检索结果-内蒙古大学图书馆

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： George, Remya P. Ahmad, Nazia Hussain, Sajithunisa Khan, Rubina Liyakat Quraishi, Suhail Javed Singh, Anupam Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Mriirs Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350387490

VANETs are an emerging area of interest in wireless communications mainly because of the features like high speed and mobility and dynamic topology. The current study presents a comprehensive evaluation of multi-variant routing protocol adapted for VANETs, wherein based on the protocol's behavior they are categorized by topology based, geographic based, broadcast based, and cluster based. These protocols are assessed based on parameters such as latency, loss packet ratio, and overhead. Additionally, we outline their practical implications and the directions for further research. © 2024 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

Robust AI-Enabled Electronic Components Authentication and Anti -Counterfeiting 9

Robust AI-Enabled Electronic Components Authentication and A...

引用

9th International Conference on science, Technology, engineering and mathematics, ICONSTEM 2024

作者： Banu, E. Afreen Priyanka, R. Thiruramanathan, P. Senthilnathan, T. Vithya, V.T. Vinoth, K. Saveetha School of Engineering Simats Department of Computer Science Engineering Tamil Nadu Chennai602105 India S.A Engineering College Department of Computer Science Engineering Tamil Nadu Chennai600077 India Ramco Institute of Technology Department of Physics Tamil Nadu Rajapalayam626117 India Sri Venkateswara College of Engineering Department of Applied Physics Tamil Nadu Sriperumbudur602117 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Electrical And Electronics Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350365092

In consumer electronics, aircraft, health care, and defense, fake electronic parts compromise system security and reliability. This study presents an AI-powered electrical component authentication and anti-counterfeiting system for efficiency and robustness. To prevent counterfeit electronic components, a multi-layered defense uses cutting-edge authentication and AI algorithms. Deep learning models for picture identification, spectroscopic analysis, and other cutting-edge component physical attribute evaluation. A decentralized blockchain platform can record the whole supply chain from producers to consumers. Smart contracts automate and enforce quality standards, eliminating many counterfeiting layers. An Integrated sensors and triggers detect physical tampering, alarm, and authenticate. Tracking and reporting component movements in real time permits quick response to suspicious activity. Stakeholders can easily validate components with AI algorithms via web or mobile apps. NFC, RFID, and QR code authentication is simple. To prevent counterfeiting, the framework specifies a stringent electrical component validation method. Advanced AI and physical and digital security measures provide comprehensive electrical component security throughout their lifespan. © 2024 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Advanced Architectures and Management Strategies for Energy Efficiency in IoT Systems 4

Advanced Architectures and Management Strategies for Energy ...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Singh, Anupam George, Remya P. Ahmad, Nazia Hussain, Sajithunisa Khan, Rubina Liyakat Quraishi, Suhail Javed Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Mriirs Haryana Faridabad India

ISBN: (纸本)9798350387490

Internet of Things (IoT) systems penetration has brought tremendous innovations in different areas from homes to industries. But, the upsurge in the usage of IOT devices to can lead to a massive demand for energy consumptions hence call for new solutions on how this energy may be used efficiently. In the current study, the authors focus on the state of the art solutions and approaches to the energy management in IoT context. It offers an understanding of how power optimisations in hardware designs are done, including in low-power SoC solutions and in more potent STT-MRAM architectures that decrease power usage at the macro component level. Pertaining to conservation, the study also reviews the energy-savvy architectures of the network, a new system of access point, and routing protocols that employ the least energy. In addition, the methods of use of renewable energy resources for IoT operation and energy harvesting techniques are also proposed. On the management front, the paper presents overall frameworks on the methods for developing EMS, utilization of big data technology for energy use measurement and control in real-time. It also looks into techniques of demand response management and load managing that reduces fluctuations in energy demand. Discussed here are some of the simple yet effective solutions for controlling and reducing energy consumption in buildings where actual performance evaluations are made by showing marked enhancement in the systems and substantial energy saving. The following are the research propositions and avenues for future research, and the emerging technologies that could be applied in the future to influence the energy efficiency of IoT systems. © 2024 IEEE.

关键词： MRAM devices

来源：评论

学校读者我要写书评

暂无评论

Analysis of Book User Ratings and Publication Trends in the Publication Industry using ML Models 4

Analysis of Book User Ratings and Publication Trends in the ...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Quraishi, Suhail Javed Singh, Anupam George, Remya P. Ahmad, Nazia Hussain, Sajithunisa Khan, Rubina Liyakat Mriirs Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (纸本)9798350387490

The central idea on the current work involves the computation of book user ratings and publication trends within the publication industry based on a large data set, which includes the user profile details and comprehensive metadata associated with books. The research focuses on examining the association between demographic characteristics and rating activities and age as a variable with a relatively small effect on these trends. The rationale also includes the need to examine the effects that genres, historical ratings, and social factors have on users. Furthermore, analysis of publication trends shows that these are deeply influenced technically and culturally and brought to the light important changes in the publication industry. © 2024 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Crow Search Optimization to Identify Adversary Nodes in Wireless Networks 2

Crow Search Optimization to Identify Adversary Nodes in Wire...

引用

2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Rajaram, Gnanajeyaraman Thangaraj, T. Stephen Packialatha, A. Deepa, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Applied Machine Learning Tamil Nadu Chennai India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Adversary node detection systems have become a vital component due to the malware attacks. These systems are very important for network security due to internet's widespread development and increased accessibility to global data systems. This paper presents a Crow Search Optimization Algorithm (CSOA) to detect the adversary node in a wireless network. The Rivest, Shamir, Adleman (RSA) algorithm transmits secure data at first. It uses the public key to encrypt the data and the private key to decrypt the data and the adversary nodes can't access or modify the real data. One of the significant problems is when an unknown attack occurs on the network due to the high volume of data while the detection's accuracy and false alarm rate decline. The proposed system aims to improve accuracy by identifying the adversary nodes using CSOA algorithm. From the results, it is observed that the CSOA mechanism reduces the loss ratio and improves throughput of the network. © 2023 IEEE.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Optimized Machine Learning Techniques for Precise Breast Cancer Detection in Mammograms

引用

SN computer science 2025年第4期6卷 1-22页

作者： Kiran, Puttegowda Veeraprathap, V. Rajashekhar, U. Mahantesh, mathapati Sudheesh, K.V. Bharath, Kumara Prabhavathi, K. Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Department of Electronics and Communication Engineering ATME College of Engineering Mysuru Karnataka Mysuru India Department of ECE Government Engineering College Huvinahadagali Karnataka Vijayanagara India Department of Computer Science & Engineering Amruta Institute of Engineering and Management Sciences Karnataka Bengaluru India Department of ECE M S Ramaiah University of Applied Science (MSRUAS) Karnataka Bangalore India Department of ECE BGS Institute of Technology Adichunchanagiri University B. G. Nagara Karnataka Mandya India

Breast cancer has recently overtaken cervical cancer as the predominant cancer type in Indian urban areas. Despite considerable research and the development of automated diagnostic machines, current methods are far from perfect, necessitating more reliable medical assessments. Moreover, there's been relatively little research on Indian datasets compared to international resources, despite significant differences due to factors like denser breasts, varying textures, lesion sizes, and compositions. To address this gap, our work focuses on using machine learning to automate breast cancer detection, tailoring models to Indian breast types and utilizing metadata for more accurate assessments. Our analyses show promising results, with the best single model achieving a per-image AUC of 0.95, and averaging four models increasing AUC to 0.98 (sensitivity: 86.7%, specificity: 96.1%) on an independent test set from the INbreast database. These findings highlight the potential of deep learning techniques in improving mammography accuracy and reducing false positives and negatives in clinical settings. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomedical applications Breast cancer Deep neural networks Faster-RCNN Machine learning models Mammograms Object detection YOLO V3

来源：评论

学校读者我要写书评

暂无评论

A Robust Cold-Start Approach Using Phylogram 10

A Robust Cold-Start Approach Using Phylogram

引用

10th International Conference on Optimization and Applications, ICOA 2024

作者： Da Silva, Breno Caetano Franco, Jorge Luiz Branco, Hermes Manoel Galvão Castelo Tokuda, Eric Delbem, Alexandre Claudio Botazzo Federal Institute of Maranhão Computer Science Department Teresina Brazil Inst. of Math. and Computer Science University of São Paulo São Carlos Brazil Federal University of Piaui Electrical Engineering Department Teresina Brazil

ISBN: (纸本)9798350387353

In Active Learning (AL) applications, it is crucial to develop a model that efficiently represents data using a limited number of labeled samples. In this context, an oracle is often employed to label samples that are deemed representative and informative. However, one of the primary limitations of AL lies in the need for an initial set of labeled samples, which may not be representative of the entire data space. To address this issue, cold-start strategies are implemented with the goal of selecting an appropriate initial set of samples for labeling, ensuring that the initial model is robust and effective. We propose a novel approach - PhyIL - that harness the advantages of phylogram analysis and community detection techniques to smartly perform the cold-start selection in AL. The method facilitates the choice of samples that best represent the diversity and complexity of the data, which is fundamental for the subsequent success of the active learning process. We evaluated the proposed approach using nine real-world databases and nine traditional ML models. The findings indicate that PhyIL offers an effective and viable solution for the initial sample selection, when compared to the random selection. © 2024 IEEE.

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

引用

Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

Maximize Leader Election Algorithm Performance in Bi-Directional Ring Networks With Both Links Down Between Two Nodes 25

Maximize Leader Election Algorithm Performance in Bi-Directi...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Al Refai, Mohammed N. Fawareh, Hamed Aljawawdeh, Hamzeh Alshraiedeh, Fuad Sameh Al-Quraan, Mohammad Al-Odat, Hisham Ali Otoom, Ahmed Jamhawi, Zeyad M. Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Zarqa Jordan Applied Science Private University Department of Cyber Security Amman Jordan Applied Science Private University Department of Computer Science Amman Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The process of selecting a leader to avoid instability in distributed system network after leader failure is called Leader Election Algorithm (LEA). This process becomes more complicated when it faces intermittent or complete links disconnected. the network becomes a one-way behavior if both lines of communication between two points are disrupted and thus the network becomes very slow, especially in systems where time is a critical issue, and the process of communication between points becomes much slower if the network leader is next to the fault or close to it, so the criteria will be established to ensures that the system leader in the Bidirectional ring communication network is in a place that allows next leader to communicate with all points as quickly as possible, and this will be during the election *** research presents a solution for leader failure problem in bidirectional ring network. This research takes into account the existence of intermittent or disconnection links between nodes. It will produce three algorithms to determine a new leader in the network with maximum network performance. It uses the least probability of time steps and the number of messages needed to elect the new leader in bidirectional ring network with N communicated nodes. so that the system remains able to work and communicate with its best capacity during this problem with the number of message complexity O(N) in the worst case. The third scenario minimizes the value of proper leader ID IDCurrent/IDCurrent-idStepCount', and maximizes the value of proper leader place N - (N - nStepCount). © 2024 IEEE.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Adaptive Content Forwarding Mechanism for Platoon based Vehicular Named Data Networks

Adaptive Content Forwarding Mechanism for Platoon based Vehi...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Kaushik, Anu Garg, Deepanshu Nehra, Anushka Bali, Rasmeet S Baza, Mohamed Srivastava, Gautam Chandigarh University Computer Science & Engineering Department India Thapar Institute of Engineering and Technology Computer Science & Engineering Department India College of Charleston Department of Computer SC29414 United States Brandon University Department of Math and Computer Science Canada

ISBN: (纸本)9781665480451

Vehicular networking systems rely on Internet Protocol to exchange information among vehicles. With the increasing number of vehicles, the communication overhead has increased significantly a nd h as b ecome m ore c ontent centric. To resolve this problem, the Named data networking-based communication model has been used. This communication is completely based upon the content rather than the location and provides better network coverage comparatively. The vehicles used for communication purposes in a network are moving in some specific p atterns, b ased o n h aving t he s ame destination, with the same speed parameters etc. These vehicles which have common interests form a platoon. This vehicular platoon helps in various fields such as safe driving, energy efficiency and road safety. This paper provides a scheme for the applicability of NDN to the vehicular platoon. Special design features are proposed for communication purposes in V-NDN-based vehicular platoons. The backbone platoon network is used for data dissemination between the vehicles on the highway. To check the efficiency of the proposed scheme, extensive simulations have been performed on the ndnSim simulator. More precisely, different scenarios have been used and analyzed their efficiency i n t erms o f d elay and throughput. © 2022 IEEE.

关键词： Autonomous vehicles

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：