检索结果-内蒙古大学图书馆

Med-tech device security through advanced server cryptography

International Journal of Information Technology (Singapore) 2025年第2期17卷 1015-1022页

作者： Sharan, Preeta Yasmeen, M.S. Nagesh, N. Parimi, Pranav Chetan, V. Roy, Sandip Kumar Department of Electronics and Communication (R & D) The Oxford College of Engineering Bangalore India Department of Electronics and Communication Engineering The Oxford College of Engineering Bangalore India Department of Computer Science Engineering Amrita Vishwa Vidyapeetham Bangalore India Research and Development The Oxford College of Engineering Bangalore India S P Jain School of Global Management Dubai United Arab Emirates

To protect secrecy without compromising data, the study outlines a strong security framework for communications between mobile devices and servers and recommends encryption as the best option. Traditional encryption techniques are not immediately transferable due to the limitations of e-health data, including its size, redundancy, and bandwidth especially when patient data is moving across public networks. Patient privacy may be compromised as a result of this security flaw. Strong encryption measures must be put in place in the healthcare industry to combat this and ensure that confidential patient data may only be retrieved securely and by authorised personnel, protecting data integrity and privacy. To strengthen communication in the healthcare industry, the study presents a new, simplified encryption and decryption technique. We carefully examine, evaluate, and then benchmark this technique against accepted encryption standards. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Data Security Healthcare device security RSA algorithm Secured communication

来源：评论

学校读者我要写书评

暂无评论

A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

引用

Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

引用

computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

来源：评论

学校读者我要写书评

暂无评论

Deployment of neural networks for WLAN thumbprint indoor positioning system

引用

International Journal of Information Technology (Singapore) 2025年 1-7页

作者： Kushwaha, Ajay Agarwal, Trapty Singh, Manpreet MP, Sunil Sharma, Amit Jain, Romil Bhuvaneswari, P. Computer Science and Engineering Rungta College of Engineering and Technology Chhatisgarh Bhilai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Centre of Research Impact and Outcome Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Karnataka Bangalore India School of Computer Applications Lovely Professional University Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Chandigarh India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India

With the application and development of related technologies based on user location information, location-based services have become essential for daily lives. Despite this fact, Wireless Local area networks (WLAN’s) often face time complexities in streamlining and collection of data. Thus, the given paper introduces a deep-learning model based on convolutional neural network (CNN) to predict user’s indoor location from the received wireless signal strength fingerprint maps. The aim is to enhance WLAN indoor localization by streamlining the data collection process. The proposed indoor positioning model leverages a region of interest (ROI) extraction technique to simulate the wireless received signal strength intensity in WLAN’s. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of WLAN localization. To optimize and evaluate the model’s performance in real-world scenarios, experiments are conducted using simulated datasets obtained from the publicly available database such as UCI repository. The results show that the proposed model attains the highest validation accuracy (97.11%) and training accuracy (97.34%) as compared to the existing deep learning algorithms used for indoor localization. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CNN Deep learning Indoor localization Region of interest (ROI) Signal strength WLAN

来源：评论

学校读者我要写书评

暂无评论

A Resourceful Energy Management and Improved Security in Wireless Sensor Networks by Optimized Energy-Based Security Prototype

引用

SN computer Science 2025年第3期6卷 1-13页

作者： Arya, Nancy Deore, Shailesh Shivaji Sharma, Mahendra Verma, Amit Patibandla, R. S. M. Lakshmi Krishna, K. Murali Pareek, Piyush Kumar Department of Computer Science and Engineering School of Engineering & Sciences GD Goenka University Sohna Gurugram India Department of Computer Engineering SSVPS B S DEORE College of Engineering Maharashtra Dhule India DoIT IMT College of Engineering UP Greater Noida India Department of Computer Science University Centre for Research and Development Chandigarh University Gharuan Punjab Mohali India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Electronics Communication Engineering Rajalakshmi Engineering College TN Chennai India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

One must admit that while using a number of remote sensing points with limited nodes, it is crucial to optimize resource usage as intensely as possible and have a secure network with the least compromises, even though WSNs are applied in increasing numbers of industries. When deploying WSN, power utilization and ability to withstand a number of threats and attacks are important factors to be considered. This work presents a technique for a routing protocol that determines the shortest path between two nodes in a network while at the same time trying to conserve energy. Lifetime and energy consumption of the networks are two important factors when designing new protocols for WSN. An important problem of finding a load-sharing routing method with less energy consumption is difficult. TBEBR routing protocol optimizes the parameters that lead to minimal energy dissipation to enhance the lifetime of the network by identifying the path with the least energy consumption possible. Throughput, overhead, packet loss and end to end delay were used as performance parameters to assess the routing protocol. The analysis of the overall efficiency of the routing concept as well as the method of its application was described. As far as the performance of network and nodes is concerned, it is clear that TBEBR outperforms both DSR and AODV. For WSNs, the proposed routing protocol aims at minimizing the energy issue, which is a disadvantage of the technology. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DoS attack EWMA Unit energy requirement WSN

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

引用

computer Modeling in engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

来源：评论

学校读者我要写书评

暂无评论

Software defined network (SDN) based data server computing system

引用

International Journal of Information Technology (Singapore) 2025年第1期17卷 607-613页

作者： Madhura, K. Sekhar, Gillala Chandra Sahu, Amaresh Karthikeyan, M.P. Khurana, Saniya Shukla, Meenu Vashisht, Nitish Information and Communication Technology Manipal Institute of Technology Mahe Bengaluru India Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad India Ajay Binay Institute of Technology Odisha India Department of Computer Science and Information Technology Jain (Deemed to be University) Karnataka Jain Bangalore India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Rajpura India

The growing clientele and broadband demand lead to several technical restrictions in ensuring stable connections between data centers. The data on existing network devices such as switches, routers, and gateways must be routed to some particular point commonly known as software-defined network (SDN). SDN is defined as a networking approach that makes use of software-based controllers or application programming interfaces (API’s) to communicate with existing hardware and provide paths to traffic in the network. In order to overcome technical restrictions in the network, the given paper introduces an application-based enabled open flow information center network layout to accomplish transport load allocation and higher efficiency. In order to mitigate noise in the network, the proposed approach employs a data centre network (DCN) and implements it using SDN controller such as OpenDaylight (ODL). ODL is an open-source platform for automating networks of any size and scale. It allocates traffic requests among the linked servers, thereby ensuing higher throughput, optimization of load balancing, and versatility in SDN’s. Lastly, the performance of the proposed approach is validated by assessing the throughput rate (Mbps) and average delay (ms) prior to load balancing (BLD) and after load balancing (ALD). The results show that the proposed approach leads to higher throughput after load balancing optimization using ODL. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： DCN Load balancing ODL SDN Throughput

来源：评论

学校读者我要写书评

暂无评论

Reliable Multihop Routing and Congestion Aware Chicken Swarm Optimization in Vehicular Ad-Hoc Network 3

Reliable Multihop Routing and Congestion Aware Chicken Swarm...

引用

3rd International Conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Anitha, Cuddapah Al-Tarazi, Dalia Vignesh, G.D. Vasuki, A. Vinotha Giri, Jayant Dinesh, M. School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Department of Computer Science and Engineering Tirupati India Zarqa University Faculty of Engineering Department of Architecture Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia St. Joseph's College of Engineering Department of Electronics and Communication Engineering Chennai India J.P. College of Engineering Department of Computer Science and Engineering Tenkasi India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798331508456

Background: Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems such as significant movement, overcrowding, and loss of data. This research presents a novel deep reinforcement learning-based resource allocation and congestion optimization (DRLRCO) framework aimed at improving communication efficiency and high reliability using an effective learning process. Method Used: The DRLRCO framework employs agent-based learning to ensure precise data transmission, AdMAC Protocol for dynamic packet delivery, and Congestion-Aware Chicken Swarm Optimization (CSO) to enhance network performance. Result: The DRLRCO framework surpasses reinforcement learning models for comprehensive testing. Significant enhancements involve improved accuracy, reduced data loss, minimized overhead, increased throughput, and shorter average delays for real-time communication. Conclusion: The DRLRCO framework successfully tackles congestion and data loss in vehicular communication. Future studies might involve Artificial Intelligence (AI) for traffic forecasting, experimentation among high-speed vehicles and investigating blockchain for enhanced security. © 2025 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

The Role of Cyber-Physical Systems in Revolutionizing Transportation: From Connected Cars to Autonomous Vehicles 3

The Role of Cyber-Physical Systems in Revolutionizing Transp...

引用

3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Frank, Anilloy Augustine Agarwal, Trapty Sharma, Divya Dutta, Arup Kumar Gupta, Shikhar Aravindan, M.K. Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

Cyber-physical systems have technologically advanced the transportation industry with their ability to connect physical systems with virtual functions. A cyber-physical system uses real-time data generation and communication technologies to make transportation systems safer, more efficient, more sustainable and more reliable. Connected cars are one of the primary use cases of CPS in transportation. They utilize sensors, cameras, and internet connectivity to capture information regarding road conditions, traffic patterns, and other drivers on the road. This information is then communicated to the backend system for analysis and decision-making to enhance safety or alleviate traffic congestion. It has also contributed towards developing independent vehicles. They employ ACH and CPS technologies to drive on roads driving without the need for human intervention. This could lead to a huge reduction in human error-related accidents and improve the efficiency of transportation. CPS also helps build smart transportation systems or link multiple modes of transport to escape congestion and reduce travel time. For example, it involves the integration of different modes of transportation, such as public transit, ride-sharing services and self-driving cars, to minimize travel time while maximizing usage. CPS has revolutionized the transport sector into a more connected, effective and secures system. © 2025 IEEE.

关键词： Travel time

来源：评论

学校读者我要写书评

暂无评论

Real-Time Indoor Localization Using IoT and Sensor Networks for Human Activity Monitoring 3

Real-Time Indoor Localization Using IoT and Sensor Networks ...

引用

3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Ganesh, V. Muthu Alam, Intekhab Sharma, Dikshit Mohanty, Samuka Parmar, Yuvraj Krishna, Gautham Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

To develop an effective real-time indoor localization methodology by exploiting the Internet of Things (IoT) consists of sensor networks. This tech is to support a more robust HAM application that allows for precise and continuous tracking based on individual locations inside buildings. It uses a combination of IoT devices and sensors to give people in the indoor environment real-time positioning data. Real-time indoor Localization based on IoT and Sensor Networks is a technology that allows evaluating precise continuous tracking of individuals in buildings to monitor human activity by applying advanced techniques such as Wireless sensor networks. Essentially, this technology leverages IoT devices and sensors to give precise real-time location information of people inside buildings. This accurate and reliable indoor positioning uses advanced techniques such as wireless sensor networks, lowenergy Bluetooth, and IoT technologies like RFID;this technology can be achieved only in Hospitals, shopping malls, and offices where it enables you to track human activities from indoor spaces at the current moment' Installing sensors and IoT devices throughout the building connected to a central data center or via Natural Activity Limit Parametrization Real-time indoor localization using the Internet of Things (IoT) and sensor networks enables accurate tracking for human activity monitoring inside buildings. This technology uses IoT devices with sensors to give people precise real-time information on their location in an indoor space. This technology provides highly precise and reliable indoor positioning with advanced techniques such as wireless sensor networks, Bluetooth low energy, or RFID. The technology is best suited to tracking human activities in indoor spaces, including hospitals, shopping malls, and offices. This is followed by creating a data layer of network sensors and IOT devices inside the building connected to an on-premises or cloud central server. Sensors captur

关键词： Wireless sensor networks

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：