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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Alam, Ajmain Mahtab Ahad, Abdul Ahmed, Saif North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Deep Learning is a technique in which the system analyzes and learns, is one of the most common applications of artificial intelligence that has seen tremendous progress in the digital era. Because of advancements of deep learning in healthcare technology, a growing number of studies have sought to anticipate and assess certain diseases in advance. Stroke illness research is a hot topic right now, especially among the elderly. Stroke, which is lethal in the elderly, is a condition that needs constant medical attention and monitoring due to its high recurrence and fatality rates. So far, the majority of investigations into stroke disease have relied on MRI or CT imaging for straightforward categorization. The goal of this is to use deep learning to detect whether there are initial signs of a brain stroke using CT or MRI images. Using CT or MRI scan pictures, a classifier can predict brain stroke. By using four Pre-trained models such as ResNet-50, Vision Transformer (Vit), MobileNetV2 and VGG-19, we obtained our desired results. The study h ighlights the capabilities of various pretrained CNN models and provides a comparison with Vit models and attempts to discuss limitations of various architectures. © 2024 IEEE.

关键词： computerized tomography

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Memory Compact and Computationally Efficient Methods for Modeling Saturation in Synchronous Machines 3

Memory Compact and Computationally Efficient Methods for Mod...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Rahman, Shadman Saqlain Tahim, Ekamjot Singh Kaushik, Abhay Jatskevich, Juri University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798331540319

Synchronous machines are commonly used in power generation, electric propulsion, and industrial automation. Accurate modeling of these machines is essential, particularly in addressing challenges posed by nonlinear characteristics such as magnetic saturation. Traditional models often rely on computationally intensive methods to account for these nonlinearities, leading to slow simulations and high memory requirements. This paper introduces two new models: the enhanced explicit flux correction (EFC-2D) model, which captures magnetic saturation using two-dimensional lookup tables (2D-LUTs) with improved computational efficiency, and the single polynomial (S-POLY) model, which represents saturation using polynomials to significantly reduce memory usage. The proposed models are compared to the advanced one-dimensional explicit flux correct model (EFC-1D) and the model in Matlab/Simulink's Simscape electrical Specialized Power Systems (SPS) toolbox. Simulation results, including both offline and real-time tests, show that EFC-2D and S-POLY models are computationally faster than conventional methods, with S-POLY being more memory-efficient than EFC-1D and EFC-2D models. These improvements make the proposed models ideal for high-performance simulations requiring both speed and memory optimization. © 2024 IEEE.

关键词： MATLAB

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Edge AI: A survey

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 71-92页

作者： Singh, Raghubir Gill, Sukhpal Singh Department of Computer Science University of Bath Bath United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

Artificial Intelligence (AI) at the edge is the utilization of AI in real-world devices. Edge AI refers to the practice of doing AI computations near the users at the network's edge, instead of centralised location like a cloud service provider's data centre. With the latest innovations in AI efficiency, the proliferation of Internet of Things (IoT) devices, and the rise of edge computing, the potential of edge AI has now been unlocked. This study provides a thorough analysis of AI approaches and capabilities as they pertain to edge computing, or Edge AI. Further, a detailed survey of edge computing and its paradigms including transition to Edge AI is presented to explore the background of each variant proposed for implementing Edge Computing. Furthermore, we discussed the Edge AI approach to deploying AI algorithms and models on edge devices, which are typically resource-constrained devices located at the edge of the network. We also presented the technology used in various modern IoT applications, including autonomous vehicles, smart homes, industrial automation, healthcare, and surveillance. Moreover, the discussion of leveraging machine learning algorithms optimized for resource-constrained environments is presented. Finally, important open challenges and potential research directions in the field of edge computing and edge AI have been identified and investigated. We hope that this article will serve as a common goal for a future blueprint that will unite important stakeholders and facilitates to accelerate development in the field of Edge AI. © 2023 The Authors

关键词： Machine learning

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PAC-MC: An Efficient Password-Based Access Control Framework for Time Sequence Aware Media Cloud

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5632-5648页

作者： Wang, Haiyan Liu, Penghui Zhong, Xiaoxiong Luo, Fucai Xiao, Bin Yang, Yuanyuan Peng Cheng Laboratory Department of New Networks Shenzhen518000 China Zhejiang Gongshang University School of Computer Science and Technology Hangzhou China Hong Kong Polytechnic University Department of Computing Hong Kong Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY11794 United States

Cloud storage makes it easier for users to access and share data remotely, but it often requires integration with cryptographic technologies to address consumer-oriented applications, such as fine-grained data access, secure data sharing and retrieval. This paper focuses on the fine-grained access problem of media applications based on time sequence, that is, certain critical media applications based on time sequences should ideally be accessible only to authorized clients. The traditional keyword-based searchable encryption (SE) allows effective search and access over encrypted data while preserving data privacy, but most existing solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users within a specified time range). In this paper, we propose PAC-MC, an efficient password-based access control framework for media cloud relying on content control with the time sequence attribute. PAC-MC not only supports multi-keyword search using any monotonic boolean formulas but also allows media owners to control content-encryption keys for different time periods with an updatable password. Furthermore, it supports the self-retrieval of content-encryption keys. In addition, PAC-MC is provably secure under the standard model. Finally, the detailed performance evaluation results and experimental comparisons indicate that PAC-MC is very efficient and outperforms the previous solutions in terms of computation, communication, and storage costs. © 2025 IEEE.

关键词： Media Encryption Access control Cloud computing Servers Passwords Keyword search Copyright protection Streaming media Performance evaluation

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A GMM-CGABC Based Energy Replenishment Scheme for On-Demand Wireless Rechargeable Sensor Networks 1

A GMM-CGABC Based Energy Replenishment Scheme for On-Demand ...

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1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024

作者： Mouli, S.Sudhamsu Veeraiah, T. Mahesh, K. Singh, M.P. Ecole Centrale School of Engineering Mahindra University Department of Computer Science and Engineering Hyderabad India National Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350349658

Wireless Sensor Networks (WSNs) are constrained by the limited energy capacity of Sensor Nodes (SNs), which hinders their perpetual operation. The advent of Wireless Energy Transfer (WET) technology has emerged as a promising solution to this challenge, enabling the creation of Wireless Rechargeable Sensor Networks (WRSNs). Recent research has shown growing interest in integrating Wireless Mobile Chargers (WMCs) into WRSNs to replenish the energy reserves of critical SNs through WET. A key aspect of WET involves using one or more WMCs to recharge SNs, with optimizing the charging schedule (i.e., the sequence in which SNs are charged) crucial in on-demand WRSNs. In addition, spatiotemporal constraints imposed by the requesting SNs must be considered when devising the WMC's charging schedule. To address these challenges, this work proposes the integration of a Gaussian Mixture Model with a Chaotic Gaussian Artificial Bee Colony (GMM-CGABC) to develop an efficient recharge schedule that revitalizes SNs. This model is designed to optimize SN deployment, maximize network coverage, minimize energy consumption, and extend network longevity. The proposed method determines the optimal charging path for WMCs within each cluster to minimize Average Charging Latency (ACL) while incorporating a novel strategy for fitness function evaluation. Extensive simulations demonstrate that the proposed system outperforms state-of-the-art algorithms, including First Come, First Served (FCFS), Nearest Job Next with Preemption (NJNP), and the Gravitational Search Algorithm (GSA), in terms of charging latency. © 2024 IEEE.

关键词： Inductive power transmission

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PairingNet: A Multi-Frame Based Vehicle Trajectory Prediction Deep Learning Network

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IEEE Access 2023年 11卷 29566-29575页

作者： Chen, Guan-Wen Sun, Min-Te Ik, Tsi-Ui National Yang Ming Chiao Tung University College of Computer Science Department of Computer Science Hsinchu30010 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan320317 Taiwan

The traffic infrastructure of a city requires evaluation and improvement through a large amount of data analysis. The construction and laborious work of traditional methods make computer vision flourish in traffic analysis. Among different computer vision technologies for intelligent transportation system, one of the most important algorithms is multiple object tracking (MOT). At present, MOT used in traffic analysis has several shortcomings, such as lack of the output of vehicle speed and movement direction, and the consideration of single factor in trajectory tracking. These limitations have affected the results of traffic analysis. This research proposes an end-to-end deep learning network, called PairingNet. In addition to retaining the function and accuracy of the original detection network, PairingNet integrates the calculation of vehicle trajectory into the network through the feature fusion of consecutive images, which is introduced to predict the movement direction and speed of the vehicle. These additional features can be used to better track the trajectories of vehicles. In addition, a pipeline is designed to reduce the loading latency incurred by using the consecutive frames as the input for PairingNet. The experiment results indicate that the vehicle identification of PairingNet reaches 96% accuracy, surpassing the original YOLOv3 as the backbone structure, and reaches a near 100% accuracy rate in the predicted vehicle position. Moreover, with the pipeline process, the inference speed of PairingNet is very close to the original YOLOv3. In MOT results, the MOTA of PairingNet also has a high performance of 91%. © 2013 IEEE.

关键词： Trajectories

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Predicting groundwater levels at Colorado state of USA using ARIMA and ANN models 2

Predicting groundwater levels at Colorado state of USA using...

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2nd International Conference on Foundation and Large Language Models, FLLM 2024

作者： Ghafoor, Hifsa Umer, Rahila Fatima Muhammad, Jan Rauf, Zahid Khan, Pamir Balochistan University of it Engineering & Management Sciences Quetta Department of Computer Science Quetta87300 Pakistan Balochistan University of it Engineering & Management Sciences Quetta Department of Computer Engineering Quetta87300 Pakistan Balochistan University of it Engineering & Management Sciences Quetta Department of Electrical Engineering Quetta87300 Pakistan Baluchistan University of it Engineering and Management Science Quetta Computer Science Quetta87300 Pakistan

ISBN: (纸本)9798350354799

variations in groundwater levels that are noticed in different parts of the world are typically the primary focus of research carried out in hydrology. The fluctuating levels of groundwater can be ascribed to several different factors, some of which include an increase in the demand for water, inappropriate irrigation practices, improper management of soil, and unregulated extraction from aquifers. It is necessary to effectively manage groundwater resources to have a trustworthy method of measuring and forecasting groundwater levels. The dynamics of groundwater are inherently uneven and difficult to understand. As a result, applying methods driven by data could potentially yield significant gains in hydrology. In this study, two data-driven models were utilized to estimate groundwater levels at a total of 4 monitoring wells located in Colorado state, USA. These models included auto regressive integrated moving average (ARIMA) and artificial neural network (ANN). The comparative analysis of the algorithms made use of a data set that had one month's worth of information from each of the years 1980 to 2019. The models were put through their paces, and the results of those tests were statistically and visually assessed. To evaluate the relative accuracy and precision of the models, the mean square error (MSE), root-mean-square error (RMSE), and mean absolute error (MAE) were utilized. When compared to ARIMA, the analysis demonstrates that ANN can produce the most accurate forecasts of groundwater levels in Colorado state USA. © 2024 IEEE.

关键词： Groundwater resources

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A Linear-Time Optimal Broadcasting Algorithm in Stars of Cliques

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Journal of Graph Algorithms and Applications 2024年第1期28卷 385-388页

作者： Damaschke, Peter Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg GothenburgSE-41296 Sweden

For the telephone broadcast model, an O(n log n)-time algorithm for constructing an optimal broadcasting scheme in a star of cliques with a total of n vertices was recently presented by Ambashankar and Harutyunyan at IWOCA 2024. In the present note we give a considerably shorter and purified algorithm description and correctness proof. Moreover, we improve the time complexity to O(n). © 2024, Brown University. All rights reserved.

关键词： Consensus algorithm

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Paillier Cryptography Based Message Authentication Code for IoMT Security

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computer Systems science & engineering 2023年第3期44卷 2209-2223页

作者： S.Siamala Devi Chandrakala Kuruba Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science and Engineering Vignan Nirula Institute of Technology and ScienceGuntur522009India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Health care visualization through Internet of Things(IoT)over wireless sensor network(WSN)becomes a current research attention due to medical sensor evolution of *** digital technology-based communication system is widely used in all *** of medical thing(IoMT)assisted health-care application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human *** smart medical devices may consume with limited resources and also the data generated by these devices are large in *** IoMT based applications suffer from the issues such as security,anonymity,privacy,and *** overcome these issues,data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and *** article proposed a well-organized data aggregation and secured transmission *** data generated by the sensor are collected and *** nodes(AN)received the compressed data and concatenate *** concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptogra-phy-based encryption with Message Authentication code(MAC).Fog node extracts the forwarded data from AN using Fog message extractor method(FME)with *** proposed system ensures data integrity,security and also protects from security *** proposed model is simulated in Net-work Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security,privacy and also reduces the communication *** node usages in between Aggregator and base station,will reduce the cloud server/base station computational overhead and storage *** proposed ideology is compared with existing data aggregation schemes in terms of computational cost,storage cost,communication cost and energy *** of communica

关键词： Fog IoMT wireless sensor network cloud aggregation encryption decryption energy

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Electronic Waste Management Using Smart Contracts on the Blockchain Platform

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SN computer science 2024年第7期5卷 1-30页

作者： Rafiee, Azam Feyzi, Farid Shahbahrami, Asadollah Department of Computer Engineering Faculty of Engineering University of Guilan Rasht Iran Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Faculty of Science and Engineering University of Groningen Groningen Netherlands

Nowadays, electronic waste is no longer considered ordinary waste;instead, it is recognized as valuable and hazardous waste containing significant amounts of precious metals. Therefore, it should not be disposed of illegally in any location. Traditional processes for managing electronic waste may not be suitable for the industry's future needs due to environmental pollution, high costs, and low efficiency. Consequently, the management of electronic waste has become a necessity in today's world. This research focuses on utilizing smart contracts to establish stable and regular waste management processes and activities. The proposed method encompasses all steps involved in waste management, including collection, separation, routing, transportation, destruction, and recycling. These steps are managed within a blockchain platform and under a smart contract. To simulate and evaluate the effectiveness of the proposed method, colored petri nets (CPNs) have been employed. The results demonstrate that the presented approach establishes a balanced situation in terms of reliability, response time, and availability parameters when providing services to customers within the context of a smart city. These results are achieved through various steps such as registration, authentication, interaction, routing, and checking the capacity of smart bins. The utilization of blockchain technology and smart contracts ensures transparency, efficiency, and accountability throughout the waste management process. The use of CPNs for simulation and evaluation further validates the effectiveness of the proposed method in achieving reliable and timely waste management services in a smart city environment. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain Electronic waste Petri net Smart contract

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