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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Luo, Long
Article Type: Research Article
Abstract: This paper proposes a lightweight human action recognition algorithm for pedestrian behavior recognition. First, the skeleton feature information is input into the HRNet network model. In order to selectively enhance more details containing the target features and suppress irrelevant or weak features, an external attention mechanism is added to the HRN child model. Secondly, in order to extract the temporal characteristics of the target feature vector and ensure the continuity of actions in human behavior recognition, a dual-stream network based on HRNet and Long Short-Term Memory (LSTM) is constructed; finally, due to the huge model, it cannot be well transplanted …to embedded. Therefore, this paper uses depthwise separable convolution to lightweight the network model. The experimental results show that in terms of human behavior recognition, the method in this paper has better recognition accuracy than Two-stream, Multi-streamCNN, Cov3DJ, ConvNets, JTM, ASM-3, RF+SW, hd-CNN and TPSMMs. Show more
Keywords: External attention mechanism, lightweight, the network model, depthwise separable convolution, dual-stream network introduction
DOI: 10.3233/JIFS-239704
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Lavanya, J. | Kavi Priya, S.
Article Type: Research Article
Abstract: The paper addresses the optimization challenges in cloud resource task execution within the container paradigm, introducing the Multi-Objective Comprehensive Container Scheduling and Resource Allocation (MOCCSRA) scheme. It aims to enhance cost-effectiveness and efficiency by utilizing the Tuna Swarm Optimization (TSO) technique to optimize task planning and resource allocation. This novel approach considers various objectives for task scheduling optimization, including energy efficiency, compliance with service level agreements (SLAs), and quality of service (QoS) metrics like CPU utilization, memory usage, data transmission time, container-VM correlation, and container grouping. Resource allocation decisions are guided by the VM cost and task completion period factors. …MOCCSRA distinguishes itself by tackling the multi-objective optimization challenge for task scheduling and resource allocation, producing non-dominated Pareto-optimal solutions. It effectively identifies optimal tasks and matches them with the most suitable VMs for deploying containers, thereby streamlining the overall task execution process. Through comprehensive simulations, the results demonstrate MOCCSRA’s superiority over traditional container scheduling methods, showcasing reductions in resource imbalance and notable enhancements in response times. This research introduces an innovative and practical solution that notably advances the optimization field for cloud-based container systems, meeting the increasing demand for efficient resource utilization and enhanced performance in cloud computing environments. Show more
Keywords: Cloud container, task scheduling, resource allocation, DSTS, multi-objective optimization, tuna swarm optimizer, pareto optimality
DOI: 10.3233/JIFS-234262
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Su, Jiafu | Xu, Baojian | Liu, Hongyu | Chen, Yijun | Zhang, Xiaoli
Article Type: Research Article
Abstract: As an emerging concept in knowledge management (KM), green knowledge management plays a crucial role in the sustainable development of enterprises. A reasonable assessment of an enterprise’s green knowledge management capabilities can help the company effectively manage the embedded green knowledge within its operational processes, thereby achieving self-reinforcement of competitive advantages for the enterprise. Therefore, this paper proposes a multi-criteria classification method based on interval-valued intuitionistic fuzzy entropy weight method-TOPSIS-Sort-B (EWM-TOPSIS-Sort-B) to assess the green knowledge management capabilities of enterprises. In this method, expert assessments are expressed using interval-valued intuitionistic fuzzy sets. A new entropy weight method is introduced into …TOPSIS-Sort-B to determine the weights of various evaluation indicators, and TOPSIS-Sort-B is employed to classify and rate each evaluation scheme. It is worth noting that this paper has improved the TOPSIS-Sort-B method by not converting interval-valued intuitionistic fuzzy sets into precise values throughout the entire evaluation process, thus avoiding information loss. Finally, we applied a case of knowledge management capability assessment to validate the proposed method, and conducted sensitivity analysis and comparative analysis on this approach. The analysis results indicate that variations in the parameter ϑ of the interval-valued intuitionistic fuzzy aggregation operator lead to changes in criterion weights and the comprehensive evaluation matrix, resulting in unordered changes in the final classification results. Due to the absence of transformation of interval values in this study, compared to the four classification methods of TOPSISort-L, the classification results are more detailed, and the evaluation levels are more pronounced. Show more
Keywords: Interval-valued intuitionistic fuzzy set, TOPSIS-Sort-B, entropy weight method, green knowledge management capability
DOI: 10.3233/JIFS-239001
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Xiao, Le | Chen, Xiaolin | Shan, Xin
Article Type: Research Article
Abstract: News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using Large Language Model(LLM) with powerful natural language understanding and generative capabilities. We also designed News Summary Generator (NSG), …which aims to select and evolve the event pattern population and generate news summaries, so that using LLM extracts structured event patterns from events contained in news paragraphs, evolves the event pattern population using a genetic algorithm, and selects the most adaptive event patterns to input into LLM in order to generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability. Show more
Keywords: News summary generation, large language model, genetic algorithm, evolution
DOI: 10.3233/JIFS-237685
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Zheng, Quanchang
Article Type: Research Article
Abstract: We investigate the semi-online problem of MapReduce scheduling on two parallel machines. We aim to minimize the makespan. Jobs are released over-list, and each job includes a map task and a reduce task. The job’s map task can be preemptive and scheduled simultaneously onto different machines, however, the reduce task is non-preemptive. The job’s reduce task needs to wait for its map task to complete before starting. We consider the following two versions: Firstly, we know the processing time of the largest reduce task beforehand, and then design a 4/3-competitive optimal semi-online algorithm. Secondly, we know in advance the value …of the reduce task with the largest processing time and the the total sum of the processing times. Then we present a 4/3-competitive semi-online algorithm. We conclude that the algorithm is the best possible when the largest reduce task meets certain conditions. Show more
Keywords: MapReduce system, semi-online, scheduling, competitive ratio, makespan
DOI: 10.3233/JIFS-239276
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Cui, Jinrong | Sun, Haosen | Kuang, Ciwei | Xu, Yong
Article Type: Research Article
Abstract: Effective fire detection can identify the source of the fire faster, and reduce the risk of loss of life and property. Existing methods still fail to efficiently improve models’ multi-scale feature learning capabilities, which are significant to the detection of fire targets of various sizes. Besides, these methods often overlook the accumulation of interference information in the network. Therefore, this paper presents an efficient fire detection network with boosted multi-scale feature learning and interference immunity capabilities (MFII-FD). Specifically, a novel EPC-CSP module is designed to enhance backbone’s multi-scale feature learning capability with low computational consumption. Beyond that, a pre-fusion module …is leveraged to avoid the accumulation of interference information. Further, we also construct a new fire dataset to make the trained model adaptive to more fire situations. Experimental results demonstrate that, our method obtains a better detection accuracy than all comparative models while achieving a high detection speed for video in fire detection task. Show more
Keywords: Object detection, fire detection, efficient, multi-scale feature learning, interference immunity
DOI: 10.3233/JIFS-238164
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Lu, Mingzhen
Article Type: Research Article
Abstract: The idea of sustainable development has become more important in resolving environmental issues and fostering a healthy coexistence of human endeavors with the natural world. Internet of Things (IoT) technology is expanding across many industries, and it is also advancing in agriculture and the agricultural environment. The planning and design for intelligent gardens using a unique Sunflower Optimized-Enhanced Support Vector Machine (SFO-ESVM) is thoroughly analyzed and researched in this study. The development and plan of intelligent gardens are investigated using agricultural IoT technologies and agricultural landscapes. First, we used the SFO method to select the best garden plan inspired by …the mathematical patterns observed in sunflower seed groupings. Next, we use an ESVM model to assess how well each plant species fits into the planned garden. The SFO-ESVM considers several variables, such as soil qualities, climatic information, plant traits, and ecological requirements, to choose the best plants. Additionally, we create an intelligent control system that combines sensors, actuators, and IoT technologies to track and regulate the environmental parameters of the garden. The SFO-ESVM-based conceptual planning and design framework for smart gardens is proposed and systematically extended to give scientific direction for the agricultural IoT of smart gardens. The proposed method was then tested in a real-world garden environment. The outcomes show that the SFO-ESVM framework-based intelligent design and execution of the sustainable development-oriented garden combines ecological principles with innovative optimization methods. Show more
Keywords: Intelligent design and realization, garden, internet of things (IoT), sustainable development, sunflower optimized-enhanced support vector machine (SFO-ESVM)
DOI: 10.3233/JIFS-234540
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: He, Shun | Li, Chaorong | Wang, Xingjie | Zeng, Anping
Article Type: Research Article
Abstract: This paper proposes a watermarking method that can be used for the copyright protection of DNN models, utilizing learnable block-wise image transformation techniques and a secret key to embed a watermark. A black-box watermarking approach is used, which does not require a specific predefined training or trigger set, allowing for the remote verification of model ownership. As a result, this method can achieve copyright protection using authentication methods for DNN models. Results of experiments on established datasets [1, 2 ] indicate that the original watermark is not easily overwritten by pirated watermarks. Moreover, its performance in pruning attack experiments is …similar to that observed in the studies cited above. However, our approach demonstrates stronger robustness against fine-tuning attacks, while also achieving higher image classification accuracy. Show more
Keywords: DNN watermark, block-wise image transformation, black-box watermark, robustness
DOI: 10.3233/JIFS-240274
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Han, Xinyue | Yao, Wei
Article Type: Research Article
Abstract: The aim of this paper is to present basic concepts of lattice-valued fuzzy mathematical morphology. We use a complete residuated lattice as the codomain of fuzzy sets, a pair of fuzzy powerset operators, called the fuzzy erosion operator and the fuzzy dilation operator, is defined and their properties and relationships are studied. The pair of two operators forms a Galois adjunction and so that the induced fuzzy opening operator and fuzzy closing are an interior operator and a closure operator respectively. It is shown that the dilation stable sets and the erosion stable sets are equivalent, which form a fuzzy …Alexandrov topology. Show more
Keywords: Fuzzy mathematical morphology, complete residuated lattice, fuzzy dilation, fuzzy erosion, dilation stable set, erosion stable set, fuzzy Alexandrov topology
DOI: 10.3233/JIFS-238540
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Long, Huimin | Zheng, Hang | Chen, Ming | Liu, Chengjian
Article Type: Research Article
Abstract: The detection of communication signals in heterogeneous electromagnetic environments currently relies primarily on a one-dimensional statistical feature threshold method. However, this approach is highly sensitive to dynamic changes in the environment, fluctuations in signal-to-noise ratios, and complex noise. To address these limitations, this paper proposes a novel time-frequency diagram based on high-order accumulation for signal detection. Traditional time-frequency diagrams suffer from poor noise suppression ability and unclear features. However, higher-order cumulants can effectively overcome these shortcomings. Currently, methods based on higher-order cumulants are typically limited to one-dimensional signals. Yet, two-dimensional time-frequency signal diagrams can represent a broader array of features. …This paper employs higher-order accumulation to extract time-frequency features from the received signal, thereby transforming the conventional radio detection problem into an image recognition challenge. By merging the advantages of higher-order accumulations and time-frequency diagrams, we propose the use of higher-order accumulation time-frequency diagrams for signal detection. Extensive experimental simulations demonstrate that the proposed time-frequency diagram exhibits strong anti-noise performance and effectively suppresses frequency bias from multiple perspectives. The performance of the Higher-Order Cumulant-Time Frequency (HOC-TF) indicated lower Root Mean Square Error (RMSE) compared with the Short-Time Fourier Transform-Time Frequency (STFT-TF) and Wavelet Transform-Time Frequency (WT-TF). Additionally, compared to the STFT-TF and WT-TF methodologies, the novel time-frequency diagram introduced demonstrates superior stability using the Singular Value Decomposition (SVD) method. Moreover, by combining the new time-frequency diagram with the deep learning YOLOV5 network, signal detection and modulation identification of communication signals can be achieved. Show more
Keywords: Signal detection, higher-order cumulant, novel time-frequency diagram
DOI: 10.3233/JIFS-237988
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Ruth Isabels, K. | Arul Freeda Vinodhini, G. | Anandan, Viswanathan
Article Type: Research Article
Abstract: This work tackles the problem of maximizing machining parameters to improve the strength and resilience of 17-4 precipitation hardening (17-4 PH SS) stainless steel, which is renowned for its strong ductility but challenging machinability. We investigate different turning input parameter combinations and machining environments (dry, oil, ionic liquid), focusing on cutting temperature and flank wear as critical parameters. We analyze eighteen experimental outcomes using a VIKOR multi-criteria decision-making (MCDM) technique using CRITIC and intuitionistic fuzzy VIKOR. Expert analyses emphasize how important the machining environment is, especially when using ionic liquids (IL). Expert preferences are taken into consideration as the hybrid …CRITIC intuitionistic fuzzy R-VIKOR technique balances flank wear and cutting temperature. Criteria similarity is evaluated by the Jaccard distance coefficient, but opponent’s subjective regret and group utility are given priority in the R-VIKOR method. Compromise values are determined by an enhanced normalization technique, and parameter analysis shows that the approach is more accurate and effective than previous ones. The machining parameters for (17-4 PH SS) are being optimized by this research, which is important for businesses that need high-performance materials with intricate machining requirements. Show more
Keywords: Cutting temperature, flank wear, CRITIC, IF R-VIKOR MCDM, Jaccard coefficient
DOI: 10.3233/JIFS-241509
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Sheng, Wenshun | Shen, Jiahui | Huang, Qiming | Liu, Zhixuan | Ding, Zihao
Article Type: Research Article
Abstract: A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for multi-target tracking (S-YOFEO) is proposed with the aim of addressing the issue of target ID transformation and loss caused by the increase of practical background complexity. For the purpose of further enhancing the representation of small-scale features, a small target detection head is first introduced to the detection layer of YOLOv8 in this paper with the aim of collecting more detailed information by increasing the detection resolution of YOLOv8. Secondly, …the Omni-Scale Network (OSNet) feature extraction network is implemented to enable accurate and efficient fusion of the extracted complex and comparable feature information, taking into account the restricted computational power of DeepSORT’s original feature extraction network. Again, a novel adaptive forgetting Kalman filter algorithm (FSA) is devised to enhance the precision of model prediction and the effectiveness of parameter updates to adjust to the uncertain movement speed and trajectory of pedestrians in real scenarios. Following that, an accurate and stable association matching process is obtained by substituting Efficient-Intersection over Union (EIOU) for Complete-Intersection over Union (CIOU) in DeepSORT to boost the convergence speed and matching effect during association matching. Last but not least, One-Shot Aggregation (OSA) is presented as the trajectory feature extractor to deal with the various noise interferences in the complex scene. OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. According to the trial results, S-YOFEO has made some developments as its precision can reach 78.2% and its speed can reach 56.0 frames per second (FPS). Show more
Keywords: Pedestrian tracking, YOLOv8, DeepSORT, association matching
DOI: 10.3233/JIFS-237208
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Tino Merlin, R. | Ravi, R.
Article Type: Research Article
Abstract: This study introduces a tailored data acquisition and communication framework for IoT smart applications, focusing on enhancing efficiency and system performance. The proposed Quality-Driven IoT Routing (EQR-SC) for smart cities utilizes IoT-enabled wireless sensor networks. Additionally, a noteworthy contribution is the introduction of the Chaotic Firefly Optimization (CFOA) algorithm for IoT sensor cluster formation, potentially optimizing the organization and efficiency of IoT sensor networks in smart cities. Trust-based cluster Head Selection is enhanced by employing the Weighted Clustering Algorithm (WCA), which assigns weights to nodes based on trustworthiness and relevant metrics to select reliable cluster heads. The proposal of a …lightweight data encryption technique enhances data security among IoT sensors, ensuring the privacy and integrity of transmitted information. To optimize pathfinding within the IoT platform, the research employs the Bellman-Ford algorithm, ensuring efficient data routing while accommodating negative edge weights when necessary. Finally, a thorough performance analysis, conducted through network simulation (NS2), provides insights into the effectiveness of the proposed OQR-SC technique, allowing for valuable comparisons with existing state-of-the-art methods. Show more
Keywords: QoS, IoT smart applications, wireless sensor networks
DOI: 10.3233/JIFS-240308
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Deng, Lulu | Zhang, Changlun | He, Qiang | Wang, Hengyou | Huo, Lianzhi | Mu, Haibing
Article Type: Research Article
Abstract: The semantic segmentation of high-resolution remote sensing images has broad application prospects in land cover classification, road extraction, urban planning and other fields. To alleviate the influence of the large data volume and complex background of high-resolution remote sensing images, the usual approach is to downsample them or cut them into small pieces for separate processing. Even if combining the two methods can improve the segmentation efficiency, it ignores the differences between the middle and the edge regions. Therefore, we consider the characteristics of large and irregular region in high-resolution remote sensing images, and then propose an irregular adaptive refinement …network to locate the irregular edge region, which will be refined adaptively. Specifically, on the basis of effectively preserving the global and local information, the prediction confidence is calculated to locate pixel points that are poorly segmented, so as to form irregular regions requiring further refinement, avoiding to ‘over-refine’ intermediate region with good segmentation. At the same time, considering the difference in the refinement degree of different pixels, we propose to adaptively integrate the local segmentation results to refine the coarse segmentation results. In addition, in order to bridge the gap between the two extreme ends of the scale space, we introduce a multi-scale framework. Finally, we conducted experiments on the Deepglobe dataset showing that the proposed method performed 0.37% to 0.87% better than the previous state-of-the-art methods in terms of mean Intersection over Union (mIoU). Show more
Keywords: High spatial resolution, remote sensing image, semantic segmentation, adaptive
DOI: 10.3233/JIFS-232958
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Du, Baigang | Rong, Yuying | Guo, Jun
Article Type: Research Article
Abstract: Quality Function Deployment (QFD) is a powerful approach for improving product quality that can transform customer requirements (CRs) into engineering characteristics (ECs) during product manufacturing. The limitations of traditional QFD methods lead to imprecise quantification of CRs and difficulty in accurately mapping customer needs. To address these issues, this paper introduces an innovative QFD approach that integrates extended hesitant fuzzy linguistic term sets (EHFLTSs), CRITIC, and cumulative prospect theory. The method expresses the subjectivity and hesitancy of decision makers when evaluating the relationship between ECs and CRs using EHFLTSs, considering the conflicts among CRs. The CRITIC is used to comprehensively …evaluate the comparison strength and conflict between indicators, and the cumulative prospect theory is utilized to derive the prioritization of ECs. A case study is presented to demonstrate the effectiveness of the proposed approach. Show more
Keywords: Extended hesitant fuzzy linguistic term set, cumulative prospect theory, quality function deployment, CRITIC
DOI: 10.3233/JIFS-237217
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Martín-del-Campo-Rodríguez, C. | Batyrshin, Ildar | Sidorov, Grigori
Article Type: Research Article
Abstract: Word embeddings have been successfully used in diverse tasks of Natural Language Processing, including sentiment analysis and emotion classification, even though these embeddings do not contain any emotional or sentimental information. This article proposes a method to refine pre-trained embeddings with emotional and sentimental content. To this end, a Multi-output Neural Network is proposed to learn emotions and sentiments simultaneously. The resulting embeddings are tested in emotion classification and sentiment analysis tasks, showing an improvement compared with the pre-trained vectors and other proposes in the state-of-the-art for fine-grained emotion classification.
Keywords: Word embedding, multi-output neural network, VAD, polarity, emotion classification
DOI: 10.3233/JIFS-219354
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Mathi, Senthilkumar | Jothi, Uma | Saravanan, G. | Ramalingam, Venkadeshan | Sreejith, K.
Article Type: Research Article
Abstract: Mobile devices have risen due to internet growth in recent years. The next generation of internet protocol is evolving for mobile devices to generate their addresses and get continuous services across networks to support the enormous number of addresses in network-based mobility. The mobile device updates its current location to its home network and the correspondent users through a binding update scheme in the visited network. Numerous studies have investigated binding update schemes to verify the reachability of the mobile device at its home network. However, most schemes endure security threats due to the incompetence of authenticating user identity and …concealing the temporary location of mobile devices. To address these issues, this paper proposes a secure and efficient binding update scheme (One-CLU) by incorporating a one-key-based cryptographically generated address (CGA) to validate and conceal the address ownership of mobile devices with minimal computations. The security correctness of the proposed One-CLU scheme is verified using AVISPA – a model checker. Finally, the simulation and the numerical results showthat the proposed scheme significantly reduces communication payloads and costs for the binding update, binding refresh, and packet delivery. Show more
Keywords: Mobile communication, routing, privacy, cryptography, communication security
DOI: 10.3233/JIFS-219422
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Al-Azani, Sadam | Almeshari, Ridha | El-Alfy, El-Sayed
Article Type: Research Article
Abstract: Speaker demographic recognition and segmentation analytics play a key role in offering personalized experiences across different automated industries and businesses. This paper aims at developing a multi-label demographic recognition system for Arabic speakers from audio and associated textual modalities. The system can detect age groups, genders, and dialects, but it can be easily extended to incorporate more demographic traits. The proposed method is based on deep learning for feature learning and recognition. Representations of audio modality are learned through 3D spectrogram and AlexNet CNN-based architecture. An AraBERT transformer is employed for learning representations of the textual modality. Additionally, a method …is provided for fusing audio and textual representations. The effectiveness of the proposed method is evaluated using the Saudi Audio Dataset for Arabic (SADA), which is a recently published database containing audio recordings of TV shows in different Arabic dialects. The experimental findings show that when using models with standalone modalities for multi-label demographic classification, textual modality using AraBERT performed better than the audio modality represented using 3D spectrogram along with AlexNet CNN-based architecture. Furthermore, when combining both modalities, audio and textual, significant improvement has been attained for all demographic traits. Show more
Keywords: Demographic, 3D spectrogram, AraBERT, multi-label classification, Arabic LLMs, multimodal deep learning
DOI: 10.3233/JIFS-219389
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Midula, P. | Shine, Linu | George, Neetha
Article Type: Research Article
Abstract: Fabrication of semiconductor wafers is a complex process and chances of defect wafers are high. Because of defective wafers the circuit patterns will not be created correctly and it is necessary to identify them. Manual identification of defects are time consuming and expensive. Deep learning methods are widely used for defect detection. In this paper we propose a simple Convolutional Neural Network (CNN) model for classification of nine defects in wafers. A custom CNN consisting of 9 layers is used for the classification of defects as Center, Donut, Edge-Loc, Edge-Ring, Loc, Random, Scratch, Near-full, and None. Performance of the model …is evaluated using WM-811K dataset. Results shows that the model classifies the defects with high confidence score and an accuracy of 99.1% is achieved using this method. Further, the convolution operation in the CNN is realized using Coordinate Rotation Digital Computer (CORDIC) algorithm. The model is implemented in Field Programmable Gate Arrays (FPGA) and proved less complex method and consume less computational power than conventional methods. Show more
Keywords: CNN, CORDIC, FPGA, wafer maps
DOI: 10.3233/JIFS-219430
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Kaur, Amandeep | Rama Krishna, C. | Patil, Nilesh Vishwasrao
Article Type: Research Article
Abstract: Software-Defined Networking (SDN) is a modern networking architecture that segregates control logic from data plane and supports a loosely coupled architecture. It provides flexibility in this advanced networking paradigm for any changes. Further, it controls the complete network in a centralized using controller(s). However, it comes with several security issues: Exhausting bandwidth and flow tables, Distributed Denial of Service (DDoS) attacks, etc. DDoS is a powerful attack for Internet-based applications and services, traditional and SDN paradigms. In the case of the SDN environment, attackers frequently target the central controller(s). This paper proposes a Kafka Streams-based real-time DDoS attacks classification approach …for the SDN environment, named KS-SDN-DDoS. The KS-SDN-DDoS has been designed using highly scalable H2O ML techniques on the two-node Apache Hadoop Cluster (AHC). It consists of two modules: (i) Network Traffic Capture (NTCapture) and (ii) Attack Detection and Traffic Classification (ADTClassification). The NTCapture is deployed on the two nodes Apache Kafka Streams Cluster (AKSC-1). It captures incoming network traffic, extracts and formulates attributes, and publishes significant network traffic attributes on the Kafka topic. The ADTClassification is deployed on the two nodes Apache Kafka Streams Cluster (AKSC-2). It consumes network flows from the Kafka topic, classifies it based on the ten attributes, and publishes it to the decision Kafka topic. Further, it saves attributes with outcome to the Hadoop Distributed File System (HDFS). The KS-SDN-DDoS approach is designed and validated using the recent “DDoS Attack SDN dataset”. The result shows that the proposed system gives better classification accuracy (100%). Show more
Keywords: Control plane, real-time, dynamic network, Apache Hadoop, data plane, Kafka streams
DOI: 10.3233/JIFS-219405
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Xu, Ying | Ji, Xinrong | Zhu, Zhengyang
Article Type: Research Article
Abstract: With the increasing penetration of distributed energy resources (DER) in microgrids, DER power inverters have become a critical asset for providing power support to these microgrids. Meanwhile, the grid-forming (GFM) inverters, among these DER inverters, have gained significant attention in microgrid applications for their capability to enable the DERs to operate in different microgrid conditions and various operation modes. Moreover, with the implementation of these GFM inverters, smooth operation mode transition, GFM functions as well as black start functions can be obtained to improve the operation of the microgrid systems. In this article, a generalized control method for a single-phase …GFM inverter is developed for community microgrid applications, facilitating smooth operation behavior in both operation modes with grid support functions and stable transition for different microgrid conditions. The control design procedure and function analysis of the proposed control method are explained in detail based on the community microgrid system. The effectiveness of the method in this paper is demonstrated on a 10 kW single-phase GFM inverter prototype with comparison to a model predictive method in recent literature. Show more
Keywords: Grid-forming inverter, microgrid, grid-support function, stable transition
DOI: 10.3233/JIFS-236902
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Tian, Jing | Zhao, Ziqi | Lin, Zheng | Zhang, Fengling | Chen, Renzhen
Article Type: Research Article
Abstract: Inter-shaft bearings are an essential component of aircraft engines, and their operational status determines the safety of aircraft engine operation. Therefore, to improve the accuracy of fault type prediction and enrich the feature information in vibration signals of aircraft engine inter-shaft bearings, this paper proposes an STFT-CNN model based on the AlexNet architecture, extending its application to the research of aircraft engine inter-shaft bearing fault diagnosis. This approach addresses the common reliance on personnel experience for fault type diagnosis in traditional aircraft engine inter-shaft bearing fault diagnosis. Firstly, real vibration fault signals from inter-shaft bearings are collected through experiments to …enrich feature information in non-stationary signals using STFT time-frequency methods. Secondly, utilizing the high interpretability of the STFT-CNN model, fault feature data from inter-shaft bearings under various operating conditions are extracted to refine our understanding of fault feature information. Finally, leveraging the robustness of the STFT-CNN model, fault types are classified and predicted. The training process involves comparative analysis using different pooling algorithms, time-frequency analysis methods, and various deep learning network models. The results demonstrate that the STFT-CNN model, employing the maximum pooling algorithm, outperforms other models in predicting inter-shaft bearing faults, achieving an average fault prediction accuracy of 98.8% . Show more
Keywords: Inter-shaft bearings, STFT-CNN model, pooling algorithms, feature extraction, classification prediction
DOI: 10.3233/JIFS-240044
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Li, Yibing | Jiang, Shijin | Wang, Lei
Article Type: Research Article
Abstract: With explosive growth of industrial big data, workshop scheduling faces problems such as high complexity, multi-dimensionality and low stability. Recent years, the wide application of deep learning provides new idea for scheduling problem. In this paper, a hybrid deep convolution network and differential evolution algorithm is proposed to solve the non-permutation flow shop scheduling problem with the goal of minimizing total completion time. Mining relationship between job attributes and process priority by deep convolutional network is core idea of this method. In this paper, differential evolution algorithm is used to obtain the data set for deep learning, and neighborhood search …algorithm is used to optimize scheduling solution. Additionally, a method combining k-means algorithm and data statistics is proposed, which provides a reasonable way for priority division. The experimental results show that this method can greatly improve scheduling efficiency. Show more
Keywords: Differential evolution algorithm, convolutional neural network, K-means algorithm; priority, flow shop scheduling
DOI: 10.3233/JIFS-236874
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Duvvuri, Kavya | Kanisettypalli, Harshitha | Masabattula, Teja Nikhil | Amudha, J. | Krishnan, Sajitha
Article Type: Research Article
Abstract: Glaucoma is an eye disease that requires early detection and proper diagnosis for timely intervention and treatment which can help slow down further progression and to manage intraocular pressure. This paper aims to address the problem by proposing a novel approach that combines a model-based Reinforcement Learning (RL) approach, called DynaGlaucoDetect, with ocular gaze data. By leveraging the RL algorithms to simulate and predict the dynamics of glaucoma, a model-based approach can improve the accuracy and efficiency of glaucoma detection by enabling better preservation of visual health. The RL agent is trained using real experiences and synthetic experiences which are …generated using the model-based algorithm Dyna-Q. Two different Q-table generation methods have been discussed: the Direct Synthesis Method (DSM) and the Indirect Synthesis Method (IdSM). The presence of glaucoma has been detected by comparing the reward score a patient obtains with the threshold values obtained through the performed experimentation. The scores obtained using DSM and IdSM have been compared to understand the learning of the agent in both cases. Finally, hyperparameter tuning has been performed to identify the best set of hyperparameters. Show more
Keywords: Glaucoma detection, model-based RL, Dyna-Q algorithm, reward system
DOI: 10.3233/JIFS-219400
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Wang, Jing | Gao, Tingting | Du, Hongxu | Tu, Chuang
Article Type: Research Article
Abstract: To address the issue of final delivery route planning in the community group purchase model, this study takes into full consideration logistics vehicles of different energy types. With the goal of minimizing the sum of vehicle operating costs, delivery timeliness costs, goods loss costs, and carbon emissions costs, a multi-objective optimization model for community group purchase final delivery route planning is constructed. An improved genetic algorithm with a hill-climbing algorithm is utilized to enhance adaptive genetic operators, preventing the algorithm from getting stuck in local optima and improving the solution efficiency. Finally, a case study simulation is conducted to validate …the feasibility of the model and algorithm. Experimental results indicate that currently, among the three types of vehicles, fuel logistics vehicles still have an advantage in terms of vehicle usage cost. Electric logistics vehicles exhibit the poorest performance with the highest cost per hundred kilometers, but their sole advantage lies in their high energy release efficiency, enabling optimal low-carbon vehicle performance. Battery-swapping logistics vehicles perform the best in terms of carbon emissions, combining the advantages of both fuel-based and electric logistics vehicles. Therefore, battery-swapping logistics vehicles are a favorable choice for replacing fuel-based logistics vehicles in the future, offering promising prospects for future development. Show more
Keywords: Community group-buying, the route problem of end-distribution, improved genetic algorithm, carbon emission cost
DOI: 10.3233/JIFS-234773
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Gao, Dongling | Ma, Suhong | Kong, Xiangchuan
Article Type: Research Article
Abstract: In today’s Higher Education System (HES), Smart Learning (SL), also known as Intelligent Learning (IL) or Adaptive Learning (AL), plays an increasingly vital role. No longer is the traditional, one-size-fits-all method of education suitable for filling the several demands of students. Using SL technologies powered by Artificial Intelligence (AI) and Machine Learning (ML) algorithms can potentially revolutionize the HES. An emerging area of study, edge-based SL helps use Edge Computing (EC) to provide learners with instantaneous, specialized, and context-aware learning. Internet of Things (IoT) devices are becoming increasingly well-liked, and data is proliferating. Using video data as a primary source …of learning content and delivering it via EC infrastructure is what is referred to as “Video Streaming (VS)” in Edge-Based Learning (EBL). By examining the importance of providing mobile video clients with a high-quality visual experience—especially considering that video streaming (VS) traffic makes up a significant amount of mobile network traffic—the research gap is filled. The proposed Content Delivery Scheme (CDS), which is based on long short-term memory, is intended to improve security and privacy protocols, accelerate network service response times, and increase application intelligence. The project intends to close the current gap in edge-based Smart Learning (SL) technologies, namely in the distribution of video material for adaptive learning in higher education, by concentrating on these elements. Given that VS traffic forms a considerable portion of mobile network traffic, this paper aims to investigate the significance of delivering a performing visual experience to mobile video clients. Fast network service response, enhanced application intelligence, and enhanced security and privacy are all made possible by the proposed LSTM-based Content Delivery Scheme (CDS). The proposed approach attains minimal stall time of 2347 ms, which outperforms the existing techniques. Show more
Keywords: Higher education system, IoT, machine learning, e-Learning, edge computing, content delivery scheme, security
DOI: 10.3233/JIFS-237485
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ayub, Mohammed | El-Alfy, El-Sayed M.
Article Type: Research Article
Abstract: Energy is a critical resource for daily activities and lifestyles with direct impacts on the economy, health and environment. Therefore, monitoring its efficient use is essential to reduce energy waste and lessen related concerns such as global warming and climate change. One of the prominent and evolving solutions is Non-Intrusive Load Monitoring (NILM) smart meters, which enables consumers to track their per-appliance energy consumption more effectively. Some recent approaches have proposed deep learning as a powerful tool for energy disaggregation. However, it is difficult to employ these models in resource-constrained end devices for effective energy monitoring. In this paper, we …explore and evaluate a lightweight improved model for multi-target non-intrusive load monitoring based on MobileNet architectures. With extensive experiments using the ENERTALK dataset, the results show that MobileNetV3-large is the most appealing for energy disaggregation as it requires about 55% less storage for trained model and about 6% less training time than MobileNetV2 with almost the same performance. On average, version 3 large has a 17.63% reduction in SAE and requires 54.21% and 8.93% less space and less training time than version 2, respectively. Moreover, the average performance is boosted using an ensemble multi-target MobileNet model across all houses, leading to significant reduction of MAE, SAE, and RMSE errors of about 6%, 48%, and 4%, respectively. In comparison to other work, the proposed MMNet-NILM shows superior performance for the majority of appliances in terms of all considered evaluation metrics. Show more
Keywords: Multi-target MobileNet, ENERTALK, Lightweight NILM, energy disaggregation, ensemble MobileNet
DOI: 10.3233/JIFS-219426
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Yang, Yeling
Article Type: Research Article
Abstract: Vocal music training for college students impacts the social and emotional aspects of better learning. This impact must be classified progressively to improve the social and musical connectivity coinciding with real-time emotions. Therefore, an intermittent analysis of music learning is required for augmenting socio-emotional changes to the learning method. This article introduces Impact-centric Learning Analysis (ILA) using the Fuzzy Control Algorithm (FCA) for the purpose above. The control algorithm operates in two linear stages: in the first stage, the socio-emotional impact of the learning on the students is analyzed, pursued by the learning changes in the second stage. This first …stage inputs student activity scores based on real-time implications. The lowest scores are classified independently in the second stage, and learning changes are carried out. The learning change is targeted to meet the maximum (optimal) impact score from the first stage using fuzzy differentiations based on training sessions and student performance. Therefore, the proposed algorithm generates an optimal impact for the considered features (socio-emotional), preventing trivial vocal music sessions. Show more
Keywords: Fuzzy control, impact optimization, socio-emotional learning, vocal music
DOI: 10.3233/JIFS-233922
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Sindge, Renuka Sambhaji | Dutta, Maitreyee | Saini, Jagriti
Article Type: Research Article
Abstract: Video Super Resolution (VSR) applications extensively utilize deep learning-based methods. Several VSR methods primarily focus on improving the fine-patterns within reconstructed video frames. It frequently overlooks the crucial aspect of keeping conformation details, particularly sharpness. Therefore, reconstructed video frames often fail to meet expectations. In this paper, we propose a Conformation Detail-Preserving Network (CDPN) named as SuperVidConform. It focuses on restoring local region features and maintaining the sharper details of video frames. The primary focus of this work is to generate the high-resolution (HR) frame from its corresponding low-resolution (LR). It consists of two parts: (i) The proposed model decomposes …confirmation details from the ground-truth HR frames to provide additional information for the super-resolution process, and (ii) These video frames pass to the temporal modelling SR network to learn local region features by residual learning that connects the network intra-frame redundancies within video sequences. The proposed approach is designed and validated using VID4, SPMC, and UDM10 datasets. The experimental results show the proposed model presents an improvement of 0.43 dB (VID4), 0.78 dB (SPMC), and 0.84 dB (UDM10) in terms of PSNR. Further, the CDPN model set new standards for the performance of self-generated surveillance datasets. Show more
Keywords: Super-resolution, image super-resolution, video super-resolution, recurrent network, residual learning
DOI: 10.3233/JIFS-219393
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Ezeji, Ijeoma Noella | Adigun, Matthew | Oki, Olukayode
Article Type: Research Article
Abstract: The rise of decision processes in various sectors has led to the adoption of decision support systems (DSSs) to support human decision-makers but the lack of transparency and interpretability of these systems has led to concerns about their reliability, accountability and fairness. Explainable Decision Support Systems (XDSS) have emerged as a promising solution to address these issues by providing explanatory meaning and interpretation to users about their decisions. These XDSSs play an important role in increasing transparency and confidence in automated decision-making. However, the increasing complexity of data processing and decision models presents computational challenges that need to be investigated. …This review, therefore, focuses on exploring the computational complexity challenges associated with implementing explainable AI models in decision support systems. The motivations behind explainable AI were discussed, explanation methods and their computational complexities were analyzed, and trade-offs between complexity and interpretability were highlighted. This review provides insights into the current state-of-the-art computational complexity within explainable decision support systems and future research directions. Show more
Keywords: Explainable decision support systems, computational complexity, optimization, explainable artificial intelligence, review
DOI: 10.3233/JIFS-219407
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Liu, Fuchen | Zhou, Sijia | Zhang, Dezhou | Wang, Xiaocui
Article Type: Research Article
Abstract: Deep learning has demonstrated remarkable advantages in the field of human pose estimation. However, traditional methods often rely on widening and deepening networks to enhance the performance of human pose estimation, consequently increasing the parameter count and complexity of the networks. To address this issue, this paper introduces Ghost Attentional Down network, a lightweight human pose estimation network based on HRNet. This network leverages the fusion of features from high-resolution and low-resolution branches to boost performance. Additionally, GADNet utilizes GaBlock and GdBlock, which incorporate lightweight convolutions and attention mechanisms, for feature extraction, thereby reducing the parameter count and computational complexity …of the network. The fusion of relationships between different channels ensures the optimal utilization of informative feature channels and resolves the issue of feature redundancy. Experimental results conducted on the COCO dataset, with consistent image resolution and environmental settings, demonstrate that employing GADNet leads to a reduction of 60.7% in parameter count and 61.2% in computational complexity compared to the HRNet network model, while achieving comparable accuracy levels. Moreover, when compared to commonly used human pose estimation networks such as Cascaded Pyramid Network (CPN), Stacked Hourglass Network, and HRNet, GADNet achieves high-precision detection of human keypoints even with fewer parameters and lower computational complexity, our network has higher accuracy compared to MobileNet and ShuffleNet. Show more
Keywords: Human pose estimation, high-resolution network, attention mechanism, feature redundancy
DOI: 10.3233/JIFS-233501
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Vusirikkayala, Gowthami | Madhu Viswanatham, V.
Article Type: Research Article
Abstract: Detecting communities within a network is a critical component of network analysis. The process involves identifying clusters of nodes that exhibit greater similarity to each other compared to other nodes in the network. In the context of Complex networks (CN), community detection becomes even more important as these clusters provide relevant information of interest. Traditional mathematical and clustering methods have limitations in terms of data visualization and high-dimensional information extraction. To address these challenges, graph neural network learning methods have gained popularity in community detection, as they are capable of handling complex structures and multi-dimensional data. Developing a framework for …community detection in complex networks using graph neural network learning is a challenging and ongoing research objective. Therefore, it is essential for researchers to conduct a thorough review of community detection techniques that utilize cutting-edge graph neural network learning methods [102 ], in order to analyze and construct effective detection models. This paper provides a brief overview of graph neural network learning methods based on community detection methods and summarizes datasets, evaluation metrics, applications, and challenges of community detection in complex networks. Show more
Keywords: Community detection (CD), complex networks (CN), graph neural network (GNN), deep learning (DL), communities, clusters
DOI: 10.3233/JIFS-235913
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2024
Authors: Abu-Sharkh, Osama M.F. | Surkhi, Ibrahim | Zabin, Hamzah | Alhasan, Maher
Article Type: Research Article
Abstract: As the entire world is becoming increasingly a global village, the need for reliable, smooth, and easy-to-use applications that facilitate the communication process between people speaking different languages worldwide becomes essential, especially in the tourism industry. While numerous online and mobile applications attempt to bridge the linguistic gap using text-to-text, text-to-voice, or voice-to-text-to-voice translators, they often fall short due to constraints such as the need for a single shared device, manual setup of speaker’s gender and preferred language, and an inability to communicate from a distance. These applications struggle to mimic the practical nature of real-time multilingual conversations where immediate …and clear communication is paramount. This paper introduces an intelligent peer-to-peer polyglot voice-to-voice mobile application to facilitate the communication of people speaking different languages worldwide transparently mimicking a live conversation whether the involved parties are close to each other or at a nearby distance. People can interact with others transparently using their preferred language, irrespective of others’ languages, while the application automatically recognizes the language, gender of the speaker, and spoken words with very high accuracy. Five languages were implemented in the developed application as a proof-of-concept, and it is designed to smoothly and simply adapt more in future updates. Show more
Keywords: Multilingual, intelligent, text-to-voice, translation, voice-to-text
DOI: 10.3233/JIFS-219388
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Tariq, Sana | Amin, Asjad
Article Type: Research Article
Abstract: The emergence of machine learning in the recent decade has excelled in determining new potential features and nonlinear relationships existing between the data derived from the DNA sequences of genetic diseases. Machine learning also enhances the process of handling data with maximum predicted variables compared to observations during the data mining process of prediction. In this context, our study presents a deep learning model for predicting Transcription Factor Binding Sites (TFBS) in DNA sequences, with a focus on features within genetic data associated with diseases. Transcription Factors (TFs) play a crucial role in modulating gene expression by binding to TFBS. …The accurate prediction of TFBS is essential for understanding genome function and evolution. Thus, we develop an efficient deep learning model that considers TFBS prediction as a nucleotide-level binary classification task. In our proposed model, first we create an input matrix using the original DNA sequences. Next, we encode these DNA sequences using one-hot encoding, representing them as a sequence of numerical values. We then employ three convolutional layers, allowing our model to capture intricate patterns and motif features over a larger spatial range. To capture important features within the DNA sequence and to focus on them, we incorporate an attention layer. Finally, a dense layer, consisting of two fully connected layers and a dropout layer, calculates the probability of TF binding site occurrence based on the features learned by the proposed model. Our experimental results, using in-vivo datasets obtained from Chip-seq, demonstrate the superior performance of our proposed deep learning model in TFBS prediction compared to other existing state-of-the-art methods. The improvement in accuracy is due to additional layers of CNN and then an attention layer in the model. Thus, this result in a better performance of our approach in predicting the transcription factor binding sites and enhancing our understanding of gene regulation and genome function. Show more
Keywords: Transcription factor binding sites, one-hot encoding, convolutional layer, attention layer
DOI: 10.3233/JIFS-238159
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Vu, Hoai Nam | Nguyen, Quang Dung | Nguyen, Thuy Linh | Tran-Anh, Dat
Article Type: Research Article
Abstract: In the real world, the appearance of similar rice varieties depends on various factors such as resolution, angle, lighting conditions, and perspective. Additionally, complex environmental factors and characteristics of each rice type, such as enhanced light intensity, cross-polarization, and shading, rice background color, and image similarity, play a role. This indicates that the data augmentation process may enhance the accuracy of crop identification, particularly in the context of self-supervised machine learning. The aim of this research is to develop a precise rice segmentation method based on the improved Mask R-CNN (Region-based Convolutional Neural Network) with multitask data augmentation. The Mask …R-CNN model is enhanced by incorporating multitask input to improve feature extraction for rice. Experimental results demonstrate that the improved Mask R-CNN model can accurately segment various rice types under different conditions, such as different background colors and varying sizes of rice grains. The achieved precision, recall, F1 score, and segmentation mean Average Precision (mAP) are 95.5%, 96.3%, 95.9%, and 0.924, respectively. The average runtime on the test set is 0.35 seconds per image. Our method outperforms two comparative approaches, showcasing its ability to accurately segment rice in the market deployment phase with near real-time performance. This study establishes the foundation for the accurate detection of valuable agricultural products. Show more
Keywords: Multi-augmentation, deep learning, Mask RCNN, rice recognition, fusion metric
DOI: 10.3233/JIFS-241133
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Wang, Lin | Ye, Hongling | Wang, Pengfei | Xu, Chi | Qian, Aiwen
Article Type: Research Article
Abstract: To enhance the control performance of semi-active suspension systems, this research proposes a particle swarm optimization algorithm (PSO) with adaptive nonlinear correction of inertia weights, which is then integrated with a proportional integral differential (PID) algorithm. To this end, this research establishes quarter semi-active and passive suspension models of automobiles by utilizing the Matlab/Simulink simulation platform. In this foundation, this research further compares the advantages and disadvantages regarding performance indexes of semi-active suspension controlled by the adaptive inertia weighted particle swarm optimization (APSO) algorithm and the PID algorithm, as well as the PID-controlled semi-active suspension and passive suspension through simulation. …Simulation results indicate that performance indicator values for different suspension types increase with higher pavement grades. Compared with passive suspension, the semi-active suspension controlled by APSO and PID algorithms presents significantly improved performance indexes, with reductions of at least 31.61% in root mean square (RMS) concerning body vertical acceleration, 1.78% in suspension dynamic deflection, and 22.13% in tire dynamic loads. Moreover, analysis of suspension system frequency response characteristics demonstrates a significant decrease in droop acceleration transmission rate for the semi-active suspension with APSO and PID algorithms across the whole frequency range compared with that of the PID-controlled suspension and passive suspension. On the same note, despite the higher values of suspension dynamic deflection and tire dynamic load transfer rate in certain frequency bands, they are generally within acceptable suspension limits. Simply put, the findings confirm the feasibility of applying the APSO algorithm in PID-controlled semi-active suspension systems, which effectively improves both vehicle ride comfort and handling stability. Show more
Keywords: Semi-active suspension, PID control, improved particle swarm optimization algorithm
DOI: 10.3233/JIFS-234812
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wang, Xiao | Wang, Dan | Zhou, Jincheng
Article Type: Research Article
Abstract: The correspondence between the decision space and the objective space is often many-to-one in multi-objective optimization problems. Therefore, a class of problems with such mapping relationships is defined as a MMOPs. For these problems, how to ensure the final solution converges to each Pareto solution set and guarantees the diversity of the algorithm is an urgent problem. The method of the paper with opposition-based strategy, a multimodal multi-objective optimization algorithm, is proposed. The algorithm proposed is called MMODE_OP, which is framed by a differential evolutionary algorithm, and opposition-based learning is applied to the initialization phase and generation-hopping phase to filter …out the more promising individuals in the population for iteration to enhance the global search capability and the diversity of population. In addition, different Gaussian perturbation strategies are adopted with iteration to achieve the search of the neighborhood, which can further not only improve the quality of the Pareto solution set but also enable the convergence of the Pareto solution set quickly. This method improves the algorithm’s local and global search ability, and enables multiple the Pareto solution set and improving the convergence. In the meantime, adaptive scaling factors and crossover factors are designed in this paper to enhance the improved search capability. Finally, the experiment results of MMODE_OP and other excellent algorithms on 13 test problems corroborate the proposed methods have superior performance. Show more
Keywords: Multimodal, multi-objective, differential evolutionary algorithm, opposition-based learning
DOI: 10.3233/JIFS-233826
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Thampi, Sabu M. | El-Alfy, El-Sayed M. | Berretti, Stefano
Article Type: Editorial
DOI: 10.3233/JIFS-219381
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: David Raj, G. | Mukherjee, Saswathi | Jasmine, R.L.
Article Type: Research Article
Abstract: To enhance the reliability of the document retrieval system, the most efficient techniques such as Query Expansion (QE) are utilized. It has offered more adequate queries for the user when assimilated over original or initial queries by adding up one or more expansion keywords. Moreover, these techniques are more effective to enhance the performance of document retrieval and return the unnecessary information. In recent times, searching the suitable documents in the huge datasets is tiresome work. Generally, the automatic QE is used to address the refining query. A typical technique for QE has included the extracted close expression and the …related documents clustering by utilizing the clusters. However, classical clustering poses some issues to QE. Hence, a novel optimized bi-clustering mechanism is proposed in this paper for patent retrieval by QE. The ultimate aim of this implemented model is to retrieve the patent information by expanding the request query. Initially, the patent-related data is collected from standard data sources in terms of abstract and text. It is then given to the text pre-processing stage. Consequently, the pre-processed text or word is converted into vector formation by using the Multi-cascade Transformer Network (MTN). Finally, the retrieval process is done by proposing the Optimal Bi-Clustering (OptBi-C) process, in which the parameters are optimally determined by a hybrid algorithm of Reptile Search Algorithm (RSA) and Lion Algorithm (LA) termed as Iteration-based Reptile Search and Lion Algorithm (IRSLA). Thus, the performance of the model is examined with certain metrics and compared with traditional techniques. The precision of the implemented patent retrieval system using the QE model is maximized by 8.82% of DHOA-OptBi-C, 7.35% of HHO-OptBi-C, 10.29% of RSA-OptBi-C, and 7.35% of LA-OptBi-C respectively when the number of retrieved data is 10. Moreover, the recall of the designed patent retrieval system using the QE model is enhanced by 21.83% of KNN, 24.13% of CNN, 19.54% of FUZZY, and 11.49% of Bi-clustering respectively when the number of retrieved data is 6. Thus, the findings demonstrate that the system improves the retrieval performance. Show more
Keywords: Patent retrieval system, query expansion, multi cascaded transformer network, iteration-based reptile search and lion algorithm, optimal bi-clustering
DOI: 10.3233/JIFS-241138
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Borse, Rushikesh | Das, Rochishnu | Dash, Devasish | Yadav, Akshay
Article Type: Research Article
Abstract: In the wake of the contemporary competitive business landscape, the retention of employees has become one of the most important yet difficult tasks for any corporate. Retaining top-performing employees not only improves organizational performance but also reduces recruitment costs. In this study, the authors investigate the major drivers leading to employee attrition and using machine learning algorithms implemented on a well proven and validated IBM HR data set. Although the data set tags the samples for a target variable (attrited and non-attrited), the work presented in this paper comes up with another labelling (1. likely to leave, 2. On the …verge of leaving, 3. will stay). The data set is evaluated over top 10 Machine learning algorithms and a competitive analysis is made between them based on various factors. The best model has shown a prediction accuracy of over 85% +. Managers are provided with insights and recommendations at the end that will help companies to proactively identify at-risk employees and implement effective retention strategies. Show more
Keywords: Employee attrition, machine learning, early detection of attrition, artificial neural network
DOI: 10.3233/JIFS-219410
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Senthamil Selvi, M. | Senthamizh Selvi, R. | Subbaiyan, Saranya | Murshitha Shajahan, M.S.
Article Type: Research Article
Abstract: Accurate prediction of grid loss in power distribution networks is pivotal for efficient energy management and pricing strategies. Traditional forecasting approaches often struggle to capture the complex temporal dynamics and external influences inherent in grid loss data. In response, this research presents a novel hybrid time-series deep learning model: Gated Recurrent Units with Temporal Convolutional Networks (GRU-TCN), designed to enhance grid loss prediction accuracy. The proposed model integrates the temporal sensitivity of GRU with the local context awareness of TCN, exploiting their complementary strengths. A learnable attention mechanism fuses the outputs of both architectures, enabling the model to discern significant …features for accurate prediction. The model is evaluated using well-established metrics across distinct temporal phases: training, testing, and future projection. Results showcase Resulting in encouraging Figures for mean absolute error, root mean squared error, and mean absolute percentage error, the model’s capacity to capture both long-term trends and transitory patterns. The GRU-TCN hybrid model represents a pioneering approach to power grid loss prediction, offering a flexible and precise tool for energy management. This research not only advances predictive accuracy but also lays the foundation for a smarter and more sustainable energy ecosystem, poised to transform the landscape of energy forecasting. Show more
Keywords: Accurate prediction, grid loss, power distribution networks
DOI: 10.3233/JIFS-235579
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Abuhoureyah, Fahd | Yan Chiew, Wong | Zitouni, M. Sami
Article Type: Research Article
Abstract: Human Activity Recognition (HAR) utilizing Channel State Information (CSI) extracted from WiFi signals has garnered substantial interest across various domains and applications. This field’s potential paths and applications extend beyond CSI-based HAR and include smart homes, assisted living, security, gaming, surveillance, and context-aware computing. The ability of deep learning algorithms to effectively process and interpret CSI data opens up new possibilities for accurate and robust human activity recognition in real-world scenarios. However, traditional Recurrent Neural Networks (RNN) models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), rely solely on their internal memory cells to maintain information over …time. Important details might be diluted or lost within the memory cells in complex CSI sequences. To address this limitation, we propose a lightweight approach that incorporates a multi-head adaptive attention weight mechanism MHAAM into the HAR framework. The multi-head attention mechanism allows the model to attend to different informative patterns within the CSI data simultaneously, capturing fine-grained temporal dependencies and improving the model’s ability to recognize complex activities. The implemented models effectively filter out noise and irrelevant information by assigning higher weights to informative CSI features, further enhancing activity classification accuracy. Experimental evaluations and comparative analyses of HAR for seven activities demonstrate that attention-based RNN models with multi-head attention consistently outperform traditional RNN models. The multi-head attention mechanism achieves improved generalization and testing for seven common human activities and environments, leading to a higher complex human activity classification accuracy of up to 98.5%. Show more
Keywords: Multi-head adaptive attention mechanism, channel state information (CSI), WiFi sensing, activity recognition, WiFi sensing, MHAAM
DOI: 10.3233/JIFS-234379
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Singh, Pardeep | Lamsal, Rabindra | Singh, Monika | Shishodia, Bhawna | Sitaula, Chiranjibi | Chand, Satish
Article Type: Research Article
Abstract: Social media platforms play a crucial role in providing valuable information during crises, such as pandemics. The COVID-19 pandemic has created a global public health crisis, and vaccines are the key preventive measure for achieving herd immunity. However, some individuals use social media to oppose vaccines, undermining government efforts to eliminate the virus. This study introduces the “GeoCovaxTweets” dataset, consisting of 1.8 million geotagged tweets related to COVID-19 vaccines from January 2020 to November 2022, originating from 233 countries and territories. Each tweet includes state and country information, enabling researchers to analyze global spatial and temporal patterns. An extensive set …of analyses are performed on the dataset to identify prominent topic clusters and explore public opinions across different vaccines and vaccination contexts. The study outlines the dataset curation methodology and provides instructions for local reproduction. We anticipate that the dataset will be valuable for crisis computing researchers, facilitating the exploration of Twitter conversations surrounding COVID-19 vaccines and vaccination, including trends, opinion shifts, misinformation, and anti-vaccination campaigns. Show more
Keywords: COVID-19 discourse, COVID-19 pandemic, sentiment analysis, social media, topic clustering, twitter dataset
DOI: 10.3233/JIFS-219418
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Article Type: Research Article
Abstract: The recognition and regulation of buildings are essential aspects of urban management to prevent illegal constructions and maintain public safety and resources. Traditional machine learning methods for building recognition often suffer from low accuracy and weak generalization capabilities due to their reliance on manually designed features. Traditional machine learning methods for building recognition often suffer from low accuracy and weak generalization capabilities due to their reliance on manually designed features. Therefore, the study of automatic, accurate building identification method is very necessary. Based on this, Introducing advanced algorithms like Faster R-CNN and DRNet signifies a significant step towards automating accurate …building identification. The utilization of Faster R-CNN as a basic training model combined with DRNet demonstrates promising results in accurately recognizing buildings. The experimental analysis highlights the potential of the proposed method, achieving an impressive 82.1% mean Average Precision (mAP) for landmark buildings. Accurate prediction of building coordinates further strengthens the effectiveness of the proposed approach. Comparative analysis showcases the superiority of the proposed model in recognizing buildings not only in normal images but also in complex environmental settings. The successful implementation of advanced algorithms in building recognition contributes to more efficient urban management and development. Continued research in automatic building identification methods is crucial for addressing challenges in urban planning and management, ensuring sustainable city development. Show more
Keywords: Deep learning, Faster R-CNN, building identification, classification algorithm, building extraction, urbanization
DOI: 10.3233/JIFS-241838
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Lamani, Dharmanna | Shanthi, T.S. | Kirubakaran, M.K. | Roopa, R.
Article Type: Research Article
Abstract: Accurately classifying products in e-commerce is critical for enhancing user experience, but it remains challenging due to data quality issues and the dynamic nature of product categories. Customers are increasingly relying on visual information to make informed purchasing decisions, emphasizing the importance of accurate product classification using images. In this paper, an innovative approach called SSWSO_LeNet is proposed for product image classification in e-commerce. The method involves preprocessing the input images using Region of Interest (RoI) and Adaptive Wiener Filters to improve image quality and reduce unwanted distortions. Data augmentation techniques are then applied to increase the diversity of the …dataset and the robustness of the model. To address this, we propose SSWSO_LeNet, integrating Squirrel Search Algorithm (SSA) and War Strategy Optimization (WSO) with LeNet. SSA mimics southern flying squirrels’ foraging behavior to find global optima efficiently, while WSO balances exploration and exploitation stages, enhancing classification accuracy. Experimental results show SSWSO_LeNet outperforms state-of-the-art models with an impressive accuracy of 0.976, sensitivity of 0.877, and specificity of 0.857. By leveraging SSA, WSO, and LeNet, SSWSO_LeNet not only improves classification accuracy but also reduces reliance on human editors, decreasing both cost and time in e-commerce product classification. Show more
Keywords: E-commerce, SSA, WSO, SSWSO_LeNet, product classification
DOI: 10.3233/JIFS-241682
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Tripathi, Diwakar | Reddy, B. Ramachandra | Dwivedi, Shubhra | Shukla, Alok Kumar | Chandramohan, D. | Dewangan, Ram Kishan
Article Type: Research Article
Abstract: Nature-inspired algorithms as problem-solving methodologies are extremely effective in discovery of optimized solutions in multi-dimensional and multi-modal problems. Because of qualities like “self-optimization”, “flexibility” and etc., nature-inspired algorithms for problem solving are effectively optimal. Feature selection is an approach to find approximate optimal subset of the features which are more relevant towards the particular outcome. In this study, we focused on how feature selection may improve the credit scoring model’s performance for prediction. Nature-inspired algorithms are applied for feature selection to improve the predictive performance of the credit scoring model. Additionally, four benchmark credit scoring datasets collected from the UCI …repository are used to test feature selection by several Nature-inspired algorithms aggregated with “Random Forest (RF)”, “Logistic Regression (LR),” and “Multi-layer Perceptron (MLP)” for classification and results are compared in terms of classification accuracy and G-measures. Show more
Keywords: Nature-inspired algorithms, credit score, feature selection, classification
DOI: 10.3233/JIFS-219413
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Faraz, Ansar Ali | Khan, Hina | Aslam, Muhammad | Albassam, Mohammed
Article Type: Research Article
Abstract: When data are hazy or uncertain, estimators given under classical statistics are ineffective. Given that it deals with uncertainty, neutrosophic statistics is the sole alternative. Due to the vast range of applications, extensive research has been done in this area. The objective of this study is to determine the most accurate predictions for the population mean with the least amount of mean square error. We have created neutrosophic ratio type estimators, when working with ambiguous, hazy, and neutrosophic-type data, the proposed estimation methods are very useful for computing results. These estimators produce findings that are not single-valued but rather have …an interval form, where our population parameter may lie more frequently. Since we have an estimated interval with the unknown population mean value given a minimal mean square error, it improves the estimators’ efficiency. Real life neutrosophic line losses data and simulation are both used to analyze the effectiveness of the proposed neutrosophic ratio-type estimators. Additionally, a comparison is made to show how helpful Neutrosophic ratio type estimator is in comparison to existing estimators. Show more
Keywords: Neutrosophic, conventional statistics, estimation, ratio estimators, mean square error
DOI: 10.3233/JIFS-240153
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Saravanan, Krithikha Sanju | Bhagavathiappan, Velammal
Article Type: Research Article
Abstract: The advancements in technology, particularly in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI) can be advantageous for the agricultural sector to enhance the yield. Establishing an agricultural ontology as part of the development would spur the expansion of cross-domain agriculture. Semantic and syntactic knowledge of the domain data is required for building such a domain-based ontology. To process the data from text documents, a standard technique with syntactic and semantic features are needed because the availability of pre-determined agricultural domain-based data is insufficient. In this research work, an Agricultural Ontologies Construction framework (AOC) is proposed for …creating the agricultural domain ontology from text documents using NLP techniques with Robustly Optimized BERT Approach (RoBERTa) model and Graph Convolutional Network (GCN). The anaphora present in the documents are resolved to produce precise ontology from the input data. In the proposed AOC work, the domain terms are extracted using the RoBERTa model with Regular Expressions (RE) and the relationships between the domain terms are retrieved by utilizing the GCN with RE. When compared to other current systems, the efficacy of the proposed AOC method achieves an exceptional result, with precision and recall of 99.6% and 99.1% respectively. Show more
Keywords: Anaphora resolution, term extraction, relationships identification, RoBERTa model, regular expressions, graph convolutional network, domain ontology
DOI: 10.3233/JIFS-237632
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Immanuel, Rajeswari Rajesh | Sangeetha, S.K.B.
Article Type: Research Article
Abstract: Human emotions are the mind’s responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep learning and machine learning techniques for emotion recognition through EEG (electroencephalogram) signals. This paper presents an investigation based on a real-time dataset that comprises 15 subjects, consisting of 7 males and 8 females. The EEG signals of these subjects were recorded during exposure to video stimuli. The collected real-time data underwent preprocessing, followed by the extraction of features using various methods tailored for this purpose. The study …includes an evaluation of model performance by comparing the accuracy and loss metrics between models applied to both raw and preprocessed data. The paper introduces the EEGEM (Electroencephalogram Ensemble Model), which represents an ensemble model combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to achieve the desired outcomes. The results demonstrate the effectiveness of the EEGEM model, achieving an impressive accuracy rate of 95.56%. This model has proven to surpass the performance of other established machine learning and deep learning techniques in the field of emotion recognition, making it a promising and superior tool for this application. Show more
Keywords: EEG signal, emotion, CNN, LSTM, ensemble learning, feature extraction
DOI: 10.3233/JIFS-237884
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Srinivasan, Manohar | Senthilkumar, N.C.
Article Type: Research Article
Abstract: The Internet of Things (IoT) has many potential uses in the day-to-day operations of individuals, companies, and governments. It makes linking all devices to the internet a realistic possibility. Convincing IoT devices to work together to implement several real-world applications is a challenging feat. Security issues impact innovative platform applications due to the current security state in IoT-based operations. As a result, intrusion detection systems (IDSs) tailored to IoT platforms are essential for protecting against security breaches caused by the Internet of Things (IoT) that exploit its vulnerabilities. Issues with data loss, dangers, service interruption, and external hostile assaults are …all part of the IoT security landscape. Designing and implementing appropriate security solutions for IoT environments is the main emphasis of this research. Within the Internet of Things (IoT) context, this research creates a Spotted Hyena Optimizer (SHO-EDLID) method for intrusion detection using ensemble deep learning. The main goal of the demonstrated SHO-EDLID method was to detect and categorize intrusions in an Internet of Things setting. It comprises many subprocesses, including feature selection, categorization, and pre-processing. The SHO-EDLID method uses a SHO-based feature selection strategy to identify the best feature subsets. It then used an ensemble of three DL models— a deep belief network (DBN), a stacked autoencoder (SAE), and a bidirectional recurrent neural network (BiRNN)— to detect and name cyberattacks. Finally, the DL models’ parameters are tuned using the Adabelief optimizer. A comprehensive simulation was run to illustrate that the offered model performed better. According to a thorough comparative analysis, the suggested method outperformed other recent approaches. Purpose of the Manuscript : To identify the best feature subsets, the SHO-EDLID method used the SHO-based feature selection method... Afterward, cyberattack identification and tracking were carried out using an ensemble of three DL models: DBN, SAE, and BiRNN. The final step in optimizing the DL models’ parameters is the Adabelief optimizer. The main comparative results : The proposed model present the Comparative analysis of SHO-EDLID algorithm with other existing systems and its outperform the performance in precision 97.50, accuracy 99.56, Recall 98.42, F-Measure.97.95. Show more
Keywords: Security, internet of things, deep learning, ensemble learning, spotted hyena optimizer
DOI: 10.3233/JIFS-240571
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Yang, Cheng | Xu, Xinrui
Article Type: Research Article
Abstract: The quality of building materials will affect the implementation effect of construction projects. To ensure the service capacity of building materials, it is necessary to do a good job in selecting suppliers. In the specific evaluation of building material suppliers, after evaluation, suppliers with poor quality are excluded to ensure the quality of material supply, reasonably improve the construction effect of the building project, meet the construction needs of the building project, and improve the quality of the building project. The selection and application of building material suppliers (BMSs) is a multiple-attribute group decision-making (MAGDM) technique. In this study, the …2-tuple linguistic neutrosophic number combined grey relational analysis (2TLNN-CGRA) technique is constructed based on the classical grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). Finally, a numerical example for building material supplier selection was constructed and some comparisons is constructed to illustrate the 2TLNN-CGRA technique. The main contribution of this study is constructed: (1) the 2TLNN-CGRA technique is implemented to cope with the MAGDM under 2TLNSs; (2) the 2TLNN-CGRA technique is implemented in line with the 2TLNN Hamming distance (2TLNNHD) and 2TLNN Euclidean distance (2TLNNED) simultaneously under 2TLNSs; (3) the numerical example for building material supplier selection is implemented to show the 2TLNN-CGRA technique; and (4) some efficient comparative studies are constructed with several existing decision techniques. Show more
Keywords: Multiple-attribute group decision-making (MAGDM), 2TLNSs, 2TLNN-CGRA technique, building material suppliers
DOI: 10.3233/JIFS-221334
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Liu, Dapeng
Article Type: Research Article
Abstract: In order to improve the remanufacturing efficiency of scrap mechanical parts and comprehensively detect their surface fault status, this paper proposes a color three-dimensional reconstruction method of scrap mechanical parts based on an improved semi-global matching (SGM) algorithm. In experiments, this method demonstrated significant performance advantages in dealing with complex mechanical component structures and large illumination interference environments. Experimental results show that the three-dimensional color model reconstructed by this method has clear texture and small dimensional error, and is suitable for online analysis of surface fault information of scrap mechanical parts in actual production lines. Through quantitative analysis, compared with …the traditional SGM method, the method in this paper improves the structural similarity index (SSIM) by an average of 19.8% and reduces the mean square error (MSE) by an average of 33.1%. Show more
Keywords: Waste mechanical parts, binocular vision, SGM, Color 3D reconstruction
DOI: 10.3233/JIFS-237214
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Jansi Rani, J. | Manivannan, A.
Article Type: Research Article
Abstract: This paper focuses on solving the fully fuzzy transportation problem in which the parameters are triangular Type-2 fuzzy numbers due to the instinctive of human imprecision. To deal with uncertainty more precisely, a triangular Type-1 fuzzy transportation problem is reformed as a transportation problem with triangular Type-2 fuzzy parameters in this paper. In order to compare triangular Type-2 fuzzy numbers, a new ranking(ordering) technique is proposed by extending the Yager’s function. However, two efficient algorithmic approaches namely, triangular Type-2 fuzzy zero suffix method (TT2FZSM) and triangular Type-2 fuzzy zero average method (TT2FZAM) are proposed to generate the initial transportation cost …of the fully triangular Type-2 fuzzy transportation problem. Both TT2FZSM and TT2FZAM are converging towards an optimal solution. In addition to TT2FZSM and TT2FZAM, the modified distribution method is applied to ensure optimality. Subsequently, we carry out a comprehensive discussion of the obtained results to establish the validation of the proposed approach. Show more
Keywords: Transportation problem, triangular type-2 fuzzy number, ranking function, optimal solution
DOI: 10.3233/JIFS-237652
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Yan, Huiming | Yan, Zilin | Wang, Weiling | Liu, Shuyue
Article Type: Research Article
Abstract: In recent years, the burgeoning imperative of energy-efficient building management practices has surged dramatically, underscoring an urgent mandate for comprehensive studies that integrate cutting-edge optimization algorithms with precise heating load forecasting techniques. These studies are not merely endeavors; they represent concerted efforts to increase building energy efficiency and address mounting concerns regarding sustainability and resource utilization. In the intricate domain of heating, ventilation, and air conditioning (HVAC) systems, energy optimization challenges are being meticulously confronted through rigorous exploration and the application of innovative problem-solving methodologies. This pioneering study introduces groundbreaking methodologies by seamlessly integrating two state-of-the-art optimization algorithms— the Red …Fox Optimization and the Golden Eagle Optimizer— with the Decision Tree model. This fusion is aimed at enhancing the accuracy of heating load predictions and streamlining HVAC system optimization processes, marking a significant leap toward achieving heightened energy efficiency and operational efficacy in building management practices. The study emphasizes the significance of precise heating load prediction in advancing energy efficiency, realizing cost savings, and fostering environmental sustainability in building management. Furthermore, it delves into the multifaceted impact of various building features on heating load, encompassing variables such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. These insights furnish actionable intelligence for refined decision-making processes in both building design and operation. Based on the results, the DT single model experienced the weakest performance among the three models, with R 2 = 0.975 and RMSE = 1.608. The model DTFO (DT + FOX) achieves an extraordinary R 2 value of 0.996 and RMSE value of 0.961 for heating load prediction, surpassing the performance benchmarks set by other models. This achievement holds considerable promise for aiding engineers in crafting energy-efficient buildings, particularly within the swiftly evolving landscape of smart home technologies. Show more
Keywords: Decision tree, heating load, red fox optimization, golden eagle optimizer
DOI: 10.3233/JIFS-240283
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Sriraam, Natarajan | Chinta, Babu | Suresh, Seshadhri | Sudharshan, Suresh
Article Type: Research Article
Abstract: Assessing fetal growth and development requires accurate identification of the fetal area contour and measurement of the Crown-Rump Length (CRL). In this paper, we presented a unique method for autonomously segmenting the fetal region in ultrasound images and calculating the CRL based on the U-Net architecture. Because of its capacity to capture both global and local information, the U-Net model is a popular choice for image segmentation tasks. Our method employs the U-Net model to extract the fetal region contour and measure the CRL, resulting in a dependable and efficient prenatal evaluation solution.
Keywords: Fetal, segmentation, U-Net, ultrasound image
DOI: 10.3233/JIFS-219403
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Macias, Cesar | Soto, Miguel | Cardoso-Moreno, Marco A. | Calvo, Hiram
Article Type: Research Article
Abstract: Mental and cognitive well-being is of paramount significance for human beings. Consequently, the early detection of issues that may culminate in conditions such as depression holds great importance in averting adverse outcomes for individuals. Depression, a prevalent mental health disorder, can severely impact an individual’s quality of life. Timely identification and intervention are critical to prevent its progression. Our research delves into the application of Machine Learning (ML) and Deep Learning (DL) techniques to potentially facilitate the early recognition of depressive tendencies. By leveraging the cognitive triad theory, which encapsulates negative self-perception, a pessimistic outlook on the world, and a …bleak vision of the future, we aim to develop predictive models that can assist in identifying individuals at risk. In this regard, we selected The Cognitive Triad Dataset, which takes into account six different categories that encapsulate negative and positive postures about three different contexts: self context, future context and world context. Our proposal achieved great performance, by relying on a strict preprocessing analysis, which led to the models obtaining an accuracy value of 0.97 when classifying aspect contexts; 0.95 when classifying sentiment-aspects; and a value of 0.93 in accuracy was achieved under the aspect-sentiment paradigm. Our models outperformed those reported in the literature. Show more
Keywords: Cognitive triad inventory, depression detection, machine learning, deep learning, natural language processing
DOI: 10.3233/JIFS-219333
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Mundada, Shyamal | Jain, Pooja | Kumar, Nirmal
Article Type: Research Article
Abstract: Sustainable agriculture revolves around soil organic carbon (SOC), which is essential for numerous soil functions and ecological attributes. Farmers are interested in conserving and adding additional soil organic carbon to certain fields in order to improve soil health and productivity. The relationship between soil and environment that has been discovered and standardized throughout time has enhanced the progress of digital soil-mapping techniques; therefore, a variety of machine learning techniques are used to predict soil properties. Studies are thriving at how effectively each machine learning method maps and predicts SOC, especially at high spatial resolutions. To predict SOC of soil at …a 30 m resolution, four machine learning models—Random Forest, Support Vector Machine, Adaptive Boosting, and k-Nearest Neighbour were used. For model evaluation, two error metrics, namely R2 and RMSE have been used. The findings demonstrated that the calibration and validation sets’ descriptive statistics sufficiently resembled the entire set of data. The range of the calculated SOC content was 0.06 to 1.76 %. According to the findings of the study, Random Forest showed good results for both cases, i.e. evaluation using cross validation and without cross validation. Using cross validation, RF confirmed highest R2 as 0.5278 and lowest RMSE as 0.1683 for calibration dataset while without cross validation it showed R2 as 0.8612 and lowest RMSE as 0.0912 for calibration dataset. The generated soil maps will help farmers adopt precise knowledge for decisions that will increase farm productivity and provide food security through the sustainable use of nutrients and the agricultural environment. Show more
Keywords: Machine learning, remote sensing data, digital soil mapping, spatial predictions, precision farming
DOI: 10.3233/JIFS-240493
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Zheng, Danjing | Song, Xiaona | Song, Shuai | Peng, Zenglong
Article Type: Research Article
Abstract: This paper investigates an observer-based boundary controller design for interconnected nonlinear partial differential equation (PDE) systems. First, the Takagi–Sugeno (T–S) fuzzy model is adopted to accurately describe the target systems. Then, boundary measurements are employed to reduce the number of sensors. Next, considering the phenomenon of abnormal interference that may lead to measurement outliers and observer parameters’ uncertainties, an outlier-resistant non-fragile observer expressed by a saturation function is designed to guarantee the desired control objectives. Moreover, the boundary control approach is employed to trade-off the cost of system design and system performance. Furthermore, utilizing the membership function-dependent Lyapunov functions and …free-weight matrixes, sufficient conditions ensuring the closed-loop systems’ exponential stability are obtained while decreasing the conservativeness of the system stability analysis. Finally, the proposed method’s feasibility and effectiveness are validated by an example. Show more
Keywords: Boundary measurements, boundary control, interconnected nonlinear partial differential equation systems, membership function-dependent Lyapunov functions, outlier-resistant non-fragile observer
DOI: 10.3233/JIFS-238858
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Hayel, Rafa | El Hindi, Khalil | Hosny, Manar | Alharbi, Rawan
Article Type: Research Article
Abstract: Instance-Based Learning, such as the k Nearest Neighbor (kNN), offers a straightforward and effective solution for text classification. However, as a lazy learner, kNN’s performance heavily relies on the quality and quantity of training instances, often leading to time and space inefficiencies. This challenge has spurred the development of instance-reduction techniques aimed at retaining essential instances and discarding redundant ones. While such trimming optimizes computational demands, it might adversely affect classification accuracy. This study introduces the novel Selective Learning Vector Quantization (SLVQ) algorithm, specifically designed to enhance the performance of datasets reduced through such techniques. Unlike traditional LVQ algorithms that …employ random vector weights (codebook vectors), SLVQ utilizes instances selected by the reduction algorithm as the initial weight vectors. Importantly, as these instances often contain nominal values, SLVQ modifies the distances between these nominal values, rather than modifying the values themselves, aiming to improve their representation of the training set. This approach is crucial because nominal attributes are common in real-world datasets and require effective distance measures, such as the Value Difference Measure (VDM), to handle them properly. Therefore, SLVQ adjusts the VDM distances between nominal values, instead of altering the attribute values of the codebook vectors. Hence, the innovation of the SLVQ approach lies in its integration of instance reduction techniques for selecting initial codebook vectors and its effective handling of nominal attributes. Our experiments, conducted on 17 text classification datasets with four different instance reduction algorithms, confirm SLVQ’s effectiveness. It significantly enhances the kNN’s classification accuracy of reduced datasets. In our empirical study, the SLVQ method improved the performance of these datasets, achieving average classification accuracies of 82.55%, 84.07%, 78.54%, and 83.18%, compared to the average accuracies of 76.25%, 79.62%, 66.54%, and 78.19% achieved by non-fine-tuned datasets, respectively. Show more
Keywords: Machine learning, instance based learning, learning vector quantization, k-nearest neighbor, value difference metric (VDM)
DOI: 10.3233/JIFS-235290
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Lu, Yang | Liu, Fengjun | Cao, Bin
Article Type: Research Article
Abstract: English text analysis is required for quantitative grammar, phrase, and word assessment to improve its usage in conversation, drafting, etc. In particular, a teaching system requires the flawless and precise use of English words, phrases, and sentences for fundamental and knowledge-based learning. Data integration and interoperability, data volume, and data variety pose difficulties for text data analytics. This article discusses a heterogeneous English teaching system text analysis solution that integrates a Genetic Algorithm (GA) and Deep Learning (DL). The Text Analytical Model (TAM) uses fused methods (FM) to handle words and their placement for sentence framing. The framed teaching sentence …is analyzed lexically for its precision and meaning with conventional features. Initially, the possible word combinations using the crossover and mutation operations of the genetic process are performed. The outcome of the genetic process forecasts different possible sentence combinations for delivering the English context to students. The mutation process identifies the most precise lexical sentence that fits the subject and context. Based on precision, the DL model is trained to reduce the initial population of the GA process; this is achieved in English teaching through repetitions or drilling performed for different sentences and words. The learning converges towards precision in delivering context-based words and sentences by reducing unnecessary crossovers in the genetic process to reduce computational complexity. This feature, therefore, achieves high-precision convergence with less computation time compared to methods of the same kind. TAM-FM improves the precision convergence, forecast probability, and population refinement by 9.5%, 11.39%, and 8.81%, respectively. TAM-FM reduces the computation time and complexity by 9.67% and 8.3%, respectively. Show more
Keywords: Convergence, deep learning, English teaching, genetic algorithm, text analysis
DOI: 10.3233/JIFS-236249
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Reka, S | Karthik Sainadh Reddy, Dwarampudi | Dhiraj, Inti | Suriya Praba, T
Article Type: Research Article
Abstract: Polycystic Ovary Syndrome (PCOS) is a hormonal condition that typically affects female during the time of their reproduction. It is identified by the disruptions in hormonal balance, particularly an increase in levels of androgen (male hormone) in the female body. PCOS can lead to various symptoms and health complications including irregular menstrual cycles, ovarian cysts, fertility issues, insulin resistance, weight gain, acne, and excess hair growth. The real-world PCOS detection is a challenging task whilst PCOS specific cause is unknown and its symptoms are unclear. Thus, accurate and timely diagnosis of PCOS is crucial for effective management and prevention of …long-term complications. In such cases, Machine learning based PCOS prediction model support diagnostic process, address potential errors and time constraints. Machine learning algorithms can analyze large set of patient data, including medical history, hormonal profiles, and imaging results, to assist in the diagnosis of PCOS. In particular, the performance of data analysis chore and prediction model is improved by ensemble feature selection strategies. These methods concentrate on selecting a subset of pertinent features from a broader range of features. The unstable nature of the outcome of feature selection algorithm is a frequent issue in practical applications, when it is applied multiple times on similar dataset or with slight modifications in the data. Thus, evaluating the robustness of feature selection algorithm is most important. To address these issues and quantify the robustness, this study uses Jenson-Shannon divergence, an information theoretic approach with ensemble feature selection method to handle the various findings, such as complete ranking, half ranking and top-k lists (without ranking). Furthermore, this article proposes a hybrid machine learning classifier with SMOTE – SVM for the prompt detection of PCOS and the performance of the model is compared with a number of other individual classifiers including KNN (K-Nearest Neighbour), Support Vector Machine (SVM), AdaBoost, LR –Logistic Regression, NB –Nave Bayes, RF –Random Forest, Decision Tree. The proposed SWISS-AdaBoost classifier surpassed other models with 97.81% of accuracy and AUC of 99.08%. Show more
Keywords: Polycystic ovary syndrome (PCOS), Jenson-shannon divergence, SVM (Support Vector Machine), K-nearest neighbour, logistic regression, decision tree, naive bayes and AdaBoost
DOI: 10.3233/JIFS-219402
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Ezhilarasie, R. | MohanRaj, I. | Ramakrishnan, Thiruvikram Gopichettipalayam | Madhavan, Vyas | Narayan, Keshav | Umamakeswari, A.
Article Type: Research Article
Abstract: Internet of Things (IoT) devices are major stakeholders of contemporary network bandwidth. The proliferation of IoT devices and the demand for latency-free communication in time-critical applications has proven the drawback of cloud-based solutions. Edge computing is an paradigm that reduces the application’s response time by utilizing computation and storage proximate to each devices. Privacy in cloud computing is attained by system virtualization, containerization, among other evolved technologies. As privacy remains a primary concern, there is a need to test the feasibility of resource-constrained edge devices. Hence, this work aimed to examine the usability of such devices in edge computing by …benchmarking on different runtime environments. The results reveal that a standard mechanism was achieved for defining the criteria to identify the suitable edge devices for computation offloading, particularly for a set of smart traffic surveillance use cases. Further, an optimization algorithm was designed to generate an optimum schedule that decides the best device to execute a particular task from the set of suitable edge devices to enhance energy and execution time in a global view. Based on the feasibility study and optimal schedule, a makespan that is nearly 11 times better than local execution for the considered traffic surveillance workflow was achieved. Show more
Keywords: Container, docker, edge computing, IoT, LXC, offloading, single board computer
DOI: 10.3233/JIFS-219424
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Bukya, Hanumanthu | Bhukya, Raghuram | Harshavardhan, A.
Article Type: Research Article
Abstract: Fog computing has several undeniable benefits, such as enhancing near-real-time response, reducing transmission costs, and facilitating IoT analysis. This technology is poised to have a significant impact on businesses, organizations, and our daily lives. However, mobile user equipment struggles to handle the complex computing tasks associated with modern applications due to its limited processing power and battery life. Edge computing has emerged as a solution to this problem by relocating processing to nodes at the network’s periphery, which have more computational capacity. With the rapid evolution of wireless technologies and infrastructure, edge computing has become increasingly popular. Nevertheless, managing fog …computing resources remains challenging due to resource constraints, heterogeneity, and distant nodes. For delay-sensitive intelligent IoT applications within the fog computing architecture, cooperation and communication processing resources in 6 G and future networks are essential. This study proposes a joint computational and optimized resource allocation (JCORA) technique to accelerate the processing of data from intelligent IoT sensors in a cell association environment. The proposed technique utilizes an uplink and downlink power allocation factor and the shortest job first (SJF) task scheduling system to optimize user fairness and decrease data processing time. This is a complex assignment due to several non-convex limitations. The suggested JCORA-SJF model simultaneously optimizes time partitioning, computing task processing mode selection, and target sensing location selection to maximize the weighted total of task processing and communication performance. The simulation results demonstrate the effectiveness of the proposed JCORA-SJF algorithms, and the system’s scalability is also examined. Show more
Keywords: Fog computing, Internet of Things (IoT), resource allocation, edge computing networks, optimized resource allocation (JCORA), shortest job first (SJF)
DOI: 10.3233/JIFS-219421
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Singh, Pardeep | Singh, Monika | Singh, Nitin Kumar | Das, Prativa | Chand, Satish
Article Type: Research Article
Abstract: Social media platforms play vital roles in disseminating information during crisis situations. Many rescue agencies, media outlets, and volunteers regularly monitor this data to identify and analyze disasters, ultimately mitigating life risks. However, effectively categorizing these messages based on information types is crucial for enhancing the situational awareness of emergency responders. This paper addresses the challenge of analyzing informal crisis-related social media texts by classifying disaster event tweets into 10 humanitarian categories associated with 19 major natural disaster events. We fine-tune seven state-of-the-art pre-trained transformer models and compare their performance with the recently introduced domain-specific models, i.e., CrisisTransformers. We empirically …found that CrisisTransformers outperform seven strong baseline transformer models in classifying disaster-specific tweets from the HumAID dataset, achieving a macro-averaged F1 score of 0.77. Our work contributes to the crisis computing field by improving the classification of disaster-related tweets and enhancing the capabilities of emergency responders and disaster management organizations. Show more
Keywords: Transformers, crisis computing, disaster classification, Twitter, disaster response
DOI: 10.3233/JIFS-219419
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Muppavarapu, Vamsee | Ramesh, Gowtham
Article Type: Research Article
Abstract: The W3C linked building data group is working on modeling the information for integrating building information with building life cycle data using Semantic Web technologies. The community has proposed a set of semantic models such as ifcOWL and Building Topology Ontology (BOT), to model various applications across Architecture, Engineering, Construction, and Operation (AECO) domain. On the other hand, the Semantic Web of Things (SWoT) group proposed standard semantic models such as M3-lite and BOSH ontologies for describing the sensor networks, observations, and sensor measurements. Both the aforementioned domains have their own siloed applications and with the evolution of the smart …home domain, there is a need to combine the knowledge of building information with the sensor knowledge to develop cross-domain applications. However, in order to develop such downstream applications leveraging advantages from both domains requires interoperable knowledge. This paper proposes an interoperable ontology, Building Topology Ontology for Smart Homes (BOTSH), with the aim of aligning the building domain with sensors domain semantic models. The BOTSH ontology facilitates capturing knowledge from both domains and helps in developing cross-domain applications. The potential of the proposed model was demonstrated using a real-life building model based on the competency questions framed by the domain experts. Show more
Keywords: Semantic web of things, building information models, building topology, sensors and observations, smart homes, knowledge graphs, semantic applications
DOI: 10.3233/JIFS-219425
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Pillai, Leena G. | Muhammad Noorul Mubarak, D. | Sherly, Elizabeth
Article Type: Research Article
Abstract: Speech production is a complex sequential process which involve the coordination of various articulatory features. Among them tongue being a highly versatile active articulator responsible for shaping airflow to produce targeted speech sounds that are intellectual, clear, and distinct. This paper presents a novel approach for predicting tongue and lip articulatory features involved in a given speech acoustics using a stacked Bidirectional Long Short-Term Memory (BiLSTM) architecture, combined with a one-dimensional Convolutional Neural Network (CNN) for post-processing with fixed weights initialization. The proposed network is trained with two datasets consisting of simultaneously recorded speech and Electromagnetic Articulography (EMA) datasets, each …introducing variations in terms of geographical origin, linguistic characteristics, phonetic diversity, and recording equipment. The performance of the model is assessed in Speaker Dependent (SD), Speaker Independent (SI), corpus dependent (CD) and cross corpus (CC) modes. Experimental results indicate that the proposed model with fixed weights approach outperformed the adaptive weights initialization with in relatively minimal number of training epochs. These findings contribute to the development of robust and efficient models for articulatory feature prediction, paving the way for advancements in speech production research and applications. Show more
Keywords: Acoustic-to-articulatory inversion, smoothing techniques, articulatory features, weight initialization, bidirectional long short-term memory
DOI: 10.3233/JIFS-219386
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Sheshadri, Shailashree K. | Gupta, Deepa
Article Type: Research Article
Abstract: Non-Autoregressive Machine Translation (NAT) represents a groundbreaking advancement in Machine Translation, enabling the simultaneous prediction of output tokens and significantly boosting translation speeds compared to traditional auto-regressive (AR) models. Recent NAT models have adeptly balanced translation quality and speed, surpassing their AR counterparts. The widely employed Knowledge Distillation (KD) technique in NAT involves generating training data from pre-trained AR models, enhancing NAT model performance. While KD has consistently proven its empirical effectiveness and substantial accuracy gains in NAT models, its potential within Indic languages has yet to be explored. This study pioneers the evaluation of NAT model performance for Indic …languages, focusing mainly on Kashmiri to English translation. Our exploration encompasses varying encoder and decoder layers and fine-tuning hyper-parameters, shedding light on the vital role KD plays in facilitating NAT models to capture variations in output data effectively. Our NAT models, enhanced with KD, exhibit sacreBLEU scores ranging from 16.20 to 22.20. The Insertion Transformer reaches a SacreBLEU of 22.93, approaching AR model performance. Show more
Keywords: Neural machine translation, auto-regressive translation, non-autoregressive translation, Levenshtein Transformer, insertion transformer, knowledge distillation
DOI: 10.3233/JIFS-219383
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Bai, Xiaojun | Jia, Haiyang | Fu, Yanfang | Ji, Yu | Li, Suyang
Article Type: Research Article
Abstract: Predicting the remaining life of aircraft engines is paramount in aviation maintenance management. It helps formulate maintenance schedules, reduce maintenance expenses, and enhance flight safety. Traditional methods for predicting the remaining life of an engine suffer from significant errors and limited generalization capabilities. This paper introduces a predictive model based on Long Short-Term Memory (LSTM) networks and Feedforward Neural Networks (FNN) to improve prediction accuracy. Furthermore, the model’s hyperparameters undergo optimization using the Gannet Optimization Algorithm (GOA). Leveraging the N-CMAPSS dataset for prediction and transfer learning experiments, the results highlight the significant advantages of the proposed model in forecasting the …remaining life of aircraft engines. When subjected to training and testing on the DS02 equipment dataset, the root mean square error (RMSE) registers at 5.04. At that time, the score function reached a value of 1.39, surpassing the performance of current state-of-the-art prediction methods. Additionally, in terms of its transfer learning capabilities, the model demonstrates minimal fluctuations in RMSE when applied directly to datasets of various other engine models. It consistently maintains a high level of predictive accuracy. Show more
Keywords: Remaining life prediction, N-CMAPSS dataset, long short-term memory network, Gannet Optimization Algorithm (GOA)
DOI: 10.3233/JIFS-236225
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Anbumani, A. | Jayanthi, P.
Article Type: Research Article
Abstract: GLOBOCAN 2020 states that, after lung cancer, breast cancer is the most common cancer worldwide, affecting many women [1 ]. AI-based computer-assisted detection/diagnosis techniques can assist radiologists in diagnosing breast cancer earlier. Mammography is one of the most widely used and effective methods for detecting and treating breast cancer. This research proposes a customised deep-learning model for breast cancer categorization. To effectively categorise the breast cancer mammography image, two customised CNN models are proposed. Three real-time datasets such as MIAS, CBIS-DDSM, and INbreast were used to evaluate the efficacy of the proposed categorization strategy. The results show that the proposed …method effectively classifies the image and obtains 98.78%, 97.84% and 96.92% accuracy for the datasets MIAS, INbreast and CBIS-DDSM. Show more
Keywords: Breast cancer, CNN, deep learning, mammography, classification
DOI: 10.3233/JIFS-232896
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Cruz, Elsy | Santos, Lourdes | Calvo, Hiram | Anzueto-Rios, Álvaro | Villuendas-Rey, Yenny
Article Type: Research Article
Abstract: In recent years, multiple studies have highlighted the growing correlation between breast density and the risk of developing breast cancer. In this research, the performance of two convolutional neural network architectures, VGG16 and VGG19, was evaluated for breast density classification across three distinct scenarios aimed to compare the masking effect on the models performance. These scenarios encompass both binary classification (fatty and dense) and multi-class classification based on the BI-RADS categorization, utilizing a subset of the ABC-Digital Mammography Dataset. In the first experiment, focusing on cases with no masses, VGG16 achieved an accuracy of 93.33% and 90.00% for two and …four-class classification. The second experiment, which involved cases with benign masses, yielded a remarkable accuracy of 95.83% and 93.33% with VGG16, respectively. In the third and last experiment, an accuracy of 88.00% was obtained using VGG16 for the two-class classification, while VGG19 delivered an accuracy of 93.33% for the four-class classification. These findings underscore the potential of deep learning models in enhancing breast density classification, with implications for breast cancer risk assessment and early detection. Show more
Keywords: Mammography, breast tissue density, convolutional neural networks
DOI: 10.3233/JIFS-219378
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Zheng, Z. | Gao, J.B. | Weng, Z.
Article Type: Research Article
Abstract: The body size parameter of cattle is an important index reflecting the growth and development and health condition of cattle. The traditional manual contact measurement is not only a large workload and difficult to measure, but also prone to problems such as affecting the normal life habits of cattle. In this paper, we address this problem by proposing a contactless body size measurement method for cattle based on machine vision. Firstly, the cattle is confined to a fixed space using a position-limiting device, and images of the body of the cattle are taken from three directions: top, left, and right, …using multiple cameras. Secondly, the image is segmented using a fuzzy clustering algorithm based on neighborhood adaptive local spatial information improvement, and the image is processed to extract the contour images of the top view and side view. The key points of body measurements were extracted using interval division and curvature calculation for the side view images, and the key point information was extracted using skeleton extraction and pruning for the top view images, which realized the measurements of body height(BH), rump height(RH), body slanting length(BSL), and abdominal circumference(AC) parameters of the cattle. The correlation between body size and weight data obtained by contactless methods was investigated and the modeled using one-factor linear regression, one-factor nonlinear regression, multivariate stepwise regression, RBF network fitting, BP neural network fitting, support vector machine, and particle swarm optimization-based support vector machine methods, respectively. Information on body size parameters was collected from 137 cattles, and the results showed that the maximum errors between the measured and actual values of BH, RH, BSL and AC were 5.0%, 4.4%, 3.6%, and 5.5%, respectively. Correlation of BH, RH, BSL and AC with weight obtained by non-contact methods was > 0.75. The BH parameter can be selected in the single-factor growth monitoring. The multi-body scale can reflect the growth status of cattle more comprehensively, in which RH, BSL and AC are important detection parameter; the multi-factor nonlinear model can reflect the growth characteristics of cattle more comprehensively. The contactless measurement method proposed in the paper can effectively improve the work efficiency and reduce the stress reaction of cattle, which is a long-term and effective monitoring method, and is of great significance in promoting accurate and welfare cattle rearing. Show more
Keywords: Image processing, body size measurement, fuzzy clustering, non-contact measurement, cattle weight estimation
DOI: 10.3233/JIFS-238016
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Vidhya, S.S. | Mathi, Senthilkumar | Anantha Narayanan, V. | Neelakanta Iyer, Ganesh
Article Type: Research Article
Abstract: The Internet of Things lies in establishing low-power and lossy networks created by interconnecting many wireless devices with limited resources. Fascinatingly, an IPv6 routing protocol for low-power and lossy networks has become a common practice for these applications. Even though this protocol addresses the challenges of low-power networks, many issues concerning the quality of service and energy consumption are open to the research community. The protocol relies on a destination-oriented directed acyclic graph, and the root selection depends on some constraints and metrics associated with an objective function (OF). The conventional OFs select parents based on a single metric, such …as the expected transmission count or the number of nodes to travel. The current paper proposes an enhancement to the OF metric, aiming to decrease node energy and enhance the quality of service. This improvement is achieved by the factors, including the received signal strength indicator, node distance, power, link quality indicator, and expected transmission count, to select reliable communication links. The minimum power needed for reliable communication is predicted from the received signal strength indicator, node distance, receiver power, and link quality indicator using a nonlinear support vector machine. The OF value of the candidate node is computed from the power level and expected transmission count combined using the Takagi-Sugeno fuzzy model. The proposed OF is implemented in the Cooja simulator and compared against minimum rank with hysteresis OF and OF zero. A considerable improvement in the packet delivery ratio and a 37.5% reduction in energy consumption is obtained. Show more
Keywords: Classification, fuzzification, power prediction, received signal strength indicator, transmission power, link quality indicator, low power networks, TSK fuzzy model
DOI: 10.3233/JIFS-219420
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Mathi, Senthilkumar | Ramalingam, Venkadeshan | Sree Keerthi, Angara Venkata | Abhirup, Kothamasu Ganga | Sreejith, K. | Dharuman, Lavanya
Article Type: Research Article
Abstract: Long-term evolution in wireless broadband communication aims to provide secure communication for users and a high data rate for a fourth-generation network. Even though the fourth-generation network provides security, some loopholes lead to several attacks on the fourth-generation network attacks. The denial-of-service attack occurs when the user communicates with a rogue base station, and the radio base station in fourth-generation long-term evolution networks ensures that the user is attached to the rogue node assigned network. The location leak attack occurs when the packets are sniffed to find any user’s location using its temporary mobile subscriber identity. Prevention of rogue base …station and location leak attacks helps the system achieve secure communication between the participating entities. Earlier works in long-term evolution mobility management do not address preventing attacks such as denial-of-service, rogue base stations and location leaks and suffer from computational costs while providing security features. Hence, the present paper addresses the vulnerability of these attacks. It also investigates how these attacks occur and exposes communication in the fourth-generation network. To mitigate these vulnerabilities, the paper proposes a novel authentication scheme. The proposed scheme is simulated using Network Simulator 3, and the security analysis of the proposed scheme is shown using AVISPA –a security tool. Numerical analysis demonstrates that the proposed scheme significantly reduces communication overhead and computational costs associated with the fourth-generation long-term evolution authentication mechanism. Show more
Keywords: Authentication, long-term evolution, denial-of-service, attack, location leak, confidentiality
DOI: 10.3233/JIFS-219406
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Zheng, Lina | Wang, Yini | Wang, Sichun
Article Type: Research Article
Abstract: Due to the relatively high cost of labeling data, only a fraction of the available data is typically labeled in machine learning. Some existing research handled attribute selection for partially labeled data by using the importance of an attribute subset or uncertainty measure (UM). Nevertheless, it overlooked the missing rate of labels or the choice of the UM with optimal performance. This study uses discernibility relation and the missing rate of labels to UM for partially labeled data and applies it to attribute selection. To begin with, a decision information system for partially labeled data (pl-DIS) can be used to …induce two equivalent decision information systems (DISs): a DIS is constructed for labeled data (l-DIS), and separately, another DIS is constructed for unlabeled data (ul-DIS). Subsequently, a discernibility relation and the percentage of missing labels are introduced. Afterwards, four importance of attribute subset are identified by taking into account the discernibility relation and the missing rate of labels. The sum of their importance, which is determined by the label missing rates of two DISs, is calculated by weighting each of them and adding them together. These four importance may be seen as four UMs. In addition, numerical simulations and statistical analyses are carried out to showcase the effectiveness of four UMs. In the end, as its application for UM, the UM with optimal performance is used to attribute selection for partially labeled data and the corresponding algorithm is proposed. The experimental outcomes demonstrate the excellence of the proposed algorithm. Show more
Keywords: Partially labeled data, pl-DIS, uncertainty measure, attribute selection, the missing rate of labels, discernibility relation
DOI: 10.3233/JIFS-240581
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Rao, Vishisht Srihari | Vinay, P. | Uma, D.
Article Type: Research Article
Abstract: A hazy image is characterized by atmospheric conditions that reduce the image’s clarity and contrast, thereby making it less visible. This degradation in image quality can hinder the performance of advanced computer vision tasks such as object detection and identifying open spaces which need to perform with high accuracy in important real world applications such as security surveillance and autonomous driving. In the recent past, the use of deep learning in image processing tasks have shown a remarkable improvement in performance, in particular, Convolutional Neural Networks (CNNs) perform superior to any other type of neural network in image related tasks. …In this paper, we propose the addition of Channel Attention and Pixel Attention layers to four state-of-the-art CNNs, namely, GMAN, U-Net, 123-CEDH and DMPHN, used for the task of image dehazing. We show that the addition of these layers yields a non-trivial improvement on the quality of the dehazed images which we show qualitatively with examples and quantitatively by obtaining PSNR and SSIM scores of 28.63 and 0.959 respectively. Through the experiments, we show that the addition of the mentioned attention layers to the GMAN architecture yields the best results. Show more
Keywords: Dehazing, deep neural network, convolutional neural network, attention
DOI: 10.3233/JIFS-219391
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Agrawalla, Bikash | Shukla, Alok Kumar | Tripathi, Diwakar | Singh, Koushlendra Kumar | Ramachandra Reddy, B.
Article Type: Research Article
Abstract: Software fault prediction, which aims to find and fix probable flaws before they appear in real-world settings, is an essential component of software quality assurance. This article provides a thorough analysis of the use of feature ranking algorithms for successful software failure prediction. In order to choose and prioritise the software metrics or qualities most important to fault prediction models, feature ranking approaches are essential. The proposed focus on applying an ensemble feature ranking algorithm to a specific software fault dataset, addressing the challenge posed by the dataset’s high dimensionality. In this extensive study, we examined the effectiveness of multiple …machine learning classifiers on six different software projects: jedit, ivy, prop, xerces, tomcat, and poi, utilising feature selection strategies. In order to evaluate classifier performance under two scenarios—one with the top 10 features and another with the top 15 features—our study sought to determine the most relevant features for each project. SVM consistently performed well across the six datasets, achieving noteworthy results like 98.74% accuracy on “jedit” (top 10 features) and 91.88% on “tomcat” (top 10 features). Random Forest achieving 89.20% accuracy on the top 15 features, on “ivy.” In contrast, NB repeatedly recording the lowest accuracy rates, such as 51.58% on “poi” and 50.45% on “xerces” (the top 15 features). These findings highlight SVM and RF as the top performers, whereas NB was consistently the least successful classifier. The findings suggest that the choice of feature ranking algorithm has a substantial impact on the fault prediction models’ predictive accuracy and effectiveness. When using various ranking systems, the research also analyses the trade-offs between computing complexity and forecast accuracy. Show more
Keywords: Software fault prediction, ensemble techniques, feature ranking, random forests, support vector machine
DOI: 10.3233/JIFS-219431
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Su, Xue | Chen, Lijun
Article Type: Research Article
Abstract: Incomplete real-valued data often misses some labels due to the high cost of labeling data. This paper investigates for partially labeled incomplete real-valued data and considers its application in semi-supervised attribute reduction. There are two decision information systems (DISs) in a partially labeled incomplete real-valued data DIS (p-IRVDIS): a labeled incomplete real-valued data DIS (l-IRVDIS) and a unlabeled incomplete real-valued data DIS (u-IRVDIS). The degree of importance on an attribute subset in a p-IRVDIS are defined using an indistinguishable relation and conditional information entropy. It is the weighted sum of l-IRVDIS and u-IRVDIS using the missing rate of label to …measure p-IRVDIS uncertainty. Based on the degree of importance, an adaptive semi-supervised attribute reduction algorithm in a p-IRVDIS is proposed. This algorithm can automatically adapt to various missing rates of label. The experimental results on 8 datasets reveal that the proposed algorithm performs statistically better than some state-of-the-art algorithms. Show more
Keywords: p-IRVDIS, the degree of importance, semi-supervised attribute reduction, indiscernibility relation, conditional information entropy
DOI: 10.3233/JIFS-239559
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Tahir Kidwai, Umar | Akhtar, Nadeem | Nadeem, Mohammad | Alroobaea, Roobaea Salim
Article Type: Research Article
Abstract: In recent years, the surge in online content has necessitated the development of intelligent recommender systems capable of offering personalized suggestions to users. However, these systems often encapsulate users within a “filter bubble”, limiting their exposure to a narrow range of content. This study introduces a novel approach to address this issue by integrating a novel diversity module into a knowledge graph-based explainable recommender system. Utilizing the Movie Lens 1M dataset, this research pioneers in fostering a more nuanced and transparent user experience, thereby enhancing user trust and broadening the spectrum of recommendations. Looking ahead, we aim to further refine …this system by incorporating an explicit feedback loop and leveraging Natural Language Processing (NLP) techniques to provide users with insightful explanations of recommendations, including a comprehensive analysis of filter bubbles. This initiative marks a significant stride towards creating a more inclusive and informed recommendation landscape, promising users not only a wider array of content but also a deeper understanding of the recommendation mechanisms at play. Show more
Keywords: Recommender system, explainable recommendations, filter bubble, knowledge graph, diversity
DOI: 10.3233/JIFS-219416
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Li, Xin | Hao, Miao | Ru, Changhai | Wang, Yong | Zhu, Junhui
Article Type: Research Article
Abstract: With the development of science and technology, people have higher and higher requirements for robots. The application of robots in industrial production is also increasing, and there are more applications in people’s lives. Therefore, robots must have a better ability to receive and process the external environment. Therefore, visual servo system appears. Pose estimation is a major problem in the current vision system. It has great application value in positioning and navigation, target tracking and recognition, virtual reality and motion estimation. Therefore, this paper put forward the research of robot arm pose estimation and control based on machine vision. This …paper first analyzed the technology of machine vision, and then carried out experiments. The accuracy and stability of the two methods for robot arm pose estimation were compared. The experimental results showed that when the noise of Kalman’s centralized data fusion method was 1 pixel, the maximum error of the X-axis angle was only 0.55, and the average error was 0.02. In Kalman’s distributed data fusion method, the average error of X-axis displacement was 0.06, and the maximum value was 17.66. In terms of accuracy, Kalman’s centralized data fusion method was better. In terms of stability, Kalman’s centralized data fusion method was also better. However, in general, these two methods had very good results, and could accurately control the position and posture of the manipulator. Show more
Keywords: Position and attitude estimation of manipulator, machine vision, kalman filter, world coordinate system
DOI: 10.3233/JIFS-237904
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wang, Wei | Xu, Dehao | Lv, Jing | Rong, Jian | He, Donggang | Li, Shuangshuang
Article Type: Research Article
Abstract: The factors of water quality in the intensive marine stichopus japonicus aquaculture process are changing with seasons, so water temperature, salinity, pH value and nitrite were selected as auxiliary variables to measure the concentration of ammonia nitrogen. FCM (Fuzzy C-means) algorithm was adopted to classify them. Based on the EM (Expectation Maximization) algorithm, fuzzy sub-models of ammonia nitrogen concentration were constructed around each operating point, and finally the fuzzy sub-models were combined according to the posterior distribution of the characteristics of the sampling data. Based on the data collected at Xinyulong Marine Biological Seed Technology Co., Ltd, in Dalian China, …the ammonia nitrogen concentration prediction model was tested and verified. Show more
Keywords: Water quality, stichopus japonicus, expectation maximization, multi-model
DOI: 10.3233/JIFS-239032
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Shuangyuan, Li | Qichang, Li | Mengfan, Li | Yanchang, Lv
Article Type: Research Article
Abstract: With the development of information technology, the number and methods of cyber attacks continue to increase, making network security issues increasingly important. Intrusion detection has become a vital means of dealing with cyber threats. Current intrusion detection methods predominantly rely on machine learning. However, machine learning suffers from limitations in detection capability and the requirement for extensive feature engineering. Additionally, current intrusion detection datasets face the challenge of data imbalance. To address these challenges, this paper proposes a novel solution leveraging Generative Adversarial Networks (GANs) to balance the dataset and introduces an attention mechanism into the generator to efficiently extract …key feature information, the mechanism can effectively sort the key information of the data and quickly capture important features. Subsequently, a combination of 1D Convolutional Neural Networks (1DCNN) and Bidirectional Gated Recurrent Units (BiGRU) is employed to construct a classification model capable of extracting both spatial and temporal features. Furthermore, Particle Swarm Optimization (PSO) is utilized to optimize the input weights and hidden biases of the model, so as to further improve the accuracy and robustness of the model. Finally, the model is trained and implemented for network intrusion detection. To demonstrate the applicability of the model, experiments were conducted using the NSL-KDD dataset and the UNSW-NB15 dataset. The final results showed that the proposed model outperformed other models, achieving accuracies of 99.15% and 97.33% on the respective datasets. This indicates that the model improves the efficiency of network intrusion detection and better ensures the effectiveness of network security. Show more
Keywords: Intrusion detection, GAN, 1DCNN, BiGRU, PSO
DOI: 10.3233/JIFS-236285
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Liu, Xia | Zhang, Xianyong | Chen, Jiaxin | Chen, Benwei
Article Type: Research Article
Abstract: Attribute reduction is an important method in data analysis and machine learning, and it usually relies on algebraic and informational measures. However, few existing informational measures have considered the relative information of decision class cardinality, and the fusion application of algebraic and informational measures is also limited, especially in attribute reductions for interval-valued data. In interval-valued decision systems, this paper presents a coverage-credibility-based condition entropy and an improved rough decision entropy, further establishes corresponding attribute reduction algorithms for optimization and applicability. Firstly, the concepts of interval credibility, coverage and coverage-credibility are proposed, and thus, an improved condition entropy is defined …by virtue of the integrated coverage-credibility. Secondly, the fused rough decision entropy is constructed by the fusion of improved condition entropy and roughness degree. By introducing the coverage-credibility, the proposed uncertainty measurements enhance the relative information of decision classes. In addition, the nonmonotonicity of the improved condition entropy and rough decision entropy is validated by theoretical proofs and experimental counterexamples, with respect to attribute subsets and thresholds. Then, the two rough decision entropies drive monotonic and nonmonotonic attribute reductions, and the corresponding reduction algorithms are designed for heuristic searches. Finally, data experiments not only verify the effectiveness and improvements of the proposed uncertainty measurements, but also illustrate the reduction algorithms optimization through better classification accuracy than four comparative algorithms. Show more
Keywords: Rough sets, Attribute reduction, Interval-valued decision systems, Algebraic measures and informational measures, Coverage-credibility-based rough decision entropy
DOI: 10.3233/JIFS-239544
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Li, Zexin | Li, Qiulin | Li, Zepeng | Huang, Lixia | Pu, Song | Luo, Zunhao
Article Type: Research Article
Abstract: Tourist attraction recommendation (TAR) problem has gained attention due to its potential to enhance tourist services. Existing studies focus on meeting tourists’ individual needs, but overlook the tour operator’s interests as the TAR service provider. The TAR problem is more challenging due to the high variability of customer demand, which is difficult to predict accurately beforehand. This paper examines TAR in response to random changes in tourist demand, aiming to minimize transportation costs, cooperation expenses between tour operators and attractions, ticket booking fees, and promotion costs, where ambiguity set is defined by means, mean absolute deviations, and the support set. …Firstly a distributionally robust model is proposed to identify suitable attractions for cooperation, along with determining the associated costs of ticket booking, promotion, and tourist transportation, while considering chance constraint on the service level. Subsequently, the model is reformulated into a tractable mixed integer linear programming model using duality theory. Numerical experiments illustrate that the proposed model outperforms both the stochastic programming model and the deterministic model in terms of risk level by out-of-sample test. In particularly, considering uncertainty and distributional ambiguity can make the model more accurate and credible. Show more
Keywords: Attraction recommendation, distributionally robust optimization, demand uncertainty
DOI: 10.3233/JIFS-238169
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Tian, Wen | Zhang, Yining | Fang, Qin | Liu, Weidong
Article Type: Research Article
Abstract: In order to solve the problem of imbalance between traffic demand and airspace capacity of high-altitude air route network, reduce unnecessary delay costs, and improve air route operation efficiency, the resource allocation problem of multi-objective air route network for CTOP program is studied. Taking the affected flights in the congested area of air routes as the research object, taking into account the constraints of actual flight operation, FCA time slot resource availability limit, FCA capacity limit, etc., aiming at minimizing the total delay time of each flight and maximizing the fairness of airlines, a multi-objective optimization model for air route …network resource allocation is established, and an improved NSGA-II algorithm is designed to solve the model. Based on the actual operation data of air routes in East China, the Pareto optimal solution set is obtained and compared with the traditional RBS algorithm, the average delay time is reduced by 5.49% and the average fair loss degree is reduced by 66.76%. The results show that the proposed multi-objective optimization model and the improved NSGA-II algorithm have better performance, which can take into account the fairness of each airline on the basis of reducing the total delay cost, realize the allocation of optimal flight trajectories and time slot resources, and provide a reference scheme for air traffic control resource scheduling. Show more
Keywords: Air traffic flow management, resource allocation, collaborative trajectory options program (CTOP), multi-objective optimization, genetic algorithm
DOI: 10.3233/JIFS-233588
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Velusamy, Saravanan | Murugan, Pallikonda Rajasekaran | Vishnuvarthanan, G. | Thiyagarajan, Arunprasath | Ramaraj, Kottaimalai | Kamalakkannan, Vidyavathi
Article Type: Research Article
Abstract: Due to the advantages of Electrocardiogram (ECG) signals, which are challenging to replicate yet easy to get, ECG-based identification has become a new path in biometric recognition research. These classic feature extraction techniques require Hand-crafted or feature-specific implications. The methods used for selection and integration of features, are time-consuming. The main objective of this study is develop deep learning approach to study the features of ECG data digital characteristics, thus saving a lot of signal pre-processing steps. This research proposed novel technique in X-wave recognition of ECG signal using max-min threshold technique and classification of ECG signal. This signal has …been processed for noise removal and normalization. Then this processed signal has been used to recognize X-wave from ECG signal. From recognized X-wave, the ECG signal has been classified using Improved Support Vector Machine (ISVM). The QRS complex has been detected using Stacked Auto-Encoder with Neural Networks (SAENN). The study took raw ECG signals and entropy-based features evaluated from extracted QRS complexes. Exams are based on classifying heart disorders into two, five, and twenty classes. The experimental findings showed that our suggested model attained a high classification accuracy of 97%, precision of 89%, recall of 90%, F-1 score of 88%. Show more
Keywords: Electrocardiogram, X-wave recognition, QRS complex, cross-validation, entropy-based features, classification
DOI: 10.3233/JIFS-241456
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Gong, Zengtai | Zhang, Yuanyuan
Article Type: Research Article
Abstract: In this paper, we focus on generalized fuzzy complex numbers and propose a straightforward matrix method to solve the dual rectangular fuzzy complex matrix equations C · Z ˜ + L ˜ = R · Z ˜ + W ˜ , in which C and R are crisp complex matrices and Z ˜ , L ˜ and M ˜ …are fuzzy complex number matrices. The existing methods for solving fuzzy complex matrix equations involve separately calculating the extended solution and the corresponding parameters of the real and imaginary parts, whereby we obtain the algebraic solution of the equations. By means of the interval arithmetic and embedding approach, the n × n dual rectangular fuzzy complex linear systems could be converted into 2n × 2n fuzzy linear systems, which are also equivalent to the 4n × 4n real linear systems. By directly solving the 4n × 4n real linear systems, the algebraic solutions can be obtained. The general dual rectangular fuzzy complex matrix equations and dual rectangular fuzzy complex linear systems are investigated by the generalized inverses of matrices. Finally, some examples are given to illustrate the effectiveness of method. Show more
Keywords: Fuzzy number, fuzzy complex number, rectangular fuzzy complex number, dual rectangular fuzzy complex matrix equations
DOI: 10.3233/JIFS-239305
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Aguilar-Canto, Fernando | Luján-García, Juan Eduardo | Espinosa-Juárez, Alberto | Calvo, Hiram
Article Type: Research Article
Abstract: Inferring phylogenetic trees in human populations is a challenging task that has traditionally relied on genetic, linguistic, and geographic data. In this study, we explore the application of Deep Learning and facial embeddings for phylogenetic tree inference based solely on facial features. We use pre-trained ConvNets as image encoders to extract facial embeddings and apply hierarchical clustering algorithms to construct phylogenetic trees. Our methodology differs from previous approaches in that it does not rely on preconstructed phylogenetic trees, allowing for an independent assessment of the potential of facial embeddings to capture relationships between populations. We have evaluated our method with …a dataset of 30 ethnic classes, obtained by web scraping and manual curation. Our results indicate that facial embeddings can capture phenotypic similarities between closely related populations; however, problems arise in cases of convergent evolution, leading to misclassifications of certain ethnic groups. We compare the performance of different models and algorithms, finding that using the model with ResNet50 backbone and the face recognition module yields the best overall results. Our results show the limitations of using only facial features to accurately infer a phylogenetic tree and highlight the need to integrate additional sources of information to improve the robustness of population classification. Show more
Keywords: Convolutional neural networks, deep learning, hierarchical clustering, phylogenetic tree
DOI: 10.3233/JIFS-219343
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-09, 2024
Authors: Li, Yuangang | Gao, Xinrui | Ni, Hongcheng | Song, Yingjie | Deng, Wu
Article Type: Research Article
Abstract: In this paper, an adaptive differential evolution algorithm with multi-strategy, namely ESADE is proposed to solve the premature convergence and high time complexity for complex optimization problem. In the ESADE, the population is divided into several sub-populations after the fitness value of each individual is sorted. Then different mutation strategies are proposed for different populations to balance the global exploration and local optimization. Next, a new self-adaptive strategy is designed adjust parameters to avoid falling into local optimum while the convergence accuracy has reached its maximum value. And a complex airport gate allocation multi-objective optimization model with the maximum flight …allocation rate, the maximum near gate allocation rate, and the maximum passenger rate at near gate is constructed, which is divided into several single-objective optimization model. Finally, the ESADE is applied solve airport gate allocation optimization model. The experiment results show that the proposed ESADE algorithm can effectively solve the complex airport gate allocation problem and achieve ideal airport gate allocation results by comparing with the current common heuristic optimization algorithms. Show more
Keywords: Differential evolution, multi-strategy, self-adaptive strategy, gate allocation, optimization
DOI: 10.3233/JIFS-238217
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Sowndeswari, S. | Kavitha, E. | Krishnamoorthy, Raja
Article Type: Research Article
Abstract: The development of tiny sensing nodes efficient for wireless communication in Wireless Sensor Networks (WSNs) can be attributed to the rapid advancements in processors and radio technology. Data transmission occurs through multi-hop routing in WSN, which relies on nodes’ cooperation. The collaboration between nodes has rendered these networks susceptible to various attacks. It is imperative to employ a security scheme to evaluate the dependability of nodes in distinctive malicious nodes from non-malicious nodes. In recent years, there has been a growing significance placed on security-based routing protocols with energy constraints as valuable mechanisms for enhancing the security and performance of …WSNs. A novel solution called the Deep Learning-based Hybrid Energy Efficient and Security System (DL-HE2S2) is introduced to address these challenges. The research workflow encompasses various essential stages, namely the deployment of nodes, the creation of clusters, the selection of cluster heads, the detection of malevolent nodes within each group, and the determination of optimal paths intra- and inter-clusters employing the routing algorithm for efficient packet transmission. The design of the algorithm is focused on achieving energy efficiency and enhancing network security while also taking into account various performance metrics, including a mean network lifetime of 187.244 hours, a throughput of 59.88 kilobits per second, an end-to-end latency of 11.939 milliseconds, a packet loss of 14.9%, a packet delivery ratio of 99.194%, network security at 92.026%, and energy usage of 19.424 J. This research examines the algorithm’s scalability and efficiency across various network sizes using a Network Simulator (NS-2). DL-HE2S2 offers valuable insights that can be applied to practical implementations in multiple applications. Show more
Keywords: Wireless sensor networks, energy efficiency, secured routing, cluster
DOI: 10.3233/JIFS-235322
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Xu, Liwen | Chen, Jiali
Article Type: Research Article
Abstract: Node classification in graph learning faces significant challenges due to imbalanced data, particularly for under-represented samples from minority classes. To address this issue, existing methods often rely on synthetic minority over-sampling techniques, introducing additional complexity during model training. In light of the challenges faced, we introduce GraphECC, an innovative approach that addresses numerical anomalies in large-scale datasets by supplanting the traditional CE loss function with an Enhanced Complementary Classifier (ECC) loss function’a novel modification to the CCE loss. This alteration ensures computational stability and mitigates potential numerical anomalies by incorporating a slight offset in the denominator during the computation of …the complementary probability distribution. In this paper, we present a novel training paradigm, the Enhanced Complementary Classifier (ECC), which offers “imbalance defense for free” without the need for extra procedures to improve node classification accuracy.The ECC approach optimizes model probabilities for the ground-truth class, akin to the cross-entropy method. Additionally, it effectively neutralizes probabilities associated with incorrect classes through a “guided” term, achieving a balanced trade-off between the two aspects. Experimental results demonstrate that our proposed method not only enhances model robustness but also surpasses the widely used cross-entropy training objective.Moreover, we demonstrate the versatility of our method by seamlessly integrating it with various well-known adversarial training techniques, resulting in significant gains in robustness. Notably, our approach represents a breakthrough, as it enhances model robustness without compromising performance, distinguishing it from previous attempts.The code for GraphECC can be accessed from the following link:https://github.com/12chen20/GraphECC . Show more
Keywords: Imbalanced node classification, trade-off optimization, enhanced complementary classifier (ECC), graph learning, minority classes
DOI: 10.3233/JIFS-239663
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Ma, Nana | Wang, Lili | Long, Yuting
Article Type: Research Article
Abstract: Music has been utilized throughout history as a medium for cultural communication and artistic expression, embodying various nations’ and societies’ ideologies and experiences. Music culture communication is crucial for encouraging cultural diversity and understanding and developing social cohesion and community building among people. Music teaching management is the process of setting up, arranging, and executing music education programs in a manner that successfully teaches students the essential skills and information necessary for becoming proficient musicians. Users’ exact preferences for various areas of attraction cannot be determined, nor are users’ choices for traditional music recommendations sufficiently accurate. A recommender system estimates …or anticipates people’s preferences and offers appropriate recommendations. First, the sparsity problem emerges when insufficient data is accessible for the recommendation, and the coverage is one of the key drawbacks of social labeling. Cold start issues might be difficult since new music learners might not have given sufficient details about their musical tastes. Hence, the Hybridized Fuzzy logic-based Content and Collaborative Music Recommendation (HFC2MR) system is proposed to create personalized music teaching plans that are effective and engaging for each student based on their music preferences and learning outcomes. Enhanced Fuzzy C-Means clustering is used in collaborative recommendations to group users based on their shared musical tastes and to provide each user with more individualized, accurate music recommendations based on other users’ listening habits and preferences in the same cluster. Subsequently, an assessment of the recommender system using parameters like accuracy, precision, f1-score, and recall ratio is shown with optimal cluster selection. The coverage ratio is used to compare experimental data based on skill capacity covered through the assessment of music teaching. RMSE metric is used to evaluate the accuracy of students’ performance based on music attributes related to teaching goals. Show more
Keywords: Music teaching management, fuzzy logic, recommender system, clustering and similarity
DOI: 10.3233/JIFS-232422
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Zhou, Yue | Chen, Qiwei
Article Type: Research Article
Abstract: Studying the evolution of karst rocky desertification (KRD) in control areas of diverse geomorphologic types and its correlation with land use provides valuable insights for identifying priority areas and implementing effective treatment measures. Employing Remote Sensing (RS) and GIS, this research quantitatively examines the evolution of KRD and its relationship with land use in the karst mountain and gorge areas of Guizhou Province over the period 2010 to 2020. The findings reveal continuous improvement in KRD across the study areas, albeit with noticeable regional disparities. Notably, the karst mountain region exhibited significantly higher change areas and rates of KRD, non-KRD, …light KRD, and moderate KRD compared to the gorge area, underscoring better desertification control in the former region. A discernible correlation emerges between different karst geomorphologic types, the distribution and changes in land use types, and the evolution of KRD. Land use change emerges as a pivotal factor influencing the improvement of KRD in these areas. Changes in land use patterns corresponded with a decrease in KRD in dry land, other woodland, grassland, and bare land across both regions. However, the response of KRD to land use patterns varied across control areas with different geomorphologic environments, resulting in geographical differentiation in KRD evolution. Key land use conversions, notably from shrubland to forestland and dry land to garden land in the gorge, and shrubland to forestland in the mountain, contributed significantly to KRD dynamics in these regions. Notably, in the gorge area, KRD primarily occurred in garden land, other woodland, dry land, and grassland. In contrast, in the mountain area, KRD was prevalent in shrubland, dry land, and grassland, highlighting distinct responses and contributions to its evolution. The study observes substantial land use change in KRD-improved areas, particularly in the gorge region. Notably, the responsiveness of KRD to woodland conversions (shrubland, forestland, other woodland) varied across different geomorphologic environments. The dynamics of rocky desertification occurrence (RDO) and the occurrence structure of KRD in various land use types exhibited significant differences between the two regions. The gorge area demonstrated generally higher RDO, with a relatively stable and simpler occurrence structure of KRD compared to the more dynamic and varied structure observed in the mountain area. The sequencing of KRD occurrence in both areas displayed stability in specific land use types, with varying intensities noted between them. Show more
Keywords: Karst, rocky desertification, land use, evolution, geomorphology
DOI: 10.3233/JIFS-241536
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Qin, Hao | Zou, Yanli | Yu, Guoliang | Liu, Huipeng | Tan, Yufei
Article Type: Research Article
Abstract: In the process of mapping outdoor undulating and flat roads, existing LiDAR SLAM systems often encounter issues such as map distortion and ghosting. These problems arise due to the low vertical resolution of multi-line LiDAR, which easily leads to the occurrence of odometry height drift during the mapping process. To address this challenge, this study propose a novel LiDAR SLAM system named SOHD-LOAM, designed specifically to suppress odometry height drift. This system encompasses several critical components, including data preprocessing, front-end LiDAR odometry, back-end LiDAR mapping, loop detection, and graph optimization. SOHD-LOAM leverages the road gradient limitation algorithm and the height …smoothing algorithm as its core, while also integrating the Kalman filter, loop detection, and graph optimization techniques. To evaluate the performance of SOHD-LOAM, the comprehensive experiments are conducted with using KITTI datasets and real-world scenes. The experimental results demonstrate that SOHD-LOAM achieves superior accuracy and robustness in global odometry compared to the state-of-the-art LEGO-LOAM. Specifically, the height error of the sequences 00, 05 experiment was found to be 40.62% and 61.92% lower than that of LEGO-LOAM. Additionally, the maps generated by SOHD-LOAM exhibit no distortion or ghosting, thereby significantly enhancing map quality. Show more
Keywords: Autonomous driving, SLAM, odometry height drift, road gradient limitation, height smoothing, loop detection
DOI: 10.3233/JIFS-235708
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Wei, YuHan | Kim, Young-Ju
Article Type: Research Article
Keywords: Camel herd algorithm (CHA), camel-bat swarm optimization (CBSO), cultural and creative product (CCP) Design, graphic design
DOI: 10.3233/JIFS-236320
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Lalitha, S. | Sridevi, N. | Deekshitha, Devarasetty | Gupta, Deepa | Alotaibi, Yousef A. | Zakariah, Mohammed
Article Type: Research Article
Abstract: Speech Emotion Recognition (SER) has advanced considerably during the past 20 years. Till date, various SER systems have been developed for monolingual, multilingual and cross corpus contexts. However, in a country like India where numerous languages are spoken and often humans converse in more than one language, a dedicated SER system for mixed-lingual scenario is more crucial to be established which is the focus of this work. A self-recorded database that includes speech emotion samples with 11 diverse Indian languages has been developed. In parallel, a mixed-lingual database is formed with three popular standard databases of Berlin, Baum and SAVEE …to represent mixed-lingual environment for western background. A detailed investigation of GeMAPS (Geneva Minimalistic Acoustic Parameter Set) feature set for mixed-lingual SER is performed. A distinct set of MFCC (Mel Frequency Cepstral Coefficients) coefficients derived from sine and cosine-based filter banks enriches the GeMAPS feature set and are proven to be robust for mixed-lingual emotion recognition. Various Machine Learning (ML) and Deep Learning (DL) algorithms have been applied for emotion recognition. The experimental results demonstrate GeMAPS features classified from ML has been quite robust for recognizing all the emotions across the mixed-lingual database of the western languages. However, with diverse recording conditions and languages of the Indian self-recorded database the GeMAPS with enriched features and classified using DL are proven to be significant for mixed-lingual emotion recognition. Show more
Keywords: Emotion, GeMAPS, mixed-lingual, sine, cosine filter bank
DOI: 10.3233/JIFS-219390
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Bisht, Akhilesh | Gupta, Deepa
Article Type: Research Article
Abstract: Neural Machine Translation (NMT) for low resource languages is a challenging task due to unavailability of large parallel corpus. The efficacy of Transformer based NMT models largely depends on scale of the parallel corpus and the configuration of hyperparameters implemented during model training. This study aims to delve into and elucidate the impact of hyperparameters on the performance of NMT models for low resource languages. To accomplish this, a series of experiments are conducted using an open-source Hindi-Kangri corpus to train both supervised and semi-supervised NMT models. Throughout the experimentation process, a significant number of discrepancies were identified within the …data-set, necessitating manual correction. The best translation performance evaluated with respect to the metrics such as BLEU (0–1), SacreBLEU (0–100), Chrf (0–100), Chrf+ (0–100), Chrf++ (0–100) and TER (%) is (0.15, 14.98, 41.43, 41.49, 38.77, 68.20) for Hindi to Kangri direction, and (0.283, 28.17, 49.71, 50.64, 48.63, 51.25) for Kangri to Hindi direction. Show more
Keywords: Neural machine translation, low resource language, low resource MT, transformers, semi-supervised MT, Kangri, natural language processing
DOI: 10.3233/JIFS-219384
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Momena, Alaa Fouad | Gazi, Kamal Hossain | Mukherjee, Asesh Kumar | Salahshour, Soheil | Ghosh, Arijit | Mondal, Sankar Prasad
Article Type: Research Article
Abstract: Use of the Internet of Everything (IoE), the number of smart gadgets increasing rapidly giving the side effect of huge data, which has led to issues with traditional cloud computing models like inadequate security, slow response times, poor privacy, and bandwidth overload. Conventionally, cloud computing is no longer adequate for supporting the diversified needs of the user and the extraordinary society of data processing, so edge computing technologies have been revealed. This study considers edge computing in an educational institute in a scientific way. Multi criteria decision making (MCDM) is one of the most suitable decision making processes that propose …to choose optimal alternatives by considering multiple conflicting criteria. Entropy weighted method is considered to evaluate factor weight. Weighted Aggregated Sum Product Assessment (WASPAS) and Combined Compromise Solution (CoCoSo) based MCDM methodologies examine the ranking of alternatives for this study. Multiple decision makers (DMs) give opinions with Pentagonal Fuzzy Soft Set (PFSS) to express the uncertainty and fuzziness of the data set. The set operations and arithmetic operations of PFSS are discussed in detail. Also, a new de-fuzzification method of PFSS is proposed in this study. Calculated the criteria weight and prioritized the alternative based on source data. Lastly, sensitivity analysis and comparative analysis are conducted to check the stability of the result. Show more
Keywords: Edge computing, Academic institute, PFSS, Entropy, WASPAS, CoCoSo
DOI: 10.3233/JIFS-239887
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Jaiseeli, C. | Raajan, N.R.
Article Type: Research Article
Abstract: Medical and satellite image analysis require incredibly high resolution. Super-resolution combines several low-resolution images of the same scene to generate a high-resolution image. The Super resolution employing deep learning techniques still has an illumination issue. This paper proposes a novel CGIHE-VDSR algorithm that integrates the Very Deep Super Resolution (VDSR) Network with Color Global Image Histogram Equalization (CGIHE) to improve image resolution. In the proposed method, the low-resolution image is first histogram equalized using the CGIHE algorithm. Then, the VDSR network is applied to the histogram equalized image for super-resolution. The comparison of real-time data with the benchmark images is …done using the proposed algorithm in the MATLAB platform. The PSNR and SSIM metrics demonstrate that the super resolution image obtained using the proposed method is significantly better than the existing methods. Show more
Keywords: Histogram equalization, super-resolution, CNN, subsample image, VDSR, residual
DOI: 10.3233/JIFS-219392
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Javed, Hira | Sufyan Beg, M.M. | Akhtar, Nadeem | Alroobaea, Roobaea
Article Type: Research Article
Abstract: Vlogs, Recordings, news, sport coverages are huge sources of multimodal information that do not just limit to text but extend to audio, images and videos. Applications such as summary generation, image/video captioning, multimodal sentiment analysis, cross modal retrieval requires Computer Vision along with Natural Language Processing techniques to extract relevant information. Information from different modalities must be leveraged in order to extract quality content. Hence, reducing the gap between different modalities is of utmost importance. Image to text conversion is an emerging field and employs the use of encoder decoder architecture. Deep CNNs extract the feature of images and sequence …to sequence models are used to generate text description. This paper is a contribution towards the growing body of research in multimodal information retrieval. In order to generate the textual description of images, we have performed 5 experiments using the benchmark Flickr8k dataset. In these experiments we have utilized different architectures - simple sequence to sequence model, attention mechanism, transformer-based architecture to name a few. The results have been evaluated using BLEAU score. Results show that the best descriptions are attained by making use of transformer architecture. We have also compared our results with the pretrained visual model vit-gpt2 that incorporates visual transformer. Show more
Keywords: Multimodal, captioning, summarization, etc
DOI: 10.3233/JIFS-219394
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Kostiuk, Yevhen | Tonja, Atnafu Lambebo | Sidorov, Grigori | Kolesnikova, Olga
Article Type: Research Article
Abstract: In this paper, we investigate the issue of hate speech by presenting a novel task of translating hate speech into non-hate speech text while preserving its meaning. As a case study, we use Spanish texts. We provide a dataset and several baselines as a starting point for further research in the task. We evaluated our baseline results using multiple metrics, including BLEU scores. We used a cross-validation approach and an average of the metrics per fold for evaluation. We achieved a 0.236 sentenceBLEU score on four folds. This study aims to contribute to developing more effective methods for reducing the …spread of hate speech in online communities. Show more
Keywords: Hate speech, translation, Spanish
DOI: 10.3233/JIFS-219348
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
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