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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Juhi Dwivedi Bramah Hazela Kamlesh Kumar Singh Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Lucknow Uttar Pradesh India Department of Electronics & Communication Enginerring Amity School of Engineering & Technology Amity University Lucknow Uttar Pradesh India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

Since cardiovascular diseases (CVDs) continue to be the largest cause of death worldwide, precise and effective diagnostic techniques are essential. Phonocardiography (PCG), which records minute heart sounds, has the potential to identify cardiac defects early. In order to automate the detection of heart abnormalities, this study explores the use of convolutional neural networks (CNNs) in PCG signal analysis. Five different kinds of heart sounds were pre-processed from a large dataset. To extract complex characteristics from PCG signals, a sophisticated CNN architecture was developed that consists of numerous convolutional and pooling layers. The model was able to identify between normal and pathological cardiac sounds with an accuracy of more than 90 %. Techniques for data normalisation, augmentation, and visualisation were used to improve training effectiveness and validate the model. This study provides an ethical and effective method of identifying circulatory irregularities at an early stage, demonstrating the promise of AI in transforming cardiac diagnostics.

关键词： Heart Training Deep learning Ethics Accuracy Data visualization Data models Convolutional neural networks Signal analysis Phonocardiography

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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications 7

Next-Generation Computational Models for Enhanced Interpreta...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gowtham Raj, R. Sreekanth, S. Nijhawan, Ginni Gupta, Shubhi Sharma, Saurabh Mounika, T. New Horizon College of Engineering Deparment of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lloyd Law College Plot No. 11 Knowledge Park II Greater Uttar Pradesh Noida201314 India Lovely Professional University Phagwara India Amity University Amity School of Engineering & Technology Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts. © 2024 IEEE.

关键词： Frequency domain analysis

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Long Short-Term Memory and KNN based Approach for Smart Irrigation System using IoT

Long Short-Term Memory and KNN based Approach for Smart Irri...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Soni, Deepak Kumar Venkatesh, Koudagani Alone, Vinod N. Hazela, Bramah Chauhan, Birendra Kumar Kumar, T Ch Anil Galgotias University Department of Civil Engineering Maharashtra Greater Noida India Holy Mary Institute of Technology and Science Department of Civil Engineering Telangana Hyderabad India Vppcoe & VA Sion Computer Engineering Department Maharashtra Mumbai India Amity University Uttar Pradesh Amity School of Engineering & Technology Department of Computer Science & Engineering Lucknow Campus Uttar Pradesh India Iimt University School of Basic Sciences and Technology Uttar Pradesh Meerut India Vfstr Department of Mechanical Engineering andhra Pradesh Vadlamudi India

ISBN: (纸本)9798350333602

The inefficient use of water in common irrigation practices leads to significant water loss. In order to save water and speed up the process, an intelligent irrigation system is required. With the development of machine learning and the Internet of Things, an intelligent system may now be built to perform this task automatically with minimal human intervention. This study showcases an LSTM-RNN-trained recommendation system for enabling effective water use with minimal farmer interaction, this is all made possible through the Internet of Things. Internet of Things (IoT) sensors are deployed all around the farm to gather information about the soil and surrounding environment. The data is then stored and processed using ML methods on a cloud server, and the farmer will be provided with irrigation recommendations based on the findings. Preprocessing, Feature Selection, and Model Training are the three main components of the proposed system. Preprocessing with an M3 filter is employed. RFE is used to perform feature selection. In the end, it's put to use in LSTM - RNN model training. When compared to LSTM and RNN models, the proposed method performs better. © 2023 IEEE.

关键词： Internet of things

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A Smart NIDS Design for SDN-based Cloud IoT Network using Optimal Path Selection and RNN 9

A Smart NIDS Design for SDN-based Cloud IoT Network using Op...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Kumar, Narendra Aeron, Anurag Dawar, Ishaan Kumar, Santosh Department of Computer Science and Engineering Amity University Jharkhand Ranchi India School of Computing DIT University Dehradun Dehradun India Computer science and Information Technology Siksha 'O' Anusandhan University Bhubaneshwar India

ISBN: (纸本)9798350383201

The Internet of Things (IoT) has greatly increased the possibility for developing intelligent connections and applications in many facets of daily life. Traditional security solutions frequently fail to solve security issues in cloud-based IoT systems, proving ineffective and insufficient. Software Defined Networking (SDN), which effectively detects and monitors network security vulnerabilities thanks to its programmable characteristics, presents a viable solution to these problems. In order to strengthen computer systems and fend off threats to network security, machine learning (ML) techniques have recently been included into SDN-Network Intrusion Detection Systems (NIDS). Deep learning (DL), one of these cutting-edge ML techniques, has become more popular in the SDN environment. For SDN-based cloud IoT networks, this paper design an efficient path selection system in this study utilizing DL approaches. The strategy comprises employing a Recurrent Neural Network (RNN) to identify threats and a selection mechanism to pick the best route for data delivery. This study performed tests on two datasets, KDD and UNSWNB15, and the results show that the suggested RNN outperformed existing DL methods by achieving an amazing accuracy ranging from 91% to 95% on both datasets. © 2023 IEEE.

关键词： Internet of things

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Security Strengthen and Detection of Deepfake Videos and Images Using Deep Learning Techniques

Security Strengthen and Detection of Deepfake Videos and Ima...

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IEEE International Conference on Communications Workshops, ICC

作者： Sumran Talreja Abhay Bindle Vimal Kumar Ishan Budhiraja Pronaya Bhattacharya School of Computer Science Engineering and Technology Bennett University Greater Noida India Electronics and Communication Engineering Maharishi Markandeswar University Mullana-Ambala Haryana Computer Science and Engineering Amity School of Engineering and Technology Amity University Kolkata India

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

The identification of fraudulent movies or images created using deep learning algorithms is the subject of this research and attempts an in-depth investigation of Deepfake Detection. Deepfakes are created by manipulating or replacing certain parts of an original video or image using machine learning algorithms, usually concentrating on face features. Deepfake detection's main goal is to precisely recognize and distinguish these altered media from real movies and photos. This study looks at a number of deepfake detection techniques, including forensic methods, machine learning algorithms, and picture analysis. These approaches' efficiency and performance are assessed based on their capacity to accurately identify and categories deep-fakes. The paper also examines the difficulties and restrictions of deepfake detection, such as the development of more complex and convincing deepfakes. Further, prospective uses and future possibilities for deepfake detection research are examined, with an emphasis on improving detection skills and creating effective countermeasures. Overall, this research offers insightful information about cutting-edge methods and developments in Deepfake Detection, giving a greater comprehension of its importance in resolving the issues brought on by manipulated media in the current digital era.

关键词： Deep learning Deepfakes Machine learning algorithms Image resolution Conferences Forensics Face recognition

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Advanced Traffic Flow Prediction Using LSTM Networks and Big Data Integration for Urban Mobility Optimization 7

Advanced Traffic Flow Prediction Using LSTM Networks and Big...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Rangaiah, Yagateela Pandu Revathi, V. Dutt, Amit Sharma, Smita Al-Rubaye, Taqi Mohammed Khattab Rani, K. Pushpa Institute of Aeronautical Engineering Department of Electronics and Communication Enfineering Dundigal Hyderabad500043 India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lovely Professional University Phagwara India Amity University Amity School of Engineering & Technology Greater Noida India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

As urbanization expands at a rate faster than ever before we experience traffic jams which call for sophisticated predictive methods in order to improve the traffic systems. Hypothesized from the heuristic analysis of big traffic data, the present paper outlines a new traffic flow prediction model. The proposed methodology uses big real-time traffic data and historical big data with a machine learning approach that can detect and analyse temporal and spatial dependencies. The model incorporates DL concepts like Long Short-Term Memory (LSTM) networks which capture the sequential dependencies and the complex correlation in traffic flows. Moreover, we also have Feature Selection mechanism to filter the traffic data indicators and thus decrease the data dimensions and increase the prediction accuracy. The integration of big data analytics is helpful in real-time adjustments to changes in traffic and is more accurate. By testing the method on different traffic datasets, the authors show that the proposed method outperforms the traditional models. The findings of this research are novel and significant because this framework can be easily applied and implemented for the traffic flow prediction across various cities, helping in management of traffic and thereby limit traffic congested areas. © 2024 IEEE.

关键词： Traffic congestion

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Utilizing Deep Reinforcement Learning for Advanced Pattern Extraction in Big Data Analytics and Ontology Systems 2

Utilizing Deep Reinforcement Learning for Advanced Pattern E...

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

作者： Karthik, A. Asha, V. Bhardwaj, Garima Nijhawan, Ginni Khan, Irfan Bader, Nabaa M. Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Computer Applications Bangalore India Amity University Amity Business School Uttar Pradesh Greater Noida India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

The development of big data has provided unparalleled prospects for uncovering novel patterns and insights in several domains. However, the complex structure and volume of data need the use of advanced methods to successfully extract significant information. This research presents a new framework that utilizes Deep Reinforcement Learning (DRL) to improve pattern extraction in big data analytics and ontology systems. DRL, which combines deep learning with reinforcement learning, excels at dealing with data spaces that have a large number of dimensions. This makes it a good option for effectively exploring and analyzing complex datasets. The suggested framework effectively utilizes sophisticated ontological structures to integrate DRL, enabling it to accurately identify and extract complex patterns that may be overlooked by standard approaches. The study showcases the effectiveness of the framework by conducting extensive tests on diverse big datasets, demonstrating that the DRL-enhanced system surpasses previous methods in terms of both accuracy and speed. The study also addresses architectural design, the choice of reinforcement learning methods, and the found implementation issues. Moreover, it delves into the consequences of these discoveries for subsequent investigations and real-world implementations, emphasizing the capacity of DRL to transform the identification of patterns in large-scale data settings. This work enhances the area of big data analytics by offering a strong technique for extracting complex patterns, which in turn enables better informed decision-making and discovery in scientific and commercial sectors. © 2024 IEEE.

关键词： Deep reinforcement learning

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Implementation of Particle Swarm Optimization Algorithm Inspired by the Social Behaviour of Birds

Implementation of Particle Swarm Optimization Algorithm Insp...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Neha Tyagi Deepshikha Bhargava Anil Ahlawat Amity School of Engineering & Technology Amity University Greater Noida India Computer Science & Engineering KIET Group of Institution Ghaziabad India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Particle swarm optimization (PSO) is an evolutionary computation technique based on the social behaviour of flocking bird. The PSO method was first proposed by Kennedy and Eberhart in 1995, and has since developed into a generic optimization framework, well suited for application in virtually any industry. In this article, we seek to give an overarching introduction to PSO starting from basic understanding to core structure visit. This paper provides a complete introduction to PSO, protecting its fundamental ideas, which includes particle illustration, fitness assessment, and key hyperparameters including inertia weight, cognitive weight, and social weight. We use the mechanics of PSO, that specialize in how particle replace their positions based totally on personal best (pbest) and Global best (gbest) positions to stability exploration and exploitation. Significant advancements and modifications in PSO are reviewed, highlighting their effect on robustness and performance. These techniques consist of more desirable mastering strategies, fuzzy common sense for dynamic parameter tuning, mutation strategies, Lévy Flight, and competition-primarily based learning. The effectiveness of these innovations is represented via packages in energy structures, image processing, and function optimization.

关键词： Technological innovation Heuristic algorithms Image processing Power system dynamics Birds Robustness Trajectory Particle swarm optimization Optimization Tuning

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Analysis and optimisation of emissions and fuel economy of biodiesel fuelled CI engine using Taguchi technique

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International Journal of Ambient Energy 2024年第1期45卷

作者： Saluja, Rajesh Kumar Singh, Narender Kumar, Vineet Vashisht, Priyanka Kumar, Niraj Amity Institute of Aerospace Engineering Amity University Uttar Pradesh Noida India Department of Automobile Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Amity University Haryana India Department of Mechanical Engineering Amity School of Engineering & Technology Amity University-Uttar Pradesh Noida India

The objectives of the present work are to examine the combined effect of engine operating and injection parameters and to optimise these parameters for better fuel economy, performance, and reduced emissions. The fuels used were diesel and biodiesel blends of Jatropha and Karanja biodiesel. The results indicate that diesel gives higher thermal efficiency at low loads, however, the 20% jatropha blend was slightly more efficient, at high loads. It was observed that the BSFC increases when the EGR is varied from 0% to 20%. Even though the smoke opacity increases with increased EGR, advanced injection at 28o BTDC decreases smoke. Increased EGR also reduced NOx emissions by up to 11%. Blending also has a significant impact on the output parameters. Taguchi analysis was applied to optimise the performance and emission characteristics of the diesel engine and set of input values were obtained for the optimum performance under different conditions. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Diesel engines

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Graph-Based Machine Learning Approaches for Fraud Detection in Financial Networks 7

Graph-Based Machine Learning Approaches for Fraud Detection ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kotiyal, Arnav Hussein, Layth Deepak, A. Rana, Aryan Manjunatha Dixit, Krishna Kant Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Electrical Engineering Mathura India Mlr Institute of Technology Department of CSE-AI & Ml Telangana Hyderabad India

ISBN: (纸本)9798350350067

Fraud detection in financial networks presents a significant challenge due to the complexity and volume of transactions. Traditional detection methods often struggle with scalability and accuracy when faced with evolving fraudulent schemes. In this paper, we explore the application of graph-based machine learning techniques to enhance fraud detection in financial networks. By representing financial transactions as graph structures, we capture the relationships between entities, allowing for the identification of suspicious patterns that may indicate fraudulent behavior. We examine several graph-based algorithms, including Graph Neural Networks (GNNs) and Random Walk-based approaches, and compare their performance with conventional machine learning methods. The use of graph representations facilitates the extraction of features such as transaction flows and network centrality, which are critical in identifying anomalies. Furthermore, we propose an enhanced detection model that integrates community detection techniques with supervised learning for improved accuracy. Our experimental results, conducted on a real-world financial dataset, demonstrate that graph-based models outperform traditional models in detecting fraud with higher precision and recall. The study highlights the potential of these techniques to provide scalable and robust solutions for real-time fraud detection, contributing to a more secure financial ecosystem. © 2024 IEEE.

关键词： Adversarial machine learning

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