Advanced Fraud Detection Using Machine Learning Techniques in Accounting and Finance Sector
Accounting; Finance Sector; Fraud Detection; Machine Learning; Techniques
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.
Bhowte Y.W.; Roy A.; Raj K.B.; Sharma M.; Devi K.; Lathasoundarraj P.
Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
Institute of Electrical and Electronics Engineers Inc.
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
Deep Learning Enabled Parent Involvement and Its Influence on Student Academic Achievement Analysis
Deep Learning; Ethical Considerations; Longitudinal Study; Neural Network; Parental Engagement; Parental Involvement; Student Academic Achievement; Technology in Education
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.
Baruah A.; Kaur A.; Venugopal P.R.; Raghunandan S.; Sreedevi C.; Atrayan L.
https://doi.org/10.1109/ICONSTEM60960.2024.10568816 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198842543&doi=10.1109%2fICONSTEM60960.2024.10568816&partnerID=40&md5=99db5838b9dd3defd965e3b758d5ab90
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
Bibliometric Analysis: A Trends and Advancement in Clustering Techniques on VANET
Bibliometric analysis; Cluster formation; Cluster head; Cluster stability; Clustering; VANET; VOS viewer
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.
Lecture Notes in Networks and Systems, Vol-995, pp. 251-263.
Springer Science and Business Media Deutschland GmbH
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
Theoretical Studies ond(?,p)n atAstrophysical Energies
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.
Springer Proceedings in Physics, Vol-304 SPPHY, pp. 815-817.
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
Strategic Integration of HR, Organizational Management, Big Data, IoT, and AI: A Comprehensive Framework for Future-Ready Enterprises
AI integration; Big data analytics; Future-ready enterprises; HR management; IoT implementation; Organizational efficiency; Strategic integration
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.
Chavan V.; Nagendra; Mohammad Roshif U.; Abhijith R.; Joshi A.; Jha D.
https://doi.org/10.1109/ICONSTEM60960.2024.10568704 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198825703&doi=10.1109%2fICONSTEM60960.2024.10568704&partnerID=40&md5=655b2e4bea7c266e3cf6e9de514286c8
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
Brain Tumor Localization Using Deep Ensemble Classification and Fast Marching Segmentation
Brain tumor detection; Deep learning; Ensemble; Fast marching; Segmentation
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.
Joshey A.; Thiruthuvanathan M.M.; Aarushi
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 151-164.
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
Enhancing Mobility: A Smart Cane with Integrated Navigation System and Voice-Assisted Guidance for the Visually Impaired
audio feedback; Blind mobility; haptic feedback; Navigation; Smart Cane; Ultrasonic sensor; vibration motor; voice control
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.
Muktha D.S.; Niveditha G.; Pinto N.A.; Sinha S.
Proceedings - 2024 13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024, pp. 1124-1129.
https://doi.org/10.1109/CSNT60213.2024.10546111 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196767291&doi=10.1109%2fCSNT60213.2024.10546111&partnerID=40&md5=ae03094e8cb81e78fe8b11ea347c2dfc
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
An Analysis of Manufacturing Machine Failures and Optimization Using Replacement Year Prediction
Failure rate; Machine learning; Manufacturing failures; Operations research; Random forest classifier; Replacement years; Statistical analysis
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(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 311-324.
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
Analysing Employee Management Using Machine Learning Techniques and Solutions in Human Resource Management
Human Resource management; Machine Learning
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.
Chandana C.; Sarkar A.; Deshmukh K.; Kulkarni P.; Bikash Acharjee P.; Lourens M.
4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024
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
AI Based Seamless Vehicle License Plate Recognition Using Raspberry Pi Technology
Customized Vehicle Management; Indian Number Plate Recognition; Raspberry Pi; Tesseract OCR; YOLOv8
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.
Proceedings - 2024 13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024, pp. 387-392.
https://doi.org/10.1109/CSNT60213.2024.10546153 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196766531&doi=10.1109%2fCSNT60213.2024.10546153&partnerID=40&md5=f9dfce4f8bdaf1cbd9cdf1b8e35f433c
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
Cognitive outcomes prediction in children using machine learning and big data analytics
big data analytics; Cognitive outcomes; Machine learning
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.
Behera N.R.; Thiyagarajan V.S.; Manoharan G.; Veena C.; Bikash Acharjee P.; Murthy A.
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
Analysis of Multinomial Classification for Legal Document Categorization
Legal document classification; Machine learning; Natural language processing
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.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 177-183.
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
Enhancing Network Security with Comparative Study of Machine Learning Algorithms for Intrusion Detection
Intrusion detection; K-nearest neighbors (KNN); Logistic regression (LR); Machine learning (ML); Random forest (RF)
With the ever-increasing network systems and dependency on digital technologies, ensuring the security and integrity of these systems is of paramount importance. Intrusion detection systems (IDS) play a major role in sheltering such systems. Intrusion detection systems are technologies that are designed to monitor network and system activities and detect suspicious, unauthorized, malicious behavior. This research paper conducts a comprehensive comparative analysis of three popular machine learning algorithmsK-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR)in the context of intrusion detection using the renowned NSL-KDD dataset. Preprocessing techniques are applied, and the dataset is split for rigorous evaluation. The findings of this research highlight the effectiveness of Random Forest in detecting intrusions, showcasing its potential for real-world network security applications. This study contributes to the field of intrusion detection and offers valuable insights for network administrators and cybersecurity professionals to enhance network protection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Thachil M.; Siby A.; Kumar K.P.; Chowdary C.R.; Karthikeyan H.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 345-354.
Thachil M., Computer Science & Engineering, CHRIST University, Bangalore, India; Siby A., Computer Science & Engineering, CHRIST University, Bangalore, India; Kumar K.P., Computer Science & Engineering, CHRIST University, Bangalore, India; Chowdary C.R., Computer Science & Engineering, CHRIST University, Bangalore, India; Karthikeyan H., Computer Science & Engineering, CHRIST University, Bangalore, India
Cost Effective and Energy Efficient Drip Irrigation System for IoT Enabled Smart Agriculture
DC submersible motor; IoT; Node MCU; Raspberry Pi; Remote monitoring; Smart drip irrigation; Soil moisture sensor; Solenoid valve
The conventional methods of smart farming consume a significant percentage of the resources such as water, electricity, and manpower. This approach demands more time, money, and effort. The state of the art drip irrigation methods make use of the solenoid valve to control the water flow. The problem with such a system is reflected in its power consumption which is a significant factor for large-scale demands. The method proposed in this paper addresses this problem by developing an automated drip irrigation system that replaces components used in conventional methods with its economical counterparts in the market. A system using Node MCU, DC submersible motor, and soil moisture sensor is developed to automate the irrigation process ensuring efficient water and energy consumption. Since the proposed system utilizes economically cheaper components, it provides an upper edge over other systems in terms of expenditure and in turn economically feasible for large-scale demands. A mobile application is also developed to control, monitor, and schedule irrigation processes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Fuhad M.; George S.K.; Elappila M.; Nagaraju S.; Reddy V.; Krishna A.V.N.; Jose S.M.
Lecture Notes in Networks and Systems, Vol-955 LNNS, pp. 199-210.
Fuhad M., CHRIST (Deemed to be University), Bengaluru, 560074, India; George S.K., CHRIST (Deemed to be University), Bengaluru, 560074, India; Elappila M., CHRIST (Deemed to be University), Bengaluru, 560074, India; Nagaraju S., CHRIST (Deemed to be University), Bengaluru, 560074, India; Reddy V., CHRIST (Deemed to be University), Bengaluru, 560074, India; Krishna A.V.N., CHRIST (Deemed to be University), Bengaluru, 560074, India; Jose S.M., University of Liverpool, L69 3BX, Liverpool, United Kingdom
Sliced Bidirectional Gated Recurrent Unit with Sparrow Search Optimizer for Detecting the Attacks in IoT Environment
bidirectional gated recurrent unit; internet of things; multi-head self-attention mechanism; network intrusion detection systems; network traffic; sparrow search optimization algorithm
In an era characterized by pervasive interconnectivity facilitated by the widespread adoption of Internet of Things (IoT) devices across diverse domains, novel cybersecurity challenges have emerged, underscoring the imperative for robust intrusion detection systems. Conventional security frameworks, constrained by their closed-system architecture, struggle to adapt to the dynamic threat landscape marked by the continual emergence of unprecedented attacks. This paper presents a methodology aimed at mitigating the open set recognition (OSR) challenge within IoT-specific Network Intrusion Detection Schemes (NIDS). Leveraging image-based representations of data, our approach focuses on extracting geographical traffic patterns. We observe that the Recurrent Neural Network exhibits suboptimal classification accuracy and lacks parallelizability for attack analysis tasks. Our investigation concludes that the Sparrow Search Optimization Algorithm (SSOA) serves as a foundation for constructing an effective assault classification model. This research contributes significantly to the field of network security by emphasizing the importance and ramifications of meticulous hyperparameter tuning. It represents a critical stride toward developing IDSs capable of effectively navigating the evolving cyber threat landscape. In the experimental analysis of proposed model reached the accuracy and 0.963% respectively. 2024 IEEE.
Gondkar S.R.; Siva Balan R.V.; Kavitha S.; Gondkar R.R.
3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024
Gondkar S.R., Bms Institute Of Technology And Management, Department Of Electronic And Communication Engineering, Bangalore, India; Siva Balan R.V., Christ (Deemed-to-be-University), Department Of Computer Science, Bangalore, India; Kavitha S., Nitte Meenakshi Institute Of Technology, Department Of Mca, Bangalore, India; Gondkar R.R., Christ (Deemed-to-be-University), Department Of Computer Science, Bangalore, India
Blockchain Integrated Retail Logistics Chain: An Adoption Perspective
blockchain; cold chain; logistics; retail
The task of managing public health and safety is a multifaceted and delicate one that demands the careful upkeep of numerous processes and systems, with a particular emphasis on cold chain logistics. The primary objective of this research is to investigate how blockchain technology can meet the needs of a retail cold chain. To accomplish this goal, we employed a comprehensive technological adoption model, partial least squares structural equation modeling, and a quantitative cross-sectional survey approach to ascertain stakeholder adoption intentions toward a blockchain-enabled cold supply chain. Our findings suggest that blockchain technology has the capacity to effectively facilitate the goals of the retail cold chain. 2024 IEEE.
Kumar N.; Kaul M.; Rawandale C.J.; Singh S.; Gupta A.K.
Kumar N., Christ University, School of Business and Management, Bangalore, India; Kaul M., Symbiosis International Deemed University, Symbiosis Law School, Noida, Pune, India; Rawandale C.J., Symbiosis International Deemed University, Symbiosis Law School, Noida, Pune, India; Singh S., Symbiosis International Deemed University, Symbiosis Law School, Noida, Pune, India; Gupta A.K., Amity University, Amity International Business School, Noida, India
An Investigation on Machine Learning Models in Classification and Identifications Cervical Cancer Using MRI Scan
cervical cancer; classification; machine learning; medical imaging; preprocessing
This study analyzes the effectiveness of machine learning models in the classification of cervical cancer using a dataset of 900 cancer and 200 non-cancer images gathered from online resources and hospitals. The dataset, covering both CT and MRI images, undergoes rigorous preprocessing, including standardization, normalization, and noise reduction, to enhance its quality for model training. Four machine learning models, namely VGG16, CNN, KNN, and RNN, are recruited to predict cancer and non-cancer cases. During the testing phase, VGG16 emerges as the most accurate, achieving an impressive accuracy of 95.44%, followed by CNN at 92.3%, KNN at 89.99%, and RNN at 86.233%. Performance parameters, such as precision, recall, F1 score, and accuracy, are fully analyzed, providing insights into each model's strengths and capabilities. These discoveries not only contribute to the advancement of cervical cancer diagnostic techniques but also underscore the potential of machine learning in medical imaging. The study emphasizes the relevance of model selection and provides a framework for future research endeavors seeking to enhance the accuracy and performance of cervical cancer diagnosis through the merger of advanced computational techniques with standard diagnostic practices. 2024 IEEE.
Singh J.; Tan K.T.; Sahu D.; Upreti K.; Kshirsagar P.R.; Elangovan M.
International Conference on E-Mobility, Power Control and Smart Systems: Futuristic Technologies for Sustainable Solutions, ICEMPS 2024
Singh J., Singapore Institute of Technology, 10 Dover Drive, Singapore; Tan K.T., Singapore Institute of Technology, 10 Dover Drive, Singapore; Sahu D., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Upreti K., Christ (Deemed To Be University), Department of Computer Science, Ghaziabad, India; Kshirsagar P.R., J D College of Engineering & Management, Deptartment of Electronics & Telecom. Engineering, Nagpur, India; Elangovan M., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Department of Biosciences, Chennai, India
Pixels to Pathogens: A Deep Learning Approach to Plant Pathology Detection
Deep Learning; EfficientNetB3; Mobile Application; Pathology detection; Plant Disease; Regularization; Transfer Learning
It is known that accurately identifying, early and timely treatment and elimination of the plant diseases is essential for crop protection and healthy crop growth. In traditional or conventional methods, identification and classification were done by testing in laboratories or through visual inspection by farmers. Now going through the testing in labs is very time consuming, while the visual inspection requires enough experience and knowledge. To solve this problem, our study proposes a robust plant pathogen detection method based on a Deep Learning approach on a large dataset containing about 38 categories of different species like Maize, Potatoes, Tomatoes, Bell Pepper, Peach, Strawberry etc. and diseases like rust, molds, blight (late and early). This crop disease detection model leverages the power of the EfficientNetB3 architecture, a state-of-art convolutional neural network(CNN). The main backbone is served by EfficientNetB3and then it is fine-tuned using different hyperparameters and other regularization techniques like weight decay, dropout method and optimizers like RAdam,to enhance the overall accuracy coupled with dynamic learning rate adjustment. In the testing set of the dataset, the proposed model shows encouraging accuracy of about 99.25%, high precision of about 97.35%. A thorough evaluation of the model's functionality is given by the help of training and validation line chart and loss chart that gives the in-depth information on the prediction. And then we implemented the detection model in our mobile application whose interface screen shots are given below. In the application the image can be taken by camera or fed from folders and it will detect the type of disease. 2024 IEEE.
Saha A.; Sharma V.; Mondal R.; Mishra S.; Daramola I.; Abbas A.
https://doi.org/10.1109/ICIPTM59628.2024.10563558 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197703419&doi=10.1109%2fICIPTM59628.2024.10563558&partnerID=40&md5=bef815f71481fca9e09ee6ceea3cc290
Saha A., Kalinga Institute of Industrial Technology, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Mondal R., Kalinga Institute of Industrial Technology, India; Mishra S., Kalinga Institute of Industrial Technology, India; Daramola I., University of Bolton, School of Creative Technologies, United Kingdom; Abbas A., University of Bolton, School of Creative Technologies, United Kingdom
Comparative Analysis of Machine Learning Models in Predicting Academic Outcomes: Insights and Implications for Educational Data Analytics
Academic outcomes; Educational Data Analytics; Machine Learning models
In the evolving landscape of educational research, the predictive analysis of student performance using data science has garnered significant interest. This study investigates the influence of diverse factors on academic outcomes, ranging from personal demographics to socioeconomic conditions, to enhance educational strategies and support mechanisms. We employed a diverse ml models to analyze a information containing academic records and socioeconomic information. The models tested include Logistic Regression, Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVC), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, and Decision Trees. The process involved comprehensive data preprocessing, exploratory analysis, model training, and evaluation based on metrics such as precision, recall, accuracy, and F1 score. The results indicate that ensemble methods, specifically RF and GB, demonstrate superior efficacy in accurately predicting categories of student performance such as 'Enrolled,' 'Graduated,' and 'Dropped Out.' These models excelled in handling the complex interplay of varied predictors affecting student success. The results further underline the potential of advanced ensemble ML techniques in significantly outperforming the prediction accuracy in the academic domain, hence facilitating the tailoring of educational interventions to foster improved engagement and better outcomes for students. This has provided a comparative analysis of the methods that guide the future application of predictive analytics in education. 2024 IEEE.
Maheshwari A.; Malhotra A.; Hada B.S.; Ranka M.; Basha M.S.A.
International Conference on Smart Systems for Applications in Electrical Sciences, ICSSES 2024
Maheshwari A., Christ (Deemed to be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Malhotra A., Christ (Deemed to be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Hada B.S., CHRIST (Deemed to be University), School of Commerce, Finance and Accountancy, NCR, Delhi, India; Ranka M., Dayananda Sagar College of Arts, Science and Commerce Bengaluru, India; Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to be University), GITAM School of Business, Bengaluru, India
A Heuristic Model For Personalised Risk Assesment of PCOS
early diagnosis; Machine Learning; PCOS; Random Forest; Stacking; XGBoost
According to WHO 8-13% of women are affected by Polycystic Ovary Syndrome (PCOS) out of which 70% women remain undiagnosed, it is a common endocrine disorder necessitating early diagnosis for timely intervention. In this paper a heuristic model is developed for PCOS prediction, by combining XGBoost and Random Forest through stacking techniques. Class imbalance was addressed using Random Oversampling. Cross-validation demonstrated the meta-model's superior accuracy compared to individual XGBoost and Random Forest models, highlighting its potential for reliable PCOS prediction. It is observed that the best possible results that the meta-model was able to provide was a score of 93.5% which was acquired in the 4th sample, the lowest score was 87.90% attained in the 2nd sample. To finalise the results, the mean accuracy was calculated which is 90.98% with a standard deviation of 1.96. deterministic model offers reproducible results and interpretability, aiding clinical decision-making. Future research could explore additional biomarkers and probabilistic techniques for personalized risk assessment. 2024 IEEE.
https://doi.org/10.1109/ICIPTM59628.2024.10563299 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197698342&doi=10.1109%2fICIPTM59628.2024.10563299&partnerID=40&md5=8bf32a0d4e0b0cd5bf42e093b60a8f41
Singh N., School of Sciences, Christ (Deemed to Be University), Delhi, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi, India; Gupta V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi, India