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: 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: Ma, Nana | Wang, Lili | Long, Yuting
Article Type: Research Article
Abstract: Music has been utilized throughout history as a medium for cultural communication and artistic expression, embodying various nations’ and societies’ ideologies and experiences. Music culture communication is crucial for encouraging cultural diversity and understanding and developing social cohesion and community building among people. Music teaching management is the process of setting up, arranging, and executing music education programs in a manner that successfully teaches students the essential skills and information necessary for becoming proficient musicians. Users’ exact preferences for various areas of attraction cannot be determined, nor are users’ choices for traditional music recommendations sufficiently accurate. A recommender system estimates …or anticipates people’s preferences and offers appropriate recommendations. First, the sparsity problem emerges when insufficient data is accessible for the recommendation, and the coverage is one of the key drawbacks of social labeling. Cold start issues might be difficult since new music learners might not have given sufficient details about their musical tastes. Hence, the Hybridized Fuzzy logic-based Content and Collaborative Music Recommendation (HFC2MR) system is proposed to create personalized music teaching plans that are effective and engaging for each student based on their music preferences and learning outcomes. Enhanced Fuzzy C-Means clustering is used in collaborative recommendations to group users based on their shared musical tastes and to provide each user with more individualized, accurate music recommendations based on other users’ listening habits and preferences in the same cluster. Subsequently, an assessment of the recommender system using parameters like accuracy, precision, f1-score, and recall ratio is shown with optimal cluster selection. The coverage ratio is used to compare experimental data based on skill capacity covered through the assessment of music teaching. RMSE metric is used to evaluate the accuracy of students’ performance based on music attributes related to teaching goals. Show more
Keywords: Music teaching management, fuzzy logic, recommender system, clustering and similarity
DOI: 10.3233/JIFS-232422
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Zhou, Yue | Chen, Qiwei
Article Type: Research Article
Abstract: Studying the evolution of karst rocky desertification (KRD) in control areas of diverse geomorphologic types and its correlation with land use provides valuable insights for identifying priority areas and implementing effective treatment measures. Employing Remote Sensing (RS) and GIS, this research quantitatively examines the evolution of KRD and its relationship with land use in the karst mountain and gorge areas of Guizhou Province over the period 2010 to 2020. The findings reveal continuous improvement in KRD across the study areas, albeit with noticeable regional disparities. Notably, the karst mountain region exhibited significantly higher change areas and rates of KRD, non-KRD, …light KRD, and moderate KRD compared to the gorge area, underscoring better desertification control in the former region. A discernible correlation emerges between different karst geomorphologic types, the distribution and changes in land use types, and the evolution of KRD. Land use change emerges as a pivotal factor influencing the improvement of KRD in these areas. Changes in land use patterns corresponded with a decrease in KRD in dry land, other woodland, grassland, and bare land across both regions. However, the response of KRD to land use patterns varied across control areas with different geomorphologic environments, resulting in geographical differentiation in KRD evolution. Key land use conversions, notably from shrubland to forestland and dry land to garden land in the gorge, and shrubland to forestland in the mountain, contributed significantly to KRD dynamics in these regions. Notably, in the gorge area, KRD primarily occurred in garden land, other woodland, dry land, and grassland. In contrast, in the mountain area, KRD was prevalent in shrubland, dry land, and grassland, highlighting distinct responses and contributions to its evolution. The study observes substantial land use change in KRD-improved areas, particularly in the gorge region. Notably, the responsiveness of KRD to woodland conversions (shrubland, forestland, other woodland) varied across different geomorphologic environments. The dynamics of rocky desertification occurrence (RDO) and the occurrence structure of KRD in various land use types exhibited significant differences between the two regions. The gorge area demonstrated generally higher RDO, with a relatively stable and simpler occurrence structure of KRD compared to the more dynamic and varied structure observed in the mountain area. The sequencing of KRD occurrence in both areas displayed stability in specific land use types, with varying intensities noted between them. Show more
Keywords: Karst, rocky desertification, land use, evolution, geomorphology
DOI: 10.3233/JIFS-241536
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Qin, Hao | Zou, Yanli | Yu, Guoliang | Liu, Huipeng | Tan, Yufei
Article Type: Research Article
Abstract: In the process of mapping outdoor undulating and flat roads, existing LiDAR SLAM systems often encounter issues such as map distortion and ghosting. These problems arise due to the low vertical resolution of multi-line LiDAR, which easily leads to the occurrence of odometry height drift during the mapping process. To address this challenge, this study propose a novel LiDAR SLAM system named SOHD-LOAM, designed specifically to suppress odometry height drift. This system encompasses several critical components, including data preprocessing, front-end LiDAR odometry, back-end LiDAR mapping, loop detection, and graph optimization. SOHD-LOAM leverages the road gradient limitation algorithm and the height …smoothing algorithm as its core, while also integrating the Kalman filter, loop detection, and graph optimization techniques. To evaluate the performance of SOHD-LOAM, the comprehensive experiments are conducted with using KITTI datasets and real-world scenes. The experimental results demonstrate that SOHD-LOAM achieves superior accuracy and robustness in global odometry compared to the state-of-the-art LEGO-LOAM. Specifically, the height error of the sequences 00, 05 experiment was found to be 40.62% and 61.92% lower than that of LEGO-LOAM. Additionally, the maps generated by SOHD-LOAM exhibit no distortion or ghosting, thereby significantly enhancing map quality. Show more
Keywords: Autonomous driving, SLAM, odometry height drift, road gradient limitation, height smoothing, loop detection
DOI: 10.3233/JIFS-235708
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Wei, YuHan | Kim, Young-Ju
Article Type: Research Article
Keywords: Camel herd algorithm (CHA), camel-bat swarm optimization (CBSO), cultural and creative product (CCP) Design, graphic design
DOI: 10.3233/JIFS-236320
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Lalitha, S. | Sridevi, N. | Deekshitha, Devarasetty | Gupta, Deepa | Alotaibi, Yousef A. | Zakariah, Mohammed
Article Type: Research Article
Abstract: Speech Emotion Recognition (SER) has advanced considerably during the past 20 years. Till date, various SER systems have been developed for monolingual, multilingual and cross corpus contexts. However, in a country like India where numerous languages are spoken and often humans converse in more than one language, a dedicated SER system for mixed-lingual scenario is more crucial to be established which is the focus of this work. A self-recorded database that includes speech emotion samples with 11 diverse Indian languages has been developed. In parallel, a mixed-lingual database is formed with three popular standard databases of Berlin, Baum and SAVEE …to represent mixed-lingual environment for western background. A detailed investigation of GeMAPS (Geneva Minimalistic Acoustic Parameter Set) feature set for mixed-lingual SER is performed. A distinct set of MFCC (Mel Frequency Cepstral Coefficients) coefficients derived from sine and cosine-based filter banks enriches the GeMAPS feature set and are proven to be robust for mixed-lingual emotion recognition. Various Machine Learning (ML) and Deep Learning (DL) algorithms have been applied for emotion recognition. The experimental results demonstrate GeMAPS features classified from ML has been quite robust for recognizing all the emotions across the mixed-lingual database of the western languages. However, with diverse recording conditions and languages of the Indian self-recorded database the GeMAPS with enriched features and classified using DL are proven to be significant for mixed-lingual emotion recognition. Show more
Keywords: Emotion, GeMAPS, mixed-lingual, sine, cosine filter bank
DOI: 10.3233/JIFS-219390
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Bisht, Akhilesh | Gupta, Deepa
Article Type: Research Article
Abstract: Neural Machine Translation (NMT) for low resource languages is a challenging task due to unavailability of large parallel corpus. The efficacy of Transformer based NMT models largely depends on scale of the parallel corpus and the configuration of hyperparameters implemented during model training. This study aims to delve into and elucidate the impact of hyperparameters on the performance of NMT models for low resource languages. To accomplish this, a series of experiments are conducted using an open-source Hindi-Kangri corpus to train both supervised and semi-supervised NMT models. Throughout the experimentation process, a significant number of discrepancies were identified within the …data-set, necessitating manual correction. The best translation performance evaluated with respect to the metrics such as BLEU (0–1), SacreBLEU (0–100), Chrf (0–100), Chrf+ (0–100), Chrf++ (0–100) and TER (%) is (0.15, 14.98, 41.43, 41.49, 38.77, 68.20) for Hindi to Kangri direction, and (0.283, 28.17, 49.71, 50.64, 48.63, 51.25) for Kangri to Hindi direction. Show more
Keywords: Neural machine translation, low resource language, low resource MT, transformers, semi-supervised MT, Kangri, natural language processing
DOI: 10.3233/JIFS-219384
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Momena, Alaa Fouad | Gazi, Kamal Hossain | Mukherjee, Asesh Kumar | Salahshour, Soheil | Ghosh, Arijit | Mondal, Sankar Prasad
Article Type: Research Article
Abstract: Use of the Internet of Everything (IoE), the number of smart gadgets increasing rapidly giving the side effect of huge data, which has led to issues with traditional cloud computing models like inadequate security, slow response times, poor privacy, and bandwidth overload. Conventionally, cloud computing is no longer adequate for supporting the diversified needs of the user and the extraordinary society of data processing, so edge computing technologies have been revealed. This study considers edge computing in an educational institute in a scientific way. Multi criteria decision making (MCDM) is one of the most suitable decision making processes that propose …to choose optimal alternatives by considering multiple conflicting criteria. Entropy weighted method is considered to evaluate factor weight. Weighted Aggregated Sum Product Assessment (WASPAS) and Combined Compromise Solution (CoCoSo) based MCDM methodologies examine the ranking of alternatives for this study. Multiple decision makers (DMs) give opinions with Pentagonal Fuzzy Soft Set (PFSS) to express the uncertainty and fuzziness of the data set. The set operations and arithmetic operations of PFSS are discussed in detail. Also, a new de-fuzzification method of PFSS is proposed in this study. Calculated the criteria weight and prioritized the alternative based on source data. Lastly, sensitivity analysis and comparative analysis are conducted to check the stability of the result. Show more
Keywords: Edge computing, Academic institute, PFSS, Entropy, WASPAS, CoCoSo
DOI: 10.3233/JIFS-239887
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Jaiseeli, C. | Raajan, N.R.
Article Type: Research Article
Abstract: Medical and satellite image analysis require incredibly high resolution. Super-resolution combines several low-resolution images of the same scene to generate a high-resolution image. The Super resolution employing deep learning techniques still has an illumination issue. This paper proposes a novel CGIHE-VDSR algorithm that integrates the Very Deep Super Resolution (VDSR) Network with Color Global Image Histogram Equalization (CGIHE) to improve image resolution. In the proposed method, the low-resolution image is first histogram equalized using the CGIHE algorithm. Then, the VDSR network is applied to the histogram equalized image for super-resolution. The comparison of real-time data with the benchmark images is …done using the proposed algorithm in the MATLAB platform. The PSNR and SSIM metrics demonstrate that the super resolution image obtained using the proposed method is significantly better than the existing methods. Show more
Keywords: Histogram equalization, super-resolution, CNN, subsample image, VDSR, residual
DOI: 10.3233/JIFS-219392
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Javed, Hira | Sufyan Beg, M.M. | Akhtar, Nadeem | Alroobaea, Roobaea
Article Type: Research Article
Abstract: Vlogs, Recordings, news, sport coverages are huge sources of multimodal information that do not just limit to text but extend to audio, images and videos. Applications such as summary generation, image/video captioning, multimodal sentiment analysis, cross modal retrieval requires Computer Vision along with Natural Language Processing techniques to extract relevant information. Information from different modalities must be leveraged in order to extract quality content. Hence, reducing the gap between different modalities is of utmost importance. Image to text conversion is an emerging field and employs the use of encoder decoder architecture. Deep CNNs extract the feature of images and sequence …to sequence models are used to generate text description. This paper is a contribution towards the growing body of research in multimodal information retrieval. In order to generate the textual description of images, we have performed 5 experiments using the benchmark Flickr8k dataset. In these experiments we have utilized different architectures - simple sequence to sequence model, attention mechanism, transformer-based architecture to name a few. The results have been evaluated using BLEAU score. Results show that the best descriptions are attained by making use of transformer architecture. We have also compared our results with the pretrained visual model vit-gpt2 that incorporates visual transformer. Show more
Keywords: Multimodal, captioning, summarization, etc
DOI: 10.3233/JIFS-219394
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Kostiuk, Yevhen | Tonja, Atnafu Lambebo | Sidorov, Grigori | Kolesnikova, Olga
Article Type: Research Article
Abstract: In this paper, we investigate the issue of hate speech by presenting a novel task of translating hate speech into non-hate speech text while preserving its meaning. As a case study, we use Spanish texts. We provide a dataset and several baselines as a starting point for further research in the task. We evaluated our baseline results using multiple metrics, including BLEU scores. We used a cross-validation approach and an average of the metrics per fold for evaluation. We achieved a 0.236 sentenceBLEU score on four folds. This study aims to contribute to developing more effective methods for reducing the …spread of hate speech in online communities. Show more
Keywords: Hate speech, translation, Spanish
DOI: 10.3233/JIFS-219348
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: George, Neetha | Ramachandran, Sivakumar | Jiji, C.V.
Article Type: Research Article
Abstract: Macula is the part of retina responsible for sharp and clear vision. Macular edema is caused by the accumulation of intraretinal fluid (IRF) in the macula, which is further distinguished by the compromised integrity of the blood-retinal barrier, particularly evident in the retinal vasculature. This results in swelling, that may lead to vision impairment and is the dominant sign of several ocular diseases, including age-related macular degeneration, diabetic retinopathy, etc. Quantitative analysis of the fluid regions in macular edema helps in ascertaining the severity as well as the response to treatment of the diseases. Optical coherence tomography (OCT) is a …major tool used by ophthalmologists for visualizing edema. The prevalent practice for diagnosing and treating macular edema involves measuring Central Retinal Thickness (CRT). Segmenting the IRF in OCT images offers the potential for a more accurate and better quantification of macular edema. This paper proposes a novel method combining convolutional neural network (CNN) and active contour model for segmenting the IRF to ascertain the severity of macular edema. The IRF region is initially segmented using an encoder-decoder architecture. Contour evolution is then performed on this segmented image to demarcate the IRF boundaries. The advantage of the method is that it does not require precisely labeled images for training the CNN. A comparison of the experimental results with models employing CNN alone and with other state-of-the art methods demonstrates the superior performance and consistency of the proposed method. Show more
Keywords: edema segmentation, convolutional neural network, active contour model
DOI: 10.3233/JIFS-219401
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Wu, Donghui | Wang, Jinfeng | Zhao, Wanwan | Geng, Xin | Liu, Guozhi | Qiu, Sen
Article Type: Research Article
Abstract: Gesture recognition based on wearable sensors has received extensive attention in recent years. This paper proposes a gesture recognition model (CGR_ATT) based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) fused attention mechanism to improve accuracy rate of wearable sensors. First, CNN serves as a feature extractor, learning features automatically from sensor data by performing multiple layers of convolution and pooling operations, capturing spatial features of gestures. Furthermore, a temporal modeling unit GRU is introduced to capture the temporal dynamics in gesture sequences. By controlling the information flow through gate mechanisms, it effectively handles the temporal relationships in …sensor data. Finally, an attention mechanism is introduced to assign different weights to the hidden state of the GRU. By calculating the attention weights for each time period, the model automatically selects key time periods related to gesture movements. The GR-dataset proposed in this paper involves 910 sets of training parameters. The model achieves an ultimate accuracy of 97.57% . In compare with CLA-net, CLT-net, CGR, GRU, LSTM and CNN, the experimental results demonstrate that the proposed method has superior accuracy. Show more
Keywords: Wearable gesture recognition system, CGR_ATT model, deep learning, wearable devices
DOI: 10.3233/JIFS-240427
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Visvanathan, P. | Durai Raj Vincent, P.M.
Article Type: Research Article
Abstract: A Stroke is a sudden loss of blood circulation in certain parts of the brain that results in a loss of neurological function. To save a patient from stroke, an immediate diagnosis and treatment plan must be implemented. Artificial intelligence-based machine learning algorithms play a major role in the prediction. To predict a person likely to have a stroke, stroke healthcare data records must be accessed, which is very sensitive. Data shared for machine learning training pose security risks and have concerns about privacy. To overcome this issue, Genetic Algorithm and Federated Learning (GA-FL) –based hybridization approach is proposed to …predict the risk of stroke in a person. Federated Learning was developed by Google, which can provide security to the data during the training process because every client participating in this training process needs to exchange only the training parameters without sharing the data. In addition to the security features, a genetic algorithm was used to optimize the parameters required to train a model using the perceptron neural network model. The experimental results show that our proposed research model (GA-FL) provides security and predicts the risk of stroke more accurately than any other existing algorithm. Show more
Keywords: Federated learning, genetic algorithm, stroke risk, perceptron neural network
DOI: 10.3233/JIFS-236354
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Hu, Junhua | Zhou, Yingling | Li, Huiyu | Liang, Pei
Article Type: Research Article
Abstract: To enhance infection diseases interval prediction, an improved model is proposed by integrating neighborhood fuzzy information granulation (NNIG) and spatial-temporal graph neural network (STGNN). Additionally, the NNIG model can efficiently extract the most representative features from the time series data and identifies the support upper and lower bounds. NNIG model transfers time series data from numerical level to granular level, and processes data feed it into STGNN for interval prediction. Finally, experiments are conducted for evaluation based on the COVID-19 data. The results demonstrate that the NNIG outperforms baseline models. Further, it proves beneficial in offering a valuable approach for …policy-making. Show more
Keywords: Time series, fuzzy information granulation, interval prediction, spatial-temporal graph neural network
DOI: 10.3233/JIFS-236766
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Hossain, AKM B. | Salam, Md. Sah Bin Hj. | Alam, Muhammad S. | Hossain, AKM Bellal
Article Type: Research Article
Abstract: Semantic segmentation is crucial for the treatment and prevention of brain cancers. Several neural network–based strategies were rapidly presented by research groups to enhance brain tumor thread segmentation. The tumor’s uneven form necessitates the usage of neural networks for its detection. Therefore, improved patient outcomes may be achieved with precise segmentation of brain tumor. Brain tumors can range widely in size, form, and position, making diagnosis difficult. Thus, this work offers a Multi-level U-Net (MU-Net) approach for analyzing the brain tumor data augmentation for improved segmentation. Therefore, a significant amount of data augmentation is employed to successfully train the recommended …system, removing the problem of a lack of data when using MR images for the diagnosis of multi-grade brain cancers. Here, we presented the “Multi-Level Pyramidal Pooling (MLPP)” component, where a new pyramidal pool will be employed to capture contextual data for augmentation. The “High-Grade Glioma” (HGG) datasets from the Kaggle and BraTs2021 were used to assess the proposed MU-Net. Overall Tumor (OT), Enhancing Core (EC), and Tumor Core (TC) were the three main designations to be segmented. The dice score was used to contrast the results empirically. The suggested MU-Net fared better than most existing methods. Researchers in the fields of bioinformatics and medicine might greatly benefit from the high-performance MU-Net. Show more
Keywords: Brain tumor, Data Augmentation (DA), Multi-level U-Net (MU-Net), Multi-Level Pyramidal Pooling (MLPP), Adaptive Curvelet Transform (ACT), wavelet threshold
DOI: 10.3233/JIFS-232782
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wu, Jie | Hou, Mengshu
Article Type: Research Article
Abstract: Table-based fact verification (TFV) is a binary classification task that requires understanding and reasoning about both table and text. This task poses many challenges, such as table parsing, text comprehension, and numerical reasoning. However, existing methods tend to depend solely on pre-trained models for tables, treating all types of reasoning equally and disregarding the importance of identifying logic types in inference process. In this regard, we propose MoETFV, an efficient and explanatory approach to solving TFV, which is based on a Mixture-of-Experts (MoE) framework. This approach can detect the underlying logic types of statements and leverage multiple independent experts to …emulate diverse logical reasoning. It consists of one shared expert for general semantic understanding and several specific experts with distinct responsibilities for different logical inferences. Moreover, the practical applications of the MoE method in TFV are thoroughly investigated. This model doesn’t necessitate any table pre-trained models, and aligns closely with human cognitive processes in addressing such issues. Experimental results demonstrate the innovation and feasibility of the proposed approach. Show more
Keywords: Tabular data, fact verification, mixture-of-experts, logical reasoning, natural language processing
DOI: 10.3233/JIFS-238142
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Chen, Longkai | Huang, Jingjing
Article Type: Research Article
Abstract: Urban traffic accidents impose a significant threat to public safety because of its frequent occurrence and potential for severe injuries and fatalities. Hence, an effective analysis of accident patterns is crucial for designing accident prevention strategies. Recent advancement in data analytics have provided opportunities to improve the pattern of urban traffic accidents. However, the existing works face several challenges in adapting the complex dynamics, and heterogeneity of the accident data. To overcome these challenges, we proposed an innovative solution by combining the K-means clustering and Support Vector Machine to precisely predict the traffic accident patterns. By leveraging the efficiencies of …clustering technique and machine learning, this work intends to identify the intricate patterns within the traffic database. Initially, a traffic accident database was collected and fed into the system. The collected database was pre-processed to improve and standardize the raw dataset. Further, cluster analysis is employed to identify distinct patterns within the dataset and group similar accidents into clusters. This clustering enables the system to recognize common accident scenarios and identify recent accident trends. Subsequently, a Support Vector Machine is deployed to classify accidents into distinct categories through intensive training with identified clusters. The combination enables the system to understand the complex relationships among diverse accident variables, making it an effective framework for real-time pattern recognition. The proposed strategy is implemented in Python and validated using the publicly available traffic accident database. The experimental results manifest that the proposed method achieved 99.65% accuracy, 99.53% precision, 99.62% recall, and 99.57% f-measure. Finally, the comparison with the existing techniques shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. Show more
Keywords: Support vector machine, traffic accident pattern recognition, cluster analysis, machine learning
DOI: 10.3233/JIFS-241018
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Liu, Fei
Article Type: Research Article
Abstract: In China, aesthetic education at the college level is essential for students’ quality because it improves their understanding of art, helps them progress in their professional career development, and helps them comprehend more fully the attractiveness of creative creations. As a result, it needs to prioritize aesthetic education at the institution and endeavor to nurture students’ feelings progressively and improve their aesthetic abilities at different levels. Artificial intelligence (AI) is used in this project to create a novel, interdisciplinary teaching technique that will maximize students’ artistic and intellectual potential and help them make more, better art. In this research, the …Osprey Optimization method improves the interdisciplinary teaching technique for aesthetic education based on a light Exclusive gradient-boosting mechanism (OOM-LEGBM). The exploration-exploitation dynamics of the OOM are incorporated into LEGBM, providing the students with a tangible and relatable technique to understand complex-solving processes. This research develops an enhanced quality framework for college aesthetic education based on the multi-model data fusion system about the implication and necessity of aesthetic education. The influence of college aesthetic education on students’ creative capacity and artistic literacy was investigated to inform instructional activities better to develop students’ aesthetic skills. The experimental findings suggest that the proposed approach achieved an improved accuracy of 99.90%, higher precision of 99.88%, and greater recall of 99.91%. Moreover, it obtained a minimum Root Mean Square Error (RMSE) of 0.26% and a lower Mean Absolute Error (MAE) of 0.34%, showing that the suggested model greatly improved preference learning accuracy while keeping overall accuracy at an identical level. Innovation capacity building in college aesthetic education can help students become more self-aware, improve their study habits, visually literate, and more comprehensive. Show more
Keywords: Interdisciplinary teaching, aesthetic education, curriculum, multimodal data fusion, artificial intelligence, and big data
DOI: 10.3233/JIFS-240723
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Zhou, Yancong | Xu, Chenheng | Chen, Yongqiang | Li, Shanshan | Guo, Zhen
Article Type: Research Article
Abstract: Due to the complexity of the products from the ethanol coupling reaction, the C4 olefin yield tends to be low. Finding the optimal ethanol reaction conditions requires repeated manual experiments. In this paper, a novel learning framework based on least squares support vector machine and tree-structured parzen estimator is proposed to solve the optimization problem of C4 olefin production conditions. And shapley value is introduced to improve the interpretation ability of modeling method. The experimental results show that the proposed learning framework can obtain the combination of ethanol reaction conditions that maximized the C4 olefin yield It is nearly 17.30% …higher compared to the current highest yield of 4472.81% obtained from manual experiments. Show more
Keywords: C4 olefin production, complex problem optimization, model interpretability, LSSVM, SHAP, TPE
DOI: 10.3233/JIFS-235144
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Muthu Thiruvengadam, P. | Gnanavadivel, J.
Article Type: Research Article
Abstract: The Power solutions have become indispensable for all the devices in recent years with an appropriate power conversion circuitries and control methods to ensure good dynamic response, improved stability, reliability and efficiency. The main intent of this article is to impart the designing of interval type-2 fuzzy logic controller (IT2FLC) based interleaved Sepic power factor correction (PFC) converter. This work also involves the careful design of the robust controller with enhanced precision and good power quality (PQ) performance at the AC mains. In addition, the development of IT2FLC based power solution improves the overall power conversion with stabilized output in …the perspective of its quick rise time, less overshoot and fast settling time in comparison to other traditional controllers. Further, the uncertainties and issues associated with the conventional proportional integral (PI) and fuzzy logic controllers (FLCs) are handled effectively by the proposed IT2FLC controller. Moreover, this preferred converter is modeled with an internal parasitics and its performances are evaluated and compared with other conventional Zeigler Nicholas (ZN) tuned PI controller and FLC by dint of MATLAB/Simulink platform. Finally, the experimental test bench set up of 250 W, 48 V power circuitry is devised and the test outcomes confirm the excellent transient behavior and PQ performances of the modeled power solution. Show more
Keywords: Power quality, interval type-2 fuzzy logic controller, total harmonic distortion, power factor correction, discontinuous conduction mode and continuous conduction mode
DOI: 10.3233/JIFS-230325
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Belal, Mohamad Mulham | Sundaram, Divya Meena
Article Type: Research Article
Abstract: Visualization-based malware detection gets more and more attention for detecting sophisticated malware that traditional antivirus software may miss. The approach involves creating a visual representation of the memory or portable executable files (PEs). However, most current visualization-based malware classification models focus on convolution neural networks instead of Vision transformers (ViT) even though ViT has a higher performance and captures the spatial representation of malware. Therefore, more research should be performed on malware classification using vision transformers. This paper proposes a multi-variants vision transformer-based malware image classification model using multi-criteria decision-making. The proposed method employs Multi-variants transformer encoders to show different …visual representation embeddings sets of one malware image. The proposed architecture contains five steps: (1) patch extraction and embeddings, (2) positional encoding, (3) multi-variants transformer encoders, (4) classification, and (5) decision-making. The variants of transformer encoders are transfer learning-based models i.e., it was originally trained on ImageNet dataset. Moreover, the proposed malware classifier employs MEREC-VIKOR, a hybrid standard evaluation approach, which combines multi-inconsistent performance metrics. The performance of the transformer encoder variants is assessed both on individual malware families and across the entire set of malware families within two datasets i.e., MalImg and Microsoft BIG datasets achieving overall accuracy 97.64 and 98.92 respectively. Although the proposed method achieves high performance, the metrics exhibit inconsistency across some malware families. The results of standard evaluation metrics i.e., Q, R, and U show that TE3 outperform the TE1, TE2, and TE4 variants achieving minimal values equal to 0. Finally, the proposed architecture demonstrates a comparable performance to the state-of-the-art that use CNNs. Show more
Keywords: Vision transformer, MCDM, VIKOR, MEREC, image malware classifier
DOI: 10.3233/JIFS-235154
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Wang, R | Yu, F.S | Zhao, L.Y
Article Type: Research Article
Abstract: This paper demonstrates a fuzzy decentralized dynamic surface control (DSC) scheme for switched large-scale interconnected nonlinear systems under arbitrary switching, which contains non-strict feedback form and unknown input saturation uncertainties. An auxiliary design system is established to handled input saturation. Uncertainties of non-strict feedback form are learned by fuzzy logic systems (FLSs) approximators, DSC method is designed to conquer “explosion of complexity” inherented by repeated differential of virtute controller in backstepping approach. Ii is shown that based on common Lyapunov function (CLF) design and analysis scheme, all the closed-loop systems signals are uniformly ultimately bounded (UUB), simulation results are provided …to demonstrate the effectiveness of this proposed strategy. Show more
Keywords: DSC scheme, large-scale switched nonlinear systems(LSSNs), input saturation, non-strict feedback (NSF) form
DOI: 10.3233/JIFS-238024
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Hassan, Shabbir
Article Type: Research Article
Abstract: The CPU scheduling technique influences the performance and efficiency of operating systems. Round-robin scheduling algorithm is ideal for time-shared systems, but it is not optimal for real-time operating systems since it yields more context switching, longer waiting time, and high turnaround time. The performance of the algorithm is predominantly influenced by the designated time quantum; however, determining a suitable time quantum is extremely challenging. This paper presents a CPU scheduling algorithm that provides a better tradeoff between waiting time, turnaround time, response time, and number of context switch by using hypothesis-based quanta generation approach. It combines the CPU burst …requirements of actual processes with some noisy data and plots them against the presumed CPU quanta to get quanta densities so that a polynomial regression model can fit the data points with the highest adjusted R-squared. Then applying some complex inferential statistic, the required quanta is obtained. The scheduling is dynamic in nature because it generates the next CPU quanta in reference to the quanta that have been used in the previous cycle with remaining CPU burst requirements of the process, and it is also adaptive in nature because, at each cycle, it uses ‘d’ (5, 5, 4, 3, 2) degree of freedom to calculate the Jarque-Bera Statistics to accept/reject the hypothesis. The algorithm is implemented in ‘R’ and the performance has been evaluated on a sample size of five processes with some noisy data which outperforms the conventional RR and significantly reduces the performance parameters mentioned above. Implementing this algorithm to a time-sharing or distributed environment will undoubtedly improve system performance and will help to avoid issues like thrashing, incorporate aging, CPU affinity, and starvation. Since the proposed algorithm is work-conservative, therefore can be implemented in network packet switching, statistical multiplexing, and real-time systems. Show more
Keywords: Adaptive scheduling, context switching, CPU burst, jarque-bera, kernel density estimation, kurtosis, quanta, thrashing
DOI: 10.3233/JIFS-238624
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Alqaissi, Eman | Alotaibi, Fahd | Ramzan, Muhammad Sher | Algarni, Abdulmohsen
Article Type: Research Article
Abstract: The influenza virus can spread easily, causing significant public health concern. Despite the existence of different techniques for rapid detection and prevention of influenza, their efficiency varies significantly. Additionally, there is currently a lack of a comprehensive, interoperable, and reusable real-time model for detecting influenza infection and predicting relationships within the field of influenza analysis. This study proposed a comprehensive, real-time model for rapid and early influenza detection using symptoms. Further, new relationships in the influenza field were discovered. Multiple data sources were used for the influenza knowledge graph (KG). Throughout this study, various graph algorithms were utilized to extract …significant nodes and relationship features and multiple influenza detection machine learning (ML) models were compared. Node classification and link prediction methods were employed on a multi-layer perceptron (MLP) model. Furthermore, the hyperparameters of the model were automatically tuned. The proposed MLP model demonstrated the lowest rate of loss and the highest specificity, accuracy, recall, precision, and F1-score compared to state-of-the-art ML models. Moreover, the Matthews correlation coefficient was promising. This study shows that graph data science can improve MLP model detection and assist in discovering hidden connections in influenza KG. Show more
Keywords: Influenza detection, knowledge graph, graph multi-layer perceptron model, graph algorithms, automatic tuning, real-time analysis
DOI: 10.3233/JIFS-233381
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Chen, Sian | Zuo, Yajuan | Wang, Rui
Article Type: Research Article
Abstract: Traditional rule-based and statistical methods have limitations when dealing with complex language structures and semantics. In neural network machine translation algorithms, the objective function is usually to improve the accuracy of n-ary words. However, this does not guarantee a more natural and accurate translation. To overcome these challenges, this paper proposes an optimization algorithm for English natural translation processing based on neural networks, which combines Generative Adversarial Network (GAN) and Transformer models. In GAN, the generative model uses the Transformer model to generate false samples, while the discriminative model uses a binary classifier based on convolutional neural networks and attention …mechanisms to distinguish between true and false samples. During the training process, reinforcement learning algorithms are added to evaluate and adjust the generated sentences, and the parameters of the generated model are updated. The classification results of the discriminative model are used together with the Bilingual Evaluation Basis Value (BLEU) objective function to evaluate false samples, and the results are fed back to the generating model to guide parameter updates and optimization. Extensive experiments were conducted on a standard English-Chinese machine translation dataset to evaluate our method. Compared with the benchmark model that only uses supervised learning methods, our neural network-based optimization algorithm for English natural translation processing has achieved significant improvements in translation quality. According to statistical comparison, compared with the Transformer model (BLUE = 33.63 and AP = 90%) and the deep learning model based on long-term and short-term memory (BLUE = 30.26 and AP = 83%), the GAN and Transformer models proposed as the best framework exhibit better performance in bilingual evaluation deficiency (BLEU) (34.35) and accuracy (AP = 95%). Show more
Keywords: Artificial neural network, English translation, GAN, generator, discriminator, transformer model; Adam optimization algorithm, reinforcement learning method
DOI: 10.3233/JIFS-237181
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Kannan, Jeevitha | Jayakumar, Vimala | Pethaperumal, Mahalakshmi | Shanmugam, Nithya Sri
Article Type: Research Article
Abstract: Every day, the globe becomes more contemporary and industrialized. As a result, the number of vehicles and engines is growing. However, the energy sources utilized in these engines are scarce and dwindling over time. This circumstance prompts the search for alternate fuel. As civilization develops, transportation becomes a need for daily living. The largest issue is the diminishing supply of fossil fuels and the expanding population. As a result, everyone needs alternate energy sources for their automobiles. Therefore, in this investigation, we identify the best substitute for petrol. We offer the similarity measure(SM) for a hybrid structure of a Linear …Diophantine Multi-Fuzzy Soft Set(LDMFSS) with the goal of determining this issue. Because the range of grade values has been expanded, decision-makers now have greater freedom in selecting their grade. An exemplary case study is illustrated that shows the appropriateness of our recommended approach. A comparative analysis is provided to show the outcomes of the proposed method are more achievable and beneficial than those of the existing methodologies. Additionally, its applicability and attainability are evaluated by comparing its structure to those of the already used procedures. Show more
Keywords: Linear diophantine multi-fuzzy soft set, similarity measures, fossil fuels, alternative fuel, fuel specifications
DOI: 10.3233/JIFS-219415
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 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
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]