检索结果-内蒙古大学图书馆

3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Mahalakshmi, P. Mahalakshmi, V. Vinothkumar, E.S. Senthilkumar, B. Dinesh, M. Krishnaprasanna, R. Rajalakshmi Engineering College Department of IT Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Tamil Nadu Chennai India V.S.B. Engineering College Department of EEE Tamil Nadu Karur India Saveetha Engineering College SIMATS Department of VLSI Microelectronics Tamil Nadu Chennai India Sathyabama Institute of Science and Technology Department of ECE Tamil Nadu Chennai India

ISBN: (纸本)9798350317022

Every single day, millions of people all around the world utilize various social networking platforms. The usage of social media platforms like Twitter and Face Book may result in both positive and bad results for users. These outcomes are not mutually exclusive. The most popular social networking sites have recently been a primary target for spammers who wish to disseminate large numbers of undesired and maybe harmful information. For example, Twitter has quickly become one of the most commonly used platforms ever, which has led to a large increase in the amount of spam that is posted on the network. Users who really use Twitter are irritated and frustrated by tweets from bogus accounts that promote meaningless products, services, or websites. There has also been an increase in the ease with which hazardous chemicals may be transmitted by presenting users with misleading information while using bogus identities. This has led to an increase in the likelihood that people would be harmed. In the field of contemporary social media, two of the most popular research subjects are the elimination of spam and the verification of users on Twitter. In the event that they continue to disseminate dangerous advertisements, spam accounts on social networking websites pose a substantial threat to the safety of the internet and should be eliminated as quickly as possible. This article explores the origins of spam accounts on social networks like Twitter, as well as the distinguishing characteristics of spam accounts, with the goal of improving spam identification. Twitter is one of the most popular online social networks (OSNs), and its members include ministers, business moguls, Hollywood actors, and Fortune 500 companies. The platform's 313 million monthly active users are responsible for publishing around 500 million tweets each and every month on it. Due to Twitter's rising popularity, spammers have developed an interest in the platform. These malicious actors exploit the servi

关键词： ANN Artificial Neural Network Deep Learning Principle LPSIM Social Networking Spam Identification Mechanism Twitter

来源：评论

学校读者我要写书评

暂无评论

Development of Real-Time Thermal Monitoring of GaN-based Power Inverter Modules Using Digital Twin

Development of Real-Time Thermal Monitoring of GaN-based Pow...

引用

Electronic Components and Technology Conference (ECTC)

作者： Bin He Jaydeep Saha Rahul Sadanand Bhujade Gongyue Tang Sanjib Kumar Panda Institute of Microelectronics (IME) Agency for Science Technology & Research (A*STAR) Republic of Singapore Department of Electrical & Computer Engineering (ECE) National University of Singapore Republic of Singapore

ISBN: (数字)9798350375985

ISBN: (纸本)9798350375992

This study addresses the critical challenge of thermal management in Gallium Nitride (GaN)-based Power Inverter Modules (PIM) for electric vehicles (EV), presenting a novel digital twin development for real-time electro-thermal analysis. The innovation lies in integrating a 1D Modelica model with a Computational Fluid Dynamics (CFD) model, enhanced by Reduced-Order Modeling (ROM), to simulate and monitor the GaN chips' thermal behavior under a liquid cooling loop. This comprehensive approach enables real-time thermal monitoring and provides detailed thermal visibility across the PIM. Utilizing test data from India and incorporating three types of virtual sensors, the study showcases the digital twin's application in optimizing multi-speed cooling control under varied traffic conditions, typical of congested tropical developing countries. The results offer a pathway to cost-effective, efficient thermal management solutions, contributing to the sustainability and performance of the electric vehicle ecosystem.

关键词： Temperature sensors Temperature measurement Computational modeling Thermal management Electric vehicles Real-time systems Digital twins

来源：评论

学校读者我要写书评

暂无评论

Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

引用

computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque Mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

来源：评论

学校读者我要写书评

暂无评论

Design of a Method Using Machine Learning Techniques for the Maintenance and Diagnosis of Failures in Solar Panels

Design of a Method Using Machine Learning Techniques for the...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chaturvedi, Abhay Lohumi, Yogesh Patil, Harshal Sharath, N. Vekariya, Daxa Taqui, Syed Noeman Gla University Department of Electronics and Communication Engineering Uttar Pradesh Mathura281406 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Balaji University Maharashtra Pune India Bgs Institute of Technology Adichunchanagiri University Department of Mechanical Engineering BG Nagar Mandya Karnataka571448 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat 391760 India Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Saveetha School of Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350375237

The increasing adoption of solar energy demands efficient maintenance strategies to ensure optimal performance and longevity of solar panel installations. This paper proposes a novel method leveraging machine learning techniques for the proactive maintenance and accurate diagnosis of failures in solar panels. The objective is to enhance the reliability and efficiency of solar energy systems while minimizing downtime and operational costs. The proposed method aims to empower solar panel operators with a proactive and intelligent maintenance system, allowing them to address potential issues before they escalate. By minimizing downtime and optimizing performance, this approach contributes to the sustainability and economic viability of solar energy installations. The effectiveness of the proposed method is demonstrated through experimental results and comparisons with traditional maintenance approaches. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Advanced IoT Intrusion Detection for Intelligent Homes using Optimized Cross-Contextual Transformers with a Dynamic City Game Framework 8

Advanced IoT Intrusion Detection for Intelligent Homes using...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Muthulingam, Gurusigaamani Ayyanar Dhasmana, Gulshan Purushothaman, S. Honnaiah, Sharath Ronald, Bindu Soudagar, Manzoore Elahi M. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248 002 India Sathyabama Institute of Science and Technology Department of Electronics and Communication Tamil Nadu Chennai India Credit Acceptance Karnataka Bangalore India Vimannagar Maharashtra Pune411014 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350376425

Ensuring data security against unauthorized access in smart homes is crucial, but implementing deep learning methods can be challenging due to potential issues such as accuracy loss, time complexity, and increased error rates. This study introduces a novel approach to address these challenges: the Securing Intelligent Home with a Transformer-Based IoT Intrusion Detection System (IDS) using an Optimized Cross-Contextual Point Running City Game Transformer Network (O2CPRCG-TransNet). This innovative method leverages advanced DL approaches to improve the security and reliability of Intelligent home environments. The study utilizes the IoT-bot dataset, which is pre-processed using the Grid-Constrained Data Purification Method (GCDP) to ensure high-quality input. The purified data is then optimized through the Giant Trevally Optimizer Algorithm (GTOA), which selects the most significant features to improve classification performance. Classification is performed using the O2CPRCG-TransNet architecture, which is specifically designed to address the complexities of Intelligent home security. To ensure secure data transmission, the system incorporates Somewhat Homomorphic Fuzzy-based Elliptic Curve Cryptography (SWH-FECC). Experimental results, obtained using the Python platform, demonstrate that the proposed approach achieves superior performance, with an accuracy of 98% and a recall of 0.993, compared to existing methods. These findings underscore the effectiveness of the proposed method and its potential for broader application in enhancing Intelligent home security. © 2024 IEEE.

关键词： Smart homes

来源：评论

学校读者我要写书评

暂无评论

Development of Digital Twin with External Data Resources in Manufacturing with Complex Algorithms

引用

SN computer Science 2023年第5期4卷 1-13页

作者： Vijayalakshmi, N. Roopa, Y. Mohana Ashreetha, B. Ramesh, J.V.N. Rao, E. Gurumohan Sundararajan, P.N. Department of Computer Science (MCA) Mount Carmel College Autonomous No.58 Palace Road Bengaluru 560052 India Department of CSE Institute of Aeronautical Engineering Telangana Hyderabad India Department of Electronics & Communication Engineering College School of Engineering & Technology Mohan Babu University (Erst while Sree Vidyanikethan Engineering College) Andhra Pradesh Tirupati India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur District AP Vaddeswaram India Department of Information Technology MLR Institute of Technology Hyderabad India Department of ECE PSNA College of Engineering and Technology Dindigul 624622 India

Although computational methods have historically been utilized by architecture companies, they are now becoming more and more common in popular constructions for automated process Digital Twin (DT) design, as-built assessment, and parametric simulation techniques. This study shows how workflow might well be utilized to converge on optimum and heuristic approaches to difficult issues and how optimization algorithms have the potential to help smart technology in assembly. In this study, two kinds were studied. The first involves a computational technique that has been applied to a difficult geometric problem in the manufacture of structural sheathing panels. The following illustration shows a computational process consisting of methods to automate the extraction and processing of Building Information Model (BIM) information for the output path shows significance. Learning from these examples shows how computer simulation should be effective for complex and repeated process development. Here, it must be demonstrated that computational techniques can effectively search for large-choice regions and detect costly development errors from the outset. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： BIM Complex algorithm Digital twin engineering problems Manufacturing

来源：评论

学校读者我要写书评

暂无评论

3D Brain Tumor Segmentation on U-Net Classifier 2

3D Brain Tumor Segmentation on U-Net Classifier

引用

2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Suja, G.P. Kaleeswari, P. Raajan, P. Ram Prasad, A.V.S. Aarthi, K. Gopatoti, Anandbabu Muslim Arts College Department of Computer Science Thiruvinthancode Kanyakumari629174 India Bme Psr Engg College Sivakasi India Manonmaiam Sundaranar University Muslim Arts College Department of Computer Science Thiruvinthancode Tirunelveli627 012 India Mechanical Engineering K L e F Kongu Engineering College Department of Ece Erode638052 India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9781665491990

For deep learning segmentation approaches, it can be difficult to extract the multimodal features that are present in multimodal glioma pictures, which provide a variety of aspects of tumour borders based on MRI. The application of deep learning (DL) and multimodal MRI images in the investigation of brain tumour segmentation has become increasingly useful as a result of the rapid development of current technology. In this paper, we suggest using DL to study multimodal MRI image segmentation to improve the treatment and therapeutic accurateness of brain tumour segmentation. Moreover, this research builds a computer aided medical diagnosis for brain tumours, extracts features from MRI images using discrete wavelet transform (DWT) and GLCM, and finally employs a U-Net to make the diagnosis;UNet outperforms on 3D MRI scanned pictures, as shown by extensive experimental results with 97.8% accuracy on the BraTS 2020 and 2021 datasets. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

A Novel Framework for Efficient Handover Mechanism in D2D Communication Using Networking Principles 4

A Novel Framework for Efficient Handover Mechanism in D2D Co...

引用

4th International Conference on Smart electronics and Communication, ICOSEC 2023

作者： Jayanthi, S. Raja, Mandapati Jackson, Beulah Jayasree, S. Nithya, A. Jeneetha Jebanazer, J. R. M. D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India CMR Engineering College Department of Electronics and Communication Engineering Telangana India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Chemistry Tamil Nadu India R. M. K College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798350300888

Software Defined Networking (SDN) technology utilizes software to manage and alter the current networks to improve the networking and interfacing capabilities. It does this by providing a split between the control plane and the data plane. On the SDN controller, each function is run as an application. This paper suggests a structure made up of a unified RAN and an SDN-based core network. For mobile terminals to access the services of the network operator, as well as the Internet and other cloud-based apps, all radio access points in the RAN are connected with the aid of OpenFlow interfaces. Decentralized Content Delivery Networks (CDN) manage mobility without requiring data to pass via the core network, which minimizes signaling. To gauge the signaling burden, traffic steering between the framework's different entities is examined. The suggested work establishes criteria based on a combination of fundamental factors, such as Signal Strength, Distance, and Speed, to assure service continuity of mobile nodes in heterogeneous networks. The parametric analysis of the handover is contrasted with that of the current designs. The suggested framework also provides ProSe capability for a select number of desirable mobile devices in the network and is configured for D2D connection. © 2023 IEEE.

关键词： Heterogeneous networks

来源：评论

学校读者我要写书评

暂无评论

A Robust DL Approach for Detection of Invasive Ductal Carcinoma in Whole Slide Images using DenseNet169

A Robust DL Approach for Detection of Invasive Ductal Carcin...

引用

2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Menaka, C. Sharma, Sunil Naval, Preeti Brar, Khushmeen Anand, Rohit Alabdeli, Haider Pant, Shivani Department Of Computer Science And Information Technology Bangalore India Vivekananda Global University Department Of Computer Science & Application Jaipur India Maharishi University Of Information Technology Maharishi School Of Engineering & Technology Uttar Pradesh India Chandigarh University Department Of Electronics And Communication Engineering Punjab Mohali India G.B Pant Okhla-1 Campus Department Of Ece New Delhi India The Islamic University Najaf Iraq Uttaranchal University Uttaranchal Institute Of Technology Uttarakhand India

ISBN: (纸本)9798350366846

The present research introduces a deep learning method for automatically recognizing and evaluating tissue areas that correspond to invasive ductal carcinoma (IDC) in whole slide images (WSI) of breast cancer (BCa). Learn-from-data techniques which utilize computational simulations of the learning process are called deep learning approaches. It has been demonstrated that these methodologies outperform conventional methods in resolving the challenging issues in a number of domains, particularly object and speech recognition. The main reason why making an invasive breast cancer evaluation is an exhausting and laborious process is that, it necessitates a pathologist to scan a huge number of benign regions with the aim to determine the spots that are malignant. Effectively characterizing IDC in WSI is a prerequisite for evaluating the severity of tumors and predicting patient prognosis. In order to support diagnosis, the DL approach in this study draws out many convolutional neural networks (CNNs) for perceptible semantic examination of tumor locations. A WSI database of 162 patients with IDC diagnoses is exploited to assess the technique, and 170 slides were chosen for training. The objective of the experimental analysis was to assess the accuracy of the classifier in identifying IDC tissue areas in WSI. A fusion of optimization techniques namely Best First and Evolutionary Search has been employed for feature reduction and improvement. When using DenseNet169, our approach produced the highest quantitative outcomes for the computerized identification of IDC sections in WSI with regard to accuracy 87.46% in comparison with an approach using EfficientNetB0 and ML classifiers for invasive tumor categorization employing Random Forest, BayesNet, SVM, Naïve Bayes and Decision Tree with accuracy of 80.65%. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Design of Unmanned Aerial Vehicle's Drones for the Purpose of Security 4

Design of Unmanned Aerial Vehicle's Drones for the Purpose o...

引用

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

作者： Murali, M.J. Agarwal, Manish Kumar, K. Sriram Parameshwari, S. Vanaja, T. Choubey, Ashutosh Kumar Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering TN India Ies College of Technology Department of Ece Madhya Pradesh Bhopal India Karpagam Institute of Technology Department of Information Technology TN India Nehru Institute of Technology Department of Aeronautical Engineering Coimbutore Tamil Nadu T.M.Palayam641105 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Baderia Global Institute of Engineering & Management Madhya Pradesh Jabalpur India

ISBN: (纸本)9798350360165

Government-funded, charities, and international organizations devote substantial resources to catastrophe avoidance, reduction, preparation, reply, and recovery. Planning for and responding to disasters present difficult logistical issues. Several organizations have looked at using helicopters as an extra tool for first responders to use while searching for casualties. Examining current studies on drone usage for searches and rescues is the aim of the present investigation. We find that there are issues with employing such aircraft for municipal rescue missions, identifying cell phones in an emergency using hidden sensors, and incorporating probability optimization for optimization within robot control. © 2024 IEEE.

关键词： Drones

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：