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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sunil, M.P. Shekhawat, Jyoti Vanaja, T. Mohammed, Vazeer Ali Thaware, Shailesh J. Singh, Vaishali Karnataka Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer & Information System Lewis University Illinois United States Department of Computer Engineering Vishwakarma Institute of Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350370249

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information. ©2024 IEEE.

关键词： Large datasets

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Filtering of Spurious Sonar Signals 14

Filtering of Spurious Sonar Signals

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Srutheeshna, U. Ashraf, Sharik Koshy, Litty Department of Master of Computer Application SCMS School of Engineering &Technology VidyaNagar Karukutty Ernakulam India Naval Physical and Oceanographic Laboratory P.B.NO.6 NGO Quarters Thrikkara Vazhakkala Kochi India Department of computer science & Engineering SCMS School of Engineering & Technology VidyaNagar Karukutty Ernakulam India

Navy uses a harmonic filter to extract the propel sounds in ships. Usually, Sonar processing uses the harmonic filter to remove unwanted harmonics. The proposed system is used for filtering unwanted noise from lower frequency noise using the machine learning technique. Some sonar may have higher frequency sounds, which can be difficult to hear. Using this sound frequency humans can identify their target by experience. Automate these sounds and using a trained neural network, Classify the sounds. Retrieve non-frequency voice and avoid harmonics using a neural network. © Grenze Scientific Society, 2023.

关键词： High pass filters

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Disease Detection Using Ensemble Model in Machine Learning

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SN computer science 2025年第3期6卷 1-15页

作者： Mohapatra, Rojalin Giri, Parimala Kumar Sayyad, Irfan Sahu, Amaresh Brahma, Biswajit Kamila, Nilayam Kumar Department of Computer Science and Engineering Gandhi Institute for Technological Advancement Odisha Bhubaneswar India Department of Information Technology and Computer Application Vignan’s Foundation for Science Technology and Research (Deemed to be University) Andhra Pradesh Vadlamudi India Department of MCA Ajay Binay Institute of Technology Odisha Cuttack India McKesson Corporation San Francisco CA United States Capital One Wilmington DE United States

Disease detection is a vital issue in the healthcare system. Few diseases need to be tested early if any patient discovers any disease-related symptoms in their body. Although various methods are used to test several diseases based on the symptoms, it is necessary to improve the experimental testing for disease detection through various machine learning (ML) methods. Thus, the ensemble ML Model is proposed to enhance the experimental performance per the methods. Different machine learning methods such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Gaussian Naive Bayes (GNB) and Logistic Regression (LR) are used to make ensemble models based on the combination of two or three methods. The experiment is demonstrated on the proposed ensemble model using a diabetes data set and performed with various evaluation metrics. As per the performance of the ensemble method, (SVM + RF) performed well than other methods with an accuracy of 99%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ensemble model Logistic regression Machine learning Naïve Bayes Random forest SVM Voting classifier

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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A Remote Health Care Monitoring system using internet of medical things (IoMT) 3

A Remote Health Care Monitoring system using internet of med...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Subha, S. Inbamalar, T.M. Komala, C.R. Suresh, Lakshmi R Boopathi, Sampath Alaskar, Kamal Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Thiruvallur India Hkbk College of Engineering Department of Information Science and Engineering Bangalore India Technocrats Institute of Technology Excellence Department of Computer Science and Engineering Bhopal India Muthayammal Engineering College Department of Mechanical Engineering Namakkal India Bharati Vidyapeeth Institute of Management Department of Computer Application Kolhapur India

ISBN: (纸本)9798350336238

The Internet of Medical Things (IoMT) is one of the most promising technology solutions that is currently being developed to monitor health status remotely. A risk-stratified data transmission protocol has been used to construct the IoMT architecture for remote patient monitoring. All the sub-systems have undergone performance tests as well as clinical validation. Clinical validation of IoMT software on 100 patients was successful. Digital representations' size and complexity are reduced by up to 80%, making them appropriate for use in developing narrow-band IoT networks. In particular for low-power devices, performance measurement revealed that bandwidth and energy were reduced to 97% and 95%, respectively. © 2023 IEEE.

关键词： Internet of things

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Examining Driver Sleepiness Detection Based on Visual Actions Using Haar Cascading and CNN 3

Examining Driver Sleepiness Detection Based on Visual Action...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Rawat, Vandana Manral, Pratibha Gupta, Lakshya Joshi, Nidhi Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Department of Computer Application Dehradun India Graphic Era Hill University Department of Computer Application Dehradun India

ISBN: (纸本)9798331519582

Every hour, there are numerous road accidents all over the world. Some of these accidents are caused by drunk driving, lack of sleep, inattention behind the wheel, and a variety of other factors that can be dangerous for both passengers and other road users. The most frequent circumstance is sleeping deprivation, which can cause the driver to be reckless while operating a motor vehicle. Driver drowsiness detection systems are particularly effective in identifying drowsiness by measuring the size of the driver's eyes and the rate at which they blink using a camera and software that is connected to it. The driver fatigue detection system works utterly offline, leveraging the Haar Cascade and Perclos machine learning methodologies, and can warn the driver via alarm if he or she is feeling drowsy. © 2025 IEEE.

关键词： Time division multiple access

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Intelligent Energy Management for Electric Vehicles: Fuzzy Logic and Neural Network Integration 10

Intelligent Energy Management for Electric Vehicles: Fuzzy L...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Thirumalaikumari, T. Princy, D. Lita Uma, S. Sadaieswaran, R. Malathi, P. Legapriyadharshini, N. Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Tamil Nadu Chennai India Meenakshi College of Engineering Department of Master of Computer Applications Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Mahabalipuram India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Data Analytics Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Application Tamil Nadu Chennai India

ISBN: (纸本)9798350384369

This study suggests a novel methodology for intelligent energy management in electric vehicles (EVs) through the integration of neural networks and fuzzy logic. Achieving enhanced energy efficiency for electric vehicles is a critical concern that is tackled in this research through the integration of neural networks' decision-making and pattern recognition functionalities with fuzzy logic's adaptability and learning capabilities. The proposed method seeks to optimize energy consumption, enhance overall performance, and extend driving range. The integrated system is subjected to rigorous testing using a meticulously planned experimental setup and simulation environment, which enables it to surpass the performance of more conventional approaches to energy management. The results emphasize the potential of the novel methodology to improve environmentally sustainable transportation options and propel the development of electric vehicle technology. This study not only offers valuable insights into efficient energy management but also establishes the groundwork for subsequent developments in the domain. © 2024 IEEE.

关键词： Electric Mobility Electric Vehicles Energy Management Intelligent Systems Neural Networks Smart Grid

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Data control in public cloud computing: Issues and challenges

Recent Advances in Computer Science and Communications

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Recent Advances in computer science and Communications 2021年第2期14卷 564-579页

作者： Sharma, Ashok Jha, Pranay Singh, Sartaj Department of Computer Science and Engineering Lovely Professional University Phagwara Jalandhar Punjab India School of Computer Application Lovely Professional University Jalandhar Punjab India

Background: The Advancement in the Hardware and Progress in IoT based devices has led to significant transformation in digitalization and globalization of business models in the IT World. In fact Cloud Computing has attracted many companies to expand their business by providing IT infrastructure with very less budget in pay per use model. The Expansion and Migration of Companies to Cloud Computing facilities has really brought many pros and cons and opened new area of Research. The Management of IT infrastructure as per business requirement is a great challenge for the IT Infrastructure managers because of complex business models which needs to be updated with market trends and it requires huge and updated infrastructure to accelerate their business requirements. No doubt there are many benefits of moving to Cloud but several vulnerabilities and potential threats related to security is a major concern for any business sensitive data. These security challenges place restrictions on moving on-premises workloads to the Cloud. This paper has discussed key differences in cloud models and existing various Cloud Security Architectures and challenges in cloud computing related to Data Security at Rest and in Transit. Also data controlling mechanism need to be adopted by IT Industry along with end to end security mechanism has been explained. Objective: The main objective of this paper is to discuss about the prevailing issues in cloud in terms of data security which is discouraging the Industry and organizations to move their data into public cloud and also to discuss how to enhance security mechanism in cloud during data migration and multitenant environment. Methods: Based on different reports and analysis, it has been pointed that data breach and data security are most challenging and concerning factor for any customer when someone think to migrate the workloads from On-Premises datacenter to Cloud Computing. It needs more attention in every consideration. All criteria a

关键词： Cloud security

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Frontiers in Artificial Intelligence and applications 3

Frontiers in Artificial Intelligence and Applications

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3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Yadav, Jaya Shukla, Shruti Sharma, Kanika Soni, Nupur Agarwal, Shruti Pathak, Prabhash Chandra Babasaheb Bhimrao Ambedkar University Department of Information Technology Lucknow India Babu Banarasi das University Department of Computer Application Lucknow India Mangalmay Institute of Management and Technology Department of Computer Application Greater Noida India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Lucknow India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The industrial revolution has been the main cause ever since tremendous technological advancement was observed. The ubiquitous deployment of recent information and communication technologies (ICT), namely Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technology, is hastening the world's industrial and technological transformation. This technical aggrandizement enhances the working culture and has a favorable impact on the workplace, as per the progressivist perspective. The breakneck pace of technological advancement, as well as AI, has enabled humans to replace manual labor in various industries. As being a domain of science and technology, AI develops machines and programs for computers that are intelligent and can accomplish tasks that would normally require human intelligence abilities. This paper mainly explores the frontiers of artificial intelligence and its applications in various fields. The AI Frontiers promulgate methodical concepts that are peer-reviewed cutting-edge research on the disruptive technological revolution of Artificial Intelligence. Additionally, some key viewpoints in the field of AI have been listed along with the main frontiers, including Machine Learning (ML), Deep Learning (DL), Fuzzy Logic (FL), Natural Language Processor (NLP), and Genetic Algorithm (GA). Furthermore, this paper discussed some common AI applications and a briefing about the current scenario in the worldwide market for artificial intelligence. © 2022 IEEE.

关键词： Genetic algorithms

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A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural Networks

A New Method for Predefined-Time Synchronization Control of ...

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2023 IEEE International Conference on Memristive Computing and applications, ICMCA 2023

作者： Liu, Aidi Yu, Wenyue Huang, Lili Guo, Jian Lei, Tengfei Zhao, Hui School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology China Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology China School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350316230

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory. © 2023 IEEE.

关键词： Neural network models

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