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检索条件"任意字段=Scientific Conference on Mechatronics Engineering and Computer Science"

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Deep learning approach for smart home security using 5G technology

Deep learning approach for smart home security using 5G tech...

引用

International conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Amanullah, M. Chaudhary, Sumit Yalini, R. Balaji, M. Vijaya Sudha, M. Dhanraj, Joshuva Arockia Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Indrashil University Gujarat Rajpur India Department of Electrical and Electronics Engineering Jayam College of Engineering and Technology Tamil Nadu Nallanur Dharmapuri India Department of Electronics and Communication Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Ramachandra College of Engineering Andhra Pradesh Eluru India Department of Mechatronics Engineering Hindustan Institute of Technology and Science Tamil Nadu Padur Chennai India

ISBN: (纸本)9781032669663

With the advent of 5G technology, the entire world will soon be permanently connected, which will connect everything from the biggest megacities to the smallest internet of things. Such a connected hierarchy must integrate the internet of things, smart homes, and smart cities into a single, comprehensive infrastructure. In this research, a fourlayer design is proposed that employs technologies including 5G, the internet of things and deep learning that connect and interface various elements. The research that existing deep learning methods for IoT in smart homes. Deep learning has been used in numerous domains and provides outcomes that are superior to those of human specialists. Deep learning can be typically regarded as an essential move toward genuine AI. Numerous data flows are produced in the detecting areas as a result of such IoT’s development using the 5G standards and uploaded to the cloud for further analysis. Better IoT aid may even be achieved by integrating big data extraction and deep learning in efficient ways. High dependability standards, extremely low latencies, additional capacity, increased security, and high-speed user interaction required for the upcoming 5G network. Also consider future trends, unresolved problems, and insights related to employing deep learning techniques to improve IoT security. As a result, the adoption of deep learning algorithms in the 5G networks has the prospect of enhancing people’s way of life through automating and competence. © 2024 The Author(s).

关键词： 5G mobile communication systems

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Enhancing the Accuracy of Forecasting of the partern of load usage of the Electrical appliences via Soft Computing 4

Enhancing the Accuracy of Forecasting of the partern of load...

引用

4th IEEE International conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Nagaraju, Subuddi Bhat, C. Rohith Baskar, M. Rajaram, Gnanajeyaraman Rani, L. Pattathu Bhaskar, K. Vijaya Aditya Institute of Technology and Management Department of Eee Andhra Pradesh Tekkali India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Chennai India Chennai Institute of Technology Department of Mechatronics Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Simats University Saveetha School of Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360165

precise estimations of consuming load profiles are crucial for the successful oversight of electrical energy systems in both financial & environmental terms. Based on this foundation, it is feasible to strategize & execute the utilization of manageable power generation and storage systems, along with power acquisition, considering the necessary preparation as well as technological and contract limitations. The documented electrical load profiles will significantly rise throughout the digitization of the energy sector. To enhance prediction accuracy, it is essential to create and analyze models that consider the increasing data volume and aim to enhance predicting accuracy through an increased number of data. Artificial neural networks (ANN) are being more commonly employed to address not linear issues involving large datasets influenced by humans and various other unexpected factors. The artificial neural network (ANN) technique is selected to forecast load patterns. The study aims to simulate and evaluate the efficacy & optimal performance of prediction models using an Artificial Neural Network for power consumption profiles. © 2024 IEEE.

关键词： ANN Electrical Load Prediction Energy generation Energy storage Load Profile ML

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Recent Trend Analysis of Convolutional Neural Network-based Breast Cancer Diagnosis

Recent Trend Analysis of Convolutional Neural Network-based ...

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2022 International conference on mechatronics engineering and Artificial Intelligence, MEAI 2022

作者： Liu, Mingzhe Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States

ISBN: (纸本)9781510663169

One of the most common malignancies worldwide is breast cancer. Early screening and diagnosis are important to the reduction of mortality rates of patients. In order to improve the performance and accuracy of breast cancer image screening, researchers have made significant progress in computer-aided diagnosis (CAD) systems built on convolutional neural networks (CNN). In this research, several recent CNN models of breast cancer diagnosis are discussed and explained, and multiple public datasets of breast cancer images are introduced. The detailed performances of the models are presented and compared. The limitations and potential improvements of current CNN-based CAD are discussed. Convolution neural network-based CAD are still facing challenges of shortage of public dataset and the problem of implementation in the clinical scenario. Conclusively, using a convolutional neural network to diagnose breast cancer is still at its early stage, and further developments are required to apply convolutional neural network-based cancer diagnosis to clinical practices. © 2023 SPIE.

关键词： Convolutional neural networks

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Proceedings - 2024 5th International conference on computer science, engineering, and Education, CSEE 2024

Proceedings - 2024 5th International Conference on Computer ...

引用

5th International conference on computer science, engineering, and Education, CSEE 2024

ISBN: (纸本)9798350376852

The proceedings contain 11 papers. The topics discussed include: a classroom teaching video evaluation algorithm based on deep learning and emotion recognition;virtual reality game for science education;scientific text-mining with KH Coder: troubleshooting and solutions;exploring future education: the innovative integration and practice of multimodal learning and ChatGPT;analysis and exploration of teaching research and practice in Chinese universities facing digital intelligence technology;a piano learning system constructed based on multi-direction videos and hand landmark detection;analyzing research trends on digital textbooks in Korea;development of e-learning software for aluminum alloy bending experiment based on simulation technology;and research on preprocessing technique based on gradient descent for luxury goods detection.

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Rumor Detection Based on Knowledge Graph and Graph Attention Network 7

Rumor Detection Based on Knowledge Graph and Graph Attention...

引用

7th IEEE Information Technology and mechatronics engineering conference, ITOEC 2023

作者： Zhang, Yian He, Lulu Lin, Weihuang Jiang, Chenyu Sun, Xuemei Tiangong University School of Computer Science and Technology Tianjin China

ISBN: (纸本)9798350334197

With the rapid growth of the Internet, people are exposed to a significant increase in the amount of information. However, the dissemination of false news has become rampant, causing adverse effects on society. Rumor detection has emerged as an important and challenging research direction aimed at accurately determining the authenticity of messages. This paper proposes a novel rumor detection method based on a knowledge graph and graph attention networks. The method leverages the attention mechanism of graph attention networks to learn topic-enhanced news representations and extract features related to rumor propagation. It then compares the extracted entity features with the entity features in a knowledge base using an entity comparison network. The proposed method is validated on the publicly available SLN dataset, and experimental results demonstrate a 2% improvement in micro F1 and macro F1 performance metrics compared to state-of-the-art baseline models. © 2023 IEEE.

关键词： Knowledge graph

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Research on the Framework Structure and Application of computer Network Security

Research on the Framework Structure and Application of Compu...

引用

2023 International conference on mechatronics and Intelligent Control, ICMIC 2023

作者： Wang, Jin Hu Nan open university Hunan Changsha China

ISBN: (纸本)9781510668393

In the current social construction and development, the computer network security architecture design is composed of software and hardware, which refers to the communication transmission activities with transmission control interconnection protocol as the core. With the continuous improvement of Internet information technology, a large number of Internet transaction modes, such as remote control and online payment, have emerged in the market. Although they provide convenient conditions for social residents, serious problems of cyberspace security have also arisen. In this paper, on the basis of understanding the development trend of modern science and technology innovation, according to the computer network security system framework design, clear network security service measures and management mode, in order to create a safe and comfortable network protection environment for social residents. © 2023 SPIE.

关键词： Remote control

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Deep Reinforcement Learning-Based Constrained Optimal Reconfiguration Scheme for Integrated Modular Avionics System 3

Deep Reinforcement Learning-Based Constrained Optimal Reconf...

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3rd International conference on Electrical engineering and mechatronics Technology, ICEEMT 2023

作者： Li, Daojun Tian, Yi Zhao, Changxiao Zou, Jie Civil Aviation University of China College of Safety Science and Engineering Tianjin China Civil Aviation University of China Key Laboratory of Civil Aircraft Airworthiness Technology Tianjin China Luoyang Institute of Electro-optical Equipment of Avic National Key Laboratory of Space Based Information Perception and Fusion Luoyang China

ISBN: (纸本)9798350303698

Dynamic reconfiguration is critical to improving safety of the flight, minimizing application loss, and balancing partition load. Reconfiguration optimization is a promising way to achieve superior flight safety because of the finite system resource and random failure. To optimize the reconfiguration strategy, we propose a deep reinforcement learning algorithm. The problem is formulated as a Markov decision process with five-tuple, and solved by our proposed deep deterministic policy gradient algorithm. The simulation result shows that our proposed algorithm outperforms the Actor-Critic algorithm and optimized reconfiguration strategy not only restores successfully the system application when failure occurs, but also balances the partition load, further improving the system security. © 2023 IEEE.

关键词： Reinforcement learning

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Human Activity Recognition Using Feedforward Backpropagation Neural Network

Human Activity Recognition Using Feedforward Backpropagation...

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2024 International conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Debs, Fadi Al Jurdy, Makram El Fares, Elias Haidar, Gaby Abou Owayjan, Michel Achkar, Roger Department of Computer and Communications Engineering American University of Science and Technology Beirut Lebanon Department of Mechatronics Engineering American University of Science and Technology Beirut Lebanon

ISBN: (纸本)9798331533137

This paper presents a feedforward backpropagation neural network designed to recognize human activities using IoT sensor data. Human activity recognition based on mobile phones and wearable sensor data has garnered significant attention due to its wide range of applications in healthcare, smart environments, and beyond. Previous studies in this field have utilized various sensors, such as accelerometers, gyroscopes, and orientation sensors, among others, to categorize human activities using modern wearable technologies. The proposed system employs a Human Activity Recognition framework, which collects real-time sensor data and analyzes human movements using deep learning techniques. The model achieves an accuracy of approximately 96% in identifying human motions based on the sensor data. ©2024 IEEE.

关键词： Feedforward neural networks

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Multi-Agent Collision Avoidance Method Using Fuzzy Risk Estimation and Information Sharing in Unknown Environments

Multi-Agent Collision Avoidance Method Using Fuzzy Risk Esti...

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2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Dundar, Yigit Can Bremdal, Bernt Arild Hakansson, Anne Karlsen, Randi UiT - the Arctic University of Norway Dept. of Computer Science Tromsø Norway UiT - the Arctic University of Norway Dept. of Computer Science and Computational Engineering Narvik Norway

ISBN: (纸本)9798350322972

Automated vehicles within Industry 4.0 are used as logistics units where they can move resources from one place to another safely and efficiently. The automated vehicles can be tasked to work in unknown environments where collision-free navigation is challenging due to uncertainty and lack of environmental information. Collisions can damage equipment and may even cause harm to human workers sharing the same space. In order to carry out tasks and avoid collisions in unknown environments, automated vehicles need means of self regulation and environmental awareness through sensors. This paper presents a multi-agent collision avoidance method using fuzzy risk estimation and information sharing for automated vehicles navigating unknown environments. The automated vehicles exchange information about their current state and GPS location with other automated vehicles and use fuzzy collision risk estimation based on sensor data to avoid collisions with static obstacles and each other. Additionally, the automated vehicles collectively learn about their work environment over time by creating a virtual map of the environment through shared information. To test and evaluate the method, a series of simulated tests are carried out. The results from tests show that the automated vehicles are able to avoid collisions while also accurately mapping an initially unknown work environment using the method. © 2023 IEEE.

关键词： Multi agent systems

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Implementing Data science and Big Data Analytics to Their Full Potential: Protocols for Gaining Useful Understanding and Promoting Data-Driven Innovation 5

Implementing Data Science and Big Data Analytics to Their Fu...

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5th IEEE Global conference for Advancement in Technology, GCAT 2024

作者： Arthy, M. Nivethitha, S.B. Karuppiah, S.P. Vijayanandh, T. Sumalatha, A. Ramesh, T. School of Computing College of Engineering and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Mechatronics Engineering Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Mba India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science & Engineering Tamil Nadu Chennai India Kristu Jayanti College Autonomous Department of Computer Science Bengaluru India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376685

The proper implementation of data science and big data analytics has become an essential component for gaining valuable insight and driving innovation across a variety of industries in this era, which is characterized by the constant presence of data. The strategies and protocols that are necessary to fully exploit the promise of these transformational technologies are investigated in depth by this research. A complete framework for enterprises to harness their data assets is the goal of the study, which will be accomplished by analyzing the fundamental principles of data science as well as the difficulties connected with big data. A number of advanced analytics methods, including machine learning, predictive modeling, and natural language processing, are being investigated as potential integrations into the decision-making process as part of this research. The relevance of data governance, quality, and the ethical considerations that surround data usage are also discussed in this document. It is demonstrated in this study that data-driven insights may lead to strategic advantages, operational efficiencies, and the development of creative goods and services. This is accomplished through a number of case studies and practical examples. In order for businesses to make the most of the potential that are given by data science and big data analytics, it is vital for them to have a culture of data literacy as well as a robust infrastructure, according to the results made. At the end of the study, a collection of best practices and recommendations for businesses and governments to implement in order to cultivate a data-driven environment that encourages innovation and usage of data in a responsible manner are presented. In light of the findings of this study, it is clear that data science and big data analytics require a more comprehensive approach, one that strikes a balance between technological capabilities, organizational readiness, and the influence on society. © 2024

关键词： Big Data Analytics Data Governance Data Infrastructure Data Literacy Data Quality Data science Data-Driven Decision Making Data-Driven Innovation Ethical Considerations Machine Learning Natural Language Processing Predictive Modeling Strategic Advantage

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