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2023 IEEE Technology and engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Anitha, G. Priya, S.Baghavathi Laxmikant, Kumar Pattanaik, Balachandra Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Wallaga University School of Electrical and Computer Engineering Ethiopia Africa Nekemte Ethiopia

ISBN: (纸本)9798350384659

Pneumonia, a widespread respiratory infection, poses significant health risks and requires timely detection for effective treatment. This study employs transfer learning for pattern identification and predictive modeling for classification in pneumonia detection using chest radiographs. Pre-trained deep learning models are utilized to extract informative features, which are then classified using various machine learning algorithms. A comprehensive evaluation is conducted on a benchmark dataset to assess the performance of the approach. The results demonstrate the effectiveness of transfer learning-based feature extraction combined with machine learning classification in accurate pneumonia detection. The findings contribute to improving diagnostic accuracy and have the potential to enhance healthcare decision-making processes. © 2023 IEEE.

关键词： Health risks

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Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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computer Modeling in engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation 13

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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User-Centric Approach to Fortify Cloud Data Security with Hybrid Cryptosystem Methods 4

User-Centric Approach to Fortify Cloud Data Security with Hy...

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4th International Conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sahu, Chandra Kumar Dash, Sushree Sasmita Sinha, Anupa Baghel, Rahul Sahu, Rajendra Kumar Ratha, Ashis Kumar Kalinga University Department of Electrical Engineering Chhattisgarh Raipur India Kalinga University Department of Cs and It Chhattisgarh Raipur India Iilm University School of Computer Science and Engineering Department of Computing and Security Greater Noida India

ISBN: (纸本)9798350343670

Cloud storage, provided by various cloud service providers, offers customers convenient storage space along with significant computational power and capacity, attracting users to entrust their data to these services. However, directly outsourcing sensitive data to cloud storage introduces security risks. Cipher text-policy attribute-based encryption emerges as a promising cryptographic solution in the cloud, allowing data owners (DO) to establish access policies for enhanced *** its potential, employing attribute-based encryption in an outsourced architecture introduces challenges, especially concerning revocation, which puts the security of data owners at risk. To overcome these obstacles, a highly secure and flexible cipher text-based attribute access control system is utilized in cloud storage. This system incorporates role hierarchy user grouping, expanding the capabilities of KUNodes' storage identity-based encryption (revocation).Using CloudSim for assessment, our algorithm proves more efficient in terms of computational cost, utilizing 32 MB for 150 MB of data, as indicated by the analysis findings. Combining multiple encryption methods offers several advantages. For instance, establishing a shared channel between two users' devices enables hybrid encryption for communication. While asymmetric encryption might prolong the process, symmetric approaches enhance both encryption forms, speeding up the process. As a result, the transmission mechanism becomes more secure, and the system operates more *** current focus in this field revolves around employing hybrid approaches to enhance both the security and speed of encrypting data in cloud computing environments. © 2024 IEEE.

关键词： A. User Grouping Cloud data security Cryptosystem Hybrid encryption KUNodes-storage

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A Bias-Free Revenue-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning 33

A Bias-Free Revenue-Maximizing Bidding Strategy for Data Con...

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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Tang, Xiaoli Yu, Han Li, Zengxiang Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore ENN Group Beijing China Department of Electrical and Computer Engineering The University of British Columbia Canada

ISBN: (纸本)9781956792041

Auction-based Federated Learning (AFL) is a burgeoning research area. However, existing bidding strategies for AFL data consumers (DCs) primarily focus on maximizing expected accumulated utility, disregarding the more complex goal of revenue maximization. They also only consider winning bids, leading to biased estimates by overlooking information from losing bids. To address these issues, we propose a Bias-free Revenue-maximizing Federated bidding strategy for DCs in AFL (BR-FEDBIDDER). Our theoretical exploration of the relationships between Return on Investment (ROI), bid costs, and utility, and their impact on overall revenue underscores the complexity of maximizing revenue solely by prioritizing ROI enhancement. Leveraging these insights, BR-FEDBIDDER optimizes bid costs with any given ROI constraint. In addition, we incorporate an auxiliary task of winning probability estimation into the framework to achieve bias-free learning by leveraging bid records from historical bid requests, including both winning and losing ones. Extensive experiments on six widely used benchmark datasets show that BR-FEDBIDDER outperforms eight state-of-the-art methods, surpassing the best-performing baseline by 5.66%, 6.08% and 2.44% in terms of the total revenue, ROI, and test accuracy of the resulting FL models, respectively. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

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Increase Security by Analyzing Password Strength using Machine Learning 9

Increase Security by Analyzing Password Strength using Machi...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Asaduzzaman, Abu D'Souza, Declan Uddin, Md Raihan Woldeyes, Yoel Wichita State University Department of Electrical and Computer Engineering WichitaKS United States Wichita State University School of Computing WichitaKS United States

ISBN: (纸本)9798350318241

Passwords (and login credentials) are pivotal in our daily online activities, securing 'things' including emails and e-wallets. Yet, users tend to recycle similar passwords across multiple services, posing a significant security threat. The prevalence of easily guessed passwords exacerbates the security issues. This study proposes a machine learning model leveraging Term Frequency - Inverse Document Frequency (TF-IDF) scores that helps develop stronger passwords by analyzing how frequently specific characters appear in passwords. The proposed methodology involves utilizing logistic regression and the 000WebHost leaked password wordlist for training and validation. The outcomes of the machine learning model accurately assess users' password strength, potentially bolstering the security of their diverse online accounts. © 2024 IEEE.

关键词： Machine learning

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Feature Enhancer Segmentation Network (FES-Net)for Vessel Segmentation

Feature Enhancer Segmentation Network (FES-Net)for Vessel Se...

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2023 International Conference on Digital Image computing: Techniques and Applications, DICTA 2023

作者： Khan, Tariq M. Arsalan, Muhammad Iqbal, Shahzaib Razzak, Imran Meijering, Erik School of Computer Science & Engineering Unsw Sydney Australia Qatar University Kindi Center for Computing Research Doha2713 Qatar Department of Electrical and Computer Engineering Islamabad Pakistan

ISBN: (纸本)9798350382204

Diseases such as diabetic retinopathy and age-related macular degeneration pose a significant risk to vision, highlighting the importance of precise segmentation of retinal vessels for tracking and diagnosis of progression. However, existing vessel segmentation methods that heavily rely on encoder-decoder structures struggle to capture contextual information about retinal vessel configurations, leading to challenges in reconciling semantic disparities between encoder and decoder features. To address this, we propose a novel feature enhancement segmentation network (FES-Net) that achieves accurate pixel-wise segmentation without requiring additional image enhancement steps. FES-Net directly processes the input image and utilizes four prompt convolutional blocks (PCBs) during downsampling, complemented by a shallow upsampling approach to generate a binary mask for each class. We evaluate the performance of FES-Net on four publicly available state-of-the-art datasets: DRIVE, STARE, CHASE, and HRF. The evaluation results clearly demonstrate the superior performance of FES-Net compared to other competitive approaches documented in the existing literature. © 2023 IEEE.

关键词： Image enhancement

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Equivalence Checking for Flow-Based computing using Iterative SAT Solving 24

Equivalence Checking for Flow-Based Computing using Iterativ...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Thijssen, Sven Rashed, Muhammad Rashedul Haq Ahmed, Md Rubel Singireddy, Suraj Jha, Sumit Kumar Ewetz, Rickard Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL United States Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX United States Department of Electrical and Computer Engineering University of Central Florida OrlandoFL United States Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX United States Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

ISBN: (纸本)9798400710773

Processing in-memory is projected to shatter the von Neumann bottleneck and enable acceleration of data-intensive applications. Flow-based computing is an efficient in-memory computing paradigm for accelerating the execution of Boolean logic. While recent synthesis algorithms can map complex functions into flow-based computing circuits, the functional correctness cannot be verified using state-of-the-art equivalence checking techniques. The challenge is that non-volatile memory devices are intrinsically bi-directional, which introduces cycles in the computational graph. These cycles break traditional equivalence checking methods that are based on SAT formulations. In this paper, we propose a framework for equivalence checking of flow-based computing circuits that is called FlowSAT. The framework captures each circuit using an undirected computational graph. The key idea of FlowSAT is to introduce helper variables, in the form of arrows, that dynamically convert the undirected graph into a directed graph. This facilitates equivalence checking to be performed using traditional SAT formulations. However, it is prohibitively expensive to ban all possible cycles using arrow variables. Therefore, we propose to eliminate cycles by iteratively adding constraints to the SAT formulation. Our experimental evaluation demonstrates that FlowSAT is up to an order of magnitude faster than state-of-the-art methods. The framework is capable of verifying all 20/20 benchmark circuits, while the previous state-of-the-art technique is only capable of verifying 12/20 circuits within a time limit of one hour. © 2024 Copyright is held by the owner/author(s).

关键词： Data flow graphs

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Development of an online condition monitoring based system for the partial demagnetization fault diagnosis of SPM-type BLDC motor

Energy Conversion and Economics

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Energy Conversion and Economics 2022年第2期3卷 72-84页

作者： Usman, Adil Rajpurohit, Bharat Singh School of Computing and Electrical Engineering Indian Institute of Technology-Mandi Mandi India Electrical and Computer Engineering Department University of California Santa CruzCA United States

This study investigates partial demagnetization faults arising from stator interturn faults in a surface-mounted permanent-magnet-type brushless direct current motor. Because of rotor demagnetization, the fault severity increases significantly owing to an increase in the stator phase current and temperature. The effect of such a fault is reflected in machine parameters such as the motor back-EMF and radial magnetic flux, which are used to analyse the characteristics of faults. A mathematical model of a machine under possible fault conditions is developed using the finite element method and advanced hybrid model approaches. Experimental investigations are conducted to validate the proposed methodology. Subsequently, the machine parameters used for fault diagnosis are employed to develop an online expert-based system that can detect, classify and estimate the percent increase in the values of the parameters to determine the fault severity of the machine under fault conditions. It is discovered that the proposed approach is suitable for industrial and commercial applications in electric vehicles, where the machine's state-of-health estimation is crucial for avoiding major faults that may result in its failure. © 2022 The Authors. Energy Conversion and Economics published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology and the State Grid of China Economic & Technological Research Institute Co Ltd.

关键词： Failure analysis

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Review on AI Assistant Systems for Programming Language Learning in Learning Environments 8

Review on AI Assistant Systems for Programming Language Lear...

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8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Senanayake, Savithree Karunanayaka, Kasun Ekanayake, K.V.J.P. University of Colombo School of Computing University of Colombo Colombo Sri Lanka The Open University of Sri Lanka Department of Electrical and Computer Engineering Nugegoda Sri Lanka

ISBN: (纸本)9798331509170

Artificial intelligence (AI) has made it possible to improve student engagement in the learning environment in many ways, including fusing the adaptive, individualized support of AI assistants with the motivating elements of gamification. The study examined the range of AI-powered platforms and tools that include gamification components, including AI-powered code generation aids, adaptive feedback mechanisms, and intelligent tutoring systems. These technologies meet the varied needs of students, which improves retention and comprehension, in addition to making learning more engaging and participatory. The paper also emphasizes how an AI-enabled gamified environment may facilitate group learning and help students gain a deeper comprehension of challenging programming concepts. However, it also discusses the difficulties and moral issues surrounding the application of AI in education such as loneliness, less student interaction, low motivation, etc, highlighting the necessity of cautious application and ongoing assessment. The review's conclusions indicate that gamified learning environments with AI capabilities have great potential to transform programming education and give students a more effective, customized, and interesting learning experience. © 2024 IEEE.

关键词： Adversarial machine learning

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