检索结果-内蒙古大学图书馆

FOG COMPUTING ARCHITECTURE FOR INTELLIGENT MOBILITY systems: PROSPECTS AND OBSTACLES IN AN IOT FUTURE

FOG COMPUTING ARCHITECTURE FOR INTELLIGENT MOBILITY SYSTEMS:...

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Sarishma Mishra, Vinay Kumar Karthigaikumar, P. Malik, Suraj Vigenesh, M. Marathe, Vijay Department of Computer Science & Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India CCSIT TMU India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore641032 India School of Engineering and Technology IIMT University Meerut India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore641021 India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350355338

In order to improve traffic management, protect our natural ecosystem, and boost safe driving, the automated mass transit idea was developed. Approach for this study have become more document, and the 5Vs of Big Data are used to characterize their data. Big data analytics must therefore be used if such data are to be completely utilized. The cloud storage facilities where data is handled are connected to the ITS equipment via the Internet of Vehicles (IoV). Huge data transfers from regionally dispersed products, notwithstanding, cause limited bandwidth, delays, and power usage. Additionally, using an unified method to handle big data produces high speed that lag time apps could perhaps accept. For real-time large data processing, fog computing is thought to be a potential tool. Fog software mainly evaluated cloud - based applications by distributing business applications at the program's periphery, which offers quicker answers to utility inquiries and conserves network services. Regrettably, in the changing world, it is difficult to apply cloud services and the logic design for real-time large data computation. This article suggests an innovative design for real-time big data analytics in the world. Three dimensions-intelligent intelligence, real-time big data analytics, and dimension-are combined in the suggested design. The ecosystem, big data features, the functional design for authentic big data analytics, and the several clever information technology platforms are also covered in detail in this article. More significantly, this article examines the advantages and disadvantages of assessment big data analytics and cloud technology into the surroundings. The important points and upcoming study path’s part addresses some topics that need to be taken into account for the suggested design to be implemented effectively. © 2024 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Deep Learning Model for Crop Yield Prediction 2

An Adaptive Deep Learning Model for Crop Yield Prediction

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Gadupudi, Ashwin Rani, Rudra Yamini Jayaram, B. Sharma, Nipun Deshmukh, Jotiram Krishna Reddy, Pundru Chandra Shaker California State University Computer Science Fresno United States Vnr Vignana Jyothi Institute of Engineering and Technology Information Technology Hyderabad India Rmk Engineering College Computer Science and Business System Tamil Nadu Kavaraipettai India Presidency University Electronics and Communication Engineering Karnataka Bangalore India Bharati Vidyapeeth College of Engineering Instrumentation Engineering Navi Mumbai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350387933

Agriculture is the study and practice of raising plants and animals for human consumption. Approximately 60.45% of India's land is devoted to farming, making it the country's second largest economic sector. Agriculture and the related agro-industrial sector are crucial to India's economy. Soil components (such as nitrogen-phosphorus &potassium), crop rotation, soil-moisture, air and soil-temperatures, rainfall, and other environmental factors all play important roles in successful farming. One piece of current evidence in this area is a technique that utilizes machine-learning (ML) strategies like Random-Forest (RF), Decision-Tree (DT), and ANN to pick the best crop. In this study, we employ deep learning approaches to improve the suggested model, and together with crop prediction, we obtain clear information regarding the quantities of soil attributes requisite, along with their individual costs. It's more precise than the current model. By analyzing the data provided, it aids farmers in making informed business decisions. Predicting a proper yield requires thinking about the land's climate and soil. The goal is to introduce a Python-based system that makes use of strategic thinking to anticipate the most productive harvest under specific conditions while keeping costs to a minimum. While LSTM &RNN are employed as deep-learning((DL)-strategies in this work, SVM is used as the ML-strategy. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

引用

2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Save the Bruised Striver: A Reliable Live Patching Framework for Protecting Real-World PLCs 24

Save the Bruised Striver: A Reliable Live Patching Framework...

引用

19th European Conference on computer systems, EuroSys 2024

作者： Zhou, Ming Wang, Haining Li, Ke Zhu, Hongsong Sun, Limin School of Cyber Science and Engineering Nanjing University of Science and Technology China Department of Electrical and Computer Engineering Virginia Tech China Nari Group Corporation State Grid Electric Power Research Institute School of Cyber Security University of Chinese Academy of Sciences China Institute of Information Engineering Cas China Virginia Tech. United States

ISBN: (纸本)9798400704376

Industrial Control systems (ICS), particularly programmable logic controllers (PLCs) responsible for managing underlying physical infrastructures, often operate for extended periods without interruption. Thus, it is challenging to patch security vulnerabilities of ICS in a timely manner after disclosure because it often necessitates waiting for a rare downtime window. While live patching has been introduced to avoid downtime and maintenance costs, conventional live patching methods are not viable for closed-source PLCs. Without the source code, it is difficult to understand the system behaviors and determine binary patch equivalence. To address these challenges, we present a Reliable Live Patching framework called RLPatch for applying live patches to third-party binary without source code. We design RLPatch to capture real-time conditions and dynamic behaviors of PLCs, which enables DevOps engineers to identify major non-recoverable fault (MNRF) vulnerabilities and generate hot patches. The core of RLPatch is an update agent that inserts breakpoints over the original MNRF code and then directs execution to the patches. To ensure system reliability, we use the unique constraints of PLCs to integrate the update processes with the scan cycle. We leverage RLPatch to patch 20 real vulnerabilities in three widely used Rockwell PLCs. We evaluate RLPatch in a real-world gas pipeline, demonstrating its reliability and effectiveness in practice. © 2024 Owner/Author.

关键词： Programmable logic controllers

来源：评论

学校读者我要写书评

暂无评论

Advancing Brain-computer Interaction: EEG-based Eye Movement Recognition with AI 7

Advancing Brain-Computer Interaction: EEG-based Eye Movement...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Saidala, Ravi Kumar Ramnath, M. Komala, C.R. Vidhya, N.G. Taqui, Syed Noeman Rajendiran, M. Alliance University Department of Computer Science And Engineering Karnataka Bangalore560076 India Ramco Institute of Technology Department of Artificial Intelligence And Data Science Tamil Nadu Rajapalayam626117 India Hkbk College of Engineering Department of Information Science And Engineering Bengaluru560 045 India St. Joseph's Institute of Technology Omr Department of Information Technology Tamil Nadu Chennai600119 India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350359299

The integration of Electroencephalography (EEG) and Brain-computer Interface (BCI) technologies is causing an evolution in healthcare, accessibility, and neuroscience. This multidisciplinary method offers a non-invasive means to communicate and control through intentional eye movements, which is particularly promising for patients suffering from neurological illnesses. In this study, eye movements are identified using EEG data from the EPOC Flex wireless EEG brain device. Using advanced Deep Learning models such as the Deep Belief Network (DBN) and the Deep Residual Network (Deep ResNet), and attempted to distinguish four distinct eye movements: open, close, right, and left. Some of the primary metrics utilized for assessing these models were accuracy, precision, recall, and F1 score. The Deep ResNet model gives better results with an accuracy of 96.25%, recall of 95.85%, precision of 96.66%, and an F1 score of 96.24%. The findings suggest that BCI frameworks can leverage EEG-based eye movement detection to improve rehabilitation procedures and make computers and devices easier to operate. This study's findings open a path for future research into neurotechnological applications and human-computer interaction. © 2024 IEEE.

关键词： Eye movements

来源：评论

学校读者我要写书评

暂无评论

Advanced Machine Learning Techniques for Enhancing Data Security in Cloud Computing systems 5

Advanced Machine Learning Techniques for Enhancing Data Secu...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Dhinakaran, M. Sundhari, M. Ambika, S. Balaji, V. Rajasekaran, R.Thalapathi Galgotias College of Engineering & Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India E. G. S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

In the age of cloud computing, data stored and processed in the cloud must be secure. This study examines how powerful machine learning can secure cloud computing data. Three trials assessed different machine learning models in this setting. Experiment 1 used a Random Forest model and achieved 95% accuracy, 0.92 precision, 0.96 recall, and 0.94 F1 Score. This shows that the model can accurately categorize security threats with a good balance of true and false positives. A Deep Neural Network (DNN) improved accuracy to 97% in Experiment 2. Precision, recall, and F1 Score values of 0.94, 0.98, and 0.96 demonstrate the DNN's ability to discriminate threats from normal activity. The model captures complicated patterns well, making it a powerful cloud security tool. Security analysis using reinforcement learning, specifically Q-learning, was introduced in Experiment 3. The model's 88% detection rate showed its capacity to identify threats, but its 0.05 false positive rate created a tradeoff between true and false positives. With the 0.12 false negative rate, one can infer improvements in threat detection accuracy. These results indicate that state-of-the-art machine learning is capable of protecting cloud data. The Random Forest and Deep Neural Network models have very high accuracy balanced with reasonable precision-recall trade-offs, whereas reinforcement learning through Qlearning shows promise but needs modification to improve the model's performance in terms of both accuracy and false positive rates. While rising threats need constant adaptation and learning, the model should also fulfill security needs. This study helps formulate a secure cloud computing infrastructure that can stand threats of change. © 2024 IEEE.

关键词： Cloud Computing Data Security Machine learning Q-Learning

来源：评论

学校读者我要写书评

暂无评论

Protecting Vulnerable Road users using IoT-CNN for Safety Measures 5

Protecting Vulnerable Road users using IoT-CNN for Safety Me...

引用

5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Karpagalakshmi, R.C. Sampoornam, M. Maria Vinya, Viyyapu Lokeshwari Monikapreethi, S.K. Yuvaraj, S. Srinivasan, C. Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Ranga Reddy India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379945

The increasing number of road accidents involving Vulnerable Road Users (VRUs) highlights the urgent need for innovative safety solutions. This study proposes a novel approach that leverages IoT and Convolutional Neural Network (CNN) technologies to enhance VRU safety. By deploying IoT sensors on vehicles and infrastructure, the proposed model can accurately track VRU locations and environmental conditions in real-time. A CNN-based model is developed to robustly detect and classify VRUs under various traffic scenarios, with continuous improvement through learning. The integration of IoT and CNN enables real-time communication and timely warnings, facilitating proactive interventions to prevent accidents. Experimental evaluations demonstrate the effectiveness of the proposed system in improving VRU safety. This research offers a promising solution for reducing road accidents and creating safer urban environments. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

On a New Rough Clustering Based on Optimization 13

On a New Rough Clustering Based on Optimization

引用

Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Furuta, Sakuya Endo, Yasunori Master's Program in Risk and Resilience Engineering Degree Programs in Systems and Information Engineering Graduate School of Science Annd Technology University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan Institute of Systems and Information Engineering University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan

ISBN: (纸本)9798350373332

The purpose of this study is to develop and investigate a new rough clustering based on optimization. We propose a restructured algorithm using only one variable, instead of the algorithm described using two variables in existing methods. We also perform numerical examples using artificial data and the Iris and Wine datasets. This research has the potential to be used as a method for determining false negatives and false positives in cancer diagnosis. © 2024 IEEE.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Prediction Using Artificial Intelligence technology

Breast Cancer Prediction Using Artificial Intelligence Techn...

引用

Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： V. Akila J. Anita Christaline Dept. of Electronics and Communication Engineering SRM Institute of Science and Technology Vadapalani Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The aim of the project is to compare the performance of four different machine learning algorithms for breast cancer prediction such as decision tree, logistic regression, XG boost, and CAT boost. We used a dataset of patient medical records containing various clinical factors to train and test the algorithms. Accurate diagnosis and early detection are essential for enhancing patient outcomes. Data mining is a prominent tool in the healthcare industry for processing massive amounts of data. To examine massive amounts of complicated medical data, researchers use a variety of data mining and machine learning approaches. The use of these strategies can help medical practitioners forecast the onset of breast cancer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SSpose: Self-Supervised Spatial-Aware Model for Human Pose Estimation

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5403-5417页

作者： Yu, Linfang Qin, Zhen Xu, Liqun Qin, Zhiguang Choo, Kim-Kwang Raymond University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Industrial Research Institute Chengdu610213 China University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering Chengdu610054 China Industrial Research Institute Chengdu610213 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human pose estimation (HPE) relies on the anatomical relationships among different body parts to locate keypoints. Despite the significant progress achieved by convolutional neural networks (CNN)-based models in HPE, they typically fail to explicitly learn the global dependencies among various body parts. To overcome this limitation, we propose a spatial-aware HPE model called SSpose that explicitly captures the spatial dependencies between specific key points and different locations in an image. The proposed SSpose model adopts a hybrid CNN-Transformer encoder to simultaneously capture local features and global dependencies. To better preserve image details, a multiscale fusion module is introduced to integrate coarse- and fine-grained image information. By establishing a connection with the activation maximization (AM) principle, the final attention layer of the Transformer aggregates contributions (i.e., attention scores) from all image positions and forms the maximum position in the heatmap, thereby achieving keypoint localization in the head structure. Additionally, to address the issue of visible information leakage in convolutional reconstruction, we have devised a self-supervised training framework for the SSpose model. This framework incorporates mask autoencoder (MAE) technology into SSpose models by utilizing masked convolution and hierarchical masking strategy, thereby facilitating efficient self-supervised learning. Extensive experiments demonstrate that SSpose performs exceptionally well in the pose estimation task. On the COCO val set, it achieves an AP and AR of 77.3% and 82.1%, respectively, while on the COCO test-dev set, the AP and AR are 76.4% and 81.5%. Moreover, the model exhibits strong generalization capabilities on MPII. © 2024 IEEE.

关键词： Supervised 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：