Analysis of Multinomial Classification for Legal Document Categorization
subject: Legal document classification; Machine learning; Natural language processing
description: A major area of research today is the application of Machine Learning Techniques for Document or Text Classification. Document Classification is an important aspect of Electronic Discovery in the Legal domain. The need for the process to be automated has been realized over the past few years. Multinomial Classification is a well-known Supervised Machine Learning Technique that helps us classify if there are more than two classes used for the purpose of Classification. Evaluation metrics such as Precision, Recall, and F1 Score have been used to measure the efficiency of Classification. Logistic Regression and Gradient Boosting Algorithms have outperformed other Multiclass Classification techniques. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Joseph T.; Adiyillam V.
source: Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 177-183.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981970974-8
format: Online
language: English
type: Conference paper
coverage: Joseph T., Research Scholar, Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, 560029, India; Adiyillam V., Department of Statistics and Data Science, CHRIST (Deemed to Be University), Bengaluru, 560029, India

Cognitive outcomes prediction in children using machine learning and big data analytics
subject: big data analytics; Cognitive outcomes; Machine learning
description: This study explores the potential of machine learning (ML) and big data analytics in predicting cognitive outcomes in children, aiming to enhance early identification and intervention strategies. Leveraging a diverse dataset comprising neurocognitive assessments, genetic markers, socio-economic factors, and environmental variables, our research employs advanced ML algorithms to develop predictive models. The interdisciplinary approach integrates neuroscience, psychology, and data science to discern patterns and correlations within the expansive dataset. The study emphasizes the importance of early cognitive assessment for optimal child development and academic success. By harnessing the power of big data, our models seek to uncover nuanced relationships that traditional methodologies may overlook. Preliminary results indicate promising accuracy in predicting cognitive outcomes, offering a valuable tool for educators, healthcare professionals, and policymakers. Additionally, the model's interpretability allows for a deeper understanding of the factors influencing cognitive development. Ethical considerations, privacy safeguards, and data governance are integral components of this research, ensuring responsible use of sensitive information. The implications of this study extend beyond academia, with the potential to inform educational policies, personalized learning strategies, and targeted interventions for at-risk populations. As technological advancements continue, the integration of ML and big data analytics in predicting cognitive outcomes heralds a new era in pediatric research, promoting proactive approaches to support children's cognitive well-being. 2024 IEEE.
creator: Behera N.R.; Thiyagarajan V.S.; Manoharan G.; Veena C.; Bikash Acharjee P.; Murthy A.
source: 4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835030775-7
format: Online
language: English
type: Conference paper
coverage: Behera N.R., Swiss School of Business and Management Geneva, Petit-Lancy, 1213, Switzerland; Thiyagarajan V.S., Karpaga Vinayaga College of Engineering and Technology, Department of Cse, India; Manoharan G., Sr University, School of Business, Telangana, Warangal, India; Veena C., Institute of Aeronautical Engineering, Dundigal, Hyderabad, India; Bikash Acharjee P., Christ University Pune, Computer Science, India; Murthy A., Nitte ( Deemed to Be University), Nmam Institute of Technology, Department of Mca, Udupi, India

AI Based Seamless Vehicle License Plate Recognition Using Raspberry Pi Technology
subject: Customized Vehicle Management; Indian Number Plate Recognition; Raspberry Pi; Tesseract OCR; YOLOv8
description: This research presents the implementation of an innovative Vehicle Management System designed specifically for the Christ University Project 'CampusWheels.' The system incorporates cutting-edge technologies, including YOLOv8 and Tesseract OCR, for robust license plate recognition. Addressing the unique challenges faced by Christ University in managing and securing vehicular movements within the campus, this project becomes crucial as the number of vehicles on campuses continues to grow. It not only provides an effective solution to these challenges but also introduces innovative methodologies, marking a significant departure from conventional campus management practices. The paramount importance of this project lies in its ability to enhance campus security through real-time vehicle monitoring and identification. The utilization of YOLOv8 for vehicle detection and Tesseract OCR for license plate recognition ensures a high level of accuracy in identifying and tracking vehicles entering and leaving the campus. This precision significantly contributes to the prevention of unauthorized vehicle access, a common security concern on educational campuses. Moreover, the system's ability to streamline traffic flow and improve efficiency in parking and access control addresses practical issues faced by campus administrators and security personnel. 2024 IEEE.
creator: Abreo A.; Mayur C.; Sinha S.
source: Proceedings - 2024 13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024, pp. 387-392.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835030546-3
format: Online
language: English
type: Conference paper
coverage: Abreo A., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India; Mayur C., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India; Sinha S., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India

Analysing Employee Management Using Machine Learning Techniques and Solutions in Human Resource Management
subject: Human Resource management; Machine Learning
description: In the contemporary landscape of Human Resource Management (HRM), organizations are increasingly turning to advanced technologies to streamline employee management processes. This study explores the integration of machine learning (ML) techniques as a transformative solution for optimizing HRM practices, with a specific focus on employee management. By leveraging the power of ML algorithms, this research aims to enhance decision-making, efficiency, and overall effectiveness in HRM. The study encompasses a comprehensive analysis of existing HRM challenges, such as talent acquisition, performance evaluation, and employee retention, and proposes ML-based solutions to address these issues. By applying natural language processing, pattern identification, and predictive analytics, businesses may learn a great deal about employee behavior, performance patterns, and possible areas for development. HR professionals are more equipped to make well-informed choices, customize employee experiences, and put proactive talent development initiatives into action thanks to this data-driven approach. Additionally, the study examines the moral issues and difficulties surrounding the use of ML in HRM, stressing the significance of openness, justice, and privacy. By understanding and mitigating these concerns, organizations can successfully harness the transformative potential of ML in employee management, fostering a more dynamic and adaptive HRM framework. The study's conclusions add to the growing body of knowledge on the relationship between technology and HRM and offer useful advice to businesses looking to use cutting-edge approaches to improve labor management procedures. 2024 IEEE.
creator: Chandana C.; Sarkar A.; Deshmukh K.; Kulkarni P.; Bikash Acharjee P.; Lourens M.
source: 4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835030775-7
format: Online
language: English
type: Conference paper
coverage: Chandana C., Kg Reddy College of Engineering & Technology, India; Sarkar A., Delhi Technical Campus, Department of Management Studies, Greater Noida, India, Guru Gobind Singh Indraprastha University, Delhi, India; Deshmukh K., Indira School of Business Studies, Pune, India; Kulkarni P., Lotus Business School, Pune, India; Bikash Acharjee P., Christ University, Computer Science, Pune, India; Lourens M., Durban University of Technology, Faculty of Management Sciences, South Africa

An Analysis of Manufacturing Machine Failures and Optimization Using Replacement Year Prediction
subject: Failure rate; Machine learning; Manufacturing failures; Operations research; Random forest classifier; Replacement years; Statistical analysis
description: The manufacturing industry is highly susceptible to equipment failures, leading to costly downtime, production delays, and increased maintenance expenses. Effective maintenance planning and resource allocation depend on the early detection of possible faults and the precise forecasting of replacement years. The fundamental technique for assuring operational resilience, limiting disruptions, and improving preventative maintenance processes is manufacturing failure analysis. It entails the methodical analysis of failures and spans several sectors, including the automobile, aerospace, electronics, and heavy machinery. In this research, an integrated methodology for predicting replacement years in the manufacturing industry using operations research approaches and the Python-based machine learning algorithm Random Forest Classifier (RFC) is proposed. The program first calculates the total failure rate after importing manufacturing data from a dataset. The failure rate for each manufacturing line is then determined, and the lines with a high failure rate are identified. The program uses machine learning to improve the analysis by teaching a Random Forest classifier to anticipate failures. The model's performance is assessed by measuring the accuracy of a test set. To determine machine replacement years, it also incorporates replacement theory assumptions. Based on the company's founding year and the current year, it determines the replacement year considering the machine's lifespan. This program's advantages include recognizing production lines with high failure rates, employing machine learning to forecast problems, and offering suggestions on when to replace machines. Manufacturers may enhance their processes, lower failure rates, and increase overall efficiency by utilizing statistical analysis, machine learning, andoptimizationstrategies. As technology advances, the field of failure analysis will continue to evolve, enabling firms to achieve improvements. The Author(…
creator: Saranya R.; Sneha M.; Sridevi R.
source: Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 311-324.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981970974-8
format: Online
language: English
type: Conference paper
coverage: Saranya R., PSG College of Arts and Science, Coimbatore, India; Sneha M., PSG College of Arts and Science, Coimbatore, India; Sridevi R., Christ Deemed to be UniversityCentral Campus, Bangalore, 560029, India

Enhancing Mobility: A Smart Cane with Integrated Navigation System and Voice-Assisted Guidance for the Visually Impaired
subject: audio feedback; Blind mobility; haptic feedback; Navigation; Smart Cane; Ultrasonic sensor; vibration motor; voice control
description: Blindness is a condition which affects many people, and for the affected people, quality of life can take a big hit. Most blind people already use walking sticks to feel the terrain in front of them as they move around and navigate using touch and sound. However, they cannot judge distances to objects until the cane actually hits the object. In some cases, the contact with the cane may damage the object. Hence, it may be better to have some early warning system so that there is less likelihood of causing damage. This paper presents the design and development of a 'Smart Cane' aimed at enhancing mobility and safety for visually impaired individuals. The cane incorporates ultrasonic sensors to detect objects in the user's surroundings. When an object is detected within a specified distance range, the cane provides haptic feedback through a bidirectional vibration motor, alerting the user to its presence. The microcontroller-based system processes data from both sensors and efficiently manages power consumption to ensure extended battery life. The device's design includes user-friendly controls and an ergonomic enclosure to offer ease of use and protection for the electronic components. Further, there is built-in navigation via online Map API. With the convenience of navigating oneself without external assistance, the 'Smart Cane' demonstrates great potential to improve the independence and confidence of visually impaired individuals in navigating their environments safely. 2024 IEEE.
creator: Muktha D.S.; Niveditha G.; Pinto N.A.; Sinha S.
source: Proceedings - 2024 13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024, pp. 1124-1129.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835030546-3
format: Online
language: English
type: Conference paper
coverage: Muktha D.S., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India; Niveditha G., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India; Pinto N.A., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India; Sinha S., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India

Brain Tumor Localization Using Deep Ensemble Classification and Fast Marching Segmentation
subject: Brain tumor detection; Deep learning; Ensemble; Fast marching; Segmentation
description: A brain tumor is an unusual and excessive growth of brain cells, which can be cancerous (malignant) or noncancerous (benign). These growths can be risky as they press on healthy brain tissue or expand in the brain. Detecting brain tumors early is tough for radiologists. A typical brain tumor can double in size in just 25days, and without the right treatment, patients often have limited chances of survival, about six months. Initial symptoms can be confused with other illnesses, and brain cancer is difficult to diagnose because of the complex nature of the brain and tumor locations. In this study, we propose a strategy where we first sort medical images based on the presence of a brain tumor. Then, we pinpoint the part of the image containing the tumor through segmentation. We use a combined model of MobileNet-V3 and EfficientNetV2 for image classification. To segment the tumor in the image, we use a fast marching method. The combined model's classification accuracy is 98%, and the segmentation accuracy is 99.6%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Joshey A.; Thiruthuvanathan M.M.; Aarushi
source: Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 151-164.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981970974-8
format: Online
language: English
type: Conference paper
coverage: Joshey A., Christ (Deemed to be University), Karnataka, Bangalore, India; Thiruthuvanathan M.M., Christ (Deemed to be University), Karnataka, Bangalore, India; Aarushi, Christ (Deemed to be University), Karnataka, Bangalore, India

Strategic Integration of HR, Organizational Management, Big Data, IoT, and AI: A Comprehensive Framework for Future-Ready Enterprises
subject: AI integration; Big data analytics; Future-ready enterprises; HR management; IoT implementation; Organizational efficiency; Strategic integration
description: This exploration paper proposes a comprehensive frame aimed at fostering unborn-ready enterprises through the strategic integration of Human coffers(HR), Organizational Management, Big Data, the Internet of Things (IoT), and Artificial Intelligence(AI). By synthesizing these critical factors, the frame seeks to optimize organizational effectiveness, enhance decision-making processes, and acclimatize proactively to evolving request dynamics. Through a methodical review of being literature and empirical substantiation, the paper delineates the interconnectedness of these rudiments and elucidates their collaborative impact on organizational performance and dexterity. likewise, it explores perpetration strategies and implicit challenges associated with espousing such an intertwined approach. This paper not only contributes to the theoretical understanding of strategic operation but also provides practical perceptivity for directors and directors seeking to navigate the complications of the contemporary business geography and place their associations for sustained success in a decreasingly digitized and competitive terrain. 2024 IEEE.
creator: Chavan V.; Nagendra; Mohammad Roshif U.; Abhijith R.; Joshi A.; Jha D.
source: Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036509-2
format: Online
language: English
type: Conference paper
coverage: Chavan V., Ravensbourne University London, Project Management, United Kingdom; Nagendra, Manipal Law School, Manipal Academy of Higher Education, Karnataka, Bengaluru, India; Mohammad Roshif U., Malabar College of Advanced Studies Vengara, University of Calicut, Department of Management Studies, Kerala, India; Abhijith R., Cet School of Management, College of Engineering Trivandrum, Apj Abdul Kalam Technological University, Kerala, India; Joshi A., Ba School of Business And Finance, Riga, Latvia; Jha D., Christ (Deemed To Be University, Delhi-NCR), Uttar Pradesh, India

Theoretical Studies ond(?,p)n atAstrophysical Energies
description: The photonuclear reactions using deuterium target finds application in nuclear physics, laser physics and astrophysics. The studies related to deuteron photodisintegration using polarized photons has been the focus of interest since 1998 which influenced many experimental studies which were carried out using 100% linearly polarized photons at Duke free electron Laser laboratory. Theoretical study on deuteron photodisintegration was carried out and in these studies the possibility of 3 different E1v amplitudes leading to the final n-p state in the continuum was discussed. As there is experimental evidence about the splitting of 3 E1vp- wave amplitudes at slightly higher energies, we hope that the same may be true at near threshold energies also. As the spin dependent variables are more sensitive to theoretical inputs and the data obtained on polarization observables are more sensitive to theoretical calculations, there is a considerable interest on studies related to the reaction. More recently, neutron polarization in d(?,n)p was studied at near threshold energies. In this regard the purpose of the present contribution is to extend this study to discuss proton polarization in d(?,p)n reaction using model independent irreducible tensor formalism at near threshold energies of interest to astrophysics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Kusuma M.; Shastri V.; Shilpashree S.P.
source: Springer Proceedings in Physics, Vol-304 SPPHY, pp. 815-817.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 9308989; ISBN: 978-981970288-6
format: Online
language: English
type: Conference paper
coverage: Kusuma M., School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Shastri V., School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Shilpashree S.P., School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India

Bibliometric Analysis: A Trends and Advancement in Clustering Techniques on VANET
subject: Bibliometric analysis; Cluster formation; Cluster head; Cluster stability; Clustering; VANET; VOS viewer
description: In recent years, Traffic management and road safety has become a major concern for all countries around the globe. Many techniques and applications based on Intelligent Transportation Systems came into existence for road safety, traffic management and infotainment. To support the Intelligent Transport System, VANET has been implemented. With the highly dynamic nature of VANET and frequently changing topology network with high mobility of vehicles or nodes, dissemination of messages becomes a challenge. Clustering Technique is one of the methods which enhances network performance by maintaining communication link stability, sharing network resources, timely dissemination of information and making the network more reliable by using network bandwidth efficiently. This study uses bibliometric analysis to understand the impact of Clustering techniques on VANET from 2017 to 2022. The objective of the study was to understand the trends & advancement in clustering in VANET through bibliometric analysis. The publications were extracted from the Dimension database and the VOS viewer was used to visualize the research patterns. The findings provided valuable information on the publication author, authors country, year, authors organization affiliation, publication journal, citation etc. Based on the findings of this analysis, the other researchers may be able to design their studies better and add more perception or understanding to their empirical studies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Shandilya A.; Gupta V.; Anand G.
source: Lecture Notes in Networks and Systems, Vol-995, pp. 251-263.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981973291-3
format: Online
language: English
type: Conference paper
coverage: Shandilya A., CHRIST (Deemed to Be University), Bengaluru, India; Gupta V., CHRIST (Deemed to Be University), Bengaluru, India; Anand G., CHRIST (Deemed to Be University), Bengaluru, India

Deep Learning Enabled Parent Involvement and Its Influence on Student Academic Achievement Analysis
subject: Deep Learning; Ethical Considerations; Longitudinal Study; Neural Network; Parental Engagement; Parental Involvement; Student Academic Achievement; Technology in Education
description: Studying the substantial effect that Deep Learning Enabled Parent Involvement (DLEPI) has on kid academic success. Using a made-up data set and a neural network model, we find that parents' level of involvement, as measured by the Parental Involvement Score (PIS), is positively correlated with their children's academic performance. DLEPI, driven by cutting-edge deep learning algorithms, equips parents with unique insights and suggestions regardless of where they live, therefore promoting educational equality and diversity. This study underlines the potential of technology to reduce performance inequalities and highlights its central role in increasing parental participation. Critical elements for future study include ethical issues, real-world validation, effect evaluations over time, and chances for personalization. This research lays the groundwork for reinventing education in a future where DLEPI improves student outcomes and offers a more inclusive and personalized educational environment. 2024 IEEE.
creator: Baruah A.; Kaur A.; Venugopal P.R.; Raghunandan S.; Sreedevi C.; Atrayan L.
source: Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036509-2
format: Online
language: English
type: Conference paper
coverage: Baruah A., Christ University, Department of Psychology, Delhi-NCR, India; Kaur A., Amity Institute of Psychology And Allied Sciences (AIPS), Amity University, Delhi-NCR, India; Venugopal P.R., Bhavan's Vivekananda College of Science, Humanities And Commerce, Commerce & Business Management, Sainikpuri, Secunderabad, India; Raghunandan S., Bharatiya Vidya Bhavans Vivekananda College of Science, Humanities And Commerce, Department of Commerce, Sainikpuri, Telangana, Secunderabad, India; Sreedevi C., Bharatiya Vidya Bhavans Vivekananda College of Science, Humanities And Commerce, Department of Commerce, Sainikpuri, Telangana, Secunderabad, India; Atrayan L., Saveetha School of Engineering, Simats, Department of Mechanical Engineering, Tamil Nadu, Chennai, 602105, India

Advanced Fraud Detection Using Machine Learning Techniques in Accounting and Finance Sector
subject: Accounting; Finance Sector; Fraud Detection; Machine Learning; Techniques
description: Monetary fraud, which is a deceptive method for getting cash, has turned into a typical issue in organizations and associations as of late. Customary techniques like manual checks and reviews aren't extremely precise, are costly, and consume most of the day. Attempting to get cash by lying. With the ascent of simulated intelligence, approaches based on machine learning have become more well known. can be utilized shrewdly to track down fraud by dissecting an enormous number of monetary exercises information. Thus, this work attempts to give a systematic literature review (SLR) that ganders at the literature in a systematic manner. reviews and sums up the exploration on machine learning (ML)-based fraud recognizing that has proactively been finished. In particular, the review utilized the Kitchenham strategy, which depends on clear systems. It will then, at that point, concentrate and rundowns the significant pieces of the articles and give the outcomes. Considering the Few investigations have been finished to accumulate search systems from well-known electronic information base libraries. 93 pieces were picked, examined, and integrated in light of measures for what to incorporate and what to forget about. As the monetary world gets more confounded, robbery is turning into a more serious issue in the accounting and finance industry. Fraudulent activities cost cash, yet they likewise make it harder for individuals to trust monetary frameworks. To stop this danger, we want further developed ways of tracking down fraud straightaway. This theoretical gives an outline of how machine learning strategies are utilized to further develop fraud detection in accounting and finance. 2024 IEEE.
creator: Bhowte Y.W.; Roy A.; Raj K.B.; Sharma M.; Devi K.; Lathasoundarraj P.
source: Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036509-2
format: Online
language: English
type: Conference paper
coverage: Bhowte Y.W., Sinhgad Institute of Management And Computer Applications Narhe, Department of Mba, India; Roy A., Institute of Business And Computer Studies, Siksha 'o' Anusandhan, Deemed To Be University, Odisha, Bhubaneswar, 751030, India; Raj K.B., Institute of Public Enterprise, Department of Management Studies, Hyderabad, 500101, India; Sharma M., Thakur Institute of Management Studies And Research, Department of Finance, Mumbai, 400101, India; Devi K., Dav Autonomous College, Faculty In Commerce, Odisha, Titilagarh, India; Lathasoundarraj P., School of Business And Management, Christ University, Lavasa, Maharashtra, Pune, 412112, India

Security and Privacy in Internet of Things (IoT) Environments
subject: Data Flow Analysis; Empirical Experiments; Homomorphic Encryption; Internet of Things (IoT); IoT Ecosystem; Privacy Preservation Techniques; Security Modeling; Threat Intelligence Sharing; User-Centric Design
description: Although the proliferation of IoT devices has led to unparalleled ease of use and accessibility, it has also raised serious privacy and safety issues. Using a systematic approach that incorporates security and privacy modelling, data analysis, and empirical trials, this study provides a deep dive into the topic of IoT security and privacy. Our results show how crucial it is to take precautions against 'Information Disclosure' by using strong encryption and authorization protocols. The need to protect against 'Unencrypted Data' vulnerabilities is further emphasized by vulnerability analysis. Encryption (AES-256) and other access control rules fare very well in the assessment of security systems. Furthermore, 'Homomorphic Encryption' is identified as a potential strategy to protecting user privacy while retaining data usefulness based on our review of privacy preservation strategies. A more secure and privacyaware IoT environment may be fostered thanks to the findings of this study, which have ramifications for the industry, government, consumers, and academics. Addressing the ever-evolving security and privacy issues in the IoT will need a future focus on cutting-edge security mechanisms, privacy-preserving technology, regulatory compliance, user-centric design, multidisciplinary cooperation, and threat intelligence sharing. 2024 IEEE.
creator: Bhoomadevi A.; Soundarraj P.L.; Gupta V.; Kumaravel S.K.; Deivasigamani S.; Kumar A.
source: Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036509-2
format: Online
language: English
type: Conference paper
coverage: Bhoomadevi A., Amity Institute of Public Health & Hospital Administration, Amity University, Noida, 201303, India; Soundarraj P.L., School of Business And Management, Christ University, Lavasa, Maharashtra, Pune, 412112, India; Gupta V., Mahatma Gandhi Kashi Vidyapith, Department of Commerce, Varanasi, India; Kumaravel S.K., Mathematics, Vel Tech Rangarajan Dr. Sagunthala R And D Institute of Science And Technology, TamilNadu, Chennai, India; Deivasigamani S., Digipulse Technologies Inc, 811 Church Road, Suite 169, 08002, NJ, United States; Kumar A., Swami Vivekanand Subharti University, Department of Computer Application Faculty of Science, Meerut, India

Depiction ofNifty Midcap Index Efficiency Using ARIMA
subject: ARIMA; Market efficiency; Nifty Midcap Index; Volatility
description: In recent years, the desirability of midcaps in Indian stock markets has received considerable attention from researchers, academicians, and financial analysts due to expectation of multi-bagger returns. The present study is undertaken to determine the market efficiency of Indian stock market using Nifty Midcap Index at High Frequency. The market efficiency of Nifty Midcap Index is determined using ARIMA technique. The fitted ARIMA model had a MASE value close to one. Hence, the findings suggest that the Nifty Midcap Index is inefficient. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Ashok Kumar B.; Balaji L.
source: Lecture Notes in Networks and Systems, Vol-1, pp. 123-131.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981995014-0
format: Online
language: English
type: Conference paper
coverage: Ashok Kumar B., Government First Grade College Vemagal, Kolar, 536102, India; Balaji L., Christ University, Bengaluru, 560029, India

Revolutionizing Arrhythmia Classification: Unleashing the Power of Machine Learning and Data Amplification for Precision Healthcare
subject: Arrhythmia Classification; Diagnostic Accuracy; Electrocardiogram (ECG) Signals; Healthcare Analytics; Machine Learning Models; Principal Component Analysis (PCA); Random Oversampling
description: This paper presents a comprehensive exploration of arrhythmia classification using machine learning techniques applied to electrocardiogram (ECG) signals. The study delves into the development and evaluation of diverse models, including K-Nearest Neighbors, Logistic Regression, Decision Tree Classifier, Linear and Kernelized Support Vector Machines, and Random Forest. The models undergo rigorous analysis, emphasizing precision and recall due to the categorical nature of the dependent variable. To enhance model robustness and address class imbalances, Principal Component Analysis (PCA) and Random Oversampling are employed. The results highlight the effectiveness of the Kernelized SVM with PCA, achieving a remarkable accuracy of 99.52%. Additionally, the paper discusses the positive impact of feature reduction and oversampling on model performance. The study concludes with insights into the significance of PCA and Random Oversampling in refining arrhythmia classification models, offering potential avenues for future research in healthcare analytics. 2024 IEEE.
creator: Arpan; Singh M.; Garg P.; Srivastava S.; Saggu A.K.
source: Proceedings - 2024 6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024, pp. 516-522.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835037462-9
format: Online
language: English
type: Conference paper
coverage: Arpan, School of Sciences, Christ Deemed to be university, Delhi NCR, India; Singh M., School of Sciences, Christ Deemed to be university, Delhi NCR, India; Garg P., SAITM, Delhi NCR, Gurgoan, India; Srivastava S., School of Sciences, Christ Deemed to be university, Delhi NCR, India; Saggu A.K., School of Sciences, Christ Deemed to be university, Delhi NCR, India

Deep Learning Approaches for Environmental Monitoring in Smart Cities
subject: Air pollution; Deep learning; Environmental monitoring; Smart cities
description: It introduces a novel integrated environmental monitoring system capable of doing on-the-go measurements. In metropolitan settings, air pollution is one of the most serious environmental threats to human health. The widespread use of automobiles, emissions from manufacturing processes, and the use of fossil fuels for propulsion and power generation have all contributed to this issue. Air quality predictions in smart cities may now be made using deep learning methods, thanks to the widespread adoption of these tools and their continued rapid growth. Particulate Matter (PM) with a width of less than 2.5 m (PM2.5) is one of the most perilous kinds of air pollution. To anticipate the hourly gauge of PM2.5 focus in Delhi, India, we utilized verifiable information of poisons, meteorological information, and PM2.5 fixation in the adjoining stations to make a spatial-worldly element for our CNN-LSTM-based deep learning arrangement. According to our experiments, our 'hybrid CNN-LSTM multivariate' method outperforms all of the above conventional models and allows for more precise predictions. 2024 IEEE.
creator: Latha Soundarraj P.; Jamadar V.M.; Rappai S.; Alkhafaji M.A.; Shareef A.M.; Zearah S.A.
source: Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036509-2
format: Online
language: English
type: Conference paper
coverage: Latha Soundarraj P., School of Business & Management - Mba, Christ University, Maharashtra, Pune, 412112, India; Jamadar V.M., Dr. DaulatraoAher College of Engineering, Department of Mechanical Engineering, Satara, Maharashtra, Karad, 415124, India; Rappai S., Kristu Jayanti College (Autonomous), Department of Computer Science, Kothanur, K. Narayanapura, Karnataka, Bangalore, 560077, India; Alkhafaji M.A., College of Engineering Technology, National University of Science And Technology, DhiQar, Iraq; Shareef A.M., National University of Science And Technology, DhiQar, Iraq; Zearah S.A., Al-ayen University, Scientific Research Center, Thi-Qar, Iraq

Research on Unmanned Artificial intelligence Based Financial Volatility Prediction in International Stock Market
subject: Feature Engineering; Financial Volatility Prediction; International Stock Market; Long Short-Term Memory (LSTM); Root Mean Squared Error (RMSE); Unmanned AI
description: This study digs into the area of unmanned artificial intelligence (AI) for financial volatility prediction in the worldwide stock market, delivering unique insights into the deployment of cutting-edge technology to handle the multifarious issues of market dynamics. Our research uses Long Short-Term Memory (LSTM) networks as the AI model of choice, showing its usefulness in capturing temporal relationships in financial data by analyzing past stock price data, trading volumes, and a variety of technical indicators. Our findings suggest a potential capacity to reliably predict financial market volatility after extensive data pretreatment, feature engineering, and model training. A powerful instrument for investors, fund managers, and financial institutions to make better informed and accurate investment choices, the model's low Root Mean Squared Error (RMSE) and high (R2) values highlight its practical usefulness. Beyond the purely technical, our study considers the ethical, regulatory, risk reduction, and optimization implications for the financial sector. Financial decision-making and risk management are being transformed by the increasingly globalized market environment, and the results given here provide a concrete roadmap towards the appropriate integration of unmanned AI systems. 2024 IEEE.
creator: Boggavarapu S.; Ramkumar G.; Gedamkar P.R.; Kaneria A.; Pundir S.; Selvameena R.
source: 5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 16-20.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835035137-8
format: Online
language: English
type: Conference paper
coverage: Boggavarapu S., University of Cumberlands, Prospect Heights, IL, United States; Ramkumar G., Christ University, School of Commerce, Finance and Accountancy, Department of Commerce, Bengaluru, India; Gedamkar P.R., Dr. VishwanathKarad MIT World Peace University, Department of MBA, Maharashtra, Pune, India; Kaneria A., Sri Balaji University, Balaji Institute of Technology & Management, Maharashtra, Pune, India; Pundir S., Graphic Era Deemed to be University, Department of Computer Science Engineering, Uttrakhand, Dehradun, India; Selvameena R., Dr.M.G.R Educational and Research Institute, Department of Computer Science and Engineering, Maduravoyal, Chennai, 600095, India

Optimization of Friction Stir Welding Parameters for the Optimum Hardness of AlCu Butt Joints Using the Taguchi Method
subject: Friction stir welding; Hardness; OHNS steel tool
description: In the present study, the base plates made of alloys AA6101 and C11000 (each 5 mm thick) were welded bythe FSW technique using a hardened OHNS steel weld tool. The percentage contribution of the input process parameters, such as tool rotational speed in rpm, feed rate in mm/min, and tool pin offset in mm, on the output parameter joint hardness, were examined using the experimental design Taguchi L9 and ANOVA numerical tool analysis. From the optimization method, at 1000rpm tool rotational speed, 40mm/min feed rate and weld tool pin toward AA6101 alloy side will have the highest hardness. The tool rotational speed experiences a maximum significant impact on the joint hardness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Kumar J.P.; Raj A.; Rout I.S.; Ravichandran G.; Darshan S.M.; Niranjana S.J.; Vinod S.K.
source: Lecture Notes in Mechanical Engineering, pp. 63-70.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 21954356; ISBN: 978-981973653-9
format: Online
language: English
type: Conference paper
coverage: Kumar J.P., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Raj A., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Rout I.S., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Ravichandran G., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Darshan S.M., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Niranjana S.J., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Vinod S.K., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India

Improving Consumer Engagement with AI Chatbots: Exploring Perceived Humanness, Social Presence, and Interactivity Factors
subject: AI chatbots; Artificial intelligence; Consumer engagement; Customer interaction; Human-computer interaction; Interactivity; Perceived humanness; Social presence; User experience
description: In many consumer industries, AI robots are becoming more and more popular because they let businesses communicate with their customers in a cheap and quick way. However, how well these measures work rests on how real and present people think they are in social situations. The main things that affect how customers deal with AI chatbots are looked into in this research. These are interaction, social presence, and perceived humanity.A wide range of users will be asked to fill out quantitative polls that will be used to judge how humanlike AI chatbots are, how well they can interact with others, and how much they interact with people. Additionally, performing qualitative interviews will give you a fuller picture of what customers want and how they interact with AI chatbots. Companies can make their chatbot exchanges with customers better by figuring out what makes the bots act like humans: friendly, interested, and sociable. This will allow them to make chatbots that are very specific to their customers' needs and tastes. The goal of this researchprogramme is to make customers happier, more loyal to brands, and have better experiences by creating AI chatbots that can have conversations with people like real people. 2024 IEEE.
creator: Tripathi A.P.; Dhaundiyal P.; Sharma K.; Sukheswala J.; Dhiman A.; Sharma A.K.
source: Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835038943-2
format: Online
language: English
type: Conference paper
coverage: Tripathi A.P., Christ Deemed to Be University, Uttar Pradesh, India; Dhaundiyal P., Christ Deemed to Be University, Uttar Pradesh, India; Sharma K., K P B Hinduja College of Commerce, Maharashtra, India; Sukheswala J., K P B Hinduja College of Commerce, Maharashtra, India; Dhiman A., Manipal University Jaipur, Rajasthan, India; Sharma A.K., University of Technology and Applied Sciences, College of Economics and Business Administration, Salalah, Oman

Stress Management among Employees in Information Technology Sector Using Deep Learning
subject: Deep Learning; Employees; Information Technology; Management; Stress
description: Information technology is one of the areas in India that is developing the quickest India's information technology (IT) administrations industry has become more merciless. The information technology area has been managing additional difficult issues like specialized development, administration enhancement, and worldwide overhauling starting from the beginning of this long period. Along these lines, it is unimaginable for everybody to adjust to the moving difficulties they experience in the field of information technology, which causes stress. Stress is something that individuals battle with for most of their lives. Albeit the information technology (IT) industry is notable for its hazardous turn of events and development, it is likewise portrayed by high worker burnout and stress levels. This theoretical proposes an original strategy for overseeing stress in the IT business that utilizes deep learning methods. This study utilizes deep learning calculations to expect, distinguish, and decrease stress makes all together location the earnest issue of stress among IT experts. The principal objective is to make a shrewd framework that can help organizations proactively recognize stress-related issues in their labor force and proposition specific cures. 2024 IEEE.
creator: Soundarraj P.L.; Pelleti S.; John E.P.; Gadgil A.A.; Sehrawat A.; Yadav L.K.
source: Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036509-2
format: Online
language: English
type: Conference paper
coverage: Soundarraj P.L., School of Business And Management, Christ University, Lavasa, Maharashtra, Pune, 412112, India; Pelleti S., Klh Global Business School, KoneruLakshmaiah Education Foundation (Deemed To Be University, Estd. U/S. 3 of Ugc Act 1956), Catefgory - I, NIRF University Ranking: 28, Hyderabad, India; John E.P., Srm Institute of Science & Technology, Faculty of Management, Chennai, 600026, India; Gadgil A.A., E&c, KLSGogte Institute of Technology, Karnataka, Belagavi, 590008, India; Sehrawat A., Amity Institute of Corporate Communication, Amity University, Uttar Pradesh, Noida, India; Yadav L.K., Amity Institute of Corporate Communication, Amity University, UP, Noida, India