检索结果-内蒙古大学图书馆

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

作者： Chabra, Rishu Dixit, Krishna Kant Singh, Devendra Kishore, G.Uday Al-Attabi, Kassem Thiyagarajan, G. Chitkara University Centre of Research Impact and Outreach Punjab Rajpura140401 India Gla University Department of Electrical Engineering Mathura India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Dehradun248007 India Cmr College of Engineering & Technology Department of Cse India The Islamic University Najaf Iraq S.A.College of Arts & Science School of Management-MBA Tamilnadu Chennai India

ISBN: (纸本)9798350360165

In the evolving landscape of the Internet of Drones (IoD), securing touchy and assignment-vital facts is paramount because of the wider assault floor and stringent electricity constraints specific to IoD networks. This paper introduces 'IoD-Crypt,' a completely specific cryptographic framework engineered to address those traumatic situations by offering a strong, strength-inexperienced answer tailored for battery-confined IoD packages. Leveraging specialized precomputation strategies and self-certified cryptographic primitives, IoD-Crypt achieves notable computation and conversation efficiencies. By circumventing the rules of latest public key cryptography suites, the framework guarantees a immoderate stage of protection inside the random oracle model. Through complete implementations on everyday drone processors, which consist of eight-bit AVR and 32-bit ARM, IoD-Crypt demonstrates an remarkable reduce rate in energy consumption, as lots as forty eight times lower than conventional techniques. This massive development paves the manner for at ease, sustainable IoD operations, emphasizing the framework's capability for big adoption and trying out. © 2024 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

A systematic literature review to implement large language model in higher education: issues and solutions

引用

Discover Education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence Education Large language models Learning systems Natural language processing systems Neural networks

来源：评论

学校读者我要写书评

暂无评论

Telehealth Data Security and Privacy Solutions for Sensitive Health Records using Cloud Computing and Isolation Forest Algorithm 2

Telehealth Data Security and Privacy Solutions for Sensitive...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Prathik, A. Banu, S. Parveen Sagar, B.S. Prakash, A. John Felix Thamizhamuthu, R. Velmurugan, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Protecting the confidentiality of patient's personal health information as it is sent and kept in the cloud is of the utmost importance due to the increasing number of telehealth services. This research provides a new healthcare data security approach using cloud computing and the Isolation Forest method. To identify important challenges and provide a novel technique, a thorough literature study on telehealth security, privacy issues, and cloud-based solutions. It uses the Isolation Forest method for anomaly detection in combination with encryption and access restriction systems to identify intrusion attempts. It uses simulated telehealth data in trials to test the system's work. Findings show substantial gains in privacy and data security with computational burden. Healthcare practitioners and legislators will find results useful in their pursuit of solutions to the problem of securing patients' private health information throughout the rise of telemedicine. © 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

Air Purification Robotics using Cloud and Deep Q Networks for Autonomous Systems 1

Air Purification Robotics using Cloud and Deep Q Networks fo...

引用

1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Reddy, Elangovan Guruva Damodhar, T. S. Balaji Yuvaraj, S. Vathani, B. Santha Sivakumar, S. Malathi, N. Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram India Madanapalle Institute of Technology and Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Mahalashmi Women's College of Arts and Science Department of Management Studies Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. Advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. Cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues. © 2024 IEEE.

关键词： Robotics

来源：评论

学校读者我要写书评

暂无评论

A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FOR HAND-REHABILITATION: A CASE STUDY 24

A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FO...

引用

24th International Conference on engineering Design, ICED 2023

作者： Bos, Mikaël Ivar Karahanoǧlu, Armaǧan Haarman, Juliet Nizamis, Kostas Chair of Systems Engineering and Multidisciplinary Design Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Interaction Design Research Group Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Human Media Interaction Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Home hand rehabilitation for stroke is becoming increasingly important due to logistic and financial challenges. Developing Daily-life Integrated Hand-rehabilitation Products (DIHP) aims to enable the application of at-home rehabilitation. The materials of these products are essential for their success, however, selecting materials for DIHP has not been investigated yet. Previous research on material selection showed that it is done strictly on material properties or based on a human-centered approach. Hence, in this study, we propose a hybrid model for choosing materials for DIHP. To achieve this, we first combined the findings of previous material selection processes into a comprehensive material selection model. We applied this model in a case study, in which we first selected three materials based on their properties. Following, we 3d printed a DIHP out of the chosen materials and tested the feeling of the materials with multiple expert groups. Our findings suggest that the proposed material selection method is promising and highlights that our comprehensive model provides more insights when compared to a strict material property-based selection. © The Author(s), 2023. Published by Cambridge University Press.

关键词： 3D printing

来源：评论

学校读者我要写书评

暂无评论

Adaptive Medical Image Segmentation Using Deep Convolutional Neural Networks

Adaptive Medical Image Segmentation Using Deep Convolutional...

引用

2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Babu, Sangita Pandey, Sakshi Palle, Kowstubha Prasad, P. Venkata Mallala, Balasubbareddy Pund, Sachin Computer Science King Khalid University College of Science and Arts in Rijal Alma Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Hyderabad India Chaitanya Bharathi Institute of Technology Department of Eee Hyderabad India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Nagpur India

ISBN: (纸本)9798350329773

Recent developments in deep learning have made it possible to apply it to medical image segmentation. Deep convolutional neural networks (DCNN s) are a type of deep learning model used for segmenting medical images. This approach has been found to be effective in accurately segmenting medical images, and better results in faster segmentation times than traditional methods. DCNN s use a combination of convolutional layers, pooling layers, and activation functions to detect patterns in the input images and learn a set of features from it. DCNN s have the advantage of having fewer parameters compared to other methods. This makes them easily adaptable and portable across different applications. By using the latest advances in CNN s, more accurate segmentation can be achieved in a shorter time. This makes DCNNs a powerful tool for medical image segmentation. © 2023 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 Analysis Based on Natural Language Processing (NLP) 2

Text Analysis Based on Natural Language Processing (NLP)

引用

2nd International Conference on Advances in engineering Science and technology, AEST 2022

作者： Flayeh, Azhar Kassem Hamodi, Yaser Issam Zaki, Nashwan Dheyaa Ministry of Higher Education & Scientific Research Department of Computer Engineering & Information Technology Baghdad Iraq Ministry of Higher Education & Scientific Research Computer Engineering College of Electrical and Electronic Techniques Baghdad Iraq University of Information Technology and Communications College of Engineering Media Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350334906

In the modern era of information explosion, where there is enormous information including thousands and millions of texts loaded on the Internet of over 1.2 million terabytes, there are no accurate statistics on the size of texts within this huge amount of data. Given the multiplicity of websites on the Internet and the spread of social networking services like Twitter, for example, it is stated that about 6000 tweets are posted per second in the year 2019. This work aims to analyze these texts through resorting to artificial intelligence to help solve this problem. In this article, a review is presented on the latest concepts in the field of word processing, information extraction, and implementing an interface for text analysis using the C++ programming language and Visual Studio 2015 edition. A comparison is made between two articles in terms of the number of verbs, prepositions, article strength, number of letters, with the possibility of sorting the text into verbs, nouns, object, and adjectives, as will be displayed in the program interface. © 2022 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm for Social media Content Classification and Community Detection 2

Stereoscopic Gated Graph Scalable Quantum Capsule Convolutio...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Elumalai, A. Devi, S. Rukmani Victoria, D. Rosy Salomi Jagadeeswaran, A. Mukherjee, Aparajita Vinoth, K. R.M.K. Engineering College Department of Mathematics Tamil Nadu Chennai601206 India Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Department of Computer Science Tamil Nadu Chennai602105 India Chennai Institute of Technology Department of Information Technology Tamil Nadu Chennai600069 India Annapoorana Engineering College Department of Electronics and Communication Engineering Tamil Nadu Salem636308 India University of Engineering and Management Department of Computer Science and Engineering West Bengal Kolkata700160 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331505745

Social media Content Classification and Community Detection (SMCCCD) classify content and identify communities through deep learning, and NLP. Traditional models are weak in scalability and interpretability. To solve all these problems, this paper introduces a Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm (SGGSQCCN2Nets+AHA) for social content classification and community detection. Data inputs are accessed from the Twitter dataset and are initially preprocessed by the Natural Language (NL) method. After preprocessing, content classification and community detection are done using the Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks (SGGSQCCN2Nets) and optimization is then performed with the Artificial Hummingbird Algorithm (AHA). The performance of the new SGGSQCCN2Nets+AHA model is tested on a Twitter dataset with a high accuracy rate of 99.9% and specificity of 99.8%. The proposed model is developed by utilizing Python programming language. The outcome of the proposed SGGSQCCN2Nets+AHA model enhances the performance of content classification and improves user community detection accuracy. It is effective in addressing scalability issues, obtaining complex features, and offering more interpretability. The model is also resilient against noisy data dynamically adapts to evolving social media trends and outperforms traditional deep learning techniques in real-time analysis. © 2025 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Designing an Intelligent Data Transmission Strategy Between Entities Using Wireless Communication Methodology

Designing an Intelligent Data Transmission Strategy Between ...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Geetha, R. Jayakumar, D. Devi, S. Rukmani Senthurya, S. Akula, Akhila Karthiga, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Engineering Saveetha School of Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Chennai Department of Computer Science Tamil Nadu India Mlr Institute of Technology Department of Ece Hyderabad India R.M.K Engineering College Department of It Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Data security is of the utmost importance in wireless sensor networks due to the massive amounts of data collected and sent between nodes. An improved security method for wireless sensor networks is presented in this paper. This mechanism takes into account the application, the security level, and the bit error rate simultaneously. In order to provide the user with the ability to select between unsecure and secure modes, we made use of reserved bits of the frame control field that are contained within the communication header. The Secured and Intelligent Data Transmission Protocol (SIDTP) is the name of the unified perceptual information transmission model that is established in this research. Its purpose is to compensate for the unequal distribution of cognitive radio users across different wireless LANs. For the purpose of determining whether or not the suggested scheme is effective, the proposed model will be cross-validated with the traditional data transmission model known as Wireless Data Transmission Protocol (WDTP). The approach has the potential to optimize the utilization of spectrum resources in an efficient manner. In addition, to help lessen the risks connected with wireless networks, this study lays out a technique for transmitting data using cyclic delay full diversity sensing information coding. In order to evaluate the performance of the system against a variety of correlation coefficients, we developed a system simulation model. The result of the study showed that cyclic delay diversity systems had overall interference intensity similar to frequency division duplex as well as time division duplex. As an added bonus, this enhances its anti-jamming attack capabilities. The proposed system has been planned and implemented, and significant trials have been carried out in an industrial setting. Additionally, a prototype of the system has been designed. Compared to conventional communication interfaces that are only equipped with a single wireless interf

关键词： Data transfer

来源：评论

学校读者我要写书评

暂无评论

Dijkstra's Algorithm with Bellman-Ford for Shortest Path Discovery in Big Data 2

Dijkstra's Algorithm with Bellman-Ford for Shortest Path Dis...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Karthikeyan, M. Sekar, Satheeshkumar Basha, H. Anwer Epsiba, P. Ganesh, E.N. Meenakshi, B. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Many applications, from logistics to social networks, rely on quickly navigating the shortest pathways inside large-scale graphs. For shortest route finding in huge data settings, this study aims to compare Dijkstra's method with the Bellman-Ford algorithm. Applying these methods to large datasets will allow us to compare their speed, accuracy, and scalability. Graph density and negative weight cycles are two of the intrinsic properties of big data settings that will be used to determine which method is most suited to each. Optimal algorithm selection for given data situations is guided by insights into each algorithm's operational strengths and shortcomings, which are revealed via rigorous testing and performance metrics analysis. This comparison helps real-world applications of each method and provides valuable information for future advancements in large data processing shortest route calculation algorithms. In a study comparing Dijkstra's Algorithm and Bellman-Ford using the RandomGraph1 dataset, the results showed that for nodes 100-500, the former's ms value ranged from 5.2 to 52.3 and the latter's ms value ranged from 6.8 to 68.9. Similarly, for nodes 100-500, the MB value range for Dijkstra's Algorithm was 10.2-46.7 and for Bellman-Ford, it was 14.8-71.9. Data from a different SparseGraph1 dataset reveals that, for values between 100 and 500, Dijkstra's Algorithm has a percentage range of 98.5 to 99.8, while for Bellman-Ford, it varies from 97.2 to 99.3. © 2024 IEEE.

关键词： Graph algorithms

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：