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IEEE International Conference on Systems, Man and Cybernetics

作者： Sevil Zanjani Miyandoab Shahryar Rahnamayan Azam Asilian Bidgoli Nature-Inspired Computational Intelligence (NICI) Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Engineering Brock University St. Catharines ON Canada Faculty of Science Wilfrid Laurier University Waterloo ON Canada

A supervised feature selection method selects an appropriate but concise set of features to differentiate classes, which is highly expensive for large-scale datasets. Therefore, feature selection should aim at both minimizing the number of selected features and maximizing the accuracy of classification, or any other task. However, this crucial task is computationally highly demanding on many real-world datasets and requires a very efficient algorithm to reach a set of optimal features with a limited number of fitness evaluations. For this purpose, we have proposed the binary multi-objective coordinate search (MOCS) algorithm to solve large-scale feature selection problems. To the best of our knowledge, the proposed algorithm in this paper is the first multi-objective coordinate search algorithm. In this method, we generate new individuals by flipping a variable of the candidate solutions on the Pareto front. This enables us to investigate the effectiveness of each feature in the corresponding subset. In fact, this strategy can play the role of crossover and mutation operators to generate distinct subsets of features. The reported results indicate the significant superiority of our method over NSGA-II, on five real-world large-scale datasets, particularly when the computing budget is limited. Moreover, this simple hyper-parameter-free algorithm can solve feature selection much faster and more efficiently than NSGA-II.

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Training Artificial Neural Networks by Coordinate Search Algorithm

arXiv

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arXiv 2024年

作者： Rokhsatyazdi, Ehsan Rahnamayan, Shahryar Miyandoab, Sevil Zanjani Bidgoli, Azam Asilian Tizhoosh, H.R. Lab Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Department of Engineering Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada Rhazes Lab Department of Artificial Intelligence and Informatics Mayo Clinic RochesterMN United States

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously;for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore, instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

关键词： Optimization

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Ethics in AI through the Practitioner’s View: A Grounded Theory Literature Review

arXiv

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arXiv 2022年

作者： Pant, Aastha Hoda, Rashina Tantithamthavorn, Chakkrit Turhan, Burak Dept. of Software Systems and Cybersecurity Monash University Melbourne Australia Faculty of Info. Tech. and Electrical Engineering University of Oulu Oulu Finland

The term ethics is widely used, explored, and debated in the context of developing Artificial Intelligence (AI) based software systems. In recent years, numerous incidents have raised the profile of ethical issues in AI development and led to public concerns about the proliferation of AI technology in our everyday lives. But what do we know about the views and experiences of those who develop these systems – the AI practitioners? We conducted a grounded theory literature review (GTLR) of 38 primary empirical studies that included AI practitioners’ views on ethics in AI and analysed them to derive five categories: practitioner awareness, perception, need, challenge, and approach. These are underpinned by multiple codes and concepts that we explain with evidence from the included studies. We present a taxonomy of ethics in AI from practitioners’ viewpoints to assist AI practitioners in identifying and understanding the different aspects of AI ethics. The taxonomy provides a landscape view of the key aspects that concern AI practitioners when it comes to ethics in AI. We also share an agenda for future research studies and recommendations for practitioners, managers, and organisations to help in their efforts to better consider and implement ethics in AI. © 2022, CC BY-NC-ND.

关键词： Artificial intelligence

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Performance comparison of a software defined and wireless sensor network

Performance comparison of a software defined and wireless se...

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2020 International Symposium on Networks, Computers and Communications, ISNCC 2020

作者： Samridhi, Samridhi Liscano, Ramiro Ontario Tech University Faculty of Engineering and Applied Science Dept. of Electrical Computer and Software Engineering OshawaOntario Canada

ISBN: (纸本)9781728156286

The paper analyses the performance of a software defined wireless sensor network (SD-WSN) to a more conventional ad-hoc wireless sensor network (WSN). Recently SD-WSN has increased in popularity due to the demand of managed and service oriented sensor networks. In this analysis, we perform several simulation experiments comparing convergence time, packet reception ratio, and data delivery time for the SDN-WISE SD-WSN system and an ad-hoc WSN based the RPL routing protocol. The experimental results confirm the expected overhead required in a SD-WSN compared to an ad-hoc approach and exposes some design flaws in the SDN-WISE architecture. © 2020 IEEE.

关键词： Wireless sensor networks

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： Mohsen Tajdinian Behzad Behdani Harold R. Chamorro Vijay K. Sood Hitachi Energy Research Center Hitachi Energy Västerås Sweden Department of Electrical Sustainable Energy Delft University of Technology Delft The Netherlands School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Ontario Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Failures of Induction Motors (IMs) can lead to unscheduled downtime and interruption in industry processes. This paper concentrates on the detection of the stator’s inter-turn faults which are one of the most frequent causes of failures in IMs. The proposed detection method is based on a similarity index that uses the current waveform. To be more specific, the proposed algorithm presents a full-cycle sliding-window-based index based on cosine similarity that only uses current signals for detection of the stator’s inter-turn faults. The proposed index cuts the phase difference before/after the disturbance and, as a result, it only depends on the size variations of the current waveform. The proposed method is technically unaffected by non-fault transient conditions including voltage imbalance, voltage sag, voltage swell, and heavy load changes. The performance of the proposed method is validated with numerous simulated scenarios and has good accuracy and speed of convergence.

关键词： Industries Voltage fluctuations Induction motors Accuracy Fault detection Power quality Stators

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Enhanced Security Protocol for VLSI Systems: Modified AES Algorithm for Robust Data Transmission

Enhanced Security Protocol for VLSI Systems: Modified AES Al...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Jinsha Lawrence Dinesh Kumar Budagam P. Mukilan Kavitha Veerappan E. Chandrasekhar K. Barathi Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education (Deemed to be University) Coimbatore India Department of MS in Software Engineering Security Architect at VISA California United States Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College of Engineering Coimbatore India Department of Electronics and Communication Engineering Kongu Engineering College Erode India Department of Electronics and Communication Engineering Chaitanya Bharathi Institute of Technology (A) Hyderabad India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The development of compact and efficient devices has been made possible by the growth of Very Large Scale Integration (VLSI) technologies, which has transformed modern electronics. However, there are cautions regarding the security of data, especially when it comes to transmission due to the extensive usage of technology. To address these, this study proposes a modified Advanced Encryption Standard (AES) algorithm as a solution for improved security protocol in VLSI systems. The proposed algorithm are intended to support the encryption procedure, increasing stronger production against cryptographic attacks while preserving the system's effectiveness and speed factors that are critical aspects for VLSI applications. Through extensive simulations and testing, the modified AES algorithm demonstrated significant security enhancements the operational efficiency of the VLSI system. The attained outcomes of the proposed work shows that the Modified AES strategy provides a workable technique to protect data in VLSI-based devices while maintaining data communication integrity and confidentiality. The proposed modified AES algorithm demonstrates a significant improvement in performance with a propagation delay of 7 ns, power consumption of 29 mW, and a computational overhead of 62 bits, leading to enhanced efficiency in cryptographic operations. This study highlights that the modified AES algorithm improve security in modern electronic systems and maintain performance.

关键词： Protocols Power demand System performance Very large scale integration Throughput Hardware Encryption Security Data communication Standards

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A Mathematical Model for Capacity Loss Optimization of Electrical Vehicles Iron-Phosphate-Based Supercharged Batteries Using Bees Algorithm

A Mathematical Model for Capacity Loss Optimization of Elect...

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International Middle-East Power System Conference, MEPCON

作者： D. Mourad A. Yousef M. Sammany Z. Shawa A. Steef A. Atalla Department of Electr. Eng Faculty of Eng University of PSAU Al-Kharj KSA Department of Electr. Tech Faculty of Tech and Indl. Edu University of Helwan Cairo Egypt Department of Electrical Matareya College of Technology Cairo Egypt Department of Pharmacy Practice Faculty of Pharmacy Heliopolis University for Sustainable Development Cairo Egypt Department of Software Engineering Faculty of Information Engineering University of Aleppo Aleppo Syria Faculty of Informatics Engineering Idlib Syria Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (纸本)9781665463645

The recent orientation towards using Electrical Vehicles (EVs), as an alternative to fossil-fuelled- powered vehicles, led to increasing the interest in producing super charged batteries, which is the critical component of EVs and the key of its development and rapid spread. Iron-Phosphate-Based Supercharged Battery (IP-BSBs) has proved its efficiency as a competitor to lead and lithium batteries. Now, it became necessary to increase its efficiency, by the optimum design, to appropriately fit its correspondent vehicle. However, conventional calibration models used to obtain the optimal design parameters often lead to a dramatic waste of time, effort, and resources (cost), without any guarantee to reach the optimal solution. In this paper, a mathematical model is proposed to optimize the capacity loss of IP-BSBs under real manufacturing conditions. The proposed model was solved using meta-heuristic search algorithm represented by Bees Algorithm (BA). Simulation results have shown the precision of our model and the possibility of obtaining an optimal design of IP-BSBs compared to its counterparts of widely existing types.

关键词： Solid modeling Simulation Metaheuristics Whales Voltage Rats Pareto optimization

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A Novel Phishing Attack Detection System Based on Web Platforms by Using Deep Learning Methodology

A Novel Phishing Attack Detection System Based on Web Platfo...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Tella Gandhi Porkodi M Sheela Shantha Kumari P K Vijaya Vardan Reddy S P Jayant Giri Ahmad Abdelhafiz Ali Samhan Dept of CSIT MLR Institute of Technology Dundigal Hyderabad Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of CSE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of EC-ACT R.M.K. Engineering College Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Global economic expansion facilitated by technology has led to an evolution in phishing attacks in recent years. An uptick in phishing and other sophisticated online attacks, together with an uptick in impersonation frauds, has contributed to a general uptick in fraudulent activity losses this year. To safeguard users' online activities, a more effective phishing detection mechanism is necessary, since the worldwide impact of these attacks will only grow. Using the URL and website content such photos, text, and frames, this study aimed to build a deep learning-based phishing detection system to fill this demand. When it comes to image and natural language processing, deep learning methods work wonders. Binary Learning with Phishing Detection Strategy (BLPDS) is a novel approach to efficiently detect phishing attacks introduced in this paper. To assess its efficacy, it is cross-validated with the conventional Long Short-Term Memory (LSTM) method. Applying the BLPDS to big datasets allowed for a thorough experimental evaluation and comparison of its efficacy in detecting phishing web pages and attacks. The precise proof demonstrating the efficacy of the suggested system is shown in the result section.

关键词： Deep learning Uniform resource locators Training Target recognition Phishing Web pages Natural language processing Hazards Fraud Long short term memory

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A Comprehensive Review of Active EV Battery Cell Voltage Balancing Systems: Current Issues and Prospective Solutions

A Comprehensive Review of Active EV Battery Cell Voltage Bal...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Chandan Chetri Alvin Huynh Sheldon S. Williamson Department of Electrical Computer and Software Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

The performance of a battery pack is greatly affected by an imbalance between the cells. Cell balancing is a very important criterion for Battery Management System (BMS) to operate properly. This paper presents the various cell balancing topologies which have been presented by various researchers in the past years. A novel topology has also been discussed, which improves the overall efficiency of the components being used. Various comparison parameters such as Control, Speed, Efficiency, Power Effects, Manufacturability, Size, and Cost have been considered.

关键词： Industrial electronics Electric potential Costs Equalizers Battery management systems Transformers Power electronics

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Service Migration for Delay-Sensitive IoT Applications in Edge Networks

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IEEE Transactions on Services Computing 2025年

作者： Li, Xiaocui Zhou, Zhangbing Wang, Yasha Deng, Shuiguang Hung, Patrick C. K. School of Information Engineering Beijing100083 China Peking University National Research & Engineering Center of Software Engineering Beijing100871 China Peking University New Generation Information Technology Research Institute Tianjin300380 China School of Information Engineering Frontiers Science Center for Deep-Time Digital Earth Beijing100083 China Institut Polytechnique de Paris Télécom SudParis Palaiseau91120 France Zhejiang University College of Computer Science and Technology Hangzhou310012 China Ontario Tech University Faculty of Business and Information Technology Oshawa Canada

The proliferation of Internet of Things (IoT) applications prompts extraordinary demands for the collaboration of large amounts of computational resources provided by IoT devices in edge networks, and these applications are mostly delay-sensitive. Generally, these resources are encapsulated as IoT services. Thereafter, IoT applications can be performed, such that the collaboration of their sub-tasks is achieved through the composition of functionally complementary and geographically contiguous IoT services. The status of computational resources in IoT devices may change continuously along with their occupancy and release by IoT services. Considering the resource-scarceness of IoT devices, when the workload of IoT devices increases due to more services to be processed, certain IoT devices may hardly have enough remaining resources to co-host more instances of certain IoT services prescribed by forthcoming IoT applications with strict constraints. As a result, the delay satisfaction of both on-running and forthcoming IoT applications may be negatively impacted, or even hardly be satisfied any longer. To solve this issue, this paper proposes a rEsource-Efficient service Configuration (E2rC) mechanism, which aims to optimize the configuration of computational resources provided by IoT devices with respect to complex requirements prescribed by IoT applications, through service migration techniques. This service migration problem is formulated as markov multi-phases decisions, which is solved through our enhanced Deep Reinforcement Learning (DRL) approach with a two-layer Q-network. Extensive experiments have been conducted upon the dataset of our testbed system. Evaluation results show that our E2rC is more efficient than the state-of-art counterparts in satisfying delay constraints of IoT applications, while reducing the energy consumption and improving the resource utilization efficiency of IoT devices. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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