检索结果-内蒙古大学图书馆

Joint trust-based detection and signature-based authentication technique for secure localization in underwater wireless sensor network

引用

Multimedia Tools and Applications 2025年 1-20页

作者： Kumar, Manni Goyal, Nitin Qaisi, Ramy Mohammed Aiesh Najim, Mohd Gupta, Sachin Kumar Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India Department of Computer Science Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Electronics and Communication Engineering Central University of Jammu UT of J&K Jammu181143 India

Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to existing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Integrating the Internet of Things to Protect Electric Vehicle Control Systems from Cyber Attacks

引用

IAENG International Journal of Applied Mathematics 2024年第3期54卷 433-440页

作者： Bhukya, Baddu Naik Venkataiah, V. Kuchibhatla, S. Mani Koteswari, S. Kumari, R.V.S. Lakshmi Raju, Yallapragada Ravi Department of Electrical and Electronics Engineering Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Department of Computer Science Engineering CMR College of Engineering & Technology Telangana Hyderabad India Department of Electrical and Electronics Engineering ACE Engineering College Telangana Hyderabad India Department of Electronics and Communication Engineering Pragati Engineering College Kakinada India Andhra Pradesh Surampalem India Department of Electrical and Electronics Engineering Gayatri Vidya Parishad College of Engineering for Women Andhra Pradesh Visakhapatnam India Department of Computer Science and Technology Madanapalle Institute of Technology and Science Andhra Pradesh India

The Internet of Things (IoT) facilitates the delivery of intelligent services by sensing, gathering, processing, and exchanging data from millions of linked smart devices. The Internet of Things (IoT), which is based on communication infrastructure, provides real-time cyber-physical device monitoring, control, and sensing, such as electric autos. Traditional communication infrastructure's vulnerability to cyberattacks hampered IoT's capacity to investigate these potential applications. This study proposes an algorithm for monitoring and controlling electric vehicles through the Internet of Things communication network to prevent fake data injection attacks. A state-space model depicts a fully autonomous electric car equipped with a vision system. Intelligent sensors and actuators from the Internet of Things are utilized to monitor and change system conditions, compensating for the large distance between the electric car and the control center. Sensing data is delivered from the vehicle to the central command center via an insecure, attack-prone communication path. The mean square error approach is used to derive states in the most effective state estimation system for comprehending and showing autos. A semi-definite programming approach is used to create an optimal control algorithm to manage the vehicle states. The simulation results show how precisely and successfully the proposed algorithms can foresee and regulate a vehicle's state. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure communication Network

引用

China communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

来源：评论

学校读者我要写书评

暂无评论

Eco-friendly Smart Sensor Adjustment using Deep Reinforcement Learning Approach 2

Eco-friendly Smart Sensor Adjustment using Deep Reinforcemen...

引用

2nd International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2024

作者： Reethika, A. Lavanya, S. Sheetal, Attar Mahay Vanitha, C.N. Priyadharsini, S. Jenolin rex, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Mahendra College of Engineering Salem India

ISBN: (纸本)9798331541217

Today's networking and telecommunications industries greatly benefit from the growth of highly sturdy and energy-effective Internet of Things through communication sensors. Device efficiency may be increased while sensor energy consumption is reduced by calibrating sensors to use less power. Reinforcement learning (RL), which has garnered significant interest from researchers, is being widely applied to achieve intelligent automation in several academic disciplines today. Resource optimization has not received much attention in research, despite the Internet of Things using a large variety of sensors. By investigating a revolutionary approach to power conservation with the use of reinforcement learning, the creative study aims to produce a novel generation of Internet of Things with standardized power sources to optimize source consumption. A closed-grid, numerous power source controlling system for sensor consumption has been revealed. Our recommended strategy, which uses Deep Q learning, can help Internet of Things sensors optimize their operation. This study focused only on energy-effective sensor and simulation results show that the planned approach works well. © 2024 IEEE.

关键词： Telecommunication industry

来源：评论

学校读者我要写书评

暂无评论

A Bioinformatics Approach to Identify Hub Genes Across Schizophrenia, Anxiety, Bipolar Disorder, and Depressive Disorder for Network-Based Drug Discovery 2

A Bioinformatics Approach to Identify Hub Genes Across Schiz...

引用

2nd International Conference on Information and communication Technology, ICICT 2024

作者： Biswas, Md. Emran Hasan, M.D. Galib Hossain, Md. Jobare Basar, Md. Abul Hossain, Md. Selim Department of Electronics and Communication Engineering HSTU Bangladesh Department of Computer Science and Engineering Engineering Uttara University Bangladesh Department of Information and Communication Technology MBSTU Bangladesh

ISBN: (纸本)9798331508227

Mental health disorders represent a significant challenge in society, requiring immediate attention and comprehensive knowledge to mitigate their widespread impacts. Although considerable research has focused on hub gene identification for one or two illnesses, there is a lack of literature examining multiple common disorders and their implications for drug discovery. This study utilizes a bioinformatics methodology to identify shared and critical genes linked to anxiety disorders, bipolar disorders, schizophrenia, and depressive disorders, along with potential therapeutic targets for these conditions. We methodically collected gene-related data from the National Center for Biotechnology Information (NCBI) for these disorders. Through protein-protein interaction analysis, we discovered hub genes, including GSK3B, MAPT, NR3C1, ESR1, TNF, SOD1, APP, CREB1, NOS1, APOE, GNB3, DISC1, SIRT1, CACNA1C, and BDNF. Additionally, we identified potential therapeutic agents targeting these hub genes, including Kaempferol, Simvastatin (CTD 00007319), Luteolin, Clozapine (CTD 00005693), Curcumin, Resveratrol, Melatonin (CTD 00006260), Felodipine, Apigenin, Docosahexaenoic Acid, Diazepam, and Alprazolam, using the DSigDB database. This study seeks to elucidate the genetic connections across mental health diseases and to find therapeutic compounds that may provide common treatment advantages. ©2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

引用

Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

引用

computer Systems science & engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

来源：评论

学校读者我要写书评

暂无评论

OFDM-CFO and Resource Scheduling Algorithm Using Fuzzy Linear-CFO

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 2513-2525页

作者： M.Prabhu B.Muthu Kumar Department of Electronics and Communication Anna UniversityChennai600025India Department of Computer Science and Engineering Syed Ammal Engineering CollegeRamanathapuram623502India

Orthogonal Frequency-Division Multiplexing(OFDM)is the form of a digital system and a way of encoding digital data across multiple frequency com-ponents that are used in telecommunication *** Frequency Offset(CFO)inaccuracy is a major disadvantage of *** paper proposed a fea-sible and elegant fuzzy-based resource allocation technique,that overcomes the constraints of the *** suggested Fuzzy linear CFO estimation(FL-CFO)not only estimates the CFO with increased precision but also allocates resources effectively,and achieves maximum utilization of dynamic *** sug-gested FL-CFO error estimation algorithm in OFDM systems employing 1-bit Quadrate errors ADC(1-bit QE)is utilized to extract the precise ***-ally,the base station(BS)manages the Resource Units(RU),which could be used to distribute resources in such a manner that the user requests are *** assign resources to a certain job,fuzzy rules are *** the residence duration exceeds the resource requirement time then the particular resources are provided to the highest-priority *** a result,the job performance will not be halted due to a lack of allocated *** metrics such as utilization,rate of failure,and life span,as well as energy consumption,are used to assess the pro-posed FL-CFO.

关键词： Orthogonal frequency division multiplexing(OFDM) carrier

来源：评论

学校读者我要写书评

暂无评论

Circuit Design using Quantum Dot Technology 1

Circuit Design using Quantum Dot Technology

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Manjunath, T.C. Kuravatti, Prakash Rajarajeswari College of Engineering Department of Computer Science & Engineering Bangalore India Rajeev Institute of Technology Department of Electronics and Communication Engineering Hassan India

ISBN: (纸本)9798331529246

In this paper, the harnessing QCA for High-Performance, Low-Power Arithmetic Circuits w.r.t. the focusing on Multipliers and Square Circuits is presented. The background in relation to this work is presented next. Quantum-dot Cellular Automata (QCA) is an emerging technology in nano-electronics that offers promising advantages over traditional CMOS (Complementary Metal-Oxide-Semiconductor) technology, including low power consumption, high operational speeds, and exceptional integration capabilities. These features position QCA as a potential successor to CMOS in various applications. Multipliers, critical components in digital signal processing, and square circuits, essential for mathematical computations, significantly impact system throughput and power efficiency. This research focuses on designing QCA-based multiplier and square circuits to leverage these advantages. Methods, Results, and Concluding Remarks w.r.t. the work is given here. The QCA-based multiplier was constructed using binary adders and AND gates, while a comparative analysis was conducted between QCA and CMOS technologies to evaluate performance. The evaluation considered parameters such as cell count, area, and energy dissipation, utilizing the QCA Designer tool for validation. Results demonstrate that QCA designs offer significant improvements in terms of reduced power dissipation and area requirements compared to CMOS. These findings highlight the potential of QCA technology to enhance the efficiency and integration of critical components in digital systems, marking it as a transformative approach in nano-electronics. © 2025 IEEE.

关键词： CMOS integrated circuits

来源：评论

学校读者我要写书评

暂无评论

Horizontal Voting Ensemble Based Predictive Modeling System for Colon Cancer

引用

computer Systems science & engineering 2023年第8期46卷 1917-1928页

作者： Ushaa Eswaran S.Anand Department of Electronics and Communication Engineering Infant Jesus College of EngineeringTuticorinTamil NaduIndia Department of Computer Science and Engineering Infant Jesus College of EngineeringTuticorinTamil NaduIndia

Colon cancer is the third most commonly diagnosed cancer in the *** colon AdenoCArcinoma(ACA)arises from pre-existing benign polyps in the mucosa of the ***,detecting benign at the earliest helps reduce the mortality *** this work,a Predictive Modeling System(PMS)is developed for the classification of colon cancer using the Horizontal Voting Ensemble(HVE)*** different patterns inmicroscopic images is essential to an effective classification system.A twelve-layer deep learning architecture has been developed to extract these *** developedHVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed *** thousand(10000)microscopic images are taken to test the classification performance of the proposed PMS with the HVE *** microscopic images obtained from the colon tissues are classified intoACAor benign by the proposed *** prove that the proposed PMS has∼8%performance improvement over the architecture without using the HVE *** proposed PMS for colon cancer reduces the misclassification rate and attains 99.2%of sensitivity and 99.4%of *** overall accuracy of the proposed PMS is 99.3%,and without using the HVE method,it is only 91.3%.

关键词： Colon cancer microscopic images medical image processing ensemble approach computer aided diagnosis texture analysis

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：