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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: Midula, P. | Shine, Linu | George, Neetha
Article Type: Research Article
Abstract: Fabrication of semiconductor wafers is a complex process and chances of defect wafers are high. Because of defective wafers the circuit patterns will not be created correctly and it is necessary to identify them. Manual identification of defects are time consuming and expensive. Deep learning methods are widely used for defect detection. In this paper we propose a simple Convolutional Neural Network (CNN) model for classification of nine defects in wafers. A custom CNN consisting of 9 layers is used for the classification of defects as Center, Donut, Edge-Loc, Edge-Ring, Loc, Random, Scratch, Near-full, and None. Performance of the model …is evaluated using WM-811K dataset. Results shows that the model classifies the defects with high confidence score and an accuracy of 99.1% is achieved using this method. Further, the convolution operation in the CNN is realized using Coordinate Rotation Digital Computer (CORDIC) algorithm. The model is implemented in Field Programmable Gate Arrays (FPGA) and proved less complex method and consume less computational power than conventional methods. Show more
Keywords: CNN, CORDIC, FPGA, wafer maps
DOI: 10.3233/JIFS-219430
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Kaur, Amandeep | Rama Krishna, C. | Patil, Nilesh Vishwasrao
Article Type: Research Article
Abstract: Software-Defined Networking (SDN) is a modern networking architecture that segregates control logic from data plane and supports a loosely coupled architecture. It provides flexibility in this advanced networking paradigm for any changes. Further, it controls the complete network in a centralized using controller(s). However, it comes with several security issues: Exhausting bandwidth and flow tables, Distributed Denial of Service (DDoS) attacks, etc. DDoS is a powerful attack for Internet-based applications and services, traditional and SDN paradigms. In the case of the SDN environment, attackers frequently target the central controller(s). This paper proposes a Kafka Streams-based real-time DDoS attacks classification approach …for the SDN environment, named KS-SDN-DDoS. The KS-SDN-DDoS has been designed using highly scalable H2O ML techniques on the two-node Apache Hadoop Cluster (AHC). It consists of two modules: (i) Network Traffic Capture (NTCapture) and (ii) Attack Detection and Traffic Classification (ADTClassification). The NTCapture is deployed on the two nodes Apache Kafka Streams Cluster (AKSC-1). It captures incoming network traffic, extracts and formulates attributes, and publishes significant network traffic attributes on the Kafka topic. The ADTClassification is deployed on the two nodes Apache Kafka Streams Cluster (AKSC-2). It consumes network flows from the Kafka topic, classifies it based on the ten attributes, and publishes it to the decision Kafka topic. Further, it saves attributes with outcome to the Hadoop Distributed File System (HDFS). The KS-SDN-DDoS approach is designed and validated using the recent “DDoS Attack SDN dataset”. The result shows that the proposed system gives better classification accuracy (100%). Show more
Keywords: Control plane, real-time, dynamic network, Apache Hadoop, data plane, Kafka streams
DOI: 10.3233/JIFS-219405
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Xu, Ying | Ji, Xinrong | Zhu, Zhengyang
Article Type: Research Article
Abstract: With the increasing penetration of distributed energy resources (DER) in microgrids, DER power inverters have become a critical asset for providing power support to these microgrids. Meanwhile, the grid-forming (GFM) inverters, among these DER inverters, have gained significant attention in microgrid applications for their capability to enable the DERs to operate in different microgrid conditions and various operation modes. Moreover, with the implementation of these GFM inverters, smooth operation mode transition, GFM functions as well as black start functions can be obtained to improve the operation of the microgrid systems. In this article, a generalized control method for a single-phase …GFM inverter is developed for community microgrid applications, facilitating smooth operation behavior in both operation modes with grid support functions and stable transition for different microgrid conditions. The control design procedure and function analysis of the proposed control method are explained in detail based on the community microgrid system. The effectiveness of the method in this paper is demonstrated on a 10 kW single-phase GFM inverter prototype with comparison to a model predictive method in recent literature. Show more
Keywords: Grid-forming inverter, microgrid, grid-support function, stable transition
DOI: 10.3233/JIFS-236902
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Tian, Jing | Zhao, Ziqi | Lin, Zheng | Zhang, Fengling | Chen, Renzhen
Article Type: Research Article
Abstract: Inter-shaft bearings are an essential component of aircraft engines, and their operational status determines the safety of aircraft engine operation. Therefore, to improve the accuracy of fault type prediction and enrich the feature information in vibration signals of aircraft engine inter-shaft bearings, this paper proposes an STFT-CNN model based on the AlexNet architecture, extending its application to the research of aircraft engine inter-shaft bearing fault diagnosis. This approach addresses the common reliance on personnel experience for fault type diagnosis in traditional aircraft engine inter-shaft bearing fault diagnosis. Firstly, real vibration fault signals from inter-shaft bearings are collected through experiments to …enrich feature information in non-stationary signals using STFT time-frequency methods. Secondly, utilizing the high interpretability of the STFT-CNN model, fault feature data from inter-shaft bearings under various operating conditions are extracted to refine our understanding of fault feature information. Finally, leveraging the robustness of the STFT-CNN model, fault types are classified and predicted. The training process involves comparative analysis using different pooling algorithms, time-frequency analysis methods, and various deep learning network models. The results demonstrate that the STFT-CNN model, employing the maximum pooling algorithm, outperforms other models in predicting inter-shaft bearing faults, achieving an average fault prediction accuracy of 98.8% . Show more
Keywords: Inter-shaft bearings, STFT-CNN model, pooling algorithms, feature extraction, classification prediction
DOI: 10.3233/JIFS-240044
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Li, Yibing | Jiang, Shijin | Wang, Lei
Article Type: Research Article
Abstract: With explosive growth of industrial big data, workshop scheduling faces problems such as high complexity, multi-dimensionality and low stability. Recent years, the wide application of deep learning provides new idea for scheduling problem. In this paper, a hybrid deep convolution network and differential evolution algorithm is proposed to solve the non-permutation flow shop scheduling problem with the goal of minimizing total completion time. Mining relationship between job attributes and process priority by deep convolutional network is core idea of this method. In this paper, differential evolution algorithm is used to obtain the data set for deep learning, and neighborhood search …algorithm is used to optimize scheduling solution. Additionally, a method combining k-means algorithm and data statistics is proposed, which provides a reasonable way for priority division. The experimental results show that this method can greatly improve scheduling efficiency. Show more
Keywords: Differential evolution algorithm, convolutional neural network, K-means algorithm; priority, flow shop scheduling
DOI: 10.3233/JIFS-236874
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Duvvuri, Kavya | Kanisettypalli, Harshitha | Masabattula, Teja Nikhil | Amudha, J. | Krishnan, Sajitha
Article Type: Research Article
Abstract: Glaucoma is an eye disease that requires early detection and proper diagnosis for timely intervention and treatment which can help slow down further progression and to manage intraocular pressure. This paper aims to address the problem by proposing a novel approach that combines a model-based Reinforcement Learning (RL) approach, called DynaGlaucoDetect, with ocular gaze data. By leveraging the RL algorithms to simulate and predict the dynamics of glaucoma, a model-based approach can improve the accuracy and efficiency of glaucoma detection by enabling better preservation of visual health. The RL agent is trained using real experiences and synthetic experiences which are …generated using the model-based algorithm Dyna-Q. Two different Q-table generation methods have been discussed: the Direct Synthesis Method (DSM) and the Indirect Synthesis Method (IdSM). The presence of glaucoma has been detected by comparing the reward score a patient obtains with the threshold values obtained through the performed experimentation. The scores obtained using DSM and IdSM have been compared to understand the learning of the agent in both cases. Finally, hyperparameter tuning has been performed to identify the best set of hyperparameters. Show more
Keywords: Glaucoma detection, model-based RL, Dyna-Q algorithm, reward system
DOI: 10.3233/JIFS-219400
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Ameen, Zanyar A. | Mohammed, Ramadhan A. | Al-shami, Tareq M. | Asaad, Baravan A.
Article Type: Research Article
Abstract: This paper introduces a new fuzzy structure named “fuzzy primal.” Then, it studies the essential properties and discusses their basic operations. By applying the q-neighborhood system in a primal fuzzy topological space and the Łukasiewicz disjunction, we establish a fuzzy operator (·) ⋄ on the family of all fuzzy sets, followed by its core characterizations. Next, we use (·) ⋄ to investigate a further fuzzy operator denoted by Cl ⋄ . To determine a new fuzzy topology from the existing one, the earlier fuzzy operators are explored. Such a new fuzzy topology is called primal fuzzy topology. Various properties of …primal fuzzy topologies are found. Among others, the structure of a fuzzy base that generates a primal fuzzy topology. Furthermore, the concept of compatibility between fuzzy primals and fuzzy topologies is introduced, and some equivalent conditions to that concept are examined. It is shown that if a fuzzy primal is compatible with a fuzzy topology, then the fuzzy base that produces the primal fuzzy topology is itself a fuzzy topology. Show more
Keywords: Fuzzy primal, fuzzy grill, fuzzy ideal, primal fuzzy topology, fuzzy ideal topology
DOI: 10.3233/JIFS-238408
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
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
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