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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: 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: 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
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