检索结果-内蒙古大学图书馆

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Purushottam, Vaishnavi V Meena, S. Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Bengaluru India Buddha Institute of Technology Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Quantum Key Distribution or QKD revolutionizes secure communication using quantum mechanics. Unlike traditional cryptographic methods vulnerable to advances in computing power, QKD guarantees unconditional security by detecting any eavesdropping attempt. Quantum Key Distribution (QKD) constitutes a through development in the realm of secure communications by utilizing the ideas of quantum mechanics to provide potentially unbreakable encryption. This study examines the most current advancements in QKD, including the recently announced Measurement-Device-Independent QKD (MDI-QKD) and protocols like BB84 and E91. We analyze the real-world implementation, including satellite-based QKD and quantum repeaters. Furthermore, we discuss the challenges and opportunities of integrating QKD into present communication infrastructure. The significance of QKD in attaining secure communication networks for the next generation is underscored by our findings, which also have ramifications for forthcoming research and applications. Ultimately, this research aims to contribute to the development of robust and impenetrable communication networks in an increasingly interconnected world. © 2024 IEEE.

关键词： Quantum communication

来源：评论

学校读者我要写书评

暂无评论

EMRChain: Electronic Medical Records Management system using Blockchain 3

EMRChain: Electronic Medical Records Management System using...

引用

3rd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Rajasekharan, Arjun Koshy, Rani College of Engineering Trivandrum Dept. of Computer Science and Engineering Kerala Thiruvananthapuram India College of Engineering Trivandrum Department of Computer Science and Engineering Kerala Thiruvananthapuram India

ISBN: (纸本)9798350354348

Electronic Medical Records (EMRs) are traditionally managed by central authorities, posing significant security risks such as data breaches, limited interoperability, and restricted patient control. This system leverages blockchain technology to address these issues, ensuring data remains immutable, transparent, and highly secure. Utilizing the Ethereum network and IPFS technology, our system provides a decentralized framework for storing and sharing medical records. Healthcare professionals, patients, and authorized parties can seamlessly exchange and access records through Ethereum's decentralized structure and smart contracts, enabling automated and auditable interactions. To enhance confidentiality, all data uploaded to IPFS undergoes AES 256-bit encryption, with the encrypted Content Identifier (CID) stored on the blockchain. This ensures that intercepted CIDs cannot be accessed without the decryption key. IPFS technology facilitates decentralized storage and retrieval, eliminating the need for a centralized server and improving scalability and resilience. Patients retain full control over their health information, granting or revoking access as necessary. This system ensures secure patient identification, precise access management, and data confidentiality while allowing authorized parties to track and trace record access and modifications. Our blockchain-based EMR management system addresses the inherent risks of traditional centralized systems by providing a secure, transparent, and decentralized solution. By leveraging Ethereum and IPFS, it enhances data security, patient control, and interoperability, thus paving the way for more robust and patient-centric healthcare information management. This approach ensures that healthcare data remains secure, patients maintain control over their information, and authorized parties can effectively manage and access the records. © 2024 IEEE.

关键词： Ethereum

来源：评论

学校读者我要写书评

暂无评论

Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Rathod, Vijayalaxmi N. Goudar, R.H. Dhananjaya, G.M. Patil, Minal Hukkeri, Geetabai S. Kaliwal, Rohit B. Dept. of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi India Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru India

ISBN: (纸本)9798331505134

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in healthcare, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

A Lightweight CNN-SVM Explainable AI Approach for Classification and Visualization of Grape Leaf Disease 3

A Lightweight CNN-SVM Explainable AI Approach for Classifica...

引用

3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Peyal, Hasibul Islam Leion, Zannatul Mawa Abdal, Md. Nazmul Islam, Md. Injamul Miraz, Shafiun Remon, Mahmudur Rashid Kontho, Md. Mahin Rahaman Tasnim, Nishat Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Rajshahi Bangladesh North South University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Sher-e-Bangla Agricultural University Dept. of Agroforestry and Environmental Science Dhaka Bangladesh

ISBN: (纸本)9798350388282

Grape is highly esteemed as a significant agricultural crop in Bangladesh. Plant diseases primarily result from the presence of pathogens and pest insects, leading to a significant decline in productivity if not properly addressed. The fundamental aim of this research is to employ a streamlined CNN-SVM architecture, utilizing deep learning techniques, to accurately categorize grape leaves into three distinct disease classes and one healthy class. The proposed model surpasses the accuracy of the previously trained transfer learning models VGG-16 and VGG-19 while having approximately 257× to 267× times fewer parameters (0.537 M). On average, the proposed model achieves a classification accuracy of 99.18%, which is significantly higher than the 93.42% and 91.94% achieved by the transfer learning models, respectively. With a precision, recall, and F1 score close to 99%, the suggested model provides excellent results. The model's outstanding performance is further validated by its remarkable Area Under Curve (AUC) score of 99.98%. In addition to using less disc space (about 6 MB), the suggested model because of being lightweight shows a significant decrease in parameters. To visually display the disease identified by the proposed model, a transparent AI methodology has been utilized, specifically the Gradient Weighted Class Activation Mapping (Grad-CAM) technique. To better understand which area was responsible for the classification, a heatmap has been created. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A Mobile Application for the Crowdsensing of Road Pavements Conditions 22

A Mobile Application for the Crowdsensing of Road Pavements ...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Delnevo, Giovanni Bartolucci, Nicola Mirri, Silvia Salomoni, Paola Prandi, Catia University of Bologna Dept. of Computer Science and Engineering Italy University of Bologna Bachelor Degree in Computer Science and Engineering Italy

ISBN: (纸本)9798331508050

Urban environments face significant challenges due to deteriorating road pavements, affecting all transportation modes' safety and comfort. Traditional methods of road assessment are costly and infrequent, but advancements in mobile technology and crowdsensing offer real-time, large-scale data collection solutions. This paper introduces 'ShakeSensing', a mobile application that uses Android device sensors to measure vibrations experienced by cyclists and scooter users, providing insights into road quality. The collected data, analyzed to assess road conditions, can benefit both users and municipal authorities, promoting safer and more comfortable travel. © 2025 IEEE.

关键词： Urban transportation

来源：评论

学校读者我要写书评

暂无评论

Enhancing Solar Energy Efficiency: A Deep Learning Approach for Dust Detection

Enhancing Solar Energy Efficiency: A Deep Learning Approach ...

引用

2024 International Conference on Smart Electronics and Communication systems, ISENSE 2024

作者： Vigna Harish, R. Ramya, A. Siva, S. Santhi, S. Kalaiselvi, S. Anitha, V. National Engineering College Dept. of Information Technology Tuticorin India National Engineering College Dept. of Computer Science and Engineering Tuticorin India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528126

In recent decades, power generation from PV systems has become increasingly popular. However, several environmental variables, such as dust deposition on PV panels, have significantly reduced PV energy production. Several approaches are under investigation to identify dust and decrease power loss. These include thermal imaging, image processing, sensors, and IoT devices, machine learning, and deep learning. The orientation of a solar panel and the angular inclination relative to the horizontal plane affect one of the environmental factors that impact its performance: since they have an impact on how much solar energy that enters into the photovoltaic panel's surface. Dust, for one, is another example of environmental factors: the quantity of energy output can be influenced by coming into contact with dust. Dust particles lying on the panel's surface have the potential to reduce light from reaching the solar modules, which would drastically reduces the energy output. Cleaning or reducing modules is critical, but these procedures must be continuously monitored and assessed for their effects. Image processing algorithms can be used to detect dust to optimize solar panels. In this paper, a novel dataset from Kaggle comprising images of dusty and clean solar panels is presented, aiming to facilitate the research on automated dust detection algorithms. The dataset is utilized to evaluate the performance of existing state-of-the-art (SOTA) image classification methods. Subsequently, a new Convolutional Neural Network (CNN) architecture, ResNet-50 with an additional attention mechanism, is proposed, specifically designed for solar panel dust detection. The experimental results shows that the proposed method outperforms other SOTA algorithms, achieving an accuracy level of 96.2%. © 2024 IEEE.

关键词： Thermography (imaging)

来源：评论

学校读者我要写书评

暂无评论

Detecting Retinal Damage From OCT Images 15

Detecting Retinal Damage From OCT Images

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vineetha, Kundrapu Ali, Md Jaffer Prathima, Narisetty Singh, Tripty Duraisamy, Prakash Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Wisconsin-Green Bay Dept. of Computer Science and Engineering Wisconsin United States

ISBN: (纸本)9798350370249

Optical coherence tomography (OCT) is a crucial imaging modality in ophthalmology, offering detailed retinal images essential for diagnosing various retinal disorders. In this study, we investigate the application of convolutional neural networks (CNNs) and transfer learning to classify retinal OCT images. We compare a custom CNN model with two pretrained models, MobileNet and VGG16, to explore their effectiveness in categorizing retinal diseases. Our study focuses on improving classification accuracy through image enhancement techniques, training methodologies, and model evaluation using confusion matrices. The results highlight the VGG16-based CNN model as the most effective, demonstrating its potential for accurate classification of retinal disorders based on OCT images. Furthermore, we delve into strategies to further enhance the performance of these models, such as data augmentation, finetuning, and hyperparameter tuning. We discuss the importance of these techniques in optimizing model performance and reducing overfitting. Additionally, we emphasize the significance of evaluating models using confusion matrices to gain insights into their performance across different classes. Overall, our findings suggest that CNNs, particularly the VGG16based model, hold promise for improving the automated diagnosis of retinal diseases using OCT imaging, which could lead to better patient outcomes in ophthalmology. © 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

Optimization of New Radio Physical Uplink Shared Channel Resources Allocation with Efficient Demodulated Referenced Signals and PT-RE Deployment

Optimization of New Radio Physical Uplink Shared Channel Res...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Upadhyay, Deepak Gupta, Mridul Sharma, Kunj Bihari Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350378092

This paper explores the resource allocation optimisation of New Radio Physical Uplink Shared Channels with an emphasis on the effective implementation of Phase Tracking Reference Signals and Demodulation Reference Signals in 5G networks. Understanding how important these reference signals are to dependable communication, we created a Python framework to model and display their distribution on a time-frequency grid. By carefully modifying important factors including mapping type, PRB allocation, and symbol allocation, our method greatly increased resource efficiency, decreased overhead, and improved signal quality. The results show how sophisticated resource allocation techniques may be used to maximise system performance, providing important information for the creation of more reliable and effective 5G NR communication systems © 2024 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Enhancing Question-Answering with Knowledge Graph Retrieval and Generation using LLMs

Enhancing Question-Answering with Knowledge Graph Retrieval ...

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Gijre, Supraj Agrawal, Rishi Laddha, Priyash Keswani, Gunjan Shri Ramdeobaba College of Engineering and Management Dept. Computer Science and Engineering Nagpur India School of Computer Science and Engineering Ramdeobaba University Nagpur India

ISBN: (纸本)9798331517953

This paper describes a novel technique to improving Large Language Models (LLMs) for document analysis that employs knowledge graphs and retrieval-augmented generation (RAG). We are working on constructing a chatbot system that can handle and analyze large documents from a variety of fields. Our approach addresses basic LLM issues including context maintenance and hallucination avoidance. The system combines document chunking, vector embedding, and similarity search with graph-based knowledge representation. Users can upload large papers and answer questions accurately. We show that integrating standard information retrieval approaches with graph-based storage and LLM capabilities improves context awareness and response accuracy across a wide range of document genres. This strategy is especially promising for complicated publications such as financial reports. © 2024 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Robust Line-Convex Polygon Intersection Computation in E2 using Projective Space Representation

引用

Machine Graphics and Vision 2023年第3-4期32卷 3-16页

作者： Skala, Vaclav Dept. of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic

This paper describes modified robust algorithms for a line clipping by a convex polygon in E2 and a convex polyhedron in E3. The proposed algorithm is based on the Cyrus-Beck algorithm and uses homogeneous coordinates to increase the robustness of computation. The algorithm enables computation fully in the projective space using the homogeneous coordinates and the line can be given in the projective space, in general. If the result can remain in projective space, no division operation is needed. It supports the use of vector-vector operations, SSE/AVX instructions, and GPU. © 2023 Institute of Information Technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： computer graphics

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：