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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Fathima, Mohammed Nazneen Singh, Prabhjot Singh, Simrandeep Chandigarh University Department of Electronic and Communication Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798331529246

This work introduces a new methodology, Vision Transformer with Attention-based Feature Mapping and Optimized Ensemble Learning (VTAF-Ensemble), which includes three advanced techniques: Vision Transformer-Based Segmentation (VT-Seg), Attention-Based Feature Mapping (AF-Map) and Optimized Ensemble Learning (OE-Ensemble), to boost the brain tumor detection and classification from Magnetic Resonance Imaging (MRI) images. Traditional practice of identifying tumor is error prone, time consuming, hence it requires automation. This thesis proposes an integrated method, which includes VT Seg for accurate segmentation, AF Map for feature enhancement, and OE Ensemble for Ensemble classification using SVM and CNN classifiers. We show that VTAF-Ensemble achieves 92.1% Intersection over Union (IoU), 94.5% Mean Average Precision (mAP), 93.2% Dice Coefficient (DC) and 95% sensitivity in segmentation. Moreover, the model also yields better results on classification metrics compared to others: Area Under the Curve (AUC) of 95.2% and F1 Score of 92.9% and recall of 93%. Overall, this hybrid approach provides substantial accuracy improvements, scalability and utility for medical image applications as they relate to medical imaging applications, in particular, brain tumor specific diagnostic applications. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Upshot of unified power flow controller on the minimization of the severity of overloading on electric power grid

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Journal of King Saud University - engineering Sciences 2024年第6期36卷 400-408页

作者： Adetona, S.O. Olaboyin, E.B. Salawu, R.I. Okafor, F.N. Department of Electrical & Electronics Engineering University of Lagos Lagos Nigeria Department of Electronic & Computer Engineering Bells University of Technology Ogun State Ota Nigeria Nigerian Electricity Regulatory Commission Abuja Nigeria

With the objective of discovering the vital line in an electrical power grid (EPG) where the unified power flow controller (UPFC) could be inserted to minimize the severity of overloading (SOL) during contingencies, an investigation into the stiffness of single line contingencies (SLC) on the EPG is presented in this study. An overloading index (OVI) is developed and used in this contribution to arrange the transmission branches of the EPG based on their cruciality and criticality throughout SLC. A harmony search algorithm (HSA), an optimization method, has been adopted to optimally set the parameters of the UPFC to achieve the best minimum SOL of the system at the optimal location. The IEEE 30-bus network was used as the test bed. Results based on the test bed show that placing a UPFC based on the ranking of a well-known contingency severity index (CSI) minimized the SOL to 2.40×1010 as against the 8.05×105 obtained when the placement is done on the ranking based on the proposed OVI. The minimization of SOL is achieved with a reactance and reactive power of 0.036pu and 0.096pu, respectively, of the UPFC. The result also reveals that the proposed OVI identifies with a higher level of precision the vital line in the test bed for placing the UPFC for the purpose of minimizing the SOL during contingencies. © 2021 The Authors

关键词： Flow control

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The Algorithm and VLSI Architecture of High-Throughput and Highly Efficient Tensor Decomposition Engine

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第7期71卷 3134-3145页

作者： Tsai, Ting-Yu Shen, Chung-An Wu, Tsung-Lin Huang, Yuan-Hao National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106335 Taiwan MediaTek Corporation Hsinchu300 Taiwan National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu300 Taiwan

Tensor decomposition is critical for compressing data and extracting key features in novel high-dimensional signal processing systems. However, due to the enormous amount of data and the highly complicated computations, designing an efficient tensor decomposition processor is very challenging. This paper presents the algorithm and VLSI architecture design of a low-latency and high-throughput tensor decomposition processor. A parallel higher-order orthogonal iteration (P-HOOI) algorithm is proposed where multiple updated matrices are computed concurrently. Thus, a low-latency tensor decomposition is achieved. Furthermore, a novel VLSI architecture is presented so that the efficiency of the component utilization is improved and the hardware complexity is greatly reduced. Therefore, the proposed tensor decomposition processor enhances the processing throughput with minimum employment of hardware components. Performance evaluations based on the post-layout estimations in the ASIC flow and based on the FPGA platform are reported in this paper. Compared with state-of-the-art designs in the literature the proposed tensor decomposition engine greatly enhances the throughput and hardware efficiency. © 2004-2012 IEEE.

关键词： Engines

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IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

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computer Modeling in engineering & Sciences 2024年第5期139卷 1487-1503页

作者： Dinesh Mavaluru Chettupally Anil Carie Ahmed I.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri Md Ezaz Ahmed School of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Computer Engineering SRM University APGunturIndia College of Computer and Information Sciences Majmaah UniversityAl Majma’ahSaudi Arabia Department of Public Health College of Applied Medical SciencesKing Khalid UniversityAbhaSaudi Arabia

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge Computing(MEC)*** network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing *** central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy *** system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT *** study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial *** results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn ***,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy *** research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

关键词： Internet of Things edge computing offloading NOMA

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Fault-tolerant visual servo control for a robotic arm with actuator faults

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Neural Computing and Applications 2024年第25期36卷 15815-15828页

作者： Li, Jiashuai Peng, Xiuyan Li, Bing Sreeram, Victor Wu, Jiawei College of Intelligent Systems Science and Engineering Harbin Engineering University Heilongjiang Nantong Harbin150001 China Department of Electrical Electronic and Computer Engineering The University of Western Australia Stirling Highway PerthWA6009 Australia

The study targets uncertain coupling faults in robotic arm actuators and proposes a new fault-tolerant visual servo control strategy. Specifically, it considers both multiplicative and additive actuator faults within the dynamic of the robotic arm, treating the coupling faults and time-varying disturbances as an aggregate of concentrated uncertainties. A radial basis function neural network-based state observer is introduced to online approximate these concentrated uncertainties, which include fault information, eliminating the need for prior knowledge of faults. Furthermore, a fault-tolerant controller based on a non-singular fast terminal sliding mode is proposed, which separately decouples the nominal quantities and concentrated uncertainties and develops individual adaptive control laws for each. This effectively reduces the detrimental impact of coupled faults and disturbances on the system’s performance, facilitating image feature trajectory tracking control with minimal jitter, high precision, and strong transient response capabilities. The stability of the state observer and the fault-tolerant controller has been substantiated through Lyapunov’s theory. Lastly, numerical simulations validate the efficacy and robustness of the proposed fault-tolerant visual servo control approach. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Robotic arms

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An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

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computer Modeling in engineering & Sciences 2024年第8期140卷 1689-1710页

作者： Md.Abdur Rahim Fahmid Al Farid Abu Saleh Musa Miah Arpa Kar Puza Md.Nur Alam Md.Najmul Hossain Sarina Mansor Hezerul Abdul Karim Department of Computer Science and Engineering Pabna University of Science and TechnologyPabna6600Bangladesh Faculty of Engineering Multimedia UniversityCyberjaya63100Malaysia Department of Computer Science and Engineering Bangladesh Army University of Science and TechnologySaidpur5311Bangladesh Department of Mathematics Pabna University of Science and TechnologyPabna6600Bangladesh Department of Electrical Electronic and Communication EngineeringPabnaUniversity of Science and TechnologyPabna6600Bangladesh Department of Electrical and Communication Engineering Pabna University of Science and TechnologyPabna6600Bangladesh

Handwriting is a unique and significant human feature that distinguishes them from one *** are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through ***,such systems are susceptible to forgery,posing security *** response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or *** response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or *** innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and *** key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive ***-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite *** meticulous amalgamation resulted in a robust set of 91 *** enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent *** the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting ***,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual ***,our experimental results unde

关键词： Bengali handwriting(BHW) person identification convolutional neural network(CNN) BiLSTM

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Modified Frequency Scheduling Droop Control for Decentralized State of Charge Balancing of Battery in Islanded Microgrid 25

Modified Frequency Scheduling Droop Control for Decentralize...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Rahman, Irham Izzatur Miyauchi, Hajime Takano, Hirotaka Gifu University Department of Electrical Electronic and Computer Engineering Gifu Japan Kumamoto University Department of Computer Science and Electrical Engineering Kumamoto Japan

ISBN: (纸本)9798350378573

This study proposes a decentralized frequency scheduling method to achieve SoC balancing with no communication protocol. Since the frequency scheduling method is based on modifying droop control of inverter, to make the power output of inverter to be proportional to the SoC, the frequency output of inverter would be affected. This study considers to minimize the frequency deviation within European Norm EN50160 (±1% of the nominal value of the frequency), while also able to satisfy SoC balancing. To achieve it, we propose fuzzy logic controller to weight the value of droop coefficient ( ) instead of linear scaling in conventional frequency scheduling. The proposed method is studied in several test to show the performance and compare to conventional method. From the result, the proposed method performs better in SoC balancing, and results in better system reliability. We also study the proposed battery balancing system in various condition of PV and load profile, as well "plug and play"condition of microgrid. The proposed method can perform well in both conditions. © 2024 IEEE.

关键词： State of charge

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Developing an Anti -Phishing Large Language Model: A Focus Group Study on Human, Technological, and Legal Factors 15

Developing an Anti -Phishing Large Language Model: A Focus G...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Thomopoulos, George A. Kiortsi, Panagiota Sigalas, Damianos Dumi Kosmopoulos, Dimitris Igglezakis, Ioannis Fidas, Christos University of Patras Department of Electrical and Computer Engineering Greece University of Thessaloniki Faculty of Law Aristotle Greece University of Patras Department of Computer Engineering and Informatics Greece

ISBN: (纸本)9798350368833

The phishing problem poses a significant threat in modern information systems, putting both individuals and businesses at risk of financial and professional harm. Owing to social media's rapid development and widespread appeal, deception of this sort is hurting millions of people and growing more dangerous. The current work, as part of the AILA (Artificial Intelligence-driven Framework and Legal Advice Tools for Phishing Prevention and Mitigation in Information Systems) project, aims to specify and validate an AI-driven multifactor (human, technology and legal) anti-phishing data model, with the implementation of focus group studies. The findings assist to provide human, technology, and legislative user model endpoints that will be identified and discussed for explicit and implicit user modeling, which will guide the development of the corresponding AI-driven user modeling and profiling mechanisms. To this end a Large Language System is planned to be employed. © 2024 IEEE.

关键词： Phishing

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Reduction Through Homogeneous Clustering: Variations for Categorical Data and Fast Data Reduction

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SN computer Science 2024年第6期5卷 671页

作者： Ougiaroglou, Stefanos Papadimitriou, Nikolaos Evangelidis, Georgios Department of Information and Electronic Engineering International Hellenic University Department of Business Administration University of Macedonia Department of Applied Informatics University of Macedonia

Reduction through Homogeneous Clustering (RHC) and its editing variant (ERHC) represent effective methods for reducing data in the context of instance-based classification. Both RHC and ERHC are based on an iterative k-means clustering procedure that builds homogeneous clusters. Therefore, they are inappropriate for data reduction tasks that need to be performed quickly, especially, when run over large training datasets. Moreover, since they are based on k-means clustering, they are inappropriate for categorical data. This paper introduces a set of variations to the RHC and ERHC algorithms. More specifically, addressing the iterative nature of k-means clustering in RHC and ERHC, we present new adaptations known as RHC2 and ERHC2. These variations strategically replace the complete execution of k-means clustering with a streamlined task, demonstrating significant improvements in speed. Additionally, we extend the scope of our study to address categorical data by introducing new variations of RHC and ERHC. The adaptations designed for handling categorical data are denoted as RHCM and ERHCM and are based on k-modes clustering. Our experimental study spans diverse datasets and includes statistical tests. The findings reveal a notable performance improvement in execution time for adaptations we propose compared to RHC, ERHC and two other prominent data reduction techniques. Moreover, RHC2 and ERHC2 are found to outperform their predecessors in data reduction effectiveness. Concerning RHCM and ERHCM, performance evaluations conducted on various categorical datasets indicate that these variations efficiently minimize the dataset size, with a relatively modest compromise in accuracy. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Categorical data Data reduction k-means k-modes k-NN Classification Prototype generation RHC

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Simulating inexact DNNs for use in Custom Hardware Architectures

Simulating inexact DNNs for use in Custom Hardware Architect...

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2024 Panhellenic Conference on electronics and Telecommunications, PACET 2024

作者： Danopoulos, Dimitrios Zervakis, Georgios Soudris, Dimitrios National Technical University of Athens School of Electrical and Computer Engineering Athens Greece University of Patras Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350318845

In response to the growing complexity of Deep Neural Network (DNN) models, the paradigm of approximate computing has emerged as a compelling approach to strike a balance between computational efficiency and model accuracy. Approximate computing involves intentionally allowing errors using inexact computations, exploiting the inherent resilience of many applications to such inaccuracies. While current state-of-the-art approaches leverage approximate multipliers for approximate DNN accelerators, there has been a notable difficulty in emulating such DNNs on common frameworks such as PyTorch, as they do not inherently support inexact arithmetic. This paper introduces a seamless PyTorch plugin, to evaluate inexact DNN models while having GPU acceleration to enable rapid emulation. Additionally, we support approximate-aware retraining and perform evaluation on several popular convolutional neural networks on Cifar10 and ImageNet datasets. © 2024 IEEE.

关键词： Deep neural networks

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