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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: 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: Ali, Zeeshan | Yin, Shi | Yang, Miin-Shen
Article Type: Research Article
Abstract: In the context of fuzzy relations, symmetry refers to a property where the relationship between two elements remains the same regardless of the order in which they are considered. Natural language processing (NLP) in engineering documentation discusses the application of computational methods or techniques to robotically investigate, analyze, and produce natural language information for manufacturing contents. The NLP plays an essential role in dealing with large amounts of textual data normally recovered in engineering documents. In this paper, we expose the idea of a bipolar complex hesitant fuzzy (BCHF) set by combining the bipolar fuzzy set (BFS) and the complex …hesitant fuzzy set (CHFS). Further, we evaluate some algebraic and Schweizer-Sklar operational laws under the presence of BCHF numbers (BCHFNs). Additionally, using the above information as well as the idea of prioritized (PR) operators, we derive the idea of BCHF Schweizer-Sklar PR weighted averaging (BCHFSSPRWA) operator, BCHF Schweizer-Sklar PR ordered weighted averaging (BCHFSSPROWA) operator, BCHF Schweizer-Sklar PR weighted geometric (BCHFSSPRWG) operator, and BCHF Schweizer-Sklar PR ordered weighted geometric (BCHFSSPROWG) operator. Basic properties for the above operators are also discussed in detail, such as idempotency, monotonicity, and boundedness. Moreover, we evaluate the best way in which NLP can be applied to engineering documentations with the help of the proposed operators. Therefore, we illustrate the major technique of multi-attribute decision-making (MADM) problems based on these derived operators. Finally, we use some existing operators and try to compare their ranking results with our proposed ranking results to show the supremacy and validity of the investigated theory. Show more
Keywords: Fuzzy set (FS), hesitant FS, bipolar complex hesitant FS, Schweizer-Sklar prioritized aggregation operators, natural language processing, multi-attribute decision-making
DOI: 10.3233/JIFS-240116
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2024
Authors: Shi, Jing | Zhang, Xiao-Lin | Wang, Yong-Ping | Gu, Rui-Chun | Xu, En-Hui
Article Type: Research Article
Abstract: Deep neural networks (DNNs) are susceptible to adversarial attacks, and one important factor is that adversarial samples are transferable, i.e., adversarial samples generated by a particular network may deceive other black-box models. However, existing transferable adversarial attacks tend to modify the input features of images directly without selection to reduce the prediction accuracy in the alternative model, which would enable the adversarial samples to fall into the model’s local optimum. Alternative models differ significantly from the victim model in most cases, and while simultaneously attacking multiple models may improve transferability, gathering numerous different models is more challenging and expensive. We …simulate various models using frequency domain transformation to close the gap between the source and victim models and improve transferability. At the same time, we destroy important intermediate layer features that influence the decision of the model in the feature space. Additionally, smoothing loss is introduced to remove high-frequency perturbations. Extensive experiments demonstrate that our FM-FSTA attack generates more well-hidden and transferable adversarial samples, and achieves a high deception rate even when attacking adversarially trained models. Compared to other methods, our FM-FSTA improved attack success rate under different defense mechanisms, which reveals the potential threats of current robust models. Show more
Keywords: Deep neural networks, adversarial samples, transferable attacks
DOI: 10.3233/JIFS-234156
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Zhao, Xianhao | Wang, Mingyang | Xin, Chaoqun | Wang, Xianjie
Article Type: Research Article
Abstract: In the field of autonomous driving, driving systems need to understand and quickly respond to changes in road scenes, which makes it equally important to enhance the accuracy and real-time performance of semantic segmentation tasks in road scenes. This article proposes a lightweight road scene semantic segmentation model LR3S that integrates global contextual information based on the DeepLabV3+ framework. LR3S utilizes a lightweight GhostNetV2 network as the backbone to capture rich semantic information in images, and uses ASPP_eSE module to enhance the capture of multi-scale and detail level semantic information. In addition, a lightweight CARAFE upsampling operator is utilized to …upsample feature maps, taking advantage of CARAFE’s large receptive field and low computational cost to prevent the loss of fine-grained features and ensure the integrity of semantic information. Experimental results demonstrate that LR3S achieves an MIoU of 74.47% on the Cityscapes dataset and obtains an MIoU of 76.01% on the PASCAL VOC 2012 dataset. Compared to baseline semantic segmentation models, LR3S significantly reduces the parameter amount while maintaining segmentation accuracy, achieving a good balance between model accuracy and real-time performance. Show more
Keywords: Semantic segmentation, road scenes, attention mechanism, GhostNetV2, CARAFE
DOI: 10.3233/JIFS-239692
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Haennah, J.H. Jensha | Christopher, C. Seldev | King, G.R. Gnana
Article Type: Research Article
Abstract: Accurate SARS-CoV-2 screening is made possible by automated Computer-Aided Diagnosis (CAD) which reduces the stress on healthcare systems. Since Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is highly contagious, the transition chain can be broken through an early diagnosis by clinical knowledge and Artificial Intelligence (AI). Manual findings are time and labor-intensive. Even if Reverse Transcription-Polymerase Chain Reaction (RT-PCR) delivers quick findings, Chest X-ray (CXR) imaging is still a more trustworthy tool for disease classification and assessment. Several studies have been conducted using Deep Learning (DL) algorithms for COVID-19 detection. One of the biggest challenges in modernizing healthcare is extracting …useful data from high-dimensional, heterogeneous, and complex biological data. Intending to introduce an automated COVID-19 diagnosis model, this paper develops a proficient optimization model that enhances the classification performance with better accuracy. The input images are initially pre-processed with an image filtering approach for noise removal and data augmentation to extend the dataset. Secondly, the images are segmented via U-Net and are given to classification using the Fused U-Net Convolutional Neural Network (FUCNN) model. Here, the performance of U-Net is enhanced through the modified Moth Flame Optimization (MFO) algorithm named Chaotic System-based MFO (CSMFO) by optimizing the weights of U-Net. The significance of the implemented model is confirmed over a comparative evaluation with the state-of-the-art models. Specifically, the proposed CSMFO-FUCNN attained 98.45% of accuracy, 98.63% of sensitivity, 98.98% of specificity, and 98.98% of precision. Show more
Keywords: COVID-19 classification, deep Learning, U-Net, Convolutional Neural Network (CNN), Moth Flame Optimization (MFO)
DOI: 10.3233/JIFS-230523
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Liu, Zhaohui | Wang, Xiao
Article Type: Research Article
Abstract: Pedestrians have random distribution and dynamic characteristics. Aiming to this problem, this paper proposes a pedestrian object detection method based on improved YOLOv5 in urban road scenes. Firstly, the last C3 module was replaced in the Backbone with the SE attention mechanism to enhance the network’s extraction of pedestrian object features and improve the detection accuracy of small-scale pedestrians. Secondly, the EIOU loss function was introduced to optimize the object detection performance of the detection network. To validate the effectiveness of the algorithm, experiments were conducted on a dataset composed of filtered Caltech pedestrian detection data and images taken by …ourselves. The experiments showed that the improved algorithm has P -value, R -value, and mAP of 98.4%, 95.5%, and 98%, respectively. Compared to the YOLOv5 model, it has increased P -value by 1.4%, R -value by 2.7%, and mAP by 1.3%. The improved algorithm also boosts the detection speed. The detection speed is 0.8 ms faster than the YOLOv5 model. It is also faster than other mainstream algorithms including Faster R-CNN and SSD. The improved algorithm enhances the effectiveness of pedestrian detection significantly and has important application value. Show more
Keywords: Road traffic safety, YOLOv5, pedestrian object detection
DOI: 10.3233/JIFS-240537
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Zhan, Huawei | Han, Chengju | Li, Junjie | Wei, Gaoyong
Article Type: Research Article
Abstract: Aiming at the problems of slow speed and low accuracy of traditional neural network systems for real-time gesture recognition in complex backgrounds., this paper proposes DMS-yolov8-a gesture recognition method to improve yolov8. This algorithm replaces the Bottleneck convolution module in the backbone network of yolov8 with variable row convolution DCNV2, and increases the feature convolution range without increasing the computation amount through a more flexible feeling field. in addition, the self-developed MPCA attention module is added after the feature output layer of the backbone layer, which improves the problem of recognizing the accuracy of difference gestures in complex backgrounds by …effectively combining the feature information of the contextual framework, taking into account the multi-scale problem of the gestures in the image, this paper introduces the SPPFCSPS module, which realizes multi-feature fusion and improves real-time accuracy of detection. Finally, the model proposed in this paper is compared with other models, and the proposed DMS-yolov8 model achieves good results on both publicly available datasets and homemade datasets, with the average accuracy up to 97.4% and the average mAP value up to 96.3%, The improvements proposed in this paper are effectively validated. Show more
Keywords: Gesture recognition, yolov8, DCNV2, MPCA, feature fusion
DOI: 10.3233/JIFS-238629
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Meenakshi, A. | Bramila, M.
Article Type: Research Article
Abstract: Molecular structures are characterised by the Hosoya polynomial and Wiener index, ideas from mathematical chemistry and graph theory. The graph representation of a chemical compound that has atoms as vertices and chemical bonds as edges is called a molecular graph, and the Hosoya polynomial is a polynomial related to this graph. As a graph attribute that remains unchanged under graph isomorphism, the Hosoya polynomial is known as a graph invariant. It offers details regarding the quantity of distinct non-empty subgraphs within a specified graph. A topological metric called the Wiener index is employed to measure the branching complexity and size …of a molecular graph. For every pair of vertices in a molecular network, the Wiener index is the total of those distances. In this paper, discussed the Hosoya polynomial, Wiener index and Hyper-Wiener index of the Abid-Waheed graphs (AW)a 8 and (AW)a 10 . This graph is similar to Jahangir’s graph. Further, we have extended the research work on the applications of the described graphs. Show more
Keywords: Wiener index, Abid-Waheed, Hosoya polynomial, diameter, distance, connected graph
DOI: 10.3233/JIFS-236051
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Lin, Jiayi
Article Type: Research Article
Abstract: At this stage, network communication technology is increasingly mature, and intelligent wearable products are also widely used in human daily life. Wearable products are popular with users because of their numerous types, complete functions and convenient services. Wearable products integrate interaction technology, and users can interact with products. However, how to improve the user’s interaction experience and reduce the user’s cognitive burden on the interaction interface is an urgent problem in the current product interaction design. Therefore, based on the analysis of the types and related technologies of wearable products, this paper made a specific analysis of the interaction design …of wearable products, and established an interaction design model. At the same time, the wearable fall detection system was also tested by machine learning algorithm. The experimental results showed that the average test result of the algorithm in this paper was 87.39%, while the average test result of the traditional algorithm was 83.79%. In terms of the missed alarm rate of fall detection, the average test result of this algorithm was 6.4%, while the average test result of the traditional algorithm was 12.33%. In terms of fall detection sensitivity, the average test result of this algorithm was 92.50%, while the average test result of the traditional algorithm was 88.24%. Compared with traditional algorithms, this method performs better, with lower missed detection rate and higher sensitivity. Innovative combination of machine learning algorithm, through three-dimensional coordinate system, differentiation and vector sum formula, improves the accuracy and reliability of fall detection. In conclusion, the algorithm in this paper can effectively optimize the relevant performance of the system, thus improving the accuracy of the system’s fall detection. Show more
Keywords: 5 G network communication technology, wearable products, interaction design, wearable fall detection system
DOI: 10.3233/JIFS-237837
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Li, Hongjun | Zhang, Jinlong
Article Type: Research Article
Abstract: This paper presents a sophisticated four-stage optimization and intelligent control algorithm tailored for two-way electric vehicle charging (EVC) stations integrated with advanced photovoltaic systems and fixed battery energy storage in commercial buildings. The primary objective is to minimize operating costs while prioritizing customer satisfaction within a dynamic and uncertain energy landscape. Our algorithm optimizes the scheduled charging and discharging of electric vehicles (EVs), local battery storage (BS) units, grid power supply, and deferred loads to balance instantaneous supply and demand. The first stage focuses on developing optimal energy management plans for the day ahead, considering factors such as projected energy …production, anticipated EVC demand, and building energy consumption patterns. Building on this foundation, the second stage introduces multilayer EV charging price structures and optimizes participation rewards for discharging, dynamically addressing EV charging patterns and price sensitivities. Approaching the commissioning timeline, the third stage refines energy management plans for the upcoming hours using real-time data and forecasts, adapting to evolving conditions for optimal resource allocation. The final stage involves real-time control and the implementation of optimized programs, dynamically adjusting charge/discharge processes, grid interactions, and load deferral to maintain supply-demand balance and minimize operating costs. Our algorithm enhances system resilience in unpredictable conditions, providing compelling incentives for active EV user participation. Coordinating the integrated system efficiently, including the commercial building’s energy load, ensures reliable service to customers while reducing costs. Extensive case studies and a comparative analysis validate the algorithm’s efficiency in significantly reducing operating costs and enhancing resilience to uncertainty. The paper concludes by highlighting the algorithm’s pioneering role in intelligent EV charging station (CHS) management, offering a cost-effective, customer-oriented, and dynamic energy control strategy for advancing global energy practices. Show more
Keywords: Electric vehicle charging, photovoltaic integration, battery energy storage, energy management optimization, commercial building integration
DOI: 10.3233/JIFS-241032
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Valadez-Godínez, Sergio | Sossa, Humberto | Santiago-Montero, Raúl
Article Type: Research Article
Abstract: The Associative Pattern Classifier (APC) was designed as an associative memory, focusing particularly on pattern classification. This implies that the training memory is constructed in a single operation and pattern classification also occurs in a single process. It is important to note that the APC translates the input patterns through a translation vector, which represents the average of all input patterns. Until now, there is no theoretical framework to explain the inner workings of the APC. Its relevance is inferred from the fact that several studies have been conducted using it as a foundation. This paper seeks to provide a …theoretical comprehension of the APC’s operation to facilitate future enhancements. We found the APC creates a system in static equilibrium through concurrent vectors at the origin (translation vector), resulting in a balanced separation of patterns. However, the APC cannot achieve complete pattern separation because of the presence of a neutral region. The neutral region is defined by all the points that define the separation hyperplanes. The points over the hyperplanes cannot be classified by the APC. Additionally, we discovered that the APC is unable to accurately classify the translation vector, which could be included as part of the input patterns. Our previous research showed that the APC is unsuccessful in achieving the linear separation of the AND function. In this research, we also broaden the examination of the AND function to illustrate that achieving linear separation is not feasible because the separation line represents a neutral region. The APC demonstrated exceptional performance when tested with artificial datasets where patterns were distributed over balanced regions, thus operating as an efficient multiclass and non-linear classifier. Nevertheless, the performance of the APC is lower when tested with real-world databases, making the APC inaccurate due to its restricted inner workings. Show more
Keywords: Classifier, pattern, associative memory, class, classification
DOI: 10.3233/JIFS-219347
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: Zhang, Wei | Zheng, Hongxuan | Zhang, Runyu
Article Type: Research Article
Abstract: In this paper, a self-organizing RBF (SORBF) neural network with an adaptive threshold is proposed based on improved particle swarm optimization (IPSO) and neural strength (NS). The parameters and structure of SORBF can be optimized simultaneously and dynamically. Moreover, the tiresome problem of threshold setting is solved. Firstly, the network size and parameters of SORBF are mapped into the particle information of PSO. Secondly, an IPSO algorithm, based on diversity inertia weight and elite knowledge guiding, is proposed to reduce the probability of the population falling into the local optimum. Then, IPSO is used for optimizing the parameters of SORBF. …Based on neuron growth intensity and competition intensity, SORBF can realize the hidden neuron addition and deletion adaptively. Moreover, the thresholds during the structure adjustment can be provided adaptively based on the network scale and neuron strength, which avoids the subjectivity setting and can improve the adaptive ability. Finally, the convergence analysis of IPSO is provided to ensure the performance of SORBF. Experiment results show that the proposed SORBF has good self-organizing ability and compact network structure compared with other methods. Show more
Keywords: RBF neural network, PSO, self-organization, neural strength, adaptive threshold
DOI: 10.3233/JIFS-239569
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Wei, Guangcun | Fu, Jihua | Pan, Zhifei | Fang, Qingge | Zhang, Zhi
Article Type: Research Article
Abstract: The text in natural scenes is often smaller compared to artificially designed text. Due to the small proportion of pixels, low resolution, less semantic information, and susceptibility to complex scenes, tiny text detection often results in many missed detections. To address this issue, this paper draws inspiration from small object detection methods and proposes TiTDet, a detection algorithm more suitable for tiny text. Due to the small proportion of pixels, low resolution, less semantic information, and susceptibility to complex scenes, tiny text detection often results in many missed detections. To address this issue, this paper draws inspiration from small object …detection methods and proposes TiTDet, a detection algorithm more suitable for tiny text. Firstly, this paper incorporates a context extraction module and an attention-guided module. These modules guide contextual information learning through a self attention mechanism, while eliminating the possible negative impact caused by redundant information. Regarding multi-scale feature fusion, this paper proposes a fine-grained effective fusion factor, making the fusion process emphasize small object learning more and highlight the feature expression of tiny texts. In terms of post-processing, this paper proposes a differentiable binarization module, incorporating the binarization process into model training. Leveraging the implicit information in the data to drive model improvement can enhance the post-processing effect. Lastly, this paper proposes a scale-sensitive loss, which can handle tiny texts more fairly, fully considering the positional relationship between the predicted and real regions, and better guiding the model training. This paper proves that TiTDet exhibits high sensitivity and accuracy in detecting tiny texts, achieving an 86.0% F1-score on ICDAR2015. The paper also compares the superiority of the method on CTW1500 and Total-Text. Show more
Keywords: Tiny text detection, context extraction module, attention-guided module, effective fusion factor, scale-sensitive loss
DOI: 10.3233/JIFS-236317
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Pandiyarajan, Abinaya | Jagatheesaperumal, Senthil Kumar | Thayanithi, Manonmani
Article Type: Research Article
Abstract: This study explores how Electronic Health Records (EHR) might be transformed in the context of the rapid improvements in cloud computing and IoT technology. But worries about sensitive data security and access management when it moves to large cloud provider networks surface. Even if they are secure, traditional encryption techniques sometimes lack the granularity needed for effective data protection. We suggest the Secure Access Policy – Ciphertext Policy – Attribute-based Encryption (SAPCP-ABE) algorithm as a solution to this problem. This method ensures that only authorized users may access the necessary data while facilitating fine-grained encrypted data exchange. The three main …phases of SAPCP-ABE are retrieval and decoding, where the system verifies users’ access restrictions, secure outsourcing that prioritizes critical attributes, and an authenticity phase for early authentication. Performance tests show that SAPCP-ABE is a better scheme than earlier ones, with faster encryption and decryption speeds of 5 and 5.1 seconds for 512-bit keys, respectively. Security studies, numerical comparisons, and implementation outcomes demonstrate our suggested approach’s efficacy, efficiency, and scalability. Show more
Keywords: Attribute-based encryption, electronic health record, access policy, cloud providers, cloud computing
DOI: 10.3233/JIFS-240341
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Huang, Ying | Li, Lang | Li, Di | Li, Yongchao
Article Type: Research Article
Abstract: AND-Rotation-XOR (AND-RX) ciphers are known for its unique round function and excellent implementation performance. As a result, AND-RX ciphers are well suited for protecting sensitive information on resource-constrained devices. AND-RX ciphers need to be passed by rigorous cryptanalysis methods before practice. Integral cryptanalysis is one of the important cryptanalysis methods. MILP-based automated model is constructed to solve the integral cryptanalysis of AND-RX ciphers. The automated model usually consumes a long time when the block length and the number of round function components are large. In this paper, we design a neural distinguisher named IABC model for fast and efficient integral …cryptanalysis. The IABC model learns to distinguish between ciphertext multisets to construct an integral distinguisher for AND-RX cipher, which ciphertext multisets from plaintext or random plaintexts. The IABC model is used for SIMON, SIMECK and SAND ciphers, which validates the neural distinguisher for AND-RX ciphers. The experimental results show that the IABC model is capable of expanding the number of rounds of integral distinguishers for AND-RX ciphers with certain accuracy. Therefore, IABC model can be effectively used for integral cryptanalysis of AND-RX ciphers. In addition, we discover that a larger number of active bits in the plaintext multiset results in a more accurate IABC model. Show more
Keywords: AND-RX cipher, integral cryptanalysis, division property, neural distinguisher
DOI: 10.3233/JIFS-238122
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ranjith, K. | Karthikeyan, K.
Article Type: Research Article
Abstract: The flow-shop scheduling problem (FSSP) has received a considerable amount of attention due to its wide-ranging applications. However, the omission of uncertainty significantly diminishes the practicality of scheduling results, underscoring its the necessity to address uncertainty in the flow shop problem. In this paper, a fuzzy two-machine flow-shop problem is considered and an effective algorithm with a fuzzy ranking method is proposed to minimize the total waiting time. The processing times are represented using trapezoidal membership functions. Furthermore, a two-stage flow shop scheduling problem is used in the proposed algorithm and various categories of fuzzy mean techniques. The experimental results …and statistical comparisons demonstrate that the proposed algorithm exhibits significant advantages in effectively solving the FFSSP (Fuzzy Flow-Shop Scheduling Problem). Show more
Keywords: Two-stage flow shop, trapezoidal fuzzy number, mean ranking techniques, waiting time
DOI: 10.3233/JIFS-235526
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Sageengrana, S. | Selvakumar, S.
Article Type: Research Article
Abstract: Distraction and fatigue are serious issues in online learning, and they directly impact educational outcomes. To achieve excellent academic achievement, students need to focus on their studies without being distracted or fatigued. Learners frequently overlook crucial information, directions, and concepts while they are passive and sleepy. They tend to miss important content, instructions, and concepts. Iris Angle Position (IAP) and electroencephalography (EEG) were used in this model to identify the behaviour of learners. Specifically, a Deep Convolutional Neural Network (DCNN) is constructed to extract IAP in order to accurately capture the learner’s facial area. EEG signals are effectively handled and …sorted using deep reinforcement learning (DRL). The learners’ facial landmarks are retrieved from a frame using the dlib toolbox. Only eye landmark points from face landmarks alone are focused on in order to determine the learner’s behaviour. When the learners EEG signals and Iris positions are monitored simultaneously, it’s helpful to identify the learner’s fatigue state (LFS) and the learner’s distraction state (LDS). The Brain Vision Algorithm (BVA) uses iris position and minimal facial landmarks, along with brain activity, to properly identify the learner’s level of distraction and exhaustion. When a student is detected as being preoccupied or sleepy, an alert goes off automatically, and the educator gets performance feedback. Iris position data and brain-computer interface-based EEG signal values are utilised to identify distraction and sleepiness. Comparative tests have demonstrated that this innovative method offers fast and high-accuracy student activity detection in virtual learning settings. Applying the suggested approach to different existing classifiers yields an F-Score of 91.92%, a recall of 93.87%, and a precision of 92.37% . The results showed that the detection rates for both distracted and sleepy phases were higher than those attained with other currently used techniques. Show more
Keywords: Drowsiness, online learning, iris position, EEG signals, distraction, brain vision algorithm
DOI: 10.3233/JIFS-237016
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Canul-Chin, Miguel Angel | Moguel-Ordóñez, Yolanda Beatriz | Martin-Gonzalez, Anabel | Brito-Loeza, Carlos | Legarda-Saenz, Ricardo
Article Type: Research Article
Abstract: Yucatan has a variety of plant species of melliferous importance. The honey produced in Yucatan has several special properties that make it one of the most demanded internationally. Analyzing the pollen grains present in honey is essential to determine its quality and identify its plants of origin. This study is a time-consuming process that must be carried out by highly trained palynologists. In this work, we propose an improved model based on a fully convolutional neural network for the automatic detection of pollen grains in microscopic images of four plant species of Yucatan to contribute to the analysis of the …honey designation of origin. Show more
Keywords: Pollen analysis, object detection, palynology, deep learning
DOI: 10.3233/JIFS-219379
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Hashmi, Hina | Dwivedi, Rakesh | Kumar, Anil | Kumar, Aman
Article Type: Research Article
Abstract: The rapid advancements in satellite imaging technology have brought about an unprecedented influx of high-resolution satellite imagery. One of the critical tasks in this domain is the automated detection of buildings within satellite imagery. Building detection holds substantial significance for urban planning, disaster management, environmental monitoring, and various other applications. The challenges in this field are manifold, including variations in building sizes, shapes, orientations, and surrounding environments. Furthermore, satellite imagery often contains occlusions, shadows, and other artifacts that can hinder accurate building detection. The proposed method introduces a novel approach to improve the boundary detection of detected buildings in high-resolution …remote sensed images having shadows and irregular shapes. It aims to enhance the accuracy of building detection and classification. The proposed algorithm is compared with Customized Faster R-CNNs and Single-Shot Multibox Detectors to show the significance of the results. We have used different datasets for training and evaluating the algorithm. Experimental results show that SESLM for Building Detection in Satellite Imagery can detect 98.5% of false positives at a rate of 8.4%. In summary, SESLM showcases high accuracy and improved robustness in detecting buildings, particularly in the presence of shadows. Show more
Keywords: Object detection, image analysis, faster R-CNN, CNN, satellite imagery, object localization
DOI: 10.3233/JIFS-235150
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Huang, De Ling | Huang, Yi Fan | Yang, Yu Qiao
Article Type: Research Article
Abstract: Practical Byzantine Fault Tolerance (PBFT), the widest-used consensus algorithm in the alliance blockchain, suffers from high communications complexity and relatively low scalability, making it difficult to support large-scale networks. To overcome these limitations, we propose a secure and scalable consensus algorithm, Vague Sets-based Double Layer PBFT (VSDL-PBFT). Roles and tasks of consensus nodes are redesigned. Three-phase consensus process of the original PBFT is optimized. Through these approaches, the communication complexity of the algorithm is significantly reduced. In order to better fit the complexity of voting in the real world, we use a vague set to select primary nodes of consensus …groups. This can greatly reduce the likelihood of malicious nodes being selected as the primary nodes. The experimental results show that the VSDL-PBFT consensus algorithm improves the system’s fault tolerance, it also achieves better performance in algorithm security, communications complexity, and transaction throughput compared to the baseline consensus algorithms. Show more
Keywords: Blockchain, consensus algorithm, Byzantine fault tolerance, PBFT
DOI: 10.3233/JIFS-239745
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Rodriguez-Bazan, Horacio | Sidorov, Grigory | Escamilla-Ambrosio, Ponciano Jorge
Article Type: Research Article
Abstract: Recently, Android device usage has increased significantly, and malicious applications for the Android ecosystem have also increased. Security researchers have studied Android malware analysis as an emerging issue. The proposed methods employ a combination of static, dynamic, or hybrid analysis along with Machine Learning (ML) algorithms to detect and classify malware into families. These families often exhibit shared similarities among their members or with other families. This paper presents a new method that combines Fuzzy Hashing and Natural Language Processing (NLP) techniques to find Android malware families based on their similarities by applying reverse engineering to extract the features and …compute fuzzy hashing of the preprocessed code. This relationship allows us to identify the families according to their features. A study was conducted using a database test of 2,288 samples from diverse ransomware families. An accuracy in classifying Android ransomware malware up to 98.46% was achieved. Show more
Keywords: Android malware analysis, android ransomware, cybersecurity, fuzzy hashing, natural language processing
DOI: 10.3233/JIFS-219367
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Arulmurugan, A. | Kaviarasan, R. | Garnepudi, Parimala | Kanchana, M. | Kothandaraman, D. | Sandeep, C.H.
Article Type: Research Article
Abstract: This research focuses on scene segmentation in remotely sensed images within the field of Remote Sensing Image Scene Understanding (RSISU). Leveraging recent advancements in Deep Learning (DL), particularly Residual Neural Networks (RESNET-50 and RESNET-101), and the research proposes a methodology involving feature fusing, extraction, and classification for categorizing remote sensing images. The approach employs a dataset from the University of California Irvine (UCI) comprising twenty-one groups of pictures. The images undergo pre-processing, feature extraction using the mentioned DL frameworks, and subsequent categorization through an ensemble classification structure combining Kernel Extreme Learning Machine (KELM) and Support Vector Machine (SVM). The paper …concludes with optimal results achieved through performance and comparison analyses. Show more
Keywords: Remote sensing, image scene classification, deep learning, feature extraction, RESNET- 101, ensemble
DOI: 10.3233/JIFS-235109
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023
Authors: Cai, Xiumei | Yang, Xi | Wu, Chengmao | Zhang, Rui
Article Type: Research Article
Abstract: Focusing on the currently available multi-view fuzzy clustering algorithms, many of which frequently lack robustness and are hence less frequently used in image segmentation. We present a multi-view fuzzy clustering image segmentation algorithm in this research, along with an autonomous view-weight learning mechanism. Firstly, to ensure that each view has the best view weight, the algorithm adds a view weight factor. Secondly, it introduces the weighted fuzzy factor and the kernel distance metric, the role of the weighted fuzzy factor is to collect the local spatial information and local grey scale information to preserve as much of the image’s detailed …information as feasible during segmentation. The role of the kernel distance metric is to lessen the influence of outliers and noisy points on image segmentation. Finally, the technique for resolving the issue of image uncertainty and fuzzy factor selection introduces the concept of interval type-2 fuzzy c-means clustering. Numerous experiments on different images demonstrate that the proposed algorithm in this paper is more robust than previous multi-view fuzzy clustering algorithms for solving noise image segmentation problems. It is also more effective at segmenting images contaminated by noise and can better retain the detailed information in the image. Show more
Keywords: Multi-view, fuzzy clustering, autonomous view-weight learning, type-2 fuzzy, image segmentation
DOI: 10.3233/JIFS-235967
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Yang, Yi | Huang, Huiling | Wu, FeiBin | Han, Jun | Ma, Mengyuan | Zhang, Yantong | Feng, Yanbing
Article Type: Research Article
Abstract: This paper introduces a novel neural network architecture and an enhanced data synthesis method that significantly boost the performance in removing complex smoke from images. The architecture features a multi-branch and multi-scale feature fusion design, which effectively integrates multiple feature streams and adaptively restores the background by identifying specific smoke characteristics within the image. A newly designed Fourier residual block is incorporated to capture frequency domain information, enabling the network to process and transform information across both spatial and frequency domains. To improve the network’s generalization ability and robustness, an in-depth analysis of the imaging process in smoky environments was …conducted, leading to an improved method for synthesizing smoke images. This methodology facilitates the creation of a more varied and realistic training dataset, substantially enhancing the neural network’s capabilities in image restoration. Experimental results show that this approach is highly effective on both synthetic and real-world smoke datasets, outperforming existing image de-smoking methods in terms of quantitative metrics and visual perception. The code for this method is available at https://github.com/Exiagit/MFSR. Show more
Keywords: Single image smoke removal, frequency domain learning, data synthesis method
DOI: 10.3233/JIFS-239146
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Nieves, Juan Carlos | Osorio, Mauricio | Rojas-Velazquez, David | Magallanes, Yazmín | Brännström, Andreas
Article Type: Research Article
Abstract: Humans have evolved to seek social connections, extending beyond interactions with living beings. The digitization of society has led to interactions with non-living entities, such as digital companions, aimed at supporting mental well-being. This literature review surveys the latest developments in digital companions for mental health, employing a hybrid search strategy that identified 67 relevant articles from 2014 to 2022. We identified that by the nature of the digital companions’ purposes, it is important to consider person profiles for: a) to generate both person-oriented and empathetic responses from these virtual companions, b) to keep track of the person’s conversations, activities, …therapy, and progress, and c) to allow portability and compatibility between digital companions. We established a taxonomy for digital companions in the scope of mental well-being. We also identified open challenges in the scope of digital companions related to ethical, technical, and socio-technical points of view. We provided documentation about what these issues mean, and discuss possible alternatives to approach them. Show more
Keywords: Conversational agents, well-being, mental health, trustworthy artificial intelligence
DOI: 10.3233/JIFS-219336
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Gomathi, S.V. | Jayalakshmi, M.
Article Type: Research Article
Abstract: This article focuses on an area of nonlinear programming problems known as linear fractional programming problems with multiple objectives. When tackling real-world linear fractional optimization problems, ambiguity and uncertainty in decision-making are inherent. This research aims to present a simple and computationally quick approach to solving multiple objective linear fractional programming problems with all decision variables and parameters described in terms of crisp. The proposed solution algorithm is based primarily on the fuzzy-based technique, and a membership function strategy. To resolve the multi-objective linear fractional programming problem, first consider the problem as a single objective function and along with the …fuzzy programming model obtain the optimal solution using LINGO software. LINGO is a software application primarily used for solving linear, nonlinear, and integer optimization problems Moreover, an e-education setup problem demonstrates the steps of the proposed method. Show more
Keywords: Linear fractional programming problem, multi-objective linear fractional programming, fuzzy mathematical programming, hyperbolic membership function
DOI: 10.3233/JIFS-234286
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Sánchez-Jiménez, Eduardo | Cuevas-Chávez, Alejandra | Hernández, Yasmín | Ortiz-Hernandez, Javier | Hernández-Aguilar, José Alberto | Martínez-Rebollar, Alicia | Estrada-Esquivel, Hugo
Article Type: Research Article
Abstract: Machine learning algorithms have been used in diverse areas among applications, including healthcare. However, to fit an effective and optimal machine learning model, the hyperparameters need to be tuned. This process is commonly referred to as Hyperparameter Optimization and comprises several approaches. We combined three Hyperparameter Optimization techniques (Bayesian Optimization, Particle Swarm Optimization, and Genetic Algorithm) with three classifiers (Random Forest, Support Vector Machine, and XGBoost) to identify the best combination of hyperparameters that maximize model performance. We use the Framingham dataset to test the proposal. For classifier performance, the Support Vector Machine obtained the best result in recall (96.40%) …and F-score (93.86%), while XGBoost obtained the best result in precision (96.30%) and specificity (96.36%). In the accuracy metric, both classifiers achieved 95%. Bayesian optimization had the best results in terms of accuracy, precision, specificity, and F-score metrics. Both Particle Swarm Optimization and Genetic Algorithm obtained the best result in the recall metric. Show more
Keywords: Bayesian optimization, framingham dataset, genetic algorithm, heart disease, hyperparameter default value, hyperparameter optimization, machine learning, particle swarm optimization, support vector machine, XGBoost
DOI: 10.3233/JIFS-219376
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Cosío-León, M.A. | Martínez-Vargas, Anabel | Rodríguez-Cortés, Gabriela
Article Type: Research Article
Abstract: It is well-known that tuning a metaheuristic is a critical task because the performance of a metaheuristic and the quality of its solutions depend on its parameter values. However, finding a good parameter setting is a time-consuming task. In this work, we apply the upper confidence bound (UCB) algorithm to automate offline tuning in a (1 + 1)-evolution strategy. Preliminary results show that our proposed approach is a less costly method.
Keywords: Upper confidence bound algorithm, meta-optimizer, bandit problems, reinforcement learning
DOI: 10.3233/JIFS-219362
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Akhmetova, Dilyara | Akhmetov, Iskander | Pak, Alexander | Gelbukh, Alexander
Article Type: Research Article
Abstract: The paper focuses on the importance of coherence and preserving the breadth of content in summaries generated by the extractive text summarization method. The study utilized the dataset containing 16,772 pairs of extractive and corresponding abstractive summaries of scientific papers specifically tailored to increase text coherence. We smoothed the extractive summaries with a Large Language Model (LLM) fine-tuning approach and evaluated our results by applying the coefficient of variation approach. The statistical significance of the results was assessed using the Kolmogorov-Smirnov test and Z-test. We observed an increase in coherence in the predicted texts, highlighting the effectiveness of our proposed …methods. Show more
Keywords: Coherence, cohesion, extractive summary, abstractive summary, GPT2, summarization, seq2seq, random forest
DOI: 10.3233/JIFS-219353
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Ibarra Carrillo, Mario Alfredo | Montiel Pérez, Jesús Yaljá | Molina Lozano, Herón
Article Type: Research Article
Abstract: Today, it is the amount of data that defines the existence of mankind. Scientists respond to the large amount of required calculations by developing hardware in several directions. One of them is to increase the number of arithmetic elements. Another direction is to create new architectures that represent new algorithms for processing numerical data. We have chosen the second direction by developing a new systolic core architecture, which implies an improvement in efficiency, i.e. performing the same task with the same number of arithmetic elements but reducing the latency. Measurements are made in terms of computational capacity and the number …of arithmetic elements involved in the operations. The results of the tests are compared with data from a number of selected articles. Today, we have achieved 3.2GFlops with only two modules. In the future, we plan to integrate up to four of our cores in a system with its own memory and management processor and at a higher operating frequency. Show more
Keywords: Systolic array, systolic tensor core, accelerated matrix multiplication, accelerated convolution
DOI: 10.3233/JIFS-219361
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: El Alaoui Elfels, Mohamed | Douiri, Moulay Rachid | Raoufi, Mustapha
Article Type: Research Article
Abstract: The generation of power in Photovoltaic systems is reduced when they operate far from their maximum power point. For optimal operation, it is essential to continuously track the maximum power point of the PV solar array. However, identifying the maximum power point is a challenge due to the nonlinear relationship of electrical characteristics of PV panels with external factors. To address this issue, we present a novel design approach for a self-organizing, self-tuning fuzzy logic controller, applied to the problem of maximum power point tracking in photovoltaic systems. We outline the basic structure of the fuzzy logic controller and address …the design problems typically associated with conventional trial-and-error schemes. We also discuss the suitability of the genetic algorithm optimization technique for determining and optimizing the fuzzy logic controller design. In our proposed approach, we translate the normalization factors, membership function parameters, and controller policy into bit-strings, which are then processed by the genetic algorithm to find a near-optimal solution. To achieve high dynamic performance, we choose a particular objective function as a performance index. We compare our approach with two variants of the maximum power point algorithm, one based on genetic algorithms and the other based on fuzzy logic, as well as with the methods described in references [34 ] and [35 ], in order to evaluate its effectiveness. Show more
Keywords: Fuzzy logic controller, genetic algorithm optimisation, optimal power, photovoltaic system
DOI: 10.3233/JIFS-231710
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Tang, Ao | Wang, Xiaofeng | Peng, Qingyuan | Wang, Junxia | Yang, Yi | He, Fei | Hua, Yingying
Article Type: Research Article
Abstract: A CNF formula with each clause of length k and each variable occurring 4s times, where positive occurrences are 3s and negative occurrences are s , is a regular (3s + s , k )-CNF formula (F 3s +s ,k formula). The random regular exact (3s + s , k )-SAT problem is whether there exists a set of Boolean variable assignments such that exactly one literal is true for each clause in the F 3s +s ,k formula. By introducing a random instance generation model, the satisfiability phase transition of the solution is analyzed by …using the first moment method, the second moment method, and the small subgraph conditioning method, which gives the phase transition point s* of the random regular exact (3s + s , k )-SAT problem for k ≥3. When s < s* , F 3s +s ,k formula is satisfiable with high probability; when s > s* , F 3s +s ,k formula is unsatisfiable with high probability. Finally, through the experimental verification, the results show that the theoretical proofs are consistent with the experimental results. Show more
Keywords: Random regular exact (3s + s, k)-SAT problem, first moment method, second moment method, small subgraph conditioning method, phase transition
DOI: 10.3233/JIFS-238254
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Kolesnikova, Olga | Yigezu, Mesay Gemeda | Gelbukh, Alexander | Abitte, Selam | Sidorov, Grigori
Article Type: Research Article
Abstract: Twitter has experienced a tremendous surge in popularity over recent years, establishing itself as a prominent social media platform with a large user base. However, with this increased usage, there has been a concerning rise in the number of individuals resorting to derogatory language and expressing their opinions in a demeaning manner toward others. This surge in hate speech has drawn significant attention to the field of sentiment analysis, which aims to develop algorithms capable of detecting and analyzing emotions expressed in social networks using intuitive approaches. This paper focuses on addressing the complex task of detecting hate speech and …aggressive behavior while performing target classification. We explored various deep-learning approaches, including LSTM, BiLSTM, CNN, and GRU. Each offers unique capabilities for capturing different aspects of the input data. We proposed an ensemble approach that combines the top three performing models. This ensemble approach benefits from the diverse strengths of each individual model showing F1 score of 0.85 for English-HS, 0.94 for English-TR, 0.92 for English-AB, 0.84 for Spanish-HS, 0.86 for Spanish-TR, 0.97 for Spanish-AB, 0.74 for multilingual-HS, 0.94 for multilingual-TR, and 0.88 for multilingual-AB. Show more
Keywords: Hate speech, aggressive behavior, target classification, ensemble learning, deep learning, target classification
DOI: 10.3233/JIFS-219350
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Valencia-Valencia, Alex I. | Gomez-Adorno, Helena | Stephens Rhodes, Christopher | Bel-Enguix, Gemma | Trueba, Ojeda | Fuentes Pineda, Gibran
Article Type: Research Article
Abstract: Social media platforms, such as Twitter (now X), are a major source of communication. Identifying communicative intentions is useful, as it encapsulates the latent motivations that drive text creation. This intention is also helpful in understanding the message, context, and audience. This study proposes a method for detecting communicative intentions in tweets using Jakobson’s language functions. We constructed a meticulously annotated dataset, drawing from the extensive RepLab2013 corpus. Our dataset underwent rigorous scrutiny by linguistic annotators who analyzed over 12,000 tweets individually. These experts identified the dominant language function within each tweet by employing diverse strategies to ensure precise labeling …quality. The outcome demonstrated a noteworthy Kappa agreement score of 0.6, reflecting a strong inter-annotator reliability. Subsequently, these functions were mapped to the corresponding intention categories. We employed logistic regression and support vector machines (SVM) algorithms to classify intention in tweets and explored various pre-processing techniques, incorporating n-grams and bag-of-words representations. Furthermore, we expanded our research using pre-trained large language models, incorporating the latest state-of-the-art techniques in natural language processing. Show more
Keywords: Intention, communicative intention, tweets, language functions, intention identification, n-grams, logistic regression, SVM, deep learning
DOI: 10.3233/JIFS-219357
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Rasham, Tahair | Kutbi, Marwan Amin | Hussain, Aftab | Chandok, Sumit
Article Type: Research Article
Abstract: The objective of this research is to propose some new fixed point theorems for fuzzy-dominated operators that satisfy a nonlinear contraction on a closed ball in a complete b -multiplicative metric space. Our strategy involves the use of a combination of two distinct kinds of mappings: one belongs to a weaker class of strictly increasing mappings, and the other is a class of dominated mappings. In order to demonstrate the validity of our new findings, we provide instances that are both illustrative and substantial. Finally, in order to illustrate the novelty of our findings, we provide applications that allow us …to derive the common solution to integral and fractional differential equations. Our findings have a significant impact on the interpretation of a large number of previously published studies, both present and historical. Show more
Keywords: Fixed point, b-multiplicative metric space, generalized nonlinear contraction, fuzzy dominated operators, graph contraction, ordered fuzzy mappings, integral equation, fractional differential equation
DOI: 10.3233/JIFS-238250
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Zhang, Xin-jie | Li, Jun-qing | Liu, Xiao-feng | Tian, Jie | Duan, Pei-yong | Tan, Yan-yan
Article Type: Research Article
Abstract: Enterprises have increasingly focused on integrated production and transportation problems, recognizing their potential to enhance cohesion across different decision-making levels. The whale optimization algorithm, with its advantages such as minimal parameter control, has garnered attention. In this study, a hybrid whale optimization algorithm (HWOA) is designed to settle the distributed no-wait flow-shop scheduling problem with batch delivery (DNWFSP-BD). Two objectives are considered concurrently, namely, the minimization of the makespan and total energy consumption. In the proposed algorithm, four vectors are proposed to represent a solution, encompassing job scheduling, factory assignment, batch delivery and speed levels. Subsequently, to generate high-quality candidate …solutions, a heuristic leveraging the Largest Processing Time (LPT) rule and the NEH heuristic is introduced. Moreover, a novel path-relinking strategy is proposed for a more meticulous search of the optimal solution neighborhood. Furthermore, an insert-reversed block operator and variable neighborhood descent (VND) are introduced to prevent candidate solutions from converging to local optima. Finally, through comprehensive comparisons with efficient algorithms, the superior performance of the HWOA algorithm in solving the DNWFSP-BD is conclusively demonstrated. Show more
Keywords: Distributed no-wait flow shop, batch delivery, hybrid whale optimization algorithm, path-relinking
DOI: 10.3233/JIFS-238627
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Fan, Zhou | Yanjun, Shen | Zebin, Wu
Article Type: Research Article
Abstract: In this article, a non-fragile adaptive fuzzy observer is proposed for nonlinear systems with uncertain external disturbance and measurement noise. Firstly, the nonlinear system is augmented by an output filtered transformation. The output with measurement disturbance is put into the state equation of the augment system. Then, we introduce fuzzy logic system (FLS) to approximate the measurement disturbance, and construct an augmented non-fragile adaptive fuzzy observer for the augment system. A Lyapunov function is constructed to reveal that the characteristic of estimation errors is uniformly ultimately boundedness (UUB). Finally, two experimental simulations are offered to confirm the validity of the …proposed design method. Show more
Keywords: Non-fragile, high-gain observer, adaptive observer, fuzzy logic system
DOI: 10.3233/JIFS-237271
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Rajesh Kannan, A. | Thirupathi, G. | Murali Krishnan, S.
Article Type: Research Article
Abstract: Consider the graph G , with the injection Ω from node set to the first p + q natural numbers. Let us assume that the ceiling function of the classical average of the node labels of the end nodes of each link is the induced link assignment Ω * . If the union of range of Ω of node set and the range of Ω * of link set is all the first p + q natural numbers, then Ω is called a classical mean labeling. A super classical mean graph is a graph …with super classical mean labeling. In this research effort, we attempted to address the super classical meanness of graphs generated by paths and those formed by the union of two graphs. Show more
Keywords: Labeling, super classical mean labeling, super classical mean graph
DOI: 10.3233/JIFS-232328
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Ihtisham, Shumaila | Mustafa, Ghulam | Qureshi, Muhammad Nauman | Manzoor, Sadaf | Alamgir, | Khan, Adnan
Article Type: Research Article
Abstract: This study explores the distribution of order statistics of the Alpha Power Pareto (APP) distribution. Alpha Power Pareto is a more flexible distribution proposed by adding an extra parameter in the well-known Pareto distribution. This paper focuses on the derivation of single and product moment of the APP order statistics. Additionally, a recurrence link for single moments of order statistics is established. Moreover, analytical formulas of Rényi and q-entropy for APP order statistics are obtained.
Keywords: Order statistics, q-entropy, rényi entropy, recurrence relation, single and product moments
DOI: 10.3233/JIFS-231873
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Shafi, Smd | Sathiya Kumar, C.
Article Type: Research Article
Abstract: Identifying diseases using chest X-rays is challenging because more medical professionals are needed. A chest X-ray contains many features, making it difficult to pinpoint the factors causing a disease. Moreover, healthy individuals are more common than those with illnesses, and various diseases occur at different rates. To diagnose the disease accurately using X-ray images, extracting significant features and addressing unbalanced data is essential. To resolve these challenges, a proposed ensemble self-attention-based deep neural network aims to tackle the problem of unbalanced information distribution by creating a new goal factor. Additionally, the InceptionV3 architecture is trained to identify significant features. The …proposed objective function is a performance metric that adjusts the ratio of positive to negative instances, and the suggested loss function can dynamically mitigate the impact of many negative observations by reducing each cross-entropy term by a variable amount. Tests have shown that ensemble self-attention performs well on the ChestXray14 dataset, especially regarding the dimension around the recipient’s characteristics curves. Show more
Keywords: Deep neural networks, cross-weighted entropy loss, data with discrepancies, feature extraction, X-ray
DOI: 10.3233/JIFS-236444
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Venkatesan, S. | Kempanna, M. | Arogia Victor Paul, M. | Bhuvanesh, A.
Article Type: Research Article
Abstract: At present, Non-Orthogonal Multiple Access (NOMA) has become the most efficient technique to solve Data Rate (DR) requirements in Visible Light Communication (VLC) systems. However, present NOMA systems show high interference and increase the Peak-to-Average Power Ratio (PAPR), especially in wider applications. To overcome this issue, several techniques have been undertaken in the past and proven to better communication performance. However, the existing studies fail to provide a better Quality of Services (QoS) for the recent multi-carrier Optical Communication System (OCS). Hence, this study put forth a novel Generalized Frequency Division Multiplexing (GFDM) scheme to minimize the PAPR in an …indoor-based NOMA-VLC system. To enhance the performance of the GFDM system, a novel Offset-based Quadrature Amplitude Modulation (OQAM) technique is introduced that enhances the signal quality and prevents the Co-Channel Interference (CCI) problems effectively. Moreover, the proposed study introduces a novel Quantum-enabled Rabbit Optimization (QRO) technique for solving Resource Allocation (RA) problems in the NOMA-VLC system. The proposed method is processed via the MATLAB platform and various performance measures like Sum Rate (SR), Signal-to-Interference Noise Ratio (SINR), and Symbol Error Rate (SER) are analyzed and distinguished with various existing studies. In the simulation scenario, the proposed method achieves the SR of 178Mbps, SINR of 16 dB, and SER of compared to conventional techniques. Show more
Keywords: Indoor optical communication, non-orthogonal multiple access, light fidelity, generalized frequency division multiplexing, resource allocation, quantum rabbit optimization, offset quadrature modulation
DOI: 10.3233/JIFS-237800
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Yang, Wenyang | Li, Mengdi
Article Type: Research Article
Abstract: The development of computer vision and artificial intelligence provides technical support for objective evaluation of classroom teaching, and promotes the implementation of personalized teaching by teachers. In traditional classroom teaching, due to limitations, teachers are unable to timely understand and evaluate the effectiveness of classroom teaching through students’ classroom behavior, making it difficult to meet students’ personalized learning needs. Using artificial intelligence, big data and other digital technologies to analyze student classroom learning behavior is helpful to understand and evaluate students’ learning situation, thus improving the quality of classroom teaching. By using the method of literature analysis, the paper sorts …out relevant domestic and foreign literature in the past five years, and systematically analyzes the methods of student classroom behavior recognition supported by deep learning. Firstly, the concepts and processes of student classroom behavior recognition are introduced and analyzed. Secondly, it elaborates on the representation methods of features, including image features, bone features, and multimodal fusion. Finally, the development trend of student classroom behavior recognition methods and the problems that need to be further solved are summarized and analyzed, which provides reference for future research on student classroom behavior recognition. Show more
Keywords: Behavior recognition, object detection, skeleton pose, deep learning
DOI: 10.3233/JIFS-238228
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Geetha, R. | Priya, E. | Sivakumar, Kavitha
Article Type: Research Article
Abstract: Purpose: Automated diagnosis of acute cerebral ischemic stroke lesions (ACISL) is an evolving science. Early detection and exact delineation of ACISL automatically from diffusion-weighted magnetic resonance (DWMR) images are crucial for initiating prompt treatment. Thus, this work aims to determine the appropriate slice out of 60 pieces using multi-fractal analysis (MFA) and to segment the lesions in DWMR images using a hybrid optimization method. Features extracted from the segmented images were clinically correlated with the modified Rankin Scale (mRS). Methods: Thirty-one real-time stroke patients’ images were collected from Rajiv Gandhi Government General Hospital, Chennai, India. Multiple …MRI slices were taken from each patient and filtered using an anisotropic diffusion filter (ADF). These filtered images were skull-stripped automatically by the maximum entropy thresholding technique incorporating mathematical morphological operations (MEM). The multi-fractal analysis (MFA) identifies the prominent slice with the significant infarct lesion. An isodata algorithm that integrated differential evolution with the particle swarm optimization method based on Kapur’s (IDPK) and Otsu’s (IDPO) approaches was attempted to segment the ACISL. Finally, the geometric and moment features extracted from the segmented lesions categorized the stroke severity and were correlated with the mRS. Results: The findings of the experimental work confirm that the suggested IDPK approach achieved usual normalized values for image similarity indices such as Sokal-Michener Coefficient (98.51%), Roger-Tanimoto Coefficient (90.16%), Sokel-Sneath-2 (91.04%), and Sorenson Index (90.04%) are superior to IDPO. Statistical significance proved that the segmented lesions’ area (r = 0.820, p < 0.0001) and perimeter (r = 0.928, p < 0.0001) were strongly correlated with the mild and moderate criteria of mRS. Conclusion: The proposed work effectively detected ischemic stroke lesions and their severity within the studied image groups. It could be a promising and potential tool to aid radiologists in validating their diagnosis. Show more
Keywords: Ischemic stroke lesion, magnetic resonance imaging, multi-fractal analysis, isodata algorithm, differential evolution with particle swarm optimization, modified Rankin Scale
DOI: 10.3233/JIFS-233883
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
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: Manju, S.C. | Swarnajyothi, K. | Geetha, J. | Somasundaram, K.
Article Type: Research Article
Abstract: The Padmakar-Ivan (PI) index of a connected graph G is given by PI (G ) = ∑e =(u ,v )∈E (G ) (|V (G ) | - N G (e )) and weighted Padmakar-Ivan index is PI w (G ) = ∑e =(u ,v )∈E (G ) (d G (u ) + d G (v )) (|V (G ) | - N G (e )) . In this paper, we present the PI index for various classes of perfect graphs, including block graphs, the line graph of unicyclic graphs, and split graphs. The theorems established in this study are applied to ascertain the PI index of chain and …cyclic silicates. Furthermore, we derive both the PI and weighted PI indices for the lexicographic product of two regular graphs and determine the exact values for the lexicographic product involving a regular graph and a complete multipartite graph. Show more
Keywords: PI index, weighted pi index, perfect graphs, block graphs, lexicographic product, regular graphs, chain and cyclic tetrahedral frameworks
DOI: 10.3233/JIFS-238204
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Chen, Jiankai | Li, Zhongyan | Wang, Xin | Zhai, Junhai
Article Type: Research Article
Abstract: Monotonic classification is a widely applied classification task where improvements in specific input values do not lead to worse outputs. Monotonic classifiers based on K-nearest neighbors (KNN) have become crucial tools for addressing such tasks. However, these models share drawbacks with traditional KNN classifiers, including high computational complexity and sensitivity to noise. Fuzzy Monotonic K-Nearest Neighbors (FMKNN) is currently the state-of-the-art KNN-based monotonic classifier, mitigating the impact of noise to some extent. Nevertheless, there is still room for improvement in reducing computational complexity and softening monotonicity in FMKNN. In this paper, we propose a prototype selection algorithm based on FMKNN, …named Condensed Fuzzy Monotonic K-Nearest Neighbors (C -FMKNN). This algorithm achieves a dynamic balance between monotonicity and test accuracy by constructing a joint evaluation function that combines fuzzy ranking conditional entropy and correct prediction. Data reduction and simplifying computations can be achieved by using C -FMKNN to filter out instance subsets under the adaptive dynamic balance between monotonicity and test accuracy. Extensive experiments show that the proposed C -FMKNN improves significantly in terms of ACCU, MAE and NMI compared with the involved KNN-based non-monotonic algorithms and non-KNN monotonic algorithms. Compared with the instance selection algorithms MCNN, MENN, and MONIPS, C -FMKNN improves the average values of ACCU, MAE, and NMI by 3.7%, 3.6% and 18.3%, respectively, on the relevant datasets. In particular, compared with the benchmark algorithm FMKNN, C -FMKNN achieves an average data reduction rate of 58.74% while maintaining or improving classification accuracy. Show more
Keywords: Monotonic classification, fuzzy monotonic K-nearest neighbor, fuzzy ranking conditional entropy, joint evaluation function, data reduction
DOI: 10.3233/JIFS-236643
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Vimala, S. | Valarmathi, K.
Article Type: Research Article
Abstract: This study proposes a novel method using hybrid CNN-LSTM networks to measure and predict the effectiveness of speech and vision therapy. Traditional methods for evaluating therapy often rely on subjective assessments, lacking precision and efficiency. By combining CNN for visual data and MFCC for speech, alongside LSTM for temporal dependencies, the system captures dynamic changes in patients’ conditions. Pre-processing of audio and visual data enhances accuracy, and the model’s performance outperforms existing methods. This approach exhibits the potential of deep learning in monitoring patient progress effectively in speech and vision therapy, offering valuable insights for improving treatment outcomes. The proposed …system’s effectiveness is assessed by various performance metrics. The suggested system’s results are compared with those of other methods already in use. The study’s findings indicate that the suggested approach is more accurate than other existing models. In conclusion, this study offers important new information on how deep learning methods are being used to track patients’ progress in speech and vision therapy. Show more
Keywords: Monitor, speech and vision, deep learning, therapy patient, recording device, CNN-LSTM, categorization
DOI: 10.3233/JIFS-237363
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Ravi, Vinayakumar
Article Type: Research Article
Abstract: Deep learning-based models are employed in computer-aided diagnosis (CAD) tools development for pediatric pneumonia (P-Pneumonia) detection. The accuracy of the model depends on the scaling of the deep learning model. A survey on deep learning shows that models with a greater number of layers achieve better performances for P-Pneumonia detection. However, the identification of the optimal models is considered to be important work for P-Pneumonia detection. This work presents a hybrid deep learning model for P-Pneumonia detection. The model leverages the EfficientNetV2 model that employs various advanced methodologies to maintain the balance between the model scaling and the performance of …the model in P-Pneumonia detection. The features of EfficientNetV2 models are passed into global weighted average pooling (GWAP) which acts like an attention layer. It helps to extract the important features that point to the infected regions of the radiography image and discard all the unimportant information. The features from GWAP are high in dimension and using kernel-based principal component analysis (K-PCA), the features were reduced. Next, the reduced features are combined together and passed into a stacked classifier. The stacked classifier is a two-stage approach in which the first stage employs a support vector machine (SVM) and random forest tree (RFT) for the prediction of P-Pneumonia using the fused features and logistic regression (LRegr) on values of prediction for classification. Detailed experiments were done for the proposed method in P-Pneumonia detection using publically available benchmark datasets. Various settings in the experimental analysis are done to identify the best model. The proposed model outperformed the other methods by improving the accuracy by 4% in P-Pneumonia detection. To show that the proposed model is robust, the model performances were shown on the completely unseen dataset of P-Pneumonia. The hybrid deep learning-based P-Pneumonia model showed good performance on completely unseen data samples of P-Pneumonia patients. The generalization of the proposed P-Pneumonia model is studied by evaluating the model on similar lung diseases such as COVID-19 (CV-19) and Tuberculosis (TBS). In all the experiments, the P-Pneumonia model has shown good performances on similar lung diseases. This indicates that the model is robust and generalizable on data samples of different patients with similar lung diseases. The P-Pneumonia models can be used in healthcare and clinical environments to assist doctors and healthcare professionals in improving the detection rate of P-Pneumonia. Show more
Keywords: Pediatric pneumonia, machine learning, deep learning, dimensionality reduction, feature fusion
DOI: 10.3233/JIFS-219397
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Vaikunta Pai, T. | Nethravathi, P.S. | Birau, Ramona | Popescu, Virgil | Karthik Pai, B.H. | Naik, Pramod Vishnu
Article Type: Research Article
Abstract: Multimodal conversational AI systems have gained significant attention due to their potential to enhance user experience and enable more interactive and engaging interactions. This vital and complex research field seeks to integrate diverse modalities, including text, images, and speech, to develop conversational AI systems capable of comprehending, perceiving, and generating responses within a multimodal framework. By seamlessly incorporating various modalities, these systems can provide a more comprehensive and immersive conversational experience, enabling users to communicate in a more natural and intuitively. This research presents a novel multimodal architecture empowered by Deep Neural Networks (DNNs) for simultaneous integration and processing of …diverse modalities. Multimodal data encompasses various sources like text, images, audio, video, or sensor data. The objective is to merge and harness information from these modalities to amplify learning and enhance performance across a spectrum of tasks. This research explores the extension of ChatGPT, a state-of-the-art conversational AI model, to handle multimodal inputs, including text and images or text and speech. We present a comprehensive analysis of the benefits and challenges of integrating various options into ChatGPT, examining their impact on understanding, interaction, and overall system performance. Through extensive experimentation and evaluation, we demonstrate the potential of multimodal ChatGPT to provide richer, more context-aware conversations, while also highlighting the existing limitations and open research questions in this evolving field. Multimodal ChatGPT outperform the current GPT-3.5 by 16.51% and it is clear that multimodal ChatGPTis capable of better performance and offer a pathway for further progress in the field of language models. Show more
Keywords: Large language model, generative pre-trained transformer, deep learning, State-Of-The-Art (SOTA), artificial intelligence (AI), reinforcement training from human feedback, natural language processing (NLP), convolutional neural networks (CNN), recurrent neural networks (RNN)
DOI: 10.3233/JIFS-239465
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Li, Ye | Zhou, Jingkang
Article Type: Research Article
Abstract: Semi-supervised learning (SSL) aims to reduce reliance on labeled data. Achieving high performance often requires more complex algorithms, therefore, generic SSL algorithms are less effective when it comes to image classification tasks. In this study, we propose ComMatch, a simpler and more effective algorithm that combines negative learning, dynamic thresholding, and predictive stability discriminations into the consistency regularization approach. The introduction of negative learning is to help facilitate training by selecting negative pseudo-labels during stages when the network has low confidence. And ComMatch filters positive and negative pseudo-labels more accurately as training progresses by dynamic thresholds. Since high confidence does …not always mean high accuracy due to network calibration issues, we also introduce network predictive stability, which filters out samples by comparing the standard deviation of the network output with a set threshold, thus largely reducing the influence of noise in the training process. ComMatch significantly outperforms existing algorithms over several datasets, especially when there is less labeled data available. For example, ComMatch achieves 1.82% and 3.6% error rate reduction over FlexMatch and FixMatch on CIFAR-10 with 40 labels respectively. And with 4000 labeled samples, ComMatch achieves 0.54% and 2.65% lower error rates than FixMatch and MixMatch, respectively. Show more
Keywords: Semi-supervised learning, negative learning, dynamic threshold, predictive stability
DOI: 10.3233/JIFS-233940
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Sun, Haobin | Chen, Bingsan | Zhang, Wenshui | Wei, Songma | Lian, Changwei
Article Type: Research Article
Abstract: In the process of production, the label on the product provides the basic product information. Due to the complex text contained on the product labels, the high accuracy recognition for online production labels has always been a challenging problem. To address this issue, a more effective method for complex text detection by improving the convolutional recurrent neural network has been proposed to enhance the recognition accuracy of complex text. Firstly, the SE-DenseNet feature extraction network has been introduced for feature extraction, aiming to improve the model’s depth and feature extraction capacity. Then, the Bi-GRU network is utilized to learn and …model the hidden states and spatial features extracted by SE-DenseNet, anticipate preliminary sequence results, reduce model parameters, and improve the model’s calculation performance. Finally, the CTC network is employed for transcription to convert each feature sequence prediction output by Bi-GRU into a label sequence, achieving complex text recognition. Experimental results on the SVT, IIIT-5K, ICDAR2013 public dataset, and a self-built dataset demonstrate that the proposed model achieves superior outcomes on both public and self-built datasets. Remarkably, the model exhibits the highest recognition accuracy of 93.2% on the ICDAR2013 public dataset, demonstrating its potential to support complex text recognition for online production labels. Show more
Keywords: Online production labels, complex text recognition, SE-DenseNet, Bi-GRU
DOI: 10.3233/JIFS-234748
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
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