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Investigate the Role of Reinforcement Learning in Optimizing Big Data Retrieval Speed

Investigate the Role of Reinforcement Learning in Optimizing...

2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Banchhor, Chitrakant O. Kumar, Mukesh Savita Kavitha, R. Francis, G. Arun Naveena, S. Sathiya Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai India

ISBN: (纸本)9798350329773

This paper investigated the capability of reinforcement studying to optimize the retrieval velocity of big data from a database. Exclusive support for learning techniques and algorithms was explored, including Q-mastering, SARSA, and Deep Q-gaining knowledge. The paper supplied a case examination to assess the impact of the one-of-a-kind reinforcement gaining knowledge of algorithms at the retrieval velocity of a database. The results confirmed that the Q-learning approach yielded the best development inside the retrieval pace, reducing the time spent retrieving big data from the database. In addition, the paper counseled ability programs of the supplied reinforcement, gaining knowledge of strategies for numerous extensive statistics retrieval problems. The report concludes by presenting sensible advice and tips for incorporating reinforcement-gaining knowledge into optimizing considerable information retrieval speed. © 2023 IEEE.

关键词： Big data

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Investigating the Benefits of Text Mining for information Analysis

Investigating the Benefits of Text Mining for Information An...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Singh, Vaishali Venkadeshwaran, K. Jha, Gaurav Arunadevi, R. Sharma, Yogesh K. Maheswaran, S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

Textual content mining is the manner of extracting significant information and insights from large volumes of unstructured textual information. Many businesses are beginning to recognize the capacity of text mining in gaining effective insights and presenting greater accurate statistics. This paper will talk the ability blessings of using text mining strategies in data analysis. It's going to present quite number possible applications, such as herbal language processing, sentiment evaluation, subject matter modeling, and predictive analytics. It also evaluation the numerous technical techniques and algorithms used for textual content mining, together with lateens semantic indexing, and will look at how text mining can be used to guide a range of various commercial enterprise targets. Sooner or later, the paper will address how text mining can help pick out useful styles and traits in unstructured information, and the way it may be utilized in tandem with conventional data processing methods to achieve higher effects. This paper will finish by way of analyzing the capacity challenges that may be encountered when using text mining and how ability dangers can be mitigated. © 2023 IEEE.

关键词： Semantics

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Origins to Innovations: A Comprehensive Journey Into Artificial Intelligence's Future Application

Origins to Innovations: A Comprehensive Journey Into Artific...

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2024 International Conference on Sustainable Computing and Integrated communication in Changing Landscape of AI, ICSCAI 2024

作者： Saraswat, Shivam Mehra, Ritika Gurpreet Yuvaraj, K. Karthigaikumar, P. Wankhede, D.S. Department of Civil Engineering School of Engineering & Technology K. R. Mangalam University Haryana Gurugram India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand 248007 India Department of Computer Applications Invertis University Uttar Pradesh Bareilly India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350355338

In the contemporary era characterized by the proliferation of extensive data sets and the advent of Industry 4.0, artificial intelligence (AI) emerges as a prominent catalyst for economic growth. It assumes a pivotal role in facilitating the integration of advanced technologies like bitcoin, virtualization, the Internet of Things (IoT), and graphics processors, hence fostering innovation and development. In this scholarly essay, a comprehensive examination of artificial intelligence (AI) and learning methodologies spanning the years 1961 to 2018 is conducted. Through an extensive examination of AI, encompassing various aspects such as causal factors, practical systems, fundamental machine learning, commercial achievements, and current advancements, this investigation serves as a highly valuable resource for both academic scholars and industry experts. There is a substantial number of challenges associated with artificial intelligence (AI). However, it is undeniable that AI has seen significant advancements, emerging as an innovative and transformative tool across all domains and applications. © 2024 IEEE.

关键词： Adversarial machine learning

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An Innovative Method to Detect Cyberbullying on Social Media 3

An Innovative Method to Detect Cyberbullying on Social Media

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

作者： Pandiaraja, P. Muthumanickam, K. Geetha, M. Mahesh, P.C. Senthil Vedhashree, S.K. Selvarathi, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Kongunadu College of Engineering and Technology Department of Information Technology Tholurpatti Tamil Nadu Trichy621215 India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639113 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641021 India

ISBN: (纸本)9798331509675

Today social media has fundamentally altered global communication and information exchange. However, as these platforms have become more widely used, so has cyber-hatred which is a significant problem that has caught academics' attention. This article has been addressed by a variety of methods from deep learning and machine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using mathematical concepts. However, sentiment-oriented data offers an accurate picture of how the public reads communications online, accurate classification necessitates a more "critical thinking"perspective. Two machine learning classifiers were employed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and multinomial naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm. © 2025 IEEE.

关键词： Adversarial machine learning

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Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends

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IET communicationS 2022年第5期16卷 400-420页

作者： Elfatih, Nada M. Hasan, Mohammad Kamrul Kamal, Zeinab Gupta, Deepa Saeed, Rashid A. Ali, Elmustafa Sayed Hosain, Md. Sarwar Department of Electrical and Electronics Engineering Red Sea University Port Sudan Sudan Faculty of Information Science and Technology The National University of Malaysia (UKM) Bangi Selangor Malaysia Amity Institute of Information Technology Amity University Uttar Pradesh Delhi NCR India Department of computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Department of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Department of Information and Communication Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh

The Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) concept have emerged from IoT technology, which refers to connecting many vehicles with various applications to the internet. The 5G new radio is based on a cloud-radio access network (CRAN), considered as the communication infrastructure for IoV. However, due to the significant challenges and issues, researchers have been working on IoV and V2X. One of the main challenges for V2X is resource allocation and management for a high-speed vehicular environment. This paper discusses and provides complete detail for resource allocation and management for IoV over 5G RAN networks focusing on artificial intelligence techniques. The paper also presented reviews on integrating the multi-layers of vehicular network architecture with AI strategy to identify advancement and future directions for resource allocation and management issues.

关键词： Internet of Things Vehicle to Everything artificial intelligence techniques resource allocation IoT technology IoV radio access networks vehicle resource management V2X 5G mobile communication Internet of Vehicles communication infrastructure artificial intelligence vehicular ad hoc networks 5G RAN networks 5G new radio high-speed vehicular environment 5G networks resource management telecommunication computing

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Real-Time Deep Learning for Dynamic Scene Understanding and Autonomous Robot Navigation

Real-Time Deep Learning for Dynamic Scene Understanding and ...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Muthukrishnan, A. Pushparani, S. Poongavanam, N. Arun, M. Ilangovan, S. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600 078 India Saveetha University Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivaganga India

ISBN: (纸本)9798331542344

In this paper, a new approach in dynamic scene understanding and robot navigation especially for autonomous robots through the integration of deep learning approach with real time processing is proposed. For object recognition and its localization on the image the system uses convolutional neural networks, also semantic segmentation relies on convolutional neural networks, for autonomous navigation, the deep reinforcement learning is used. The data for the research are gathered from publicly available databases and new data captured with web-cameras and LiDAR sensors installed on robots exploring the chosen scenarios. The deep learning models are for real time usage by using TensorFlow Lite and the models are run on Jetson Xavier. In order to investigate object detection efficiency, scene segmentation differentiate, success rate of providing navigation and system latency, various experiments were performed inside and outside the building. The outcomes also show a high degree of 'indoor' stability and an interacting environment obstacle avoidance efficiency over real-Time system operation. While these difficulties were noticed in rural outdoor environment, the system managed to navigate in space populated with dynamic obstacles and safely avoid them even if environment conditions changed, for instance, due to wind. Deep learning with sensor fusion as a solution offered accurate scene understanding to make sure that autonomous navigation is possible on both indoor and outdoor settings. Thus, the research provides valuable information on the improvement of safety and efficiency in autonomous robots for use in various industries such as self-driving cars, robots for a smart warehouse etc. © 2024 IEEE.

关键词： Convolutional neural networks

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Developing Novel Approaches for Implementing Machine Learning in Medicine 3

Developing Novel Approaches for Implementing Machine Learnin...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Mercilin Raajini, X. Inamdar, Fatima M. Kannagi, A. Agrawal, Trapty Vishnoi, Manish Yuvaraj, K. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Civil Engineering Jaipur India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350319125

Recent advances in computing hardware and software program have enabled the improvement and deployment of state-of-the-art system getting to know (ML) models in diverse scientific domain names. ML has the capacity to leverage large volumes of affected person facts to generate insights and actionable answers, allowing unique prognosis and treatment choices. In spite of promising ability, enforcing ML in medicine has been hard because of numerous realistic problems, inclusive of records bias and series technology, lack of robust evaluation frameworks, and ethical and privacy worries. To cope with these demanding situations, researchers have begun to expand novel methods to allow green and powerful ML deployment in medical practice. these tactics contain leveraging novel records resources such as wearable's and glide cytometry, the use of federated studying algorithms for privacy-preserving version development, and incorporating multi-level evaluation metrics which include explain ability and fairness. Together, these novel methods provide a roadmap for correctly and safely deploying ML in medication to higher help and enhance patient care. © 2023 IEEE.

关键词： computing generate improvement medication patient

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An Adaptive Deep Learning Model for Crop Yield Prediction 2

An Adaptive Deep Learning Model for Crop Yield Prediction

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2nd International Conference on computer, communication and Control, IC4 2024

作者： Gadupudi, Ashwin Rani, Rudra Yamini Jayaram, B. Sharma, Nipun Deshmukh, Jotiram Krishna Reddy, Pundru Chandra Shaker California State University Computer Science Fresno United States Vnr Vignana Jyothi Institute of Engineering and Technology Information Technology Hyderabad India Rmk Engineering College Computer Science and Business System Tamil Nadu Kavaraipettai India Presidency University Electronics and Communication Engineering Karnataka Bangalore India Bharati Vidyapeeth College of Engineering Instrumentation Engineering Navi Mumbai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350387933

Agriculture is the study and practice of raising plants and animals for human consumption. Approximately 60.45% of India's land is devoted to farming, making it the country's second largest economic sector. Agriculture and the related agro-industrial sector are crucial to India's economy. Soil components (such as nitrogen-phosphorus &potassium), crop rotation, soil-moisture, air and soil-temperatures, rainfall, and other environmental factors all play important roles in successful farming. One piece of current evidence in this area is a technique that utilizes machine-learning (ML) strategies like Random-Forest (RF), Decision-Tree (DT), and ANN to pick the best crop. In this study, we employ deep learning approaches to improve the suggested model, and together with crop prediction, we obtain clear information regarding the quantities of soil attributes requisite, along with their individual costs. It's more precise than the current model. By analyzing the data provided, it aids farmers in making informed business decisions. Predicting a proper yield requires thinking about the land's climate and soil. The goal is to introduce a Python-based system that makes use of strategic thinking to anticipate the most productive harvest under specific conditions while keeping costs to a minimum. While LSTM &RNN are employed as deep-learning((DL)-strategies in this work, SVM is used as the ML-strategy. © 2024 IEEE.

关键词： Forecasting

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Optimising Business Processes in Project Tracking using CRM 2

Optimising Business Processes in Project Tracking using CRM

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Ramprasath, J. Saranya, N. Arun Kumar, M. Chandru Vignesh, C. Ramprakash, P. Krishnaraj, N. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of AI & DS Coimbatore India

ISBN: (纸本)9798331534950

Customer relationship management, called CRM, is essential for businesses looking to sustain profitable growth in today's fiercely competitive business environment. This essay examines cutting-edge methods to improve clients in the digital era and thoroughly explains CRM practices. The research examines best-serving customers through various touchpoints by integrating data analytics, AI, and personalized marketing strategies. In addition, it explores the role of organizational alignment and a customer-centric culture in developing enduring customer relationships. The study uses theoretical frameworks and practical data to highlight the main obstacles and changes associated with implementing efficient CRM procedures. As a result, synthesizing pertained research and case studies provides insights into new trends and industry best practices for building long-lasting relationships with customers. The results highlight the significance of proactive involvement, ongoing innovation, and ethicizing deliberations in fostering trust and allegiance across various consumer categories. This paper adds to the current conversation on CRM by providing practice advice that companies can use to adjust to and prosper in a constantly changing market. © 2024 IEEE.

关键词： Sales

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Exploring the Quality of Service Impacts of Cloud Computing over Wireless Networks

Exploring the Quality of Service Impacts of Cloud Computing ...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Gopal, Santhosh Kumar Mohammed, Abdul Sajid Saddi, Venkata Ramana Jiwani, Nasmin Logeshwaran, J. Sciences CharlotteNC United States School of Computer and Information Sciences University of The Cumberlands KY United States Ace American Insurance Chubb Group RaleighNC United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350394399

Cloud computing is a paradigm that enables users to access statistics and programs through the use of a virtualized infrastructure. As such, it could be leveraged to lessen the prices of strolling an allotted system, even at the same time growing its competencies and flexibility. Unfortunately, cloud computing additionally has a big effect on the great of service of the underlying Wi-Fi community. In this paper, we discover the best carrier influences of cloud computing over wi-wireless networks in phrases of latency, throughput, and packet loss. Wi-Fi, we talk about the demanding situations and solutions associated with integrating Wi-Fi networks with cloud computing architectures. We then evaluate the overall performance of different eventualities, consisting of using 4G and 5G, as well as baseline overall performance without cloud integration. Our outcomes show that cloud computing can improve the general quality of service of Wi-Fi wireless networks despite the fact that there may be full-size variations between exceptional community generations. © 2024 IEEE.

关键词： Cloud computing

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