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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: 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
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