检索结果-内蒙古大学图书馆

CEER:Cooperative Energy-Efficient Routing Mechanism for Underwater Wireless Sensor Networks Using Clusters

computer Systems science & engineering 2023年第6期45卷 2587-2602页

作者： Sayyed Mudassar Shah Tariq Hussain Babar Shah Farman Ali Khalid Zaman Kyung-Sup Kwak Institute of Computer Science&Information Technology the University of Agriculture Peshawar25000Pakistan High Performance Computing and Networking Institute National Research Council(ICAR-CNR)Naples80131Italy College of Technological Innovation Zayed UniversityDubai19282UAE Department of Software Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Inha UniversityIncheon22212Korea

Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration *** most of these applications,the network is composed of several sensor nodes deployed at different depths in the *** nodes located at depth on the seafloor cannot invariably communicate with nodes close to the surface level;these nodes need multihop communication facilitated by a suitable routing *** this research work,a Cluster-based Cooperative Energy Efficient Routing(CEER)mechanism for UWSNs is proposed to overcome the shortcomings of the Co-UWSN and LEACH *** optimal role of clustering and cooperation provides load balancing and improves the network *** simulation results using MATLAB show better performance of CEER routing protocol in terms of various parameters as compared to Co-UWSN routing protocol,i.e.,the average end-to-end delay of CEER was 17.39,Co-UWSN was 55.819 and LEACH was *** addition,the average total energy consumption of CEER was 9.273,Co-UWSN was 12.198,and LEACH was *** packet delivery ratio of CEER was 53.955,CO-UWSN was 42.047,and LEACH was *** stability period CEER was 130.9,CO-UWSN was 129.3,and LEACH was *** obtained results maximized the lifetime and improved the overall performance of the CEER routing protocol.

关键词： Cluster head co-UWSN UWSN relay LEACH CEER

来源：评论

学校读者我要写书评

暂无评论

Intelligent Blood Flow Velocity Calculation using Deep Belief Network with Harmony Search Algorithm 2

Intelligent Blood Flow Velocity Calculation using Deep Belie...

引用

2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Mahendran, Radha Ala Walid, Md. Abul Vijaya Pratap, G. Pradeep, Aneesh Soumya, M. Udhayaraja, P. Institute of Science and Technology and Advanced Studies. Pv Department of Bioinformatics Vels Tamilnadu Chennai India Khulna9203 Bangladesh Sga Gov. Degree College Andhra Pradesh India New Uzbekistan University Software Engineering Tashkent Uzbekistan Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal India Excel College for Commerce and Science Department of Microbiology Tamilnadu Namakkal India

ISBN: (纸本)9798350346640

Generally, the procedure of blood flow velocity computation contains gathered data on the movement of red blood cells or other indicators of blood flow and utilizes that data to compute the velocity of blood flows with the blood vessels. The procedure of blood flows velocity computation contains evaluating the speed at which blood moves through a blood vessel. It is done utilizing several approaches like magnetic resonance imaging (MRI), Doppler flowmetry, ultrasound, particle image velocimetry (PIV), or computed tomography (CT) scans. This manuscript involves the design of Intelligent Blood Flow Velocity Calculation using Deep Belief Network with Harmony Search Algorithm (BFV-DBNHSA) technique. The proposed BFV-DBNHSA technique computes the velocity of the blood flow accurately and timely. In the presented BFV-DBNHSA technique, the major aim is to determine the interior blood flow velocity. To accomplish this, the BFV-DBNHSA technique employs DBN model to produce the features of the blood flow velocity. Moreover, the BFV-DBNHSA technique uses HSA algorithm for optimal hyperparameter selection of the DBN model. The experimental outcome investigation of the BFV-DBNHSA system is well studied under different measures. The comprehensive comparison analysis revealed the improvement of the BFV-DBNHSA technique over recent algorithms. © 2023 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Xinquan Xie, Jinheng Li, Xuechen Li, Xuguang Shen, Linlin Deng, Yongqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China National University of Singapore Show Lab Singapore Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University General Hospital Department of Stomatology Shenzhen China

ISBN: (纸本)9798350337488

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods. © 2023 IEEE.

关键词： Dental Implant Multi-module Detection Multi-slice Learning Text Guided Detection Transformer

来源：评论

学校读者我要写书评

暂无评论

2D Animation - Q Version of the AIDS Prevention Short Video 10

2D Animation - Q Version of the AIDS Prevention Short Video

引用

10th International Conference on Orange Technology, ICOT 2022

作者： Wei, Chun-Cheng Hsieh, Hui-Ming Hung, Long-Chen Chen, Ru-Yi Huang, Jiang-Fang Lu, Xin-Chun Zhong, Yu-Shan School of Software and Big Data Changzhou College of Information Technology Changzhou China Taiwan Shoufu University Department of Computer Science and Multimedia Design Tainan Taiwan Lunghwa University of Science and Technology Department of Electronic Engineering Taiwan

ISBN: (纸本)9781665454193

AIDS is a sexually transmitted disease. The medical term is called Acquired Immune Deficiency Syndrome. Medical methods such as cocktail therapy and anticancer drugs can kill AIDS. Although there are currently cured cases, prevention is still required to prevent the infection from expanding;prevention Ways, like the AIDS vaccine or preventive safe sex, can reduce the risk of infection. We hope that through the production of animated short films for viewing, we can help reduce the HIV infection rate. This short film is presented as a story similar to a micro-film by ourselves. And using the modern scene and the character design of the Q version as the material, the story content is made into 2D animation with Adobe Flash CS5. © 2022 IEEE.

关键词： Animation

来源：评论

学校读者我要写书评

暂无评论

IoHT System Authentication Through the Blockchain Technology: A Review

IoHT System Authentication Through the Blockchain Technology...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Muwafaq Jawad Ali A. Yassin Hamid Ali Abed Al-Asadi Zaid Ameen Abduljabbar Vincent Omollo Nyangaresi Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bando Kenya Department of Applied Electronics Saveetha School of Engineering SIMATS Chennai Tamilnadu India

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The growing adoption of the Internet of Things (IoT) terminology in recent years in several domains, including healthcare, has yielded multiple benefits. One of the roles is to support professionals in delivering outstanding and suitable healthcare for patients, while also empowering patients to independently track their health state. However, this wide growth is accompanied by many significant security concerns and cyber risks, including unauthorized access, data leakage, information modification, and data erasure. This article outlines the advantages of employing groundbreaking blockchain technology to provide robust and verified authentication methods for different systems. Additionally, it imparts a foundational comprehension of the technologies employed in the procedure of constructing and advancing blockchain-based platforms. In addition, we explore tools like Scythe and Avispa that are employed to assess the system’s resilience against different types of attacks.

关键词： Terminology Reviews Instruments Authentication Medical services Throughput Blockchains Internet of Things Information technology Resilience

来源：评论

学校读者我要写书评

暂无评论

Paddy Plant Disease Detection using Hybrid Deep Learning Algorithms

Paddy Plant Disease Detection using Hybrid Deep Learning Alg...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： R Suguna S Deepa T Kanimozhi M Chandru G Arun Harshini M Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Department of Computer Science VET-Institute of Arts and Science (Co-Education) College Erode Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai M.Sc (Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Agriculture is the backbone of India’s economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and ResNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial Leaf Blight, (iii) Sheath Blight, and (iv) Ufra Disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity.

关键词： Deep learning Productivity Plant diseases Microorganisms Accuracy Prediction algorithms Real-time systems Classification algorithms System analysis and design Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects

引用

SN computer science 2021年第3期2卷 219页

作者： Feroze, Aqeel Daud, Ali Amjad, Tehmina Hayat, Malik Khizar Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering Jeddah University Jeddah Saudi Arabia Department of Information Technology Faculty of Information Technology and Engineering The University of Haripur Haripur Pakistan

Anomaly detection has evolved as a successful research subject in the areas such as bibliometrics, informatics and computer networks including security-based and social networks. Almost all existing anomaly detection techniques have some limitations and do not focus specifically on detecting anomalous groups. Anomaly detection is also a crucial problem in processing large-scale datasets when our goal is to find abnormal values or unusual events. The authors decided to survey existing group anomaly detection techniques because there is a need to consider group anomalies for mitigation of risks, prevention of malicious collaborative activities, and other interesting explanatory insights by identifying groups that are not consistent with regular group patterns. In this research, we bifurcated group anomaly detection techniques into activity-based and graph-based methods. The graphical methodologies are then further classified under static versus dynamic and attributed versus plain graph methods. We have also listed the datasets used in various studies to detect group anomalies along with detected anomalies and the various performance measures used to validate the results. Towards the end, we have provided various applications of group anomaly detection and the research challenges that group anomaly detection presents to the scientific community and enlisted some of the future trends for this particular research area. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Academic social network Dynamic Graph-centric features Group anomaly detection Performance metrics Static

来源：评论

学校读者我要写书评

暂无评论

eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense 1

引用

18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Zhang, Tianning Cai, Miao Zhang, Diming Huang, Hao Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China College of Computer Engineering Jiangsu University of Science and Technology Nanjing China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Image Recognition: Next-Generation CNN Architectures for Handwritten Digits and Objects

Revolutionizing Image Recognition: Next-Generation CNN Archi...

引用

IEEE Symposium on Wireless Technology and Applications (ISWTA)

作者： Md Nurul Absur Kazi Fahim Ahmad Nasif Sourya Saha Sifat Nawrin Nova Department of Computer Science City University of New York New York USA College of Computing and Software Engineering Kennesaw State University Georgia USA Department of Computer Science Chalmers University of Technology Gothenburg Sweden

ISBN: (数字)9798350351354

ISBN: (纸本)9798350351361

This study addresses the pressing need for computer systems to interpret digital media images with a level of sophistication comparable to human visual perception. By leveraging Convolutional Neural Networks (CNNs), we introduce two innovative architectures tailored to distinct datasets: the MNIST handwritten digit dataset and the Fashion MNIST dataset. Unlike traditional machine learning methods such as Support Vector Machines (SVM) and Random Forests, our customized CNN models remarkably enhance image attribute comprehension and recognition accuracy. Specifically, the model developed for the MNIST dataset achieved an unprecedented accuracy of 98.71% without any bias, while the Fashion MNIST model reached 91.39%, marking significant advancements over conventional algorithms without any bias. This research showcases the superior efficiency of CNNs in processing and understanding digital images. It underscores the potential of deep learning technologies in bridging the gap between computational systems and human-like visual recognition. Through meticulous experimentation and analysis, we illustrate how deep CNNs require less preparatory work than other image-processing algorithms, setting a new benchmark in computer vision.

关键词： Support vector machines Handwriting recognition Visualization Accuracy Image recognition Computational modeling computer architecture

来源：评论

学校读者我要写书评

暂无评论

Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

arXiv

引用

arXiv 2024年

作者： Su, Xiaoxin Zhou, Yipeng Cui, Laizhong Guo, Song College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Shenzhen University Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology HKSAR Hong Kong

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with Compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning systems

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：