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检索条件"机构=Department of Cyber Security and Computer Engineering Technology"

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Application of Machine Learning Techniques for Heavy Rainfall Prediction using Satellite Data 5

Application of Machine Learning Techniques for Heavy Rainfal...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Balram, Gujjari Poornachandrarao, N. Ganesh, D. Nagesh, Banavath Basi, Reddy.A Kumar, M.Sunil Anurag University Ghatkesar Rd Venkatapur Telangana Hyderabad500088 India Geethanjali College of Engineering and Technology Hyderabad Telangana Cheeryal India Andhra Pradesh Tirupati India Marri Laxman Reddy Institute of Technology and Management Department of Cyber Security Hyderabad Telangana Dundigal500043 India

ISBN: (纸本)9798331504403

This study presents a large-scale lunar dataset with the goal of utilizing machine learning techniques to forecast periods of intense rain. The dataset includes features from the lunar surface such as slope, height and depth of craters, types of boulders, patterns of distribution, and the presence of shadows. The study uses well-known machine learning techniques Random Forest, AdaBoost, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), with an emphasis on the Heavy Rain Prediction target variable, which indicates the chance of heavy rain events (Yes/No). In order to provide insights into efficient heavy rain event prediction on the moon's surface, the algorithms are trained and assessed to identify patterns and linkages within the lunar dataset. The results further lunar exploration plans and provide useful recommendations for planning lunar missions and safety precautions against potentially unfavorable weather circumstances. © 2024 IEEE.

关键词： Rain

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Detection of Abnormal Crowd Behavior Based on Graph Convolutional Neural Network 12

Detection of Abnormal Crowd Behavior Based on Graph Convolut...

引用

12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Zhou, Xiang Xiao, Ruliang Fuzhou Institute of Technology Department of Computing and Information Science Fujian Fuzhou China Fujian Normal University College of Computer and Cyber Security Fujian Fuzhou China

ISBN: (纸本)9798350310153

With the increasing demand for public safety precautions, the accuracy and timeliness of abnormal crowd behavior detection is gradually improving. Therefore, with the development of the technology for the abnormal crowd behavior, it's even more demanding on how to effectively prevent the occurrence of accidents and eliminate the impact of disasters on the society. This article first introduces the current applications of abnormal behavior detection and the research and development of related technologies. Second, it proposes a method for detecting abnormal crowd behavior based on Graph Convolutional Neural Network (GCN). This method first proposes to effectively and accurately locate the abnormal behavior characteristics through the extraction of abnormal behavior characteristics from the auto-encoder of the surveillance video. With the construction of the graph classification model based on SAGPool, the classification of normal and abnormal behavior is carried out. Finally, experimental data show that data displayed with this method meet the expected requirements. © 2022 IEEE.

关键词： Convolutional neural networks

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An Extensive Analysis of cybersecurity in the Era of Advanced Threats 2

An Extensive Analysis of Cybersecurity in the Era of Advance...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Krishnaveni, S. Geetha, B.T. Rashmitha Duvvada, Sree Sathe, Manoj Amrita Soni, Mukesh Adithya Institute of Technology Department of Management Studies Kovilpalayam Coimbatore India Saveetha School of Engineering Simats Department of Ece Tamil Nadu India University of Wisconsin Madison Department of Cse United States Computer Science Pes Modern Institute of Business Studies Pune India Sharda School of Engineering & Technology Sharda University Center for Cyber Security and Cryptology Computer Science & Engineering Uttar Pradesh Greater Noida India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune India

ISBN: (纸本)9798350384277

The scientific and business communities have taken an interest in the Internet of Things (IoT) because of its tremendous impact on human life. The IoTs have emerged as a state-of-the-art framework for creating intelligent spaces. IoT cybersecurity has gained much attention lately due to multiple cyber threats that revealed serious cybersecurity threats. Traditional techniques for safeguarding networks are widely recognized. However, typical security methods are ineffective in protecting IoT networks and systems from cyber threats due to the resource-constrained nature of IoT devices and the unique features of IoT regulations. IoT-specific databases, instruments, and techniques are required to raise the bar for IoT cybersecurity. Thus, safeguarding the IoT environment against cyber threats becomes crucial. The goal of this investigation is to extensively examine the era of advanced threats. It aids in the automation of procedures for transferring real-time crucial data between gadgets without the need for human involvement. It presents a statistical structure for protecting entities and networking traffic associated with IoT wireless connectivity. When a networking session is formed, it comprises a computerized cyber-defense authentication process that identifies and blocks security assaults. A comparison of the performance of machine learning that has been shown useful is applied to protect the transport Layer (TL) of IoT devices. As an outcome, models generate precise conclusions that can be used to predict and discover risks in IoT-based platforms. © 2024 IEEE.

关键词： cyber attacks

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GRDNet: A Lightweight Railway Foreign Object Intrusion Detection Algorithm 7

GRDNet: A Lightweight Railway Foreign Object Intrusion Detec...

引用

7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Wang, Zhaonan Wei, Lin Meng, Caixia Shi, Lei Gao, Yufei Wang, Qingxian School Of Cyber Science And Engineering Zhengzhou University Zhengzhou China School Of Computer And Artificial Intelligence Zhengzhou University Zhengzhou China Zhengzhou Police College Department Of Image And Network Investigation Technology Zhengzhou China

ISBN: (纸本)9798350361445

Railway intrusion detection using convolutional neural networks has significant results, but it requires a large amount of data and arithmetic power to train and deploy neural network models, and it is still a challenge to improve detection accuracy under limited computational resources. Based on this problem, this paper proposes a lightweight railway foreign object intrusion detection algorithm: GRDNet, which firstly adopts a lightweight backbone network GhostNetV2, and makes use of the decoupled fully connected attention mechanism to aggregate local and long-distance information, which significantly reduces the number of parameters and computation while maintaining the detection performance. Next, a deep feature extractor, RSPPFCSPC, is proposed to reduce the computational effort by decreasing the number of channels in the feature map while maintaining the diversity and effectiveness of the features. Finally, a knowledge distillation algorithm is used to improve the accuracy of foreign object localization and classification and reduce the leakage rate of the model. Experiments show that the proposed method is superior to the state-of-the-art work of each comparison. © 2024 IEEE.

关键词： Object detection

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Industry 4.0 Technologies for Smart Manufacturing: A Systematic Review of Machine Learning Methods for Predictive Maintenance

Industry 4.0 Technologies for Smart Manufacturing: A Systema...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Sharma, Madhurima Sharma, Manu Sharma, Mohnish Yadav, Kuldeep Singh Shukla, Shailja Rabindranath Tagore University Department of Computer Science Engineering Bhopal India Rajiv Gandhi Proudyogiki Vishwavidyalaya Department of Cyber Forensics Bhopal India Indian Institute of Information Technology Department of Mechanical Engineering Jabalpur India Indian Institute of Technology Msp Lab Department of Electrical Engineering Delhi India

ISBN: (纸本)9798350300857

The automotive sector, which has a significant impact on economic and social growth, has been a driving force behind the development of Industry 4.0 and its associated technologies such as Smart Production, Smart Manufacturing, and the Internet of Things (IoT). One important area of focus within Industry 4.0 is the use of machine learning (ML) algorithms and predictive maintenance (PdM) methods to optimize manufacturing tool performance. With the advent of digital convergence and communication networks, it is now possible to collect vast amounts of process and performance data from different parts of manufacturing tools and use this data for diagnostic and automatic fault detection to reduce downtime and increase component utilization rates. This work provides a brief review of current developments in machine learning methods that are broadly useful for PdM in smart manufacturing within the context of Industry 4.0. The study categorizes research based on ML algorithms and focuses on the future prediction of temperature using time series and multivariate analysis techniques. By highlighting the state-of-the-art advances in ML and PdM for smart manufacturing, this paper aims to contribute to the ongoing effort to enhance manufacturing processes and improve competitiveness in the industry. © 2023 IEEE.

关键词： Machine learning

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Combined Effort Estimation Framework for Heterogeneous Projects Using Machine Learning: A Comparative Analysis

Combined Effort Estimation Framework for Heterogeneous Proje...

引用

Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Amrita Sharma Neha Chaudhary Anand Sharma Department of Computer Science Indus University Ahmedabad India Computer Science & Engineering Manipal University Jaipur India Cyber Resilience Eventus Security Ahmedabad India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

The essential role of software development effort estimation is to predict the required work to develop software. The three main types of cost estimation approaches are algorithmic models, non-algorithmic models, and machine learning models. Algorithmic models use statistical or analytical formulae to estimate. Machine learning approaches are used with algorithmic or non-algorithmic models to increase accuracy. Other techniques include regression analysis, fuzzy logic, artificial neural networks, and others soft computing, meta-heuristic algorithms, and search-based algorithms. This paper present a combined framework developed for the effort estimation for diversifying projects. The combined framework uses the databases that include the projects from procedural, agile, and object-oriented approaches. A comparison is presented for the different projects using the single model as well as with the combined models. The combined models are developed for the projects with single feature and multiple features that are selected with the correlation.

关键词： Machine learning algorithms Costs Accuracy Object oriented modeling Computational modeling Software algorithms Estimation Machine learning Prediction algorithms Software

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Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach

Breast Cancer Detection using Domain-Adversarial Training (D...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： Akash K V Esther Daniel S. Seetha S. Durga Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Information Science & Engineering CMR Institute of Technology India TIFAC CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages.

关键词： Training Accuracy Risk minimization Neural networks Transfer learning Medical services Breast cancer Data models Reliability Tumors

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The Future of Recruitment: Using Deep Learning to Build Intelligent Interview Bots

The Future of Recruitment: Using Deep Learning to Build Inte...

引用

International Conference on Computing and Networking technology (ICCNT)

作者： Shahnawaz Ahmad Shahadat Hussain Mohammed Wasid Ogala Justin Onyarin Mohd Arif Javed Ahmad School of Computer Science Engineering and Technology Bennett University Greater Noida India Cyber Security Department University of Delta PMB Agbor Delta State Nigeria Department of Computer Science Engineering and Technology Galgotias University Greater Noida India Department of Computer Science Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The hiring process for IT positions is often complex and time-consuming, posing significant challenges for businesses seeking to maintain consistency and efficiency. Interview scheduling is complicated by manual effort and scheduling difficulties, which have been further made worse by the COVID-19 pandemic, making in-person interviews difficult. To address these challenges, this study proposes a Natural Language Processing (NLP)-based Machine Learning (ML) solution that automates interview scheduling and communication in natural language. To develop the solution, two deep-learning NLP models served as the foundation of the system, and AWS serverless stack and Amazon SageMaker were used for training and inference. The solution is designed to be scalable and cost-effective, and new models can be easily added and trained on unique subjects. Furthermore, the Amazon Sumerian scene is customizable to meet branding requirements. This research offers significant implications, as the proposed ML solution can help businesses streamline their hiring process, reduce human error, and improve the accuracy of candidate assessments. The scalability and cost-effectiveness of the solution make it ideal for businesses of all sizes. The smart interview bot developed in this study can conduct live-like interviews that mimic human interactions, making the hiring process more engaging and personalized. Overall, this paper demonstrates the potential of NLP to develop a smart solution for an everyday business problem. The intelligent live-like interview process and smart interview bot developed in this research have the potential to revolutionize the hiring process by automating scheduling and reducing manual effort. By leveraging the proposed ML solution, businesses can improve the accuracy and consistency of their hiring practices, ultimately leading to more successful outcomes.

关键词： Deep learning Training Ethics Accuracy Scalability Manuals Chatbots Interviews Business Recruitment

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Unveiling the Nexus: Exploring the Societal Implications of AI-Driven cybersecurity and Data Analysis

Unveiling the Nexus: Exploring the Societal Implications of ...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Mohd Arif Shahnawaz Ahmad Mohd Nazim Javed Ahmad Ogala Justin Onyarin Department of Computer Science Engineering and Technology Galgotias University Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science Noida Institute of Engineering and Technology Greater Noida India Cyber Security Department University of Delta PMB Agbor Delta State Nigeria

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

AI when integrated with cybersecurity and data analysis forms a new field that will have great impact on the society. The present research focuses on the social consequences of integrating AI for cybersecurity and big data analytics, thus filling the gap in the existing literature. Using both qualitative and quantitative points of view, quantitative analysis and qualitative examination, the research offers an all-embracing understanding of this convergence. It aims to review and analyze the history, present usage, future prognosis of AI related cybersecurity and data analysis, including the reflection of ethical issues, privacy aspects and relevant laws. The lessons learnt are discussed based on case studies, best practices analysis, and the study provides recommendations for the effective use of AI-based solutions throughout their life cycle. The analysis broadens the knowledge on the effects of AI technology for cybersecurity and data analytics, and helps researchers, policymakers, and industry stakeholders apply the new knowledge.

关键词： Industries Data analysis Statistical analysis Reviews Market research Reflection Stakeholders computer security Artificial intelligence Prognostics and health management

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Quantum K-Means Model based on Optimization Framework

Quantum K-Means Model based on Optimization Framework

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Shao, Zexiang Zhang, Shibin Kumar, Sachin School of Cyberspace Security Chengdu University of Information Technology Chengdu610225 China Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu610225 China Ajay Kumar Garg Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9781665457033

The Quantum K-means clustering algorithm offers the advantage of quantum parallel computing, but suffers from issues related to cluster center initialization and sensitivity to noisy data due to its similarity with the K-means clustering algorithm. To address these challenges, we propose a quantum K-means model based on an optimization framework. Specifi-cally, we use a quantum genetic algorithm with pin optimization to adjust the fitness function for the Quantum K-means clustering algorithm. The optimally adapted quantum genetic algorithm reduces the sensitivity of cluster center initialization and improves the noise robustness of the Quantum K-means clus-tering algorithm using a noise reduction self-encoder. As a result, the proposed model achieves better clustering performance even in the presence of noise. Experimental results demonstrate that our proposed quantum K-means model has fewer clustering iterations, lower probability of falling into local optima, and improved noise robustness after optimizing the clustering cen-ters. The model's effectiveness in clustering is also demon-strated experimentally. © 2023 IEEE.

关键词： Noise abatement

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