检索结果-内蒙古大学图书馆

RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

computers, Materials & Continua 2024年第12期81卷 4833-4851页

作者： Weiwei Hu Kiran Sree Pokkuluri Rajesh Arunachalam Bander A.Jabr Yasser A.Ali Preethi Palanisamy School of Information Engineering Technology&MediaUniversity of HenanKaifeng475004China Department of Computer Science and Engineering Shri Vishnu Engineering College forWomenBhimavaram534202Andhra PradheshIndia Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.

关键词： Sensor node localization received signal strength indicator 3D wireless sensor network deep neural network average localization error and hybrid T cell immune with lotus effect optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain technology

引用

computer Modeling in engineering & Sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning Empowered Skin Diseases Detection in Children

引用

computer Modeling in engineering & Sciences 2024年第12期141卷 2609-2623页

作者： Meena N.Alnuaimi Nourah S.Alqahtani Mohammed Gollapalli Atta Rahman Alaa Alahmadi Aghiad Bakry Mustafa Youldash Dania Alkhulaifi Rashad Ahmed Hesham Al-Musallam Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia ICS Department King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)*** left untreated,these diseases can have severe consequences and spread,especially among *** detection is crucial to prevent their spread and improve a patient’s chances of ***,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and *** study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the *** method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and *** proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,*** illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.

关键词： Deep learning MobileNet DenseNet121 skin diseases detection transfer learning

来源：评论

学校读者我要写书评

暂无评论

Automated Brain Hemorrhage Classification and Volume Analysis

引用

computers, Materials & Continua 2023年第4期75卷 2283-2299页

作者： Maryam Wardah Muhammad Mateen Tauqeer Safdar Malik Mohammad Eid Alzahrani Adil Fahad Abdulmohsen Almalawi Rizwan Ali Naqvi Department of Computer Science Air University Multan CampusMultan60000Pakistan Department of Information Technology Bahauddin Zakariya UniversityMultan60800Pakistan Department of Engineering and Computer Science College of Computer Science and Information TechnologyAl Baha UniversityAl BahaSaudi Arabia School of Computer Science&Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea

Brain hemorrhage is a serious and life-threatening condition. It cancause permanent and lifelong disability even when it is not fatal. The wordhemorrhage denotes leakage of blood within the brain and this leakage ofblood from capillaries causes stroke and adequate supply of oxygen to thebrain is hindered. Modern imaging methods such as computed tomography(CT) and magnetic resonance imaging (MRI) are employed to get an idearegarding the extent of the damage. An early diagnosis and treatment can savelives and limit the adverse effects of a brain hemorrhage. In this case, a deepneural network (DNN) is an effective choice for the early identification andclassification of brain hemorrhage for the timely recovery and treatment of anaffected person. In this paper, the proposed research work is divided into twonovel approaches, where, one for the classification and the other for volumecalculation of brain hemorrhage. Two different datasets are used for twodifferent techniques classification and volume. A novel algorithm is proposedto calculate the volume of hemorrhage using CT scan images. In the firstapproach, the ‘RSNA’ dataset is used to classify the brain hemorrhage typesusing transfer learning and achieved an accuracy of 93.77%. Furthermore,in the second approach, a novel algorithm has been proposed to calculate thevolume of brain hemorrhage and achieved tremendous results as 1035.91mm3and 9.25 cm3, using the PhysioNet CT scan tomography dataset.

关键词： Deep learning computed tomography magnetic resonance imaging brain hemorrhage classification volume analysis

来源：评论

学校读者我要写书评

暂无评论

Enhancing Plant Leaf Disease Prediction Through Advanced Deep Feature Representations: A Transfer Learning Approach

引用

Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 469-482页

作者： Naralasetti, Veeranjaneyulu Bodapati, Jyostna Devi Department of Information Technology Vignan’s Foundation for Science Technology and Research Guntur India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Guntur India

Plant diseases present a considerable threat to the farming industry, causing significant economic losses by reducing crop yields. The emergence of deep neural network models in the realm of computer vision has brought about a groundbreaking transformation in the swift recognition and surveillance of plant diseases. In this study, we introduce an innovative approach for plant disease prediction, leveraging transfer learning techniques to enable accurate and early detection of plant diseases. The proposed leaf disease prediction framework encompasses multiple key stages, including preprocessing, feature extraction, classification, and postprocessing. Notably, our model excels at feature extraction, utilizing the pre-trained convolutional architecture of VGG16 to extract valuable information from leaf images. These features are then fed into a deep neural network model designed for disease prediction. The feature extraction method employed in our approach excels at capturing intricate disease-related details from plant images. When the deep features extracted are utilized for classification using our proposed classifier, the outcome demonstrates a substantial enhancement in disease prediction performance. Based on extensive experimentation with the benchmark plant village dataset, it is evident that our transfer learning-based approach outperforms existing methods, achieving an impressive accuracy rate of 96.56%. Besides accuracy, the proposed approach surpasses F1_score and Kappa scores for the benchmark plant village dataset. © The Institution of Engineers (India) 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Dynamic Spectrum Management and Spectrum Sharing Techniques in 5G Networks: A Survey

引用

Journal of Mobile Multimedia 2024年第5期20卷 997-1038页

作者： Mondal, Sonali Dutta, Manash Pratim Chakraborty, Swarnendu Kumar Department of Computer Science & Engineering National Institute of Technology Arunachal Pradesh India Department of Computer Science & Information Technology Cotton University Assam India

The enormous developments of gaming devices as well as mobile apps have increased the demand of bandwidth. Development of wireless applications has been affected because of the insufficient spectrum resources in the 3G and 4G network. This spectrum scarcity is the main limitation of 3G and 4G networks, which leads to the revolution of the 5G technology. Spectrum scarcity is a great challenge in wireless communication. There are two significant research areas that need to be explored such as, searching spectrum resource and maximum spectrum utilization. To address spectrum insufficiency, consistent research is needed to find out spectrum band and share the spectrum resource. In this work, complete Spectrum management framework is discussed as well as spectrum sharing techniques are classified. We have conducted a thorough survey on various spectrum sharing schemes. The literature survey has been classified based on some significant sharing techniques depending on spectrum access method, network architecture, spectrum allocation behavior etc. We have also summarized contribution as well as limitation of recent spectrum sharing approaches. To enhance 5G technology, related spectrum sharing surveys are studied and future research directions are also discussed here. © 2024 River Publishers.

关键词： 5G cognitive radio spectrum management framework spectrum sensing spectrum sharing

来源：评论

学校读者我要写书评

暂无评论

Dehazing using Generative Adversarial Network - A Review

引用

SN computer Science 2025年第1期6卷 1-17页

作者： Khatun, Amina Mostafiz, Rafid Shorif, Sumaita Binte Uddin, Mohammad Shorif Hadi, Md. Abdul Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science: Information Technology University of Nebraska Omaha Omaha United States

Dehazing is a difficult process in computer vision that seeks to improve the clarity and excellence of pictures taken under cloudy, foggy, and rainy circumstances. The Generative Adversarial Network (GAN) has been a viable method for removing haze from photos in recent years. This is because GAN can understand intricate data patterns and provide high-quality outcomes. This paper provides a thorough examination of the most advanced strategies in dehazing utilizing GAN. The study examines the various elements utilized in GAN-based dehazing, including generator and discriminator architectures, loss functions, and training strategies. It also explores the evaluation metrics employed to assess the effectiveness of GAN-based dehazing methods. It also examines the datasets often used to train and evaluate these models. This research concludes by examining prospective avenues for future study in the domain of dehazing, employing GAN to tackle the obstacles of real-time dehazing, managing intricate scenarios with various atmospheric conditions, and enhancing the resilience of GAN-based dehazing models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Atmospheric haze Dehazing Generative Adversarial Network (GAN) Performance evaluation metrics

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Cluster-head-based Energy-aware Routing using a Separable Convolution Neural Network in a Wireless Sensor Network

引用

IEIE Transactions on Smart Processing and Computing 2024年第2期13卷 105-112页

作者： Ather, Danish Mary, J. Prisca Singh, Pooja Garg, Kanika Priyadharshini, T.R. Princy, B. Anni Tiwari, Mohit Department of IT and Engineering Amity University in Tashkent Uzbekistan Department of Computer Science and Engineering NPR College of Engineering and Technology Tamil Nadu Dindigul India Department of Physics Faculty of Science SGT University Haryana Gurugram India Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Ghaziabad Modinagar India Department of Information Technology Hindusthan College of Engineering and Technology Pollachi main road Coimbatore India Department of Computer and Communication Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

Wireless sensor network (WSN) applications are added day by day owing to numerous global uses (by the military, for monitoring the atmosphere, in disaster relief, and so on). Here, trust management is a main challenge. Sensor nodes are important in wireless sensor networks, but they are easily depleted because of their short lifespan from continuous sensing activity and low battery capacity. So efficient energy utilization is a challenging task in a WSN. To minimize energy loss, clustering with an optimum path selection process is needed to retain energy in sensor nodes. This manuscript proposes multi-objective Pelican Optimization Algorithm (POA) routing to maintain energy efficiency and minimize transmission distances in wireless sensor networks. A cluster head (CH) is selected by using a Separable Convolution Neural Network (SCNN). Simulation outcomes prove the proposed technique attains 22.3% and 25.04% improvements in energy consumption when compared to the Multi-Objective CH Energy-aware Optimized Routing Approach at WSN (MOCH-EORA-WSN) and Multiple Optimum Cluster Head Multi-Objective Grasshopper Optimization with Harmony-search at WSN (MOCH-MOGOH-WSN), respectively. © 2024 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Mapping Technological Trajectories of Edge Computing: A Citation Graph Analysis

引用

IEEE Internet of Things Journal 2024年第9期11卷 16545-16560页

作者： Wagiu, Elmor Benedict Liu, Chuan-Ming Palopak, Yulianus National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan National Taiwan University of Science and Technology Department of Information Management Taipei106 Taiwan

The notion of edge computing has recently arisen as a potentially helpful advancement in distributed computing and communication infrastructure. To better understand the evolution and diffusion of technological knowledge in this field, we conducted a citation graph analysis of scholarly publications related to edge computing. Our study aims to identify the critical technological trajectories and research themes that have shaped the development and adoption of edge computing over time. We employ main path analysis (MPA) to construct a citation graph using a comprehensive publications data set from Web of Science (WoS) databases. We applied keyword co-occurrence analysis techniques to identify clusters of research topics and their interconnections. Occurrence cluster analysis results indicate that current research in edge computing is primarily centered around the architecture of edge computing, high-speed networks, mobile edge computing, computing systems, and blockchain and security technologies. We also identified the significant papers and contributors in the edge computing field and their impact on the technological trajectory of edge computing. The results reveal 35 significant papers that shape the emergence of four phases of the technology trajectory, including edge computing transition, architecture, edge future, and edge optimization. © 2014 IEEE.

关键词： computer architecture

来源：评论

学校读者我要写书评

暂无评论

Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

引用

SN computer Science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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：