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A Novel Method to Identify and Recover the Fault Nodes over 5G Wireless Sensor Network Environment

A Novel Method to Identify and Recover the Fault Nodes over ...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Vijaya Vardan Reddy, S.P. Juliet, A. Hency Jayadurga, R. Sethu, S. Pavithra, K.N. Samuthira Pandi, V. R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha University Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Department of Computer Science and Engineering Tamilnadu Chennai India R. M. D. Engineering College Department of Science and Humanities Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350361537

The significance of communication networks is growing in tandem with the proliferation of communication technologies. Present methods for maintaining 5G Wireless Sensor Networks are still restricted to routine maintenance and post-maintenance tasks. They lack a comprehensive function for monitoring the network's status, are unable to assess the network's health, and are difficult to maintain before the 5G-based WSNs seriously degrade. 5G Wireless Sensor Network faults can only be resolved by highly trained technicians due to low maintenance efficiency. As a result, errors cannot be detected or located promptly or accurately, leading to forced repairs that incur the expense of new network cables. First, the article lays forth the basics of network fault analysis. Then, it uses deep learning to simulate communication network problem diagnosis. Lastly, the experimental section compares various methodologies and analyses the findings of fault location. The results of the simulation demonstrate that the suggested approach mitigates the created model's flaws to a certain degree while simultaneously enhancing the network fault detection model's accuracy, universality, and robustness. A novel approach to autonomous placement, the Fault Node Recovery Protocol is described in this study. It is implemented in 5G mobile communications to detect faulty data according to wireless sensor network standards, and it is made to fix the problems with conventional techniques, such poor positioning precision and lengthy running time. The automated localization model for 5G mobile communication fault data, constructed using the suggested FNRP approach, is presented in this work. By comparing it to the standard Adhoc On-Demand Distance Vector Routing protocol, we can see how well the suggested system performs. © 2024 IEEE.

关键词： 5G mobile communication systems

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Neural Network Implementation for Battery Failure Detection in Electric Vehicles

Neural Network Implementation for Battery Failure Detection ...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Vijitha Dheeraj Hebri Sangeeta Singh M. Manohara Mohammad Ishrat D Raja Joseph Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Master of Computer Application Srinivas Institute of Technology Mangaluru Karnataka India Department of Electrical Engineering JSS Academy of Technical Education Noida Uttar Pradesh India School of Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India School of Aeronautical Sciences Hindustan Institute of Technology and Science Chennai Tamil Nadu India

Soil destruction and global warming have recently come to the forefront of discussion in industrialised nations as governments strive to accommodate the rising demands of their citizens. The demand for zero-emission electric cars has increased as a result of international competitiveness and technological advancements (EVs). Concerns about the high voltage of increasing numbers of electric cars are shared by an increasing number of individuals. Since the system of batteries may be at responsible for over 30% of EV accidents, it is vital to investigate how problems with LIBs are recognized. Many different kinds of problems make it hard to fix EV's LIB. Fast and precise diagnosis of battery pack problems is crucial for the immediate and ongoing safety of EV operation. Utilizing models of neural networks like multiple hidden layers (MLP) or nonlinear activation functions, this research provides a mechanism for identifying and fixing problems with electric vehicle batteries (RBF). To generate information for the BFD system, battery simulations are done in MATLAB. Accuracy may be improved by performing pre-processing steps on the information once it has been generated. After training, the two models are put to the test to see how well they perform. There are both positive and negative measures that may be used to determine which model is the best.

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Clinical Significance of Measles and Its Prediction Using Data Mining Techniques: A Systematic Review

Clinical Significance of Measles and Its Prediction Using Da...

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International Conference on Artificial Intelligence and Data Engineering, AIDE 2019

作者： Rao, Abhishek S. D’Mello, Demian Antony Anand, R. Nayak, Sneha Department of Information Science and Engineering NMAM Institute of Technology Nitte UdupiKarnataka574110 India Department of Computer Science and Engineering Canara Engineering College Bantwal India Department of Master of Computer Application NMAM Institute of Technology Nitte UdupiKarnataka574110 India Department of Biotechnology Engineering NMAM Institute of Technology Nitte UdupiKarnataka574110 India

ISBN: (纸本)9789811535130

Data science techniques used in the past era for data extraction are now being replaced by data mining methods due to a lot of contemporary trends and challenges. Data mining includes implicit data extraction, which is previously unknown but potentially useful information, with the help of databases to generate new information. Medical data mining is an area of challenges since the data involves in it is imprecise, inconsistent and massive. Medical diagnostics systems are evaluated by employing large information databases, but it endures many failures to extract data from the databases. There exists no tool to discover the major relationships concerning the data. In such a case, the core knowledge of healthcare data is extracted by applying the data mining methods, thus helping in turning raw data into useful information. Measles is an immunizable disease most likely seen in infants and young children. Measles (rubeola), is an RNA virus, belongs to the Morbillivirus genus, and Paramyxoviridae family. It is one among the six major killer diseases, with accounts to 90% secondary infection rate with susceptible contacts. In this paper, the authors present a systematic review of measles cases and its mortality with the help of the taxonomical tree structure. The published research which utilizes the application of data mining techniques for early prediction of measles in terms of methods used, algorithms utilized and results obtained are evaluated. The paper provides major consolidation of data with respect to motive, study and methods used in the literature. The authors present the summary of the research review findings and research gaps for further study and possible application. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Data mining

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HMLM: An Intelligent Artificial Intelligence Assisted Strategy to Identify UPI Frauds Based on Hybrid Markov Learning Methodology

HMLM: An Intelligent Artificial Intelligence Assisted Strate...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bharath, S. Prasad, G. Lakshmi Vara Sujatha, V. Hemajothi, S. Mani, D. Sharada Merlin, N.R. Gladiss Rajalakshmi Institute of Technology Department of Ece Chennai India Qis College of Engineering and Technology Department of Aimlds Vengamukkapalem AndhraPradesh Ongole India S.A. Engineering College Department of Master of Computer Applications Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Ece Chennai India Qis College of Engineering and Technology Department of Aimlds Vengamukkapalem Andhra Pradesh Ongole India R.M.K. Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331543617

The proliferation of Unified Payments Interface (UPI) transactions in the digital age has considerably improved financial inclusion, but it has also increased the vulnerability to fraud. This manuscript introduces a novel approach that employs Artificial Intelligence (AI) to detect UPI frauds. The approach is based on deep learning and is referred to as the Hybrid Markov Learning Methodology (HMLM). To evaluate the suggested system, it is cross-checked against the traditional Hidden Markov Model (HMM). Our approach combines the benefits of modern artificial intelligence methods with Markov models to improve the accuracy of fraud detection. Combining deep learning algorithms with probabilistic state transitions allows the hybrid Markov Learning Methodology to dynamically analyze transaction patterns and behaviors. By modeling both historical and real-time transaction data, our approach improves the identification of unusual activity and fraud patterns that conventional algorithms may ignore. From experimental data, we prove our approach minimizes false positives and improves the rate for fraud detection against the current options available. Our work advances the current state of fraud detection systems using more flexible and robust fraud detection systems, thus enabling security and confidence in UPI transactions. © 2024 IEEE.

关键词： Hidden Markov models

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AR Intelligent Real-time Method for Cultural Heritage Object Recognition 5

AR Intelligent Real-time Method for Cultural Heritage Object...

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5th IEEE International Conference on Advanced Information and Communication Technologies, AICT 2023

作者： Pisnyi, Oleh Kit, Ivan Lipianina-Honcharenko, Khrystyna Sieck, Jurgen Sachenko, Anatoliy Dobrowolski, MacIej Sapozhnyk, Grygoriy Master's Degree in Computer Science West Ukrainian National University Ternopil Ukraine Systems and Control West Ukrainian National University Department for Information-Computing Ternopil Ukraine Hochschule für Technik und Wirtschaft Berlin Berlin Germany Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics and Teleinformatics Radom Poland

ISBN: (纸本)9798350372571

In a world of rapid technological changes and growing interest in cultural heritage, the development of innovative tools for travelers and history enthusiasts becomes incredibly important. Even in the digital age, interactive ways of exploring historical landmarks remain relevant and attractive. In this research, an AR Intelligent Real-time Method for Cultural Heritage Object Recognition has been developed. The method is implemented through a chatbot, which, when using a smartphone's camera, recognizes objects of cultural heritage and provides descriptions of these objects. The chatbot opens up new possibilities for travelers, allowing them to enjoy history and cultural heritage using intelligent tools and augmented reality. © 2023 IEEE.

关键词： Augmented reality

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Dual Mode Host-Based and Cloud-Based Smartphone Intrusion Detection System

Dual Mode Host-Based and Cloud-Based Smartphone Intrusion De...

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International Conference on Futuristic Technologies (INCOFT)

作者： Gopala B M Hanumanthappa Manish Kumar Department of Computer Science and Applications Bangalore University Bangalore INDIA Department of Master of Computer Applications M S Ramaiah Institute of Technology Bangalore INDIA

Smartphones are one of the most important devices in our daily life. It produces transfers and stores a significant amount of data about individuals such as banking transactions, authentication credentials, location details, call logs, etc. If the security of smartphones is compromised, it can severely affect the individual’s life. As a result, the security system must be both efficient and dependable. We present a novel dual-mode Intrusion Detection System (IDS) that can operate both offline (without internet) and online (cloud mode). It collects application run-time data and analyses it locally on a smartphone or in the cloud, based on the user’s configuration, to predict the app’s behaviors and detect any intrusion.

关键词： Performance evaluation Cloud computing Intrusion detection Authentication Banking Behavioral sciences Security

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An Experiment to Develop an Enhanced Medical Image Security by using Deep Learning Assisted Crypto Policy

An Experiment to Develop an Enhanced Medical Image Security ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Ramkumar, G. Raja, B. Balaji, A. Sujatha, V. TamilSelvi, M. Kumar, ATA Kishore Department of ECE Saveetha School of Engineering SIMATS Chennai India Department of ECE Dr. M.G.R. Educational and Research Institute Chennai India Department of ECE Rajalakshmi Institute of Technology Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of ECE SRM Institute of Science and Technology Ramapuram Chennai India Department of ECE School of Engineering Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College AndraPradesh Tirupati India

ISBN: (纸本)9798350305708

Ensuring a high level of safety and security is imperative when transmitting medical images via open access channels. Medical images hold immense importance in various applications, particularly in real-time contexts like telemedicine. Due to the unique characteristics of medical imaging data, determining the appropriate techniques for safeguarding confidential images against unauthorized access becomes challenging. Existing encryption methods primarily cater to textual data, leaving a gap in effectively securing multimedia data such as images. Using DNN leaves these systems open to attack from adversarial samples, which are pictures with slight changes that can trick the model into producing inaccurate predictions. Utilizing the MNIST database, this research demonstrates that Deep Learning algorithms may be used to encrypted data without compromising accuracy. This finding holds significance as it offers a potential solution for preserving data security while benefiting from advanced machine learning capabilities. In addition, the research goes farther by training a model using encrypted information to categories X-ray coronary angiography images, making it a more difficult scenario. The suggested DNN model's remarkable accuracy score of 0.983 highlights its ability to make predictions with an exceptional level of correctness. © 2023 IEEE.

关键词： Cryptography Deep Learning Models Deep Learning Neural Network Medical Image Security

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Enhanced Prognostics and Health Management in Automated Driving Systems: Using Graph Neural Networks to Recognize Operational Contexts

Enhanced Prognostics and Health Management in Automated Driv...

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Prognostics and System Health Management Conference (PHM)

作者： Peng Su Yeqi Wang Conglei Xiang Erik Wendel Madhav Mishra DeJiu Chen Department of Engineering Design Mechatronics Division KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program of Electrical Engineering and Computer Science (EECS) School KTH Royal Institute of Technology Stockholm Sweden Digital Systems Division RISE Research Institutes of Sweden Gothenburg Sweden

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.

关键词： Graph neural networks Data models Safety Complexity theory Data mining Prognostics and health management Intelligent systems

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Integration of Renewable Energy with Common Clud for Centre Level Control for Domestic Uses

Integration of Renewable Energy with Common Clud for Centre ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Archana Verma Deepak Mehta Satish Kumar Jangid Master of Computer Application Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Department of Computer Science Jain (Deemed to be University) Bangalore India Department of Electrical Engineering Vivekananda Global University Jaipur India

The growing problem of water scarcity is made worse by the unchecked use of fossil fuels for irrigation water-table pumping, which contributes to both environmental degradation and global warming. An burgeoning population and rising food demand mean that sustainable agriculture has to make effective use of its energy and water resources. In reaction, the area of “smart agriculture” (SA) becomes more important, using information and communication technology (ICT) to maximize resource use and improve agricultural yields. In order to help small and medium farms overcome their financial barriers to adopting commercial solutions, this study proposes a complete SA solution that prioritizes cost-effectiveness. Tested in a real-world Smart Farm testbed, the deployed SA system demonstrated a significant decrease in water use (up to 71.8% compared to typical irrigation systems). The solution's open-source nature makes it easier for academics to adopt and modify it, particularly when it comes to creating specialized Cloud-based platforms that solve issues with water table consumption, especially in dry and sub-Saharan nations.

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Virtual Mouse Using Face Gesture and NLP

Virtual Mouse Using Face Gesture and NLP

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Ajay Zad Jagannatha S Manish Kumar S Ajitha Prakash B R Department of Masters of Computer Applications M S Ramaiah Institute of Technology Bangalore India Department of Computer Applications East Point College of Engineering Bangalore India Department of Master of Computer Applications M S Ramaiah Institute of Technology Bangalore India Department of Computer Science Government First Grade College Tumkur Karnataka India

The mouse plays a very important role in interacting with laptop’s and computer system, to perform necessary operations. In this paper, we are proposing a method to carry out mouse operations on the computer system without a physical mouse by utilizing voice commands and face-motion gestures. The system employs an internal webcam to detect facial gestures and an internal microphone to receive voice commands from the user. By employing a virtual mouse based on face gestures and natural language processing (NLP), we can accomplish all the fundamental functionalities of a mouse by issuing voice commands to the computer system. The system accepts these commands and executes corresponding mouse events such as "click," "right-click," "double-click," "scroll-up," "scroll-down," "minimize," and "close." The mobility of the mouse cursor on the monitor is achieved through face motion gestures, enabling the cursor to move up, down, left, and right, respectively.

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