Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Purchase individual online access for 1 year to this journal.
Price: EUR 315.00Impact Factor 2023: 2
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: 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: Arulmurugan, A. | Jose Moses, G. | Gandhi, Ongole | Sheshikala, M. | Arthie, A.
Article Type: Research Article
Abstract: In the current scenario, feature selection (FS) remains one of the very important functions in machine learning. Decreasing the feature set (FSt) assists in enhancing the classifier’s accuracy. Because of the existence of a huge quantity of data within the dataset (DS), it remains a colossal procedure for choosing the requisite features out of the DS. Hence, for resolving this issue, a new Chaos Quasi-Oppositional-based Flamingo Search Algorithm with Simulated Annealing Algorithm (CQOFSASAA) has been proffered for FS and for choosing the optimum FSt out of the DSs, and, hence, this lessens the DS’ dimension. The FSA technique can be …employed for selecting the optimal feature subset out of the DS. Generalized Ring Crossover has been as well embraced for selecting the very pertinent features out of the DS. Lastly, the Kernel Extreme Learning Machine (KELM) classifier authenticates the chosen features. This proffered paradigm’s execution has been tested by standard DSs and the results have been correlated with the rest of the paradigms. From the experimental results, it has been confirmed that this proffered CQOFSASAA attains 93.74% of accuracy, 92% of sensitivity, and 92.1% of specificity. Show more
Keywords: Quasi-oppositional, feature selection, Flamingo Search Algorithm, Simulated Annealing, convergence rate
DOI: 10.3233/JIFS-233557
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023
Authors: Harikumar, Yedhu | Muthumeenakshi, M.
Article Type: Research Article
Abstract: The Indian stock market is a dynamic, complicated system that is impacted by many different variables, making it difficult to anticipate its future. The utilization of deep learning and optimization techniques to forecast stock market movements has gained popularity in recent years. To foresee the Indian stock market, an innovative approach is presented in this study that combines the Grey Wolf Optimization algorithm with a hybrid Convolutional Neural Network (CNN) and Bi-Directional Long-Short Term Memory (Bi-LSTM) framework. The stock market information is first pre-processed utilizing a CNN to extract pertinent features. The Bi-LSTM system, that is intended to capture the …long-term dependencies and temporal correlations of the stock market statistics, is then fed the CNN’s outcome. The model parameters are then optimized utilizing the Grey Wolf Optimization (GWO) technique, which also increases forecasting accuracy. The findings demonstrate that, in terms of forecasting accuracy, the suggested method outperforms a number of contemporary methods, including conventional time series models, neural networks, and evolutionary algorithms. Thus, the suggested methodology provides an effective way to forecast the Indian stock market by combining deep learning and optimization approaches. Show more
Keywords: Indian stock market, grey wolf optimization, deep learning approach, bi-directional long-short term memory, convolutional neural network
DOI: 10.3233/JIFS-233716
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Vallabhaneni, Nagalakshmi | Prabhavathy, Panneer
Article Type: Research Article
Abstract: Numerous people are interested in learning yoga due to the increased tension levels in the modern lifestyle, and there are a variety of techniques or resources available. Yoga is practiced in yoga centers, by personal instructors, and through books, the Internet, recorded videos, etc. As the aforementioned resources may not always be available, a large number of people will opt for self-study in fast-paced lifestyles. Self-learning makes it impossible to recognize an incorrect posture. Incorrect poses will have a negative effect on the patient’s health, causing severe agony and long-term chronic issues. Computer vision (CV)-related techniques derive pose features and …conduct pose analysis using non-invasive CV methods. The application of machine learning (ML) and artificial intelligence (AI) techniques to an inter-disciplinary field like yoga becomes quite difficult. Due to its potent feature learning ability, deep learning (DL) has recently achieved an impressive level of performance in classifying yoga poses. In this paper, an artificial algae optimizer with hybrid deep learning-based yoga pose estimation (AAOHDL-YPE) model is presented. The presented AAOHDL-YPE model analyzes yoga video clips to estimate pose. Utilizing Part Confidence Map and Part Affinity Field with bipartite equivalent and parsing, OpenPose can be employed to determine the joint location. The deep belief network (DBN) model is then used for Yoga recognition. Finally, the AAO algorithm is utilized to enhance the EfficientNet model’s recognition performance. The results of a comprehensive experimentation analysis reveal that the AAOHDL-YPE technique produces superior results in comparison to existing methods. Show more
Keywords: Yoga posture, activity recognition, deep learning, metaheuristics, computer vision
DOI: 10.3233/JIFS-233583
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Sendhil, R. | Arulmurugan, A. | Jose Moses, G. | Kaviarasan, R. | Ramadoss, P.
Article Type: Research Article
Abstract: Occult peritoneal metastasis often emerges in sick persons having matured gastric cancer (GC) and is inexpertly detected with presently feasible instruments. Due to the existence of peritoneal metastasis that prevents the probability of healing crucial operation, there relies upon a discontented requirement for an initial diagnosis to accurately recognize sick persons having occult peritoneal metastasis. The proffered paradigm of this chapter identifies the initial phases of occult peritoneal metastasis in GC. The initial phase accompanies metabolomics for inspecting biomarkers. If the sick person undergoes the initial signs of occult peritoneal metastasis in GC, early detection is conducted. Yet, the physical …prognosis of this cancer cannot diagnose it, and so, automated detection of the images by dissecting the preoperational Computed Tomography (CT) images by conditional random fields accompanying Pro-DAE (Post-processing Denoising Autoencoders) and the labeling in the images is rid by denoising strainers; later, the ensued images and the segmented images experience the Graph Convolutional Networks (GCN), and the outcome feature graph information experience the enhanced categorizer (Greywold and Cuckoo Search Naïve Bayes categorizer) procedure that is employed for initial diagnosis of cancer. Diagnosis of cancer at the initial phase certainly lessens the matured phases of cancer. Hence, this medical information is gathered and treated for diagnosing the sickness. Show more
Keywords: Gastric Cancer, MIoT, Greywold and Cuckoo Search Naïve Bayes categorizer, Cuckoo-Grey Wolf search Correlative Naïve Bayes categorizer
DOI: 10.3233/JIFS-233510
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Priya, S. Baghavathi | Rani, P. Sheela | Chokkalingam, S.P. | Prathik, A. | Mohan, M. | Anitha, G. | Thangavel, M. | Suthir, S.
Article Type: Research Article
Abstract: Traditional testimony and electronic endorsements are extremely challenging to uphold and defend, and there is a problem with challenging authentication. The identity of the student is typically not recognized when it comes to requirements for access to a student’s academic credentials that are scattered over numerous sites. This is an issue with cross-domain authentication methods. On the one hand, whenever the volume of cross-domain authentication requests increases dramatically, the response time can become intolerable because of the slow throughput associated with blockchain mechanisms. These systems still do not give enough thought to the cross-domain scenario’s anonymity problem. This research proposes …an effective cross-domain authentication mechanism called XAutn that protects anonymity and integrates seamlessly through the present Certificate Transparency (CT) schemes. XAutn protects privacy and develops a fast response correctness evaluation method that is based on the RSA (Rivest, Shamir, and Adleman) cryptographic accumulator, Zero Knowledge Proof Algorithm, and Proof of Continuous work consensus Algorithm (POCW). We also provide a privacy-aware computation authentication approach to strengthen the integrity of the authentication messages more securely and counteract the discriminatory analysis of malevolent requests. This research is primarily used to validate identities in a blockchain network, which makes it possible to guarantee their authenticity and integrity while also increasing security and privacy. The proposed technique greatly outperformed the current methods in terms of authentication time, period required for storage, space for storage, and overall processing cost. The proposed method exhibits a speed gain of authentication of roughly 9% when compared to traditional blockchain systems. The security investigation and results from experiments demonstrate how the proposed approach is more reliable and trustworthy. Show more
Keywords: Zero Knowledge Proof, RSA accumulator, educational certificates, cross-domain authentication, blockchain
DOI: 10.3233/JIFS-235140
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2023
Authors: Lakshmi Narayanan, K. | Naresh, R.
Article Type: Research Article
Abstract: Vehicular Ad-Hoc Network (VANET) Technology is advancing due to the convergence of VANET and cloud computing technologies, Vehicular Ad-Hoc Network (VANET) entities can benefit from the cloud service provider’s favourable storage and computing capabilities. Cloud computing, the processing and storage capabilities provided by various cloud service providers, would be available to all VANET enterprises. Digital Twin helps in creating a digital view of the Vehicle. It focuses on the physical behaviour of the Vehicle as well as the software it alerts when it finds issues with the performance. The representation of the Vehicle is created using intelligent sensors, which are …in OBU of VANET that help collect info from the product. The author introduces the Cloud-based three-layer key management for VANET in this study. Because VANET connections can abruptly change, critical negotiation verification must be completed quickly and with minimal bandwidth. When the Vehicles are in movement, we confront the difficulty in timely methods, network stability, and routing concerns like reliability and scalability. We must additionally address issues such as fair network access, inappropriate behaviour identification, cancellation, the authentication process, confidentiality, and vehicle trustworthiness verification. The proposed All-Wheel Control (AWC) method in this study may improve the safety and efficiency of VANETs. This technology would also benefit future intelligent transportation systems. The Rivest–Shamir–Adleman (RSA) algorithm and Chinese Remainder Theorem algorithms generate keys at the group, subgroup, and node levels. The proposed method produces better results than the previous methods. Show more
Keywords: Cloud computing, VANET, RSA, CRT, AWC
DOI: 10.3233/JIFS-233527
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Deepak Raj, D.M. | Arulmurugan, A. | Shankar, G. | Arthi, A. | Panthagani, Vijaya Babu | Sandeep, C.H.
Article Type: Research Article
Abstract: The technique of determining the borders between several objects or regions in an image is known as edge detection. The edges of an object in an image serve as the object’s limits and can reveal crucial details about the object’s size, shape, and position. The pre-processing stage of edge detection is crucial because it can increase the precision and effectiveness of edge detection algorithms. As low-density or low-pixel values muddy the image, detecting edges in low-resolution images is difficult. This paper aims to introduce LRED, an improved edge detection model for low-resolution images based on Gaussian smoothing. Also used for …image pre-processing and smoothing is the Gaussian filter. The Gaussian smoothing method works well for spotting edges in images. Additionally, we have presented a comprehensive comparison of our proposed approach with three modern, cutting-edge detection approaches and algorithms. Investigations have been conducted on several images in addition to low-quality images to discover edges. RMSE and PSNR are two different evaluation metrics used to measure proposed methods. LRED achieved 90.25% MSE, which is slightly better than the other three approaches which show more reliable outcomes. Show more
Keywords: Edge detection, image pre-processing, image smoothing, low resolution image, metrics
DOI: 10.3233/JIFS-235332
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Niyasudeen, F. | Mohan, M.
Article Type: Research Article
Abstract: With the growing reliance on cloud computing, ensuring robust security and data protection has become a pressing concern. Traditional cryptographic methods face potential vulnerabilities in the post-quantum era, necessitating the development of advanced security frameworks. This paper presents a fuzzy-enhanced adaptive multi-layered cloud security framework that leverages artificial intelligence, quantum-resistant cryptography, and fuzzy systems to provide comprehensive protection in cloud environments. The proposed framework incorporates data encryption, access control, and intrusion detection mechanisms, with fuzzy logic systems augmenting the decision-making process for threat detection and response. The integration of artificial intelligence and quantum-resistant cryptographic techniques enhances the framework’s adaptability and …resilience against emerging threats. The implementation of fuzzy systems further improves the accuracy and efficiency of the security mechanisms, ensuring robust protection in the face of uncertainty and evolving attack vectors. The fuzzy-enhanced adaptive multi-layered cloud security framework offers a comprehensive, adaptable, and efficient solution for securing cloud infrastructures, safeguarding sensitive data, and mitigating the risks associated with the post-quantum era. Show more
Keywords: Cloud security, artificial intelligence, quantum-resistant cryptography, fuzzy systems, adaptive multi-layered framework
DOI: 10.3233/JIFS-233462
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023
Authors: Kandan, M. | Durai Murugan, A. | Ramu, Gandikota | Ramu, Gandikota | Gnanamurthy, R.K. | Bordoloi, Dibyahash | Rawat, Swati | Murugesan, | Prasad, Pulicherla Siva
Article Type: Research Article
Abstract: Privacy-Preserving Fuzzy Commitment Schemes (PPFCS) have emerged as a promising solution for secure Internet of Things (IoT) device authentication, addressing the critical need for privacy and security in the rapidly growing IoT ecosystem. This paper presents a novel PPFCS-based authentication mechanism that protects sensitive user data and ensures secure communication between IoT devices. The proposed scheme leverages error-correcting codes (ECC) and cryptographic hash functions to achieve reliable and efficient authentication. The PPFCS framework allows IoT devices to authenticate themselves without revealing their true identity, preventing unauthorized access and preserving users’ privacy. Furthermore, our PPFCS-based authentication mechanism is resilient against various …attacks, such as replay, man-in-the-middle, and brute-force attacks, by incorporating secure random nonce generation and timely key updates. We provide extensive experimental results and comparative analysis, demonstrating that the proposed PPFCS significantly outperforms existing authentication schemes in terms of security, privacy, and computational efficiency. As a result, the PPFCS offers a viable and effective solution for ensuring secure and privacy-preserving IoT device authentication, mitigating the risks associated with unauthorized access and potential data breaches in the IoT ecosystem. Show more
Keywords: Privacy-preserving, fuzzy commitment, IoT device authentication, error-correcting codes, cryptographic hash functions
DOI: 10.3233/JIFS-234100
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2023
Authors: Ganesh, Aurobind | Ramachandiran, R.
Article Type: Research Article
Abstract: Globally, the two main causes of young people dying are mental health issues and suicide. A mental health issue is a condition of physiological disorder that inhibits with the vital process of the brain. The amount of individuals with psychiatric illnesses has considerably increased during the past several years. The majority of individuals with mental disorders reside in India. The mental illness can have an impact on a person’s health, thoughts, behaviour, or feelings. The capacity of controlling one’s thoughts, emotions, and behaviour might help an individual to deal with challenging circumstances, build relationships with others, and navigate life’s problems. …With a primary focus on the healthcare domain and human-computer interaction, the capacity to recognize human emotions via physiological and facial expressions opens up important research ideas as well as application-oriented potential. Affective computing has recently become one of the areas of study that has received the greatest interest from professionals and academics in a variety of sectors. Nevertheless, despite the rise in articles published, the reviews of a particular aspect of affective computing in mental health still are limited and have certain inadequacies. As a result, a literature survey on the use of affective computing in India to make decisions about mental health issues is discussed. As a result, the paper focuses on how traditional techniques used to monitor and assess physiological data from humans by utilizing deep learning and machine learning approaches for humans’ affect recognition (AR) using Affective computing (AfC) which is a combination of computer science, AI, and cognitive science subjects (such as psychology and psychosocial). Show more
Keywords: Affective computing, mental Health, decision making, machine learning, deep learning
DOI: 10.3233/JIFS-235503
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023
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: 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: Sharma, Amit | Naga Raju, M. | Hema, P. | Chaitanuya, Morsa | Jagannatha Reddy, M.V. | Vignesh, T. | Chandanan, Amit Kumar | Verma, Santhosh
Article Type: Research Article
Abstract: Wireless Sensor Networks (WSNs) have gained significant attention in recent years due to their wide range of applications, such as environmental monitoring, smart agriculture, and structural health monitoring. With the increasing volume of data generated by WSNs, efficient data analytics techniques are crucial for improving the overall performance and reducing energy consumption. This paper presents a novel distributed data analytics approach for WSNs using fuzzy logic-based machine learning. The proposed method combines the advantages of fuzzy logic for handling uncertainty and imprecision with the adaptability of machine learning techniques. It enables sensor nodes to process and analyze data locally, reducing …the need for data transmission and consequently saving energy. Furthermore, this approach enhances data accuracy and fault tolerance by incorporating the fusion of heterogeneous sensor data. The proposed technique is evaluated on a series of real-world and synthetic datasets, demonstrating its effectiveness in improving the overall network performance, energy efficiency, and fault tolerance. The results indicate the potential of our approach to be applied in various WSN applications that demand low-energy consumption and reliable data analysis. Show more
Keywords: Wireless sensor networks, distributed data analytics, fuzzy logic, machine learning, energy efficiency
DOI: 10.3233/JIFS-234007
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Kumar, Manoj | Sharma, Sukhwinder | Mittal, Puneet | Singh, Harmandeep | Singh, Sukhwinder
Article Type: Research Article
Abstract: The rapid expansion of Internet of Things (IoT) applications and the increasing complexity of Wireless Sensor Networks (WSNs) have created a critical need for efficient load balancing strategies. This paper proposes a dynamic load balancing approach for IoT-enabled WSNs using a fuzzy logic-based control mechanism. The proposed method aims to optimize energy consumption, reduce latency, and enhance network lifetime by intelligently distributing the workload among sensor nodes. The fuzzy logic controller takes into account various parameters, such as energy levels, communication distances, and node density, to make adaptive load balancing decisions. The control mechanism allocates tasks to the most suitable …nodes, ensuring efficient utilization of resources and preventing overloading of individual nodes. Simulations are conducted in diverse network scenarios to validate the performance of the proposed approach. Results demonstrate significant improvements in energy efficiency, latency reduction, and overall network lifetime compared to traditional load balancing techniques. The fuzzy logic-based control mechanism proves to be a promising solution for addressing the dynamic and resource-constrained nature of IoT-enabled WSNs, paving the way for more robust and resilient networks in various IoT applications. Show more
Keywords: IoT, Wireless Sensor Networks, load balancing, fuzzy logic, network lifetime
DOI: 10.3233/JIFS-234075
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
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: Sitharamulu, V. | Mahammad Rafi, D. | Naulegari, Janardhan | Battu, Hanumantha Rao
Article Type: Research Article
Abstract: In this study, we investigate the viability of applying fuzzy reinforcement learning (FRL) to Internet of Things-based robots for purposes of autonomous navigation and collision avoidance. The proposed approach utilises FRL, IoT, and a sensor network to give the robot the ability to learn from its environment and act in accordance with those policies. The authors used FRL to train a mobile robot with wheels to move around and avoid obstacles, and then they put the robot through its paces in a virtual world. Results showed that the FRL-based technique improved the robot’s navigation and collision avoidance performance compared to …traditional rule-based approaches. The results of this study indicate that FRL may be a viable technique for enabling autonomous navigation and obstacle avoidance in IoT-based robotics. Show more
Keywords: Fuzzy reinforcement learning, IoT-based robotics, autonomous navigation, collision avoidance, sensor network
DOI: 10.3233/JIFS-233860
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Sivaranjani, N. | Senthil Ragavan, V.K. | Jawaherlalnehru, G.
Article Type: Research Article
Abstract: Industry experts are motivated to collect, collate, and analyse historical data in the legal sector in attempt to predict court case outcomes as the amount of historical data available in this field has increased over time. But using judicial data to predict and defend court judgements is no simple undertaking. Using Machine Learning (ML) models and traditional approaches for categorical feature encoding, previous research on predicting court outcomes using limited experimental datasets produced a number of unexpected predictions. The paper proposes an ensemble model combining Convolutional Neural Network (CNN), attention mechanism and eXtreme Gradient Boosting (XGB) algorithm. This model is …primarily based on a self-attention network, which could simultaneously capture linguistic relationships over lengthy sequences like RNN (Recurrent Neural Network) and is nevertheless speedy to train like CNN. C-XGB can obtain accuracy that surpasses the state-of-art model on numerous classification/prediction tasks simultaneously as being twice as speedy to train. The proposed C-XGB model is designed to process the documents hierarchically and calculates the attention weights. Two convolutional layers are used to calculate the attention weights, one at the word level and another at the sentence level. And finally, at the last layer, the XGB algorithm predicts the input case file’s outcome. The experimental results shows that the proposed model outperforms the existing model with 4.67% improvement in accuracy value. Show more
Keywords: Neural Networks, machine learning, legal judgment prediction, Indian Supreme Court
DOI: 10.3233/JIFS-235936
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Sugin Lal, G. | Porkodi, R.
Article Type: Research Article
Abstract: The term “educational data mining” refers to a field of study where information from academic environments is predicted using data mining, machine learning, and statistics. Education is the act of giving or receiving knowledge to or from someone who is formally studying and developing a natural talent. Over time, scholars have used data mining techniques to uncover hidden information in educational statistics and other external elements. This study suggests a unique method for analysing academic student performance that is based on data mining and machine learning. Here, the input is gathered as a dataset of student academic performance and is …processed for normalisation and noise reduction. Then, using the Boltzmann deep learning model coupled with linear kernel principal component analysis, this data’s characteristics were retrieved and chosen. Based on weights, information gain, and the Gini index, the characteristics are assessed and optimised. Following the selection of the pertinent data, conditional random field-based probabilistic clustering model is performed using RNN-based training, and the academic performance of the students is then examined using voting classifiers and sparse features. Experimental results are carried out for students academic performance dataset based on subjects in terms of training accuracy, validation accuracy, mean average precision, mean square error and correlation evaluation. Proposed technique attained accuracy of 96%, precision of 95%, Correlation Evaluation of 92% . Show more
Keywords: Student performance analysis, data mining, machine learning, clustering model, academic performance
DOI: 10.3233/JIFS-235350
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Bala, B. Kiran | Sekhar, J.C. | Al Ansari, Mohammed Saleh | Rao, Vuda Sreenivasa
Article Type: Research Article
Abstract: A plant disease that attacks the leaf causes significant yield and market value losses. A professional plant pathologist should be able to visually identify the disease by looking at the affected plant leaves, but this is unlikely to result in a more accurate diagnosis. Disease symptoms should be immediately recognisable in order to stop the spread of the illness. To find plant diseases, steps should be taken using computer assisted technologies. Numerous methods for identifying plant diseases using machine learning (ML) and deep learning (DL) have been developed and tested in numerous studies. Machine learning has the disadvantages of having …a small dataset, taking longer, and requiring more time for results interpretation. Deep learning is suggested as a solution to this. This study compares the effectiveness of both ML&DL for plant leaf disease identification with more recent investigations. The common deep learning technique involves utilising the Krill Herd Optimisation Algorithm (KHO) to segment images and the Speeded up Robust Features (SURF) to extract the images. The Artificial Bee Colony (ABC) then chooses the features. Then, a Deep Belief Network (DBN) can be used to classify the chosen image. Multiple diseases can be identified on the same leaf using this method. This study demonstrates that deep learning outperforms machine learning in terms of results. The outcome demonstrates that the deep learning method is superior for the diagnosis of plant disease when there is sufficient data available. Using this technique, the validity and consistency were also examined. Show more
Keywords: Krill herd algorithm, artificial bee colony, deep learning, SURF, machine learning, DBN
DOI: 10.3233/JIFS-234864
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Mohan, M. | Tamizhazhagan, V. | Balaji, S.
Article Type: Research Article
Abstract: Cloud computing is a new technology that provides services to customers anywhere, anytime, under varying conditions and managed by a third-party cloud provider. Even though cloud computing has progressed a lot, some attacks still happen. The recent anomalous and signature attacks use clever strategies such as low-rate attacks and attacking as an authenticated user. In this paper, a novel Attack Detection and Prevention (ADAPT) method is proposed to overcome this issue. The proposed system consists of three stages. An Intrusion Detection System is initially used to check whether there is an attack or not by comparing the IP address in …the Blacklist IP Database. If an attack occurs, the IP address will be added to the Blacklist IP database and blocked. The second stage uses Bi-directional LSTM and Bi-directional GRU to check the anomalous and signature attack. In the third stage, classified output is sent to reinforcement learning, if any attack occurs the IP address is added to the blacklist IP database otherwise the packets are forwarded to the user. The proposed ADAPT technique achieves a higher accuracy range than existing techniques. Show more
Keywords: Cloud computing, Bi-directional LSTM, Bi-directional GRU, IP address, and reinforcement learning
DOI: 10.3233/JIFS-236371
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Yu, Xingping | Yang, Yang
Article Type: Research Article
Abstract: The rapid advancement of communication and information technology has led to the expansion and blossoming of digital music. Recently, music feature extraction and classification have emerged as a research hotspot due to the difficulty of quickly and accurately retrieving the music that consumers are looking for from a large volume of music repositories. Traditional approaches to music classification rely heavily on a wide variety of synthetically produced aural features. In this research, we propose a novel approach to selecting the musical genre from user playlists by using a classification and feature selection machine learning model. To filter, normalise, and eliminate …missing variables, we collect information on the playlist’s music genre and user history. The characteristics of this data are then selected using a convolutional belief transfer Gaussian model (CBTG) and a fuzzy recurrent adversarial encoder neural network (FRAENN). The experimental examination of a number of music genre selection datasets includes measures of training accuracy, mean average precision, F-1 score, root mean squared error (RMSE), and area under the curve (AUC). Results show that this model can both create a respectable classification result and extract valuable feature representation of songs using a wide variety of criteria. Show more
Keywords: Music genre selection, user playlists, machine learning, classification, feature selection
DOI: 10.3233/JIFS-235478
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Prabu Sankar, N. | Usha, D.
Article Type: Research Article
Abstract: This research paper presents a novel approach to improving healthcare services in rural areas by leveraging the potential of Fuzzy Intelligence Systems, Internet of Bodies (IoB) devices, and Blockchain technology. It begins by exploring the design and development of a Blockchain-based Patients Record System (BPRS), which ensures secure, transparent, and tamper-proof storage of patient medical records. The paper then delves into the fabrication of advanced IoB devices, specifically designed to study and monitor the health of rural populations. These devices, integrated with Fuzzy Intelligence Systems, provide efficient and reliable data capture, interpretation, and decision-making support. The highlight of the study …is the innovative integration of the IoB enabled Patient Monitoring System with the BPRS, which ensures real-time data synchronization and secure access to patient data for authorized personnel. The system collectively promotes efficient healthcare delivery, data privacy, and patient safety in rural areas. Show more
Keywords: Fuzzy intelligence systems, blockchain-based patients record system, internet of bodies devices, rural health monitoring, integrated healthcare system
DOI: 10.3233/JIFS-233752
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Kexing, Zhang | Jiang, He
Article Type: Research Article
Abstract: Recent developments in wireless networking, big data technologies including 5G networks, healthcare big data analytics, the Internet of Things (IoT), sophisticated wearable technologies, and artificial intelligence (AI) have made it possible to design intelligent illness diagnostic models. In addition to its critical function in e-health applications, 5G-IoT is becoming a standard feature of intelligent software. Intelligent systems and architectures are necessary for e-health applications to counteract threats to the privacy of patients’ medical information. Using machine learning and IoMT, this research suggests a new approach to cloud data analysis using the 5G network in the context of a recommendation model. …This application of the 5G cloud network to the monitoring and analysis of healthcare data makes use of variational adversarial transfer convolutional neural networks. The treatment plan for abnormalities in a tolerant body is derived from this clustered outcome. Experiment analysis was performed for a number of healthcare datasets with respect to training precision, network efficiency, F-1 score, root-mean-squared error, and mean average precision as the metrics of interest. Show more
Keywords: 5G network, cloud data analysis, recommendation model, machine learning, internet of medical things (IoMT)
DOI: 10.3233/JIFS-235064
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Rao, Bommaraju Srinivasa | Banerjee, Kakoli | Anand Deva Durai, C. | Balu, S. | Sahoo, Ashok Kumar | Priyadharshini, A. | Rama Krishna, Paladugu | Kakade, Revannath Babanrao
Article Type: Research Article
Abstract: In recent years, the Internet of Things (IoT) has rapidly emerged as an essential technology, enabling seamless communication between billions of interconnected devices. These devices generate a massive amount of data that requires efficient management to ensure optimum performance in IoT environments. Dynamic load balancing (DLB) is a crucial technique employed to distribute workloads evenly across multiple computing resources, thereby reducing latency and increasing the overall efficiency of IoT networks. This paper presents a novel DLB approach based on type-2 fuzzy logic systems (T2FLS) to enhance the performance and reliability of IoT environments. The proposed T2FLS-based DLB technique addresses the …inherent uncertainties and imprecisions in IoT networks by considering various parameters, such as workload, processing capability, and communication latency. A comprehensive performance evaluation is carried out to compare the proposed method with traditional DLB approaches. Simulation results demonstrate that the T2FLS-based DLB technique significantly improves the network’s response time, throughput, and energy efficiency, while also providing better adaptability and robustness to dynamic changes in IoT environments. This study contributes to the advancement of DLB techniques in IoT networks and lays the groundwork for further research in this field. Show more
Keywords: Dynamic load balancing, internet of things, type-2 fuzzy logic systems, performance evaluation, energy efficiency
DOI: 10.3233/JIFS-234105
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Du, Xueke | Li, Wenli | Wei, Xiaowen
Article Type: Research Article
Abstract: The fees of different certification services are charged in different ways: For example, T-mall.com (one of the leading e-commerce platforms in China) uses a total certification service , where each type of seller participating in the platform must purchase certification services; Pinduoduo.com (another Chinese e-commerce platform) uses an alternative certification service , where after paying a transaction fee, each seller participating in the platform can choose whether to purchase certification services. This paper studies how the choice of certification services affects the participation decisions of both sellers and buyers, as well as the revenue and quality level (the proportion of …high-quality sellers of all participating sellers) of a platform. According to previous research, network externalities also affect sellers’ and buyers’ participation strategies. Studies on the effectiveness of different certification services for e-commerce platforms have rarely considered both positive and negative network externalities. The results of constructed game-theoretic models show that both the certification capability and the certification cost play critical roles in determining which certification services can generate more revenue. If a platform provides certification services, the total certification service always generates a higher quality level than the alternative certification service. Furthermore, the applicable scope of certification services (defined as the certification strategy space), can be broadened by increasing both the profit ratio (the ratio between the profit of H-type sellers and L-type sellers) and the value ratio (the ratio between the value of H-type sellers and L-type sellers). Counterintuitively, a higher certification capability does not always yield a higher certification fee. Show more
Keywords: Certification services, E-commerce platforms, information asymmetries, network externalities, certification capability
DOI: 10.3233/JIFS-234621
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Wang, Hanpeng | Xiong, Hengen
Article Type: Research Article
Abstract: An improved genetic algorithm is proposed for the Job Shop Scheduling Problem with Minimum Total Weight Tardiness (JSSP/TWT). In the proposed improved genetic algorithm, a decoding method based on the Minimum Local Tardiness (MLT) rule of the job is proposed by using the commonly used chromosome coding method of job numbering, and a chromosome recombination operator based on the decoding of the MLT rule is added to the basic genetic algorithm flow. As a way to enhance the quality of the initialized population, a non-delay scheduling combined with heuristic rules for population initialization. and a PiMX (Precedence in Machine crossover) …crossover operator based on the priority of processing on the machine is designed. Comparison experiments of simulation scheduling under different algorithm configurations are conducted for randomly generated larger scale JSSP/TWT. Statistical analysis of the experimental evidence indicates that the genetic algorithm based on the above three improvements exhibits significantly superior performance for JSSP/TWT solving: faster convergence and better scheduling solutions can be obtained. Show more
Keywords: Improved genetic algorithm, total weight tardiness, minimum local tardiness, PiMX
DOI: 10.3233/JIFS-236712
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Vaikunta Pai, T. | Singh, Manmohan | Shaik, Nazeer | Ashokkumar, C. | Anuradha, D. | Gangopadhyay, Amit | Rao, Goda Srinivasa | Reddy, T.Sunilkumar | Nagaraju, D.
Article Type: Research Article
Abstract: As the demand for energy in India continues to surge, accurate forecasting becomes paramount for efficient resource allocation and sustainable development. This study proposes an innovative approach to forecasting Indian primary energy demand by integrating Artificial Intelligence (AI) techniques with Fuzzy Auto-regressive Distributed Lag (FADL) models. FADL models, incorporating fuzzy logic, allow for a nuanced representation of uncertainties and complexities within the energy demand dynamics. In this research, historical energy consumption data is analysed using FADL models with both symmetric and non-symmetric triangular coefficients, enhancing the model’s adaptability to the inherent uncertainties associated with energy forecasting. This study addresses the …urgent need for enhanced energy planning models in the context of sustainable development. Our research aims to provide a comprehensive framework for predicting future Total Final Consumption (TFC) in alignment with the Indian National Energy Plan’s net-zero emissions target by 2035. Recognizing the limitations of current models, our research introduces a novel approach that integrates advanced algorithms and methodologies, offering a more flexible and realistic assessment of TFC trends. The primary objective of this study is to develop an improved energy planning model that surpasses existing projections by incorporating sophisticated algorithms. We aim to refine Show more
Keywords: Auto-regressive, distributed lag, energy consumption, forecast, triangular coefficient
DOI: 10.3233/JIFS-240729
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Ma, Chengfei | Yang, Xiaolei | Lu, Heng | He, Siyuan | Liu, Yongshan
Article Type: Research Article
Abstract: When calculating participants’ contribution to federated learning, addressing issues such as the inability to collect complete test data and the impact of malicious and dishonest participants on the global model is necessary. This article proposes a federated aggregation method based on cosine similarity approximation Shapley value method contribution degree. Firstly, a participant contribution calculation model combining cosine similarity and the approximate Shapley value method was designed to obtain the contribution values of the participants. Then, based on the calculation model of participant contribution, a federated aggregation algorithm is proposed, and the aggregation weights of each participant in the federated aggregation …process are calculated by their contribution values. Finally, the gradient parameters of the global model were determined and propagated to all participants to update the local model. Experiments were conducted under different privacy protection parameters, data noise parameters, and the proportion of malicious participants. The results showed that the accuracy of the algorithm model can be maintained at 90% and 65% on the MNIST and CIFAR-10 datasets, respectively. This method can reasonably and accurately calculate the contribution of participants without a complete test dataset, reducing computational costs to a certain extent and can resist the influence of the aforementioned participants. Show more
Keywords: Federated aggregation algorithm, contribution assessment, cosine similarity, Shapley value, equitable distribution
DOI: 10.3233/JIFS-236977
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Pandey, Sakshi Dev | Ranadive, A.S. | Samanta, Sovan | Dubey, Vivek Kumar
Article Type: Research Article
Abstract: Several methodologies have been proposed in the literature of graph theory for depicting collaboration among entities. However, in these studies, the measure of collaboration is taken based on the crisp graphical properties and discusses only its positive effects. In this manuscript, we discuss the simultaneous collaboration and competition that are observed among individuals, organizations, countries, communities and many others. The notion of bipolar fuzzy bunch graph (BFBG) is introduced in this study to effectively capture the positive and negative effects of both the terms collaboration and competition, which is jointly called coopetition. The goal of this paper is to introduce …an improved representation and analytical measure for coopetition. To further enrich the literature on competition graphs, the notion of survival and winning competition among species has been introduced and also provides its bipolar fuzzy competition degrees. We also introduce two types of coopetition measures to understand the ranking structure of entities (i.e. which node batter collaborates and competes with other nodes) in the network: a) bipolar fuzzy coopetition degree and b) bipolar fuzzy coopatition index. In the form of a bipolar fuzzy coopetition graph, we find evidence to validate our framework and computations. We gathered research articles on COVID-19 and their citations over a specific time period from a specific journal. To demonstrate our approach, we displayed bipolar fuzzy collaboration and competition of various countries on COVID-19 and classified their rankings based on their positive and negative coopetition indices. Show more
Keywords: Bipolar fuzzy bunch degree, communication potential effect (CPE), bipolar fuzzy mixed graph, winning and survival competition, coopetition degree, coopetition index
DOI: 10.3233/JIFS-234061
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Rachamadugu, Sandeep Kumar | Pushphavathi, T.P.
Article Type: Research Article
Abstract: This paper introduces an innovative approach, the LS-SLM (Local Search with Smart Local Moving) technique, for enhancing the efficiency of article recommendation systems based on community detection and topic modeling. The methodology undergoes rigorous evaluation using a comprehensive dataset extracted from the “dblp. v12.json” citation network. Experimental results presented herein provide a clear depiction of the superior performance of the LS-SLM technique when compared to established algorithms, namely the Louvain Algorithm (LA), Stochastic Block Model (SBM), Fast Greedy Algorithm (FGA), and Smart Local Moving (SLM). The evaluation metrics include accuracy, precision, specificity, recall, F-Score, modularity, Normalized Mutual Information (NMI), betweenness …centrality (BTC), and community detection time. Notably, the LS-SLM technique outperforms existing solutions across all metrics. For instance, the proposed methodology achieves an accuracy of 96.32%, surpassing LA by 16% and demonstrating a 10.6% improvement over SBM. Precision, a critical measure of relevance, stands at 96.32%, showcasing a significant advancement over GCR-GAN (61.7%) and CR-HBNE (45.9%). Additionally, sensitivity analysis reveals that the LS-SLM technique achieves the highest sensitivity value of 96.5487%, outperforming LA by 14.2%. The LS-SLM also demonstrates superior specificity and recall, with values of 96.5478% and 96.5487%, respectively. The modularity performance is exceptional, with LS-SLM obtaining 95.6119%, significantly outpacing SLM, FGA, SBM, and LA. Furthermore, the LS-SLM technique excels in community detection time, completing the process in 38,652 ms, showcasing efficiency gains over existing techniques. The BTC analysis indicates that LS-SLM achieves a value of 94.6650%, demonstrating its proficiency in controlling information flow within the network. Show more
Keywords: Recommender Systems (RS), BagofWords (BoW), Pearson Correlation Co-efficient based Latent Dirichlet Allocation (PCC-LDA), Linear Scaling based Smart Local Moving (LS-SLM), Time Frequency and Inverse Document Frequency (TF-IDF), Community detection
DOI: 10.3233/JIFS-233851
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Lalitha, V. | Latha, B.
Article Type: Research Article
Abstract: The most valuable information of Hyperspectral Image (HSI) should be processed properly. Using dimensionality reduction techniques in two distinct approaches, we created a structure for HSI to collect physiological and diagnostic information. The tissue Oxygen Saturation Level (StO2 ) was extracted using the HSI approach as a physiological characteristic for stress detection. Our research findings suggest that this unique characteristic may not be affected by humidity or temperature in the environment. Comparing the standard StO2 reference and pressure concentrations, the social stress assessments showed a substantial variance and considerable practical differentiation. The proposed system has already been evaluated on …tumor images from rats with head and neck cancers using a spectrum from 450 to 900 nm wavelength. The Fourier transformation was developed to improve precision, and normalize the brightness and mean spectrum components. The analysis of results showed that in a difficult situation where awareness could be inexpensive due to feature possibilities for rapid classification tasks and significant in measuring the structure of HSI analysis for cancer detection throughout the surgical resection of wildlife. Our proposed model improves performance measures such as reliability at 89.62% and accuracy at 95.26% when compared with existing systems. Show more
Keywords: Hyperspectral Image, dimensionality reduction, stress tests, cancer detection, fourier coefficients
DOI: 10.3233/JIFS-236935
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Fan, Jianping | Chai, Mingxuan | Wu, Meiqin
Article Type: Research Article
Abstract: In this manuscript, we construct a Multi-Criteria Decision-Making (MCDM) model to study the new energy vehicle (NEV) battery supplier selection problem. Firstly, we select criteria to build an evaluation index system. Secondly, SAWARA and MEREC methods are used to calculate subjective and objective weights in the ranking process, respectively, and PTIHFS (Probabilistic Triangular Intuitionistic Hesitant Fuzzy Set) is employed to describe the decision maker’s accurate preferences in performing the calculation of subjective weights. Then, the game theory is used to find the satisfactory weights. We use TFNs to describe the original information in the MARCOS method to obtain the optimal …alternative. Finally, a correlation calculation using Spearman coefficients is carried out to compare with existing methods and prove the model’s validity. Show more
Keywords: PTIHFS, SWARA, MEREC, MARCOS, game theory
DOI: 10.3233/JIFS-231975
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Devi, Salam Jayachitra | Doley, Juwar | Gupta, Vivek Kumar
Article Type: Research Article
Abstract: Object detection has made significant strides in recent years, but it remains a challenging task to accurately and quickly identify and detect objects. While humans can easily recognize objects in images or videos regardless of their appearance, computers face difficulties in this task. Object detection plays a crucial role in computer vision and finds applications in various domains such as healthcare, security, agriculture, home automation and more. To address the challenges of object detection, several techniques have been developed including RCNN, Faster RCNN, YOLO and Single Shot Detector (SSD). In this paper, we propose a modified YOLOv5s architecture that aims …to improve detection performance. Our modified architecture incorporates the C3Ghost module along with the SPP and SPPF modules in the YOLOv5s backbone network. We also utilize the Adam and Stochastic Gradient Descent (SGD) optimizers. The paper also provides an overview of three major versions of the YOLO object detection model: YOLOv3, YOLOv4 and YOLOv5. We discussed their respective performance analyses. For our evaluation, we collected a database of pig images from the ICAR-National Research Centre on Pig farm. We assessed the performance using four metrics such as Precision (P), Recall (R), F1-score and mAP @ 0.50. The computational results demonstrate that our method YOLOv5s architecture achieves a 0.0414 higher mAP while utilizing less memory space compared to the original YOLOv5s architecture. This research contributes to the advancement of object detection techniques and showcases the potential of our modified YOLOv5s architecture for improved performance in real world applications. Show more
Keywords: Object detection, YOLO, convolutional neural networks, pig, and computer vision
DOI: 10.3233/JIFS-231032
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Duan, Wenbiao | Yang, Mingjin | Sun, Weiliang | Xia, Mingmin | Zhu, Hui | Gu, Chijiang | Zhang, Haiqiang
Article Type: Research Article
Abstract: OBJECTIVE: A comprehensive evaluation of studies using DNA microarray datasets for screening and identifying key genes in gastric cancer is the goal of this systematic review and meta-analysis. To better understand the molecular environment associated with stomach cancer, this study aims to provide a quantitative synthesis of findings. PURPOSE: Using DNA microarray databases in a systematic manner, this study aims to analyze gastric cancer (GC) screening and gene identification efforts. Through a literature review spanning 2002–2022, this research aims to identify key genes associated with GC and develop strategies for screening and prognosis based on these …findings. METHODS: The following databases were searched extensively: Science Direct, NCKI, Web of Science, Springer, and PubMed. Fifteen studies met the inclusion and exclusion criteria; 10,134 tissues served as controls and 11,724 as GCs. The levels of critical genes, including COL1A1, COL1A2, THBS2, SPP1, SPARC, COL6A3, and COL3A1, were compared in normal and GC tissues. Rev Man 5.3 was used to do the meta-analysis. While applying models with fixed or random effects, 95% confidence intervals and weighted mean differences were computed. RESULTS According to the meta-analysis, GC tissues exhibited substantially elevated levels of important genes when contrasted with the control group. In particular, there were statistically significant increases in COL1A1 (MD = 2.43, 95% CI: 1.84–3.02), COL1A2 (MD = 2.75, 95% CI: 1.09–4.41), THBS2 (MD = 2.54, 95% CI: 1.66–3.41), SPP1 (MD = 3.64, 95% CI: 3.40–3.88), SPARC (MD = 1.57, 95% CI: 0.37–2.77), COL6A3 (MD = 2.31, 95% CI: 2.02–2.60), and COL3A1 (MD = 2.21, 95% CI: 1.59–2.82). CONCLUSIONS: The COL1A1, THBS2, SPP1, COL6A3, and COL3A1 genes were shown to have potential use in germ cell cancer screening and prognosis, according to this research. Clinical assessment and prognosis of heart failure patients may be theoretically supported by the results of this study. Show more
Keywords: DNA microarray database, gastric cancer, key genes, meta-analysis
DOI: 10.3233/JIFS-236416
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Li, Tao | Zhang, Zhongyu | Tao, Zhigang | Jia, Xinyu | Wang, Xiaolong | Wang, Jian
Article Type: Research Article
Abstract: Rock crack is one of the main factors responsible for rock failure. Uniaxial compression creep tests are performed using acoustic emission techniques, a high-sensitivity, non-radiative, non-destructive testing method to understand the influence of crack number on the precursor characteristics of short-term creep damage in the fractured rock mass. Based on the Grassberger-Procaccia (G-P) algorithm, the calculation step size for the correlation dimension value (D 2 ) of the acoustic emission ringing count rate is consistent with that for the acoustic emission b -value. The influence of the number of pre-cracks on the Acoustic emission precursor characteristics of red sandstone …creep is analyzed. The results show that near the destabilization of the specimen, the Acoustic emission accumulative ringing count surges in a stepwise manner, the Acoustic emission b -value decreases, the D 2 -value increases, the Acoustic emission amplitude shows high intensity and high frequency, and the ringing count increases sharply, all with the characteristics of failure precursors. During the accelerated creep stage of the specimens, with the increase of pre-cracks number, the precursory time points of acoustic emission b -value and D 2 -value advance, and their acoustic emission ringing counts increase sharply. Show more
Keywords: Acoustic emission, b-value, correlation dimension value (D2), precursor information, pre-cracks
DOI: 10.3233/JIFS-238964
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Hou, Xiaoyu | Luo, Chao | Gao, Baozhong
Article Type: Research Article
Abstract: Candlesticks are widely used as an effective technical analysis tool in financial markets. Traditionally, different combinations of candlesticks have formed specific bullish/bearish patterns providing investors with increased opportunities for profitable trades. However, most patterns derived from subjective expertise without quantitative analysis. In this article, combining bullish/bearish patterns with ensemble learning, we present an intelligent system for making stock trading decisions. The Ensemble Classifier through Multimodal Perturbation (ECMP) is designed to generate a diverse set of precise base classifiers to further determine the candlestick patterns. It achieves this by: first, introducing perturbations to the sample space through bootstrap sampling; second, employing …an attribute reduction algorithm based on neighborhood rough set theory to select relevant features; third, perturbing the feature space through random subspace selection. Ultimately, the trading decisions are guided by the classification outcomes of this procedure. To evaluate the proposed model, we apply it to empirical investigations within the context of the Chinese stock market. The results obtained from our experiments clearly demonstrate the effectiveness of the approach. Show more
Keywords: Trading system, ensemble learning, multimodal perturbation method, neighborhood rough set theory
DOI: 10.3233/JIFS-237087
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Zhao, Bin | Cao, Wei | Zhang, Jiqun | Gao, Yilong | Li, Bin | Chen, Fengmei
Article Type: Research Article
Abstract: Aiming at the issue that the current click-through rate prediction methods ignore the varying impacts of different input features on prediction accuracy and exhibit low accuracy when dealing with large-scale data, a click-through rate prediction method (GBIFM) which combines Gradient Boosting Decision Tree (GBDT) and Input-aware Factorization Machine (IFM) is proposed in this paper. The proposed GBIFM method employs GBDT for data processing, which can flexibly handle various types of data without the need for one-hot encoding of discrete features. An Input-aware strategy is introduced to refine the weight vector and embedding vector of each feature for different instances, adaptively …learning the impact of each input vector on feature representation. Furthermore, a fully connected network is incorporated to capture high-order features in a non-linear manner, enhancing the method’s ability to express and generalize complex structured data. A comprehensive experiment is conducted on the Criteo and Avazu datasets, the results show that compared to typical methods such as DeepFM, AFM, and IFM, the proposed method GBIFM can increase the AUC value by 10% –12% and decrease the Logloss value by 6% –20%, effectively improving the accuracy of click-through rate prediction. Show more
Keywords: Click-through rate estimation, GBIFM, GBDT, IFM
DOI: 10.3233/JIFS-234713
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Wang, Shuo | Yang, Jing | Yang, Yue
Article Type: Research Article
Abstract: Personalized recommendation systems fundamentally assess user preferences as a reflection of their emotional responses to items. Traditional recommendation algorithms, focusing primarily on numerical processing, often overlook emotional factors, leading to reduced accuracy and limited application scenarios. This paper introduces a collaborative filtering recommendation method that integrates features of facial information, derived from emotions extracted from such data. Upon user authorization for camera usage, the system captures facial information features. Owing to the diversity in facial information, deep learning methods classify these features, employing the classification results as emotional labels. This approach calculates the similarity between emotional and item labels, reducing …the ambiguity inherent in facial information features. The fusion process of facial information takes into account the user’s emotional state prior to item interaction, which might influence the emotions generated during the interaction. Variance is utilized to measure emotional fluctuations, thereby circumventing misjudgments caused by sustained non-interactive emotions. In selecting the nearest neighboring users, the method considers not only the similarity in user ratings but also in their emotional responses. Tests conducted using the Movielens dataset reveal that the proposed method, modeling facial features, more effectively aligns recommendations with user preferences and significantly enhances the algorithm’s performance. Show more
Keywords: Collaborative filtering algorithm, facial information features, emotional factors, non-interactive emotion
DOI: 10.3233/JIFS-232718
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Zhai, Shanshan | Fan, Jianping | Liu, Lin
Article Type: Research Article
Abstract: Neutrosophic cubic set (NCS) can process complex information by choosing both interval value and single value membership and indeterminacy and falsehood components. The aggregation operators based on Aczel-Alsina t-norm and t-corm are quite effective for evaluating the interrelationship among attributes. The purpose of this paper is to diagnose the interrelationship among attributes with neutrosophic cubic information, and propose a multi-attribute decision-making(MADM) method for supplier selection problem with unknown weight under neutrosophic cubic environment. We defined neutrosophic cubic Aczel-Alsina (NC-AA) operator and neutrosophic cubic Aczel–Alsina weighted arithmetic average (NCAAWAA) operator, then we discussed various important results and some properties of the …proposed operators. Additionally, we proposed a MADM method under the presence of the NC-AAWAA operator. When the weights of attributes are unknown, we use the MEREC method to determine the weights. Later, the NC-AAWAA operator and MEREC method are applied to address the supplier selection problem. Finally, a sensitivity analysis and a comparative analysis are conducted to illustrate the stability and superiority of the proposed method. The results show the NC-AAWAA operator can handle the interrelationship among complex information more effectively, and MEREC method can weight the attributes based on the removal effect of a neutrosophic cubic attribute. Show more
Keywords: Multi-attribute decision-making (MADM), neutrosophic cubic set (NCS), Aczel-Alsina aggregation operators, MEREC method
DOI: 10.3233/JIFS-235274
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Hong, Yuntao
Article Type: Research Article
Abstract: Obsessive-compulsive disorder (OCD) is a chronic disease and psychosocial disorder that significantly reduces the quality of life of patients and affects their personal and social relationships. Therefore, early diagnosis of this disorder is of particular importance and has attracted the attention of researchers. In this research, new statistical differential features are used, which are suitable for EEG signals and have little computational load. Hilbert-Huang transform was applied to EEGs recorded from 26 OCD patients and 30 healthy subjects to extract instant amplitude and phase. Then, modified mean, variance, median, kurtosis and skewness were calculated from amplitude and phase data. Next, …the difference of these statistical features between various pairs of EEG channels was calculated. Finally, different scenarios of feature classification were examined using the sparse nonnegative least squares classifier. The results showed that the modified mean feature calculated from the amplitude and phase of the interhemispheric channel pairs produces a high accuracy of 95.37%. The frontal lobe of the brain also created the most distinction between the two groups among other brain lobes by producing 90.52% accuracy. In addition, the features extracted from the frontal-parietal network produced the best classification accuracy (93.42%) compared to the other brain networks examined. The method proposed in this paper dramatically improves the accuracy of EEG classification of OCD patients from healthy individuals and produces much better results compared to previous machine learning techniques. Show more
Keywords: Obsessive-compulsive disorder (OCD), Electroencephalogram (EEG), Hilbert-Huang transform, statistical features, classification
DOI: 10.3233/JIFS-237946
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Zhao, Xixi | Gu, Liang | Duan, Xiaorong | Wang, Liguo | Li, Zhenxi
Article Type: Research Article
Abstract: Cloud storage attribute libraries usually store a large amount of sensitive data such as personal information and trade secrets. Attackers adopt diverse and complex attack methods to target the cloud storage attribute database, which makes the defense work more challenging. In order to realize the secure storage of information, an attribute based cloud storage anti-attack algorithm based on dynamic authorization access is proposed. According to the characteristic variables of the sample, the data correlation matrix is calculated, and the principal component analysis method is adopted to reduce the dimension of the data, build the anti-attack code model, simulate the dynamic …authorization access rights, and calculate the packet loss rate according to the anti-attack flow. Design the initialization stage, cluster stage and cluster center update stage to realize the attack prevention of cloud storage attribute database. The experimental results show that the proposed algorithm can accurately classify the anti-attack code, has good packet processing ability, relatively short page request time, and anti-attack success rate is higher than 90%, which can effectively ensure the stability of the algorithm. Show more
Keywords: Dynamic authorization access, cloud storage attributes, basic anti-attack algorithm, anti-attack code model, access permissions
DOI: 10.3233/JIFS-237409
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Mahendran, S. | Gomathy, B.
Article Type: Research Article
Abstract: This study addresses the escalating energy demands faced by global industries, exerting pressure on power grids to maintain equilibrium between supply and demand. Smart grids play a pivotal role in achieving this balance by facilitating bidirectional energy flow between end users and utilities. Unlike traditional grids, smart grids incorporate advanced sensors and controls to mitigate peak loads and balance overall energy consumption. The proposed work introduces an innovative deep learning strategy utilizing bi-directional Long Short Term Memory (b-LSTM) and advanced decomposition algorithms for processing and analyzing smart grid sensor data. The application of b-LSTM and higher-order decomposition in the analysis …of time-series data results in a reduction of Mean Absolute Percentage Error (MAPE) and Minimal Root Mean Square (RMSE). Experimental outcomes, compared with current methodologies, demonstrate the model’s superior performance, particularly evident in a case study focusing on hourly PV cell energy patterns. The findings underscore the efficacy of the proposed model in providing more accurate predictions, thereby contributing to enhanced management of power grid challenges. Show more
Keywords: Smart grids, deep learning, PV cells, error rate and absolute error, prediction
DOI: 10.3233/JIFS-238850
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Ning, Yi | Liu, Meiyu | Guo, Xifeng | Liu, Zhiyong | Wang, Xinlu
Article Type: Research Article
Abstract: Accurate load forecasting is an important issue for safe and economic operation of power system. However, load data often has strong non-stationarity, nonlinearity and randomness, which increases the difficulty of load forecasting. To improve the prediction accuracy, a hybrid short-term load forecasting method using load feature extraction based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and refined composite multi-scale entropy (RCMSE) and improved bidirectional long short time memory (BiLSTM) error correction is proposed. Firstly, CEEMDAN is used to separate the detailed information and trend information of the original load series, RCMSE is used to reconstruct the feature …information, and Spearman is used to screen the features. Secondly, an improved butterfly optimization algorithm (IBOA) is proposed to optimize BiLSTM, and the reconstructed components are predicted respectively. Finally, an error correction model is constructed to mine the hidden information contained in error sequence. The experimental results show that the MAE, MAPE and RMSE of the proposed method are 645 kW, 0.96% and 827.3 kW respectively, and MAPE is improved by about 10% compared with other hybrid models. Therefore, the proposed method can overcome the problem of inaccurate prediction caused by data and inherent defects of models and improve the prediction accuracy. Show more
Keywords: Short-term load forecasting, complete ensemble empirical mode decomposition with adaptivenoise, refined composite multi-scale entropy, improved butterfly optimization algorithm, bidirectional long short time memory neural network
DOI: 10.3233/JIFS-237993
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Limei, Nong | Dongfan, Wu | Bo, Zhang
Article Type: Research Article
Abstract: Garden landscape is the combination of nature and humanity, with high aesthetic value, ecological value and cultural value, has become an important part of people’s life. Modern people have a higher pursuit for the spiritual food such as garden landscape after the material life is satisfied, which brings new challenges to the construction of urban garden landscape. As an advanced type of machine learning, deep learning applied to landscape image recognition can solve the problem of low quality and low efficiency of manual recognition. Based on this, this paper proposes a garden landscape image recognition algorithm based on SSD (Single …Shot Multibox Detector), which realizes accurate extraction and recognition of image features by positioning the target, and can effectively improve the quality and efficiency of landscape image recognition. In order to test the feasibility of the algorithm proposed in this paper, experimental analysis was carried out in the CVPR 2023 landscape data set. The experimental results show that the algorithm has a high recognition accuracy for landscape images, and has excellent performance compared with traditional image recognition algorithms. Show more
Keywords: Deep learning, garden landscape, image recognition, target detection; image analysis
DOI: 10.3233/JIFS-239654
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ramkumar, N. | Karthika Renuka, D.
Article Type: Research Article
Abstract: In recent times, the rapid advancement of deep learning has led to increased interest in utilizing Electroencephalogram (EEG) signals for automatic speech recognition. However, due to the significant variation observed in EEG signals from different individuals, the field of EEG-based speech recognition faces challenges related to individual differences across subjects, which ultimately impact recognition performance. In this investigation, a novel approach is proposed for EEG-based speech recognition that combines the capabilities of Long Short Term Memory (LSTM) and Graph Attention Network (GAT). The LSTM component of the model is designed to process sequential patterns within the data, enabling it to …capture temporal dependencies and extract pertinent features. On the other hand, the GAT component exploits the interconnections among data points, which may represent channels, nodes, or features, in the form of a graph. This innovative model not only delves deeper into the connection between connectivity features and thinking as well as speaking states, but also addresses the challenge of individual disparities across subjects. The experimental results showcase the effectiveness of the proposed approach. When considering the thinking state, the average accuracy for single subjects and cross-subject are 65.7% and 67.3% respectively. Similarly, for the speaking state, the average accuracies were 65.4% for single subjects and 67.4% for cross-subject conditions, all based on the KaraOne dataset. These outcomes highlight the model’s positive impact on the task of cross-subject EEG speech recognition. The motivations for conducting cross subject are real world applicability, Generalization, Adaptation and personalization and performance evaluation. Show more
Keywords: Electroencephalography, recurrent neural network, long short term memory, gated recurrent unit, graph convolution network and graph attention network
DOI: 10.3233/JIFS-233143
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Peng, Weishi | Fang, Yangwang | Ma, Yongzhong
Article Type: Research Article
Abstract: Although many scholars say that their algorithms are better than others in the state estimation problem, only a fewer convincing algorithms were applied to engineering practices. The reason is that their algorithms outperform others only in some aspects such as the estimation accuracy or the computation load. To solve the problem of performance evaluation of state estimation algorithms, in this paper, the comprehensive evaluation measures (CEM) for evaluating the nonlinear estimation algorithm (NEA) is proposed, which can comprehensively reflect the performance of the NEAs. First, we introduce three types of the NEAs. Second, the CEM combining the flatness, estimation accuracy …and computation time of the NEAs, is designed to evaluate the above NEAs. Finally, the superiority of the CEM is verified by a numerical example, which helps decision makers of nonlinear estimation algorithms theoretically and technically. Show more
Keywords: Performance evaluation, nonlinear estimation algorithm, comprehensive metrics, error spectrum, EKF, UKF, PF
DOI: 10.3233/JIFS-231376
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Valdez-Rodríguez, José E. | Rangel, Nahum | Moreno-Armendáriz, Marco A.
Article Type: Research Article
Abstract: Visual detection of fingering on the trumpet is an increasingly interesting topic in music research. The ability to recognize and track the movements of the trumpet player’s fingers during the performance of a musical piece can provide valuable information for analyzing and improving instrument technique. However, this is a largely unexplored task, as most works focus on audio quality rather than instrument fingering techniques. Developing techniques for identifying essential finger positions on a musical instrument is crucial, as poor fingering techniques can harm instrument performance. In this work, we propose the visual detection of this fingering using convolutional neural networks …with a proprietary dataset created for this purpose. Additionally, to improve the results and focus on the essential parts of the instrument, we use self-attention mechanisms by extracting these features automatically. Show more
Keywords: Fingering detection, Convolutional Neural Networks, Self-attention mechanisms, Visual detection, Trumpet
DOI: 10.3233/JIFS-219342
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Ganesh, M.A. | Saravana Perumaal, S. | Gomathi Sankar, S.M.
Article Type: Research Article
Abstract: The current framework for detecting Fake License Plates (FLP) in real-time is not robust enough for patrol teams. The objective of this paper is to develop a robust license plate authentication framework, based on the Vehicle Make and Model Recognition (VMMR) and the License Plate Recognition (LPR) algorithms that is implementable at the edge devices. The contributions of this paper are (i) Development of license plate database for 547 Indian cars, (ii) Development of an image dataset with 3173 images of 547 Indian cars in 8 classes, (iii) Development of an ensemble model to recognize vehicle make and model from …frontal, rear, and side images, and (iv) Development of a framework to authenticate the license plates with frontal, rear, and side images. The proposed ensemble model is compared with the state-of-the-art networks from the literature. Among the implemented networks for VMMR, the Ensembling model with a size of 303.2 MB achieves the best accuracy of 89% . Due to the limited memory size, Easy OCR is chosen to recognize license plate. The total size of the authentication framework is 308 MB. The performance of the proposed framework is compared with the literature. According to the results, the proposed framework enhances FLP recognition due to the recognition of vehicles from side images. The dataset is made public at https://www.kaggle.com/ganeshmailecture/datasets . Show more
Keywords: Vehicle make and model recognition, fake license plate detection, license plate authentication, intelligent transportation system
DOI: 10.3233/JIFS-230607
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2024
Authors: Yin, Songyi | Wang, Yu | Fu, Yelin
Article Type: Research Article
Abstract: The environmental, social, and governance (ESG) rating method is a powerful tool that can help investors to judge the investment value of companies based on the information disclosure. However, mainstream ESG rating methods ignore the distinction between companies with incomplete information disclosure and companies without information disclosure, which decreases the initiative and enthusiasm of companies to disclose information. In this study, a self-disclosure ESG (SDESG) rating method is proposed to evaluate companies’ ESG performance capabilities. First, based on the fuzzy set, fuzzy data is defined and applied to the SDESG rating method. Second, analogous to the academic reward system of …a university, a reward mechanism of disclosure is used in the SDESG rating method. Finally, the effectiveness and reliability of the SDESG rating method are demonstrated through Refinitiv’s case. The results show that the SDESG rating method can distinguish companies with incomplete information disclosure from companies without information disclosure and allow companies that proactively disclose information to obtain better ESG scores under each industry. The implications of the study would increase companies’ enthusiasm to disclose information and maintain transparency within a company. Show more
Keywords: ESG rating method, information disclosure, fuzzy set, reward mechanism
DOI: 10.3233/JIFS-230777
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Wang, Dan | Yao, Jingfa | Zhang, Yanmin
Article Type: Research Article
Abstract: Nowadays, automatic human activity recognition from video images is necessary for monitoring applications and caring for disabled people. The use of surveillance cameras and the processing of the obtained images leads to the achievement of a smart, accurate system for the recognition of human behavior. Since human detection in different scenes is associated with many challenges, several approaches have been implemented to detect human activity from video image processing. Due to the complexity of human activities, background noises and other factors affect the detection. For the solution of these problems, two deep learning-based algorithms have been described in the current …article. According to the convolutional neural networks, the LSTM + CNN method and the 3D CNN method have been used to recognize the human activities in the images of the video. Each algorithm is explained and analyzed in detail. The experiments designed in this paper are performed by two datasets: the HMDB-51 dataset and the UCF101 dataset. In the HMDB-51 dataset, the highest obtained accuracy for CNN + LSTM method was equal to 70.2 and for method 3D CNN equal to 54.4. In the UCF101 dataset, the highest obtained accuracy for CNN + LSTM method was equal to 95.1 and for method 3D CNN equal to 90.8. Show more
Keywords: Long short-term memory (LSTM), video processing, deep learning, human activity recognition, convolutional neural network (CNN)
DOI: 10.3233/JIFS-236068
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Viet, Hoang Huu | Uyen, Nguyen Thi | Cao, Son Thanh | Nguyen, Long Giang
Article Type: Research Article
Abstract: The Student-Project Allocation with preferences over Projects problem is a many-to-one stable matching problem that aims to assign students to projects in project-based courses so that students and lecturers meet their preference and capacity constraints. In this paper, we propose an efficient two-heuristic algorithm to solve this problem. Our algorithm starts from an empty matching and iteratively constructs a maximum stable matching of students to projects. At each iteration, our algorithm finds an unassigned student and assigns her/his most preferred project to her/him to form a student-project pair in the matching. If the project or the lecturer who offered the …project is over-subscribed, our algorithm uses two heuristic functions, one for the over-subscribed project and the other for the over-subscribed lecturer, to remove a student-project pair in the matching. To reach a stable matching of a maximum size, our two heuristics are designed such that the removed student has the most opportunities to be assigned to some project in the next iterations. Experimental results show that our algorithm is efficient in execution time and solution quality for solving the problem. Show more
Keywords: Approximation algorithm, heuristic search, matching problem, student-project allocation problem
DOI: 10.3233/JIFS-236300
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Huang, Jinsong | Hou, Hecheng | Li, Xiaoying | Zhang, Ziyi | Jia, Qi
Article Type: Research Article
Abstract: In the context of the digital era, the factors influencing the cognitive load of the full ecological smart home on the elderly are mostly interconnected. Most existing studies have conducted single correlation analyses, ignoring the fact that cognitive load is the result among multiple interactions of multiple factors. Furthermore, the color, material and Finishing of the product design can also impact on the user’s perceptual needs. Therefore, exploring the grouping dynamics of cognitive load and users’ perceptual needs for color (C), material (M), and Finishing (F) of smart products can provide insights for inclusive design of smart homes. The article …analyzes the asymmetric multiple concurrent causal effects of full ecological smart homes on the cognitive load of the elderly from a histological perspective using fuzzy set Qualitative Comparative Analysis (fsQCA) based on the four elements of Innovation Diffusion Theory. At the same time, principal component analysis and quantitative theory I class method are used to explore the quantitative relationship between color, material, Finishing and users’ perceptual imagery of the product. The results of the study showed that there were no necessary conditions leading to high or low cognitive load in the fsQCA analysis, indicating that the problem was the result of the interaction of multiple conditions, and the final analysis yielded three histological pathways leading to low cognitive load and one pathway leading to high load in older adults. Moreover, the study identifies the combination of colors, materials, and finishes that best represent user preferences. This study establishes a dialogue between theory, results, and cases in analyzing of the group dynamics of the impact of full ecological smart homes on the cognitive load of the elderly. It provides a theoretical basis for the development of digital inclusion enhancement strategies. Show more
Keywords: Smart home, cognitive load, diffusion of innovation, qualitative comparative analysis (QCA), human-computer interaction
DOI: 10.3233/JIFS-237212
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Jansi Sophia Mary, C. | Mahalakshmi, K.
Article Type: Research Article
Abstract: Intrusion Detection (ID) in cloud environments is vital to maintain the safety and integrity of data and resources. However, the presence of class imbalance, where normal samples significantly outweigh intrusive instances, poses a challenge in constructing a potential ID system. Deep Learning (DL) methods, with their capability to automatically study complex patterns and features, present a promising solution in various ID tasks. Such methods can automatically learn intricate features and patterns from the input dataset, making them suitable for detecting anomalies and finding intrusions in cloud environments. Therefore, this study proposes a Class Imbalance Data Handling with an Optimal Deep …Learning-Based Intrusion Detection System (CIDH-ODLIDS) in a cloud computing atmosphere. The CIDH-ODLIDS technique leverages optimal DL-based classification and addresses class imbalance. Primarily, the CIDH-ODLIDS technique preprocesses the input data using a Z-score normalization approach to ensure data quality and consistency. To handle class imbalance, the CIDH-ODLIDS technique employs oversampling techniques, particularly focused on synthetic minority oversampling techniques such as Adaptive Synthetic (ADASYN) sampling. ADASYN generates synthetic instances for the minority class depending on the available data instances, effectively balancing the class distribution and mitigating the impact of class imbalance. For the ID process, the CIDH-ODLIDS technique utilizes a Fuzzy Deep Neural Network (FDNN) model, and its tuning procedure is performed using the Chaotic Tunicate Swarm Algorithm (CTSA). CTSA is employed to choose the learning rate of the FDNN methods optimally. The experimental assessment of the CIDH-ODLIDS method is extensively conducted on three IDS datasets. The comprehensive comparison results confirm the superiority of the CIDH-ODLIDS algorithm over existing techniques. Show more
Keywords: Cloud computing, security, deep learning, intrusion detection system, tunicate swarm algorithm, class imbalance data
DOI: 10.3233/JIFS-237900
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Lomas-Barrie, Victor | Reyes-Camacho, Michelle | Neme, Antonio
Article Type: Research Article
Abstract: Horizontal gene transference is a biological process that involves the donation of DNA or RNA from an organism to a second, unrelated organism. This process is different from the more common one, vertical transference, which is present whenever an organism or pair of organisms reproduce and transmit their genetic material to the descendants. The identification of segments of genetic material that are the result of horizontal transference is relevant to construct accurate phylogenetic trees, on one hand, and to detect possible drug-resistance mechanisms, on the other, since this movement of genetic material is the main cause behind antibiotic resistance in …bacteria. Here, we describe a novel algorithm able to detect sequences of foreign origin, and thus, possible acquired via horizontal transference. The general idea of our method is that within the genome of an organism, there might be sequences that are different from the vast majority of the remaining sequences from the same organism. The former are candidate anomalies, and thus, their origin may be explained by horizontal transference. This approach is equivalent to a particular instance of the authorship attribution problem, that in which from a set of texts or paragraphs, almost all of them were written by the same author, whereas a minority has a different authorship. The constraint is that the author of each text is not known, so the algorithm has to attribute the authorship of each one of the texts. The texts detected to be written by a different author are the equivalent of the sequences of foreign origin for the case of genetic material. We describe here a novel method to detect anomalous sequences, based on interpretable embeddings derived from a common attention mechanism in humans, that of identifying novel tokens within a given sequence. Our proposal achieves novel and consistent results over the genome of a well known organism. Show more
Keywords: Horizontal gene transference, anomaly detection, embeddings, natural language processing, genomics
DOI: 10.3233/JIFS-219337
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Weng, Zhi | Bai, Rongfei | Zheng, Zhiqiang
Article Type: Research Article
Abstract: Cattle detection and counting is one of the most important topics in the development of modern agriculture and animal husbandry. The traditional manual monitoring methods are inefficient and constrained by factors such as site. To solve the above problems, a SCS-YOLOv5 cattle detection and counting model for complex breeding scenarios is proposed. The original SPPF module is replaced in the YOLOv5 backbone network with a CSP structured SPPFCSPC. A CA (Coordinate Attention) mechanism is added to the neck network, as well as the SC (Standard Convolution) of the Neck network is replaced with a light convolution GSConv and Slim Neck …is introduced, and training strategies such as multi-scale training are also employed. The experimental results show that the proposed method enhances the feature extraction ability and feature fusion ability, balances the localization accuracy and detection speed, and improves the use effect in real farming scenarios. The Precision of the improved network model is improved from 93.2% to 95.5%, [email protected] is improved from 94.5% to 95.2%, the RMSE is reduced by about 0.03, and the FPS reaches 88. Compared with other mainstream algorithms, the comprehensive performance of SCS-YOLOv5 s is in a leading position, with fewer missed and false detections, and the strong robustness and generalization ability of this model are proved on multi-category public datasets. Applying the improvement ideas in this paper to YOLOv8 s also yields an increase in accuracy. The improved method in this study can greatly improve the accuracy of cattle detection and counting in complex environments, and has good real-time performance, so as to provide technical support for large-scale cattle breeding. Show more
Keywords: Cattle detection, counting, attention mechanism, occlusion, complex environments
DOI: 10.3233/JIFS-237231
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Lugo-Torres, Gerardo | Valdez-Rodríguez, José E. | Peralta-Rodríguez, Diego A.
Article Type: Research Article
Abstract: The use of generative models in image synthesis has become increasingly prevalent. Synthetic medical imaging data is of paramount importance, primarily because medical imaging data is scarce, costly, and encumbered by legal considerations pertaining to patient confidentiality. Synthetic medical images offer a potential answer to these issues. The predominant approaches primarily assess the quality of images and the degree of resemblance between these images and the original ones employed for their generation.The central idea of the work can be summarized in the question: Do the performance metrics of Frechet Inception Distance(FID) and Inception Score(IS) in the Cycle-consistent Generative Adversarial Networks …(CycleGAN) model are adequate to determine how real a generated chest x-ray pneumonia image is? In this study, a CycleGAN model was employed to produce artificial images depicting 3 classes of chest x-ray pneumonia images: general(any type), bacterial, and viral pneumonia. The quality of the images were evaluated assessing and contrasting 3 criteria: performance metric of CycleGAN model, clinical assessment of respiratory experts and the results of classification of a visual transformer(ViT). The overall results showed that the evaluation metrics of the CycleGAN are insufficient to establish realism in generated medical images. Show more
Keywords: Synthetic chest x-ray, cycle generative adversarial network, pneumonia, image-to-image translation, visual transformer
DOI: 10.3233/JIFS-219373
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ramírez-Martínez, Angel | Chong-Quero, J. Enrique | Cervantes-Culebro, Héctor | Cruz-Villar, Carlos A.
Article Type: Research Article
Abstract: This paper presents a data-driven control approach for a five-bar robot with compliant joints. The robot consists of a parallel mechanism with compliant elements that introduce uncertainties in modeling and control. To address this fact, it is implemented a model-less data-driven controller based on a Feedforward Neural Network Module (FNNM) that identifies the inverse dynamics of the robot. The FNNM is incorporated into a coordination of Feedforward Control Method (CFCM) to achieve precise trajectory tracking. Experiments compare the compliant joints robot to a bearing-joint robot performing pick-and-place tasks from 0.15 to 3.15 Hz. Results show the compliant robot maintaining trajectory tracking …up to 1.25 Hz with a Root Mean Square Error (RMSE) of 9.02 mm. Show more
Keywords: Data-driven, five-bar robot, compliant joints, vision-based
DOI: 10.3233/JIFS-219364
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Chen, Kang | Song, Changming | Cheng, Dongxu | Li, Hao
Article Type: Research Article
Abstract: Video anomaly detection (VAD) has garnered substantial attention from researchers due to its broad applications, including fire detection, drop detection, and vibration detection. In the current context of VAD, existing methods prioritize detection efficiency but overlook the impact of motion and appearance information. Additionally, achieving accurate predictions while retaining motion and appearance information poses a significant challenge. This paper proposes a novel semi-supervised method for VAD based on Generative Adversarial Network (GAN) structures with dual generators and dual discriminators, namely Dual-GAN. The future frame generator utilizes an improved encoder-decoder network to preserve more spatial information. Motion information for the future …flow generator is obtained by estimating optical flow between reconstruction frames, complementing the optical flow between prediction frames. The introduction of a frame discriminator and a motion discriminator against the frame generator enhances the realism of prediction frames, which facilitates the identification of unexpected abnormal events. This method significantly outperforms comparative approaches in synthesizing video frames and predicting future flows, showcasing its effectiveness in handling diverse video data. Extensive experiments are performed on four publicly available datasets to ensure a comprehensive evaluation of the model performance. Further exploration could include refining the model architecture, exploring additional datasets, and adapting the methodology to specific application domains. Show more
Keywords: Anomaly detection, generative adversarial network, dual discriminators, future flow
DOI: 10.3233/JIFS-237831
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Karthikeyan, M. | Colak, Ilhami | Sagar Imambi, S. | Joselin Jeya Sheela, J. | Nair, Sruthi | Umarani, B. | Alagusabai, Andril | Suriyakrishnaan, K. | Rajaram, A.
Article Type: Research Article
Abstract: This research paper introduces a cutting-edge approach to electric demand forecasting by incorporating the Temporal Fusion Transformer (TFT). As the landscape of demand forecasting becomes increasingly intricate, precise predictions are vital for effective energy management. To tackle this challenge, we leverage the sequential and temporal patterns in an extensive electric demand dataset spanning from 2003 to 2014. Our proposed Temporal Fusion Transformer model combines attention mechanisms with the transformer architecture, enabling it to adeptly capture intricate temporal dependencies. Thorough data preprocessing, including temporal embedding and external features, enhances prediction accuracy. Through rigorous evaluation, the TFT model surpasses existing forecasting techniques, …showcasing its capacity for accurate, resilient, and adaptive predictions. This research contributes to the advancement of electric demand forecasting, harnessing the TFT’s capabilities to excel in capturing diverse temporal patterns. The findings hold the potential to enhance energy management and support decision-making in the energy sector, bridging the gap between innovation and practical utility. Show more
Keywords: Electric demand forecasting, temporal fusion transformer, energy management, time-series analysis, transformer architecture
DOI: 10.3233/JIFS-236036
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Arenas Muñiz, Andrés Antonio | Mújica-Vargas, Dante | Rendón Castro, Arturo | Luna-Álvarez, Antonio | Vela-Rincón, Virna V.
Article Type: Research Article
Abstract: The selection of an appropriate trajectory for self-driving vehicles involves the analysis of several criteria that describe the generated trajectories. This problem evolves into an optimization problem when it is desired to increase or decrease the values for a specific criterion. The contribution of this thesis is to explore the use and optimization of another technique for decision-making, such as TOPSIS, with a sufficiently robust method that allows the inclusion of multiple parameters and their proper optimization, incorporating human experience. The proposed approach showed significantly higher safety and comfort performance, with about 20% better efficiency and 80% fewer safety violations …compared to other state-of-the-art methods, and in some cases outperforming in comfort by about 30.43%. Show more
Keywords: Decision-making, human experience, trajectory selection, self-driving
DOI: 10.3233/JIFS-219365
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Téllez-Velázquez, Arturo | Delice, Pierre A. | Salgado-Leyva, Rafael | Cruz-Barbosa, Raúl
Article Type: Research Article
Abstract: This paper performs an analysis comparing two evolutionary explainable fuzzy models that make inferences in a pipeline with a blood test data set for COVID-19 classification. Firstly, data is preprocessed by the following stages: cleaning, imputation and ranking feature selection. Later, we perform a comparative analysis between several clustering methods used in an Evolutionary Clustering-Structured Fuzzy Classifier (ECSFC) to solve this classification problem using the Differential Evolution (DE) algorithm. Complementarily, we find that the Fuzzy Decision Tree model produces similar performance when is tuned with the DE algorithm (EFDT). The obtained results show that, simpler models are easier to explain …qualitatively, i.e., increasing the number of clusters in ECSFC model or the maximum depth of the tree in EFDT model, does not necessarily help to obtain simplified and accurate models. In addition, although the EFDT model is by itself an intuitively explainable model, the ECSFC, with the help of the proposed Weighted Stacked Features Plot, generates more intuitive models that allow not only highlighting the features and the linguistic terms that defines a patient with COVID-19, but also allows users to visualize in a single graph and in specific colors the analyzed classes. Show more
Keywords: COVID-19, blood tests, fuzzy classifier, fuzzy decision tree, clustering, differential evolution
DOI: 10.3233/JIFS-219372
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Yao, Ziyang
Article Type: Research Article
Abstract: The traditional multi-task Takagi-Sugeno-Kang (TSK) fuzzy system modeling methods pay more attention to utilizing the inter-task correlation to learn the consequent parameters but ignore the importance of the antecedent parameters of the model. To this end, we propose a novel multi-task TSK fuzzy system modeling method based on multi-task fuzzy clustering. This method first proposes a novel multi-task fuzzy c-means clustering method that learns multiple specific clustering centers for each task and some common clustering centers for all tasks. Secondly, for the consequent parameters of the fuzzy system, the novel low-rank and row-sparse constraints are proposed to better implement multi-task …learning. The experimental results demonstrate that the proposed model shows better performance compared with other existing methods. Show more
Keywords: Multi-task fuzzy clustering, TSK fuzzy system, low-rank, row-sparsity, joint learning
DOI: 10.3233/JIFS-232312
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Gao, Lijun | Liu, Kai | Liu, Wenjun | Wu, Jiehong | Jin, Xiao
Article Type: Research Article
Abstract: As machine learning models become increasingly integrated into practical applications and are made accessible via public APIs, the risk of model extraction attacks has gained prominence. This study presents an innovative and efficient approach to model extraction attacks, aimed at reducing query costs and enhancing attack effectiveness. The method begins by leveraging a pre-trained model to identify high-confidence samples from unlabeled datasets. It then employs unsupervised contrastive learning to thoroughly dissect the structural nuances of these samples, constructing a dataset of high quality that precisely mirrors a variety of features. A mixed information confidence strategy is employed to refine the …query set, effectively probing the decision boundaries of the target model. By integrating consistency regularization and pseudo-labeling techniques, reliance on authentic labels is minimized, thus improving the feature extraction capabilities and predictive precision of the surrogate models. Evaluation on four major datasets reveals that the models crafted through this method bear a close functional resemblance to the original models, with a real-world API test success rate of 62.35%, which vouches for the method’s validity. Show more
Keywords: Model extraction, unsupervised learning, selection of strategies, active learning
DOI: 10.3233/JIFS-239504
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Yu, Xiaobing | Zhang, Yuexin | Wang, Xuming
Article Type: Research Article
Abstract: Sensors are often deployed in harsh environments, in which some threats may endanger the safety of sensors. In this paper, a sensor deployment model is developed in Wireless Sensor Networks (WSNs), in which the coverage rate and the threat risk are considered simultaneously. The model is established as an optimization problem. An adaptive ranking teaching learning-based optimization algorithm (ARTLBO) is proposed to solve the problem. Learners are divided into inferior and superior groups. The teacher phase is boosted by replacing the teacher with the top three learners, and the learner phase is improved by providing some guidance for inferior learners. …The experimental results show that the proposed ARTLBO algorithm can effectively optimize the model. The fitness values of the proposed model found by the proposed ARTLBO are 0.4894, 0.4886, which are better than its competitors. The algorithm can provide a higher coverage rate and lower threat risk. Show more
Keywords: WSNs, teaching-learning-based optimization, sensor deployment, coverage rate
DOI: 10.3233/JIFS-240215
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Zhang, Lixin | Yin, Hongtao | Li, Ang | Hu, Longbiao
Article Type: Research Article
Abstract: In large-scale scenes, how to quickly obtain paths while ensuring the shortest possible path length is a key issue. Rapidly-exploring Random Tree (RRT) have the characteristic of quickly exploring the state space, but it is often difficult to obtain a short path. To overcome this problem, this paper proposes an improved RRT algorithm based on equidistance retention strategy and A* local search(ERRRT-A*). First, RRT is used for large-step global fast exploration to obtain approximate paths. Then, an equidistance retention strategy is used to discard most of the points and retain a small number of key points. Finally, A* is used …to search between each segment to obtain a new path. The ERRRT-A* algorithm is compared with other commonly used algorithms on maps of different size in terms of path length and planning time. Simulation results indicate that compared with other algorithms, this algorithm achieves fast planning in large-scale scenes while obtaining short path length, which can effectively balance the path length and planning time. Show more
Keywords: Path planning, large-scale scenes, unmanned vehicles, RRT
DOI: 10.3233/JIFS-238695
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: López-Jasso, Edgar | Felipe-Riverón, Edgardo M. | Valdez-Rodríguez, José E.
Article Type: Research Article
Abstract: This study underscores the crucial role of image preprocessing in enhancing the outcomes of multimodal image registration tasks using scale-invariant feature selection. The primary focus is on registering two types of retinal images, assessing a methodology’s performance on a set of retinal image pairs, including those with and without microaneurysms. Each pair comprises a color optical image and a gray-level fluorescein image, presenting distinct characteristics and captured under varying conditions. The SIFT methodology, encompassing five stages, with preprocessing as the initial and pivotal stage, is employed for image registration. Out of 35 test retina image pairs, 33 (94.28%) were successfully …registered, with the inability to extract features hindering automatic registration in the remaining pairs. Among the registered pairs, 42.42% were retinal images without microaneurysms, and 57.57% had microaneurysms. Instead of simultaneous registration of all channels, independent registration of preprocessed images in each channel proved more effective. The study concludes with an analysis of the fifth registration’s resulting image to detect abnormalities or pathologies, highlighting the challenges encountered in registering blue channel images due to high intrinsic noise. Show more
Keywords: Image SIFT registration, microaneurysms counting, retina image analysis, multimodal registration, image processing
DOI: 10.3233/JIFS-219374
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Pradeepkumar, G. | Kavitha, S.
Article Type: Research Article
Abstract: To provide the best possible performance in precisely segmenting clinical images, several approaches are used. Convolutional neural networks are one method used in it to extract its features, which combine several models with several additional methods. It also improves the efficiency of generalisation between categorised and uncategorized image categories. The method proposed combines multi-style image fusion with two-dimensional fracture image representation. The photographs on this page have been updated with a variety of images to improve concentration sharing and achieve the desired visual look. The border detection algorithm is then used to extract the exact border of the image from …the contrast extended images. It will then be divided into basic and comprehensive layers. The fused image was then created using augmented end layers. Show more
Keywords: Segmenting, clinical images, extract features, categorized image, uncategorized image, multi style, border detection, image extraction
DOI: 10.3233/JIFS-239695
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Wei, Xiao | Lin, Yidian
Article Type: Research Article
Abstract: Legal judgment prediction(LJP) has achieved remarkable results. However, existing methods still face problems such as difficulties in obtaining key feature words for charges, which impose limitations on the improvement of prediction results. To this end, we propose a legal judgment prediction model with legal feature Word subgraph Label-Embedding and Dual-knowledge Distillation(WLEDD). Compared with traditional methods, our method has two contributions: (1) To mitigate the impact of overly sparse tail class data and high similarity text representations, we capture the critical features related to the charges by fusing LDA and legal feature word subgraphs. Then we encode them as label information …to obtain highly distinguished representations of legal documents. (2) To solve the problem of high difficulty in some subtasks in LJP, we perform subtask-oriented compression of models to construct a student model with lower complexity and higher accuracy through dual knowledge distillation. Moreover, we exploit the logical association between the subtasks to constrain the labels of articles by charge prediction results. It greatly reduces the difficulty of article prediction. Experimental results on four datasets show that our approach significantly outperforms the baseline models. Compared with the state-of-art method, the F1 value of WLEDD for charge prediction has increased by an average of 2.57% . For article prediction, the F1 value has increased by an average of 1.09% . In addition, we demonstrate its effectiveness through ablation experiments and analytical experiments. Show more
Keywords: Legal judgment prediction, knowledge distillation, label embedding, legal text mining
DOI: 10.3233/JIFS-237323
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Yang, Hong | Wang, Lina
Article Type: Research Article
Abstract: The paper focuses on how to improve the prediction accuracy of time series and the interpretability of prediction results. First, a novel Prophet model based on Gaussian linear fuzzy approximate representation (GF-Prophet) is proposed for long-term prediction, which uniformly predicts the data with consistent trend characteristics. By taking Gaussian linear fuzzy information granules as inputs and outputs, GF-Prophet predicts with significantly smaller cumulative error. Second, noticing that trend extraction affects prediction accuracy seriously, a novel granulation modification algorithm is proposed to merge adjacent information granules that do not have significant differences. This is the first attempt to establish Prophet based …on fuzzy information granules to predict trend characteristics. Experiments on public datasets show that the introduction of Gaussian linear fuzzy information granules significantly improves prediction performance of traditional Prophet model. Compared with other classical models, GF-Prophet has not only higher prediction accuracy, but also better interpretability, which can clearly give the change information, fluctuation amplitude and duration of a certain trend in the future that investors actually pay attention to. Show more
Keywords: Fuzzy number, gaussian linear fuzzy information granule, the prophet model, long-term prediction
DOI: 10.3233/JIFS-230313
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Canul-Chin, Miguel Angel | Moguel-Ordóñez, Yolanda Beatriz | Martin-Gonzalez, Anabel | Brito-Loeza, Carlos | Legarda-Saenz, Ricardo
Article Type: Research Article
Abstract: Yucatan has a variety of plant species of melliferous importance. The honey produced in Yucatan has several special properties that make it one of the most demanded internationally. Analyzing the pollen grains present in honey is essential to determine its quality and identify its plants of origin. This study is a time-consuming process that must be carried out by highly trained palynologists. In this work, we propose an improved model based on a fully convolutional neural network for the automatic detection of pollen grains in microscopic images of four plant species of Yucatan to contribute to the analysis of the …honey designation of origin. Show more
Keywords: Pollen analysis, object detection, palynology, deep learning
DOI: 10.3233/JIFS-219379
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Hashmi, Hina | Dwivedi, Rakesh | Kumar, Anil | Kumar, Aman
Article Type: Research Article
Abstract: The rapid advancements in satellite imaging technology have brought about an unprecedented influx of high-resolution satellite imagery. One of the critical tasks in this domain is the automated detection of buildings within satellite imagery. Building detection holds substantial significance for urban planning, disaster management, environmental monitoring, and various other applications. The challenges in this field are manifold, including variations in building sizes, shapes, orientations, and surrounding environments. Furthermore, satellite imagery often contains occlusions, shadows, and other artifacts that can hinder accurate building detection. The proposed method introduces a novel approach to improve the boundary detection of detected buildings in high-resolution …remote sensed images having shadows and irregular shapes. It aims to enhance the accuracy of building detection and classification. The proposed algorithm is compared with Customized Faster R-CNNs and Single-Shot Multibox Detectors to show the significance of the results. We have used different datasets for training and evaluating the algorithm. Experimental results show that SESLM for Building Detection in Satellite Imagery can detect 98.5% of false positives at a rate of 8.4%. In summary, SESLM showcases high accuracy and improved robustness in detecting buildings, particularly in the presence of shadows. Show more
Keywords: Object detection, image analysis, faster R-CNN, CNN, satellite imagery, object localization
DOI: 10.3233/JIFS-235150
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Huang, De Ling | Huang, Yi Fan | Yang, Yu Qiao
Article Type: Research Article
Abstract: Practical Byzantine Fault Tolerance (PBFT), the widest-used consensus algorithm in the alliance blockchain, suffers from high communications complexity and relatively low scalability, making it difficult to support large-scale networks. To overcome these limitations, we propose a secure and scalable consensus algorithm, Vague Sets-based Double Layer PBFT (VSDL-PBFT). Roles and tasks of consensus nodes are redesigned. Three-phase consensus process of the original PBFT is optimized. Through these approaches, the communication complexity of the algorithm is significantly reduced. In order to better fit the complexity of voting in the real world, we use a vague set to select primary nodes of consensus …groups. This can greatly reduce the likelihood of malicious nodes being selected as the primary nodes. The experimental results show that the VSDL-PBFT consensus algorithm improves the system’s fault tolerance, it also achieves better performance in algorithm security, communications complexity, and transaction throughput compared to the baseline consensus algorithms. Show more
Keywords: Blockchain, consensus algorithm, Byzantine fault tolerance, PBFT
DOI: 10.3233/JIFS-239745
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Rodriguez-Bazan, Horacio | Sidorov, Grigory | Escamilla-Ambrosio, Ponciano Jorge
Article Type: Research Article
Abstract: Recently, Android device usage has increased significantly, and malicious applications for the Android ecosystem have also increased. Security researchers have studied Android malware analysis as an emerging issue. The proposed methods employ a combination of static, dynamic, or hybrid analysis along with Machine Learning (ML) algorithms to detect and classify malware into families. These families often exhibit shared similarities among their members or with other families. This paper presents a new method that combines Fuzzy Hashing and Natural Language Processing (NLP) techniques to find Android malware families based on their similarities by applying reverse engineering to extract the features and …compute fuzzy hashing of the preprocessed code. This relationship allows us to identify the families according to their features. A study was conducted using a database test of 2,288 samples from diverse ransomware families. An accuracy in classifying Android ransomware malware up to 98.46% was achieved. Show more
Keywords: Android malware analysis, android ransomware, cybersecurity, fuzzy hashing, natural language processing
DOI: 10.3233/JIFS-219367
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Arulmurugan, A. | Kaviarasan, R. | Garnepudi, Parimala | Kanchana, M. | Kothandaraman, D. | Sandeep, C.H.
Article Type: Research Article
Abstract: This research focuses on scene segmentation in remotely sensed images within the field of Remote Sensing Image Scene Understanding (RSISU). Leveraging recent advancements in Deep Learning (DL), particularly Residual Neural Networks (RESNET-50 and RESNET-101), and the research proposes a methodology involving feature fusing, extraction, and classification for categorizing remote sensing images. The approach employs a dataset from the University of California Irvine (UCI) comprising twenty-one groups of pictures. The images undergo pre-processing, feature extraction using the mentioned DL frameworks, and subsequent categorization through an ensemble classification structure combining Kernel Extreme Learning Machine (KELM) and Support Vector Machine (SVM). The paper …concludes with optimal results achieved through performance and comparison analyses. Show more
Keywords: Remote sensing, image scene classification, deep learning, feature extraction, RESNET- 101, ensemble
DOI: 10.3233/JIFS-235109
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023
Authors: Cai, Xiumei | Yang, Xi | Wu, Chengmao | Zhang, Rui
Article Type: Research Article
Abstract: Focusing on the currently available multi-view fuzzy clustering algorithms, many of which frequently lack robustness and are hence less frequently used in image segmentation. We present a multi-view fuzzy clustering image segmentation algorithm in this research, along with an autonomous view-weight learning mechanism. Firstly, to ensure that each view has the best view weight, the algorithm adds a view weight factor. Secondly, it introduces the weighted fuzzy factor and the kernel distance metric, the role of the weighted fuzzy factor is to collect the local spatial information and local grey scale information to preserve as much of the image’s detailed …information as feasible during segmentation. The role of the kernel distance metric is to lessen the influence of outliers and noisy points on image segmentation. Finally, the technique for resolving the issue of image uncertainty and fuzzy factor selection introduces the concept of interval type-2 fuzzy c-means clustering. Numerous experiments on different images demonstrate that the proposed algorithm in this paper is more robust than previous multi-view fuzzy clustering algorithms for solving noise image segmentation problems. It is also more effective at segmenting images contaminated by noise and can better retain the detailed information in the image. Show more
Keywords: Multi-view, fuzzy clustering, autonomous view-weight learning, type-2 fuzzy, image segmentation
DOI: 10.3233/JIFS-235967
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Yang, Yi | Huang, Huiling | Wu, FeiBin | Han, Jun | Ma, Mengyuan | Zhang, Yantong | Feng, Yanbing
Article Type: Research Article
Abstract: This paper introduces a novel neural network architecture and an enhanced data synthesis method that significantly boost the performance in removing complex smoke from images. The architecture features a multi-branch and multi-scale feature fusion design, which effectively integrates multiple feature streams and adaptively restores the background by identifying specific smoke characteristics within the image. A newly designed Fourier residual block is incorporated to capture frequency domain information, enabling the network to process and transform information across both spatial and frequency domains. To improve the network’s generalization ability and robustness, an in-depth analysis of the imaging process in smoky environments was …conducted, leading to an improved method for synthesizing smoke images. This methodology facilitates the creation of a more varied and realistic training dataset, substantially enhancing the neural network’s capabilities in image restoration. Experimental results show that this approach is highly effective on both synthetic and real-world smoke datasets, outperforming existing image de-smoking methods in terms of quantitative metrics and visual perception. The code for this method is available at https://github.com/Exiagit/MFSR. Show more
Keywords: Single image smoke removal, frequency domain learning, data synthesis method
DOI: 10.3233/JIFS-239146
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Nieves, Juan Carlos | Osorio, Mauricio | Rojas-Velazquez, David | Magallanes, Yazmín | Brännström, Andreas
Article Type: Research Article
Abstract: Humans have evolved to seek social connections, extending beyond interactions with living beings. The digitization of society has led to interactions with non-living entities, such as digital companions, aimed at supporting mental well-being. This literature review surveys the latest developments in digital companions for mental health, employing a hybrid search strategy that identified 67 relevant articles from 2014 to 2022. We identified that by the nature of the digital companions’ purposes, it is important to consider person profiles for: a) to generate both person-oriented and empathetic responses from these virtual companions, b) to keep track of the person’s conversations, activities, …therapy, and progress, and c) to allow portability and compatibility between digital companions. We established a taxonomy for digital companions in the scope of mental well-being. We also identified open challenges in the scope of digital companions related to ethical, technical, and socio-technical points of view. We provided documentation about what these issues mean, and discuss possible alternatives to approach them. Show more
Keywords: Conversational agents, well-being, mental health, trustworthy artificial intelligence
DOI: 10.3233/JIFS-219336
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Gomathi, S.V. | Jayalakshmi, M.
Article Type: Research Article
Abstract: This article focuses on an area of nonlinear programming problems known as linear fractional programming problems with multiple objectives. When tackling real-world linear fractional optimization problems, ambiguity and uncertainty in decision-making are inherent. This research aims to present a simple and computationally quick approach to solving multiple objective linear fractional programming problems with all decision variables and parameters described in terms of crisp. The proposed solution algorithm is based primarily on the fuzzy-based technique, and a membership function strategy. To resolve the multi-objective linear fractional programming problem, first consider the problem as a single objective function and along with the …fuzzy programming model obtain the optimal solution using LINGO software. LINGO is a software application primarily used for solving linear, nonlinear, and integer optimization problems Moreover, an e-education setup problem demonstrates the steps of the proposed method. Show more
Keywords: Linear fractional programming problem, multi-objective linear fractional programming, fuzzy mathematical programming, hyperbolic membership function
DOI: 10.3233/JIFS-234286
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Sánchez-Jiménez, Eduardo | Cuevas-Chávez, Alejandra | Hernández, Yasmín | Ortiz-Hernandez, Javier | Hernández-Aguilar, José Alberto | Martínez-Rebollar, Alicia | Estrada-Esquivel, Hugo
Article Type: Research Article
Abstract: Machine learning algorithms have been used in diverse areas among applications, including healthcare. However, to fit an effective and optimal machine learning model, the hyperparameters need to be tuned. This process is commonly referred to as Hyperparameter Optimization and comprises several approaches. We combined three Hyperparameter Optimization techniques (Bayesian Optimization, Particle Swarm Optimization, and Genetic Algorithm) with three classifiers (Random Forest, Support Vector Machine, and XGBoost) to identify the best combination of hyperparameters that maximize model performance. We use the Framingham dataset to test the proposal. For classifier performance, the Support Vector Machine obtained the best result in recall (96.40%) …and F-score (93.86%), while XGBoost obtained the best result in precision (96.30%) and specificity (96.36%). In the accuracy metric, both classifiers achieved 95%. Bayesian optimization had the best results in terms of accuracy, precision, specificity, and F-score metrics. Both Particle Swarm Optimization and Genetic Algorithm obtained the best result in the recall metric. Show more
Keywords: Bayesian optimization, framingham dataset, genetic algorithm, heart disease, hyperparameter default value, hyperparameter optimization, machine learning, particle swarm optimization, support vector machine, XGBoost
DOI: 10.3233/JIFS-219376
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Cosío-León, M.A. | Martínez-Vargas, Anabel | Rodríguez-Cortés, Gabriela
Article Type: Research Article
Abstract: It is well-known that tuning a metaheuristic is a critical task because the performance of a metaheuristic and the quality of its solutions depend on its parameter values. However, finding a good parameter setting is a time-consuming task. In this work, we apply the upper confidence bound (UCB) algorithm to automate offline tuning in a (1 + 1)-evolution strategy. Preliminary results show that our proposed approach is a less costly method.
Keywords: Upper confidence bound algorithm, meta-optimizer, bandit problems, reinforcement learning
DOI: 10.3233/JIFS-219362
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Akhmetova, Dilyara | Akhmetov, Iskander | Pak, Alexander | Gelbukh, Alexander
Article Type: Research Article
Abstract: The paper focuses on the importance of coherence and preserving the breadth of content in summaries generated by the extractive text summarization method. The study utilized the dataset containing 16,772 pairs of extractive and corresponding abstractive summaries of scientific papers specifically tailored to increase text coherence. We smoothed the extractive summaries with a Large Language Model (LLM) fine-tuning approach and evaluated our results by applying the coefficient of variation approach. The statistical significance of the results was assessed using the Kolmogorov-Smirnov test and Z-test. We observed an increase in coherence in the predicted texts, highlighting the effectiveness of our proposed …methods. Show more
Keywords: Coherence, cohesion, extractive summary, abstractive summary, GPT2, summarization, seq2seq, random forest
DOI: 10.3233/JIFS-219353
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Ibarra Carrillo, Mario Alfredo | Montiel Pérez, Jesús Yaljá | Molina Lozano, Herón
Article Type: Research Article
Abstract: Today, it is the amount of data that defines the existence of mankind. Scientists respond to the large amount of required calculations by developing hardware in several directions. One of them is to increase the number of arithmetic elements. Another direction is to create new architectures that represent new algorithms for processing numerical data. We have chosen the second direction by developing a new systolic core architecture, which implies an improvement in efficiency, i.e. performing the same task with the same number of arithmetic elements but reducing the latency. Measurements are made in terms of computational capacity and the number …of arithmetic elements involved in the operations. The results of the tests are compared with data from a number of selected articles. Today, we have achieved 3.2GFlops with only two modules. In the future, we plan to integrate up to four of our cores in a system with its own memory and management processor and at a higher operating frequency. Show more
Keywords: Systolic array, systolic tensor core, accelerated matrix multiplication, accelerated convolution
DOI: 10.3233/JIFS-219361
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: El Alaoui Elfels, Mohamed | Douiri, Moulay Rachid | Raoufi, Mustapha
Article Type: Research Article
Abstract: The generation of power in Photovoltaic systems is reduced when they operate far from their maximum power point. For optimal operation, it is essential to continuously track the maximum power point of the PV solar array. However, identifying the maximum power point is a challenge due to the nonlinear relationship of electrical characteristics of PV panels with external factors. To address this issue, we present a novel design approach for a self-organizing, self-tuning fuzzy logic controller, applied to the problem of maximum power point tracking in photovoltaic systems. We outline the basic structure of the fuzzy logic controller and address …the design problems typically associated with conventional trial-and-error schemes. We also discuss the suitability of the genetic algorithm optimization technique for determining and optimizing the fuzzy logic controller design. In our proposed approach, we translate the normalization factors, membership function parameters, and controller policy into bit-strings, which are then processed by the genetic algorithm to find a near-optimal solution. To achieve high dynamic performance, we choose a particular objective function as a performance index. We compare our approach with two variants of the maximum power point algorithm, one based on genetic algorithms and the other based on fuzzy logic, as well as with the methods described in references [34 ] and [35 ], in order to evaluate its effectiveness. Show more
Keywords: Fuzzy logic controller, genetic algorithm optimisation, optimal power, photovoltaic system
DOI: 10.3233/JIFS-231710
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Tang, Ao | Wang, Xiaofeng | Peng, Qingyuan | Wang, Junxia | Yang, Yi | He, Fei | Hua, Yingying
Article Type: Research Article
Abstract: A CNF formula with each clause of length k and each variable occurring 4s times, where positive occurrences are 3s and negative occurrences are s , is a regular (3s + s , k )-CNF formula (F 3s +s ,k formula). The random regular exact (3s + s , k )-SAT problem is whether there exists a set of Boolean variable assignments such that exactly one literal is true for each clause in the F 3s +s ,k formula. By introducing a random instance generation model, the satisfiability phase transition of the solution is analyzed by …using the first moment method, the second moment method, and the small subgraph conditioning method, which gives the phase transition point s* of the random regular exact (3s + s , k )-SAT problem for k ≥3. When s < s* , F 3s +s ,k formula is satisfiable with high probability; when s > s* , F 3s +s ,k formula is unsatisfiable with high probability. Finally, through the experimental verification, the results show that the theoretical proofs are consistent with the experimental results. Show more
Keywords: Random regular exact (3s + s, k)-SAT problem, first moment method, second moment method, small subgraph conditioning method, phase transition
DOI: 10.3233/JIFS-238254
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Kolesnikova, Olga | Yigezu, Mesay Gemeda | Gelbukh, Alexander | Abitte, Selam | Sidorov, Grigori
Article Type: Research Article
Abstract: Twitter has experienced a tremendous surge in popularity over recent years, establishing itself as a prominent social media platform with a large user base. However, with this increased usage, there has been a concerning rise in the number of individuals resorting to derogatory language and expressing their opinions in a demeaning manner toward others. This surge in hate speech has drawn significant attention to the field of sentiment analysis, which aims to develop algorithms capable of detecting and analyzing emotions expressed in social networks using intuitive approaches. This paper focuses on addressing the complex task of detecting hate speech and …aggressive behavior while performing target classification. We explored various deep-learning approaches, including LSTM, BiLSTM, CNN, and GRU. Each offers unique capabilities for capturing different aspects of the input data. We proposed an ensemble approach that combines the top three performing models. This ensemble approach benefits from the diverse strengths of each individual model showing F1 score of 0.85 for English-HS, 0.94 for English-TR, 0.92 for English-AB, 0.84 for Spanish-HS, 0.86 for Spanish-TR, 0.97 for Spanish-AB, 0.74 for multilingual-HS, 0.94 for multilingual-TR, and 0.88 for multilingual-AB. Show more
Keywords: Hate speech, aggressive behavior, target classification, ensemble learning, deep learning, target classification
DOI: 10.3233/JIFS-219350
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Valencia-Valencia, Alex I. | Gomez-Adorno, Helena | Stephens Rhodes, Christopher | Bel-Enguix, Gemma | Trueba, Ojeda | Fuentes Pineda, Gibran
Article Type: Research Article
Abstract: Social media platforms, such as Twitter (now X), are a major source of communication. Identifying communicative intentions is useful, as it encapsulates the latent motivations that drive text creation. This intention is also helpful in understanding the message, context, and audience. This study proposes a method for detecting communicative intentions in tweets using Jakobson’s language functions. We constructed a meticulously annotated dataset, drawing from the extensive RepLab2013 corpus. Our dataset underwent rigorous scrutiny by linguistic annotators who analyzed over 12,000 tweets individually. These experts identified the dominant language function within each tweet by employing diverse strategies to ensure precise labeling …quality. The outcome demonstrated a noteworthy Kappa agreement score of 0.6, reflecting a strong inter-annotator reliability. Subsequently, these functions were mapped to the corresponding intention categories. We employed logistic regression and support vector machines (SVM) algorithms to classify intention in tweets and explored various pre-processing techniques, incorporating n-grams and bag-of-words representations. Furthermore, we expanded our research using pre-trained large language models, incorporating the latest state-of-the-art techniques in natural language processing. Show more
Keywords: Intention, communicative intention, tweets, language functions, intention identification, n-grams, logistic regression, SVM, deep learning
DOI: 10.3233/JIFS-219357
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Rasham, Tahair | Kutbi, Marwan Amin | Hussain, Aftab | Chandok, Sumit
Article Type: Research Article
Abstract: The objective of this research is to propose some new fixed point theorems for fuzzy-dominated operators that satisfy a nonlinear contraction on a closed ball in a complete b -multiplicative metric space. Our strategy involves the use of a combination of two distinct kinds of mappings: one belongs to a weaker class of strictly increasing mappings, and the other is a class of dominated mappings. In order to demonstrate the validity of our new findings, we provide instances that are both illustrative and substantial. Finally, in order to illustrate the novelty of our findings, we provide applications that allow us …to derive the common solution to integral and fractional differential equations. Our findings have a significant impact on the interpretation of a large number of previously published studies, both present and historical. Show more
Keywords: Fixed point, b-multiplicative metric space, generalized nonlinear contraction, fuzzy dominated operators, graph contraction, ordered fuzzy mappings, integral equation, fractional differential equation
DOI: 10.3233/JIFS-238250
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Zhang, Xin-jie | Li, Jun-qing | Liu, Xiao-feng | Tian, Jie | Duan, Pei-yong | Tan, Yan-yan
Article Type: Research Article
Abstract: Enterprises have increasingly focused on integrated production and transportation problems, recognizing their potential to enhance cohesion across different decision-making levels. The whale optimization algorithm, with its advantages such as minimal parameter control, has garnered attention. In this study, a hybrid whale optimization algorithm (HWOA) is designed to settle the distributed no-wait flow-shop scheduling problem with batch delivery (DNWFSP-BD). Two objectives are considered concurrently, namely, the minimization of the makespan and total energy consumption. In the proposed algorithm, four vectors are proposed to represent a solution, encompassing job scheduling, factory assignment, batch delivery and speed levels. Subsequently, to generate high-quality candidate …solutions, a heuristic leveraging the Largest Processing Time (LPT) rule and the NEH heuristic is introduced. Moreover, a novel path-relinking strategy is proposed for a more meticulous search of the optimal solution neighborhood. Furthermore, an insert-reversed block operator and variable neighborhood descent (VND) are introduced to prevent candidate solutions from converging to local optima. Finally, through comprehensive comparisons with efficient algorithms, the superior performance of the HWOA algorithm in solving the DNWFSP-BD is conclusively demonstrated. Show more
Keywords: Distributed no-wait flow shop, batch delivery, hybrid whale optimization algorithm, path-relinking
DOI: 10.3233/JIFS-238627
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Fan, Zhou | Yanjun, Shen | Zebin, Wu
Article Type: Research Article
Abstract: In this article, a non-fragile adaptive fuzzy observer is proposed for nonlinear systems with uncertain external disturbance and measurement noise. Firstly, the nonlinear system is augmented by an output filtered transformation. The output with measurement disturbance is put into the state equation of the augment system. Then, we introduce fuzzy logic system (FLS) to approximate the measurement disturbance, and construct an augmented non-fragile adaptive fuzzy observer for the augment system. A Lyapunov function is constructed to reveal that the characteristic of estimation errors is uniformly ultimately boundedness (UUB). Finally, two experimental simulations are offered to confirm the validity of the …proposed design method. Show more
Keywords: Non-fragile, high-gain observer, adaptive observer, fuzzy logic system
DOI: 10.3233/JIFS-237271
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Rajesh Kannan, A. | Thirupathi, G. | Murali Krishnan, S.
Article Type: Research Article
Abstract: Consider the graph G , with the injection Ω from node set to the first p + q natural numbers. Let us assume that the ceiling function of the classical average of the node labels of the end nodes of each link is the induced link assignment Ω * . If the union of range of Ω of node set and the range of Ω * of link set is all the first p + q natural numbers, then Ω is called a classical mean labeling. A super classical mean graph is a graph …with super classical mean labeling. In this research effort, we attempted to address the super classical meanness of graphs generated by paths and those formed by the union of two graphs. Show more
Keywords: Labeling, super classical mean labeling, super classical mean graph
DOI: 10.3233/JIFS-232328
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]