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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Chitra Sabapathy Ranganathan Arshadh Ariff Mohamed Abuthahir Riazulhameed G. Swathi Pavithra M.R S. Senthil C. Srinivasan Associate Vice President Mphasis Corporation Chennai Tamil Nadu India Architect - Data Engineer Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Assistant Professor Department of Management Studies Rajalakshmi School of Business Chennai Tamil Nadu India Assistant Professor Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research paper offers a groundbreaking study on precision nutrition, an intelligent Internet of Things ecosystem that aims to transform the control of nutritional intake. The system provides continuous nutritional analysis and individualized health insights due to its integration of cutting-edge sensor technology with data analytics. Real-time monitoring of the food and drinks' nutritional content is made possible by the ecosystem thanks to a network of intelligent wearable devices equipped with spectroscopic and ion-selective sensors. The technology delivers tailored health insights and dietary recommendations by using algorithms for machine learning and an extensive library of nutrients. In this article, the architecture, sensor integration, data processing, and validation procedures of the Internet of Things ecosystem are further detailed. The suggested system is accurate and effective, as shown by the results of extensive validation studies. The findings of this study highlight the potentially game-changing role that the Internet of Things (IoT) ecosystem may play in enabling people to improve their health via educated dietary decisions, contribute to the development of precision nutrition research, and maximize their health results.

关键词： Data analysis Ecosystems Transforms Propulsion Nutrients Real-time systems Internet of Things

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Recent Advancements in Predicting the Behaviour of Unsaturated and Expansive Soils

Recent Advancements in Predicting the Behaviour of Unsaturat...

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Indian Geotechnical Conference, IGC 2019

作者： Banerjee, Aritra Puppala, Anand J. Congress, Surya S. C. Chakraborty, Sayantan Pedarla, Aravind Assistant Professor Department of Civil and Environmental Engineering University of Delaware NewarkDE19716 United States A.P. and Florence Wiley Chair Professor Zachry Department of Civil and Environmental Engineering Texas A&M University College StationTX77840 United States Associate Research Scientist Zachry Department of Civil and Environmental Engineering Texas A&M University College StationTX77840 United States Assistant Professor Department of Civil Engineering Birla Institute of Technology and Science Pilani Rajasthan333031 India Geotechnical Engineer CDM Smith DallasTX75251 United States

ISBN: (纸本)9789813365896

This study focuses on the advancements of characterization of unsaturated expansive soils which would allow better prediction of its swelling and mechanical behaviours. The paper has been divided into three parts. Firstly, a novel model is developed by considering two micro-soil parameters, soil-specific surface area and internal pore size distribution, to predict the natural swelling in expansive clayey soils. The model was validated by comparing the predictions with experimental results for eight soils. Secondly, the paper deals with the diffused-double-layer (DDL) theory-related electrostatic forces from individual clay minerals of expansive soils and their influence on soil swelling. A DDL-based model was also developed, which was validated for swell prediction for fourteen expansive soils. A good correlation was observed for both models when the results were compared with experimental results. Finally, the paper demonstrated the use of vapour pressure technique to control suction beyond 5 MPa for performing suction-controlled repeated load triaxial (RLT) test at high suction state. This novel technique mitigates the limitation of axis-translation technique for maintaining suction till the air-entry-value (AEV) of the ceramic disk. These new models and unsaturated laboratory testing techniques would aid in better characterization of expansive soils in their unsaturated state. This would also provide accurate prediction of swell and stiffness behaviours of soils, thereby reducing the uncertainties in the design of civil infrastructure built on expansive soils. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data Processing Logic

Empirical Evaluation of Stock Market Prediction System using...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： S. Prabakar Ankit Kumar R Jayakarthik D. Venkatesh Pratheeba R S Bhola Khan Finance and Accounting ISBR Business School Bangalore Department of Computer Application Faculty of Science Swami Vivekanand Subharti University Meerut Dept of Computer science Saveetha College of Liberal Arts and Sciences SIMATS Assistant Professor of Commerce School of Commerce and Management Mohan Babu University Tirupati Department of Computer Science and Engineering Velammal Institute of Technology Panchetti Department of Regional Economics MJP Rohilkhand University Bareilly India

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets.

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Heart Rate Anomaly Detection in Healthcare Using Elliptic Envelope and Local Forest

Heart Rate Anomaly Detection in Healthcare Using Elliptic En...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Chandralekha, E. Vinodhini, S. Kandasamy, V. Rama, P. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai600062 India Department of Information Technology Velammal Engineering College Tamilnadu Chennai600062 India Assistant Professor Panimalar Engineering College Tamilnadu Chennai600123 India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Chennai603203 India

Medical professionals use Anomaly Detection(AD) to identify patients with potential health problems in the heart rate data (HRD), an essential metric related to cardiovascular health. In this research, the most effective AD techniques were identified in detecting unusual variations of the heart rate pattern. Synthetic HRD data with anomalous rates of 15%, 20%, and 25% was generated and standardized it using min-max scaling for consistency among the datasets. Normal HR was based on function with a mean of 70 and a standard deviation of 5 while anomalous values were generated with a mean of 120 and a standard deviation of 10. Among the evaluated models (Elliptic Envelope, one-class SVM, Local Outlier Factor, and Isolation Forest), the Elliptic Envelope (EE) and Isolation Forest (IF) models obtain the best scores in terms of recall and precision - 0.92 and 0.89, respectively. Overall, the results of this research indicate that equally robust AD models should be implemented within clinical systems and wearable devices that serve as a digital space for early cardiac risk identification, prompting adequate intervention and, as a result, health improvement. © 2025 The Author(s).

关键词： Cardiology

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Optimized fuzzy C-means clustering algorithm for cotton fibre quality analysis

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Materials Today: Proceedings 2021年 45卷 2082-2086页

作者： R. Dhanapal D. Selvapandian Assistant Professor Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore 638107 India

Exact determination of cotton fibre quality is a huge help for turning factories to purchase cotton as per the least prerequisites. The nature of cotton fibre is decided on numerous elements, for example, staple length, development, finesses, neatness, tenacity , and quality, to make reference to. The essential target of the framework is to accomplish the institutionalization at various levels in the cotton production network the board in the conveyed mechanical systems condition with exact estimation and characterization procedures. The current framework is executed in information mining procedures to foresee the nature of cotton fibre and to establish the most noteworthy connection between's short fibre file and yellowness. The proposed framework incorporates a bunching strategy to group cotton quality fibre. The proposed optimized principal component analysis (OPCA), and Fuzzy C-means bunching in huge dataset. These proposed calculations are executed inside the Map-Reduce method and cuckoo searching technique is applied for reduces the time complexity it is implemented in Hadoop stage to anticipate dampness content, micronaire, extension, and group quality.

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An investigation on the use of waste materials from industrial processes in clay brick production

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Materials Today: Proceedings 2023年

作者： Padmalosan, P. Vanitha, S. Sampath Kumar, V. Anish, M. Tiwari, Rajesh Dhapekar, Nishikant Kishor Yadav, Anil Singh Department of Civil Engineering Sathyabama Institute of Science and Technology Jeppiar Nagar Chennai India School of Mechanical Engineering Assistant Professor Sathyabama Institute of Science and Technology Jeppiar Nagar Chennai India Department of Management Studies Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Civil Engineering Kalinga University Raipur 492101 Chhattisgarh India Department of Public Relations Swatantra Sahayak Foundation Bhopal MP 462037 India

Clay brick manufacturing is an industry that often makes use of several types of high-temperature firing processes. In order to maintain this temperature in the kiln, large amounts of wood, coal, biomass and other fuels are burned, which is a substantial source of air pollution. The manufacture of bricks needs to make use of waste products in order to accomplish the goal of lowering the amount of trash produced in the environment. In India, the usage of textile sludge and quarry dust are helping the country to get less clay than it would otherwise need. This is due to the fact that there is an inadequate supply of clay. The primary purpose of this research is to investigate the possibility of using textile sludge and quarry dust as supplementary ingredients for the clay used in the production of bricks, in addition to determining the mechanical qualities of the bricks themselves. The percentages of weight contributed by quarry dust and textile sludge were, respectively, 10%, 20%, and 30%. The test is conducted as per BIS standards to determine the compressive strength, water absorption, and efflorescence of the material. It is noticed that the compressive strength of the brick decreases if there is a rise in the quantity of sludge, quarry dust, water absorption, or efflorescence that they contain. These materials could help to reduce the amount of waste that contributes to the contamination of natural and industrial settings. © 2023 Elsevier Ltd. All rights reserved.

关键词： Brick

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Design, Modelling, and Analysis of Novel Solar PV System using MATLAB

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Materials Today: Proceedings 2022年 51卷 756-763页

作者： Ayan Banik Anurag Shrivastava Ravindra Manohar Potdar Sanjiv Kumar Jain Santosh Gopal Nagpure Mukesh Soni National Institute of Technical Teachers' Training & Research Kolkata India Principal and Professor ECE Lakshmi Narain College of Technology and Science Indore India Associate Professor Department of E&TC Engineering Bhilai Institute of Technology Chhattisgarh India Assistant Professor Department of Electrical Engineering Medi-Caps University Indore India Assistant Professor Department of E&TC Engineering D. Y. Patil School of Engineering Academy Pune India Research Scholar Department of Computer Science and Engineering Jagran Lakecity University Bhopal India

In order to meet the world's future energy demand, alternative renewable green energy supplies must be widely promoted. Among all DERs, the most prevalent is Solar PV for a number of important reasons. In this study, the authors analyze several elements of the Single-Phase Solar Photovoltaic Rooftop System with MATLAB. In order to analyze the characteristics of the output current, a new grid synchronization methodology has been devised. For maximum power tracking, the INC control technique has been adopted. The output of the solar DC power system has been synchronized with the grid through hysteresis current loop control. Extensive testing verifies the practicality of the proposed power grid model and promising outcomes and results.

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Responsible AI on National Cybersecurity Strategy with Enhancement of Cybersecurity in Education

Responsible AI on National Cybersecurity Strategy with Enhan...

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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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Automatic Vehicle License Plate Recognition Using Optimal Deep Learning Model

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computers, Materials & Continua 2021年第5期67卷 1881-1897页

作者： Thavavel Vaiyapuri Sachi Nandan Mohanty M.Sivaram Irina V.Pustokhina Denis A.Pustokhin K.Shankar College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science&Engineering IcfaiTechICFAI Foundation for Higher EducationHyderabadIndia Assistant professor Research Research CenterLebanese French UniversityErbil44001Iraq Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudiIndia

The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the *** VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing *** current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN *** presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and *** Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in *** SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN *** HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge *** experimentation outcome verified that the presented method was better under several *** projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.

关键词： Deep learning license plate recognition intelligent transportation segmentation

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A Deep Learning Approach to Automated Sleep Stages Classification Using Multi-Modal Signals

A Deep Learning Approach to Automated Sleep Stages Classific...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Satapathy, Santosh Kumar Kondaveeti, Hari Kishan Sreeja, S.R. Madhani, Hiral Rajput, Nitinsingh Swain, Debabrata Information and Communication Technology Pandit Deendayal Energy University Gujarat Gandhi Nagar382007 India VIT-AP University Assistant Professor CSE Group IIIT Sri City Andhra Pradesh Amaravati522237 India Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhi Nagar382007 India

Sleep is one of the elements most vital to human life. However, the modern lifestyle continues to push people to neglect this critical requirement. With a vast majority of people falling victim to various sleep disorders, it has become increasingly essential to have a robust system for diagnosing and treating such ailments. Sleep stage classification is one of the primary steps for identifying sleep-related anomalies. Sleep stages are classified according to the frequency and nature of signals received during a polysomnography test. Since the early days, this has been performed manually with the help of trained technicians. However, manual scoring is often prone to error and subjectivity and requires tremendous time and effort. It is, therefore, essential to automatize this process. Several challenges from the correct selection of features remain to be faced in the machine learning-based sleep stage classification system. As an alternative, Deep Learning, capable of automatic feature extraction, proves far more reliable for this task. This experimental study analyses both techniques to compare and decide on a better approach. Three popular Machine Learning classifiers, namely Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), and a neural network comprising CNN and LSTM, have been trained on a vast base of diverse data. The proposed model reported an accuracy of 87.4% with CNN+LSTM. © 2022 Elsevier B.V.. All rights reserved.

关键词： Feature extraction

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