检索结果-内蒙古大学图书馆

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Arularasan, R. Rama Prabha, K.P. Vinitha, R. Praveena, T. Nithila, Ezhil E Ravi, S. Agni College of Technology Department of Mechatronics Engineering Tamilnadu Chennai India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Chennai India Karpagam Academy of Higher Education Department of Electronics and Communications Engineering Tamil Nadu Coimbatore India Srm Institute of Science and Technology Department of Mechatronics Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Saveetha School of Engineering Department of Vlsi Microelectronics Tamilnadu Chennai India

ISBN: (纸本)9798350364569

Water conservation and sustainable farming in agriculture depend heavily on effective irrigation. Water waste and less-than-ideal crop yields result from traditional irrigation systems' reliance on set schedules and crude sensor-based techniques, which make them frequently inaccurate and unable to adjust to shifting crop and soil conditions. The study proposes a sophisticated system that combines Internet of Things-connected drones for accurate irrigation and real-time monitoring. IoT sensors send data to a central system that uses machine learning algorithms for dynamic irrigation planning, tracking soil moisture and environmental conditions continually. These plans are carried out by drones fitted with thermal and multispectral cameras, guaranteeing targeted water distribution. The results show notable improvements: operational expenses were reduced by 20%, crop yields increased by up to 20%, and water use efficiency increased by 20%. The novel strategy improves agricultural productivity and sustainability while simultaneously addressing the shortcomings of existing systems. © 2024 IEEE.

关键词： Drones Irrigation Optimization Precision Agriculture Sustainable Farming Water Conservation

来源：评论

学校读者我要写书评

暂无评论

Reliability and QoS Aware Intelligent UAV Communication with Throughput Maximization in Wireless Networks

Reliability and QoS Aware Intelligent UAV Communication with...

引用

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Lalitha, S.D. Suresh Kumar, K. Ramya, M. Harirajkumar, J. Ravi, S. R.M.K. Engineering College Department of CSE Chennai India Panimalar Engineering College Deprtment of MBA Chennai India S.A. Engineering College Department of ECE Chennai India Sona College of Technology Department of Electronics and Instrumentation Engineering Salem India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha University Chennai India

ISBN: (纸本)9798350319200

To perform effective traditional terrestrial communication among the wireless networks, Unmanned Ariel Networks (UAVs) are created which provides the way to perform next generation communication system. This technology consists of certain special characteristics like high speed communication and unpredictable mobility among the devices. Due to this drawback in certain period reliability issues are occurred that increases the power utilization and delay occurrences among the devices which lead to affect the overall stability. For that purpose in this Reliability and Quality of Service (QoS) Aware Intelligent UAV Communication with Throughput Maximization (RQIUT) is performed among wireless network. The core processes of this model are system model, energy consumption model, QoS requirements and throughput maximization process. Through this processes the communication among the devices are carried out without delay and unwanted power utilization and that leads to improve the performance. The implementation of this model is carried out in NS3 and the parameters which are concentrated in this analysis are energy efficiency and packet delivery ratio. From the research outcome it is proven that RQIUT perform much better when compared with the earlier baseline researches. © 2023 IEEE.

关键词： Intelligent Throughput Maximization Intelligent UAV Communication Network Reliability Quality of Service (QoS) Unmanned Ariel Networks (UAVs)

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approach for Predicting Output Current Ripple in Electric Vehicle Chargers 7

Deep Learning Approach for Predicting Output Current Ripple ...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Madumathi, K. Saravanan, T.S. Khandan, K. Lakshmi Raja, R. Kannan, Sathish Rajendiran, M. Mahendra Institute of Technology Department of Electrical And Electronics Engineering Namakkal Tamil Nadu Mallasamudram637503 India Rajalakshmi Engineering College Department of Electrical And Electronics Engineering Chennai Tamil Nadu Thandalam602105 India Madanapalle Institute of Technology & Science Department of Electrical And Electronics Engineering Andhra Pradesh Madanapalle517325 India Pandian Saraswathi Yadav Engineering College Department of Electronics And Communication Engineering Sivagangai Tamil Nadu Madurai630561 India Saveetha Institute of Medical And Techinical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350359299

Significant attention in the field of Electric Vehicle (EV) battery development has been directed towards enhancing the longevity of the batteries. EV batteries commonly experience severe power ripples throughout the charging and power conversion operations, which might cause battery deterioration, especially in low-temperature conditions. The term "ripple"generally refers to undesired variations in the AC voltage when applied to a DC power source. Ripple current is generated by motor drivers, power conversion elements, DC power outlets, on-board chargers, and brake charging. This research addresses the essential need for predicting ripple current to maintain the quality and longevity of EV batteries. The study used Deep Learning (DL) models, namely Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM), to predict output current ripple in EV chargers. The EV charger circuit simulation provides a total of 336 data points, 300 of which are used for training and 36 are stored for model testing. Both models are evaluated using performance criteria throughout the testing phase. Bi-LSTM achieves a better coefficient of determination (R2), with a value of 0.9879. A range of error metrics are employed to conduct a full examination, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and root mean squared error (RMSE). The simulation findings reveal that Bi-LSTM significantly improves the prediction accuracy of output current ripple. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Identification and Containment of Intrusion Attacks in an IIoT Network based on Machine Learning and Blockchain Technology 2

Identification and Containment of Intrusion Attacks in an II...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Motamarri, Nikhita Satish, G. Uma, H.R. Barve, Amit Patil, Harshal Taqui, Syed Noeman Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering and Technology Department of Computer Science and Engineering Vijayawada Andhra Pradesh 520001 India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Sheriguda Ibrahimpatnam Telangana Hyderabad501510 India BGS Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Mandya Karnataka 571448 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Department of Computer Science and Engineering Pune Maharashtra 412115 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamilnadu Chennai602105 India

ISBN: (纸本)9798350327533

The Blockchain technology is leveraged to establish a secure and tamper-resistant decentralized ledger, providing a transparent and immutable record of all transactions and communications within the IoT network. By implementing smart contracts, we ensure the integrity and authenticity of device interactions, mitigating the risk of unauthorized access and data manipulation. Complementing the Blockchain, Machine Learning (ML) algorithms are employed to analyze and identify patterns indicative of malicious activities. The proposed system employs anomaly detection models, trained on historical data, to continuously monitor and adapt to emerging threats. This dynamic threat detection mechanism enhances the ability of the IoT network to recognize and respond to sophisticated intrusion attempts in real-Time. Furthermore, the proposed framework incorporates a decentralized identity management system using Blockchain, ensuring that only authenticated and authorized devices can participate in the IoT network. This not only safeguards against unauthorized access but also protects the privacy and integrity of the data generated and exchanged by IoT devices. To validate the effectiveness of our approach, we conducted experiments in a simulated IoT environment, considering various attack scenarios. The results demonstrate a significant improvement in the overall security posture of the IoT network, with reduced false positives and swift identification of intrusions. In conclusion, the integration of Blockchain and ML techniques provides a robust and adaptive defense mechanism for IoT device networks. This holistic approach addresses the challenges of security, privacy, and data integrity, paving the way for a more resilient and trustworthy IoT ecosystem in the face of evolving cyber threats. © 2024 IEEE.

关键词： Blockchain Integrity Internet of Things (IoT) Intrusion Intrusion Detection System Machine Learning Security

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Drug Discovery: Computational Methods and Applications 5

AI-Driven Drug Discovery: Computational Methods and Applicat...

引用

5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Rao, Gali Nageswara Gunasundari, C. Tilak Babu, S.B.G. Pavithra, G. Dwivedi, Vijay Kumar Shaik, Bazani Aditya Institute of Technology and Management Department of Information Technology Andhra Pradesh Srikakulam India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Aditya Engineering College Department of ECE Surampalem India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Surguja India Ramachandra College Of Engineering Department of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350351378

Artificial intelligence (AI) and its revolutionary effects on the medication development process. The project explores the potential integration of artificial intelligence tools into the different phases of drug development using state-of-the-art computational methods. This project's overarching goal is to assess how well target selection, lead compound optimization, and toxicity prediction are served by data analytics, predictive modelling, and machine learning algorithms. This research aims to analyze contemporary uses and breakthroughs in artificial intelligence (AI) to better understand how it improves drug discovery pipeline efficiency and accuracy. Beyond this, it delves into the challenges and opportunities of AI-driven drug discovery, with an emphasis on finding fresh approaches to old biomedical problems. The dynamic environment at the crossroads of AI and pharmaceutical sciences is better-understood thanks to this comprehensive analysis. It opens the door to more efficient drug development processes and the development of new therapeutic approaches. © 2024 IEEE.

关键词： Computational methods

来源：评论

学校读者我要写书评

暂无评论

Investigating Blockchain Security Mechanisms for Tamper-Proof Data Storage 2

Investigating Blockchain Security Mechanisms for Tamper-Proo...

引用

2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Thilakavathy, P. Jayachitra, S. Aeron, Anurag Kumar, Narendra Ali, Syed Sumera Malathy, M. Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Psna College of Engineering and Technology Department of Ece Tamilnadu Dindigul India School of Computing Dit University Dehradun Computer Science and Engineering India Amity University Department of Computer Science and Engineering Jharkhand Ranchi India Csmss Chh Shahu College of Engineering Electronics and Computer Engineering Department Maharashtra Aurangabad India Vel Tech High Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India

ISBN: (纸本)9798350369960

Across a wide range of sectors, blockchain technology has emerged as a game-changing option for secure data storage. This research dives into the mechanisms that enable blockchain to protect data integrity and immutability. This research investigates how blockchain technology reduces the chances of data tampering and unauthorized adjustments by examining its underlying concepts and features, such as decentralization, cryptography, consensus procedures, and immutability. Blockchain networks are decentralized, so they don't rely on any one entity to function. This eliminates one potential point of failure or manipulation. Data is protected from tampering and disclosure thanks to the use of hash functions and digital signatures, two examples of strong cryptographic algorithms. By confirming and agreeing on transactions, consensus methods like Proof of Work (PoW) and Proof of Stake (PoS) keep the blockchain ledger up-to-date and accurate. By linking blocks together and recording timestamps, blockchain makes it practically impossible to alter data after it has been recorded. The distributed ledger's availability to the public and ease of use encourage accountability and deter dishonest participants. Smart contracts eliminate the need for third parties by automating operations based on predetermined rules that cannot be altered after they have been created. This study also delves into blockchain fork resistance, incentives for truthful involvement, and other network security measures. It also covers the security implications of both permissioned and permissionless blockchains. Blockchain technology provides strong security procedures, but it has several drawbacks that must be taken into account. These include issues with scalability and the possibility of security flaws in smart contracts. Therefore, securing security and performance requires cautiously considering particular use case needs and picking the right blockchain platform and configuration. For blockchain networ

关键词： Blockchain Consensus Algorithms Cryptography Immutable Ledger

来源：评论

学校读者我要写书评

暂无评论

TRACING THE MOTION IN HIGH-SPEED VIDEO FRAMES USING DEEP LEARNING

TRACING THE MOTION IN HIGH-SPEED VIDEO FRAMES USING DEEP LEA...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gayathri, R. Harsha, B.K. Sharmiladevi, D. Sajitha, L.P. Rao, Vuda Sreenivasa Nirmala, G. Department of ECE Rajalakshmi Engineering College Tamilnadu Chennai India Department of ECE CMR Institute of Technology Karnataka Bengaluru India Department of Electronics and Communication Engineering Sona College of Technology Tamilnadu Salem India Department of Computer Science and Business System R. M. K Engineering College Tamilnadu Kavaraipettai India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Science and Engineering R.M.D Engineering College Chennai India

ISBN: (纸本)9798350305708

One of the most important jobs of a self-driving vehicle's visual system is to identify and categorize obstacles along the route. Cars need to be detected, categorized, localized, and linked across frames if they are to be tracked. With the low cost and high utility of visual information, vision-based technologies have gained popularity for this purpose. In this paper, we develop a deep learning-based motion tracking from high-speed video frames. The model is developed using deep convolutional neural network that makes it to be applicable for classification of objects in the video frames. The deep convolutional neural network classifies the obstacles present in the pathway and offers a better classification pattern than the conventional models. © 2023 IEEE.

关键词： Application Convolutional Neural Network Deep Learning High-Speed Video Motion Vehicle Video Frames

来源：评论

学校读者我要写书评

暂无评论

Internet of Medical Things (IoMT): Opportunities and Security Challenges 5

Internet of Medical Things (IoMT): Opportunities and Securit...

引用

5th International Conference on electronics and Sustainable Communication Systems, ICESC 2024

作者： Ashokkumar, N. Yatam, Narayana Reddy Chandra, I. Anita Christaline, J. Bhairavi, R. Thiruveni, M. Department of Electronics and Communication Engineering AndraPradesh Tirupati517102 India Micron Technologies Telangana Hyderabad500081 India Rajalakshmi Institute of Technology Chennai600124 India School of Computer Science and Engineering Vellore Institute of Technology Chennai campus Chennai600127 India Srm Institute of Science and Technology Department of Ece Tamilnadu Chennai600026 India Psna College of Engineering and Technology Department of Ece Tamilnadu Dindigul6246221 India

ISBN: (纸本)9798350379945

The IoMT is nothing but a technology phase breakthrough in the healthcare industry that uses wearables, and other low-level digital data collection devices for better patient care and operational efficiency. Through this research, we seek to bring together the opportunities and security threats related to IoMT with a special focus on machine learning algorithms as a predictive tool for health assessment. Empirical experiments were conducted to determine the performance of four machine learning techniques (Random Forest, Support Vector Machine, K-Nearest Neighbors, and Deep Learning Neural Network) using different set of data covering medical sensor data and patient's health records. The prediction results show that the DNN-based model had higher accuracy than other algorithms, its deep learning producing the right outcomes of 0. 88, precision of 0. 89, recall of 0. With more than eighty-seventy-five percent recall rate and F1-score of 0. 88. The comparison will enlighten the advantages and shortcomings of various algorithms in using the IoMT data to predict the health outcomes, showing the prospect of the DNN as one of the better algorithms in the space. Along with this, this study brings to the foreground the significance of focusing on strong cybersecurity measures to ensure that patients' data remains protected and IoMT ecosystems are free from flaws. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Inclusion of IoT, ML, and Blockchain Technologies in Next Generation Industry 4.0 Environment

引用

Materials Today: Proceedings 2023年 80卷 3471-3475页

作者： Shrivastava, Anurag Murali Krishna, K. Lal Rinawa, Moti Soni, Mukesh Ramkumar, Gowtham Jaiswal, Sushma ECE Lakshmi Narain College of Technology and Science Indore India Department of Electronics and Communication Engineering Vignan's Institute of Information Technology Andhra Pradesh Duvvada Visakhapatnam India Department of Mechanical Engineering Government Engineering College Jhalawar Rajasthan India Computer Science & Engineering Department Jagran Lackcity University Bhopal India SRM Institute of Science and Technology Chennai India Koni Bilaspur495009 India

Industry 4.0 is a revolutionizing concept which has a more significant implication to numerous industries of various sectors worldwide. Industry 4.0 acts as a technical catalyst in accelerated growth through systematic adaptation and new-generation cutting-edge engineering tools. IoT, Blockchain, Artificial Intelligence, Augmented Reality, 3D printing, Big-data analytics, cloud-computing are some of the primary technologies which work in join hand to make the vision of Industry 4.0 successful. In addition, it promotes autonomous industry, which in turn considerably reduces human intervention, thus resulting in efficient working and safeguard companies' interests. Due to the faster adaption of Industry 4.0, authors have attempted to define each and every concept related to it and to explore a futuristic roadmap, special focusing on its implication in the contemporary market. In the 21st century Industry, 4.0 is the need of the hour, so it has become essential to understand the state-of-the-art technologies involved. © 2021

关键词： Industry 4.0

来源：评论

学校读者我要写书评

暂无评论

The Efficient Energy Routing Strategy Making Through Hybrid Techniques of ANFIS and SVM 4

The Efficient Energy Routing Strategy Making Through Hybrid ...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Shanmathi, M. Thirumurugan, T. Suhasini, S. Manoj Kumar, N. Abdulaali, Hayder Saadoon Alani, Huthaifa Al-Saidi, Imad Al-Din Hussein Saveetha Engineering College Department of Electronics and Communication Engineering Chennai India College of Engineering & Technology Department of Ece Christ Puducherry India Cvr College of Engineering Hyderabad India Soundarya Institute of Management and Science Department of Computer Science karnataka Bangalore India National University of Science and Technology College of Technical Engineering Dhi qar64001 Iraq Al-Farahidi University Baghdad Iraq Al-Kunooze University College Computer Engineer Department Basra Iraq

ISBN: (纸本)9798350360165

Due to their reliance on batteries for power, conventional wireless sensor networks (WSNs) continue to have significant energy-related limitations. Consequently, prolonging the lifespan and enhancing the performance of WSNs requires improved energy efficiency. The aim of this study is to address WSN energy utilisation by selecting relay nodes and cluster heads (CH) using adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). The CH is required for data transmission and collection from all other nodes in the cluster, and the book concentrates on advanced optimisation approaches to reduce power usage at this node. By dramatically decreasing energy consumption at the relay node and CH by 30%, the recommended optimisation strategy enhances WSN's energy-efficient operations and extends its lifespan. This paper presents the development and impact of the revolutionary optimisation technique. © 2024 IEEE.

关键词： Fuzzy inference

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：