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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: Brintha, K. | Joseph Jawhar, S.
Article Type: Research Article
Abstract: Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research. A rapid advancement in deep learning has promoted new possibilities for research in this field. In this work, a novel deep learning-based FOD-YOLO net is proposed for detecting the fasteners faults and objects in the railway tracks. There are two basic components in the deep learning-based YOLOv8: the backbone and the head. YOLOv8 utilizes an improved version of the CSPDarknet53 network for detecting objects on the railway track. The head of YOLOv8 …consists of EfficientNet with various convolutional layers with squeeze and excitation blocks for detecting any defect in the track fasteners. These layers are liable for detecting the objectness scores, bounding boxes and class probabilities structured with fully connected layers for the objects and faults in tracks. Based on the results from the Yolo network, the alert message is sent to the loco pilot to avoid accidents using fuzzy logic. The experimental fallouts of proposed FOD-YOLO net achieve higher accuracy and yields better evaluation results with 98.14% accuracy, 98.84% precision and 95.94% recall. From the experimental results, the FOD-YOLO net improves the overall accuracy range by 5.44%, 4.72%, 0.73%, and 13.18% better than Fast RCNN, YOLOv5s-VF, YOLO-GD, and 2D-SSA + Deep network respectively. Show more
Keywords: Railway track, object detection, fault detection, deep learning, Yolo network, fuzzy logic
DOI: 10.3233/JIFS-236445
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Dagal, Idriss | Akín, Burak | Dari, Yaya Dagal
Article Type: Research Article
Abstract: In this paper, an improved constant current step based on the grey wolf optimization (CCS-GWO) algorithm for photovoltaic systems is investigated. The development of grey wolf optimization has been widely spread over photovoltaic applications. This method is one of the metaheuristic swarm optimization algorithm groups inspired by an optimum means of chasing prey by grey wolves. The proposed technique applies constant current steps to the pack of wolves (alpha, beta, and omega) by monitoring the average of the internal current step and external current step in order to target the leader alpha wolf position. Moreover, the proposed technique solves the …convergence process issues, low convergence speed, and premature local optima problems of the traditional GWO algorithm. This CCS-GWO algorithm accurately tracks the maximum power point from the photovoltaic systems for load charging in different partial shading conditions (PSCs). A number of standard benchmark functions are presented with low average cost functions and their corresponding standard deviation values. The simulation results revealed that the proposed CCS-GWO approach outperforms the existing GWO and GA algorithms in terms of efficiency (98.55%) and tracking time (0.3 s). Show more
Keywords: Grey wolf optimization, metaheuristics, photovoltaics, maximum power point.
DOI: 10.3233/JIFS-224535
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Teng, Wei | Li, Yan | Sun, Hongxing | Chen, Haojie
Article Type: Research Article
Abstract: In the present study, three hybrid models include support vector regression-salp swarm optimization (SVR-SSO), support vector regression-biogeography-based (SVR-BBO), and support vector regression-phasor particle swarm optimization (SVR- PPSO) was applied to forecast pond ash’s CBR value modified with lime sludge (LS) and lime (LI). In the developed models, five variables were selected as inputs. It can result that the developed integrated models have R2 bigger than 0.9952. It means the agreement between observed and forecasted values by hybrid models is mainly similar to represent the highest accuracy. In both the training and testing stages, PSO-SVR results from better performance than the …BBO-SVR model, with R2, RMSE, MAE, and PI equal to 0.9983, 0.6439, 0.3181, and 0.0081 for training data, and 0.9975, 0.7319, 0.4135, and 0.0141 for testing data, respectively. So, by considering the OBJ index, the OBJ value for PSO-SVR is 12.966, lower than BBO-SVR at 16.9957. Therefore, the PSO-SVR model outperforms another model to estimate the CBR of pond ash modified with LI and LS, consequently being recognized as the proposed model that makes it to be used for practical applications. Show more
Keywords: California bearing ratio, phasor particle swarm optimization, biogeography-based optimization, salp swarm optimization, support vector regression
DOI: 10.3233/JIFS-220745
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Li, Biao | Tang, Shoufeng | Li, Wenyi
Article Type: Research Article
Abstract: Pose estimation plays a crucial role in human-centered vision applications and has advanced significantly in recent years. However, prevailing approaches use extremely complex structural designs for obtaining high scores on the benchmark dataset, hampering edge device applications. In this study, an efficient and lightweight human pose estimation problem is investigated. Enhancements are made to the context enhancement module of the U-shaped structure to improve the multi-scale local modeling capability. With a transformer structure, a lightweight transformer block was designed to enhance the local feature extraction and global modeling ability. Finally, a lightweight pose estimation network— U-shaped Hybrid Vision Transformer, UViT— …was developed. The minimal network UViT-T achieved a 3.9% improvement in AP scores on the COCO validation set with fewer model parameters and computational complexity compared with the best-performing V2 version of the MobileNet series. Specifically, with an input size of 384×288, UViT-T achieves an impressive AP score of 70.2 on the COCO test-dev set, with only 1.52 M parameters and 2.32 GFLOPs. The inference speed is approximately twice that of general-purpose networks. This study provides an efficient and lightweight design idea and method for the human pose estimation task and provides theoretical support for its deployment on edge devices. Show more
Keywords: Pose estimation, multi-branch structure, lightweight network, context enhancement, attention mechanism
DOI: 10.3233/JIFS-231440
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 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: Myithili, K.K. | Beulah, R.D.
Article Type: Research Article
Abstract: The concept of intuitionistic fuzzy soft set is applied to generalize the theory of transversals in hypergraphs. The notion of transversals of an Intuitionistic Fuzzy Soft Hypergraphs (IFSHGs) and locally minimal transversals of IFSHGs are pioneered with some of its specifications. It is also proved that H ˜ is (μ, ν )-tempered IFSHGs if H ˜ is support simple, elementary and simply ordered. Then, an algorithm is developed and proposed to find the minimal transversals of IFSHGs. An application is also identified in selecting appropriate location for the …installation of wind turbines. Finally the proposed algorithm works in finding the suitable place for wind turbine installation. As a result the proposed algorithm is helpful in making decisions. Show more
Keywords: Transversals, locally minimal transversals, (μ, ν)-tempered IFSHGs
DOI: 10.3233/JIFS-222714
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Yu, Jie | Zhang, Jubin
Article Type: Research Article
Abstract: The rapid growth of the Internet of Things (IoT) brings sweeping changes in various industries. Healthcare industries have become a prime example where the Internet of Healthcare Things (IoHT) is making significant progress, particularly in how we approach real-time patient care. Traditional systems for monitoring older people and people with special needs are frequently expensive, require a large workforce, and fall short of providing real-time data. This paper introduces the “3-Tier Health Care Architecture,” an integrated approach to mitigating these issues. This architecture capitalizes on IoHT technologies and is constructed around three principal tiers: Sensor, Fog, and Cloud. The Sensor …Tier employs Health Metrics Acquisition Units (HMAUs) fitted with an nRF5340 Development Kit, capturing an extensive range of health-related metrics via wearable sensors. These metrics are then relayed to the Local Processing Units (LPUs) in Fog Tier, which operates on Raspberry Pi Zero 2 W microprocessors for the initial data processing before forwarding to the cloud. The Cloud Tier uses a hybrid CNN-LSTM Machine Learning (ML) model to perform Real-Time Healthcare Monitoring (RTHM) status assessments and includes an Early Warning System for immediate alert issuance. The proposed architecture is resilient, scalable, and efficient, serving as a fortified and all-encompassing solution for RTHM. This enables quick medical interventions, thus elevating healthcare quality and potentially life-saving. Show more
Keywords: IoT, machine learning, internet of healthcare things, healthcare monitoring, CNN, LSTM
DOI: 10.3233/JIFS-237483
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Chen, Jie | Yin, Chuancun
Article Type: Research Article
Abstract: Probabilistic linguistic term sets (PLTSs) provide a flexible tool to express linguistic preferences, and several multi-criteria decision models based on PLTSs have been recently developed. In this framework, distortion risk measures are extensively used in finance and insurance applications, but are rarely applied in fuzzy systems. In this paper, distortion risk measures are applied to fuzzy tail decisions. In particular, three tail risk measurement methods are put forward, referred to as probabilistic linguistic VaR (PLVaR), expected probability linguistic VaR (EPLVaR), and Wang tail risk measure and extensively study their properties. Our novel methods help to clarify the connections between distortion …risk measure and fuzzy tail decision-making. In particular, the Wang tail risk measure is characterized by consistency and stability of decision results. The criteria and expert weights are unknown or only partially known during the decision making process, and the maximising PLTSs deviations are showed how to determine them. The theoretical results are showcased on an optimal stock fund selection problem, where the three tail risk measures are compared and analyzed. Show more
Keywords: Probabilistic linguistic term sets, probabilistic linguistic VaR, expected probability linguistic VaR, Wang tail risk measure, maximizing deviation method
DOI: 10.3233/JIFS-234218
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Cao, Heling | Han, Dong | Chu, Yonghe | Tian, Fangchao | Wang, Yun | Liu, Yu | Jia, Junliang | Ge, Haoyang
Article Type: Research Article
Abstract: Automatic program repair (APR) is crucial to improve software quality. Recently, neural machine translation (NMT) based modeling for bug fixes has demonstrated great potential. However, these approaches still have two major challenges. One is that their search space is limited due to the out-of-vocabulary (OOV) problem. The other is that the NMT-based APR models tend to ignore past translation information, which often leads to over-translation and under-translation. To address the above challenges, we propose MNRepair, a new NMT-based APR approach that combines multiple mechanisms to fix bugs in source code. Specifically, we devise an encoder-decoder NMT framework with the attention …mechanism. Our framework combines the copy mechanism to overcome the OOV problem that occurs with source code. To deal with the over-translation and under-translation, we utilize a coverage mechanism to record past translation information. MNRepair is able to capture a wide range of repair operators and fix 26 bugs in Defects4J. Our evaluation shows the effectiveness of multiple mechanisms in the repair process. Show more
Keywords: Automatic program repair, neural machine translation, multiple mechanisms
DOI: 10.3233/JIFS-234037
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Kalaimathi, M. | Balamurugan, B.J. | Nagar, Atulya K.
Article Type: Research Article
Abstract: Let G = (V , E ) be a simple graph. A 1-1 function f : V → ℕ , where ℕ is the set of natural numbers, is said to induce a k -Zumkeller graph G if the induced edge function f * : E → ℕ defined by f * (xy ) = f (x ) f (y ) satisfies the following conditions:(i) f * (xy ) is a Zumkeller number for every xy ∈ E . (ii) …The total number of distinct Zumkeller numbers on the edges of G is k . A Mycielski transformation of a graph is a larger graph having more vertices and edges. In this article, the Mycielski transformation of a graphs such as path, cycle and star graphs have been computed and their k -Zumkeller graphs have been investigated by reducing the number of distinct Zumkeller numbers. AMS Subject Classification: 05C78 f * (xy ) is a Zumkeller number for every xy ∈ E . The total number of distinct Zumkeller numbers on the edges of G is k . Show more
Keywords: Zumkeller numbers, k-Zumkeller graph, Mycielski transformation
DOI: 10.3233/JIFS-231095
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
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