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A Wavelet Features and Machine Learning Founded Error Analysis of Sound and Trembling Signal

SN computer science 2023年第6期4卷 1-7页

作者： Shukla, Prashant Kumar Roy, Vandana Chandanan, Amit Kumar Sarathe, Vivek Kumar Mishra, Pankaj Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur 522302 India Department of Electronics Communication Gyan Ganga Institute of Technology and Sciences MP Jabalpur India Department of CSIT Guru Ghasidas Vishvavidyalay CG Bilaspur India Department of Applied Physics Amity School of Engineering and Technology Amity University Madhya Pradesh Maharajpura Dang Gwalior India

The sound and vibration signals coming from the spinning machinery are non-stationary signals, and to analyse them, we require information on both their time and frequency components. The signal in the time domain is converted by the Fourier transform into one in the frequency domain, which does not include any information about the passage of time Wavelet transforms, can represent non-stationary signals in time and frequency. It can breakdown source signals of different time and frequency resolutions, which are characteristic of rotating machine fault mechanisms. Rotating the machine achieves this. Wavelet can filter raw waveforms and compress massive amounts of data without losing information. Thus, eliminating background noise improves sound-based defect diagnosis. Sound and vibration data recovered wavelet energy features and wavelet energy to entropy characteristics. Eight decomposition stages extracted wavelet features. Wavelet characteristics have nine coefficients: one approximate and eight detailed. Decision trees were used to select the best wavelet features. Wavelet energy features outperform wavelet energy-to-entropy ratio features. The classification algorithm's mean classification efficiency percentage for vibration signals of class 24 is 94.23 and for sound signals, 96.73. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Frequency Noise Vibration signal Wavelet

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A Stacked BiLSTM based Approach for Bus Passenger Demand Forecasting using Smart Card Data

A Stacked BiLSTM based Approach for Bus Passenger Demand For...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Balaji, V. Anand, Taruna Abid, Tahera Ramya, D. Chauhan, Amit Hazela, Bramah Aditya Engineering College Department of Eee Andhra Pradesh Surampalem India Graphic Era Deemed to Be University Department of Humanities and Social Sciences Dehradun India Nawab Shah Alam Khan College of Engineering and Technology Department of It Telangana Hyderabad India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India School of Sciences Department of Life Sciences Karnataka Bengaluru India Amity University Amity School of Engineering and Technology Lucknow Uttar Pradesh India

ISBN: (纸本)9798350313987

Demand forecasting is crucial in the business sector. Despite the inherent uncertainty of the future, it is essential for any firm to be able to accurately predict the market for both short- and long-term planning in order to place itself in a profitable position. The proposed approach focus on the passenger transport sector because it is particularly vulnerable to fluctuations in consumer demand for perishable commodities. At every stage of the planning process from initial network designs to final pricing of inventory for each vehicle in a route-an accurate prediction of demand is essential. Forecasting passenger demand is crucial since passenger transportation is responsible for a substantial chunk of global commerce. The suggested method relies on three distinct techniques: data preparation, feature selection, and model training. Data modification, cleansing, and reduction are the three sub-processes that make up preprocessing. When it comes to feature selection, partition-based clustering algorithms like k-means are the norm. Let's go on to training the models with stacked BiLSTM. The proposed method is demonstrably superior to both LSTM and BiLSTM, the two most common competing approaches. The proposed method had a success rate of 98.45 percent. © 2023 IEEE.

关键词： Demand Forecasting Electronic Ticketing Machines (ETMs) Long Short-Term Memory (LSTM)

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Survey Paper: Study of Natural Language Processing and its Recent Applications 2

Survey Paper: Study of Natural Language Processing and its R...

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2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Shivahare, Basu Dev Singh, Arun Kumar Uppal, Nilesh Rizwan, Ashar Vaathsav, Vangala Sri Suman, Shashikant Amity School of Engineering and Technology Dept. of Computer Science Greater Noida India Png University of Technology Dept. of Mathematics and Computer Science Port Morobe Lae Papua New Guinea

ISBN: (纸本)9781665474160

Natural Language Processing (NLP) is a field of research related to developing computer systems that can process native language intelligently. Considering natural language such as English has always been one of the key issues of the practical feasibility of research, both because of the key language that works with human ingenuity and because of the richness of potential applications. The main functions of natural language processing are machine translation, speech analysis, automatic summarization, conference resolution, speech recognition, etc. NLP is used to make the virtual computer interface easier for humans. This paper gives you an overview of NLP from scratch and discusses some of the key applications. © 2022 IEEE.

关键词： Speech recognition

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Viability of Viola-Jones Algorithm for Drowsiness Detection in Drivers

Viability of Viola-Jones Algorithm for Drowsiness Detection ...

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International Conference for Convergence of technology (I2CT)

作者： Kireet Joshi Paras Jain Gamini Dhiman Vishan Kumar Gupta Harvinder Singh Ujjwal Sharma Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India Amity School of Engineering & Technology Amity University Punjab Mohali India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

This research paper investigates the viability ofusing the Viola-Jones algorithm for drowsiness detection in drivers. The algorithm is designed to detect the presence of a face, locate the eyes within the face region, and then calculate the Eye Aspect Ratio (EAR) to determine if the eyes are open or closed. The study uses a dataset of images of the driver’s face and tests the algorithm's performance under various lighting, pose, and expression variations. The results show that the Viola-Jones algorithm is a robust and effective method for detecting drowsiness in drivers, achieving high accuracy rates even in challenging conditions. The paper concludes by discussing the potential applications of this technology in real-world settings, such as in the development of driver assistance systems for improving road safety.

关键词： Machine learning algorithms Lighting Ear Road safety Faces Vehicles Convergence

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Towards Fortifying Digital Image Security: “Blockchain Integration with CryptoMark Approaches”

Towards Fortifying Digital Image Security: “Blockchain Inte...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Vivek Kumar Sharma Renuka Arora Chandra Shekhar Yadav Deepti Mehrotra Amity School of Engineering and Technology Greater Noida Campus Amity University Gautam Buddha Nagar Uttar Pradesh India Department of Computer Science and Engineering Amity School of Engineering and Technology Noida Campus Amity University Gautam Buddha Nagar Uttar Pradesh India School of Computer Applications Noida Institute of Engineering and Technology Greater Noida Dr APJ Abdul Kalam Technical University Gautam Buddha Nagar Uttar Pradesh India Department of Information Technology Amity School of Engineering and Technology Noida Campus Amity University Gautam Buddha Nagar Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

In the rapidly evolving digital landscape, Guaranteeing the safety and confidentiality of digital assets, particularly images, has become imperative. Blockchain has arisen as a leading solution for ensuring decentralized and unchangeable data storage, backed by strong cryptographic security measures. This paper presents a systematic review that investigates the impact of blockchain on enhancing security and privacy for encrypted and watermarked digital images. By synthesizing existing literature, our objective is to comprehensively analyze the efficacy of blockchain integration in safeguarding the integrity and confidentiality of digital image assets. Our review covers various aspects including encryption techniques, watermarking methods, and blockchain implementations. Additionally, we assess the research goals, motivations, and future directions in leveraging blockchain for image security and privacy enhancement. This review provides critical insights for scholars, professionals, and lawmakers into blockchain technology’s ability to greatly improve digital image preservation across a wide range of applications, including the health sector and intellectual ownership administration.

关键词： Privacy Digital images Memory Watermarking Blockchains Safety Encryption Informatics Systematic literature review

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Advanced Machine Learning Approach for Cyber Attack Detection in Cloud Computing

Advanced Machine Learning Approach for Cyber Attack Detectio...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

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

ISBN: (纸本)9798331527495

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. © 2025 IEEE.

关键词： Recurrent neural networks

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Online Affinity of Instructing Methods for Using Personal-Efficacy and Reading Capacious During Covid-19 5th

Online Affinity of Instructing Methods for Using Personal-Ef...

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5th International Conference on Communication, Device and Networking, ICCDN 2021

作者： PremaLatha, V. Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Bhoi, Akash Kumar Ghosh, Uttam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Guntur Vaddeswaram522502 India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur603203 India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Sikkim Gangtok737102 India Department of Computer Science and Data Science Meharry School of Computational Sciences Nashville NashvilleTN United States

ISBN: (纸本)9789811920035

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be "more expensive, difficult, and time-consuming". Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes "comprehension reading work" is treated as fundamental in the proposal of disclosures. In case of any, "the programme appeared to positively affect contributing students". Techniques of classification like "decision tree and Mixed Model Database Miner (MMDBM)" are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection. © 2023, The Author(s), under exclusive lice

关键词： students

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Analyzing Automatic Code Generation for Learning Models in Generative AI 11

Analyzing Automatic Code Generation for Learning Models in G...

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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

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Advanced Machine Learning Approach for Cyber Attack Detection in Cloud Computing

Advanced Machine Learning Approach for Cyber Attack Detectio...

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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

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

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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

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