检索结果-内蒙古大学图书馆

11th IEEE International Conference on Software Defined Systems, SDS 2024

作者： Jain, Alok William, P. Ayasrah, Firas Tayseer Lakshmi, G. Prasanna Diwan, Tarun Dhar Ca United States Amity University Sanjivani College of Engineering Deaprtment of Information Technology Dubai United Arab Emirates Al Ain University College of Education Humanities and Science Al Ain United Arab Emirates Sandip University School of Computer Science and Engineering Maharashtra Nashik India Atal Bihari Vajpayee University Bilaspur India

ISBN: (纸本)9798331518325

Independent code generation models produced by generative AI provide a new way to software development. These models automatically generate code using machine learning based on input samples. This study examines the fundamentals, applications, problems, and future prospects of AI-related automated code generation technologies. Model-based software, domain-specific code, and testing procedures are examples of these research topics. Performance analysis and assessment are used to assess the efficacy, efficiency, and reliability of several automated code generating methods. The fact that these models have pros and cons and room for development is highlighted. © 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

Optimal power management and robust control scheme for AC–DC microgrid involving renewable and conventional sources

引用

Electrical engineering 2025年 1-17页

作者： Buduma, Parusharamulu Pal, Amrindra Kumba, Krishna Gundu, Venkateswarlu K., Subbaramaiah Department of Electrical and Electronics Engineering Lendi Institute of Engineering and Technology Vizianagaram Visakhapatnam India Department of Electronics & Communication Engineering Amity University Lucknow India School of Electrical Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

Hybrid AC–DC microgrid systems have recently emerged as a promising method for connecting AC loads with AC microgrid (ACM) and DC loads with DC microgrid (DCM). The bidirectional power converter (BPC) allows the AC–DC microgrid to distribute power between the ACM and DCM. Effective supervisory power sharing and robust control methods are needed to share power among the AC–DC microgrid sources and ensure seamless operation during different uncertain conditions. Also, suitable islanding and grid synchronization techniques are needed to connect and disconnect the microgrid with the utility grid seamlessly. In this study, an optimal power management strategy is considered for the sharing of power among the sources. To implement the optimal power management, a robust optimization problem for a restricted data set is used. The goal of optimization is to distribute power while maximizing the use of renewable energy, minimizing the use of conventional energy, and using minimal energy transfer through the BPC. To achieve robust control commitment during the parameter uncertainty, a nested loop robust control scheme is considered. To achieve the islanding detection and grid synchronization, Synchro-Extraction Transform and Optimal Linear Kalman Filter-based methods are proposed, respectively. Considering different operational conditions and uncertainty scenarios, the performance and stability robustness of the proposed AC–DC microgrid system are analysed in MATLAB/SIMULINK.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Dynamics of Digital Economics: A Comprehensive Exploration 6

Unveiling the Dynamics of Digital Economics: A Comprehensive...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Singla, Nihira Rana, Ajay Verma, Tanvi Singh, Deepankar Independent Researcher Punjab Ludhiana India Amity School of Engineering and Technology Department of Computer Science Amity University Uttar Pradesh Greater Noida India Chitkara Business School Chitkara University Punjab India Government PG College Department of Commerce Etah Uttar Pradesh Jalesar India

ISBN: (纸本)9798350304480

The rapid spread of digital technologies has led to a paradigm shift in financial activity, commonly referred to as digital finance This paper presents a comprehensive analysis of the development of digital finance, with various aspects such as digital platforms, cryptocurrencies, e-commerce, and data-driven decisions. Let's delve into the transformative impact of technology, examine the challenges and opportunities it presents, and propose possible frameworks for understanding and organizing this evolving landscape. Through interdisciplinary research, we seek to provide a nuanced understanding of the complex interactions between technology, finance and society in the digital age. © 2023 IEEE.

关键词： cryptocurrencies digital age digital technologies e-commerce

来源：评论

学校读者我要写书评

暂无评论

Advanced Machine Learning Approach for Cyber Attack Detection in Cloud Computing

Advanced Machine Learning Approach for Cyber Attack Detectio...

引用

Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Shashi Bhushan Singh Yadav Nitin Arvind Shelke Jagendra Singh Yasmeen Deependra Pandey Himanshi Department Computer Science & Engineering BBD Northern India Institute of Technology Lucknow India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CSE-DS CVR College of Engineering Hyderabad India Department of ECE Amity School of Engineering and Technology Amity University Lucknow India Department of Information Technology Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Cloud computing has become the most important part of modern IT infrastructure, which is scalable and flexible. However, with such wide-scale adoption, new vulnerabilities have been introduced into the system, making it a favorite target for cyber-attacks, especially Distributed Denial-of-Service (DDoS) and Man-in-the-Cloud (MitC) attacks. Security measures in cloud environments are more than ever required to be effective. This study introduces a state-of-the-art approach using machine learning techniques to identify and prevent such attacks; the four machine learning algorithms that have been used here are Support Vector Machine, Artificial Neural Network, Recurrent Neural Network, and k-Nearest Neighbors. A dataset of 3,200 simulated records has been used for the purpose of training and testing; 70% of data were used for training purposes, and 30% of data were used for testing purposes. The models were rated based on precision, running time, and memory usage. The results highlighted that SVM was the strongest model and had an accuracy of 97.86%, while ANN was about 93.4% followed by RNN which was about 89.45% and KNN at 86.7%. This would mean that among the above models, the SVM is the most efficient with respect to real-time attack detection as it is in high precision and has efficiency in resource use. The proposed approach can be very well applied to real-time cloud security systems, offering a robust solution in identifying and mitigating cyber threats in dynamic cloud environments, ensuring enhanced protection against evolving attack vectors.

关键词： Support vector machines Cloud computing Accuracy Recurrent neural networks Computational modeling Machine learning Artificial neural networks Nearest neighbor methods Real-time systems Cyberattack

来源：评论

学校读者我要写书评

暂无评论

Sentimental Technique Implementation on Textual Data

Sentimental Technique Implementation on Textual Data

引用

International Conference on Contemporary Computing and Informatics (IC3I)

作者： Baggam Sai Karthik Dhavileswarapu NSSVV Durga Bharat Md Afroz Alam Bandari Tharun Sai Mrinalini Rana Smita Sharma Suman Avdhesh Yadav School of Computer Science and Engineering Lovely Professional University Kapurtha1a Punjab India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh India

The increasing amount of real-time or near-real-time data in the big data age provides organizations with opportunities to make data-driven decisions. However, social media data, such as that generated by Twitter, is typically unstructured and difficult to manage. This paper proposes an efficient text data pre-processing approach and develops an algorithm for training decision tree, random forest, SVM, and logistic regression classifiers on a dataset. The algorithm weights the sentiment score based on the hashtag and cleaned text weights, and the most efficient data pre-processing strategy is identified using a novel technology called the hash vectorizer. The effectiveness of the algorithm is tested against manually sorted sentiments and sentiments created before text data pre-processing, and the resultsshow its accuracy is comparable to human-annotated feelings. This research contributes to the existing body of knowledge by providing a precise method for cleaning social media data and a new approach to sentiment analysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Explainable Federated Learning Based Secure and Transparent Object Detection Model for Autonomous Vehicles

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Devarajan, Ganesh Gopal Thirunavukkarasan, M. Mahalinghum, Maheswari Arockiam, Daniel Wu, Mu-En Mahapatra, Rajendra Prasad Delhi NCR Campus SRM Institute of Science and Technology Department of Computer Science and Engineering Faculty of Engineering and Technology Uttar Pradesh Ghaziabad201204 India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore632014 India Sathyabama institute of science and technology School of computing Department of Computer science and engineering Tamil Nadu Chennai India CSE-ASET Amity University Madhya Pradesh India National Taipei University of Technology Taipei Taiwan

Intelligent Transportation Systems (ITS) and autonomous vehicles (AVs) are two fast-moving research fields. Understanding the surroundings and acting quickly is essential for AV control, especially in dynamic environments. Object detection algorithms must function properly under these demanding circumstances. Even though deep learning models are excellent at detecting objects, they are sometimes criticized for having unclear decision-making. Recently, explainable artificial intelligence (XAI) has improved these judgments' interpretability. This paper proposes a federated learning system that protects data privacy while enabling decentralized training across many AVs of an upgraded object detection algorithm, Yolov7-E. Grad-CAM visualization techniques are used to illustrate the deep learning models, and for easier interpretability, semantic data is fused with object detection. Enhanced security through federated learning, reducing data privacy risks by 40%. Based on comparative studies, it is demonstrated that the suggested model, which utilizes the federated learning paradigm, attains higher mAP and is appropriate for AV decision mechanisms while maintaining data privacy. © 1975-2011 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Utilizing Blockchain for Effective Urban Street Vendor Management 4

Utilizing Blockchain for Effective Urban Street Vendor Manag...

引用

4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Singh, Pushpa Dash, Snigdha Gupta, Aditya Kumar Singh, Narendra Yadav, Prashant Dev Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India School of Business Galgotias University Greater Noida India Amity International Business School Amity University Noida India Gl Bajaj Institute of Technology and Management Department of Management Studies Greater Noida India Noida India

ISBN: (纸本)9798350393248

Blockchain technology provides efficient, transparent and secure street vendor management. The paper aims to investigate the role of blockchain in street vendor management in the city. The study proposes to solve the problem at both ends for municipal corporations to manage street vendors and have a transparent system to address their issues. We suggest a model to improve the functioning of the municipal corporation and help the local administration. The paper focuses on a Blockchain-based Street Vendor Management (SVM) that automates the registration process and allot a street ID to the street vendor. SVM utilised the power of blockchain technology to offer a secure, efficient, immutable and transparent system. Ethereum-based smart contracts are used to build a standard nationwide registration process. The main players of SVM are street vendors, local administrators, and global administrators. Further, the proposed model can be integrated to perform transactions among customers, street vendors, local and global administrators. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Fog Computing Enabled Healthcare 4.0 3rd

Fog Computing Enabled Healthcare 4.0

引用

3rd International Conference on Recent Innovations in Computing, ICRIC 2020

作者： Parveen, Shaheen Singh, Pawan Arora, Deepak Department of Computer Science & Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow Campus Lucknow India

ISBN: (纸本)9789811582967

Industry 4.0 has revolutionized the utilization of information technology (IT) in the contemporary scenario. Different disruptive technologies have imparted their contribution in the success of Industry 4.0. In the transition from Industry 1.0 to 4.0, the ideas from mechanical to electrical engineering, from electrical to telecommunication and information technology and then artificial intelligence have switched abruptly. Artificial intelligence (AI), big data analytics (BDA) and machine learning (ML) contributed its leading role. The competence of artificial intelligence brings machine at the capability to handle the human health issues. In Healthcare 4.0, the whole efforts are to resolve the multi-dimensional problems of treatments faced in daily life. Healthcare 4.0 is the rebellion paradigm where hospital-centric environment, artificial intelligence and computing resources are integrated to provide the real-time treatment to patients. The fog computing can play the major role for real-time treatment. This paper illustrates the applicability of fog computing in Healthcare 4.0 and the transition of hospital-centric healthcare (HCH) system to patient-centric care (PCC). The solution for tracking health record is also proposed in this paper by analysing the case study of diabetic patient in India. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

Analysis of Blockchain technology to Protect Data Access Using Intelligent Contract Mechanism for 5G Networks 6

Analysis of Blockchain Technology to Protect Data Access Usi...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： William, P. Rai, Anjani Kumar Madan, Parul Kumar, C Praveen Shrivastava, Anurag Rana, Ajay Sanjivani College of Engineering Department of Information Technology Savitribai Phule Pune University Maharashtra Pune India Institute of Engineering & Technology GLA University Uttar Pradesh Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

With constantly changing internet models automating and digitising countless residential and industrial applications, the world of today has made significant progress in the sharing of healthcare data and the implementation of crucial actions. The two most crucial factors in the setting of healthcare data exchange today are privacy and data integrity. In order to prevent unauthorised individuals from viewing or accessing sensitive data, such as medical information, encryption is essential. The intermediary nodes shouldn't be given too much weight because conventional encryption techniques could not be deployable through them. The current security alternatives for IoT-driven healthcare monitoring frameworks are discussed in this article along with the numerous security challenges that exist in the current 5G network design and how blockchain technology may be used to encrypt data between nodes. The suggested approach outperformed the alternatives and the present strategies for IoT-driven healthcare monitoring frameworks'security solutions, according to a variety of performance factors utilised to test it. A number of performance parameters were used to test the suggested technique, and the results showed that it performed better than the alternatives. © 2023 IEEE.

关键词： Block chain Data Integrity Encryption Healthcare IoT Security

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Framework with Learning without Forgetting for Intelligent Surveillance in IoT-enabled Home Environments in Smart Cities

Recent Advances in Computer Science and Communications

引用

Recent Advances in computer science and Communications 2025年

作者： Dalal, Surjeet Dahiya, Neeraj Verma, Amit Faujdar, Neetu Rathee, Sarita Jaglan, Vivek Rani, Uma Le, Dac-Nhuong Department of Computer Science & Engineering Amity University Haryana Gurugram India Department of Computer Science & Engineering SRM University Sonipat Haryana Delhi-NCR India Department of Computer Science and Engineering Maharaja Agrasen University Baddi India Department of Computer Science & Applications GLA University UP Mathura India Department of Electronics and Communication Engineering School of Engineering and Technology JECRC University Rajasthan Jaipur303905 India Department of Computer Science & Engineering Amity University M.P. Gwalior India Department of Computer Science & Engineering K. R. Mangalam University Haryana Gurugram India Dean of Faculty of Information Technology Haiphong University Viet Nam

Background: Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal. Objectives: Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage. Method: This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data pre-processing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience. Result: The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning. Conclusion: By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings. © 2024 Bentham science Publishers.

关键词： Smart homes

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：