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： Singh, Saumya Kumar, Sourabh Pandey, Prashant Tripathi, D.K. Tripathi, Ravi Prakash Rajkiya Engineering College Department of Electronics Engineering Uttar Pradesh Sonbhadra India Rajkiya Engineering College Department of Computer Science Uttar Pradesh Sonbhadra India Rajkiya Engineering College Department of Civil Engineering Uttar Pradesh Sonbhadra India

ISBN: (纸本)9798350368109

Urbanization has led to a significant rise in traffic congestion, which in turn has worsened air pollution and contributed to the problem of illegal parking. These issues not only harm the environment but also decrease the efficiency of urban systems. A potential solution involves better vehicle management and optimized use of parking spaces. Our approach leverages software and sensor integration to allow users to locate nearby parking spots, reserve them, and make cashless payments via a website. The system includes OTP verification for secure access to designated parking spaces, while sensors provide real-time updates on slot availability, enhancing security and making the parking process more convenient and efficient. © 2024 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Review of dehazing techniques: challenges and future trends

引用

Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

引用

computers, Materials & Continua 2023年第9期76卷 3859-3876页

作者： Weizheng Wang Xiangqi Wang Xianmin Pan Xingxing Gong Jian Liang Pradip Kumar Sharma Osama Alfarraj Wael Said College of Information Science and Engineering HunanWomen’s UniversityChangsha410138China School of Mathematics and Statistics Hunan First Normal UniversityChangsha410138China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenAB243FXUK Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial *** remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to ***,image denoising compromises the classification accuracy of original *** address these challenges in AE defense through image denoising,this paper proposes a novel AE detection *** proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network *** used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting *** analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected *** technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by *** proposed approach demonstrates excellent detection performance against mainstream AE *** results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.

关键词： Deep neural networks adversarial example image denoising adversarial example detection machine learning adversarial attack

来源：评论

学校读者我要写书评

暂无评论

Optimization Models for Teaching Load Formation of University Teachers 1

Optimization Models for Teaching Load Formation of Universit...

引用

IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Chigambayeva, Diana Goryakin, Makar Batova, Polina Department of Computational and Data Science Astana IT University Astana Kazakhstan Depatment of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

Functional and mathematical models for the distribution of academic workload at the stage of preparing the educational process at a university are considered, which make it possible to largely determine the uniformity of teachers’ workload with various types of academic work when fulfilling the criteria and restrictions adopted for the distribution. ©2024 IEEE.

关键词： functional model mathematical model study load educational process educational work types of educational work

来源：评论

学校读者我要写书评

暂无评论

VisionSaver: Revolutionizing Ocular Disease Detection with AI-Powered Real-Time Screening 1

VisionSaver: Revolutionizing Ocular Disease Detection with A...

引用

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Benedict Vinusha, V. Indhuja, V. Reddy, Medarametla Varshitha Nikhitha, Nagalla Pramila, R. Priyanka R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai601206 India

ISBN: (纸本)9798350372748

Detecting ocular diseases is crucial for preventing vision impairment and blindness. However, various obstacles such as insufficient awareness, elevated healthcare expenses, and the necessity for technological advancements impede effective screening. This ground-breaking device, VisionSaver, presents a novel method of identifying eye pathologies. By employing a multi-layered deep Convolutional Neural Network, VisionSaver outperforms traditional models in the extraction of complex characteristics from medical images. VisionSaver stands out for its real-time detection capabilities, which are made possible by an easy-to-use Graphical User Interface. This feature gives medical professionals accurate results and makes it possible to make decisions quickly. The system's adaptability guarantees precise identification of a broad range of ocular diseases, encompassing Diabetic Retinopathy, glaucoma, and even uncommon ocular conditions. VisionSaver enhances efficiency and retains crucial features by integrating sophisticated techniques like convolutional layers, pooling layers, dense layers, and VGG models. This innovative strategy holds the potential to transform eye healthcare, ushering in a proactive treatment era and significantly enhancing patient outcomes. VisionSaver combines data collection and preparation, data preprocessing, feature extraction, model selection, and model training to provide a robust solution for ocular disease detection achieving the accuracy of 91%. The system's real-time capabilities, user-friendly interface, and adaptability make it a powerful tool in the fight against ocular diseases. © 2024 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Feature Fusion and Machine Learning Method for Effective Home Monitoring and Early Detection of Sleep Apnea 5th

A Lightweight Feature Fusion and Machine Learning Method fo...

引用

5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Sarkar, Subrata Dhar, Debjit Sarkar, Rajib Saha, Sanjoy K. Chakraborti, Tapabrata Computer Science and Engineering Department Jadavpur University Kolkata India Computer Science Department Derozio Memorial College Kolkata India The Alan Turing Institute University College London London United Kingdom

ISBN: (纸本)9789819638628

According to the American National Heart, Lung, and Blood Institute (NHLBI), undiagnosed Sleep Apnea (SA) may raise the risk of high blood pressure, diabetes, heart disease, and stroke. A lot of these complications can be managed effectively or even prevented through early diagnosis of SA, which often goes undetected since it occurs mostly during the natural daily sleep cycle. Thus a low cost and non-invasive solution is urgently needed that may be used for home monitoring over longer periods, instead of high cost and obtrusive clinical methods such as polysomnography. Towards this goal, we propose a simple yet effective computational technique that combines feature fusion with machine learning (ML) in an interpretable manner from electrocardiogram (ECG) signals with high fidelity demonstrated through strong experimental results on benchmark public datasets. We have deliberately chosen a non deep learning based ML method that is transparent and fast, because this work is meant to be a proof of concept for future translation into practical application using smart portable ECG monitors, thereby facilitating early detection of SA and improve patient health. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sleep research

来源：评论

学校读者我要写书评

暂无评论

Accurate Classification of Tessellated Retinal Disease with Deep Learning Model 6

Accurate Classification of Tessellated Retinal Disease with ...

引用

6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Anvesh, Kachi Reshmi, Bharati M Basaveshwar Engineering College Department of Computer Science and Engineeering Bagalkote India Vardhaman College of Engineering Hyderabad India Basaveshwar Engineering College Department of Artificial Intelligence and Machine Learning Bagalkote India

ISBN: (纸本)9798350306613

We provide a novel Tessellated Eye Classifier (TEC) model designed for accurate identification of retinal diseases, with a focus on Tessellation Disease (TSL). Our trials were carried out on the AWS cloud platform, utilizing cutting-edge technology such as optimized RAM and high-performance GPU computers. The TEC model is based on the VGG-16 architecture and has extra Convolutional layers, global average pooling, and a customized output layer designed for the categorization of normal circumstances and retinal disorders, such as TSL. To improve the performance of the model, training and validation were carried out using a variety of fundus images with consistent color channels and resolutions. After undergoing repeated training, the customized TEC model successfully classified retinal disorders with a notable test set accuracy of roughly 79%. This study provides insights into the complexity of the neural network by analyzing parameter distribution in both our proprietary TEC model and the VGG-16 base model in depth. These results highlight the importance of the TEC model for patient care and early diagnosis;more research is being done to optimize the model further to increase its accuracy and therapeutic usefulness. © 2024 IEEE.

关键词： AWS Cloud Platform Deep Learning Model Model Optimization Optometry Retinal Disease Classification Tessellated Eye Classifier VGG-16 Architecture

来源：评论

学校读者我要写书评

暂无评论

Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

引用

International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

来源：评论

学校读者我要写书评

暂无评论

CNN based Leaves Disease Detection in Potato Plant

CNN based Leaves Disease Detection in Potato Plant

引用

International Conference on Information Systems and computer Networks (ISCON)

作者： Riya Verma Rahul Mishra Prachi Gupta Pooja Shivani Trivedi Department of Computer Science & Engineering ABES Engineering College Ghaziabad UP

Potatoes are one of the most popular vegetables worldwide, but they are severely affected by potato leaf diseases such as early blight and late blight. Early detection and appropriate action are crucial to prevent substantial financial losses for farmers. In this study, we propose a technology that uses image processing methods to accurately detect and diagnose potato leaf diseases. The presented model employs CNN, a machine learning algorithm that performs better than others, for image classification. The model uses normal and disease-impacted potato leaves to differentiate between normal and abnormal potato leaf properties. After being analysed by the algorithm, the potato leaf is classified as normal or diseased. Our model achieves high precision with a 97% accuracy rate. This technology has the potential to reduce economic losses for potato farmers and improve the efficiency of disease detection in potato crops.

关键词： Plant diseases Machine learning algorithms Operating systems Crops Classification algorithms Convolutional neural networks Reliability

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Machine Learning Classifiers for Sudden Cardiac Arrest Detection

引用

Journal of The Institution of Engineers (India): Series B 2022年 1-7页

作者： Banerjee, Prakash Bhattacherjee, Saptak Dasgupta, Kousik Sen, Shibaprasad Department of Electrical and Electronics Engineering University of Engineering and Management Kolkata India School of Mechatronics and Robotics Indian Institute of Engineering Science and Technology Shibpur Howrah India Department of Computer Science and Engineering Kalyani Government Engineering College Kalyani India Department of Computer Science and Engineering University of Engineering and Management Kolkata India

Sudden cardiac arrest (SCA) is a type of cardiovascular disease which attacks a person so promptly that it gives minimum time for hospitalization and may also lead to the patient death. In order to save the patient from death, rapid detection of SCA just after the onset of symptoms with a high accuracy is very critical. Although numerous approaches of SCA detection based on electrocardiogram have been carried out, there is still need of standard method for SCA detection with high precession and fast detection. Hence, the present work proposes the use of four machine learning classifiers for detection of SCA based on temporal features extracted from ECG. Performance analysis of the classifiers used in this study on 19 SCA and 20 normal, i.e., healthy subjects, yields classification accuracies of 97.4%, 94.8%, 97.4% and 97.4% for SVM, decision tree, Naïve Bayes and KNN, respectively. Experimental results thus obtained demonstrate the potential of the proposed method as a promising alternative for SCA detection. © 2022, The Institution of Engineers (India).

关键词： Electrocardiograms

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：