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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: Prasad, Mal Hari | Swarnalatha, P.
Article Type: Research Article
Abstract: The model-based methods were utilized in order to produce the test cases for the behavioral model of a software system. Run test cases habitually or physically facilitates premature identification of requirement errors. Regression test suite design is thought-provoking as well as significant task in this automated test design. General techniques of regression testing comprise rerunning formerly accomplished tests as well as inspecting whether program behavior has modified as well as formerly fixed faults have recurred. Regression testing is carried out with the intension of assessing a system skillfully by means of logically picking the right least set of tests essential …to suitably cover a particular modification. Then again, the relapse testing occasions of experiment prioritization, test suite decrease, and relapse test choice are commonly focused on conditions, which recognize the experiments to pick or the experiment to run thusly in independent framework. As indicated by experiment prioritization, experiments are very much arranged ward upon some condition just as experiments with greatest need are run first to achieve a presentation objective. If there should be an occurrence of test suite decrease/minimization, experiment, which end up being ended over the long haul are dismissed from the test suite with the intension of making a minor arrangement of experiments. In the event of relapse test determination, from a prevalent unique suite, a subset of experiments is picked. Show more
Keywords: Test case prioritization, test criteria, generalized predictive control, rudder performance testing system
DOI: 10.3233/JIFS-233547
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Vinoth Kumar, M. | Supreeth, B.R. | Hariprabhu, M. | Shanmuga Priya, P. | Ahmed, Ahmed Najat | Nagrare, Trupti | Mathur, Shruti | Manikandan, G.
Article Type: Research Article
Abstract: Containerized data centers (CDCs) have experienced rapid growth in recent years, owing to their modular and scalable nature. However, ensuring reliability and early fault detection in these complex systems is critical. This paper presents a novel Fuzzy Logic-based Fault Detection (FLFD) framework for CDCs using Digital Twins (DTs). The proposed approach employs DTs to create accurate virtual representations of the CDCs, which enable real-time monitoring and analysis of the physical systems. This paper focuses on three main aspects: (1) the development of a comprehensive DT model for CDCs, (2) the design and implementation of a FLFD algorithm, and (3) the …validation of the proposed approach through extensive simulations and real-world case studies. The FLFD algorithm leverages fuzzy logic principles to identify and localize faults in the system, thereby enhancing the overall fault detection accuracy and reducing false alarms. Results demonstrate the effectiveness of the proposed framework, with significant improvements in fault detection performance and system reliability. The FLFD approach offers a promising solution for proactive maintenance and management in containerized data centers, paving the way for more efficient and resilient operations. Show more
Keywords: Fuzzy logic, fault detection, containerized data centers, digital twins, proactive maintenance
DOI: 10.3233/JIFS-233736
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023
Authors: Famila, S. | Jawahar, A. | Arthi, A. | Supriya, N. | Ramadoss, P.
Article Type: Research Article
Abstract: The maximization of lifetime in Wireless Sensor Networks (WSNs) is always made feasible by conserving energy and maintaining synchronization in the connectivity between its nodes. The selection of Cluster head (CH) methodology used during data dissemination process from the CH to the BS determines the energy conversation which is necessary for extending the network’s lifetime. Initially, the nodes are localized using Graphical Recurrent Neural Network. In this research, a hybrid monarchy butterfly and chicken swarm optimization based cluster head selection (HMB-CSO-CHS) method is used to enhance the lifespan of sensor networks. This suggested HMB-CSO-CHS Scheme uses the benefits of the …Hybrid Monarchy butterfly and chicken swarm optimization algorithm for the efficient selection of cluster heads by establishing reliable tradeoffs between their exploitation and exploration potentials with optimized convergence rate. The simulation-based investigation of the suggested HMB-CSO-CHS Scheme confirms its effectiveness in reducing the rate of mortality among the sensor nodes such that remarkable improvement in lifetime can be realized in the network When analyzing HMB-CSO-CHS method, it is noted that energy consumption and packet delivery ratio is completely reduced when comparing with existing methods. Show more
Keywords: Monarchy butterfly, chicken swarm optimization, cluster head selection, exploitation, exploration, best individual solution
DOI: 10.3233/JIFS-233681
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023
Authors: Venkata Vidyalakshmi, Guggilam | Gopikrishnan, S.
Article Type: Research Article
Abstract: In the realm of Internet of Things (IoT) sensor data, missing patterns often occur due to sensor glitches and communication problems. Conventional missing data imputation methods struggle to handle multiple missing patterns, as they fail to fully leverage the available data as well as partially imputed data. To address this challenge, we propose a novel approach called Univariate data Imputation using Fast Similarity Search (UIFSS). The proposed method solved the missing data problem of IoT data using fast similarity search that can suits different patterns of missingness. Exploring similarities between data elements, a problem known as all-pairs-similarity-search, has been extensively …studied in fields like text analysis. Surprisingly, applying this concept to time series subsequences hasn’t seen much progress, likely due to the complexity of the task. Even for moderately sized datasets, the traditional approach can take a long time, and common techniques to speed it up only help a bit. Notably, for very large datasets, our algorithm can be easily adapted to produce high-quality approximate results quickly. UIFSS consists of two core components:Sensor sorting with Similar Node Clustering (SSNC) and Imputation Estimator using Fast Similarity Search(IEFSS). The SSNC, encompassing missing sensor sorting depending on their entropy to guide the imputation process. Subsequently, IEFSS uses global similar sensors and captures local region volatility, prioritizing data preservation while improving accuracy through z-normalized query based similarity search. Through experiments on simulated and bench mark datasets, UIFSS outperforms existing methods across various missing patterns. This approach offers a promising solution for handling missing IoT sensor data and with improved imputation accuracy. Show more
Keywords: Data imputation, internet of things, spatial correlation, univariate data, data quality, similarity search
DOI: 10.3233/JIFS-233446
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2023
Authors: Praba, M.S. Bennet | Subashka Ramesh, S.S.
Article Type: Research Article
Abstract: A unique system that offers traffic management, mobility management, and proactive vulnerability identification is the vehicular ad hoc network (VANET). With the use of efficient deep learning algorithms, intrusion prevention practices can improve their reliability. Many assaults, like Sybil, Blackhole, Wormhole, DoS attack, etc. expose them to risk. These intrusions compromise efficiency and dependability by taking advantage of network connectivity. The use of amazingly precise learning models to anticipate a variety of threats in VANET has not yet been thoroughly explored. To categorize numerous attacks on the VANET scenario, we develop a novel efficient integrated Long Short Term Memory (LSTM) …paradigm. The system employs the Panthera Leo Hunting Optimization (PLHO) method to modify the hyper-parameters of the systems to enhance the LSTM model’s detection rate under different threat situations. SUMO-OMNET++and Veins, two well-known modeling programs were utilized to gather the various VANET variables for both normal and malicious scenarios. The improved LSTM model was evaluated using actual information that had been recorded. The outcomes from the various learning models were merged with performance measures to show the algorithm’s efficiency and individuality. As the space between nearer vehicles reduces abruptly, a collision happens. So, to provide a realistic collision prevention system, it is necessary to collect exact and detailed information on the distance between every vehicle and all of the nearby vehicles. We suggest using a Carbon Nanotube Network (CNT) combined with the other Nanodevices to achieve reliability on the scale of millimeters. Modeling findings that the proposed novel approach succeeded with strong recognition capabilities. Show more
Keywords: Vehicular ad-hoc networks, traffic management, long short term memory, panthera leo hunting, nanotechnology devices
DOI: 10.3233/JIFS-234401
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023
Authors: Elangovan, D. | Subedha, V.
Article Type: Research Article
Abstract: Opinion Mining and Sentiment Analysis acts as a pivotal role in facilitating businesses to actively operate on enhancing the business strategies and accomplish detailed insights of the consumer’s feedback regarding the products. In recent times, deep learning (DL)technique has been used for many sentiment analysis tasks and has attained effective outcomes. Huge quantity of product reviews is being posted by the customer on different e-commerce and social networking platforms which can assist the developers to improve the quality of the products. The study focuses on the design of Sentiment Classification on Online Product Reviews using Dwarf Mongoose Optimization with Attention …based Deep Learning (DMO-ABDL) model. The proposed DMO-ABDL technique analyzes the product reviews for the identification of sentiments. To accomplish this, the DMO-ABDL technique performs different stages of preprocessing to transform the actual data into suitable format. Furthermore, the Glove technique is employed for word embedding process. Moreover, attention based long short-term memory (ALSTM) approach was exploited for sentiment classification and its hyperparameters can be optimally chosen by the DMO technique. A comprehensive set of experiments were performed in order to guarantee the enhanced sentiment classification performance of the DMO-ABDL algorithm. A brief comparative study highlighted the supremacy of the DMO-ABDL technique over other existing approaches under different measures. Show more
Keywords: Sentiment analysis, natural language processing, hybrid models, deep learning, hyperparameter optimization
DOI: 10.3233/JIFS-233611
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Abd Algani, Yousef Methkal | Babu, K. Suresh | Beram, Shehab Mohamed | Al Ansari, Mohammed Saleh | Tapia-Silguera, Ruben Dario | Borda, Ricardo Fernando Cosio | Bala, B. Kiran
Article Type: Research Article
Abstract: Growing older is a phenomenon that is associated with increasingly complex health situations as a result of the coexistence of several chronic diseases. As a result, there is a downward tendency in both older people and their caretakers’ quality of life, which frequently results in frailty. There are numerous solutions available to treat the issue, which primarily affects older people. The basic and most popular imaging method for predicting cognitive impairment is magnetic resonance imaging. Furthermore, few of the earlier models had a definite level of accuracy when diagnosing the condition. Further, there is a critical need to put in …place a stronger, more reliable approach to precise prediction. When compared to other procedures, using magnetic resonance images to predict cognitive decline is the safest and most straightforward. The advanced concept for a better optimized strategy to predict cognitive impairment at an early stage is presented in this research. The hybrid krill herd and grey wolf optimization method is offered as a solution to address the challenges in locating the impacted area. In a short amount of time, a significant number of MRI images are analyzed, and the results show a more precise or higher rate of recognition. Show more
Keywords: Fuzzy model, soft computing, cognitive impairment, dementia, fuzzy C-Means clustering
DOI: 10.3233/JIFS-233695
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023
Authors: Pradeep, M. | Sivaji, U. | Nithya, B. | Kadiravan, G. | Preethi, D. | Painam, Ranjith Kumar
Article Type: Research Article
Abstract: The mapping function must identify the reference model and detect coordinate arrangement by observing a repository with deep learning. Progression model with coordinate arrangement composition should have various positional displacements from one location to another. A prerogative classification model is an evolution of factor accomplishment in a repository method. Coordinate arrangement with calculation method must formulate a model locality twirl in classification method of a reference in dominance factor of perpetuity position observation by procession of reference localities. In a procession model observation by location, tendency method should be rotated from locality position into another coordinate method, with a PDD …factor measuring DPA of cadent RFT with an origin of 92.6, a cadent DS intermediate factor of 95.2, culmination factor of cadent RFT of 94.1. The docile exploratory arrangement of heuristic parameters is used in existing system to perceive phenomena such as sprout, enrollment discernment, demeanour, gravest perforation measure, Model of a heretic in apprehension method by premonition incongruity. Annotation should identify classification process using a proposed model to obtain massive measure of imputation function, In PDD measure of DPA in Cadent DS, with inception of 96.1, intercession of Cadent RFT in 92.6, with crowning of Cadent RFT in 96.4, 93.2 Show more
Keywords: MRCAI, Goin Twirl, maginot, idiosyncrasy outline, coffer atavism, flocculent utter eminence kedge
DOI: 10.3233/JIFS-234739
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023
Authors: Ahamed, Ayoobkhan Mohamed Uvaze | Joel Devadass Daniel, D.J. | Seenivasan, D. | Rukumani Khandhan, C. | Radhakrishnan, S. | Daya Sagar, K.V. | Bhardwaj, Vivek | Nishant, Neerav
Article Type: Research Article
Abstract: Time-sensitive programs that are linked to smart services, such as smart healthcare as well as smart cities, are supported in large part by the fog computing domain. Due to the increased speed limitation of the cloud, Cloud Computing (CC) is a competent platform for fog in data processing, but it is unable to meet the demands of time-sensitive programs. The procedure of resource provisioning, as well as allocation in either a fog-cloud structure, takes into account dynamic changes in user requirements, and resources with limited access in fog devices are more difficult to manage. Due to the continual changes in …user requirement factors, the deadline represents the biggest obstacle in the fog computing structure. Hence the objective is to minimize the total cost involved in scheduling by maximizing resource utilization. For dynamic scheduling in the fog-cloud computing model, the efficiency of hybridization of the Grey Wolf Optimizer (GWO) and Lion Algorithm (LA) is developed in this study. In terms of energy costs, processing costs, and communication costs, the created GWOMLA-based Deep Belief Network (DBN) performed better and outruns the other traditional models. Show more
Keywords: Fog-cloud computing environment, deep learning, deep belief network (DBN), lion algorithm (LA), grey wolf optimizer (GWO).
DOI: 10.3233/JIFS-234030
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023
Authors: Rajesh Kanna, R. | Ulagamuthalvi, V.
Article Type: Research Article
Abstract: Diagnosis is given top priority in terms of farm resource allocation, because it directly affects the GDP of the country. Crop analysis at an early stage is important for verifying the efficient crop output. Computer vision has a number of intriguing and demanding concerns, including disease detection. After China, India is the world’s second-largest creator of wheat. However, there exist algorithms that can accurately identify the most prevalent illnesses of wheat leaves. To help farmers keep track on a large area of wheat plantation, leaf image and data processing techniques have recently been deployed extensively and in pricey systems. In …this study, a hybrid pre-processing practice is used to remove undesired distortions while simultaneously enhancing the images. Fuzzy C-Means (FCM) is used to segment the affected areas from the pre-processed images. The data is then incorporated into a disease classification model using a Convolutional Neural Network (CNN). It was tested using Kaggle data and several metrics to see how efficient the suggested approach was. This study demonstrates that the traditional Long-Short Term Memory (LSTM) technique achieved 91.94% accuracy on the input images, but the hybrid pre-processing model with CNN achieved 95.06 percent accuracy. Show more
Keywords: Plant leaves diseases, convolutional neural network, fuzzy c-means, wheat production, pre-processing techniques
DOI: 10.3233/JIFS-233672
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Prabu Shankar, K.C. | Shyry, S. Prayla
Article Type: Research Article
Abstract: Early detection of diseases in men and women can improve treatment and reduce the risk involved in human life. Nowadays techniques which are non-invasive in nature are popularly used to detect the various types of diseases. Histopathological analysis plays a major role in finding the nature of the disease through medical images. Manual interpretation of these medical imaging takes time, is tedious, subjective, and can have human errors. It has also been discovered that the interpretation of these images varies amongst diagnostic labs. As computer power and memory capacity have increased, methodologies and medical image processing techniques have been developed …to interpret and analyse these images as a substitute for human involvement. The challenge lies in devising an efficient pre-processing technique that helps in analysing, processing and preparing the medical image for further diagnostics. This research provides a hybrid technique that reduces noise in the NITFI medical image by using a 2D adaptive median filter at level 1. The edges of the filtered medical image are preserved using the modified CLAHE algorithm which preserves the local contrast of the image. Expectation Maximization (EM) algorithm extracts the ROI part of the image which helps in easy and accurate identification of the disease. All the three steps are run over the 3D image slices of a NIFTI image. The proposed method proves that it achieves close to ideal RMSE, PSNR and UQI values as well as achieves an average runtime of 37.193 seconds for EM per slice. Show more
Keywords: 2D adaptive, expectation maximization, NIFTI, UQI, edge preservation, 3D slice, computational intelligence
DOI: 10.3233/JIFS-233931
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023
Authors: Rajendran, Aishwarya | Ganesan, Sumathi | Rathis Babu, T.K.S.
Article Type: Research Article
Abstract: Brain tumor is observed to be grown in irregular shape and presented deep inside the tissues that led to cancer. Human brain tumor identification and categorization are performed with high latency, but also an essential task for the medical experts. The assistance through the automated diagnosis is generally utilized for the advancement in the diagnosis ability in order to get superior accuracy in brain tumor detection. Although the researches are enhancing the brain tumor detection performance, the highly challenging is to segment the brain tumor since it has variability concerning the tumor type, contrast, image modality and also in other …factors. To meet up all the challenges, a novel classification method is introduced using segmentation and machine learning approaches. Initially, the required images are collected from benchmark data sources. The input images are undergone for pre-processing stage, where it is done via “Contrast Limited Adaptive Histogram Equalization (CLAHE) and filtering methods”. Further, the pre-processed imagesare given as input to two classifier models as “Residual Network (ResNet) and Gated Recurrent Unit (GRU)”, in which the model provide the result as normal and abnormal images. In the second part, obtained abnormal image acts an input for segmentation step. In segmentation, it is needed to extract the relevant features by texture and spatial features. The resultant features are subjected for optimizing, where the optimal features are acquired through Adaptive Coyote Optimization Algorithm (ACOA). Then, the extracted features are fed into machine learning model like “Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF)” to render the segmented image. Finally, the hybrid classification named Hybrid ResGRUis developed by integrating the ResNet and GRU, where the hyper parameters are tuned optimally using developed ACOA, thus it is used for classifying the abnormal image that belongs to benign stage or malignant stage. The experimental results are evaluated, and its performance is analyzed by various metrics. Hence, the proposed classification model ensures effective segmentation and classification performance. Show more
Keywords: Brain tumour segmentation and classification, adaptive coyote optimization algorithm, residual network, gated recurrent unit, ensemble machine learning-based tumor segmentation, deep learning-based classification
DOI: 10.3233/JIFS-233546
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Abdus Subhahan, D. | Vinoth Kumar, C.N.S.
Article Type: Research Article
Abstract: The worldwide deforestation rate worsens year after year, ultimately resulting in a variety of severe implications for both mankind and the environment. In order to track the success of forest preservation activities, it is crucial to establish a reliable forest monitoring system. Changes in forest status are extremely difficult to manually annotate due to the tiny size and subtlety of the borders involved, particularly in regions abutting residential areas. Previous forest monitoring systems failed because they relied on low-resolution satellite images and drone-based data, both of which have inherent limitations. Most government organizations still use manual annotation, which is a …slow, laborious, and costly way to keep tabs on data. The purpose of this research is to find a solution to these problems by building a poly-highway forest convolution network using deep learning to automatically detect forest borders so that changes over time may be monitored. Here initially the data was curated using the dynamic decomposed kalman filter. Then the data can be augmented. Afterward the augmented image features can be fused using the multimodal discriminant centroid feature clustering. Then the selected area can be segmented using the iterative initial seeded algorithm (IISA). Finally, the level and the driver of deforestation can be classified using the poly-highway forest convolution network (PHFCN). The whole experimentation was carried out in a dataset of 6048 Landsat-8 satellite sub-images under MATLAB environment. From the result obtained the suggested methodology express satisfied performance than other existing mechanisms. Show more
Keywords: Deforestation, dynamic decomposed kalman filter, multimodal discriminant centroid feature clustering, iterative initial seeded algorithm, poly-highway forest convolution network
DOI: 10.3233/JIFS-233534
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Agrawal, Monika | Moparthi, Nageswara Rao
Article Type: Research Article
Abstract: Sentiment analysis (SA)at the sentence, aspect, and document levels determines the sentiment of particular aspect phrases in a given sentence. Due to their capacity to extract sentiment information from text in aspect-level sentiment classification, neural networks (NNs) have achieved significant success. Generally speaking, sufficiently sizable training corpora are necessary for NNs to be effective. The performance of NN-based systems is reduced by the small size of the aspect-level corpora currently available. In this research, we suggest a gated bilateral recurrent neural network (G-Bi-RNN) as a foundation for multi-source data fusion, their system offers sentiment information that several sources. We develop …a uniform architecture specifically to include information from sentimental lexicons, including aspect- and sentence-level corpora. To further provide aspect-specific phrase representations for SA, we use G-Bi-RNN, a deep bilateral Transformer-based pre-trained language model. We assess our methods using SemEval 2014 datasets for laptops and restaurants. According to experimental findings, our method consistently outperforms cutting-edge techniques on all datasets. We use a number of well-known aspect-level SA datasets to assess the efficacy of our model. Experiments show that when compared to baseline models, the suggested model can produce state-of-the-art results. Show more
Keywords: Sentiment analysis (SA), gated bilateral recurrent neural network (G-Bi-RNN), language model
DOI: 10.3233/JIFS-234076
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Pughazendi, N. | Valarmathi, K. | Rajaraman, P.V. | Balaji, S.
Article Type: Research Article
Abstract: Internet of Things (IoT) devices installed in hospital direct data unceasingly; in this manner, energy usage augments with the number of broadcasts too. In this paper, Reliable Cluster based Data Collection Framework (RCDCF) for IoT-Big Data Healthcare Applications (HA) is developed. During clustering process, the connected IoT devices are grouped into clusters. In clustering technique, the available IoT devices are gathered into groups. The device with high battery capacity and processing ability is selected as a cluster head (CH). Each member of the cluster is allocated multiple slots by applying a general function pooled by the Fog node and the …entire devices. To perceive and eliminate outliers from the sensor data, Density-based spatial clustering of applications with noise (DBSCAN) method is utilized. To forecast the objective and subjective behaviours of the equipments, a Random Forest Deep Neural Network (RF-DNN) based classification model is utilized. By experimental results, it has been shown that RCDCF achieves 19% and 20% reduced energy consumption at Cloud and Fog centers, respectively. Moreover, RCDCF has 2.1% and 1.3% increased correctness of data at Cloud and Fog data centers, respectively, when compared to the existing framework. Show more
Keywords: Internet of Things (IoT), big data, cloud, clustering, health care solution, slot allocation, Random Forest Deep Neural Network (RF-DNN), categorization
DOI: 10.3233/JIFS-233505
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023
Authors: Subburaj, S. | Murugavalli, S. | Muthusenthil, B.
Article Type: Research Article
Abstract: SLR, which assists hearing-impaired people to communicate with other persons by sign language, is considered as a promising method. However, as the features of some of the static SL could be the same as the feature in a single frame of dynamic Isolated Sign Language (ISL), the generation of accurate text corresponding to the SL is necessary during the SLR. Therefore, Edge-directed Interpolation-based Recurrent Neural Network (EI-RNN)-centered text generation with varied features of the static and dynamic Isolated SL is proposed in this article. Primarily, ISL videos are converted to frames and pre-processed with key frame extraction and illumination control. …After that, the foreground is separated with the Symmetric Normalised Laplacian-centered Otsu Thresholding (SLOT) technique for finding accurate key points in the human pose. The human pose’s key points are extracted with the Media Pipeline Holistic (MPH) pipeline approach and to improve the features of the face and hand sign, the resultant frame is fused with the depth image. After that, to differentiate the static and dynamic actions, the action change in the fused frames is determined with a correlation matrix. After that, to engender the output text for the respective SL, features are extracted individually as of the static and dynamic frames. It is obtained from the analysis that when analogized to the prevailing models, the proposed EI-RNN’s translation accuracy is elevated by 2.05% in INCLUDE 50 Indian SL based Dataset and Top 1 Accuracy 2.44% and Top 10 accuracy, 1.71% improved in WLASL 100 American SL. Show more
Keywords: Isolated Sign Language (ISL), Sign Language Recognition (SLR), Edge directed Interpolation based Recurrent Neural Network (EIRNN), text generation, word level sign language, Media Pipeline Holistic (MPH)
DOI: 10.3233/JIFS-233610
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Prasath, N. | Arun, A. | Saravanan, B. | Kamaraj, Kanagaraj
Article Type: Research Article
Abstract: Intelligent Fuzzy Edge Computing (IFEC) has emerged as an innovative technology to enable real-time decision-making in Internet of Things (IoT)-based Digital Twin environments. Digital Twins provide virtual models of physical systems, facilitating predictive maintenance and optimization. However, implementing real-time decision-making in these environments is challenging due to massive data volumes and need for quick response times. IFEC addresses this by offering a flexible, scalable and efficient platform for real-time decision-making. This paper presents an overview of key aspects of IFEC including fuzzy logic, edge computing and Digital Twins. The use of fuzzy logic in IFEC provides an adaptive framework for …handling uncertainties in data. Edge computing enables localized processing, reducing latency. The integration of Digital Twins allows system monitoring, analysis and optimization. Potential applications of IFEC are highlighted in domains such as manufacturing, healthcare, energy management and transportation. Recent advancements in IFEC are also discussed, covering new fuzzy inference systems, edge computing architectures, Digital Twin modeling techniques and security mechanisms. Overall, IFEC shows great promise in enabling real-time decision-making in complex IoT-based Digital Twin environments across various industries. Further research on IFEC will facilitate the ongoing digital transformation of industrial systems. Show more
Keywords: Intelligent fuzzy edge computing, real-time decision making, IoT-based digital twins, predictive maintenance, fuzzy logic, edge computing
DOI: 10.3233/JIFS-233495
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023
Authors: Vishnukumar, Ravula | Ramaiah, Mangayarkarasi
Article Type: Research Article
Abstract: The Internet’s evolution resulted in a massive amount of data. As a result, the internet has become more sophisticated and vulnerable to massive attacks. The attack detection system is a key feature for system security in modern networks. The IDS might be signature-based or detect anomalous behavior. Researchers recently created several detection algorithms for identifying network intrusions in vehicular network security, but they failed to detect intrusions effectively. For this reason, the optimal Deep Learning approach, namely Political Fractional Dingo Optimizer (PFDOX)-based Deep belief network is introduced for attack detection in network security for vehicles. The Internet of Vehicle simulation …is done initially, and then the input data is passed into the pre-processing phase, which removes noise present in the data. Then, the feature extraction module receives the pre-processed data. The Deep Maxout Network is trained using the Fractional Dingo optimizer (FDOX)is utilized to detect normal and abnormal behavior. Fractional calculus and Dingo optimizer (DOX) are combined to create the proposed FDOX. Finally, intruder/attack types are classified using the Deep Belief Network, which is tuned using the PFDOX. The PFDOX is created by the assimilation of the DOX, Fractional Calculus, and Political Optimizer (PO). The experimental result shows that the designed PFDOX_DBN for attack type classification offers a better result based on f-measure, precision, and recall with the values of 0.924, 0.916, and 0.932, for the CIC-IDS2017 dataset. Show more
Keywords: Deep maxout network, intrusion detection, deep belief network, dingo optimizer, fractional calculus, political optimizer
DOI: 10.3233/JIFS-233581
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023
Authors: Nandipati, Bhagya Lakshmi | Devarakonda, Nagaraju
Article Type: Research Article
Abstract: Lung cancer incidence and mortality continue to rise rapidly around the world. According to the American Cancer Society, the five-year survivability for individuals in the metastasis phases is significantly lower, highlighting the importance of early lung cancer diagnosis for effective therapy and improved quality of life. To achieve this, it is crucial to combine PET’s sensitivity for recognizing abnormal regions with CT’s anatomical localization for evaluating PET-CT images in computer-assisted detection implementations. Current PET-CT image evaluation methods either run each modality independently or aggregate the data from both, but they often overlook the fact that different visual features encode different …types of data from different modalities. For instance, high atypical PET uptake within the lungs is more crucial for identifying tumors compared to physical PET uptake in the heart. To address the challenges of fine-grained issues during feature extraction and fusion, we propose an interpretable deep learning-based solution for lung cancer diagnosis using CT and PET images. This involves building an Optimal Adversarial Network for merging and an Optimal Attention-based Generative Adversarial Network with Classifier (Opt_att-GANC) to augment the classification of the existence and nonexistence of lung cancer based on extracted features. The performance of the Opt_att-GANC is compared with existing methodologies like global-feature encoding U-Net (GEU-Net), 3D Dense-Net, and 3D Convolutional Neural Network Technique (3D-CNN). Results show that the proposed Opt_att-GANC achieves an F1-score of 67.08%, 93.74% accuracy, 92% precision, 92.1% recall, and 93.74% recall. The prospective study aims to enhance the precision degree with reduced duration by incorporating an ensemble neural network paradigm for feature extraction. Show more
Keywords: Lung cancer, fuzzy fusion, feature extraction, classification, neural networks, Adversarial network, PET
DOI: 10.3233/JIFS-233491
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Li, Zhongliang | Tu, Xuezhen | Gao, Hong | Huang, Shiyue | Ma, Zongmin
Article Type: Research Article
Abstract: With the development of artificial intelligence, deep-learning-based log anomaly detection proves to be an important research topic. In this paper, we propose LogCSS, a novel log anomaly detection framework based on the Context-Semantics-Statistics Convolutional Neural Network (CSSCNN). It is the first model that uses BERT (Bidirectional Encoder Representation from Transformers) and CNN (Convolutional Neural Network) to extract the semantic, temporal, and correlational features of the logs. We combine the features with the statistic information of log templates for the classification model to improve the accuracy. We also propose a technique, DOOT (Deals with the Out-Of-Templates), for online template matching. The …experimental research shows that our framework improves the average F1 score of the six best algorithms in the industry by more than 5% on the open-source dataset HDFS, and improves the average F1 score of the six best algorithms in the industry by more than 8% on the BGL dataset, LogCSS also performs better than other similar methods on our own constructed dataset. Show more
Keywords: Anomaly detection, convolutional neural network, intelligent operation and maintenance, data mining
DOI: 10.3233/JIFS-235801
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Wang, Caichuan | Li, Jiajun
Article Type: Research Article
Abstract: With the continuous changes and development of financial markets, it has brought many difficulties to investment decision-making. For the multi-objective investment decision-making problem, the improved Ant colony optimization algorithms was used to improve the effectiveness and efficiency of the multi-objective investment decision-making. Therefore, based on intelligent Fuzzy clustering algorithm and Ant colony optimization algorithms, this paper studied a new multi-objective investment decision model, and proved the advantages of this method through comparative analysis of experiments. The experimental results showed that the improved Ant colony optimization algorithms has significantly reduced the system’s construction costs, operating costs and financial costs, all of …which were controlled below 41% . Compared with the traditional Ant colony optimization algorithms, this method had lower values in policy risk, technical risk and market risk, and can effectively control risks. Meanwhile, the environmental, economic, and social benefits of this method were all above 58%, and the average absolute return rate and success rate in this experiment were 21.5450% and 69.4083%, respectively. Therefore, from the above point of view, the multi-objective investment decision model based on intelligent Fuzzy clustering algorithm and the improved Ant colony optimization algorithms can effectively help decision-makers to find the best investment decision-making scheme, and can improve the accuracy and stability of decision-making. This research can provide reference significance for other matters in the field of investment decision-making. Show more
Keywords: Multi-objective investment, investment decision model, improved ant colony algorithm, intelligent fuzzy clustering algorithm, traditional ant colony algorithm
DOI: 10.3233/JIFS-234704
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Wang, Yuansen | Lv, Guibin | He, Jialin | Cheng, Feng | Li, Dongke
Article Type: Research Article
Abstract: To comprehensively and objectively evaluate the actual safety condition in road and bridge engineering construction, the road and bridge engineering construction safety risk evaluation index system is constructed combined with the factors induced by emergencies in the road and bridge engineering construction process. Aiming at the dynamic uncertainty of road and bridge construction safety risk, using Fuzzy Set Theory and an improved similar aggregation method to determine the prior probabilities and conditional probabilities of network nodes, and then selecting the transition probabilities of nodes through expert opinions and incident reports, leading to the development of a dynamic evaluation model for …safety risks in road and bridge engineering construction based on Fuzzy Dynamic Bayesian Network, this model can make the construction safety risk prediction result accurately. Taking the Hebi City Provincial Highway 304 reconstruction project as an example for analysis, the results indicate that the model can accurately predict the probability of changes in safety risks in road and bridge engineering construction. Additionally, it can identify critical risk factors and provide crucial supporting information for decision-makers to optimize risk management strategies. Show more
Keywords: Road and bridge engineering, similar aggregation method, Dynamic Bayesian Network, risk analysis
DOI: 10.3233/JIFS-236301
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wu, Yanqiu | Liu, Min | Sun, Dehong
Article Type: Research Article
Abstract: Person re-identification relies on discriminative features. However, most researches focus on extracting features from the high-layer of network while ignoring the middle-layer features, some important details are overlooked frequently. To address this issue, we propose a Multi-Scale and Multi-Patch Feature Fusion Network(MSPF). We employ modified OSFA to extract, align, and fuse the feature maps in the middle-layer of network, which can compensate for the lack of detailed information in the high-level network features. To obtain richer detailed global features of pedestrian, we construct a multi-patch feature fusion module(MPF). We concatenate the global features extracted from modified OSFA and MPF to …obtain global features with richer detailed representations. Cross-entropy loss, triplet loss and center loss are combined to constrain our model. We evaluate the performance of our model on Market-1501, CUHK03_labeled and DukeMTMC. The results prove that our method is superior to the state-of-the-art approaches. Show more
Keywords: Person re-identification, multi-scale, multi-patch, feature fusion
DOI: 10.3233/JIFS-237113
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Xu, Zhedong | Su, Yongbo | Guo, Fei
Article Type: Research Article
Abstract: In the process of digital transformation and development in various industries, there are more and more large-scale optimization problems. Currently, swarm intelligence optimization algorithms are the best method to solve such problems. However, previous experimental research has found that there is still room for improvement in the performance of using existing swarm intelligence optimization algorithms to solve such problems. To obtain the high-precision optimal value of whale optimization algorithm (WOA) for solving large-scale optimization problems, the optimization problem knowledge model is studied to guide the iterative process of WOA algorithm, and a novel whale optimization algorithm based on knowledge model …guidance (KMGWOA) is proposed. First, a population update strategy based on multiple elite individuals is proposed to reduce the impact of the local optimal values, and the knowledge model to guide population update is constructed by combining the proposed population update strategy with the population update strategy based on global optimal individual. Second, a collaborative reverse learning knowledge model with multiple elite and poor individuals in the solution space is proposed to prevent long-term non-ideal region search. The above two knowledge models guide the iterative process of WOA algorithm in solving large-scale optimization problems. The performance of the KMGWOA algorithm guided by the proposed knowledge models is tested through the well-known classical test functions. The results demonstrate that the proposed KMGWOA algorithm not only has good search ability for the theoretical optimal value, but also achieves higher accuracy in obtaining the optimal value when it is difficult to obtain the theoretical optimal value. Moreover, KMGWOA algorithm has fast convergence speed and high effective iteration percentage. Show more
Keywords: Knowledge model, whale optimization algorithm, large-scale problem, population update strategy, collaborative reverse learning
DOI: 10.3233/JIFS-236930
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Wang, Qi | Lu, TongWei
Article Type: Research Article
Abstract: Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extraction methods and detection methods based on convolutional neural network (CNN) is poor, and the information provided by the features extracted from the network is limited and single. In this paper, an end-to-end ringed residual U-Net is proposed to detect image splicing forgery by blending features of non-natural regions. Some regions with significant differences from the image background are defined as non-natural regions(such as the irregular border at the splicing of …images). In this paper, a feature enhancement module for non-natural regions is constructed, which the image through the pooling of four different scales, and these features are then combined with the original image and input to the backbone network for processing, aiming to highlight regions of the image that differ significantly from the background. Therefore, after adding the feature enhancement module for non-natural regions to the end-to-end ring residual U-Net, more attention will be paid to the tampering regions in the feature extraction stage, image manipulation detection and localization will also become more accurate. Compared with some mainstream methods, this method achieves better performance on the three standard datasets(CASIA2.0, NIST2016, COLUMBIA). In addition, it has excellent robustness under JPEG compression attack and noise corruption attack. Show more
Keywords: Convolutional neural network, image splicing forgery detection, non-natural regions
DOI: 10.3233/JIFS-232025
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Deng, Xiangyu | Hu, Yiman | Yang, Yahan
Article Type: Research Article
Abstract: With the development of artificial intelligence technology, the digital transformation of student-oriented education becomes particularly important. How to promote real-time interaction between teachers and students in the classroom is an urgent issue which is needed to pay attention to. Based on the facial expression features of students in a classroom, this paper analyzes the changes in angles between facial expression feature points using Dlib. Additionally, this paper proposes a novel algorithm for extracting variable scale template edge trend features. The algorithm adaptively processes the template based on the edge trend features of expression feature points, and use the proposed template …slope normalization algorithm to achieve multi-scale template edge trend extraction. Then, DNN are used to recognize different listening expressions. The experimental results show that the proposed algorithm has faster recognition speed and better robustness when applied to classroom expression recognition. By identifying students’ class status to remind teachers to adjust their class progress, the goal of improving classroom learning effectiveness is achieved Show more
Keywords: Dlib face recognition, learning effectiveness, expression recognition, DNN prediction, feature extraction
DOI: 10.3233/JIFS-237143
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Mathavan, N. | Ramesh, G.
Article Type: Research Article
Abstract: A groundbreaking study employs interval arithmetic to address the challenging multi-objective interval traveling salesperson problem. Customizing methods like a nearest neighbor, branch and bound, two-way heuristics, and dynamic programming effectively resolve this complex problem. Preserving interval values without the need for classical form conversion is a significant advantage. Researchers validated this approach through extensive experiments, consistently demonstrating superior outcomes compared to existing methods. These algorithmic approaches were optimized for Python 3.11 64-bit to enhance processing speed and efficiency.
Keywords: Multi-objective interval traveling salesperson problem, new interval arithmetic, weighted sum method, Python program
DOI: 10.3233/JIFS-235966
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Byeon, Haewon | Tammina, Manoj Ram | Soni, Mukesh | Kuzieva, Nargiza | Jindal, Latika | Keshta, Ismail | Kulkarni, Mrunalini Harish
Article Type: Research Article
Abstract: Online health consultations are becoming more popular as a result of technological improvements. Patients routinely look for information about medical disorders online, which could jeopardize the privacy of medical records and increase the workload of healthcare professionals. Nonetheless, academics continue to be extremely concerned about issues related to the quality characteristics that relate to the current architectural models, such as energy consumption, latency, resource utilization, scalability, and packet loss. This method, however, also results in a significant strain being placed on medical experts who must sort through vast amounts of medical records to extract certain information. This paper presents a …novel ciphertext policy attribute-based encryption method coupled with fuzzy logic to overcome these issues. This solution uses a hybrid structure of IPFS and blockchain to store data and enables complex bidirectional access control. Before being added to IPFS, medical records are encrypted. To ensure data integrity, related IPFS hash indexes are then added to the blockchain. Utilizing attribute-based technology, users’ data is encrypted to give them fine-grained bidirectional access control. A thorough security analysis proves the system’s resilience, especially when faced with chosen plaintext assaults inside the random oracle model. Tests for this study were conducted using 10–50 attribute sets. This paper’s technique solely makes use of a hash operation. All things considered; the study demonstrates that the proposed design is more efficient than earlier schemes. Thus, from the comparison study above, it can be concluded that the system presented in this work is more efficient. Results from simulations provide additional support for the suggested methodology by highlighting the improved computing efficiency of users as compared to baseline conventional systems. This study demonstrates how technological advancement and healthcare requirements can coexist harmoniously, paving the way for secure and effective online medical consultations that are powered by fuzzy logic. Show more
Keywords: Fuzzy logic, data analysis, online health consultation, advanced encryption system
DOI: 10.3233/JIFS-235893
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Wan, Yifei | Huang, Qi | Wu, Yin | Li, Songling
Article Type: Research Article
Abstract: By designing a digital power grid multi-source data security collaborative management platform, the system configuration problem of the OMS system and the power grid management platform for the main distribution network of the power grid is solved. A design method for the digital power grid multi-source data security collaborative management platform based on discrete particle swarm optimization algorithm is proposed. Based on the design concept of SOA, realize the overall design framework of the platform according to the design method of multi-layer technical system in the business presentation layer, business process and composition layer, service layer, component layer and resource …layer, realize the basic layer design of the system management platform through the basic application platform design scheme of XML configuration, implement the query, processing and output representation of the grid’s multivariate data using B/S architecture protocol, and use the Spring Framework The platform software architecture is implemented using J2EE technology and multi module component design scheme. The discrete particle swarm optimization algorithm is used for the fusion and scheduling of multi-source data in the digital power grid. The interface design and functional construction of the power grid management platform are implemented in the OMS system of the power grid main distribution network, and the logical model of the transformation project is constructed to achieve platform optimization and construction. Tests have shown that the designed digital power grid multi-source data security collaborative management platform has good human-machine interaction, strong data fusion scheduling ability, reduced resource and subsystem coupling, and supports the flexibility of physical deployment and maintenance. Show more
Keywords: Discrete particle swarm optimization algorithm, digital power grid, multi source data, safety, collaborative management platform, main distribution network OMS system of the power grid
DOI: 10.3233/JIFS-237849
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Zhou, Mi | Xiong, Xue-Di | Pei, Feng
Article Type: Research Article
Abstract: Marine high-end equipment reflects a country’s comprehensive national strength. The safety assessment of it is very important to avoid accident either from human or facility factors. Attribute structure and assessment approach are two key points in the safety assessment of marine high-end equipment. In this paper, we construct a hierarchical attribute structure based on literature review and text mining of reports and news. The hierarchical attribute structure includes human, equipment, environment and management level. The correlations among these attributes are analyzed. The assessment standards of attributes are described in details. Different evaluation grades associated with attributes are transformed to a …unified one by the given rules. As for the assessment approach, the evidential reasoning approach is applied for uncertain information fusion. Group analytical hierarchical process is used to generate attribute weights from a group of experts, where process aggregation method and result aggregation method are combined in a comprehensive way. The importance of expert is computed by the uncertainty measure of expert’s subjective judgment. A drilling platform is finally assessed by the proposed attribute structure and assessment approach to illustrate the effectiveness of the assessment framework. Show more
Keywords: Safety assessment, marine high-end equipment, evidential reasoning, uncertainty, group analytical hierarchical process
DOI: 10.3233/JIFS-237750
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-26, 2024
Authors: Xiao, Yongxia | Tang, Xiao
Article Type: Research Article
Abstract: In the interval-valued intuitionistic fuzzy environment, a new multi-attribute three-way decision-making model is proposed to address the problems that the relative loss function in the existing multi-attribute three-way decision-making model does not consider the degree of hesitancy, and the alternative conditional probabilities are given subjectively by the authors, which lacks objectivity. First, three types of ideal solutions are introduced, and the correlation coefficients between the evaluated values and ideal solutions are utilized to construct alternative relative loss functions. Second, the ELECTRE-I method is generalized to the interval-valued intuitionistic fuzzy environment to establish the outranking relation and a method for estimating …the conditional probability of alternatives is given. Finally, the model is used to experimentally analyze examples to illustrate the effectiveness and rationality of the model. Show more
Keywords: Interval-valued intuitionistic fuzzy sets, multi-attribute decision-making, three-way decision, correlation coefficient, outranking relation
DOI: 10.3233/JIFS-236356
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Gao, Zhihui | Han, Meng | Liu, Shujuan | Li, Ang | Mu, Dongliang
Article Type: Research Article
Abstract: The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization …problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively. Show more
Keywords: Beluga whale optimization algorithm, harris hawk optimization algorithm, high utility itemsets mining, good point set, intelligent optimization algorithm
DOI: 10.3233/JIFS-236793
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-36, 2024
Authors: Wang, Zhongan | Li, Honghai | Pang, Minghao | Wu, Yingna | Yang, Rui | Wu, Zhiwei | Cai, Guoshuang
Article Type: Research Article
Abstract: Detection and classification methods for the melt pool state in laser direct energy deposition (L-DED) can significantly help predict defects and mechanical properties of L-DED metal parts. Although traditional machine learning algorithms based on physical modeling methods and convolutional neural networks have recently been introduced into melt pool state identification, these methods rely on complex artificially designed features or cannot simultaneously detect defects in multiple dimensions. In this paper, a novel bilateral stream neural network was designed for melt pool identification, which performs defect identification in two label dimensions simultaneously. Two sets of single-channel experiments were designed to collect the …dataset captured by a high-speed camera. By cutting the metal parts and marking them with professional equipment operated by professionals, the dataset was labeled according to the bonding condition and dilution rate criteria. Without an additive model structure, the model achieved 95.2% accuracy in identifying defects in the bonding condition and 92.8% in determining deficiencies in the dilution rate. In order to explain the identification mechanism of the model, the CAM method was utilized for the visual display of the model recognition process, which provides a potential application solution for the online monitoring method of the L-DED. Show more
Keywords: Laser direct energy deposition, melt pool state, bilateral stream neural network
DOI: 10.3233/JIFS-236589
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Kalaipriya, O. | Dhandapani, S.
Article Type: Research Article
Abstract: Lung cancer is one of the leading causes of mortality from cancer. Lung cancer is a kind of malignant lung tumor characterized by uncontrolled cell proliferation in lung tissues. Even though CT scans are the most often used imaging technology in medicine, clinicians find it challenging to interpret and diagnose cancer from CT scan pictures. As a result, computer-aided diagnostics can assist clinicians in precisely identifying malignant cells. Many computer-aided approaches were explored and applied, including image processing and machine learning. A comparison of the various classification methodologies will assist in enhancing the accuracy of lung cancer detection systems that …employ robust segmentation and classification algorithms presented in this research. This research proposed to enhance existing segmentation and classification-basedmethodsof human lung cancer detection with optimization in techniques. The workflow includes initial preprocessing of medical images, for segmentation a novel hybrid methodology is developed by combining enhanced k-means clustering and random forest and classification with an Artificial neural network enhanced with PSO parameter and feature optimization. Show more
Keywords: Machine learning, K-means, ANN, random forest, PSO, image processing technique
DOI: 10.3233/JIFS-233845
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Kadry, Heba | Samak, Ahmed H. | Ghorashi, Sara | Alhammad, Sarah M. | Abukwaik, Abdulwahab | Taloba, Ahmed I. | Zanaty, Elnomery A.
Article Type: Research Article
Abstract: Coronavirus is a new pathogen that causes both the upper and lower respiratory systems. The global COVID-19 pandemic’s size, rate of transmission, and the number of deaths is all steadily rising. COVID-19 instances could be detected and analyzed using Computed Tomography scanning. For the identification of lung infection, chest CT imaging has the advantages of speedy detection, relatively inexpensive, and high sensitivity. Due to the obvious minimal information available and the complicated image features, COVID-19 identification is a difficult process. To address this problem, modified-Deformed Entropy (QDE) algorithm for CT image scanning is suggested. To enhance the number of training …samples for effective testing and training, the suggested method utilizes QDE to generate CT images. The retrieved features are used to classify the results. Rapid innovations in quantum mechanics had prompted researchers to use Quantum Machine Learning (QML) to test strategies for improvement. Furthermore, the categorization of corona diagnosed, and non-diagnosed pictures is accomplished through Quanvolutional Neural Network (QNN). To determine the suggested techniques, the results are related with other methods. For processing the COVID-19 imagery, the study relates QNN with other existing methods. On comparing with other models, the suggested technique produced improved outcomes. Also, with created COVID-19 CT images, the suggested technique outperforms previous state-of-the-art image synthesis techniques, indicating possibilities for different machine learning techniques such as cognitive segmentation and classification. As a result of the improved model training/testing, the image classification results are more accurate. Show more
Keywords: Coronavirus, quantum machine learning, quanvolutional neural network, Q-deformed entropy
DOI: 10.3233/JIFS-233633
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Sran, Sukhwinder Singh | Singh, Harmandeep | Mittal, Puneet | Kumar, Manoj | Sharma, Sukhwinder
Article Type: Research Article
Abstract: With the rapid adoption of cloud storage for business and personal use, data security has become a significant concern. This study investigates the effectiveness of advanced encryption algorithms to ensure the integrity, confidentiality, and availability of data stored in cloud environments. The research focuses on the implementation and evaluation of three encryption algorithms: AES-256, ChaCha20, and Threefish, comparing their performance in terms of computational complexity, key generation, and resistance to various attacks. The study utilizes a testbed consisting of a simulated cloud storage environment, where the encryption algorithms are deployed and assessed based on encryption/decryption time and throughput. Results indicate …that the ChaCha20 algorithm outperforms both AES-256 and Threefish in terms of encryption/decryption speed while maintaining strong security. Moreover, the findings suggest that the combination of these encryption algorithms can enhance data security by providing a multi-layered defense mechanism against potential threats. The research contributes to the advancement of cloud storage security by identifying optimal encryption algorithms and proposing a robust solution for safeguarding sensitive information. Show more
Keywords: Cloud storage, data security, encryption algorithms, AES-256, ChaCha20, Threefish
DOI: 10.3233/JIFS-234043
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Chen, Meng | Wang, Xue-ping
Article Type: Research Article
Abstract: In this article, we characterize triangular norms that have not the limit property, which are applied for exploring the characterizations of function f : [0, 1] → [0, 1] with f ( x ) = lim n → ∞ x T ( n ) for a triangular norm T when the function f is continuous. In particular, we prove that a continuous t-norm T satisfies that f (x ) >0 for all x ∈ (0, 1) if and only if 0 is an accumulation point of its non-trivial idempotent elements, and the function …f is continuous on [0,1] if and only if T = T M . Show more
Keywords: Triangular norm, the limit property, the contrary limit property, Archimedean property, continuity
DOI: 10.3233/JIFS-237999
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Hussain, Abrar | Amjad, Alina | Ullah, Kifayat | Pamucar, Dragan | Ali, Zeeshan | Al-Quran, Ashraf
Article Type: Research Article
Abstract: Supplier selection is a very crucial process within a business or commercial enterprise because it depends upon different components like reliability, customer need, services, cost and reputation. A suitable supplier is familiar with developing a relationship between customer needs and business. To serve this purpose, the multiple attribute group decision-making (MAGDM) technique is a well-known and efficient aggregation model used to evaluate flexible optimal options by considering some appropriate criteria or attributes. Experts face some sophisticated challenges during the decision-making process due to uncertain and ambiguous information about human opinions. To address such conditions, we explore the notion of spherical …fuzzy sets (SFS) and their reliable operations. Some flexible operational laws of Dombi t-norms are also developed in light of spherical fuzzy (SF) information. Combining the theory of Hamy mean (HM) models and Dombi aggregation tools, some robust strategies are also studied in this research work. The main objectives of this article are to propose some dominant strategies in the presence of SF information including spherical fuzzy Dombi Hamy mean (SFDHM), spherical fuzzy Dombi weighted Hamy mean (SFDWHM), spherical fuzzy Dombi Dual Hamy mean (SFDDHM) and spherical fuzzy Dombi weighted Dual Hamy mean (SFDWDHM) operators. The MAGDM techniques are utilized to evaluate the flexibility of our derived methodologies under considering SF information. An experimental case study is utilized to evaluate a notable supplier enterprise under consideration of our developed methodologies. Finally, a comprehensive overview of our research work is also presented. Show more
Keywords: Spherical fuzzy values, Dombi aggregation tools, Hamy mean and Dual Hamy mean, multi-attribute group decision-making system
DOI: 10.3233/JIFS-234514
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2024
Authors: Luo, Binghui | Liu, Xin | Qin, Long | Jiao, Xiaolong | Li, Wengui
Article Type: Research Article
Abstract: The short text matching models can be roughly divided into representation-based and interaction-based approaches. However, current representation-based text matching models often lack the ability to handle sentence pairs and typically only perform feature interactions at the network’s top layer, which can lead to a loss of semantic focus. The interactive text matching model has significant shortcomings in extracting differential information between sentences and may ignore global information. To address these issues, this article proposes a model structure that combines a dual-tower architecture with an interactive component, which compensates for their respective weaknesses in extracting sentence semantic information. Simultaneously, a method …for integrating semantic information is proposed, enabling the model to capture both the interactive information between sentence pairs and the differential information between sentences, thereby addressing the issues with the aforementioned approaches. In the process of network training, a combination of cross-entropy and cosine similarity is used to calculate the model loss. The model is optimized to a stable state. Experiments on the commonly used datasets of QQP and MRPC validate the effectiveness of the proposed model, and its performance is stably improved. Show more
Keywords: Short text matching, representational structure, interactive structure, BERT, multi-angle information
DOI: 10.3233/JIFS-230268
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Diao, Xiu-Li | Zhang, Hao-Ran | Zeng, Qing-Tian | Song, Zheng-Guo | Zhao, Hua
Article Type: Research Article
Abstract: At present, the Chinese text field is facing challenges from low resource issues such as data scarcity and annotation difficulties. Moreover, in the domain of cigarette tasting, cigarette tasting texts tend to be colloquial, making it difficult to obtain valuable and high-quality tasting texts. Therefore, in this paper, we construct a cigarette tasting dataset (CT2023) and propose a novel Chinese text classification method based on ERNIE and Comparative Learning for Low-Resource scenarios (ECLLR). Firstly, to address the issues of limited vocabulary diversity and sparse features in cigarette tasting text, we utilize Term Frequency-Inverse Document Frequency (TF-IDF) to extract key terms, …supplementing the discriminative features of the original text. Secondly, ERNIE is employed to obtain sentence-level vector embedding of the text. Finally, contrastive learning model is used to further refine the text after fusing the keyword features, thereby enhancing the performance of the proposed text classification model. Experiments on the CT2023 dataset demonstrate an accuracy rate of 96.33% for the proposed method, surpassing the baseline model by at least 11 percentage points, and showing good text classification performance. It is thus clear that the proposed approach can effectively provide recommendations and decision support for cigarette production processes in tobacco companies. Show more
Keywords: Low-resource, Cigarette Tasting, Contrastive Learning, Text classification
DOI: 10.3233/JIFS-237816
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ledesma Roque, Diana Anahí | Kolesnikova, Olga | Menchaca Méndez, Ricardo
Article Type: Research Article
Abstract: This study addresses the issue of semantic similarity in sentences using the BERT model through various aggregation techniques, such as max-pooling, mean-pooling, and an LSTM network applied to the output of the BERT model. Subsequently, the linguistic interpretability of the BERT-Base transformer model is analyzed through the unsupervised learning approach, specifically through dimensionality reduction using autoencoders and clustering algorithms, utilizing the representation of the classification token CLS. The results highlight that the CLS classification token achieves better abstractions than the proposed methods. In terms of interpretability, it is observed that sequence length is relevant in the early layers, with …a gradual decrease across the layers. Additionally, attention to semantic similarity is concentrated in the intermediate and upper layers, especially in layers 6, 8, 9, and 10. All these findings were obtained by addressing the semantic similarity task using the STS-Benchmark dataset. Show more
Keywords: Linguistic interpretability, aggregation methods, unsupervised learning, attention mechanisms, token CLS
DOI: 10.3233/JIFS-219359
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Cardoso-Moreno, Marco A. | Luján-García, Juan Eduardo | Yáñez-Márquez, Cornelio
Article Type: Research Article
Abstract: In this study, a thorough analysis of the proposed approach in the context of emotion classification using both single-modal (A-13sbj) and multi-modal (B-12sbj) sets from the YAAD dataset was conducted. This dataset encompassed 25 subjects exposed to audiovisual stimuli designed to induce seven distinct emotional states. Electrocardiogram (ECG) and galvanic skin response (GSR) biosignals were collected and classified using two deep learning models, BEC-1D and ELINA, along with two different preprocessing techniques, a classical fourier-based filtering and an Empirical Mode Decomposition (EMD) approach. For the single-modal set, this proposal achieved an accuracy of 84.43±30.03, precision of 85.16±28.91, and F1-score of …84.06±29.97. Moreover, in the extended configuration the model maintained strong performance, yielding scores of 80.95±22.55, 82.44±24.34, and 79.91±24.55, respectively. Notably, for the multi-modal set (B-12sbj), the best results were obtained with EMD preprocessing and the ELINA model. This proposal achieved an improved accuracy, precision, and F1-score scores of 98.02±3.78, 98.31±3.31, and 97.98±3.83, respectively, demonstrating the effectiveness of this approach in discerning emotional states from biosignals. Show more
Keywords: Emotion classification, signal preprocessing, convolutional neural network, ECG, GSR, EMD
DOI: 10.3233/JIFS-219334
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Yigezu, Mesay Gemeda | Kolesnikova, Olga | Gelbukh, Alexander | Sidorov, Grigori
Article Type: Research Article
Abstract: The rise of social media and micro-blogging platforms has led to concerns about hate speech, its potential to incite violence, psychological trauma, extremist beliefs, and self-harm. We have proposed a novel model, Odio-BERT for detecting hate speech using a pretrained BERT language model. This specialized model is specifically designed for detecting hate speech in the Spanish language, and when compared to existing models, it consistently outperforms them. The study provides valuable insights into addressing hate speech in the Spanish language and explores the impact of domain tasks.
Keywords: BERT, hate speech, domain task, fine tune, Odio-BERT, Spanish
DOI: 10.3233/JIFS-219349
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Liang, Weijing | Xue, Ye | Xu, Jing
Article Type: Research Article
Abstract: With the increasing global disaster risks, constructing more inclusive, flexible, and resilient communities has become crucial for effectively carrying out disaster prevention, mitigation, and relief work. However, existing research on community resilience mostly focuses on the selection of key factors and the assessment of community resilience, lacking in-depth exploration of the interactions between factors and simulation studies of key paths. Therefore, this paper applies the Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) method to select important factors of community resilience. Based on this, the maximum average difference entropy method is used to analyze the relationships and influence mechanisms among …different factors, thus identifying the key factors and key paths affecting community resilience. The Fuzzy Cognitive Map (FCM) is then used to simulate the paths. The study finds that factors of community resilience can be categorized as input, intermediary, and output types, and further analysis of their influence mechanisms reveals four key paths and four key factors. Through pathway simulation, different improvement states of community resilience are observed when triggering the input-type factors of the key paths. Therefore, under limited resources, a phased and systematic approach to enhancing community resilience should be adopted. The contribution of this study lies in providing a comprehensive analysis of factors and pathway selection methods, and through pathway simulation, it offers a scientific basis and decision support for improving and constructing community resilience in practice. Show more
Keywords: Fuzzy cognitive map, fuzzy DEMATEL, maximum average difference entropy method, community resilience, simulation analysis
DOI: 10.3233/JIFS-232234
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: Zhang, Shuguang | Xie, Chengyuan | Zhang, Heng | Gong, Wenzheng | Liu, Lingjie | Zhi, Xuntao
Article Type: Research Article
Abstract: Graph Convolutional Networks (GCN) are prevalent techniques in collaborative filtering recommendations. However, current GCN-based approaches for collaborative filtering recommendation have limitations in effectively embedding neighboring nodes during node and neighbor information aggregation. Furthermore, weight allocation for the user (or item) representations after convolution of each layer is too uniform. To resolve these limitations, we propose a new Graph Convolutional Collaborative Filtering recommendation method based on temporal information during the node aggregation process (TA-GCCF). The method aggregates and propagates information using Gated Recurrent Units, while dynamically updating features based on the timing and sequence of interactions between nodes and their neighbors. …Concurrently, we have developed a convolution attention coefficient to ascertain the significance of embedding at distinct layers. Experiments on three benchmark datasets show that our method significantly outperforms the comparison methods in the accuracy of prediction. Show more
Keywords: Graph convolutional neural network, collaborative filtering, recommendation, gated recurrent units, temporal information
DOI: 10.3233/JIFS-238307
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
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: Xiao, Yanjun | Pei, Eryue | Shi, Linhan | Peng, Kai | Liu, Weiling
Article Type: Research Article
Abstract: In order to solve the problem that Switched Reluctance Motor (SRM) generates torque pulsation phenomenon during operation, which reduces the stability of loom spindle operation, this paper proposes and designs a multi-algorithm fusion-based SRM control strategy from the point of view of control strategy research. Combined with the operating characteristics of the loom, the causes of SRM torque pulsation are analyzed from the point of view of SRM control strategy, and combined with the spindle control indexes, the voltage chopper control and torque distribution function are introduced to construct the SRM control strategy scheme for the loom. On this basis, …an optimization strategy based on the fusion of fuzzy control algorithm, particle swarm algorithm and simulated annealing algorithm is proposed to optimize the torque distribution function, and the algorithmic process of SRM control strategy is verified through comparative tests. The results show that the control strategy can make its torque pulsation reduced to less than 10%, the speed rise time is less than 0.1 s, and the relative error of the speed is less than 0.05%, which meets the index requirements of the spindle drive. This proves that the SRM torque pulsation can be reduced by the multi-algorithm fusion control strategy without increasing the hardware cost, which provides a useful reference for solving the SRM torque pulsation problem under the requirement of low cost. Show more
Keywords: Rapier loom, switched reluctance motor, torque distribution function, multi-algorithm fusion
DOI: 10.3233/JIFS-233138
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-25, 2024
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