检索结果-内蒙古大学图书馆

2025 International Conference on Computing for sustainability and Intelligent Future, COMP-sIF 2025

作者： sagar, b.J. Job Karuna Divya, M.v. Kale, Namita Anand, Neetu Gaurav, Kumar shashidhara, shrividya Joginpally B.R Engineering College Department of Cse Telangana Hyderabad India East West Institute of Technology Department of Computer Science Bangalore India MET'S Institute Of Engineering Department of Information Technology Nashik India Maharaja Surajmal Institute Department of Computer Applications Delhi India Bms Institute of Technology and Management Department of Cse Bangalore India

ISBN: (纸本)9798331538538

The availability of the internet and enhanced applications, more specifically, the utilization of more devices in networks, and improved computational applications has created complexity in the control of network traffic. Network traffic forecasting is an essential component in low-latency networks, as well as in real-time applications that deserve excellent service quality and efficient resource allocation. This study therefore focuses on identifying whether deep learning models can further improve the accuracy in forecasting traffic flows in a network. Therefore, with the help of LsTM networks and Convolutional Neural Networks, the work will build temporal dependencies and spatial correlations of network traffic data. There are major and minor adaptations to some classic methodologies that conform the proposed framework;moreover, it is applied to two real datasets to highlight the capacity of the model in anticipating traffic congestion with a higher level of accuracy than the traditional methodologies. Evaluation of models in dynamic network conditions is done through common parameters such as prediction accuracy, lowered latency, and scalability. saved models show that deep learning-based solutions reduce latency at high loads much more effectively, which will help network administrators in finding efficient solutions. This study demonstrate how deep learning can aid in the prediction of traffics, and the manner in which it is changing the efficiency and effectiveness of modern-day network systems. It makes the foundation for improved understanding of traffic patterns in order to apply it to optimize future generation of communication networks. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Text Transmission Through Li-Fi in WsN Using smart based secure Morse Code Algorithm

Text Transmission Through Li-Fi in WSN Using Smart Based Sec...

引用

Multi-Agent systems for Collaborative Intelligence (ICMsCI), International Conference on

作者： I. vasudevan P. Malathi s. siddik P.R Therasa M.Mohamed Nasurudeen R.M.Dilip Charaan Dept.of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Dept of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Dept.of EEE Pavaai Engineering College Namakkal India Dept.of CSE R.M.K Engineering College Kavaraipettai India Dept.of CSE V.S.B Engineering College Karur India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Cyber security is one the most challenging task in today's world. Most of the existing Li-Fi technology in wireless sensor network is to detect the data within the short range of distance less accuracy. In this research, we proposed a smart based secure Morse Code (ssMC) technique. Here there are two part, first is encoding and next is decoding. This proposed encoding technique takes user input in text based form and convert these text to standard morse code pattern and decoding does in the opposite direction. The transmitter parts encode the user input and send the pattern through laser module. The receiver part receives the pattern sends to microcontroller and then decodes and displays the data. In our experiment results shows that the proposed technique can transmit data over long distance with help of morse code encoding decoding algorithm. This technique takes 5ms for the whole transmission and process the transmission accurately.

关键词： Wireless communication Wireless sensor networks Codes Transmitters Receivers Light fidelity Encoding Decoding computer crime standards

来源：评论

学校读者我要写书评

暂无评论

Permissioned blockchain security Pqfabric from Quantum and Classical Attacks

Permissioned Blockchain Security Pqfabric from Quantum and C...

引用

Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： C. Tamilarasi K bhavadrarani Thiagarajan Kittappa N. subashini v. Anitha vimalraj E Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science & Technology Chennai Department of Computer Science and Engineering V.S.B. Engineering College Karur. Department of Mathematics Rajalakshmi Institute of Technology Chennai Department of Mathematics Sathyabama Institute of Science and Technology Chennai Department of CSE Panimalar Engineering College Poonamallee Chennai School of Computing Mohan Babu University Tirupati Andhra Pradesh

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Old style cryptographic techniques are genuinely compromised by the development of quantum computing, which requires the formation of safety ideal models that are impervious to quantum mistakes. A new permissioned blockchain security system called PqFabric is intended to oppose both traditional and quantum attacks. PqFabric ensures the validness, mystery, and respectability of exchanges in a quantum-empowered future by including post-quantum cryptographic techniques like hash-based and grid based marks. As opposed to ordinary blockchains, PqFabric utilizes a half breed agreement process that gets information trades and members characters by melding byzantine Fault Tolerance (bFT) with quantum-safe encryption. The system gives protection against calculation based attacks by shor and Grover, tending to huge imperfections in existing blockchain structures. besides, when quantum-safe guidelines advance, its secluded plan empowers smooth enhancements. With this procedure, PqFabric is situated as a ground breaking answer for organizations and areas wishing to safeguard their blockchain networks from new cryptographic risks.

关键词： Production systems Technological innovation Quantum computing supply chains standards organizations Oral communication Throughput Fabrics blockchains Encryption

来源：评论

学校读者我要写书评

暂无评论

Developing a Robust Intrusion Detection system Using sMOTE and Hybrid svNN Model 4

Developing a Robust Intrusion Detection System Using SMOTE a...

引用

4th International Conference on sentiment Analysis and Deep Learning, ICsADL 2025

作者： Kiranmayee, b. Devi, M. sharmila susheela, Kalva Dhumpati, Raghu Penubaka, Kiran Kumar Reddy Naidu, U Ganesh Kurnool India Santhiram Engineering College Department of Cse Nandyal India Sir C R Reddy College for Women Department of Computer Science Eluru India Bahrain Polytechnic College Department of CSE Bahrain Mlr Institute of Technology Department of CSE-AIML Hyderabad India B v Raju Institute of Technology Telangana Medak India

ISBN: (纸本)9798331523923

This paper proposes an integrated approach to build up IDs with proper effectiveness toward the rising need for strong network security. Network traffic anomaly detection and classification are one of the major aims and enhance the security layer against various types of cyber threats. This study is a methodical approach in which a diverse set of data is first extracted from Kaggle. The collected dataset is a comprehensive one that includes various kinds of network traffic data. The first step includes preprocessing the data, i.e., handling missing values, removing erroneous entries, and dealing with outliers using the Interquartile Range (IQR) method. To counter class imbalance, synthetic Minority Oversampling Technique (sMOTE) is utilized in generating synthetic samples in underrepresented classes for generalization of models. The feature selection algorithm relies on the Chi-square Lasso Feature selection method. This method unites Chi-square tests to select statistically significant features with Lasso regularization for feature dimension reduction with the goal of overfitting minimization. For building models, a hybrid of support vector Machine and K-Nearest Neighbors, which is termed as svNN, is used. by using soft voting to aggregate the outputs of svM and KNN, the proposed svNN model improves the prediction accuracy, the degree of robustness, and adaptability. The presented methodology has achieved high classification accuracy 93.28%, precision 93.49%, recall 93.28%, F1 score 93.23%, and low RMsE of 0.2592. These metrics demonstrate that the model is reliable and of practical use for intrusion detection in dynamic, high-dimensional environments of networks, providing a proper solution to modern security network challenges. © 2025 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Wireless Charging Technologies for Electric vehicles: state of the Art and Future Outlook

Wireless Charging Technologies for Electric Vehicles: State ...

引用

IEEE students' Conference on Electrical, Electronics and computer science (sCEECs)

作者： sarathkumar D b.K. Parrthipan Murugesan Manivel T. Jayakumar R subramani Rao s. sambooranalaxmi Department of Electrical and Electronics Engineering Tamil Nadu India Department of Mechanical Engineering Kamaraj College of Engineering and Technology Vellakulam Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronics Engineering Nandha Engineering College Tamilnadu India Department of Electrical and Electronics Engineering V.S.B Engineering College Karur Department of Electronics and Communication Engineering PSR Engineering College Sivakasi Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Wireless charging technology for electric vehicles (Evs) represents a significant advancement in the transition to sustainable transportation, offering a convenient and efficient alternative to traditional plug-in charging systems. This paper provides a comprehensive review of the state-of-the-art wireless charging techniques, with a focus on inductive power transfer (IPT), resonant inductive coupling, and emerging capacitive and magnetic resonance methods. Key components of wireless charging systems, including power electronics, coil design, alignment techniques, and communication protocols, are discussed. We also address the challenges related to efficiency, safety, standardization, and scalability, particularly in dynamic charging systems designed for highway applications. The paper further explores the integration of wireless charging into smart grids, considering the potential for vehicle-to-grid (v2G) applications and renewable energy sources. A comparative analysis of existing technologies and their readiness for widespread adoption is provided, alongside an outlook on future trends, including high-power charging, autonomous charging infrastructure, and advances in materials and control algorithms. The paper concludes by identifying research gaps and potential directions for innovation in wireless charging for Evs.

关键词： vehicle-to-grid Electric potential Technological innovation Inductive charging Dynamics Magnetic resonance Transportation Power electronics Inductive coupling vehicle dynamics

来源：评论

学校读者我要写书评

暂无评论

A Text Detection Method based on Multi-scale selective Fusion Feature Pyramid and Multi-semantic spatial Network for visual IoT

引用

IEEE Internet of Things Journal 2025年第11期12卷 15997-16008页

作者： Wang, Manli Dou, Zeya Chen, Wei Wang, Zehua Yu, Fei Richard Leung, victor C. M. Henan Polytechnic University School of Physics and Electronic Information Henan Jiaozuo454003 China China University of Mining and Technology School of Computer Science and Technology Jiangsu Xuzhou221116 China China University of Mining and Technology School of Mechanical Electronic and Information Engineering Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University School of Information Technology OttawaONK1S 5B6 Canada

With the rapid development of visual Internet of Things (vIoT) and text detection technology, they have been widely combined and applied to many industrial production sites, such as label text detection, achieving impressive results. However, there are still many shortcomings in the text detection technology: 1) The existing vIoT system has very limited detection precision for text with large scale changes, especially for some small-scale text detection;2) The existing text detection algorithms cannot meet the actual situation, as the labels often contain handwritten texts, and the text to be detected is arbitrary shape;3) In the actual detection, there are many creases or defects on the text label. To solve the above problems, this paper designs a text detection method based on a multi-scale selection fusion feature pyramid and multi-semantic spatial network to assist the vIoT system in detecting label text. Firstly, a multi-scale selective fusion feature pyramid is designed, which not only uses the texture extraction module to effectively improve the text texture feature and multi-scale feature extraction ability, but also uses the cross-scale selective fusion block to selectively fuse the features of different stages to reduce the influence of pollution on detection. In addition, a multi-semantic spatial network is designed to capture the multi-semantic spatial information of each feature channel by using the multi-scale deep shared one-dimensional convolution, which effectively integrates global context dependence and multi-semantic spatial prior. Experimental results show that the comprehensive index F-measure on the public datasets ICDAR2015, Total-Text and CTW1500 is increased by 5.7%, 3.3%and 3.8% respectively. Furthermore, the precision, recall, and F-measure on the dataset Label-Text are 94.6%, 90.7%and 92.6% respectively. The label text detection vIoT system we designed has been deployed in the field and achieved excellent performance. The code of our pro

关键词： semantics

来源：评论

学校读者我要写书评

暂无评论

Drug Traceability and Health Monitoring system with Hyperledger Fabric 6th

Drug Traceability and Health Monitoring System with Hyperled...

引用

6th International Conference on blockchain and Trustworthy systems, blocksys 2024

作者： vijayakumar, P. Rajkumar, s.C. sivaraman, Audithan Jayarekha, P. Chandra sekaran, s. Department of Computer Science and Engineering University College of Engineering Tindivanam Villupuram Tamil Nadu Tindivanam604001 India Department of Computer Science and Engineering Anna University Regional Campus Madurai Keelakuilkudi Tamil Nadu Madurai625019 India Department of Information Technology Netaji Subhas University of Technology Jafferpur110073 India Department of Information Science and Engineering B.M.S. College of Engineering Karnataka Bangalore560019 India Department of Computer Science and Engineering P.S.V. College of Engineering and Technology Tamil Nadu Krishnagiri635108 India

ISBN: (纸本)9789819614134

The proposed system aims to transform drug traceability and patient authentication through the seamless integration of Hyperledger Fabric. This integration is designed to enhance transparency, security, and efficiency within the pharmaceutical supply chain. The system establishes a resilient framework that enables various stakeholders, including manufacturers and patients, to engage with a distributed ledger system powered by Hyperledger Fabric. Through the utilization of QR code technology and biometric authentication, the system ensures the legitimacy of medications and validates the identities of patients, thereby promoting trust and responsibility in healthcare transactions. Performance evaluation indicates that the proposed system exhibits an 85% increase in efficiency compared to current systems, providing improved transaction processing capacity, reduced latency, enhanced scalability, and heightened security measures. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2025.

关键词： Distributed ledger

来源：评论

学校读者我要写书评

暂无评论

Analysis of brain Health Using Machine Learning and Artificial Intelligence Technology: Modern Drug Discovery Perspective

Analysis of Brain Health Using Machine Learning and Artifici...

引用

Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Arunkumar Thirunagalingam M. s. Usha s. Geetha A Dhanamathi s. Dhivya M.Tamil Thendral Business Intelligence and Reporting Department of CSE B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Computer Science and Engineering VS.B. ENGINEERING COLLEGE Karur Department of CSE Roever Engineering College Perambalur Department of Computer Science and Engineering Sri Venkateswara College of Engineering Sriperumbudur Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The creation and discovery of pharmaceuticals may be considered the most important translational science activity that improves human invulnerability and happiness. In the pharmaceutical sector, strategies to reduce costs and speed up the development of new drugs have sparked a rigorous and fascinating brainstorming session. The use of classified big data in conjunction with remarkably improved computer power and cloud storage has enabled the application of artificial intelligence (AI), particularly the deep-learning (DL) component, in all *** healthy individuals, ML evaluation of neuroimaging data may accurately predict ordered age; illness and mental impairment have been linked to departures from normal brain maturation. Convolutional neural networks (CNN), a DL-based predictive display technique, were applied to both pre-handled and raw T1-weighted X-ray data in order to further evaluate the eligibility of “brain-predicted age” as a biomarker of individual contrasts in the brain ageing process. brain-predicted age is a very accurate, remarkably stable, and genetically valid trait that exhibits assurance as a biomarker of brain maturation. Additionally, age may be accurately predicted from raw T1-MRI data, which greatly reduces the amount of time needed to compute fresh data and accelerates the process of delivering immediate data on brain health in clinical settings.

关键词： Drugs Industries Neuroimaging Costs Translational research Pipelines Genetics Reliability Convolutional neural networks Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Decentralized and Distributed Control strategies for Microgrids: A Review of Key Techniques and Applications

Decentralized and Distributed Control Strategies for Microgr...

引用

IEEE students' Conference on Electrical, Electronics and computer science (sCEECs)

作者： sarathkumar D P. Maheswari Murugesan Manivel T. Jayakumar R. sivakumar b.K. Parrthipan Department of Electrical and Electronics Engineering Kongu Engineering College Tamil Nadu India Department of Electrical and Electronics Engineering Excel Engineering College Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronics Engineering Nandha Engineering College Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B Engineering College Tamil Nadu India Department of Mechanical Engineering Kamaraj College of Engineering and Technology Vellakulam Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Microgrids have emerged as a key solution for enhancing the flexibility, reliability, and sustainability of power systems. As the penetration of renewable energy sources and distributed generation increases, effective control strategies are essential for ensuring stable and efficient microgrid operation. This paper presents a comprehensive review of decentralized and distributed control strategies for microgrids, highlighting their significance in enabling scalability, robustness, and resilience. Key techniques, such as primary, secondary, and tertiary control layers, are discussed, with a focus on the transition from centralized to decentralized paradigms. The review explores the application of advanced algorithms like multi-agent systems (MAs), consensus-based control, and artificial intelligence (AI) for optimizing power sharing, voltage regulation, frequency stability, and energy management in islanded and grid-connected modes. Additionally, the paper analyzes the challenges and opportunities in implementing decentralized and distributed control, with real-world case studies and simulation-based insights to demonstrate their effectiveness in various microgrid configurations. Finally, potential future research directions are suggested to address emerging trends such as peer-to-peer energy trading, blockchain-based energy systems, and the integration of electric vehicles (Evs) into microgrid networks.

关键词： Electric potential Reviews Decentralized control Microgrids Power system stability Peer-to-peer computing Power system reliability sustainable development Resilience Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

AI-Powered Monitoring for Mitigating Human-Animal Conflicts in Agricultural and Forest Zones

AI-Powered Monitoring for Mitigating Human-Animal Conflicts ...

引用

Data science, Agents & Artificial Intelligence (ICDsAAI), International Conference on

作者： R. Murugadoss Department of Artificial Intelligence and Data Science V.S.B. College of Engineering Technical Campus Coimbatore Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Human-animal conflicts pose a significant challenge in forest zones and agricultural fields, leading to resource losses and threats to endangered wildlife. These conflicts have escalated in recent years, necessitating innovative solutions for continuous monitoring and intervention. This study presents a novel approach for mitigating such conflicts by leveraging image processing and Artificial Intelligence (AI). Motion detection techniques are employed to identify activity, and content-based image classification algorithms analyze the captured visuals. The proposed method integrates advanced feature extractors, data augmentation, and AI to develop a robust detection network. Additionally, the system enhances safety analysis and certification for high-speed trains by identifying objects and animals in real time. Using the COCO dataset for training and validation, the study demonstrates the potential of AI to streamline conventional safety measures and ensure the coexistence of humans and animals in ecologically sensitive areas.

关键词： Animals Wildlife Forestry Feature extraction Data augmentation Real-time systems safety Object recognition Artificial intelligence Monitoring

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：