检索结果-内蒙古大学图书馆

Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Anil Sharma Dr R Deepa S Sowmiya Rakesh Kumar T. Kuppuraj R. Pavaiyarkarasi Department of Electronics & Communication Engineering (ECE) IES College Of Technology Bhopal M. P India Department of Science and Humanities (English) Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Engineering & Applications (CE&A) GLA University Mathura Department of Computer Applications Karpagam Academy of Higher Education Coimbatore TN Department of ECE R.M.K. Engineering College RSM Nagar Kavaraipettai Tamilnadu

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

As 5G deployment picks up steam, there’s been a noticeable change in the field of wireless research toward imagining what 6G will look like for communication in the future. The purpose of this article is to present the anticipated communication requirements and technology for the 6G future. The creation of digital twin realms that accurately replicate the biological and physical worlds in real time is the key to the future of connectedness as it is currently envisioned. The goal of this integration is to bring experiences from the digital, biological, and physical realms together. The development of novel man-machine layouts through productive local devices, the ubiquitous distribution of general computing across individual devices and the cloud, as well as multi-sensory integration of data for the creation of multi-verse corresponds and exciting mixed-reality experiences, and accurate sensing and actuation for efficient control of the physical world are emerging themes that are shaping 6G system specifications and technologies. The 6G air link and network will be supported by artificial intelligence, which will use data, computation, and energy as key resources to achieve higher performance. This paper also explores other revolutionary technologies that will likely shape 6G: cognitive spectrum sharing, integrating localization and sensing into system design, meeting strict latency and reliability performance requirements, new network architecture concepts involving sub-networks while RAN-Core convergence, and creative security and privacy strategies.

关键词： 6G mobile communication Location awareness Privacy Wireless sensor networks 5G mobile communication Layout Communication channels

来源：评论

学校读者我要写书评

暂无评论

A multidimensional time series data on wireless network to improve the detection performance of unsupervised learning algorithms

A multidimensional time series data on wireless network to i...

引用

2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Paul, Chayan Ranjith, S. Vijayashanthi, R.S. Begam, R. Benazir Sabitha, R. KoneruLakshmaiah Education Foundation Department of Artificial Intelligence and Data Science AP Vaddeswaram India Jeppiaar Engineering College Semmenchery Department of Electronics and Communication Engineering Chennai600119 India S.A. Engineering College Department of Electronics and Communication Engineering Poonamallee Avadi Road Tamil Nadu Thiruverkadu600077 India Rajalakshmi Engineering College Department of ECE Tamil Nadu Chennai602105 India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

In recent years, only with developments of cellular networking technologies as well as the implementation of 4G LTE networks, the skills and options provided to end-users had expanded to a level that seems to be limitless. Smart phone users who travel by public transportation rely on high-speed internet infrastructure and expect to have a stable, greater connectivity for the length of their journey. Because of the high requirements placed on wireless networks and the regular changes in the fundamental radio channel, customers frequently encounter rapid and surprising alterations in the connectivity reliability of their connections. The old traditional process of evaluating and inspecting infrastructure cellular technology should be no longer sufficient to fulfill the demands of the manufacturing industry. Consequently, an accurate mobile network cellular irregularity diagnostic method is required to monitor for problems that lead and to improving the effectiveness of device construction and operation. It is estimated that there are thousands of mistakes and exclusions in the current single-dimensional abnormality identification technique, and also that the present multifunctional abnormality diagnostic technique has a poor detection accuracy on multifunctional time series analysis. Multidimensional statistics depending on network service device abnormality classification optimization approach described in this work is predicated on machine learning that use limited of information to support in the instruction of tremendous unmonitored techniques, thereby enhancing the detection accuracy of unsupervised classification techniques. This paper conducts experimental studies to validate the functionality of the optimization techniques, and it shows a significant progress over the four generally used unmonitored techniques, that can greatly enhance the abnormality detection performance of the existing techniques, according to the authors. © 2022 IEEE.

关键词： Wireless networks

来源：评论

学校读者我要写书评

暂无评论

Intelligent and Smart Health Care Monitoring System based on Deep Convolutional Neural Network Model

Intelligent and Smart Health Care Monitoring System based on...

引用

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： G. Anitha R. Sankar A. Vimala Juliet V. Dhivya B. Thiyaneswaran Ata Kishore Kumar Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai Department of Master of Computer Applications S.A. Engineering College Chennai Department of Electronics & Instrumentation Engineering SRM Institute of Science & Technology Chennai Department of ECE Vel Tech MultiTech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of ECE Sona College of Technology Salem Department of ECE School of Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Andhra Pradesh

Remote health monitoring apps have revolutionized the healthcare industry as a whole with the advent of Internet of Things (IoT) technology. Strategies used to get insights through the analysis of lifestyle patterns as well as behaviors are also crucial to tailored healthcare and preventative services. Opportunities for both the investigation of illnesses and the forecasting of abnormal health situations are made possible by the use of intelligent data retrieval as well as categorization methods. The broad use of IoT has improved telemedicine capabilities and introduced more precise remote patient monitoring, both of which have had a favorable effect on the world at large. The impact of the Internet of Things (IoT) becomes deeper as it enhances healthcare infrastructure for patients, doctors, and hospitals. Through the usage of secure patient portals, healthcare providers have easier access to up-to-date patient information, allowing for more precise diagnosis and treatment. The prototype's potential for efficient healthcare monitoring is reinforced by the success of similar systems. In this research, we automated the AlexNet Model building process by experimenting with several approaches. In addition, a whole new method of diagnosis has been created. The experimental data shows that the suggested algorithm is superior to the state-of-the-art methods. The total accuracy of the suggested model is 97.13%, which is significantly higher than the accuracy of any of the other models compared. This demonstrates the model's efficiency and places it as a potentially game-changing breakthrough in healthcare.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Interpreting the Evidence on Life Cycle to Improve Educational Outcomes of Students Based on Generalized ARC-GRU Approach 3

Interpreting the Evidence on Life Cycle to Improve Education...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Madumidha, S. Tripathi, Anju Srinivasan, P. Wategaonkar, Somnath Raghunath Chauhan, Amit Suganthi, D. Sri Krishna College of Technology Department of Information Technology Coimbatore India Mangalmay Institute of Management and Technology Dehi NCR Gr.Noida India Sona College of Technology Department of Ece Tamilnadu Salem India Bharati Vidyapeeth College of Engineering Department of Electronics and Telecommunication Navi Mumbai India School of Sciences Department of Life Sciences Karnataka Bengaluru India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Chennai India

ISBN: (纸本)9798350381931

Research on the effects of teachers' fatigue on students' learning has been significantly less common than research on the effects of teachers' fatigue on teachers' own performance. Therefore, the purpose of this research is to see if teachers' emotional weariness has any bearing on their students' performance in the classroom. Consideration is given to a student's grades and their impressions of whether or not the system receive assistance from teachers, as well as to the student's general outlook on school, confidence in their own abilities, and faith in the availability of faculty support. Data preparation, feature extraction, and model training are the first steps in the proposed approach. Indicators of the quality of the education being provided are eliminated (by outlier removal and feature scaling). k-mean clustering approach is a technique of clustering which is commonly used in feature extraction. Following feature extraction, GARCH-GRU models are trained. The proposed approach is superior to two popular alternatives, ARCH and GRU. Using the provided method, the system were able to achieve a maximum accuracy of 97.07%. © 2024 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

An Innovative Method to Predict Real Estate Prices Using Convolutional Block Attention Module 4

An Innovative Method to Predict Real Estate Prices Using Con...

引用

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

作者： Babu, G.N.P.V. Vijayalakshmi, N.S. Yadav, Sameer Manigandan, E. Gulati, Chahat Pundir, Aditya Kumar Singh Rudraram Telangana Hyderabad India Nehru Memorial College Department of Economics Karnataka Sullia India University of Allahabad Department of Commerce and Business Administration Uttar Pradesh Prayagraj India Saveetha College of Liberal Arts and Sciences Thandalam Department of Computer Applications Tamilnadu Chennai India Bits Pilani Pilani Campus Department of ECE Rajasthan India Arya College of Engineering and Information Technology Department of Electronics and Communication Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350300888

Both real estate brokers and owners can benefit strategically from foresight into rising or falling property values. This study intends to develop a novel method to accurately forecast real estate prices using machine learning models and actual transaction data. In machine learning models, the attributes of the real estate and the transaction prices are the independent variables and the dependent variables, respectively. Preprocessing, feature selection, and model training are used in the proposed method. Preprocessing involves the use of normalization. The selection of features is based on PCA and sequential analysis. CBAM is the model that is utilized for training. When compared to GRU and LSTM, two additional models, the proposed technique performs well. © 2023 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Endoscopic Image Processing: Evaluating the Robustness of Deep Neural Networks 5

Endoscopic Image Processing: Evaluating the Robustness of De...

引用

5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Gireesh Babu, C.N. Babu, Kaparapu Satish Suneel, Sajja Rao, K.Srinivasa Muralitharan, Divya Aarthi, R. Bms Institute of Technology and Management Dept.of CSE/ISE Karnataka Bengaluru India Geetanjali College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Hyderabad Telangana Dundigal500043 India K.S.R.M. College of Engineering Department of Ai&ml Andhra Pradesh Kadapa516003 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.D Engineering College Department of Ece R. S. M Nagar Gummidipoondi601206 India

ISBN: (纸本)9798331504403

The Hybrid CNN-Transformer Model (HCNNT) is a novel model which exploits Convolutional Neural Networks (CNN) for spatial feature extraction and Transformers for capturing contextual and long range dependencies in order to carry complex image classification. In order to evaluate the algorithm proposed, a new model has been introduced and trained on a data set of gastrointestinal (GI) image classification, which is derived from a balanced data set with 226 images available on Kaggle. Although the cascaded CNN part is to learn elaborate visual characteristics, the improved understanding of complex patterns and relationships is achieved by the Transformer component to process these features. When both are integrated, we gain a powerful system that has high accuracy in distinguishing GI bleeding condition from normal condition. Extensive training and optimization of the hybrid model using binary cross-entropy loss and Adam optimizer has achieved state-of-the-art performance. Through the experiments evaluated on the HCNNT model, it is evident that an accuracy of 97.34% proves to be significantly beneficial, its practicality in terms of reliability for medical image classification that essentially improves the accuracy of diagnosis in gastrointestinal healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Evaluation of Secured Electronic Voting System Design based on Face Biometric Authentication Policy 8

A Comprehensive Evaluation of Secured Electronic Voting Syst...

引用

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

作者： Potluri, Pandarinath Jayakarthik, R. Agarwal, Shivam Shobana, S. Venkata Padmavathi, S. Aarthi, R. Swarnandhra College of Engineering and Technology Cse Department Sitarampuram West Godavari district Narsapuram534275 India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Gl Bajaj Institute of Technology and Management Department of Information Technology Gr. Noida India R.M.K Engineering College Department of It Chennai India Gst Gitam Deemed to be University Electrical and Electronics Engineering Hyderabad campus Telangana India R.M.D Engineering College Department of Ece R. S. M Nagar Gummidipoondi601206 India

ISBN: (纸本)9798350376425

In the field of electronic voting systems, ensuring secure and reliable authentication mechanisms is paramount to safeguarding the integrity and fairness of the democratic process. Face biometric authentication has emerged as a promising approach, integrating advanced deep learning algorithms for accurate and efficient verification of voters based on their facial features. This paper presents a comprehensive overview of the system architecture and design considerations for integrating face biometric authentication into electronic voting systems. It describes the hardware and software components essential for system functionality, discusses the selection of appropriate technologies for face recognition and encryption, and elaborates on the design of user interfaces, database structures, and ballot generation systems. Furthermore, the paper delves into the implementation of face detection and recognition algorithms, emphasizing the importance of anti-spoofing measures and multi-factor authentication mechanisms to enhance security. It also addresses voter registration processes, ballot generation, the voting process, and vote counting and verification procedures within the context of face biometric authentication. Additionally, the paper highlights the significance of robust security measures, including encryption techniques, access controls, and regular security audits, to mitigate cyber threats and ensure the integrity of the electoral process. By integrating face biometric authentication into electronic voting systems, this paper aims to contribute to the development of secure, transparent, and inclusive electoral systems, fostering trust and confidence in democratic governance, with an accuracy rate of 96%. © 2024 IEEE.

关键词： Voting machines

来源：评论

学校读者我要写书评

暂无评论

A High-Band Compact Microstrip Patch Antenna for 5G Wireless Technologies 6

A High-Band Compact Microstrip Patch Antenna for 5G Wireless...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Santhakumar, G. Muthukumar, R. Asa Jyothi, G. Balakumar, S. Maranan, Ramya Bonde, Roshan Sri Krishna College of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Erode Sengunthar Engineering College Department of EEE Tamil Nadu Erode India Malla Reddy Engineering College ECE Department Maisamma Guda Telangana Hyderabad India Arba Minch University Faculty of Electrical & Computer Engineering Arba MinchPin: 21 Ethiopia Saveetha School of Engineering Department of Research and Innovation SIMATS Tamil Nadu Chennai India Dr. Babasaheb Ambedkar Technological University Lonere Raigad India

ISBN: (纸本)9798350304480

This examination presents a High Increase, Overhauled Bandwidth Patch antenna for 5G Undertakings. Using an inset-took care of taking care of method for the microstrip patch antenna wave band, the double band is achieved. Two rectangular apertures are embedded on the patch's transmitting component to provide it a high increase. The anticipated antenna Consolidating three stages at the edge of the rectangular patch increases bandwidth. Fifth-generation (5G) networks clearly outperform 4G because to reduced idleness, more transmission speed, greater bandwidth, and a higher possibility of integrating with more notable various devices. For impending 5G association technologies, this paper studies and presents a microstrip patch antenna working at 28 GHz. The outcome of this reenactment is superior to the previous one. It typically acts as a dependable choice for 5G wireless innovation as a result. The performance of the suggested antenna is superior to that of earlier antennas spread in logical diaries. © 2023 IEEE.

关键词： 5g High-Band Microstrip Patch Antenna Wireless Technologies

来源：评论

学校读者我要写书评

暂无评论

Paddy Leaf Disease Detection Using an Optimized Deep Neural Network

引用

computers, Materials & Continua 2021年第7期68卷 1117-1128页

作者： Shankaranarayanan Nalini Nagappan Krishnaraj Thangaiyan Jayasankar Kalimuthu Vinothkumar Antony Sagai Francis Britto Kamalraj Subramaniam Chokkaligam Bharatiraja Department of Computer Science&Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Electronics&Communication Engineering SSM Institute of Engineering and TechnologyDindigul624002India Department of Mechanical Engineering Rohini College of Engineering and TechnologyPalkulam629401India Department of ECE Karpagam Academy of Higher EducationCoimbatore641021India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the *** diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming *** of diseases from the plant images is an active research topic which makes use of machine learning(ML)approaches.A novel deep neural network(DNN)classification model is proposed for the identification of paddy leaf disease using plant image *** errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm(CSA)during both the standard pre-training and fine-tuning *** DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload,ensuring high classification *** leaf images were first preprocessed,and the areas indicative of disease were initially extracted using a k-means clustering *** was then applied to eliminate regions not indicative of ***,a set of features were extracted from the previously isolated diseased ***,the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations.

关键词： Leaf classification paddy leaf deep learning metaheuristics optimization crow search algorithm

来源：评论

学校读者我要写书评

暂无评论

An Automation Innovation of Gearbox Vehicle Control by Using Machine Learning Based Robotic Operation

An Automation Innovation of Gearbox Vehicle Control by Using...

引用

2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Ramakrishna, M.M. Atyam, Nageswara Rao Chaurasia, Nikhil Sahaya Senthamil, L. Shah, Mohd Asif Raja, V.L. Dr. M.G.R Educational and Research Institute Dept of ECE/ICE Tamil Nadu Chennai India Cmr Institute of Technology Electrical and Electronics Department Karnataka Bengaluru India Sage University Department of Computer Science & Engineering Madhya Pradesh Bhopal India Psna College of Engineering and Technology Electrical and Electronics Engineering Tamil Nadu Dindigul India University School of Business Chandigarh University Punjab Mohali India School of Computing Science Tamil Nadu Pallavaram Chennai India

ISBN: (纸本)9798350323887

There are many vehicle exchanges around the world are using the manual transmission and automatic transmission are the most popular. At this time, many popular manufacturers have started using the robot version in their new products. The gearbox of the robot is, in fact, mechanical;it has an additional automatic clutch and gear shift. Accordingly, the operation of the transmission is not completely dependent on the driver, as in other options, but on the electronic control unit. For the proper functioning of the transmission the driver needs to correctly transmit only the incoming information. In this paper an automation innovation was proposed to control the vehicle gear shifting functions by using the machine learning based on the robotic operation model. An automaton or robot, after some time, must first learn the device of a new invention. The automatic gearbox received a friction type clutch. It is a disk set or a built-in separate mechanism. The most reliable and durable design can be called the double clutch design. © 2023 IEEE.

关键词： Vehicle transmissions

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：