检索结果-内蒙古大学图书馆

Enhanced Electric Vehicle charging strategy through Graph Convolutional Networks integrated with deep reinforcement learning

引用

International Journal of information technology (Singapore) 2025年 1-13页

作者： Ghode, Shilpa Digalwar, Mayuri Computer Science and Engineering Department Indian Institute of Information Technology Maharashtra Nagpur 441108 India Information Technology Department G H Raisoni College of Engineering Hingna Road Maharashtra Nagpur 440016 India

The increasing adoption of Electric Vehicles (EVs) necessitates advanced charging infrastructure. This manuscript presents a novel approach that integrates Graph Convolutional Networks (GCNs) and the Deep Dyna Reinforcement Learning (D2RL) algorithm to optimize EV charging. GCNs are utilized to model charging stations as interconnected nodes, incorporating factors such as connectivity and distance. The D2RL algorithm effectively schedules charging by considering vehicle location, battery status, station availability, and electricity costs. Our approach demonstrates enhanced efficiency over traditional methods, achieving a high infrastructure utilization rate and substantial energy savings across various sample sizes. The proposed model maintains its effectiveness even at larger scales, showcasing its scalability and adaptability. Additionally, it improves critical performance metrics related to energy impact and demand response, contributing to more efficient EV charging while reducing energy costs and minimizing environmental impact. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Battery life enhancement Charging infrastructure optimization Deep dyna reinforcement learning Electric Vehicles Graph Convolutional Networks

来源：评论

学校读者我要写书评

暂无评论

Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

引用

computer Systems Science & engineering 2023年第5期45卷 1655-1677页

作者： G.Nallasivan V.Ramachandran Roobaea Alroobaea Jasem Almotiri Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21974Saudi Arabia

An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver *** of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s *** changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis,suggesting that the cause may be alcohol instead of liver *** Two-Dimensional(2D)ultrasound data sets contain an accuracy rate of 85.9%and a 2D Computed Tomography(CT)data set of 91.02%.The most recent work on designing a Three-Dimensional(3D)ultrasound imaging system in or close to real-time is *** this article,a Deep Learning(DL)model is implemented and modified to fit liver CT segmentation,and a semantic pixel classification of road scenes is *** architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers.A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training *** the normal class,we obtained 100%precision for chronic cirrhosis hepatitis(73%),offset cirrhosis(59.26%),and offensive cirrhosis(91.67%)for chronic hepatitis or cirrhosis(73,0%).The aim is to develop a computer-Aided Detection(CAD)screening tool to detect *** results proved 98.33%exactness,94.59%sensitivity,and 92.11%case with Convolutional Neural Networks(CNN)*** the classifier’s performance did not differentiate so clearly at this level,it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve(AUC)and accuracy.

关键词： Deep learning liver tumor CNN CT accuracy

来源：评论

学校读者我要写书评

暂无评论

Unguided Machine Learning-Based Computation Offloading for Near-Memory Processing 38

Unguided Machine Learning-Based Computation Offloading for N...

引用

38th International Conference on VLSI Design, VLSID 2025

作者： Maity, Satanu Ghose, Manojit Kumar, Avinash Chakraborty, Anol Chakraborty, Ankit Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India Jorhat Engineering College Department of Computer Science and Engineering Assam India

ISBN: (纸本)9798331522445

Recent data-intensive applications encounter memory wall bottlenecks in the traditional processor-centric computing architecture due to the need for frequent and extensive off-chip data movement. The emerging 3D-enabled near-memory processing (NMP) is a potential solution where computation can be offloaded and performed near memory, eliminating the need for expansive off-chip data transfer. Recently, some crucial works in this context have been proposed that use a supervised machine learning approach that requires a well-labeled dataset, which necessitates creating a time-consuming simulation process. Furthermore, their major focus was on profiling and offloading the entire application to the NMP side, causing a sub-optimal performance because of the low processing power on the NMP cores. This paper presents an unguided machine-learning-based approach (UCO) for computation offloading in NMP architecture that offloads only the memory-intensive regions of an application. The approach extracts several application performance characteristics and creates two clusters using them - one for the CPU side and another for the NMP side. The proposed approach's effectiveness is demonstrated through a series of comprehensive experiments with standard simulators and dataintensive benchmark applications. The results indicate that UCO improves performance (1.31x and 1.10x), saves energy (16% and 12%), and reduces off-chip data transfer (78% and 73%) compared to conventional and state-of-the-art approaches. © 2025 IEEE.

关键词： Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Integration of Blockchain and IPFS: healthcare data management & sharing for IoT Environment

引用

Multimedia Tools and Applications 2024年 1-22页

作者： Mishra, Rajiv Kumar Yadav, Rajesh Kumar Nath, Prem Department of Computer Science & amp Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & amp Engineering Delhi Technological University Delhi India Department of Computer Science HNB Garhwal University Srinagar India

The immense volume of data generated and collected by smart devices has significantly enhanced various aspects of our daily lives. However, safeguarding the sensitive information shared among these devices is crucial. Ensuring the security of the Internet of Things (IoT) ecosystem from unauthorized access is imperative. Blockchain technology emerges as a promising solution to address these security concerns. Nevertheless, the effectiveness of Blockchain in handling the extensive data generated by smart devices is challenged by the rapid pace of IoT data generation and the slower transaction validation speed within Blockchain networks. This research aims to resolve these issues by integrating Blockchain with the Inter-Planetary File System (IPFS), creating a robust framework for secure data recording on a distributed storage network while enabling authorized access to the stored data. The proposed mechanism involves defining and recording access policies and cryptographic hash content on the Blockchain network, while storing the actual IoT-generated data on IPFS to enhance the confidentiality, integrity, and availability (CIA) triad. Performance assessments of the proposed scheme demonstrate its security and practicality, validating its potential for real-world application. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Robust Device-Free Mmwave Sensing with Specular Reflection Interference Mitigation

引用

IEEE Transactions on Mobile Computing 2025年第7期24卷 5749-5764页

作者： Liu, Yulin Wang, Jie Gao, Qinghua Pan, Miao Fang, Yuguang Dalian Maritime University School of Information Science and Technology Dalian116026 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Device-Free mmWave Sensing (DFWS) could sense target state by analyzing how target activities influence the surrounding mmWave signals. It has emerged as a promising sensing technology. However, when employing DFWS indoors, specular reflection interference arises due to the specular reflectors. This interference often induces ghost targets, impacting the accurate estimation of the number and position of targets, resulting in degradation in sensing performance. To tackle this issue, we delve into the generation mechanism of specular reflection interference and analyze its multi-domain characteristics. Through exploration, we discern its temporal sparsity, spatial symmetry or collinearity, and frequency correlation characteristics, and propose four metrics to measure them, accordingly. Specifically, we propose a temporal characteristic quantitative evaluation metric based on identity matching, spatial symmetry and collinearity quantitative evaluation metrics based on geometric analysis, and a frequency correlation quantitative evaluation metric based on Doppler velocity correction, respectively. Based on these metrics, we design a novel Specular Reflection Interference Mitigation (SRIM) method and develop a robust SRIM-DFWS prototype system based on a 60 GHz mmWave radar to validate our proposed method. Experimental results demonstrate that our proposed method could achieve accurate and effective mitigation of specular reflection interference in device-free target tracking. © 2002-2012 IEEE.

关键词： Radar interference

来源：评论

学校读者我要写书评

暂无评论

Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

引用

computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

来源：评论

学校读者我要写书评

暂无评论

Alzheimer's Disease Detection using Machine Learning Models 3

Alzheimer's Disease Detection using Machine Learning Models

引用

3rd International Conference on Electrical, Electronics, information and Communication Technologies, ICEEICT 2024

作者： Nayaki, A. Periya Arrchit Ramana, M.S. Prithivi Raj, S.D. Santhosh, L. Swathi, B. Abinesh, N. Thanabal, M.S. Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350369083

Alzheimer's disease is the most common cause of dementia in older persons, with its incidence rising rapidly. This neurodegenerative disorder affects brain function, impacting memory and cognition, and presents significant social, financial, and economic challenges. Early prediction of Alzheimer's disease is essential for effective treatment and reducing harm. This study utilizes machine learning algorithms to predict Alzheimer's disease using the "MRI and Alzheimer's"dataset from the Open Access Series of Imaging Studies (OASIS). The models evaluated include Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, AdaBoost, LightGBM, XGBRF Classifier, and a Voting Classifier. Their respective accuracies are 84.21 percent for Logistic Regression, 84.21 percent for Support Vector Machine, 81.58 percent for Decision Tree, 94.74 percent for Random Forest, 94.74 percent for AdaBoost, 52.63 percent for LightGBM, 89.47 percent for XGBRF Classifier, and 98.17 percent for the Voting Classifier. The Voting Classifier yielded the highest accuracy, indicating its effectiveness in predicting Alzheimer's disease. © 2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Secure Software Development Life Cycle: An Approach to Reduce the Risks of Cyber Attacks in Cyber Physical Systems and Digital Twins 1

引用

2nd International Conference on Computational Intelligence in Machine Learning, ICCIML 2022

作者： Seelaboyina, Radha Vadla, Sai Prakash Chary Teerthala, Sree Alekhya Pedduri, Veena Vani Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India Department of Information Technology Geethanjali College of Engineering and Technology Hyderabad India

ISBN: (数字)9789819979547

ISBN: (纸本)9789819979530

Digital Twin is an implementation of a physical object or system into a digital space for efficient monitoring and better scalability. It is a virtual representation which acts as a digital counterpart for Cyber Physical Systems or processes in real-time and addresses every instance of its total life cycle. Nevertheless, it also has huge prospects even in the conventional and well-developed industries like agriculture, construction, medicine and automation. By large, it serves as a key example of how the Industry4.0 concept is revolutionizing the industries by facilitating vast numbers of opportunities and efficiencies in them. In this paper, we dig a deep dive into the various risks of cybersecurity associated with Cyber Physical Systems, Digital Twin technology and explore the opportunities of twins protecting themselves. Additionally, we look at the implementation of Secure Software Development Life-Cycle (SSDLC) on Digital Twins and Cyber Physical Systems for mitigating the proposed risks from the very roots. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Fortifying Healthcare Data Security in the Cloud:A Comprehensive Examination of the EPM-KEA Encryption Protocol

引用

computers, Materials & Continua 2024年第5期79卷 3397-3416页

作者： Umi Salma Basha Shashi Kant Gupta Wedad Alawad SeongKi Kim Salil Bharany Computer Science and Information Technology Jazan UniversityJazanSaudi Arabia CSE Eudoxia Research UniversityNew CastleUSA Department of Information Technology College of Computer ScienceQassim UniversityBuraydahSaudi Arabia National Center of Excellence in Software Sangmyung UniversitySeoulKorea Department of Computer Science and Engineering Lovely Professional UniversityPhagwaraPunjabIndia

A new era of data access and management has begun with the use of cloud computing in the healthcare *** the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity *** use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional *** suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.

关键词： Cloud computing healthcare data security enhanced parallel multi-key encryption algorithm(EPM-KEA)

来源：评论

学校读者我要写书评

暂无评论

Gun Detection: A Comparative Study of RetinaNet, EfficientDet and YOLOv8 on Custom Dataset 21

Gun Detection: A Comparative Study of RetinaNet, EfficientDe...

引用

21st IEEE International Conference on computer Applications, ICCA 2024

作者： Khin, Pyone Pyone Htaik, Nay Min Mandalay Technological University Department of Computer Engineering and Information Technology Mandalay Myanmar

ISBN: (纸本)9789998601673

The escalating threat of armed robberies has prompted the urgent need for advanced detection systems. Current solutions in the market lack the ability to autonomously identify and signal the presence of firearms during robbery incidents. To overcome this limitation, this research proposes a novel approach based on deep learning techniques, specifically employing the RetinaNet, EfficientDet, and YOLOv8 (You Only Look Once) models for gun detection. The objective of this study is to develop a sophisticated system that can automatically and accurately identify firearms in surveillance footage during robbery activities. The three selected models bring unique strengths to the system, contributing to its overall efficacy. The proposed system aims to revolutionize the way we address security challenges associated with armed robberies. By leveraging the capabilities of deep learning models, it promises to provide a more proactive and accurate means of gun detection, thus improving overall public safety. The implementation proposed in this study employs a custom robbery detection dataset specifically designed for robbery detection, encompassing classes such as gun, no-gun, knife, no robbery activity, and robbery activity. This study offers a comparison of gun detection algorithms, specifically focusing on different three models which are RetinaNet, EfficientDet and YOLOv8. Through a meticulous evaluation of the three models' performance on this custom dataset, the good results are obtained according to mAP. Among them, it becomes apparent that the YOLOv8 architecture surpasses others, achieving a remarkable accuracy of 91.4% Mean Average Precision (mAP) in robbery detection. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：