检索结果-内蒙古大学图书馆

2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021

作者： Elangovan, V.R. Joe, A. J. Rajeswari Akila, D. Shankari, K. Hema Suseendran, G. Agurchand Manmull Jain College Department of Computer Science Chennai India Srikanyaka Parameswari Arts and Science College for Women Department of Computer Applications Chennai India Vels Institute of Science Technology Advanced Studies Department of Information Technology Chennai India Women's Christian College Department of Computer Application Chennai India

ISBN: (纸本)9781728194912

This paper presented the methodology to apply segmentation techniques on human heart atherosclerosis images using a hybrid segmentation methods with FCM+kmeans combined segmentation algorithm. Coronary heart disease (CHD) can be caused by atherosclerosis as one of its primary reason. Cardiovascular diseases are significant causes of mortality in the world. The proposed methodology for atherosclerosis segmentation for medical image performs better with some advantages, like improved sensitivity, specificity and accuracy of segmentation. Thus overall performance metrics of the new combined algorithm. © 2021 IEEE.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

iCOIL: Scenario Aware Autonomous Parking Via Integrated Constrained Optimization and Imitation Learning

iCOIL: Scenario Aware Autonomous Parking Via Integrated Cons...

引用

International Conference on Distributed computing Systems Workshop

作者： Lexiong Huang Ruihua Han Guoliang Li He Li Shuai Wang Yang Wang Chengzhong Xu Center for Cloud Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Department of Computer Science University of Hong Kong IOTSC University of Macau

Autonomous parking (AP) is an emerging technique to navigate an intelligent vehicle to a parking space without any human intervention. Existing AP methods based on mathematical optimization or machine learning may lead to potential failures due to either excessive execution time or lack of generalization. To fill this gap, this paper proposes an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable AP. The iCOIL method has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis (HSA) model to determine the better mode under various scenarios. We implement and verify iCOIL on the Macao Car Racing Metaverse (MoCAM) platform. Results show that iCOIL properly adapts to different scenarios during the entire AP procedure, and achieves significantly larger success rates than other benchmarks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

GUEST EDITORIAL

引用

China Communications 2023年第2期 3-6页

作者： Yuan Wu Xu Chen Feng Lyu Xianfu Chen Xumin Huang Jie Gao Yueyue Dai Tony Q.S.Quek Yan Zhang the State Key Laboratory of Internet of Things for Smart City University of Macau the Department of Computer and Information Science University of Macau Sun Yat-sen University Sun Yat-sen University Institute of Advanced Networking and Computing Systems Sun Yat-sen University National and Local Joint Engineering Laboratory the School of Computer Science and Engineering Central South University the VTT Technical Research Centre of Finland the School of Automation Guangdong University of Technology the School of Information Technology Carleton University Huazhong University of Science and Technology Singapore University of Technology and Design(SUTD) the IEEE Communications Society Technical Committee on Green Communications and Computing(TCGCC)

With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and l...

关键词：

来源：评论

学校读者我要写书评

暂无评论

MAM: Multimodel Attention Mechanism for Social Media Natural Disaster Management Tweet Classification

MAM: Multimodel Attention Mechanism for Social Media Natural...

引用

ACS/IEEE International Conference on computer Systems and Applications

作者： M. Sangeetha R. Manjula Devi Bhisham Sharma Subrata Chowdhury Imed Ben Dhaou Department of Information Technology Dr.N.G.P. Institute of Technology Coimbatore Tamilnadu India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Tamilnadu India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies Chittoor Andra Pradesh India Department of Computer Science Hekma School of Engineering Computing and Design Dar Al-Hekma University Jeddah Saudi Arabia

People have been using social media as a category for exchanging content for decades. It has revolutionized communication and enhanced the sharing of information during emergency situations. The key features of social media are collective action, connectivity, comprehensiveness, and clarity. Consequently, it performed a significant function in natural disaster management by keeping track of and reporting disaster-related incidents. The volume and diversity of the data acquired from social media during a natural disaster pose the greatest challenge. For natural disaster management it is extremely difficult to derive actionable information from the data collected from social platforms. Various strategies have been presented in the literature to address the difficulties posed by social media for natural disaster management. The proposed work is a semi-supervised machine learning model for detecting and classifying tweets. The proposed work centre’s on preparing the data by performing cleansing and various transformations, generating word embedding vectors with DistillBERT, using the vision transformer, the image model was constructed. The attention mechanism is utilized for text and image model integration. In the proposed work the data pertaining to seven distinct natural disasters, such as cyclones, floods, and earthquakes are analyzed. A novel decision diffusion technique is proposed for classifying them into informative and non-informative groups and evaluating the accuracy of the results. The MAM model increases the accuracy to 97% for crisisMMD dataset.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PACELC: Enchantment multi-dimension TensorFlow for value creation through Big Data 4

PACELC: Enchantment multi-dimension TensorFlow for value cre...

引用

4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020

作者： Yasmin, A. Kamalakkannan, S. Vels Institute of Science Technology Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology Advanced Studies Department of Information Technology Chennai India

ISBN: (数字)9781728154640

ISBN: (纸本)9781728154640

New online mode learns more about different kinetic models. Frequency algorithm reduces the loss function, which directly compensates for the error between the required and the actual acceleration. It allows the use of green acceleration principles such as speed accelerators and TensorFlow as a helper function of the robot mode. The use of direct loss eliminates the problem of learning outside the scope of indirect loss programs, usually in their current state. The power of re-learning creates a trend online tip according to standard non-linear parameters updating and updating online can correct frequency varied operating error during big data real-world generation. JEDEC reduced the machine learning sequence by a combined multi-dimension robust management robust study is planned for future tasks. This paper describes the operation and control of the controller for analytical, there is a clear link between the size of the compressor, the vibration level and the lens pool, learning new machine learning tools. In particular, JEDEC(Joint Electron Device Engineering Council) would like to use relational PACELC(Partition exists for Availability/consistency Else Latency/consistency) theoretical analysis to obtain the same summary and intensity The results of frequency case will also focus on increasing demand Important task balance, especially Restore model other types of contractions, as well as the connection between this reduced style adapter control and the learned control style multi-dimensions in sushisen algorithm using reduces these updates Deep Lanning Networks. Therefore, BDA data Partitioning helps to reduce the complexity of the calculation in the learning process and classification of data storage. © 2020 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Granger Causality for Compressively Sensed Sparse Signals

arXiv

引用

arXiv 2022年

作者： Kathpalia, Aditi Nagaraj, Nithin Department of Complex Systems Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic Consciousness Studies Programme National Institute of Advanced Studies Bengaluru India

Compressed sensing is a scheme that allows for sparse signals to be acquired, transmitted and stored using far fewer measurements than done by conventional means employing Nyquist sampling theorem. Since many naturally occurring signals are sparse (in some domain), compressed sensing has rapidly seen popularity in a number of applied physics and engineering applications, particularly in designing signal and image acquisition strategies, e.g., magnetic resonance imaging, quantum state tomography, scanning tunneling microscopy, analog to digital conversion technologies. Contemporaneously, causal inference has become an important tool for the analysis and understanding of processes and their interactions in many disciplines of science, especially those dealing with complex systems. Direct causal analysis for compressively sensed data is required to avoid the task of reconstructing the compressed data. Also, for some sparse signals, such as for sparse temporal data, it may be difficult to discover causal relations directly using available data-driven/ model-free causality estimation techniques. In this work, we provide a mathematical proof that structured compressed sensing matrices, specifically Circulant and Toeplitz, preserve causal relationships in the compressed signal domain, as measured by Granger Causality. We then verify this theorem on a number of bivariate and multivariate coupled sparse signal simulations which are compressed using these matrices. We also demonstrate a real world application of network causal connectivity estimation from sparse neural spike train recordings from rat prefrontal cortex. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Assisted Protein Structure Prediction: An AI Approach for Biochemical Insights

Machine Learning-Assisted Protein Structure Prediction: An A...

引用

International Conference on science Technology Engineering and Management (ICONSTEM)

作者： S. Radha Mahendran Akriti Dogra Dinesh Mendhe S. B G Tilak Babu Shriniket Dixit Surya Pratap Singh Department of Bioinformatics Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Oral & Maxillofacial Surgery Manav Rachna Dental College Faridabad Haryana Office of Research Computing Rutgers State University of New Jersey New Brunswick New Jersey Department of ECE Aditya Engineering College Surampalem School of Computer Science Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Multi-Disciplinary Research Unit UPUMS Etawah UP

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Recently, machine learning has changed the field of bioinformatics by making it possible to identify and understand protein structures in ways that were not possible before. This paper gives a thorough examination of a unique way for guessing protein structure. It uses complex machine learning algorithms to give you deep biological information. We use cutting-edge deep learning models along with traditional computer methods to improve the accuracy and usefulness of predicting protein structures. The suggested AI-assisted method uses a variety of protein structures and attention processes to find complex spatial connections and long-range interactions within protein sequences. It does this by using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and CNNs. Using machine learning to predict protein structures can be very useful, and this work shows how it can effectively connect computational biology and experimental research. Our method not only makes predictive modeling better, but it also gives researchers a useful way to learn more about the complex links between protein structure and function as the field grows. The study's results could speed up the process of finding new drugs and help us learn more about how different diseases work at the molecular level.

关键词： Proteins Drugs Deep learning Recurrent neural networks Machine learning algorithms Computational modeling Predictive models

来源：评论

学校读者我要写书评

暂无评论

Distributed optimization for IoT attack detection using federated learning and Siberian Tiger optimizer

引用

ICT Express 2025年第3期11卷 542-546页

作者： Gupta, Brij B. Gaurav, Akshat Alhalabi, Wadee Arya, Varsha Alharbi, Eman Chui, Kwok Tai Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India School of Cybersecurity Korea University Seoul South Korea Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul South Korea Ronin Institute Montclair NJ United States International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taichung 413 Taiwan Department of Computer Science Immersive Virtual Reality Research Group King Abdulaziz University Jeddah Saudi Arabia Hong Kong Metropolitan University Hong Kong SAR China Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The rapid growth of IoT devices has heightened the risk of botnet attacks, calling for scalable and distributed detection solutions. In this context, this study proposes a distributed optimization system for IoT attack detection using CNN model utilizing federated learning. After optimizing the hyperparameters of the model at the server, the Siberian Tiger Optimization (STO) method distributes these values to clients for dispersed training. Our model achieves accuracy, recall, and precision of 0.89978, 0.94355, and 0.94455, respectively, using the N-BaIoT dataset. These findings show, in spite of latency issues, the efficiency of federated learning in distributed IoT security systems. © 2025

关键词： CNN model Distributed optimization Federated learning IoT attack detection Siberian Tiger Optimization (STO)

来源：评论

学校读者我要写书评

暂无评论

Blockchain Innovations in Education: Empowering Students with Smart Contracts

Blockchain Innovations in Education: Empowering Students wit...

引用

Disruptive Technologies (ICDT), International Conference on

作者： M. Sheerin Banu A. Sevuga Pandian Bindu Madhavi P. Sasikumar Vishal KS L Sherin Beevi Department of Information Technology R.M.K. Engineering College Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru Karnataka India KL Business School Koneru Lakshmaiah Education Foundation (Deemed to be University) Andhra Pradesh India School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Commerce School of Commerce VET Institute of Arts and Science Erode Tamilnadu India Department of Computer Science and Engineering R.M.D Engineering College

A lot of people are interested in how blockchain technology can be used in education because it has the potential to change everything. The main point of this study is to investigate how the Internet of Things (IoT), smart contracts, and blockchain technology can be used to give students more power in the classroom. Traditional school systems have a lot of problems when it comes to being open, safe, and effective. When old-fashioned ways of keeping records and managing data are used, wrong information, fraud, and not being able to access data are possible outcomes. In the past, though, studies have been done on separate parts of these technologies. This work suggests a complete plan to fix current issues in the education field that makes use of how blockchain technology, smart contracts, and the IoT can work together. The study uses a mixed-methods approach, which means that it uses both qualitative and quantitative evaluations. The results show that there is an effect that is good for student empowerment because it makes it easier for students to get to learning materials and generally lowers the amount of work that needs to be done.

关键词：

来源：评论

学校读者我要写书评

暂无评论

E-Learning in Data Analytics on Basis of Rule Mining Prediction in DM Environment 2

E-Learning in Data Analytics on Basis of Rule Mining Predict...

引用

2nd International Conference on Physics and Applied sciences, ICPAS 2021

作者： Nagarathinam, T. Elangovan, V.R. Obaid, Ahmed J. Akila, D. Tuyen, Dang Quang Department of Computer Science Mass College of Arts and Sceince Kumbakonam India Department of Computer Applications Agurchand Manmull Jain College Meenambakkam Chennai India Faculty of Computer Science and Mathematics University of Kufa Iraq Department of Information Technology Vels Institute of Science Technology and Advanced Studies Chennai India Viet Nam

Nowadays, most of the information science inquires about a spotlight on affiliation rule to decide explicit examples and rules from huge information. Affiliation guideline is worked by basic information duration device, for example, WEKA which incorporates arrangement, bunching, affiliation runs, etc. When all is said in done, the affiliation standard could be pertinent for enormous E-Learning datasets like they were pertinent towards the formless arrangement. Within this exam, the uses for affiliating rule after gathering under straight information for a considerable length of time from LMS have been explored. Intriguing quality measurements and other applicable understudy inclinations are caught in the wake of thinking about a few affiliation rule calculations. Some imagined introductions of rules and their applicable outcomes will be exhibited in wording reasonableness in eLearning conditions. © Published under licence by IOP Publishing Ltd.

关键词： E-learning

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：