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Intelligent & Innovative Practices in engineering & Management (IIPEM), International Conference on

作者： Bhagyashree Ashok Tingare G. Prasanna Lakshmi Vijayshri Khedkar Rakesh Thoppaen Suresh Babu Tarun Dhar Diwan Pravin B Khatkale Department of Artificial Intelligence and Data Science D Y Patil College of Engineering Akurdi Pune School of Computer Science and Engineering Sandip University Nashik Maharashtra Department of Computer Science & Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Senior Technical Architect HEXAWARE TECHNOLOGIES INC New Jersey Assistant Professor and Controller of Examination (COE) Atal Bihari Vajpayee University Bilaspur India Sanjivani University Kopargaon Maharashtra India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

For national cybersecurity to keep up with the digital transition, AI is more important than ever. Responsible AI improves cybersecurity and education by emphasizing openness and ethics. Ethical design is stressed from the outset in responsible AI. This article analyzes how artificial intelligence (AI) influences the national cybersecurity strategy, focusing on cybersecurity education development and effectiveness. The National Cybersecurity Strategy will frame this article. The major results show that responsible AI improves threat detection and response, cybersecurity infrastructure, talent shortage filling, collaborative research, and public trust. Educational institutions may now offer innovative training programs thanks to artificial intelligence. Completing this aim requires closing the gap between conventional methodologies and modern challenges. Closing gaps is vital. Functional artificial intelligence improves national cybersecurity and educational quality. This analysis shows that this conclusion is possible. This allows experts to properly react to shifting dangers. This is why it matters.

关键词： Training Ethics Focusing Collaboration Threat assessment Filling Artificial intelligence computer security

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A novel prediction of remaining useful life time of rolling bearings using convolutional neural network with bidirectional long short term memory

Journal of Engineering Research (Kuwait)

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Journal of engineering Research (Kuwait) 2024年

作者： Balamurugan, R. Takale, Dattatray G. Parvez, M. Muzammil Gnanamurugan, S. Associate Professor Department of Mechanical Engineering M.Kumarasamy College of Engineering Karur India Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Assistant Professor/ ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Avadi Chennai India

Detection of bearing problems increases the importance of the service life of rotating machinery. Convolutional neural networks (CNNs) are often used in current research, and databases built on deep learning (DL) models have improved capabilities in the field of defect diagnosis. We use the publicly available Case Western Reserve University (CWRU) dataset to compare the classification accuracy and gain more adaptive knowledge and insights about the proposed approach. Extensive tests and evaluations are performed on the dataset to verify the diagnostic effectiveness of the recommended method in different situations. To demonstrate the superiority of the proposed method, we compare multiple views of the same dataset with similar tasks. CNN supports degraded index sequences to reduce noise and stop temporal oscillations. A new CNN-BiLSTM model is used to capture current and historical inspection data and predict the RUL's service life and supported power levels. Regarding production, we follow health rates. The proposed method was evaluated by accelerating the bearing motion to failure, and the results demonstrated its advantages in terms of more accurate RUL prediction. According to the experimental results, the proposed center distance measurement method is a new and valuable means for intelligent bearing diagnosis. Experimental results using 48 K and 12 K CWRU datasets show that the overall accuracy of the BiLSTM method is 99.80% and 98.3%, respectively, which is better in diagnosis than some popular models. © 2024 The Authors

关键词： BiLSTM CNN CWRU dataset Rolling bearings life time

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Automated data preprocessing and training interface for machine learning applications

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AIP Conference Proceedings 2024年第1期2942卷

作者： Anagha Challa Akhila Vutukuri Maithreyi Kenguva Sateesh Kumar Kanagala Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India Assistant Professor Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

Machine learning is a promising tool for analyzing and predicting data. However, it is very time-consuming and challenging to manually process the data. There are also many missing values in the data that can affect the accuracy of the models. We propose an automated approach to pre-process the data. It efficiently performs the various steps involved in the data cleaning process by converting the categorical values to a label encoded value, sampling the data, and replacing the missing values with the appropriate central tendency. This project takes the raw data as input and provides a clean error-free dataset that is suitable for training machine learning models. It also aims to use a web application to automate data pre-processing and training of classification and regression models. It uses multiple algorithms and normalization strategies to get the optimum model based on the desired metric. It allows users to download the clean dataset as well as the trained models after the models have been trained. Multiple datasets, including binary classification and regression, are used to test this model.

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Cloud-Based Smart Sensing System for Real-Time Health Management of Dementia Patients

Cloud-Based Smart Sensing System for Real-Time Health Manage...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： R. Latha R. Ramya U. Arul Nilamadhab Mishra S. Dhanalakshmi Professor Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Trichy Tamil Nadu India Department of Mathematics S.A Engineering College Chennai Tamil Nadu India Professor Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Assistant Professor Department of Electronics and Communication Engineering Sri Muthukumaran Institute of Technology Chennai Tamil Nadu India

The paper describes an innovative technique of monitoring updates on the health of people with dementia by integrating smart sensing technologies with a cloud-based platform. Because dementia is a degenerative neuropsychiatric disease that develops with time, constant patient monitoring is essential for ensuring the health and safety of those living with the condition. The proposed system uses smart sensing devices, including wearable and ambient sensors, to monitor dementia patients' health in real-time. Data like heart rates, movements used, and ambient conditions are collected and sent to a server in the cloud for analysis. The camera can monitor and analyze visual data, capture crucial information, and help evaluate the patient's well-being. Multiple patients can be monitored effectively in real time because of the cloud-based system's scalability, accessibility, and storage capacities. For healthcare providers to recognize irregular movements, identify possible risks to health, and implement preventative measures, doctors utilize the moment data analytics devices to gather valuable insights from the acquired data. In addition, the cloud-based solution improves collaborative care and enhances patient support by allowing for continuous communication and information sent among caregivers, doctors, nurses, and family members. Predefined thresholds or anomalous occurrences may trigger alerts and messages, allowing for quick action in reaction to emergencies. Modern technology and data analytics increase the health and happiness of people with dementia.

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Preservation of data using magic squares in Asymmetric key cryptography

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Materials Today: Proceedings 2022年

作者： Srinivasa Rao, Mannava Murari, Thejovathi Sukirtha Priya, N.D. Raghunandan, K.R. Dept of ECE PVP Siddhartha Institute of Technology India Computer Science and Engineering Acharya Nagarjuna University Andhra Pradesh Guntur India Dept of CSE Government College of Engineering Salem India Senior Assistant Professor Dept of ECE N.M.A.M. Institute of Technology Nitte affiliated to VTU Belagavi India

In data communication the information that is being communicated should be confidential. Many algorithms have been designed to keep the data secure. But malicious attacks on these algorithms to break its security have made it necessary to design new algorithms. To overcome this problem a new method using magic squares has been proposed in this paper. The magic square is constructed on constraints such as order of the matrix n and starting number a. © 2021

关键词： Cryptography

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Artificial super intelligence: Barriers and limitations

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AIP Conference Proceedings 2024年第1期3209卷

作者： Rahul Goyal Neha Sharma Amanpreet Kaur Student Department of Computer Science and Engineering Chitkara University Institute of Engineering & technology Chitkara University Punjab India. Assistant Professor Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Professor Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India.

In urban studies, artificial intelligence systems are a widely recognized technology that provides an alternate approach to dealing with complicated and dynamic issues. Despite the risks of implementing more powerful artificial intelligence, many big tech companies are working towards it. So, this paper aims to show the barriers to artificial intelligence. Also, this paper illustrates barriers in machines that prevent artificial superintelligence from being achieved. Further, the paper discusses how machine neural networks differ from human neural networks and the existential threats posed by artificial intelligence and superintelligence.

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Machine Learning in K–12 Educational Settings

Machine Learning in K–12 Educational Settings

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Ramy Riad Al-Fatlawy Dadamuxamedov Alimjon R. Udayakumar Ashish Sharma Jaspreet Kaur S. Padmanaban Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Senior Lecturer at the Department of "Modern Information and Communication Technologies" of International Islamic Academy of Uzbekistan Tashkent Uzbekistan Dean CS & IT Kalinga University India Department of Computer Engineering and Applications GLA University Mathura India Assistant Professor Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Science and Humanities Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The many real-world applications of machine learning-based approaches over the past several decades have shown the potential of data-driven methodology in a variety of computing fields. Higher education computer curricula are beginning to include machine learning, and more and more institutions are incorporating it into K–12 computer instruction. Given the growing prevalence of computational learning in K–12 computer teaching, it is imperative to investigate how agency and intuition develop in these kinds of settings. However, considering the challenges educators and schools already have in integrating conventional learning, understanding the challenges connected with teaching algorithms for learning across grades K–12 provides an even more challenging barrier for computer education research. the curriculum's incorporation of artificial intelligence and *** article outlines the potential for data mining education in grades K–12. These advancements include modifications to technology, philosophy, and practice. The study puts the present findings in the larger framework of computing education and discusses some differences that K–12 computer educators should keep in mind while tackling this issue. The study focuses on key components of the fundamental shift that is required in order to successfully integrate machine learning into more thorough K–12 computing curricula. A critical first step is to abandon the notion that next-generation computational thinking requires rule-based, "traditional" programming.

关键词： Training Technological innovation Philosophical considerations Education Machine learning Learning (artificial intelligence) Market research Telecommunication computing Programming profession Next generation networking

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Machine learning models for evaluating the benefits of business intelligence systems

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Journal of High technology Management Research 2023年第2期34卷

作者： Tripathi, Mano Ashish Madhavi, Kilaru Kandi, V.S. Prasad Nassa, Vinay Kumar Mallik, Banitamani Chakravarthi, M. Kalyan Department of Humanities and Social Sciences Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj India Department of Business Management Velagapudi Ramakrishna Siddhartha Engineering College Kanuru Andhra Pradesh Vijayawada India Greenfields Andhra Pradesh Vaddeswaram India Department of Computer Science Engineering Rajarambapu Institute of Technology Rajaramnagar Maharashtra Sangli India Department of Mathematics Centurion University of Technology and Management Paralakhemundi Odisha Gajapati India Senior Assistant Professor School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi India

Due to the uncertainty of the market and the intensity of rivalry, business owners and managers are often compelled to experiment with a wide variety of strategies for enhancing their company's performance. By enhancing the timeliness and quality of inputs to the decision-making process, Business Intelligence (BI) is one such idea and tool that combines operational data with analytical tools to show complex and competitive information to planners and decision-makers. Business intelligence (BI) tools help companies rapidly generate insights that guide managers toward operational efficiencies, lead them to new opportunities, and set them apart from the competition. The literature study shows that there is a debate about whether BI tools have an effect on the quality of decisions and the development of businesses. The present research explores the varied empirical facets of BI application through ML models. This study concluded with a discussion of how Machine Learning models can be used to assess the value of BI tools. Machine learning models, fed with historical data and a wealth of input features, can foresee the effect of new systems on metrics like revenue development, customer behavior, and inventory management. Using these models, businesses will be able to better evaluate potential investments in new tools and systems. © 2023 Elsevier Inc.

关键词： Decision making

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ADVANCED ATTACK MITIGATION IN IOT GATEWAY PROTOCOLS

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computers & Security 2025年 157卷

作者： K. Praveen Kumar Dr. N. Suresh Kumar Assistant Professor Department of Information Technology Kakatiya Institute of Technology and Science Warangal (KITSW) Telangana India Research Scholar Department of Computer Science and Engineering GITAM University Visakhapatnam Andhra Pradesh India Associate Professor Department of Computer Science Engineering GITAM Institute of Technology GITAM (Deemed to be University) Visakhapatnam India

With the increasing number of users on the internet, numerous cyberattacks are becoming more and more common. Proper detection of these attacks by Intrusion Detection Systems (IDS) is extremely important, particularly for IoT networks. Deep learning methods have proved to be very promising for enhancing IDS performance. This paper presents an end-to-end system for attack detection and prevention in IoT networks with the use of data augmentation, preprocessing, feature extraction, and deep machine learning algorithms. The class imbalance is resolved using the Enhanced Synthetic Minority Over-Sampling Technique (ESMOTE), and preprocessing operations normalize and clean the data for improved model performance. Feature extraction involves statistical features and Shannon entropy-based features, which are fused and sent through a feature selection process. A new 2D-LICM hyper-chaotic map combined with Walrus Optimization (2D-LICMHy-CM_WO) is used to enhance feature selection through enhanced search diversity, convergence rate, and eliminating redundancy. The Dense Convolutional Spatial Attention-based Enhanced Bi-GRU (DCSAtten_EBi-GRU) effectively extracts attack pattern dependencies for precise detection, and an Enhanced Double Deep Q-Learning Network (DoubleDQN) offers dynamic adaptive real-time countermeasures. Experimental findings prove that the proposed solution can obtain a 99.6% detection accuracy with an F1-score of 0.98 and outperforms current IDS models in false positive rate and detection time.

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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