检索结果-内蒙古大学图书馆

arXiv 2023年

作者： De Marchi, Alberto Mehlitz, Patrick University of the Bundeswehr Munich Department of Aerospace Engineering Institute of Applied Mathematics and Scientific Computing Neubiberg85577 Germany Philipps-Universität Marburg Department of Mathematics and Computer Science Marburg35032 Germany

A broad class of optimization problems can be cast in composite form, that is, considering the minimization of the composition of a lower semicontinuous function with a differentiable mapping. This paper investigates the versatile template of composite optimization without any convexity assumptions. First- and second-order optimality conditions are discussed. We highlight the difficulties that stem from the lack of convexity when dealing with necessary conditions in a Lagrangian framework and when considering error bounds. Building upon these characterizations, a local convergence analysis is delineated for a recently developed augmented Lagrangian method, deriving rates of convergence in the fully nonconvex *** Codes 49J52, 49J53, 65K10, 90C30, 90C33 © 2023, CC BY.

关键词： Lagrange multipliers

来源：评论

学校读者我要写书评

暂无评论

Analyzing the function of smart security in the context of the internet of things

Analyzing the function of smart security in the context of t...

引用

Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： K. Venkatagurunatham Naidu Anchu Krishnan Pillai C. E. Rajaprabha Rajesh Sharma R Atowar Ul Islam Shaik Khasimbee Dept. of Computer Science and Engineering Dr M.G.R Educational and Research Institute Chennai Applied Science and Humanities Xavier Institute of Engineering Mumbai Maharashtra Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore Department of Computer Science and Engineering School of Electrical Engineering and Computing Adama Science and Technology University Nazareth Ethiopia Department of Computer Science and Electronics University of Science and Technology Meghalaya Baridua Meghalaya Department of Computer Science and Engineering Aditya College of Engineering Surampalem Andhra Pradesh

Technology is getting better day by day and human beings are using the latest technology possible for their needs and safety. Many devices have been invented that use the internet and with this human safety issues are also taken care of up to a great extent. GPS tracking devices, SHE (Society Harnessing Equipment), AESHS (Advanced Electronic System for Human Safety), ILA Systems are one of the most useful applications and devices that are used for human safety. Human safety and especially the safety of girls have become an emerging and sensitive issue in the present world scenario. This article deals with the role of the Internet of Things in advancing smart security. The issues are taken care of with the help of these upgraded devices that are easily accessible from a remote location with an internet connection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IoT Based Smart Fish-Feeder and Monitoring System

IoT Based Smart Fish-Feeder and Monitoring System

引用

2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022

作者： Abu-Khadrah, Ahmed Issa, Ghassan F. Aslam, Shaukat Shahzad, Muhammad Ateeq, Karamath Hussain, Munawar Saudi Electronic University College of Computing and Informatics Riyadh Saudi Arabia Skyline University College School of Information Technology Sharjah United Arab Emirates NCBAandE School of Computer Science Lahore Pakistan Riphah International University Department of Computing and Applied Sciences Lahore Pakistan Skyline University Bsit Faculty Sharjah United Arab Emirates

ISBN: (纸本)9781665409209

People have always enjoyed having pets around them, but in today's hectic world, it might be difficult to properly care for pets, particularly fish, which require more attention. When fish owners are out of town, their fish may be left with a feed shortfall or over-feed. Another consideration is the water quality (temperature, PH level, and so on). As a result, a solution is provided in this article to address the aforementioned issues. Two components comprise the proposed system. The first is for fish eating, while the second is for water feature and level monitoring. The first module also includes electrical, automatic, and communication parts that are connected to the Internet of Things (IoT) to allow fish to be fed on a schedule or remotely through a web interface. Microcontrollers with PH, Ammonia, Temperature and other sensors are included in the second module, which is used to monitor the aquarium's environment. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Enhancing Web Application Efficiency: Exploring Modern Design Patterns within the MVC Framework

Enhancing Web Application Efficiency: Exploring Modern Desig...

引用

Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Ankit Kumar Saroj Kumar Pandey Sunny Prakash Kamred Udham Singh Teekam Singh Gaurav Kumar Department of Computer Engineering & Applications GLA University Mathura UP India Department of Applied Science and Humanities GL Bajaj Institute of Technology and Management India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun

The growing demand for web applications across various sectors has led to challenges in developing efficient and high-performance online apps. The traditional approach to web app development may struggle to meet the performance requirements of modern online applications. To address this, the Model-View-Controller (MVC) framework has become an essential tool for creating interactive online apps. While the MVC framework has limitations, several new design patterns have emerged from its architecture that offers improvements. This paper explores some of the thoroughly researched design patterns of the MVC framework. The Composite View pattern merges several views to facilitate the development of complex user interfaces, while the Intercepting Filter design pattern filters and processes requests before they reach the controller, enhancing security and speed. The Service Layer design pattern creates an abstraction layer between the data access layer and the controller to increase scalability and maintainability. Employing MVC-based design patterns can help developers enhance the functionality and efficiency of web apps, ensuring they meet the expectations of modern consumers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Retraction Note: An efficient quantum based D2D computation and communication approach for the internet of things

引用

Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Mekala, M. S. Srivastava, Gautam Chun-Wei Lin, Jerry Dhiman, Gaurav Park, Ju H. Jung, Ho-Youl Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea RLRC for Autonomous Vehicle Parts and Materials Innovation Yeungnam University Gyeongsan Korea Department of Math and Computer Science Brandon University Brandon Canada Western Norway University of Applied Sciences Gaurav Dhiman Bergen Norway Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical Engineering Yeungnam University Gyeongsan Korea Research Centre for Interneural Computing China Medical University Taichung Taiwan University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

来源：评论

学校读者我要写书评

暂无评论

An Overview of Tools and Techniques in Network Forensics

An Overview of Tools and Techniques in Network Forensics

引用

Information Technology (ACIT)

作者： Mahmoud Asassfeh Mohammad Rasmi Al-Mousa Hasan Al-Dweikat Malak Hamad Al-Mashagbeh Suha Afaneh Ghassan Samara Ali Abu Zaid Mohammed Khouj Essam Al Daoud Department of Cybersecurity Zarqa University Zarqa Jordan Cybersecurity and Cloud Computing Department Applied Science Private University Amman Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Network forensics pertains to a subfield of digital forensics that deals with network security. It is utilized in conjunction with computer network traffic monitoring and analysis, which acts as an intrusion detection system and firewall and facilitates the collection of data and digital proof for network security. This platform is designed to analyze and monitor data in a real-time network environment. It is a crucial instrument for identifying and resolving instances of real-time network misuse. This research shows the differences between network forensics and digital forensics as well as the tools and techniques used in network forensics. It also discusses building forensics platforms, developing network analysis methodologies, and talking about the future of network forensics.

关键词： Performance evaluation Digital forensics Intrusion detection Network analyzers Network security Search problems Real-time systems Logic Monitoring Stress

来源：评论

学校读者我要写书评

暂无评论

Two-stage CNN-based framework for leukocytes classification

引用

computers in Biology and Medicine 2025年 187卷 109616-109616页

作者： Khan, Siraj Sajjad, Muhammad Escorcia-Gutierrez, José Dhahbi, Sami Hijji, Mohammad Muhammad, Khan Department of Computer Science Islamia College University Peshawar25120 Pakistan Department of Computational Science and Electronics Universidad de la Costa CUC Barranquilla080002 Colombia Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Leukocytes are pivotal markers in health, crucial for diagnosing diseases like malaria and viral infections. Peripheral blood smear tests provide pathologists with vital insights into various medical conditions. Manual leukocyte counting is challenging and error-prone due to their complex structure. Accurate segmentation and classification of leukocytes remain challenging, impacting both accuracy and efficiency in blood microscopic image analysis. To overcome these limitations, we propose a robust two-stage CNN framework that integrates YOLOv8 for precise segmentation and MobileNetV3 for effective classification. Initially, WBCs are segmented using YOLOv8m-seg, extracting ROIs for subsequent analysis. Then, features from segmented ROIs are used to train MobileNetV3, classifying WBCs into lymphocytes, monocytes, basophils, eosinophils, and neutrophils. This framework significantly advances leukocyte categorization, enhancing diagnostic performance and patient outcomes. The proposed technique achieved impressive accuracy rates of 99.56 %, 99.19 % and 98.89 % during segmentation and 99.28 %, 99.63 % and 98.49 % during classification on Raabin-WBC, PBC and LISC datasets, respectively, outperforming state-of-the-art methods. © 2024 Elsevier Ltd

关键词： White blood cells Blood smear images Image segmentation Deep learning MobileNetV3 YOLOv8 Image classification

来源：评论

学校读者我要写书评

暂无评论

Detecting Covid-19 in Chest X-Ray Images with Convolutional Neural Network 2

Detecting Covid-19 in Chest X-Ray Images with Convolutional ...

引用

2nd International Conference on computer science and Engineering, IC2SE 2021

作者： Rashid, Nurullainy Mat Shuib, Liyana Eke, Christopher Ifeanyi Azmi, Nor A. Faculty of Computer Science and IT University of Malaya Department of Information Systems Kuala Lumpur Malaysia Faculty of Computing Federal University of Lafia Department of Computer Science Nasarawa State Nigeria School of Diagnostic and Applied Health Sciences Faculty of Health Sciences Bangi Malaysia

ISBN: (纸本)9781665400459

The World Health Organization (WHO) had declared Coronavirus disease (COVID-19), a deadly respiratory illness, as a pandemic in March 2020. The number of infected and deaths in COVID-19 positive patients has been rising at a distressing rate. A massive number of chest X-rays to analyse and the exhaustion from monotonous routine work may become prone to the escalation of error rate. For this reason, the use of the artificial intelligence-based tool will be beneficial in detecting subtle COVID-19, as well as to prevent false diagnoses on patients. In this study, several multi-class image classifications were developed using pre-Trained models, namely VGG16, ResNet50, MobbileNetV2, and Xception. A publicly available dataset of 14,220 chest X-ray images comprised of four different classes was used. Extensive evaluation and analysis of the Convolutional Neural Network model in predicting COVID-19 positives have been conducted. The MobileNetV2 model has been demonstrated as the best performing model out of four classifiers with an overall accuracy score of 92.48%. The model also yields 92.67% precision and 92.48% recall score, thus exhibiting a high success in the detection of COVID-19. © 2021 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network

引用

computers in Biology and Medicine 2024年 174卷 108146-108146页

作者： Khan, Siraj Sajjad, Muhammad Abbas, Naveed Escorcia-Gutierrez, José Gamarra, Margarita Muhammad, Khan Department of Computer Science Islamia College University Peshawar25120 Pakistan Department of Computational Science and Electronics Universidad de la Costa CUC Barranquilla080002 Colombia Department of System Engineering Universidad del Norte Puerto Colombia081007 Colombia Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems. © 2024 Elsevier Ltd

关键词： Classification (of information)

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：