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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: Vela-Rincón, Virna V. | Mújica-Vargas, Dante | Luna-Álvarez, Antonio | Arenas Muñiz, Andrés Antonio | Cruz-Prospero, Luis A.
Article Type: Research Article
Abstract: Image segmentation is a very studied area, looking for the best clustering of pixels. However, it is sometimes a complicated task, especially when these pixels are at the edges of regions, where there is a gradient and it is difficult to decide to which region to assign it. Hesitating fuzzy sets (HFS) better describe these situations, allowing to have multiple possible values for each element, giving more flexibility. This type of sets has been mainly applied in decision-making problems, obtaining better results than other types of fuzzy sets. This research proposes a fast and automatic method based on fuzzy hesitant …clustering (FAHFC), which does not require parameters since it is capable of determining the number of clusters, using the Calinski-Harabasz index, in which the segmentation is performed, solving the initialization problem in clustering; it also proposes an alternative to construct the HFS through the use of fuzzy relations. The experiments show superiority in terms of clustering quality and convergence over some selected state-of-the-art algorithms. Show more
Keywords: Fuzzy clustering, hesitant fuzzy sets, image segmentation, Calinski-Harabasz index
DOI: 10.3233/JIFS-219370
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Zhou, Ning | Ren, Zhihao
Article Type: Research Article
Abstract: Traffic flow prediction is a significant application of deep learning in spatio-temporal forecasting analysis. Existing research faces challenges that hinder prediction accuracy. One challenge is the inadequate capturing of spatial dependencies in traffic flow due to fixed pre-defined graph structures. Moreover, manually designed prior graphs still have limitations in extracting spatial features. Another challenge is the instability in short-term prediction performance when pre-defined graphs are completely abandoned in favor of parameter training. Additionally, ordinary RNN sequence convolution methods also struggle to capture long-term sequential patterns in large historical traffic data, leading to gradient vanishing or exploding issues. To address these …challenges, we proposes a graph convolutional network for traffic flow prediction. We combine an improved prior graph with an adaptive graph to form a dual-branch spatio-temporal neural network. In the first branch, we introduce a time graph based on rough data inference to complement the predefined static graph. In the second branch, we construct an adaptive learning framework that dynamically learns the adjacency matrix and captures global road information. By utilizing enhanced multi-scale gated convolution, we extract spatio-temporal dependencies. Our method surpasses most baseline approaches according to extensive experiments conducted on public datasets. Show more
Keywords: Traffic flow prediction, graph convolutional network, adaptive graph, rough data inference, dual-branch
DOI: 10.3233/JIFS-236819
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Milovanović, Vladimir | Aleksić, Aleksandar | Milenkov, Marjan | Sokolović, Vlada
Article Type: Research Article
Abstract: The paper aims to present a hybrid model for measuring the performance of business processes in complex organizations based on the subjective decision-making of expert teams. The subject of the research is finding ways to measure, analyze and improve the key performance indicators (KPIs) process. Obtaining the values of KPIs, which reflect the real state of the process, creates a basis for their ranking, i.e. insight into KPIs that are extremely important for the process as well as KPIs that are of lesser importance, but as such are not excluded from consideration because they are necessary for the beginning, realization …and completion of the process. The model was compiled through five phases and was tested through a case study in a real business organization, which deals with the maintenance of complex combat systems. The obtained results helped the management to take certain measures in order to improve the performance of the maintenance process. In the model, it is proposed to form two expert teams, which make assessments based on experience and express them in linguistic terms according to a predefined scale. Modeling of linguistic expressions is realized using intuitive fuzzy sets of a higher order, more precisely Fermatean fuzzy sets (FFS). Selecting KPIs, decomposing the process into sub-processes and assessing the relative importance of sub-processes is carried out by one team of experts, while another team carries out the assessment of KPIs at the level of each sub-process. Determining the relative importance of sub-processes is realized using the Delphi method extended to FFS while reaching a consensus. The measurement of process performance, i.e. the value of KPIs, is realized using Multi-Criteria Group Decision-Making (MCGDM), such as the ELECTRE method extended with FFS. The sensitivity analysis of the developed model is realized by uncertainty modeling with q-rung orthopair fuzzy sets. Show more
Keywords: Fermatean fuzzy set (FFSs), ELECTRE method, Delphi method, maintenance, performance
DOI: 10.3233/JIFS-238907
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Xin, Ling
Article Type: Research Article
Abstract: In the era of digital economy, the optimization of enterprise supply chain networks has become a key challenge, while the problems of traditional supply chains, including information asymmetry and lack of trust, seriously hinder the development of enterprise supply chain networks. This paper will use the blockchain distributed technology and the digital economy background to explore how to use the blockchain distributed technology to optimize the existing problems. Firstly, study the supply chain information sharing to develop resources to reduce costs, then use the application of block chain technology and smart contract to establish information sharing mechanism to help the …supply chain information more transparent and improve trust; secondly, use the block chain technology decentralized storage model to realize the decentralized supply chain research, and finally use the consensus method to improve the privacy protection of information, to avoid information asymmetry among users. Through experiments, it could be found that the optimization method of enterprise supply chain network based on blockchain distributed technology had a traceability accuracy of over 92.35% for the extracted products, with an average traceability accuracy of 93.791% for 10 products. Research on the transparency of different supply chain information was above 89.73% . By utilizing blockchain distributed technology, information protection in enterprise supply chains could be effectively improved; trust mechanisms could be better established; risk control effectiveness could be improved; optimization of enterprise supply chain networks could be better assisted. Show more
Keywords: Enterprise supply chain network optimization, blockchain distributed technology, digital economy, smart contracts, deep learning
DOI: 10.3233/JIFS-234664
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Cruz, Eddy Sánchez-Dela | Fuentes-Ramos, Mirta | Loeza-Mejía, Cecilia-Irene | José-Guzmán, Irahan-Otoniel
Article Type: Research Article
Abstract: Purpose: Vaginal infections are prevalent causes of gynecological consultations. This study introduces and evaluates the efficacy of four Machine Learning algorithms in detecting vaginitis cases in southern Mexico. Methods: Utilizing Simple Perceptron, Naïve Bayes, CART, and AdaBoost, we conducted classification experiments to identify four vaginitis subtypes (gardnerella, candidiasis, trichomoniasis, and chlamydia) in 600 patient cases. Results: The outcomes are promising, with a majority achieving 100% accuracy in vaginitis identification. Conclusion: The successful implementation and high accuracy of these algorithms demonstrate their potential as valuable diagnostic tools for vaginal infections, particularly in southern Mexico. It …is crucial in a region where health technology adoption lags behind, and intelligent software support is limited in gynecological diagnoses. Show more
Keywords: Machine learning, gynecological pathologies, vaginitis, local dataset, correct identification
DOI: 10.3233/JIFS-219377
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Xie, Mengtong | Chai, Huaqi
Article Type: Research Article
Abstract: A human resources management plan is presently recognised as one of the most important components of a corporate technique. This is due to the fact that its major purpose is to interact with people, who are the most precious asset that an organisation has. It is impossible for an organisation to achieve its objectives without the participation of individuals. An organisation may effectively plan as well as manage individual processes to support the organization’s objectives and adapt nimbly to any change if it has well-prepared HR techniques and an action plan for its execution. This investigation puts up a fresh …way for the board of directors of a private firm to increase their assets and advance their growth by using cloud programming that is characterised by networks. The small company resource has been improved by strengthening human resource management techniques, and the cloud SDN network is used for job scheduling using Q-convolutional reinforcement recurrent learning. The proposed technique attained Quadratic normalized square error of 60%, existing SDN attained 55%, HRM attained 58% for Synthetic dataset; for Human resources dataset propsed technique attained Quadratic normalized square error of 62%, existing SDN attained 56%, HRM attained 59%; proposed technique attained Quadratic normalized square error of 64%, existing SDN attained 58%, HRM attained 59% for dataset. Show more
Keywords: Small business management, cloud software defined networks, human resource management, task scheduling, recurrent learning
DOI: 10.3233/JIFS-235379
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Cortés-Antonio, Prometeo | Valdez, Fevrier | Melin, Patricia | Castillo, Oscar
Article Type: Research Article
Abstract: The computing with words is an approach that has unique characteristics and advantages to model cognitive processes, this article explains the relationship and difference between type-1 and type-2 fuzzy sets in the definition of linguistic values. Here, we perform a compressive review and justify because type-2 sets are more appropriate in modeling linguistic values, and a heuristic procedure by examples is carried out to define linguistic values on a continuous variable. A visual comparison of a rule-based system, when linguistic values use crips, type-1, and type-2 fuzzy sets in modeling a cognitive system.
Keywords: Type-2 and type-1 fuzzy sets, linguistic values and variables, rule-based systems, cognitive computing
DOI: 10.3233/JIFS-219368
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Li, Zhiyuan | Hu, Chunhua | Hou, Zhanshan
Article Type: Research Article
Abstract: This study goes into the complexities of innovation and entrepreneurial skills by developing a detailed linear model and exploring the essential components that make up these talents. A multi-objective function model is presented to assess the effectiveness of using and distributing educational resources in this setting. For this assessment, the study uses the grey correlation method. Through a series of experimental simulations, the study demonstrates that the optimisation approach significantly improves the utilisation and allocation efficiency of educational resources committed to innovation and entrepreneurship by 18.72% and 20.98%, respectively. This results in a more balanced resource utilisation, which helps to …enhance the allocation of educational resources. A major conclusion of this study is the correlation value of 0.3177 with ideal entrepreneurship, which indicates a high degree of excellence in innovation and entrepreneurship education reached across the population analysed.. Show more
Keywords: Linear spatial model, grey correlation, resource allocation, multi-objective optimization, innovation and entrepreneurship
DOI: 10.3233/JIFS-236992
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Li, Jia | Xue, Shuaihao | Li, Minghui | Shi, Xiaoqiu
Article Type: Research Article
Abstract: Combining the harmony search algorithm (HS) with the local search algorithm (LS) can prevent the HS from falling into a local optimum. However, how LS affects the performance of HS has not yet been studied systematically. Therefore, in this paper, it is first proposed to combine four frequently used LS with HS to obtain several search algorithms (HSLSs). Then, by taking the flexible job-shop scheduling problem (FJSP) as an example and considering decoding times, study how the parameters of HSLSs affect their performance, where the performance is evaluated by the difference rate based on the decoding times. The simulation results …mainly show that (I) as the harmony memory size (HMS) gradually increases, the performance of HSLSs first increases rapidly and then tends to remain unchanged, and HMS is not the larger the better; (II) as harmony memory considering rate increases, the performance continues to improve, while the performance of pitch adjusting rate on HSLSs goes to the opposite; Finally, more benchmark instances are also used to verify the effectiveness of the proposed algorithms. The results of this paper have a certain guiding significance on how to choose LS and other parameters to improve HS for solving FJSP. Show more
Keywords: Algorithm analysis, local search, harmony search, flexible job-shop scheduling problem
DOI: 10.3233/JIFS-239142
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Ma, Dongdong | He, Xiaohai | Wang, Meiling | Fang, Qingmao | Zhu, Han | Hu, Ping
Article Type: Research Article
Abstract: Knowledge graph question answering aims to answer natural language questions using structured knowledge graph data. The key to achieving this is having a correct semantic understanding of the question phrases. Query graph generation is an important step for knowledge graph question answering systems to tackle complex questions. Unlike simple single-hop questions, complex questions often require reasoning between multiple triples to get the right answer due to multiple entities, relationships and constraints, making it difficult to generate correct query graphs. In previous studies, researchers have primarily focused on improving the extraction and representation of question features, neglecting the prior structural information …implicated in the question itself. In this paper, we propose a question structure classifier to classify the question structure, and alleviate the noise interference in query graph through classification results. In the classifier, we strengthen the information about the question structure through the attention mechanism, while weakening the irrelevant information. Moreover, a query graph sorting module based on feature cross-coding is proposed to sort candidate paths in the query graph using fine-grained feature interaction between words. Extensive experiments are conducted on two public datasets (MetaQA and WebQuestionsSP) and the experimental results show that the proposed method outperforms other baselines. Show more
Keywords: Knowledge graph question answering, relation embedding, attention enhancement, feature cross-encoding
DOI: 10.3233/JIFS-233650
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Yue, Lizhu | Lv, Yue
Article Type: Research Article
Abstract: The Vlsekriterijumska Optimizacija I Komprosmisno Resenie (VIKOR) method to some extent modifies the utility function to a value function that can consider different risk preferences. However, the weight and risk attitude parameters involved in the model are difficult to determine, which limits its application. To overcome this problem, a Poset-VIKOR model is proposed. A partial order set is a non-parametric decision-making method. Through the combination of partial order set and VIKOR model, the parameters can be “eliminated”, and a robust method that can run the model is obtained. This method uses the Hasse diagram to express the evaluation results, which …can not only directly display the hierarchical and clustering information, but also show the robustness characteristics of the alternative comparison. Show more
Keywords: VIKOR method, poset, weight, multiple attribute decision making
DOI: 10.3233/JIFS-230680
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Shao, Dangguo | Huang, Chunsheng | Liu, Cuiyin | Ma, Lei | Yi, Sanli
Article Type: Research Article
Abstract: The automatic segmentation of diabetic retinopathy (DR) holds significant importance for assisting physicians in diagnosis and treatment. Given the complexity, high inter-class similarity, and uncertainty of DR, it is crucial to integrate multiscale information between lesions and establish global correlations among them. To address these issues, a novel HRU-TNet (Hybrid Residual U-Transformer Network) algorithm for retinal lesion segmentation is proposed. In this framework, the network is augmented with lightweight self-attention residual U-modules (LSA-RSU) to capture high-frequency details of the lesions and global contextual information. The skip connections are then enhanced through interactive residual transformer fusion modules (IRTF) and channel-cross attention …(CCA), promoting dependencies among features at different scales and filtering out interfering information to guide feature fusion and eliminate ambiguity. Additionally, a novel retinal image enhancement technique is devised, employing local wavelet transformations to capture detailed components of the retinal images, thereby enhancing the representational capacity of the segmentation network. Data augmentation is also performed to ensure network adaptability to small datasets. Comprehensive experiments conducted on the publicly available IDRID and e_ophtha datasets yielded average AUC_PR values of 0.709 and 0.451, respectively. The proposed approach demonstrated superior generalization on the DDR dataset compared to other methods mentioned in the literature. These results demonstrate that our proposed method is better suited for small retinal datasets, exhibiting improved segmentation accuracy and generalization compared to existing approaches. Show more
Keywords: Lesion segmentation, fundus image enhancement, transformer, cross attention fusion, light self-attention residual
DOI: 10.3233/JIFS-240788
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: He, Xiaorong | Fang, Anran | Yu, Dejian
Article Type: Research Article
Abstract: Electronic commerce (EC) has become the most critical business activity in the world. China has become the world’s largest market for EC. Over the past three decades, numerous researches have examined the current status of the development of monolingual EC research in specific scenarios. However, the paradigm shift in EC development through the analysis of the dynamic evolution of semantic information has not yet been examined, and the distinctions and connections between multilingual EC studies have not yet been established. This study analyzed 16,207 English and 17,850 Chinese EC-related articles from the Web of Science database and CNKI by combining …the BERTopic topic model and SBERT sentence embedding-based similarity computations. The results reveal the distributions of global and local topics in the English and Chinese EC literature, analyze the semantic intricacies of topic convergence and evolution across continuous time, as well as the distinctions and connections between English and Chinese topics. Finally, the evolutionary patterns and life cycle of three crucial English and Chinese topics are explored respectively, including their emergence, development, maturity, and decline. Overall, this study provides a comprehensive overview of EC studies from a topic perspective. Show more
Keywords: Electronic commerce, BERTopic, topic modeling, topic evolution, sentence embedding
DOI: 10.3233/JIFS-232825
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Kazancı, O. | Hoskova-Mayerova, S. | Davvaz, B.
Article Type: Research Article
Abstract: In recent years, the m-polar fuzziness structure has attracted the attention of researchers and has been commonly applied in algebraic structures. In this article, we present the notion of multi-polar fuzzy hyperideals of ordered semihyperrings, which is a generalization of the concept of bi-polar fuzzy hyperideals of ordered semihyperrings. We investigate some of their associated properties. Furthermore, we characterized regular ordered semihyperring in terms of multi-polar fuzzy quasi-ideals and multi-polar fuzzy bi-ideals.
Keywords: Semihyperring, ordered semihyperring, m-polar fuzzy semihyperring, m-polar fuzzy hyperideals
DOI: 10.3233/JIFS-238654
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-09, 2024
Authors: Ameen, Zanyar A. | Mohammed, Ramadhan A. | Al-shami, Tareq M. | Asaad, Baravan A.
Article Type: Research Article
Abstract: This paper introduces a new fuzzy structure named “fuzzy primal.” Then, it studies the essential properties and discusses their basic operations. By applying the q-neighborhood system in a primal fuzzy topological space and the Łukasiewicz disjunction, we establish a fuzzy operator (·) ⋄ on the family of all fuzzy sets, followed by its core characterizations. Next, we use (·) ⋄ to investigate a further fuzzy operator denoted by Cl ⋄ . To determine a new fuzzy topology from the existing one, the earlier fuzzy operators are explored. Such a new fuzzy topology is called primal fuzzy topology. Various properties of …primal fuzzy topologies are found. Among others, the structure of a fuzzy base that generates a primal fuzzy topology. Furthermore, the concept of compatibility between fuzzy primals and fuzzy topologies is introduced, and some equivalent conditions to that concept are examined. It is shown that if a fuzzy primal is compatible with a fuzzy topology, then the fuzzy base that produces the primal fuzzy topology is itself a fuzzy topology. Show more
Keywords: Fuzzy primal, fuzzy grill, fuzzy ideal, primal fuzzy topology, fuzzy ideal topology
DOI: 10.3233/JIFS-238408
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Article Type: Research Article
Abstract: Background: Breast cancer diagnosis relies on accurate lesion segmentation in medical images. Automated computer-aided diagnosis reduces clinician workload and improves efficiency, but existing image segmentation methods face challenges in model performance and generalization. Objective: This study aims to develop a generative framework using a denoising diffusion model for efficient and accurate breast cancer lesion segmentation in medical images. Methods: We design a novel generative framework, PalScDiff, that leverages a denoising diffusion probabilistic model to reconstruct the label distribution for medical images, thereby enabling the sampling of diverse, plausible segmentation outcomes. Specifically, with the …condition of the corresponding image, PalScDiff learns to estimate the masses region probability through denoising step by step. Furthermore, we design a Progressive Augmentation Learning strategy to incrementally handle segmentation challenges of irregular and blurred tumors. Moreover, multi-round sampling is employed to achieve robust breast mass segmentation. Results: Our experimental results show that PalScDiff outperforms established models such as U-Net and transformer-based alternatives, achieving an accuracy of 95.15%, precision of 79.74%, Dice coefficient of 77.61%, and Intersection over Union (IOU) of 81.51% . Conclusion: The proposed model demonstrates promising capabilities for accurate and efficient computer-aided segmentation of breast cancer. Show more
Keywords: Diffusion model, consistent regularization, breast cancer, medical image segmentation, data augmentation
DOI: 10.3233/JIFS-239703
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Yang, Guang | Qi, Juntong | Wang, Mingming | Wu, Chong | Liu, Yansheng | Liu, Zhengjun | Ping, Yuan
Article Type: Research Article
Abstract: Target encirclement is widely used in the field of unmanned aerial vehicles(UAVs), which can effectively monitor and intercept external threats. However, the integration from target detection, localization to final tracking is difficult or costly. This article proposes a complete and inexpensive framework of the target encirclement for multiple quadrotors. The framework consists of three modules: object detection, target localization and formation tracking. Firstly, a one-stage object detector based on a convolutional neural network is used to achieve fast and accurate object detection. Then, combined with the position and attitude states of the quadrotor, a 3D target localization scheme to locate …the target position is proposed. Based on consensus theory, a time-varying formation tracking control protocol is proposed. Finally, a multiple quadrotor platform composed of one reconnaissance quadrotor and four hunter quadrotors is built with self-organizing network communication, which avoids the expensive cost of deploying object detection modules on each quadrotor platform. We deployed the framework on the multiple quadrotor platform and conducted static and dynamic localization and encirclement experiments with a minibus as the target. The result shows that the reconnaissance quadrotors can detect and accurately locate targets over 30 fps , and the average deviation of locating the target minibus could reach a minimum of 0.0712 m . The hunter quadrotors could track and encircle the dynamic moving target minibus in a time-varying formation. Experiments demonstrate the effectiveness and practicality of the proposed framework of the target encirclement for multiple quadrotors. Show more
Keywords: Multiple quadrotors, target encirclement, visual detection, target localization, time-varying formation tracking
DOI: 10.3233/JIFS-238335
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Ou, Qiqi | Zhang, Xiaohong | Wang, Jingqian
Article Type: Research Article
Abstract: Fuzzy rough sets (FRSs) play a significant role in the field of data analysis, and one of the common methods for constructing FRSs is the use of the fuzzy logic operators. To further extend FRSs theory to more diverse information backgrounds, this article proposes a covering variable precision fuzzy rough set model based on overlap functions and fuzzy β-neighbourhood operators (OCVPFRS). Some necessary properties of OCVPFRS have also been studied in this work. Furthermore, multi-label classification is a prevalent task in the realm of machine learning. Each object (sample or instance) in multi-label data is associated with various labels (classes), …and there are numerous features or attributes that need to be taken into account within the attribute space. To enhance various performance metrics in the multi-label classification task, attribute reduction is an essential pre-processing step. Therefore, according to overlap functions and fuzzy rough sets’ excellent work on applications: such as image processing and multi-criteria decision-making, we establish an attribute reduction method suitable for multi-label data based on OCVPFRS. Through a series of experiments and comparative analysis with existing multi-label attribute reduction methods, the effectiveness and superiority of the proposed method have been verified. Show more
Keywords: Fuzzy rough sets, overlap functions, fuzzy β-neighbourhood operators, attribute reduction, multi-label classification
DOI: 10.3233/JIFS-238245
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Embriz-Islas, Cesar | Benavides-Alvarez, Cesar | Avilés-Cruz, Carlos | Zúñiga-López, Arturo | Ferreyra-Ramírez, Andrés | Rodríguez-Martínez, Eduardo
Article Type: Research Article
Abstract: Speech recognition with visual context is a technique that uses digital image processing to detect lip movements within the frames of a video to predict the words uttered by a speaker. Although models with excellent results already exist, most of them are focused on very controlled environments with few speaker interactions. In this work, a new implementation of a model based on Convolutional Neural Networks (CNN) is proposed, taking into account image frames and three models of audio usage throughout spectrograms. The results obtained are very encouraging in the field of automatic speech recognition.
Keywords: CNN, artificial intelligence, deep learning, speech recognition
DOI: 10.3233/JIFS-219346
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Zavala-Díaz, Jonathan | Olivares-Rojas, Juan C. | Gutiérrez-Gnecchi, José A. | Téllez-Anguiano, Adriana C. | Alcaraz-Chávez, J. Eduardo | Reyes-Archundia, Enrique
Article Type: Research Article
Abstract: Efficient medical information management is essential in today’s healthcare, significantly to automate diagnoses of chronic diseases. This study focuses on the automated identification of diabetic patients through a clinical note classification system. This innovative approach combines rules, information extraction, and machine learning algorithms to promise greater accuracy and adaptability. Initially, the four algorithms evaluated showed similar performance, with Gradient Boosting standing out with an accuracy of 0.999. They were tested on our clinical and oncology notes, where SVM excelled in correctly labeling non-oncology notes with a 0.99. Gradient Boosting had the best average with 0.966. The combination of rules, information …extraction, and Random Forest provided the best average performance, significantly improving the classification of clinical notes and reducing the margin of error in identifying diabetic patients. The principal contribution of this research lies in the pioneering integration of rule-based methods, information extraction techniques, and machine learning algorithms for enhanced accuracy in diabetic patient identification. For future work, we consider implementing these algorithms in natural clinical settings to evaluate their practical performance. Additionally, additional approaches will be explored to improve the accuracy and applicability of clinical note-grading systems in healthcare. Show more
Keywords: NLP, diabetes, machine learning, binary classification, word frequency analysis
DOI: 10.3233/JIFS-219375
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Martinez, German | Duta, Eduard-Andrei | Sanchez-Romero, Jose-Luis | Jimeno-Morenilla, Antonio | Mora-Mora, Higinio
Article Type: Research Article
Abstract: Within various industrial settings, such as shipping, aeronautics, woodworking, and footwear, there exists a significant challenge: optimizing the extraction of sections from material sheets, a process known as “nesting”, to minimize wasted surface area. This paper investigates efficient solutions to complex nesting problems, emphasizing rapid computation over ultimate precision. We introduce a dual-approach methodology that couples both a greedy technique and a genetic algorithm. The genetic algorithm is instrumental in determining the optimal sequence for placing sections, ensuring each is located in its current best position. A specialized representation system is devised for both the sections and the material sheet, …promoting streamlined computation and tangible results. By balancing speed and accuracy, this study offers robust solutions for real-world nesting challenges within a reduced computational timeframe. Show more
Keywords: Genetic algorithm, 2D nesting, irregular pattern, cutting, industrial automation
DOI: 10.3233/JIFS-219345
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ling, Lina | Wen, Mi | Wang, Haizhou | Zhu, Zhou | Meng, Xiangjie
Article Type: Research Article
Abstract: The detection of out-of-distribution (OoD) samples in semantic segmentation is crucial for autonomous driving, as deep learning models are typically trained under the assumption of a closed environment, whereas the real world presents an open and diverse set of scenarios. Existing methods employ uncertainty estimation, image reconstruction, and other techniques for OoD sample detection. We have observed that different classes may exhibit connections and associations in varying contexts. For example, objects encountered by autonomous vehicles differ in rural road scenes compared to urban environments, and the likelihood of encountering novel objects varies. This aspect is missing in current anomaly detection …methods and is vital for OoD sample detection. Existing approaches solely consider the relative significance of each prediction class, overlooking the inter-object correlation. Although prediction scores (e.g., max logits) obtained from the segmentation network are applicable for OoD sample detection, the same problem persists, particularly for OoD objects. To address this issue, we propose the utilization of the Mahalanobis distance of max logits to evaluate the final predicted score. By calculating the Mahalanobis distance, the paper aims to uncover correlations between different classes, thus enhancing the effectiveness of OoD detection. To this end, we also extend the state-of-the-art segmentation model, DeepLabV3+, to enable OoD sample detection in this paper. Specifically, this paper proposes a novel backbone network, SOD-ResNet101, for extracting contextual and multi-scale semantic information, leveraging the class correlation feature of the Mahalanobis distance to enhance the detection performance of out-of-distribution objects. Notably, our approach eliminates the need for external datasets or separate network training, making it highly applicable to existing pretraining segmentation models. Show more
Keywords: Semantic segmentation, deep learning, anomaly segmentation, automatic driving, out-of-distribution detection
DOI: 10.3233/JIFS-237799
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Kumar Sahu, Vinay | Pandey, Dhirendra | Singh, Priyanka | Haque Ansari, Md Shamsul | Khan, Asif | Varish, Naushad | Khan, Mohd Waris
Article Type: Research Article
Abstract: The Internet of Things (IoT) strategy enables physical objects to easily produce, receive, and exchange data. IoT devices are getting more common in our daily lives, with diverse applications ranging from consumer sector to industrial and commercial systems. The rapid expansion and widespread use of IoT devices highlight the critical significance of solid and effective cybersecurity standards across the device development life cycle. Therefore, if vulnerability is exploited directly affects the IoT device and the applications. In this paper we investigated and assessed the various real-world critical IoT attacks/vulnerabilities that have affected IoT deployed in the commercial, industrial and consumer …sectors since 2010. Subsequently, we evoke the vulnerabilities or type of attack, exploitation techniques, compromised security factors, intensity of vulnerability and impacts of the expounded real-world attacks/vulnerabilities. We first categorise how each attack affects information security parameters, and then we provide a taxonomy based on the security factors that are affected. Next, we perform a risk assessment of the security parameters that are encountered, using two well-known multi-criteria decision-making (MCDM) techniques namely Fuzzy-Analytic Hierarchy Process (F-AHP) and Fuzzy-Analytic Network Process (F-ANP) to determine the severity of severely impacted information security measures. Show more
Keywords: IoT attacks, fuzzy-ANP, fuzzy-AHP, MCDM, IoT vulnerabilities
DOI: 10.3233/JIFS-233759
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Chen, Jian | Cai, Zhiming | Peng, Sheng | Lu, Fei
Article Type: Research Article
Abstract: In the era of widespread connectivity, leveraging artificial intelligence models and analyzing the vast datasets generated by smart devices are central points in IoT research. While existing studies mainly focus on improving the decision-making prowess of central systems, the potential for local optimization remains largely unexplored. This paper presents an Ensemble Voting Scheme with Multilayer Dynamic Groups (EVMDS), which assigns decision weights to IoT devices based on their attribute data. By employing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, dynamic clusters among IoT devices can be identified, the application of ensemble voting rules at each stage of …group formation, enabling layered computations to ease backend burden and achieve hierarchical decision-making capability, facilitating regional-level decision-making that strikes a balance between local and global optimization. Through simulated decision-making scenarios in a small-scale IoT environment, our experiments demonstrate the superior accuracy and reliability of the proposed approach compared to existing models. Show more
Keywords: Local optimization, Internet-of-things, ensemble-voting, DBSCAN, dynamic grouping
DOI: 10.3233/JIFS-236899
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Bochkarev, Vladimir V. | Savinkov, Andrey V. | Shevlyakova, Anna V. | Solovyev, Valery D.
Article Type: Research Article
Abstract: This work considers implementation of a diachronic predictor of valence, arousal and dominance ratings of English words. The estimation of affective ratings is based on data on word co-occurrence statistics in the large diachronic Google Books Ngram corpus. Affective ratings from the NRC VAD dictionary are used as target values for training. When tested on synchronic data, the obtained Pearson‘s correlation coefficients between human affective ratings and their machine ratings are 0.843, 0.779 and 0.792 for valence, aroused and dominance, respectively. We also provide a detailed analysis of the accuracy of the predictor on diachronic data. The main result of …the work is creation of a diachronic affective dictionary of English words. Several examples are considered that illustrate jumps in the time series of affective ratings when a word gains a new meaning. This indicates that changes in affective ratings can serve as markers of lexical-semantic changes. Show more
Keywords: Affective words, affective norms, sentiment dictionary, word valence ratings, lexical semantic change
DOI: 10.3233/JIFS-219358
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Zhang, Yingmin | Yi, Afa | Li, Shuo
Article Type: Research Article
Abstract: The constant development and application of new technologies, such as big data, artificial intelligence and the mobile Internet, have profoundly changed the personal and professional spheres. Despite these advances, finance professionals are still faced with a multitude of routine, repetitive and error-prone tasks. At the same time, they are challenged by the shift to management accounting, resulting in reduced productivity. This paper addresses these issues by introducing a financial statement filing robot developed using Robotic Process Automation (RPA) technology. The application of this robot has been shown to provide superior efficiency and accuracy, reduce the heavy burden of routine tasks, …and facilitate a smooth transition to management accounting practices. In addition, this research provides a valuable reference for the application and diffusion of RPA technology in the financial sector. Given the large amount of text data generated by financial processes, this paper proposes an automatic text categorization model. The effectiveness of the model is demonstrated as a response to address the challenges encountered in the consultation and archiving process. This contribution informs the development of text categorization robots tailored to the needs of finance professionals. Show more
Keywords: RPA technology, robot, financial statements, text classification, naive Bayes classifier model
DOI: 10.3233/JIFS-236716
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Jun, Dai | Huijie, Shi | Yanqin, Li | Junwei, Zhao | Naohiko, Hanajima
Article Type: Research Article
Abstract: Cylinder liner is an internal part of the automobile engine, which plays an important role in the automobile internal combustion engine. Therefore, it is a top priority to accurately and quickly detect the cylinder liner surface defects. In order to effectively achieve the classification and localization of surface defects on the cylinder liner, this paper establishes a dataset for surface defects on cylinder liner and proposes a based on improved YOLOv5 algorithm for detecting surface defects on cylinder liner. Firstly, a machine vision system is established to acquire on-site images and perform manual annotation to build the dataset of surface …defects on cylinder liner. Secondly, the GSConv SlimNeck mechanism is introduced to reduce the model complexity; the Bi-directional Feature Pyramid Network (BiFPN) is used to fuse the feature information at different scales to enhance the detection accuracy of small surface defects on cylinder liner; and embedding the SimAM attention mechanism to focus on the object region of interest and improve the accuracy and robustness of the model. The final improved YOLOv5 model reduces the number of model parameters by 15.8% compared to the non-improved YOLOv5. And the experimental results on our self-built dataset for cylinder liner defects show that the mAP0.5 is improved by 0.4%. This means that the accuracy of model detection was not compromised. This method can be applied to actual production processes. Show more
Keywords: Cylinder liner defect detection, YOLOv5, GSConv SlimNeck, BiFPN, SimAM
DOI: 10.3233/JIFS-237793
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Hu, Man | Sun, Dezhi | Bai, Yihan | Xiao, Han | You, Fucheng
Article Type: Research Article
Abstract: In the realm of graph representation learning, Graph Neural Networks (GNNs) have demonstrated exceptional efficacy across diverse tasks. Typically, GNNs employ message-passing schemes to disseminate node features along graph structures, culminating in learned graph representations. However, their heavy reliance on smoothed node features over graph structures, coupled with limited expressiveness in the presence of node attributes, often constrains link prediction performance. To surmount this challenge, we propose GTLP, a Graph Transformer based link prediction framework. GTLP integrates unsupervised GNNs and structure encoding, enabling a holistic consideration of both topological structures and node features. This approach preserves critical node location and …role information, enhancing the model’s expressiveness. By introducing the Graph Transformer model, GTLP adeptly incorporates neighbor information, refining embedding quality and bolstering the model’s learning and generalization capabilities. Notably, our method exhibits superior scalability, accommodating diverse techniques for information extraction, embedding learning, and sampling. Experimental results underscore GTLP’s state-of-the-art performance, outpacing various baselines across five real-world datasets. Show more
Keywords: Deep learning, graph neural networks, graph transformer, link prediction
DOI: 10.3233/JIFS-237506
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Chen, Xinying | Hu, Mingjie
Article Type: Research Article
Abstract: With the rapid proliferation of substantial textual data from sources such as social media, online comments, and news articles, sentiment analysis has become increasingly crucial. However, existing deep learning methods have overlooked the significance of part-of-speech (POS) and emotional words in understanding the emotion of text. Based on this, this paper proposes a sentiment analysis approach that combines multiple features with a dual-channel network. Firstly, the vector representation of the text is obtained through Robustly Optimized BERT Pretraining Approach (RoBERTa). Secondly, the POS features and word emotional features are separately updated using self-attention to calculate weights. Concatenating words, POS and …emotion, feature dimension reduction and fusion are achieved through a linear layer. Finally, the fused feature vector is input into a dual-channel network composed of Bidirectional Gated Recurrent Unit (BiGRU) and Deep Pyramid Convolutional Neural Network (DPCNN). Experimental results demonstrate that the proposed method achieves higher classification accuracy than the comparative methods on three sentiment analysis datasets. Moreover, the experimental results fully validate the effectiveness of the proposed approach. Show more
Keywords: Sentiment analysis, part-of-speech, RoBERTa, bidirectional gated recurrent unit, deep pyramid convolutional neural network
DOI: 10.3233/JIFS-237749
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Nisha, B. Muthu | Selvakumar, J. | Nithya, V.
Article Type: Research Article
Abstract: The provision of secure and sustainable energy services is ensured by this research, also contributing to the advancement of technology align with the Sustainable Development Goals (SDGs). The motivation behind this study stems from the critical need to bolster hardware security within cutting-edge smart grid infrastructure, and more specifically, for smart energy metering technology. To address this need, this paper introduces a feasible and modular approach for enhancing the security through the implementation of a cryptographic key generator. This key generator is based on a modified Delay-based Physically Unclonable Function (PUF), which incorporates the innovative concept of a Delay Locked …Loop(DLL).The reliability of the proposed PUFs has been rigorously assessed, demonstrating impressive performance levels of 98.02% and 99.1% across a wide temperature and supply voltage, spanning from -40°C to 80°C and (3.0-3.6) V. This is showcasing exceptional functionality within the smart meter’s operational parameters.The effectiveness of this approach is confirmed through practical testing conducted on the ZYNQ-7 ZC 702 Field-Programmable Gate Array (FPGA) platform.The outcomes are encouraging by substantial uniqueness (55.96% and 56.2%) and uniformity (51.2% and 49.15%). This research significantly advances the state of the art by surpassing previous investigations into XOR Arbiter PUF (XOR APUF) and Configurable Ring Oscillator PUF (CRO PUF) designs. Furthermore, the paper delves into an examination of the proposed design’s resilience against modeling attacks, along with comprehensive security assessments. Show more
Keywords: Sustainable development goals, smart energy meter, delay locked loop, physically unclonable function, field programmable gate array
DOI: 10.3233/JIFS-240099
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Gowri, S. | Vennila, B. | Antony Crispin Sweety, C.
Article Type: Research Article
Abstract: The primary focus of this work is to develop the concept of bipolar N-neutrosophic supra topological spaces. Also, extended some concepts such as closure and interior operators of N-neutrosophic supra topological spaces to Bipolar N-neutrosophic supra topological spaces. The properties and relationship between weak forms of bipolar N-neutrosophic supra topological open sets are also established. Further, suggested several separations amongst bipolar N-neutrosophic supra sets. Some distance between bipolar N-neutrosophic sets is introduced and an efficient approachfor group multi-criteria decision making based on bipolar N-neutrosophic sets is proposed.
Keywords: Bipolar N-neutrosophic supra topology, bipolar N-neutrosophic supra α-open set, bipolar N-neutrosophic supra semi-open, bipolar N-neutrosophic supra β-open and bipolar N-neutrosophic supra pre-open, N-valued interval neutrosophic sets
DOI: 10.3233/JIFS-224450
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Vallejos, Sebastian | Armentano, Marcelo G. | Berdun, Luis | Schiaffino, Silvia | González Císaro, Sandra | Nigro, Oscar | Balduzzi, Leonardo | Cuesta, Ignacio
Article Type: Research Article
Abstract: Product classification is a critical task for the smooth running of the purchase process in e-commerce websites. When it comes to P2P marketplaces, users can act both as sellers and as buyers, and they need to assign predefined categories to the products they want to sell. Besides being tedious for users, this task can result in ambiguous or inaccurate assignments. This article presents a method for the automatic categorization of items offered in a local P2P marketplace using a multi-level classification approach. Our experiments demonstrated a significant improvement in the classification results of the proposed solution compared to a traditional …direct classification approach. Show more
Keywords: Classification, e-commerce, NLP, P2P marketplace
DOI: 10.3233/JIFS-219344
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Brännström, Andreas | Nieves, Juan Carlos
Article Type: Research Article
Abstract: This paper introduces an automated decision-making framework for providing controlled agent behavior in systems dealing with human behavior-change. Controlled behavior in such settings is important in order to reduce unexpected side-effects of a system’s actions. The general structure of the framework is based on a psychological theory, the Theory of Planned Behavior (TPB), capturing causes to human motivational states, which enables reasoning about dynamics of human motivation. The framework consists of two main components: 1) an ontological knowledge-base that models an individual’s behavioral challenges to infer motivation states and 2) a transition system that, in a given motivation state, decides …on motivational support, resulting in transitions between motivational states. The system generates plans (sequences of actions) for an agent to facilitate behavior change. A particular use-case is modeled regarding children with Autism Spectrum Conditions (ASC) who commonly experience difficulties in everyday social situations. An evaluation of a proof-of-concept prototype is performed that presents consistencies between ASC experts’ suggestions and plans generated by the system. Show more
Keywords: Interactive agents, strategic decision-making, behavior-change systems, theory of planned behavior, Autism
DOI: 10.3233/JIFS-219335
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Li, Fuxue | Chi, Chuncheng | Yan, Hong | Zhang, Zhen | Zhao, Zhongchao
Article Type: Research Article
Abstract: Transformer-based neural machine translation (NMT) models have achieved state-of-the-art performance in the machine translation paradigm. These models learn the translation knowledge from the bilingual corpus through the attention mechanism automatically. This differs from the way human translators approach sentence translation, where prior knowledge plays a significant role. Inspired by this, a word translation augmentation (WTA) method is proposed to improve the Transformer-based NMT model. The main steps are as follows: Firstly, constructing the word alignment rules based on the training set. Next, generating the translation rules for source words according to the word alignment rules. Lastly, incorporating the potential translation …candidates for each source word into the NMT model during the training and testing procedure. In addition, the WTA method introduces the idea of Mixup for translation candidates of a source word and employs two augmentation strategies to augment the encoder. The results of experiments on several translation tasks with high-resource and low-resource indicate the effectiveness of the proposed method compared with the corresponding strong baseline, and the improvement in BLEU score achieved ranges from 0.42 to 0.63. Show more
Keywords: Neural machine translation, transformer, word embedding, word translations
DOI: 10.3233/JIFS-236170
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Jia, Liu
Article Type: Research Article
Abstract: This study explores a predictive approach using a combination of a one-dimensional convolutional neural network and support vector machine to enhance the management of cultural product trade between China and South Korea, addressing the trade deficit challenge. The methodology involves the collection and categorization of diverse data related to the trade of cultural products between the two countries, identifying data mining directions. The research incorporates the design of association rule functions to identify viable data sources, and employs a hybrid data clustering algorithm integrating technology and spectral clustering to cluster available data. The features extracted from the data mining process …are utilized as learning samples for trade prediction. Both a one-dimensional convolutional neural network and support vector machine are employed to model and predict cultural product trade between China and South Korea. Experimental results demonstrate the method’s accuracy in predicting trade situations under parameterized conditions. Throughout the prediction process, credibility measurement values and controllable correlation degrees consistently exceed 19 and 12.5, respectively, while uncertainty discrimination degrees and error coefficients remain below 12 and 6. Show more
Keywords: Big data integration, Chinese and Korean cultural products, trade prediction, data mining, convolutional neural network, support vector machine
DOI: 10.3233/JIFS-238061
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: López-López, Aurelio | Garcıa-Gorrostieta, Jesús Miguel | González-López, Samuel
Article Type: Research Article
Abstract: Emotion detection in educational dialogues, particularly within student-teacher interactions, has become a crucial research area for improving the learning experience. In this paper, we employ two models, one generic Bidirectional Encoder Representations from Transformers (BERT) and the Emotion detection model Robustly Optimized BERT Approach (EmoRoBERTa), to automatically classify emotions in a corpus of student-teacher chat interactions. Then subsequently, we validate these classifications using a scheme based on oracles, employing two generative large language models (ChatGPT and Bard). Experiments on emotion detection in dialogues between students and teachers revealed that EmoRoBERTa exhibited a reasonable level of agreement with the oracles, while …ChatGPT demonstrated the highest consistency with EmoRoBERTa’s predictions. Furthermore, we identified the impact of specific words on emotion classification, offering insights into the decision-making process of these models. The results not only highlight the prominent presence of emotions like approval, gratitude, curiosity, disapproval, amusement, confusion, remorse, joy , and surprise but also provide substantial support for the utilization of the proposed emotion detection model to enhance the student learning environment. Exploring the emotional aspects of educational dialogues holds the potential to enhance instruction methods, provide timely assistance to students in need, and create an improved learning atmosphere. Show more
Keywords: Emotion detection, learning interaction, transfer learning, large language models, active learning
DOI: 10.3233/JIFS-219340
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ratha, Ashoka Kumar | Behera, Santi Kumari | Devi, A. Geetha | Barpanda, Nalini Kanta | Sethy, Prabira Kumar
Article Type: Research Article
Abstract: With the rise of the fruit processing industry, machine learning and image processing have become necessary for quality control and monitoring of fruits. Recently, strong vision-based solutions have emerged in farming industries that make inspections more accurate at a much lower cost. Advanced deep learning methods play a key role in these solutions. In this study, we built an image-based framework that uses the ResNet-101 CNN model to identify different types of papaya fruit diseases with minimal training data and processing power. A case study to identify commonly encountered papaya fruit diseases during harvesting was used to support the results …of the suggested methodology. A total of 983 images of both healthy and defective papaya were considered during the experiment. In this study, we initially used the ResNet-101 CNN model for classification and then combined the deep features drawn out from the activation layer (fc1000) of the ResNet-101 CNN along with a multi-class Support Vector Machine (SVM) to classify papaya fruit defect detection. After comparing the performance of both approaches, it was found that Cubic SVM is the best classifier using the deep feature of ResNet-101 CNN, achieved with an accuracy of 99.5% and an area under the curve (AUC) of 1 without any classification error. The findings of this experiment reveal that the ResNet-101 CNN with the cubic SVM model can categorize good, diseased, and defective papaya pictures. Moreover, the suggested model executed the task in a greater way in terms of the F1- Score (0.99), sensitivity (99.50%), and precision (99.71%). The present work not only assists the end user in determining the type of disease but also makes it possible for them to take corrective measures during farming. Show more
Keywords: Disease classification, CNN (Convolutional Neural Network), ResNet-101, ML (Machine Learning), SVM (Support Vector Machine)
DOI: 10.3233/JIFS-239875
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Shi, Xiaolong | Kosari, Saeed | Rangasamy, Parvathi | Nivedhaa, R.K. | Rashmanlou, Hossein
Article Type: Research Article
Abstract: Modern image processing techniques are improving beyond old methods, which include advanced approaches, for example deep learning. Convolutional Neural Networks (CNNs) are excellent at automatic feature extraction, whereas Generative Adversarial Networks (GANs) produce realistic images. Transfer learning uses pre-trained models, whereas semantic segmentation identifies pixels in images. Super-resolution, style transfer, and attention mechanisms can increase the quality of images and understanding. Adversarial defenses address purposeful manipulations, while 3D image processing handles three-dimensional data. These advancements make use of improved computational power and massive datasets to revolutionize image processing capabilities. Traditional image processing algorithms frequently fail to handle the complex and …multidimensional structure of color images, particularly when dealing with uncertainty and imprecision. In this study, the 3D-EIFIM frame work is extented and scaled aggregation operations 3D-EIFIM tailored for image data are proposed. By representing each pixel as an entry of 3D-EIFIM and applying aggregation techniques to enable more effective image analysis, manipulation, and enhancement. The practical implications of this research are significant, as it can lead to advancements in fields such as computer vision, medical imaging, and remote sensing. Show more
Keywords: IFP, conjunction, disjunction, IFIM, EIFIM, 3D-IFIM, 3D-EIFIM
DOI: 10.3233/JIFS-238252
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Ruby Elizabeth, J. | Kesavaraja, D. | Ebenezer Juliet, S.
Article Type: Research Article
Abstract: The retinal illness that causes vision loss frequently on the globe is glaucoma. Hence, the earlier detection of Glaucoma is important. In this article, modified AlexNet deep leaning model is proposed to category the source retinal images into either healthy or Glaucoma through the detection and segmentations of optic disc (OD) and optic cup (OC) regions in retinal pictures. The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC regions are detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region are classified and trained by the suggested AlexNet …deep leaning model. This model classifies the source retinal image into either healthy or Glaucoma. Finally, performance measures have been estimated in relation to ground truth pictures in regards to accuracy, specificity and sensitivity. These performance measures are contrasted with the other previous Glaucoma detection techniques on publicly accessible retinal image datasets HRF and RIGA. The suggested technique as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. AIM: Segmenting the OD and OC areas and classifying the source retinal picture as either healthy or glaucoma-affected. METHODS: The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC region is detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region classified are and trained by the suggested AlexNet deep leaning model. RESULTS: The suggested method as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. CONCLUSION: This article proposes the modified AlexNet deep learning models for the detections of Glaucoma utilizing retinal images. The OD region is detected using circulatory filter and OC region is detected using k-means classification algorithm. The detected OD and OC regions are utilized to classify the retinal images into either healthy or Glaucoma using the suggested AlexNet model. The proposed method obtains 100% Sey, 93.7% Spy and 96.6% CA on HRF dataset retinal images. The proposed AlexNet method obtains 97.7% Sey, 98% Spy and 97.8% CA on RIGA dataset retinal images. The proposed method stated in this article achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. Show more
Keywords: Retina, deep learning, OD, OC, AlexNet
DOI: 10.3233/JIFS-234131
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wang, Lu
Article Type: Research Article
Abstract: In this technology world, education is also becoming one of the basic necessities of human life like food, shelter, and clothes. Even in day-to-day daily activities, the world is moving toward an automated process using technology developments. Some of the technology developments in day-to-day life activities are smartphone, internet activities, and home and office appliances. To cope with these advanced technologies, the persons must have basic educational qualification to understand and operate those appliances easily. Apart from this, the education helps the person to develop their personal growth in both knowledge and wealth. With the development of technologies, different Artificial …Intelligence techniques have been applied on the datasets to analyze these factors and enhance the teaching method. But the current techniques were applied to one or two data models that analyze either their educational performance or demographic variable. But these models were not sufficient for analyzing all the factors that affects the education. To overcome this, a single optimized machine-learning approach is proposed in this paper to analyze the factors that affect the education. This analysis helps the faculty to enhance their teaching methodology and understand the student’s mentality toward education. The proposed Hybrid Cuckoo search-particle swarm optimization was implemented on three datasets to determine the factors that affect the education. These optimal factors are determined by identifying their relations to the final results of an individual person. All these optimal factors are combined and grades are grouped to analyze the proposed optimization process performance using regression neural network. The proposed optimization-based neural network was tested on three data models and its performance analysis showed that the proposed model can achieve higher accuracy of 99% that affects the individual education. This shows that the proposed model can help the faculty to enhance their attention to the students individually. Show more
Keywords: Education, demographic factors, optimization, hybrid, cuckoo search optimization, particle swarm, regression neural network
DOI: 10.3233/JIFS-234021
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Ramasamy, Uma | Santhoshkumar, Sundar
Article Type: Research Article
Abstract: In the expansive domain of data-driven research, the curse of dimensionality poses challenges such as increased computational complexity, noise sensitivity, and the risk of overfitting models. Dimensionality reduction is vital to handle high-dimensional datasets effectively. The pilot study disease dataset (PSD) with 53 features contains patients with Rheumatoid Arthritis (RA) and Osteoarthritis (OA). Our work aims to reduce the dimension of the features in the PSD dataset, identify a suitable feature selection technique for the reduced-dimensional dataset, analyze an appropriate Machine Learning (ML) model, select significant features to predict the RA and OA disease and reveal significant features that predict …the arthritis disease. The proposed study, Progressive Feature Reduction with Varied Missing Data (PFRVMD), was employed to reduce the dimension of features by using PCA loading scores in the random value imputed PSD dataset. Subsequently, notable feature selection methods, such as backward feature selection, the Boruta algorithm, the extra tree classifier, and forward feature selection, were implemented on the reduced-dimensional feature set. The significant features/biomarkers are obtained from the best feature selection technique. ML models such as the K-Nearest Neighbour Classifier (KNNC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Naïve Bayes Classifier (NBC), Random Forest Classifier (RFC) and Support Vector Classifier (SVC) are used to determine the best feature selection method. The results indicated that the Extra Tree Classifier (ETC) is the promising feature selection method for the PSD dataset because the significant features obtained from ETC depicted the highest accuracy on SVC. Show more
Keywords: Autoimmune disease, rheumatoid arthritis, osteoarthritis, feature reduction, feature selection, machine learning algorithms
DOI: 10.3233/JIFS-231537
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Elsabagh, M.A. | Emam, O.E. | Medhat, T. | Gafar, M.G.
Article Type: Research Article
Abstract: By anticipating system defect-prone units, software-developing businesses aim to increase the quality of software. Despite the development of numerous Data Mining (DM) and Artificial Intelligence (AI) techniques in the Software Defect Prediction (SDP) field, dealing with the uncertainty of datasets persists due to noise, data distribution, class overlapping, proposed model parameters, and old data. This uncertainty issue has a negative impact on the accuracy of software defect prediction. To overcome this limitation, a model-based hybridization of Ant Colony Optimization-inspired Fuzzy Rough Feature Selection (FRAC) followed by adapting the parameters of Adaptive Neuro-Fuzzy Inference System (ANFIS) with a novel algorithm called …Turbulent Flow of Water Optimization (TFWO) is recommended. The proposed model (FRAC+TFWANFIS) performed better than contemporary literature and other optimization algorithms in SDP, such as Ant Colony Optimization (ACO), Differential Evolution (DE), ANFIS, Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Also, the performance of the proposed model is superior to that of other conventional classification techniques such as Naïve Bayes (NB), Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Fuzzy Rough Nearest Neighbor (FRNN), Fuzzy Nearest Neighbor (FNN), Bagging, C4.5, Random Forest (RF), and K-Nearest Neighbor (K-NN). Two datasets, PC3 and PC4, with large dimensions from the OPENML platform are used. The experiments are applied with regard to accuracy, Standard Deviation (SD), Root Mean Square Error (RMSE), Mean Square Error (MSE), and other measurement metrics. The uncertainty issue is addressed by the (FRAC+TFWANFIS) model with accuracy 90.8% and 91.1% for PC3 and PC4, respectively. Show more
Keywords: Adaptive Neuro-Fuzzy Inference System (ANFIS), Turbulent Flow of Water Optimization Algorithm, Software Defect Prediction (SDP), Recent and Conventional Optimization Algorithms, Uncertainty of SDP.
DOI: 10.3233/JIFS-234415
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Sun, Yilin | Li, Shufan
Article Type: Research Article
Abstract: Contemporary art design not only pursues the quality of the work itself, but also pays attention to the sensory aspects of people’s needs for art design. Traditional art design methods can be limited by time, space and other objective conditions, and often fail to achieve the designer’s expected effect, and visitors’ experience is not strong. The usage of multimedia technology in art and design can enrich its expression and enhance visitors’ experience. In order to increase the sense of interaction between the platform and users, multimedia technology is incorporated into the interactive art design platform generated by VR technology in …this paper. This article combines multimedia technology with interactive technology to construct an interactive platform for art and design, and applies it to the display of Dunhuang murals. Through the analysis of user experience feedback, the effectiveness of art and design display and interaction is verified. Display and interact with Dunhuang murals as interactive platform applications. This test is to extract women’s clothing colors from the same tradition in different times in the color extraction exploration module of the interactive platform, so as to provide accurate information for displaying women’s clothing color changes and comparing interactions. The findings show that the platform is capable of extracting and recognizing the color characteristics of the murals, accurately identifying user signals, and noticing 3D modeling of images via VR technology. This capability provides solid technical and data support for the platform’s interaction module. The interaction design, platform functionality, and layout can support the majority of users in terms of cognition, perception, and interaction, pique their interest, and enhance their experience, according to evaluation of trial user information. The interaction ends abruptly, according to a small percentage of users, and they had a bad experience overall. Show more
Keywords: Multimedia technology, art and design, interactive, platform building
DOI: 10.3233/JIFS-238001
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Sheik Faritha Begum, S. | Suresh Anand, M. | Pramila, P.V. | Indra, J. | Samson Isaac, J. | Alagappan, Chockalingam | Gopala Gupta, Amara S.A.L.G. | Srivastava, Suraj | Vidhya, R.G.
Article Type: Research Article
Abstract: Thyroid tumours are a common form of cancer, and accurate classification of their type is crucial for effective treatment planning. This research presents a hybrid approach for the classification of thyroid tumours based on their type. The proposed approach combines the use of advanced machine learning techniques with a comprehensive database of thyroid tumour samples. The database includes various features such as tumour size, shape, and texture, as well as patient-specific information. The hybrid approach aims to optimize the classification process by leveraging the diverse set of features and utilizing the power of machine learning algorithms. By harnessing the power …of machine learning algorithms, this approach has the potential to revolutionize the field of thyroid tumour classification and significantly improve patient outcomes. The optimization strategy is Particle Swarm Optimization, refining the classification performance and ensuring optimal accuracy in identifying and categorizing four types of thyroid tumours. The utilization of advanced diagnostic tools and state-of-the-art Random forest classifier techniques in this approach marks a significant advancement in the field of thyroid tumour classification. Through the augmentation of the dataset and the pre-processing techniques employed, the hybrid classification system demonstrates enhanced accuracy and reliability in distinguishing between different types of thyroid tumours. This innovative approach not only provides a more comprehensive understanding of thyroid tumours but also paves the way for personalized and effective treatment strategies, ultimately improving patient care and outcomes. Show more
Keywords: Machine learning, thyroid tumours, Particle Swarm Optimization, Random Forest classifier, innovative approach
DOI: 10.3233/JIFS-239804
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Hou, Junjian | Zhang, Bingyu | Zhong, Yudong | Zhao, Dengfeng | He, Wenbin | Zhou, Fang
Article Type: Research Article
Abstract: Online monitoring of cutting tools wear is an important component of advanced manufacturing technology, which can greatly improve the processing efficiency and reduce the production cost. In this paper, a cutting tools wear state prediction method based on acoustic imaging recognition is developed. By applying the advantages of the functional generalized inverse beamforming method in the sound field reconstruction, the acoustic signal is used as the carrier to reconstruct the three-dimensional space radiated sound field. And then, slice the reconstructed sound field image and input it into the convolutional neural network model as a sample, to process and classify the …image and mines the feature information related to state from the sound field image. By incorporating amplitude and phase information of the sound field, the presented method utilizes spatial domain mapping to accurately identify the noise source and address challenges such as low recognition rate and difficult diagnosis under weak fault conditions. Furthermore, the paper also demonstrates the recognition of sound field states through a fault experiment in sound box simulation, based on these theories. And the recognition of sound field states is achieved through a simulation fault experiment conducted on the sound box, thereby validating the feasibility of the state monitoring method based on pattern recognition of sound and image. Finally, the experimental object is selected as the four-edge carbide milling cutter, and the cutting tools wear state is monitored by integrating sound field reconstruction techniques with convolution feature extraction methods to validate the robustness of the proposed approach. Show more
Keywords: Functional generalized inverse beamforming, convolutional neural network, sound field reconstruction, state detection, acoustic imaging technology
DOI: 10.3233/JIFS-238755
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Zhang, Jianwei | Chen, Lei | Hou, Ge | Huang, Jinlin | Wang, Yong
Article Type: Research Article
Abstract: Health assessment is one of the important theoretical bases for deciding whether the diversion tunnel can operate safely and stably. A project of the TBM diversion tunnel is taken as the research object to ensure the normal operation of the diversion tunnel. Based on measured data and considering multiple safety aspects such as structural response, durability, and external factors of the diversion tunnel, a TBM diversion tunnel structural health evaluation index system is established. A new method for the TBM diversion tunnel structural health comprehensive evaluation based on Analytic Hierarchy Process-Matter Element Extension-Variable Weight Theory (AMV) is proposed to explore …the impact of AMV fluctuation with the measured results of the indicators on the weight, closeness, and health grade of each evaluation index. The high sensitivity and high-risk evaluation indicators for the structural health of the diversion tunnels are identified. It is found that the variable weight varies with the changes in various indicator values, which can accurately evaluate the health status of tunnels in real-time. The characteristic values of the tunnel grade calculated by the AHP and the AMV are 1.589 and 1.695, respectively. The results of the corresponding interval diversion tunnel are the basic safety state of grade B. Except for the two evaluation indicators of concrete strength and slurry properties, the variable weight values and grade characteristic values of other evaluation indicators increase with the increase of indicator values. The four indicators of segment settlement, segment opening, segment misalignment, and segment cracks are more sensitive to the health of the TBM diversion tunnel. This AMV can accurately evaluate the health status of the diversion tunnel structure. The research results can provide references for later maintenance work and similar projects. Show more
Keywords: Diversion tunnel, Health evaluation, AMV, AHP, susceptibility
DOI: 10.3233/JIFS-239155
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Li, Yuerong | Zhang, Yuhua | Che, Jinxing
Article Type: Research Article
Abstract: Accurate prediction of short-term electricity price is the key to obtain economic benefit and also an important index of power system planning and management. Support vector regression (SVR) based ensemble works have gained remarkable achievements in terms of high accuracy and steady performance, but they are highly dependent on data representativeness and have a high computational complexity O (k * N 3 ) of data samples and parameter selection. To further improve the data representativeness and reduce its computational complexity, this paper develops a new approach to forecast electricity price via optimal weighted ensemble. In the model, the cluster-based subsampling …algorithm is proposed to categorize the inputs being seasonally decomposed into several groups, and representative data are drawn from each group in a certain proportion to ensure that each subset trained with SVR has the same representativeness and features. Moreover, the optimal weighted combination method is presented to assign weights to the sub-SVRs to obtain the optimal support vector regression ensemble model (OWSSVRE). The experimental results show that the improved support vector regression ensemble model with the same features and representativeness of the subset has better performance in electricity price forecasting. As a result, it is suitable to support decision making in the energy and other sectors. Show more
Keywords: Electricity price forecasting, support vector regression, K-means clustering, optimal weight, subsampling
DOI: 10.3233/JIFS-236239
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Thenmozhi, R. | Sakthivel, P. | Kulothungan, K.
Article Type: Research Article
Abstract: The Internet of Things and Quantum Computing raise concerns, as Quantum IoT defines security that exploits quantum security management in IoT. The security of IoT is a significant concern for ensuring secure communications that must be appropriately protected to address key distribution challenges and ensure high security during data transmission. Therefore, in the critical context of IoT environments, secure data aggregation can provide access privileges for accessing network services. "Most data aggregation schemes achieve high computational efficiency; however, the cryptography mechanism faces challenges in finding a solution for the expected security desecration, especially with the advent of quantum computers utilizing …public-key cryptosystems despite these limitations. In this paper, the Secure Data Aggregation using Quantum Key Management scheme, named SDA-QKM, employs public-key encryption to enhance the security level of data aggregation. The proposed system introduces traceability and stability checks for the keys to detect adversaries during the data aggregation process, providing efficient security and reducing authentication costs. Here the performance has been evaluated by comparing it with existing competing schemes in terms of data aggregation. The results demonstrate that SDA-QKM offers a robust security analysis against various threats, protecting privacy, authentication, and computation efficiency at a lower computational cost and communication overhead than existing systems. Show more
Keywords: Internet of things, security, data aggregation, access control, quantum cryptography
DOI: 10.3233/JIFS-223619
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Li, Chen | Liu, Na | Xu, Zhenshun | Zheng, Guofeng | Yang, Jie | Dao, Lu
Article Type: Research Article
Abstract: Medical short text classification is of great significance to medical information extraction and medical auxiliary diagnosis. However, medical short texts face challenges such as sparse features, semantic ambiguity, and the specialized nature of the medical field, resulting in relatively low accuracy in short text classification. Taking into consideration the characteristics of medical short texts, this paper proposes a Chinese medical short text classification model based on DPECNN. First, ERNIE is utilized to learn text knowledge and information in order to enhance the model’s semantic representation capabilities. Then, the DPECNN model is employed to extract rich feature information, and the classification …results are generated through a fully connected layer. In the case of DPCNN, it only considers deep-level contextual semantic information, overlooking the correlation of adjacent semantic information between channels. To address this, ECA channel attention is introduced to account for adjacent semantic information. The use of a self-normalizing activation function helps avoid the problem of vanishing gradients. To enhance the model’s robustness and generalization ability, the FGM adversarial training algorithm is employed to perturb the data. The F1 values achieved on the THUCNews, KUAKE-QIC, and CHIP-CTC datasets are 95.00%, 79.45%, and 82.81%, respectively. Show more
Keywords: Medical text mining, Chinese short text classification, ERNIE, DPECNN, confrontation training
DOI: 10.3233/JIFS-239006
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Du, Rong | Cheng, Yan
Article Type: Research Article
Abstract: This research paper highlights the significance of vehicle detection in aerial images for surveillance systems, focusing on deep learning methods that outperform traditional approaches. However, the challenge of high computation complexity due to diverse vehicle appearances persists. The motivation behind this study is to highlight the crucial role of vehicle detection in aerial images for surveillance systems, emphasizing the superior performance of deep learning methods compared to traditional approaches. To address this, a lightweight deep neural network-based model is developed, striking a balance between accuracy and efficiency enabling real-time operation. The model is trained and evaluated on a standardized dataset, …with extensive experiments demonstrating its ability to achieve accurate vehicle detection with significantly reduced computation costs, offering a practical solution for real-world aerial surveillance scenarios. Show more
Keywords: Aerial images, vehicle detection, surveillance system, deep learning, real-time processing
DOI: 10.3233/JIFS-236059
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Pavithra, R. | Ramachandran, Prakash
Article Type: Research Article
Abstract: The Hilbert spectrum images of intrinsic mode functions (IMF) of empirical mode decomposition (EMD) analysis and variational mode decomposition (VMD) analysis of faulty machine vibration signals are used in deep convolutional neural network (DCNN) for machine fault classification in which the DCNN automatically learns the features from spectral images using convolution layer. Though both EMD and VMD analysis suit well for non-stationary signal analysis, VMD has the merit of aliasing free IMFs. In this paper, the performance improvement of DCNN classification for a non-stationary vibration signal dataset using VMD is brought out. The numerical experiment uses the Hilbert spectrum images …of 4 EMD-IMFs and 4 VMD-IMFs in DCNN to classify 10 different faults of the Case Western Reserve University (CWRU) bearing dataset. The confusion matrices are obtained and the plot of model accuracies in terms of epochs for the DCNN is analysed. It is shown that the spectrum images of one of the four EMD-IMFs, IMF0 , give a validation accuracy of 100% and in the case of VMD the spectrum images of two of the four VMD-IMFs, IMF0 , and IMF1 give a validation accuracy of 100%. This reveals that non-aliasing IMFs of VMD are better at classifying bearing faults. Further to bring out the merits of VMD analysis for non-stationary signals the numerical experiment is conducted using VMD analysis for binary fault classification of the milling dataset which is more non-stationary than the bearing dataset which is proved by plotting the statistical parameters of both datasets against time. It is found that the DCNN classification is 100% accurate for IMF3 of VMD analysis which is much better than the 81% accuracy provided by EMD analysis as per existing literature. The performance comparison highlights the merits of VMD analysis over EMD analysis and other state-of-the-art methods and ensemble learning methods. Show more
Keywords: Deep convolution neural network, empirical mode decomposition, hilbert transform, intrinsic mode function, variational mode decomposition, ensemble learning
DOI: 10.3233/JIFS-237546
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Nawshin, Sabila | Islam, Salekul | Shatabda, Swakkhar
Article Type: Research Article
Abstract: Software Defined Networking (SDN) proposes a centralized network paradigm where a central controller manages the network. While this centralizes scheme opens up previously unachievable opportunities, it also makes the network more susceptible to a varying range of cyber threats. The development of effective Intrusion Detection Systems (IDS) designed for the SDN topology is a critical need to address the different vulnerabilities SDN faces. Towards that purpose, the inSDN dataset was specifically curated for intrusion detection in SDN with various attack scenarios unique to the SDN topology. This study leveraged the inSDN dataset to introduce an innovative Intrusion Detection …System (IDS) model that amalgamates Principal Component Analysis (PCA), a dimensionality reduction technique widely employed in traditional Machine Learning (ML) to extract the principal features of the dataset and couples it with Artificial Neural Networks (ANN) to classify network traffic based on the extracted features. The proposed model attains an exceptional accuracy rate of 99.95% for multi-class classification and demonstrate that it surpasses the current state-of-the-art techniques while operating within a much simpler framework. This significantly diminishes the necessity for complex models that demand extensive computational resources when dealing with the inSDN attack dataset. The analysis of the dataset carried out in this study also provides insights into the redundancy present in the dataset and identifies the core features that contains most of the information in the dataset. Show more
Keywords: Software Defined Networking (SDN), Intrusion Detection Systems (IDS), Principle Component Analysis (PCA), Artificial Neural Network (ANN)
DOI: 10.3233/JIFS-236340
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Kumar, Geethu S. | Ankayarkanni, B.
Article Type: Research Article
Abstract: Facial Emotion Recognition (FER) is a powerful tool for gaining insights into human behaviour and well-being by precisely quantifying a wide range of emotions especially stress, through the analysis of facial images. Detecting stress using FER entails meticulously examining subtle facial cues, such as changes in eye movements, brow furrowing, lip tightening, and muscle contractions. To assure effectiveness and real-time processing, FER approaches based on deep learning and artificial intelligence (AI) techniques was created using edge modules. This research introduces a novel approach for identifying stress, leveraging the Conv-XGBoost Algorithm to analyse facial emotions. The proposed model sustain rigorous evaluation …techniques, for employing key metrics examination such as the F1 score, validation accuracy, precision, and recall rate to assess its real-world reliability and robustness. This comprehensive analysis and validation proved the model’s practical utility in facial analysis. Integrating the Conv-XGBoost Algorithm with facial emotion analysis represents a promising and highly accurate solution for efficient stress detection. The method surpasses existing literature and demonstrate significant potential for practical applications based on well-validated data. Show more
Keywords: Stress, emotion recognition, Conv-XGBoost, deep learning, facial expression
DOI: 10.3233/JIFS-237820
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Martínez Felipe, Miguel de JesÚs | Martínez Castro, JesÚs Alberto | Montiel Pérez, JesÚs Yaljá | Chaparro Amaro, Oscar Roberto
Article Type: Research Article
Abstract: In this work, the image block matching based on dissimilarity measure is investigated. Moreover, an unsupervised approach is implemented to yield that the algorithms have low complexity (in numbers of operations) compared to the full search algorithm. The state-of-the-art experiments only use discrete cosine transform as a domain transform. In addition, some images were tested to evaluate the algorithms. However, these images were not evaluated according to specific characteristics. So, in this paper, an improved version is presented to tackle the problem of dissimilarity measure in block matching with a noisy environment, using another’s domain transforms or low-pass filters to …obtain a better result in block matching implementing a quantitive measure with an average accuracy margin of ± 0.05 is obtained. The theoretical analysis indicates that the complexity of these algorithms is still accurate, so implementing Hadamard spectral coefficients and Fourier filters can easily be adjusted to obtain a better accuracy of the matched block group. Show more
Keywords: Block matching, Walsh-Hadamard discrete transform, Fourier filter, dissimilarity measure, unsupervised machine learning
DOI: 10.3233/JIFS-219341
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ensastegui-Ortega, Maria Elena | Batyrshin, Ildar | Cárdenas–Perez, Mario Fernando | Kubysheva, Nailya | Gelbukh, Alexander
Article Type: Research Article
Abstract: In today’s data-rich era, there is a growing need for developing effective similarity and dissimilarity measures to compare vast datasets. It is desirable that these measures reflect the intrinsic structure of the domain of these measures. Recently, it was shown that the space of finite probability distributions has a symmetric structure generated by involutive negation mapping probability distributions into their “opposite” probability distributions and back, such that the correlation between opposite distributions equals –1. An important property of similarity and dissimilarity functions reflecting such symmetry of probability distribution space is the co-symmetry of these functions when the similarity between probability …distributions is equal to the similarity between their opposite distributions. This article delves into the analysis of five well-known dissimilarity functions, used for creating new co-symmetric dissimilarity functions. To conduct this study, a random dataset of one thousand probability distributions is employed. From these distributions, dissimilarity matrices are generated that are used to determine correlations similarity between different dissimilarity functions. The hierarchical clustering is applied to better understand the relationships between the studied dissimilarity functions. This methodology aims to identify and assess the dissimilarity functions that best match the characteristics of the studied probability distribution space, enhancing our understanding of data relationships and patterns. The study of these new measures offers a valuable perspective for analyzing and interpreting complex data, with the potential to make a significant impact in various fields and applications. Show more
Keywords: Dissimilarity function, co-symmetry, correlation, probability distribution, negation
DOI: 10.3233/JIFS-219363
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Xu, Zhigang | Li, Yugen
Article Type: Research Article
Abstract: Construction site environment helmet detection is of great significance for protecting workers’ lives and realizing the automation of safety management. Aiming at the current object detection methods for the complex construction site environment in the small-scale helmet object detection ability is insufficient. This paper proposes a construction site environment helmet detection method based on multi-scale context and attention fusion. The method is able to aggregate the multi-scale contextual semantics of deep image features through the proposed multi-scale context module and expand the receptive field in order to improve the network’s discriminative learning ability for small-scale helmet objects. Meanwhile, the proposed …attention feature fusion module dynamically fuses features from shallow features and network decoding features to enhance the network’s ability to learn the expression of global feature dependencies and local spatial detail features of helmet objects, and further improve the network’s detection precision of helmet objects. The experimental results show that on the constructed safety helmet wearing dataset, the proposed method in this paper has good detection effect and balanced detection speed compared with the existing mainstream object detection methods. Show more
Keywords: Construction site, helmet detection, CenterNet, multi-scale context, attention feature fusion
DOI: 10.3233/JIFS-236385
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wei, Tao | Yang, Changchun | Zheng, Yanqi | Zhang, Jingxue
Article Type: Research Article
Abstract: Recently, Graph Neural Networks (GNNs) using aggregating neighborhood collaborative information have shown effectiveness in recommendation. However, GNNs-based models suffer from over-smoothing and data sparsity problems. Due to its self-supervised nature, contrastive learning has gained considerable attention in the field of recommendation, aiming at alleviating highly sparse data. Graph contrastive learning models are widely used to learn the consistency of representations by constructing different graph augmentation views. Most current graph augmentation with random perturbation destroy the original graph structure information, which mislead embeddings learning. In this paper, an effective graph contrastive learning paradigm CollaGCL is proposed, which constructs graph augmentation by …using singular value decomposition to preserve crucial structure information. CollaGCL enables perturbed views to effectively capture global collaborative information, mitigating the negative impact of graph structural perturbations. To optimize the contrastive learning task, the extracted meta-knowledge was propagate throughout the original graph to learn reliable embedding representations. The self-information learning between views enhances the semantic information of nodes, thus alleviating the problem of over-smoothing. Experimental results on three real-world datasets demonstrate the significant improvement of CollaGCL over state-of-the-art methods. Show more
Keywords: Self-supervised learning, recommendation, contrastive learning, data augmentation
DOI: 10.3233/JIFS-236497
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Yang, Dianqing | Wang, Wenliang
Article Type: Research Article
Abstract: Unmanned aerial vehicle (UAV) remote-sensing images have a wide range of applications in wildfire monitoring, providing invaluable data for early detection and effective management. This paper proposes an improved few-shot target detection algorithm tailored specifically for wildfire detection. The quality of UAV remote-sensing images is significantly improved by utilizing image enhancement techniques such as Gamma change and Wiener filter, thereby enhancing the accuracy of the detection model. Additionally, ConvNeXt-ECA is used to focus on valid information within the images, which is an improvement of ConvNeXt with the addition of the ECANet attention mechanism. Furthermore, multi-scale feature fusion is performed by …adding a feature pyramid network (FPN) to optimize the extracted small target features. The experimental results demonstrate that the improved algorithm achieves a detection accuracy of 93.2%, surpassing Faster R-CNN by 6.6%. Moreover, the improved algorithm outperforms other target detection algorithms YOLOv8, RT-DETR, YoloX, and SSD by 3.4%, 6.4%, 7.6% and 21.1% respectively. This highlights its superior recognition accuracy and robustness in wildfire detection tasks. Show more
Keywords: Fire target detection, ConvNeXt-ECA, UAV remote-sensing image, feature pyramid network
DOI: 10.3233/JIFS-240531
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Singh, Pratibha | Kushwaha, Alok Kumar Singh | Varshney, Neeraj
Article Type: Research Article
Abstract: Precise video moment retrieval is crucial for enabling users to locate specific moments within a large video corpus. This paper presents Interactive Moment Localization with Multimodal Fusion (IMF-MF), a novel interactive moment localization with multimodal fusion model that leverages the power of self-attention to achieve state-of-the-art performance. IMF-MF effectively integrates query context and multimodal features, including visual and audio information, to accurately localize moments of interest. The model operates in two distinct phases: feature fusion and joint representation learning. The first phase dynamically calculates fusion weights for adapting the combination of multimodal video content, ensuring that the most relevant features …are prioritized. The second phase employs bi-directional attention to tightly couple video and query features into a unified joint representation for moment localization. This joint representation captures long-range dependencies and complex patterns, enabling the model to effectively distinguish between relevant and irrelevant video segments. The effectiveness of IMF-MF is demonstrated through comprehensive evaluations on three benchmark datasets: TVR for closed-world TV episodes and Charades for open-world user-generated videos, DiDeMo dataset, Open-world, diverse video moment retrieval dataset. The empirical results indicate that the proposed approach surpasses existing state-of-the-art methods in terms of retrieval accuracy, as evaluated by metrics like Recall (R1, R5, R10, and R100) and Intersection-of-Union (IoU). The results consistently demonstrate IMF-MF’s superior performance compared to existing state-of-the-art methods, highlighting the benefits of its innovative interactive moment localization approach and the use of self-attention for feature representation and attention modeling. Show more
Keywords: Multimedia data retrieval, query-dependent fusion, ranking system, multimodal retrieval, video segment localization
DOI: 10.3233/JIFS-233071
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Maheswari, M. | Anitha, D. | Sharma, Aditi | Kaur, Kiranpreet | Balamurugan, V. | Garikapati, Bindu | Dineshkumar, R. | Karunakaran, P.
Article Type: Research Article
Abstract: Anomaly detection, a critical aspect of data analysis and cybersecurity, aims to identify unusual patterns that deviate from the expected norm. In this study, we propose a hybrid approach that combines the strengths of Autoencoder neural networks and Multiclass Support Vector Machines (SVM) for robust anomaly detection. The Autoencoder is utilized for feature learning and extraction, capturing intricate patterns in the data, while the Multiclass SVM provides a discriminative classification mechanism to distinguish anomalies from normal patterns. Specifically, the Autoencoder is trained on normal data to acquire a compact and efficient representation of the underlying patterns, with the reconstruction errors …serving as indicative measures of anomalies. Concurrently, a Multiclass SVM is trained to classify instances into multiple classes, including an anomaly class. The anomaly scores from the Autoencoder and the decision function of the Multiclass SVM, along with that of the Random Forest Neural Network (AE-RFNN), are combined, leveraging their complementary strengths. A thresholding mechanism is then employed to classify instances as normal or anomalous based on the combined scores. The performance of the hybrid model is evaluated using standard metrics such as precision, recall, F1-score, and the area under the Receiver Operating Characteristic (ROC) curve. The proposed hybrid anomaly detection approach demonstrates effectiveness in capturing complex patterns and discerning anomalies across diverse datasets. Additionally, the model offers flexibility for adaptation to evolving data distributions. This study contributes to the advancement of anomaly detection methodologies by presenting a hybrid solution that combines feature learning and discriminative classification for improved accuracy and generalization. Show more
Keywords: Anomaly detection, Autoencoder, Multiclass SVM, feature learning, hybrid model, cybersecurity
DOI: 10.3233/JIFS-240028
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Ren, Xinyu | Yang, Wanhe | Yang, Hui
Article Type: Research Article
Abstract: With the increasing demand for tourism, people’s travel modes are more and more diversified, and the tourism recommendation system also arises at the historical juncture. However, the current recommendation system is only recommended for a single user and does not realize the group travel recommendation. To achieve the goal of recommending its preferred attractions for multiple users, the time decay characteristics and Pearson correlation coefficient in Newton’s cooling law are used to obtain the user similarity with spatial distance factor and temporal decay factor and to obtain the score prediction results based on spatiotemporal fusion. In addition, the trust of …user communication is used to recommend, and the weights of the two scoring results are added to obtain the personalized recommendation results of member users. Finally, the study used the fusion strategy to integrate the personalized recommendation results for group preference and obtained the final group travel recommendation list. Therefore, a group travel recommendation model based on spatio-temporal integration factors was constructed. According to the experimental analysis, we can see that the average HR value of the constructed model is 0.8124, and the average NDCG value is 0.7284, which can accurately judge users’ preferences and get the most suitable group travel recommendation results, thus facilitating users to make the next plan for the tourism project. Show more
Keywords: Group recommendation, spatio-temporal fusion, score prediction, fusion strategy
DOI: 10.3233/JIFS-239548
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Shehzadi, Maham | Fahmi, Aliya | Abdeljawad, Thabet | Khan, Aziz
Article Type: Research Article
Abstract: This paper investigates the detailed analysis of linear diophantine fuzzy Aczel-Alsina aggregation operators, enhancing their efficacy and computational efficiency while aggregating fuzzy data by using the fuzzy C-means (FCM) method. The primary goal is to look at the practical uses and theoretical foundations of these operators in the context of fuzzy systems. The aggregation process is optimised using the FCM algorithm, which divides data into clusters iteratively. This reduces computer complexity and enables more dependable aggregation. The mathematical underpinnings of Linear Diophantine Fuzzy Aczel-Alsina aggregation operators are thoroughly examined in this study, along with an explanation of their purpose in …handling imprecise and uncertain data. It also investigates the integration of the FCM method, assessing its impact on simplifying the aggregation procedure, reducing algorithmic complexity, and improving the accuracy of aggregating fuzzy data sets. This work illuminates these operators performance and future directions through extensive computational experiments and empirical analysis. It provides an extensive framework that shows the recommended strategy’s effectiveness and use in a variety of real-world scenarios. We obtain our ultimate outcomes through experimental investigation, which we use to inform future work and research. The purpose of the study is to offer academics and practitioners insights on how to improve information fusion techniques and decision-making processes. Show more
Keywords: Linear diophantine fuzzy set, Aczel-Alsina operational laws, linear diophantine fuzzy Aczel-Alsina aggregation operators, fuzzy C-means algorithm
DOI: 10.3233/JIFS-238716
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Chongjuan, Wang
Article Type: Research Article
Abstract: The convergence of visual communication design with unique effects, graphic design, as well as virtual reality, which is becoming progressively more popular, has created a new paradigm for education in recent years. However, emerging evidence indicates that their integration into the world of learning is a somewhat gradual and intricate process. The present research proposes a novel algorithm and a functional model of artificial intelligence technology design to automatically arrange graphic language in visual communication design. In visual communication design, the goal orchestration function used to determine the display size of buffer images is the difference between the minimum and …maximum values of the number of orchestration screens. An ant colony method is used in visual communication design to identify the optimal locations for visuals to be presented, and ASM semantics is used to characterize the visual languages. In order to accomplish the invention and development of a visual communication design style, the suggested algorithm has to be programmed and executed. It employs sequential decision marking to characterize the visual vocabulary and accomplishes automated organization. According to the trial results, visual saturation based on AI technology can reach up to 97%, and the average user satisfaction score is 7.65. It is evident that a creative visual thinking approach can maximize the visual communication design effect and communicate fresh design concepts. Show more
Keywords: Innovation and entrepreneurship, visual communication design (VCD), hybrid optimization, adaptive network-based fuzzy inference system (ANFIS), Statistical analysis, t-test and correlation
DOI: 10.3233/JIFS-235930
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Bakhshi, Mahmood | Ahn, Sun Shin | Jun, Young Bae | Borzooei, Rajab Ali
Article Type: Research Article
Abstract: Some kinds of pseudo valuations such as positive implicative pseudo valuation, (weak) implicative pseudo valuation, and commutative pseudo valuation of various types are introduced. Several examples, properties and characterizations of them are given as well. The relationships between them and the substructures of hyper BCK -algebras are investigated, too. Finally, by giving various examples and theorems, the relationships among the proposed pseudo valuations are investigated and characterized, especially in hyper BCK -algebras with three elements.
Keywords: Hyper BCK -algebra, pseudo valuation, positive implicative pseudo valuation, implicative pseudo valuation, commutative pseudo valuation
DOI: 10.3233/JIFS-233898
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Selvaraj, Sunil Kumar | Bhat Pundikai, Venkatramana
Article Type: Research Article
Abstract: BACKGROUND: The increased depletion of ground water resources poses the risk of higher moisture stress environment for agriculture crops. The rapid increase in the moisture stress situation imposes the need of efficient agricultural research on determining the impact of moisture stress on variety of crops. OBJECTIVE: The prime objective of the proposed work is building an IoT based Plant Phenotyping Device for moisture stress experimental study on variety of crops with deep learning model for stress response detection. METHODS: In this work, IoT technology is used for building a proposed system for conducting …the moisture stress experiments on plants and adopting the image processing and convolution neural network based model for stress prediction. RESULTS: The accuracy of the proposed system was experimentally evaluated and empirical results were satisfactory in maintaining the desired level of moisture stress. Performance analysis of LeNet, AlexNet, customized AlexNet and GoogLeNet CNN models were carried out with hyper-parameters variations on the leaf images. GoogLeNet achieved a better validation accuracy of 96% among other models. The trained GoogLeNet model is used for predicting the moisture stress response and predicted results were matched with manual observation of stress response. SIGNIFICANCE: The affirmative results of proposed system would increases its adoption for in-house precision agriculture and also for conducting various moisture stress experiments on variety of crops. The confirmative detection of moisture stress tolerance level of plant provides knowledge on minimum level of water requirement for plant growth, which in-turn save the water by avoiding excess watering to plants. Show more
Keywords: IoT, sensors, Raspberry Pi, moisture stress, deep learning
DOI: 10.3233/JIFS-236885
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Ashwin, P.V. | Ansal, K.A.
Article Type: Research Article
Abstract: Image classification using polarimetric synthetic aperture radar (Pol-SAR) is becoming more important in image processing for remote sensing applications. However, in the existing techniques, during the feature extraction process, there exist some limitations including laborious endeavour for Pol-SAR image classification, identifying intrinsic features for target recognition is difficult in feature selection, and pixel-level Pol-SAR image classification is difficult for obtaining more precise and coherent interpretation consequences. Hence to overcome these issues, a novel Multifarious Stratification Stratagem in machine learning is proposed to achieve pixel-level Pol-SAR classification. In this proposed model, a novel Scrumptious Integrant Wrenching method is used for efficient …feature extraction. It is compatible with the orientation-sensitive of the Pol-SAR image which increases the variety of intra-layer features. To remove the difficulty in feature selection, a novel Episodicical Proximity Selection method is proposed in which a Split-level parallel feature selection strategy is used to select the best qualities from the extracted features. To tackle the difficulty in classification, an Elastic Net Classifier (ENC) is used that find the coefficient vector for the linear combination of the training sets. This efficiently classified the best features in the Pol-SAR images and improved the proposed system’s accuracy. As a result, the performance measures of the proposed system demonstrate that the accuracy is increased by 99.69%, precision is increased by 98.99%, recall is increased by 98.99%, sensitivity is increased by 98.99%, and F1-score is increased by 98.99% as a response. Show more
Keywords: Feature extraction, feature selection, elastic net classifier, principle component analysis, convolution layer, max-pooling layer
DOI: 10.3233/JIFS-222403
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Ning, Tao | Zhang, Tingting | Huang, Guowei
Article Type: Research Article
Abstract: Folk dance is an important intangible cultural heritage in China. In the environment where movement recognition technology is widely used, there is still no research field on the protection and inheritance of folk dance culture. In order to better protect and inherit the minority dance, screening the typical movements of 5 types of minority dance, through the dance video frame processing, obtain the key movements of 19 class dance sequence, build the national dance typical action data set, put forward a 3D CNN fusion Transformer national dance recognition network model (FCTNet), the recognition rate of 96.7% in the experiment. The …results show that the construction method of the folk dance data set is reasonable, the identification model has good performance for the classification of folk dance, and can effectively identify and record the folk dance movements, which also makes new contributions to the digital protection of folk dance. Show more
Keywords: Transformer, folk dance, cultural protection
DOI: 10.3233/JIFS-235302
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-09, 2024
Authors: Shao, Shuai | Li, Dongwei
Article Type: Research Article
Abstract: As technology evolves, the allocation and use of educational resources becomes increasingly complex. Due to the many factors involved in recommending and matching English education resources, traditional predictive control models are no longer adequate. Therefore, fuzzy predictive control models based on neural networks have emerged. To increase the effectiveness and efficiency of using English educational resources (EER), this research aims to create a neural network-based fuzzy predictive control model (T-S-BPNN) for resource suggestion and matching. The results of the study show that the T-S-BPNN model α proposed in the study starts from 0 and increases sequentially by 0.1 up to …1, observing the change in MAE values. The experiment’s findings demonstrate that the value of MAE is lowest at values around 0.5. The T-S-BPNN model, on the other hand, gradually plateaued in its adaptation rate up to 7 runs, reaching about 9.8%. The accuracy rate peaked at 0.843 when the number of recommendations reached 7. The recall rate also peaked at 0.647 when the number of recommended English courses reached 7. The R-value for each set hovered around 0.97, which is a good fit. And the R-value of the training set is 0.97024, which can indicate that the T-S-BPNN model model proposed in the study fits well. It indicates that the algorithm proposed in the study is highly practical. Show more
Keywords: Resource recommendation, english teaching, fuzzy predictive control, recommended evaluation, neural network
DOI: 10.3233/JIFS-233265
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Ammavasai, S.K.
Article Type: Research Article
Abstract: The rapid growth of the cloud computing landscape has created significant challenges in managing the escalating volume of data and diverse resources within the cloud environment, catering to a broad spectrum of users ranging from individuals to large corporations. Ineffectual resource allocation in cloud systems poses a threat to overall performance, necessitating the equitable distribution of resources among stakeholders to ensure profitability and customer satisfaction. This paper addresses the critical issue of resource management in cloud computing through the introduction of a Dynamic Task Scheduling with Virtual Machine allocation (DTS-VM) strategy, incorporating Edge-Cloud computing for the Internet of Things (IoT). …The proposed approach begins by employing a Recurrent Neural Network (RNN) algorithm to classify user tasks into Low Priority, Mid Priority, and High Priority categories. Tasks are then assigned to Edge nodes based on their priority, optimizing efficiency through the application of the Spotted Hyena Optimization (SHO) algorithm for selecting the most suitable edge node. To address potential overloads on the edge, a Fuzzy approach evaluates offloading decisions using multiple metrics. Finally, optimal Virtual Machine allocation is achieved through the application of the Stable Matching algorithm. The seamless integration of these components ensures a dynamic and efficient allocation of resources, preventing the prolonged withholding of customer requests due to the absence of essential resources. The proposed system aims to enhance overall cloud system performance and user satisfaction while maintaining organizational profitability. The effectiveness of the DTS-VM strategy is validated through comprehensive testing and evaluation, showcasing its potential to address the challenges posed by the diverse and expanding cloud computing landscape. Show more
Keywords: Task scheduling, priority, classification, edge computing, cloud, VM allocation, IoT
DOI: 10.3233/JIFS-236838
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Du, Baigang | Zhang, Fujiang | Guo, Jun | Sun, Xiang
Article Type: Research Article
Abstract: The actual operating environment of rotating mechanical device contains a large number of noisy interference sources, leading to complex components, strong coupling, and low signal to noise ratio for vibration. It becomes a big challenge for intelligent fault diagnosis from high-noise vibration signals. Thus, this paper proposes a new deep learning approach, namely decomposition-enhance Fourier residual network (DEFR-net), to achieve high noise immunity for vibration signal and learn effective features to discriminate between different types of rotational machine faults. In the proposed DEFR-net, a novel algorithm is proposed to explicitly model high-noise signals for noisy data filtering and effective feature …enhancement based on a hard threshold decomposition function and muti-channel self-attention mechanism. Furthermore, it deeply integrates complementary analysis based on fast Fourier transform in the time-frequency domain and extends the breadth of network. The performance of the proposed model is verified by comparison with five state-of-the-art algorithms on two public datasets. Moreover, the noise experimental results show that the fault diagnosis accuracy is still 85.91% when the signal-to-noise-ratio reaches extreme noise of –8 dB. The results demonstrate that the proposed method is a valuable study for intelligent fault diagnosis of rotating machines in high-noise environments. Show more
Keywords: Intelligent fault diagnosis, high noise immunity, fourier residual network, decompose-enhance algorithm, attention mechanism
DOI: 10.3233/JIFS-233190
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Shao, Changshun | Yu, Zhenglin | Tang, Jianyin | Li, Zheng | Zhou, Bin | Wu, Di | Duan, Jingsong
Article Type: Research Article
Abstract: The main focus of this paper is to solve the optimization problem of minimizing the maximum completion time in the flexible job-shop scheduling problem. In order to optimize this objective, random sampling is employed to extract a subset of states, and the mutation operator of the genetic algorithm is used to increase the diversity of sample chromosomes. Additionally, 5-tuple are defined as the state space, and a 4-tuple is designed as the action space. A suitable reward function is also developed. To solve the problem, four reinforcement learning algorithms (Double-Q-learning algorithm, Q-learning algorithm, SARS algorithm, and SARSA(λ ) algorithm) are …utilized. This approach effectively extracts states and avoids the curse of dimensionality problem that occurs when using reinforcement learning algorithms. Finally, experimental results using an international benchmark demonstrate the effectiveness of the proposed solution model. Show more
Keywords: Reinforcement learning, flexible job-shop scheduling, maximum completion time, Variation
DOI: 10.3233/JIFS-236981
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Lin, Zhiwei | Zhang, Songchuan | Zhou, Yiwei | Wang, Haoyu | Wang, Shilei
Article Type: Research Article
Abstract: Current mainstream deep learning optimization algorithms can be classified into two categories: non-adaptive optimization algorithms, such as Stochastic Gradient Descent with Momentum (SGDM), and adaptive optimization algorithms, like Adaptive Moment Estimation with Weight Decay (AdamW). Adaptive optimization algorithms for many deep neural network models typically enable faster initial training, whereas non-adaptive optimization algorithms often yield better final convergence. Our proposed Adaptive Learning Rate Burst (Adaburst) algorithm seeks to combine the strengths of both categories. The update mechanism of Adaburst incorporates elements from AdamW and SGDM, ensuring a seamless transition between the two. Adaburst modifies the learning rate of the SGDM …algorithm based on a cosine learning rate schedule, particularly when the algorithm encounters an update bottleneck, which is called learning rate burst. This approach helps the model to escape current local optima more effectively. The results of the Adaburst experiment underscore its enhanced performance in image classification and generation tasks when compared with alternative approaches, characterized by expedited convergence and elevated accuracy. Notably, on the MNIST, CIFAR-10, and CIFAR-100 datasets, Adaburst attained accuracies that matched or exceeded those achieved by SGDM. Furthermore, in training diffusion models on the DeepFashion dataset, Adaburst achieved convergence in fewer epochs than a meticulously calibrated AdamW optimizer while avoiding abrupt blurring or other training instabilities. Adaburst augmented the final training set accuracy on the MNIST, CIFAR-10, and CIFAR-100 datasets by 0.02%, 0.41%, and 4.18%, respectively. In addition, the generative model trained on the DeepFashion dataset demonstrated a 4.62-point improvement in the Frechet Inception Distance (FID) score, a metric for assessing generative model quality. Consequently, this evidence suggests that Adaburst introduces an innovative optimization algorithm that simultaneously updates AdamW and SGDM and incorporates a learning rate burst mechanism. This mechanism significantly enhances deep neural networks’ training speed and convergence accuracy. Show more
Keywords: Convolutional neural networks (CNNs), MNIST, CIFAR, deep learning, optimization algorithms, person image generation, diffusion models
DOI: 10.3233/JIFS-239157
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Gonzalez, Claudia I. | Torres, Cesar
Article Type: Research Article
Abstract: This paper presents an approach incorporating fuzzy logic techniques inside a convolutional neural network to manage uncertainty present in the multiple data sources that the model handles when training. The implementation considers the use of information and filters in the fuzzy spectrum, as well as the creation of a new layer to replace the traditional convolution layer with a fuzzy convolutional layer. The aim is to design artificial intelligence algorithms that combine the potential of deep convolutional neural networks and fuzzy logic to create robust systems that allow modeling the uncertainty present in the sources of data and that are …applied to classification problems. The fuzzification process is developed using three membership functions, including the Triangular, Gaussian, and S functions. The work was tested in databases oriented to traffic signs, due to the complexity of the different circumstances and factors in which a traffic sign can be found. Show more
Keywords: Fuzzy-neural network, fuzzy CNN, fuzzy deep learning model, fuzzy data, fuzzy convolutional
DOI: 10.3233/JIFS-219369
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Sun, Haibin | Zhang, Wenbo
Article Type: Research Article
Abstract: The ability of deep learning based bearing fault diagnosis methods is developing rapidly. However, it is difficult to obtain sufficient and comprehensive fault data in industrial applications, and changes in vibration signals caused by machine operating conditions can also hinder the accuracy of the model. The problem of limited data and frequent changes in operating conditions can seriously affect the effectiveness of deep learning methods. To tackle these challenges, a novel transformer model named the Differential Window Transformer (Dwin Transformer), which employs a new differential window self-attention mechanism, is presented in this paper. Meanwhile, the model introduces a hierarchical structure …and a new patch merging to further improve performance. Furthermore, a new fault diagnosis model dealing with limited training data is proposed, which combines the Auxiliary Classifier Generative Adversarial Network with the Dwin Transformer(DT-ACGAN). The DT-ACGAN model can generate high-quality fake samples to facilitate training with limited data, significantly improving diagnostic capabilities. The proposed model can achieve excellent results under the dual challenges of limited data and variable working conditions by combining Dwin Transformer with GAN. The DT-ACGAN owns superior diagnostic accuracy and generalization performance under limited sample data and varying working environments when compared with other existing models. A comparative test about cross-domain ability is conducted on the Case Western Reserve University dataset and Jiang Nan University dataset. The results show that the proposed method achieves an average accuracy of 11.3% and 3.76% higher than other existing methods with limited data respectively. Show more
Keywords: Transformer, generative adversarial network, cross-domains, limited data, fault diagnosis
DOI: 10.3233/JIFS-236787
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Dai, Jinpeng | Zhang, Zhijie | Yang, Xiaoyuan | Wang, Qicai | He, Jie
Article Type: Research Article
Abstract: This study explores nine machine learning (ML) methods, including linear, non-linear and ensemble learning models, using nine concrete parameters as characteristic variables. Including the dosage of cement (C), fly ash (FA), Ground granulated blast furnace slag (GGBS), coarse aggregate (G), fine aggregate (S), water reducing agent (WRA) and water (W), initial gas content (GC) and number of freeze-thaw cycles (NFTC), To predict relative dynamic elastic modulus (RDEM) and mass loss rate (MLR). Based on the linear correlation analysis and the evaluation of four performance indicators of R2 , MSE, MAE and RMSE, it is found that the nonlinear model has …better performance. In the prediction of RDEM, the integrated learning GBDT model has the best prediction ability. The evaluation indexes were R2 = 0.78, MSE = 0.0041, MAE = 0.0345, RMSE = 0.0157, SI = 0.0177, BIAS = 0.0294. In the prediction of MLR, ensemble learning Catboost algorithm model has the best prediction ability, and the evaluation indexes are R2 = 0.84, MSE = 0.0036, RMSE = 0.0597, MAE = 0.0312, SI = 5.5298, BIAS = 0.1772. Then, Monte Carlo fine-tuning method is used to optimize the concrete mix ratio, so as to obtain the best mix ratio. Show more
Keywords: Machine learning, relative dynamic elastic modulus, mass loss rate, sensitivity analysis, optimization design of mix proportions
DOI: 10.3233/JIFS-236703
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-26, 2024
Authors: Yuan, Weihao | Yang, Mengdao | Gu, Hexu | Xu, Gaojian
Article Type: Research Article
Abstract: There is scope to enhance agricultural measurement and control systems user interactivity, which typically necessitates training for users to perform specific operations successfully. With the continuous development of natural language semantic processing technology, it has become essential to augment the user-friendliness of multifaceted control and query operations in the agricultural measurement and control sector, ultimately leading to reduced operation costs for users. The study aims to focus on command parsing. The proposed AMR-OPO semantic parsing framework is based on the natural language understanding method of Abstract Meaning Representation of Rooted Markup Graphs (AMR). It transforms the user’s natural language inputs …into structured ternary (OPO) statements (operation-place-object) and converts the corresponding parameters of the user’s input commands. The framework subsequently sends the transformed commands to the relevant devices via the IoT gateway. To tackle the intricate task of parsing instructions, we developed a BERT-BiLSTM-ATT-CRF-OPO entity recognition model. This model can detect and extract entities from agricultural instructions, and precisely populate them into OPO statements. Our model shows exceptional accuracy in instruction parsing, with precision, recall, and F-value all measuring at 92.13%, 93.12%, and 92.76%, correspondingly. The findings from our experiment reveal outstanding and precise performance of our approach. It is anticipated that our algorithm will enhance the user experience offered by agricultural measurement and control systems, while also making them more user-friendly. Show more
Keywords: Natural language processing, abstract meaning representation, entity recognition, natural language understanding, human-computer interaction
DOI: 10.3233/JIFS-237280
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Li, Yingjie | Sun, Rongrong | Huang, Guangrong | Deng, Yuanbin | Zhang, Haixuan | Zhang, Delong
Article Type: Research Article
Abstract: In response to a series of issues in the distribution network, such as inadequate and inflexible utilization of flexible loads, delayed response to demand participation, and the uncertainty of new energy source output, a differentiated objective-based method for optimizing distribution network operations is proposed. Firstly, flexible loads are categorized, and corresponding mathematical models are established. Secondly, by employing chance-constrained programming to account for the uncertainty in new energy source output, a multi-objective optimization model is developed to reduce distribution network economic costs, decrease network losses, and enhance power supply reliability. Subsequently, an improved NSGA-III algorithm is introduced to address the …multi-objective model. Finally, an 11-node distribution network is used as a case study, and three different algorithms are comprehensively compared. This confirms the rationality of the optimized scheduling scheme proposed in this paper. Show more
Keywords: Distribution network, flexible load, multi-objective optimization, chance-constrained programming
DOI: 10.3233/JIFS-238367
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Yan, HongJu
Article Type: Research Article
Abstract: To solve the problem of lack of practice in Japanese teaching, a design of a Japanese remote interactive practical teaching platform based on the modern edge computing-based wireless sensor network is proposed. In terms of hardware, it mainly refits interactive mobile edge computing, wireless sensor networks, microprocessors, and other equipment, and adjusts the interface circuit. The Japanese teaching data and relevant Japanese teaching resources generated in the process of Japanese Teaching of practical courses are stored in the corresponding database table according to a certain format, and the logical relationship between database tables is used to update the database. The …software function of the platform is designed with the support of a database and hardware equipment. It consists of multiple modules, including platform user roles, interactive practical teaching and management, practical resource management and distribution, practice project information release, practice investigation statistics, and platform operation safety. Through the above design, the operation of a Japanese remote interactive practical teaching platform is realized. The test results show that there is no significant difference in the function realization of the design platform, but when multiple users are online at the same time, the interaction performance of the design platform is stronger, that is, the operation performance of the platform has obvious advantages. Show more
Keywords: Mobile edge computing, wireless sensor network, Japanese teaching platform, remote interactive learning, microprocessor, platform user roles, practical teaching, database management, interaction performance
DOI: 10.3233/JIFS-238196
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Ahani, Zahra | Shahiki Tash, Moein | Ledo Mezquita, Yoel | Angel, Jason
Article Type: Research Article
Abstract: Super-enhancers are a category of active super-enhancers densely occupied by transcription factors and chromatin regulators, controlling the expression of disease-related genes and cellular identity. Recent studies have demonstrated the formation of complex structures by various factors and super-enhancers, particularly in various cancers. However, our current knowledge of super-enhancers, such as their genomic locations, interaction with factors, functions, and distinction from other super-enhancers regions, remains limited. This research aims to employ deep learning techniques to detect and differentiate between super-enhancers and enhancers based on genomic and epigenomic features and compare the accuracy of the results with other machine learning methods In …this study, in addition to evaluating algorithms, we trained a set of genomic and epigenomic features using a deep learning algorithm and the Python-based cross-platform software to detect super-enhancers in DNA sequences. We successfully predicted the presence of super-enhancers in the sequences with higher accuracy and precision. Show more
Keywords: Super-enhancer, enhancer, genomic, epigenomic, deep learning
DOI: 10.3233/JIFS-219356
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Shahbazova, Shahnaz N. | Rzayev, Ab.G. | Asadova, R.Sh. | Jabiyev, K.M.
Article Type: Research Article
Abstract: The paper gives a systems analysis in the field of heat transfer and temperature distribution (TD) along the length of oil production wells (OPW). The analysis shows that the existing mathematical models are suitable only for determining TD along the length of casing string (CS) and are not suitable for determining TD along the length of the tubing run, since the existence of the interfacial (between the CS and the tubing) annulus of the fluid and gas layers with heat capacity and thermal conductivity that differ significantly from the heat capacity and thermal conductivity of rocks surrounding the CS. Given …the above, mathematical models taking into account the impact of the fluid and gas layers in the annulus on the heat transfer from the upward fluid flow to the tubing wall and from the wall to the interfacial annulus are developed. To ensure the technological effectiveness of the obtained model, formulas for quantitative estimation of the heat transfer of the fluid flow into the surrounding environment are given. Show more
Keywords: Heat exchange, heat transfer, heat dissipation, thermal conductivity, temperature distribution, oil production well.
DOI: 10.3233/JIFS-219366
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Bai, Yu | Hu, Qijun | Zhou, Zhenxiang | Cai, Qijie | He, Leping
Article Type: Research Article
Abstract: The interaction of several workers with intelligent construction machinery can lead to serious collisions. Typically, the safety distance is used as an indicator of the safety of worker–machine interactions (WMI). However, the degree of risk does not increase linearly with decreasing worker–machine distances. To further reveal the essence of WMI safety, this study proposes a new method for assessing the safety state of WMIs, namely, the construction safety potential field. It is used to describe the factors and patterns associated with the spatial overlap and decay of hazardous energy in WMI operations. The proposed method was tested in an earthworks …construction WMI operation and the results were valid. A preliminary discussion of the relevant parameters constituting the construction safety potential field model is presented. The contributions of the research is proposing a generic energy-based model, which provides a novel idea for the interpretation of safety issues in construction WMI operations and opens up a new foundation for the development of active safety control. Show more
Keywords: Construction site, worker–machine safety, safety field, potential function
DOI: 10.3233/JIFS-236423
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Zhang, Qian | Bai, Enrui | Shao, Mingwen | Liang, Hong
Article Type: Research Article
Abstract: Convolutional neural networks (CNNs) and Transformer architectures have traditionally been recognized as the preferred models for addressing computer vision tasks. However, there has been a recent surge in the popularity of networks based on multi-layer perceptron (MLP) structures that do not rely on convolution or attention mechanisms. These MLP architectures have demonstrated exceptional performance in image classification tasks, exhibiting lower time complexity while maintaining high accuracy. In contrast, video classification tasks involve larger amounts of data and necessitate more intricate feature extraction, resulting in increased time and resource consumption. To enhance computational efficiency and minimize resource utilization, we propose a …convolution-free and Transformer-free architecture for video classification called Video-MLP for video classification. Video-MLP utilizes a simple MLP structure to learn video features. Specifically, it comprises two types of layers: Spatial-Mixer and Temporal-Mixer, which respectively capture spatial and temporal information. The Spatial-Mixer extracts spatial information from each frame along the height and width dimensions, while the Temporal-Mixer models temporal information for the same spatial positions across frames. To improve the efficiency of spatial-temporal modeling in our network, we used a spatial-temporal information fusion approach to integrate information at different scales. Additionally, we grouped the input data along the time dimension and designed three different grouping schemes when extracting temporal information. The experimental results indicate that Video-MLP achieved accuracy rates of 87.2% on the Kinetics-400 dataset and 75.3% on the SomethingV2 dataset, outperforming models with equivalent computational complexity. Notably, Video-MLP achieved these results without using convolution and attention mechanisms, and without pre-training on large-scale image and video datasets. Show more
Keywords: MLP-based-model, video classification, computer vision, deep learning
DOI: 10.3233/JIFS-240310
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Ma, Nana | Wang, Lili | Long, Yuting
Article Type: Research Article
Abstract: Music has been utilized throughout history as a medium for cultural communication and artistic expression, embodying various nations’ and societies’ ideologies and experiences. Music culture communication is crucial for encouraging cultural diversity and understanding and developing social cohesion and community building among people. Music teaching management is the process of setting up, arranging, and executing music education programs in a manner that successfully teaches students the essential skills and information necessary for becoming proficient musicians. Users’ exact preferences for various areas of attraction cannot be determined, nor are users’ choices for traditional music recommendations sufficiently accurate. A recommender system estimates …or anticipates people’s preferences and offers appropriate recommendations. First, the sparsity problem emerges when insufficient data is accessible for the recommendation, and the coverage is one of the key drawbacks of social labeling. Cold start issues might be difficult since new music learners might not have given sufficient details about their musical tastes. Hence, the Hybridized Fuzzy logic-based Content and Collaborative Music Recommendation (HFC2MR) system is proposed to create personalized music teaching plans that are effective and engaging for each student based on their music preferences and learning outcomes. Enhanced Fuzzy C-Means clustering is used in collaborative recommendations to group users based on their shared musical tastes and to provide each user with more individualized, accurate music recommendations based on other users’ listening habits and preferences in the same cluster. Subsequently, an assessment of the recommender system using parameters like accuracy, precision, f1-score, and recall ratio is shown with optimal cluster selection. The coverage ratio is used to compare experimental data based on skill capacity covered through the assessment of music teaching. RMSE metric is used to evaluate the accuracy of students’ performance based on music attributes related to teaching goals. Show more
Keywords: Music teaching management, fuzzy logic, recommender system, clustering and similarity
DOI: 10.3233/JIFS-232422
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Zhou, Yue | Chen, Qiwei
Article Type: Research Article
Abstract: Studying the evolution of karst rocky desertification (KRD) in control areas of diverse geomorphologic types and its correlation with land use provides valuable insights for identifying priority areas and implementing effective treatment measures. Employing Remote Sensing (RS) and GIS, this research quantitatively examines the evolution of KRD and its relationship with land use in the karst mountain and gorge areas of Guizhou Province over the period 2010 to 2020. The findings reveal continuous improvement in KRD across the study areas, albeit with noticeable regional disparities. Notably, the karst mountain region exhibited significantly higher change areas and rates of KRD, non-KRD, …light KRD, and moderate KRD compared to the gorge area, underscoring better desertification control in the former region. A discernible correlation emerges between different karst geomorphologic types, the distribution and changes in land use types, and the evolution of KRD. Land use change emerges as a pivotal factor influencing the improvement of KRD in these areas. Changes in land use patterns corresponded with a decrease in KRD in dry land, other woodland, grassland, and bare land across both regions. However, the response of KRD to land use patterns varied across control areas with different geomorphologic environments, resulting in geographical differentiation in KRD evolution. Key land use conversions, notably from shrubland to forestland and dry land to garden land in the gorge, and shrubland to forestland in the mountain, contributed significantly to KRD dynamics in these regions. Notably, in the gorge area, KRD primarily occurred in garden land, other woodland, dry land, and grassland. In contrast, in the mountain area, KRD was prevalent in shrubland, dry land, and grassland, highlighting distinct responses and contributions to its evolution. The study observes substantial land use change in KRD-improved areas, particularly in the gorge region. Notably, the responsiveness of KRD to woodland conversions (shrubland, forestland, other woodland) varied across different geomorphologic environments. The dynamics of rocky desertification occurrence (RDO) and the occurrence structure of KRD in various land use types exhibited significant differences between the two regions. The gorge area demonstrated generally higher RDO, with a relatively stable and simpler occurrence structure of KRD compared to the more dynamic and varied structure observed in the mountain area. The sequencing of KRD occurrence in both areas displayed stability in specific land use types, with varying intensities noted between them. Show more
Keywords: Karst, rocky desertification, land use, evolution, geomorphology
DOI: 10.3233/JIFS-241536
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Qin, Hao | Zou, Yanli | Yu, Guoliang | Liu, Huipeng | Tan, Yufei
Article Type: Research Article
Abstract: In the process of mapping outdoor undulating and flat roads, existing LiDAR SLAM systems often encounter issues such as map distortion and ghosting. These problems arise due to the low vertical resolution of multi-line LiDAR, which easily leads to the occurrence of odometry height drift during the mapping process. To address this challenge, this study propose a novel LiDAR SLAM system named SOHD-LOAM, designed specifically to suppress odometry height drift. This system encompasses several critical components, including data preprocessing, front-end LiDAR odometry, back-end LiDAR mapping, loop detection, and graph optimization. SOHD-LOAM leverages the road gradient limitation algorithm and the height …smoothing algorithm as its core, while also integrating the Kalman filter, loop detection, and graph optimization techniques. To evaluate the performance of SOHD-LOAM, the comprehensive experiments are conducted with using KITTI datasets and real-world scenes. The experimental results demonstrate that SOHD-LOAM achieves superior accuracy and robustness in global odometry compared to the state-of-the-art LEGO-LOAM. Specifically, the height error of the sequences 00, 05 experiment was found to be 40.62% and 61.92% lower than that of LEGO-LOAM. Additionally, the maps generated by SOHD-LOAM exhibit no distortion or ghosting, thereby significantly enhancing map quality. Show more
Keywords: Autonomous driving, SLAM, odometry height drift, road gradient limitation, height smoothing, loop detection
DOI: 10.3233/JIFS-235708
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Wei, YuHan | Kim, Young-Ju
Article Type: Research Article
Keywords: Camel herd algorithm (CHA), camel-bat swarm optimization (CBSO), cultural and creative product (CCP) Design, graphic design
DOI: 10.3233/JIFS-236320
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Lalitha, S. | Sridevi, N. | Deekshitha, Devarasetty | Gupta, Deepa | Alotaibi, Yousef A. | Zakariah, Mohammed
Article Type: Research Article
Abstract: Speech Emotion Recognition (SER) has advanced considerably during the past 20 years. Till date, various SER systems have been developed for monolingual, multilingual and cross corpus contexts. However, in a country like India where numerous languages are spoken and often humans converse in more than one language, a dedicated SER system for mixed-lingual scenario is more crucial to be established which is the focus of this work. A self-recorded database that includes speech emotion samples with 11 diverse Indian languages has been developed. In parallel, a mixed-lingual database is formed with three popular standard databases of Berlin, Baum and SAVEE …to represent mixed-lingual environment for western background. A detailed investigation of GeMAPS (Geneva Minimalistic Acoustic Parameter Set) feature set for mixed-lingual SER is performed. A distinct set of MFCC (Mel Frequency Cepstral Coefficients) coefficients derived from sine and cosine-based filter banks enriches the GeMAPS feature set and are proven to be robust for mixed-lingual emotion recognition. Various Machine Learning (ML) and Deep Learning (DL) algorithms have been applied for emotion recognition. The experimental results demonstrate GeMAPS features classified from ML has been quite robust for recognizing all the emotions across the mixed-lingual database of the western languages. However, with diverse recording conditions and languages of the Indian self-recorded database the GeMAPS with enriched features and classified using DL are proven to be significant for mixed-lingual emotion recognition. Show more
Keywords: Emotion, GeMAPS, mixed-lingual, sine, cosine filter bank
DOI: 10.3233/JIFS-219390
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Bisht, Akhilesh | Gupta, Deepa
Article Type: Research Article
Abstract: Neural Machine Translation (NMT) for low resource languages is a challenging task due to unavailability of large parallel corpus. The efficacy of Transformer based NMT models largely depends on scale of the parallel corpus and the configuration of hyperparameters implemented during model training. This study aims to delve into and elucidate the impact of hyperparameters on the performance of NMT models for low resource languages. To accomplish this, a series of experiments are conducted using an open-source Hindi-Kangri corpus to train both supervised and semi-supervised NMT models. Throughout the experimentation process, a significant number of discrepancies were identified within the …data-set, necessitating manual correction. The best translation performance evaluated with respect to the metrics such as BLEU (0–1), SacreBLEU (0–100), Chrf (0–100), Chrf+ (0–100), Chrf++ (0–100) and TER (%) is (0.15, 14.98, 41.43, 41.49, 38.77, 68.20) for Hindi to Kangri direction, and (0.283, 28.17, 49.71, 50.64, 48.63, 51.25) for Kangri to Hindi direction. Show more
Keywords: Neural machine translation, low resource language, low resource MT, transformers, semi-supervised MT, Kangri, natural language processing
DOI: 10.3233/JIFS-219384
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Momena, Alaa Fouad | Gazi, Kamal Hossain | Mukherjee, Asesh Kumar | Salahshour, Soheil | Ghosh, Arijit | Mondal, Sankar Prasad
Article Type: Research Article
Abstract: Use of the Internet of Everything (IoE), the number of smart gadgets increasing rapidly giving the side effect of huge data, which has led to issues with traditional cloud computing models like inadequate security, slow response times, poor privacy, and bandwidth overload. Conventionally, cloud computing is no longer adequate for supporting the diversified needs of the user and the extraordinary society of data processing, so edge computing technologies have been revealed. This study considers edge computing in an educational institute in a scientific way. Multi criteria decision making (MCDM) is one of the most suitable decision making processes that propose …to choose optimal alternatives by considering multiple conflicting criteria. Entropy weighted method is considered to evaluate factor weight. Weighted Aggregated Sum Product Assessment (WASPAS) and Combined Compromise Solution (CoCoSo) based MCDM methodologies examine the ranking of alternatives for this study. Multiple decision makers (DMs) give opinions with Pentagonal Fuzzy Soft Set (PFSS) to express the uncertainty and fuzziness of the data set. The set operations and arithmetic operations of PFSS are discussed in detail. Also, a new de-fuzzification method of PFSS is proposed in this study. Calculated the criteria weight and prioritized the alternative based on source data. Lastly, sensitivity analysis and comparative analysis are conducted to check the stability of the result. Show more
Keywords: Edge computing, Academic institute, PFSS, Entropy, WASPAS, CoCoSo
DOI: 10.3233/JIFS-239887
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Jaiseeli, C. | Raajan, N.R.
Article Type: Research Article
Abstract: Medical and satellite image analysis require incredibly high resolution. Super-resolution combines several low-resolution images of the same scene to generate a high-resolution image. The Super resolution employing deep learning techniques still has an illumination issue. This paper proposes a novel CGIHE-VDSR algorithm that integrates the Very Deep Super Resolution (VDSR) Network with Color Global Image Histogram Equalization (CGIHE) to improve image resolution. In the proposed method, the low-resolution image is first histogram equalized using the CGIHE algorithm. Then, the VDSR network is applied to the histogram equalized image for super-resolution. The comparison of real-time data with the benchmark images is …done using the proposed algorithm in the MATLAB platform. The PSNR and SSIM metrics demonstrate that the super resolution image obtained using the proposed method is significantly better than the existing methods. Show more
Keywords: Histogram equalization, super-resolution, CNN, subsample image, VDSR, residual
DOI: 10.3233/JIFS-219392
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Javed, Hira | Sufyan Beg, M.M. | Akhtar, Nadeem | Alroobaea, Roobaea
Article Type: Research Article
Abstract: Vlogs, Recordings, news, sport coverages are huge sources of multimodal information that do not just limit to text but extend to audio, images and videos. Applications such as summary generation, image/video captioning, multimodal sentiment analysis, cross modal retrieval requires Computer Vision along with Natural Language Processing techniques to extract relevant information. Information from different modalities must be leveraged in order to extract quality content. Hence, reducing the gap between different modalities is of utmost importance. Image to text conversion is an emerging field and employs the use of encoder decoder architecture. Deep CNNs extract the feature of images and sequence …to sequence models are used to generate text description. This paper is a contribution towards the growing body of research in multimodal information retrieval. In order to generate the textual description of images, we have performed 5 experiments using the benchmark Flickr8k dataset. In these experiments we have utilized different architectures - simple sequence to sequence model, attention mechanism, transformer-based architecture to name a few. The results have been evaluated using BLEAU score. Results show that the best descriptions are attained by making use of transformer architecture. We have also compared our results with the pretrained visual model vit-gpt2 that incorporates visual transformer. Show more
Keywords: Multimodal, captioning, summarization, etc
DOI: 10.3233/JIFS-219394
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Kostiuk, Yevhen | Tonja, Atnafu Lambebo | Sidorov, Grigori | Kolesnikova, Olga
Article Type: Research Article
Abstract: In this paper, we investigate the issue of hate speech by presenting a novel task of translating hate speech into non-hate speech text while preserving its meaning. As a case study, we use Spanish texts. We provide a dataset and several baselines as a starting point for further research in the task. We evaluated our baseline results using multiple metrics, including BLEU scores. We used a cross-validation approach and an average of the metrics per fold for evaluation. We achieved a 0.236 sentenceBLEU score on four folds. This study aims to contribute to developing more effective methods for reducing the …spread of hate speech in online communities. Show more
Keywords: Hate speech, translation, Spanish
DOI: 10.3233/JIFS-219348
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: George, Neetha | Ramachandran, Sivakumar | Jiji, C.V.
Article Type: Research Article
Abstract: Macula is the part of retina responsible for sharp and clear vision. Macular edema is caused by the accumulation of intraretinal fluid (IRF) in the macula, which is further distinguished by the compromised integrity of the blood-retinal barrier, particularly evident in the retinal vasculature. This results in swelling, that may lead to vision impairment and is the dominant sign of several ocular diseases, including age-related macular degeneration, diabetic retinopathy, etc. Quantitative analysis of the fluid regions in macular edema helps in ascertaining the severity as well as the response to treatment of the diseases. Optical coherence tomography (OCT) is a …major tool used by ophthalmologists for visualizing edema. The prevalent practice for diagnosing and treating macular edema involves measuring Central Retinal Thickness (CRT). Segmenting the IRF in OCT images offers the potential for a more accurate and better quantification of macular edema. This paper proposes a novel method combining convolutional neural network (CNN) and active contour model for segmenting the IRF to ascertain the severity of macular edema. The IRF region is initially segmented using an encoder-decoder architecture. Contour evolution is then performed on this segmented image to demarcate the IRF boundaries. The advantage of the method is that it does not require precisely labeled images for training the CNN. A comparison of the experimental results with models employing CNN alone and with other state-of-the art methods demonstrates the superior performance and consistency of the proposed method. Show more
Keywords: edema segmentation, convolutional neural network, active contour model
DOI: 10.3233/JIFS-219401
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Wu, Donghui | Wang, Jinfeng | Zhao, Wanwan | Geng, Xin | Liu, Guozhi | Qiu, Sen
Article Type: Research Article
Abstract: Gesture recognition based on wearable sensors has received extensive attention in recent years. This paper proposes a gesture recognition model (CGR_ATT) based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) fused attention mechanism to improve accuracy rate of wearable sensors. First, CNN serves as a feature extractor, learning features automatically from sensor data by performing multiple layers of convolution and pooling operations, capturing spatial features of gestures. Furthermore, a temporal modeling unit GRU is introduced to capture the temporal dynamics in gesture sequences. By controlling the information flow through gate mechanisms, it effectively handles the temporal relationships in …sensor data. Finally, an attention mechanism is introduced to assign different weights to the hidden state of the GRU. By calculating the attention weights for each time period, the model automatically selects key time periods related to gesture movements. The GR-dataset proposed in this paper involves 910 sets of training parameters. The model achieves an ultimate accuracy of 97.57% . In compare with CLA-net, CLT-net, CGR, GRU, LSTM and CNN, the experimental results demonstrate that the proposed method has superior accuracy. Show more
Keywords: Wearable gesture recognition system, CGR_ATT model, deep learning, wearable devices
DOI: 10.3233/JIFS-240427
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Hu, Junhua | Zhou, Yingling | Li, Huiyu | Liang, Pei
Article Type: Research Article
Abstract: To enhance infection diseases interval prediction, an improved model is proposed by integrating neighborhood fuzzy information granulation (NNIG) and spatial-temporal graph neural network (STGNN). Additionally, the NNIG model can efficiently extract the most representative features from the time series data and identifies the support upper and lower bounds. NNIG model transfers time series data from numerical level to granular level, and processes data feed it into STGNN for interval prediction. Finally, experiments are conducted for evaluation based on the COVID-19 data. The results demonstrate that the NNIG outperforms baseline models. Further, it proves beneficial in offering a valuable approach for …policy-making. Show more
Keywords: Time series, fuzzy information granulation, interval prediction, spatial-temporal graph neural network
DOI: 10.3233/JIFS-236766
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Visvanathan, P. | Durai Raj Vincent, P.M.
Article Type: Research Article
Abstract: A Stroke is a sudden loss of blood circulation in certain parts of the brain that results in a loss of neurological function. To save a patient from stroke, an immediate diagnosis and treatment plan must be implemented. Artificial intelligence-based machine learning algorithms play a major role in the prediction. To predict a person likely to have a stroke, stroke healthcare data records must be accessed, which is very sensitive. Data shared for machine learning training pose security risks and have concerns about privacy. To overcome this issue, Genetic Algorithm and Federated Learning (GA-FL) –based hybridization approach is proposed to …predict the risk of stroke in a person. Federated Learning was developed by Google, which can provide security to the data during the training process because every client participating in this training process needs to exchange only the training parameters without sharing the data. In addition to the security features, a genetic algorithm was used to optimize the parameters required to train a model using the perceptron neural network model. The experimental results show that our proposed research model (GA-FL) provides security and predicts the risk of stroke more accurately than any other existing algorithm. Show more
Keywords: Federated learning, genetic algorithm, stroke risk, perceptron neural network
DOI: 10.3233/JIFS-236354
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Hossain, AKM B. | Salam, Md. Sah Bin Hj. | Alam, Muhammad S. | Hossain, AKM Bellal
Article Type: Research Article
Abstract: Semantic segmentation is crucial for the treatment and prevention of brain cancers. Several neural network–based strategies were rapidly presented by research groups to enhance brain tumor thread segmentation. The tumor’s uneven form necessitates the usage of neural networks for its detection. Therefore, improved patient outcomes may be achieved with precise segmentation of brain tumor. Brain tumors can range widely in size, form, and position, making diagnosis difficult. Thus, this work offers a Multi-level U-Net (MU-Net) approach for analyzing the brain tumor data augmentation for improved segmentation. Therefore, a significant amount of data augmentation is employed to successfully train the recommended …system, removing the problem of a lack of data when using MR images for the diagnosis of multi-grade brain cancers. Here, we presented the “Multi-Level Pyramidal Pooling (MLPP)” component, where a new pyramidal pool will be employed to capture contextual data for augmentation. The “High-Grade Glioma” (HGG) datasets from the Kaggle and BraTs2021 were used to assess the proposed MU-Net. Overall Tumor (OT), Enhancing Core (EC), and Tumor Core (TC) were the three main designations to be segmented. The dice score was used to contrast the results empirically. The suggested MU-Net fared better than most existing methods. Researchers in the fields of bioinformatics and medicine might greatly benefit from the high-performance MU-Net. Show more
Keywords: Brain tumor, Data Augmentation (DA), Multi-level U-Net (MU-Net), Multi-Level Pyramidal Pooling (MLPP), Adaptive Curvelet Transform (ACT), wavelet threshold
DOI: 10.3233/JIFS-232782
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wu, Jie | Hou, Mengshu
Article Type: Research Article
Abstract: Table-based fact verification (TFV) is a binary classification task that requires understanding and reasoning about both table and text. This task poses many challenges, such as table parsing, text comprehension, and numerical reasoning. However, existing methods tend to depend solely on pre-trained models for tables, treating all types of reasoning equally and disregarding the importance of identifying logic types in inference process. In this regard, we propose MoETFV, an efficient and explanatory approach to solving TFV, which is based on a Mixture-of-Experts (MoE) framework. This approach can detect the underlying logic types of statements and leverage multiple independent experts to …emulate diverse logical reasoning. It consists of one shared expert for general semantic understanding and several specific experts with distinct responsibilities for different logical inferences. Moreover, the practical applications of the MoE method in TFV are thoroughly investigated. This model doesn’t necessitate any table pre-trained models, and aligns closely with human cognitive processes in addressing such issues. Experimental results demonstrate the innovation and feasibility of the proposed approach. Show more
Keywords: Tabular data, fact verification, mixture-of-experts, logical reasoning, natural language processing
DOI: 10.3233/JIFS-238142
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Chen, Longkai | Huang, Jingjing
Article Type: Research Article
Abstract: Urban traffic accidents impose a significant threat to public safety because of its frequent occurrence and potential for severe injuries and fatalities. Hence, an effective analysis of accident patterns is crucial for designing accident prevention strategies. Recent advancement in data analytics have provided opportunities to improve the pattern of urban traffic accidents. However, the existing works face several challenges in adapting the complex dynamics, and heterogeneity of the accident data. To overcome these challenges, we proposed an innovative solution by combining the K-means clustering and Support Vector Machine to precisely predict the traffic accident patterns. By leveraging the efficiencies of …clustering technique and machine learning, this work intends to identify the intricate patterns within the traffic database. Initially, a traffic accident database was collected and fed into the system. The collected database was pre-processed to improve and standardize the raw dataset. Further, cluster analysis is employed to identify distinct patterns within the dataset and group similar accidents into clusters. This clustering enables the system to recognize common accident scenarios and identify recent accident trends. Subsequently, a Support Vector Machine is deployed to classify accidents into distinct categories through intensive training with identified clusters. The combination enables the system to understand the complex relationships among diverse accident variables, making it an effective framework for real-time pattern recognition. The proposed strategy is implemented in Python and validated using the publicly available traffic accident database. The experimental results manifest that the proposed method achieved 99.65% accuracy, 99.53% precision, 99.62% recall, and 99.57% f-measure. Finally, the comparison with the existing techniques shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. Show more
Keywords: Support vector machine, traffic accident pattern recognition, cluster analysis, machine learning
DOI: 10.3233/JIFS-241018
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Liu, Fei
Article Type: Research Article
Abstract: In China, aesthetic education at the college level is essential for students’ quality because it improves their understanding of art, helps them progress in their professional career development, and helps them comprehend more fully the attractiveness of creative creations. As a result, it needs to prioritize aesthetic education at the institution and endeavor to nurture students’ feelings progressively and improve their aesthetic abilities at different levels. Artificial intelligence (AI) is used in this project to create a novel, interdisciplinary teaching technique that will maximize students’ artistic and intellectual potential and help them make more, better art. In this research, the …Osprey Optimization method improves the interdisciplinary teaching technique for aesthetic education based on a light Exclusive gradient-boosting mechanism (OOM-LEGBM). The exploration-exploitation dynamics of the OOM are incorporated into LEGBM, providing the students with a tangible and relatable technique to understand complex-solving processes. This research develops an enhanced quality framework for college aesthetic education based on the multi-model data fusion system about the implication and necessity of aesthetic education. The influence of college aesthetic education on students’ creative capacity and artistic literacy was investigated to inform instructional activities better to develop students’ aesthetic skills. The experimental findings suggest that the proposed approach achieved an improved accuracy of 99.90%, higher precision of 99.88%, and greater recall of 99.91%. Moreover, it obtained a minimum Root Mean Square Error (RMSE) of 0.26% and a lower Mean Absolute Error (MAE) of 0.34%, showing that the suggested model greatly improved preference learning accuracy while keeping overall accuracy at an identical level. Innovation capacity building in college aesthetic education can help students become more self-aware, improve their study habits, visually literate, and more comprehensive. Show more
Keywords: Interdisciplinary teaching, aesthetic education, curriculum, multimodal data fusion, artificial intelligence, and big data
DOI: 10.3233/JIFS-240723
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
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