DR. KABIRDOSS DEVI

@vistas.ac.in

Associate Professor, School of Management Studies- VISTAS
Vels Institute of science technology and advanced studies

DR. KABIRDOSS DEVI


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https://researchid.co/kabirdossdevi

RESEARCH, TEACHING, or OTHER INTERESTS

General Business, Management and Accounting, General Business, Management and Accounting
21

Scopus Publications

272

Scholar Citations

8

Scholar h-index

8

Scholar i10-index

Scopus Publications

  • Monitoring the Manufacturing Operation Process Data through the Cloud Database
    Kavin Francis Xavier, Devi Kabirdoss, Chandramouli Seetharaman, Kotteeswaran Mani, Ganesan Veeramani, Vinoth Kumar Murali Thiyagarajan
    Journal of Advanced Research in Applied Sciences and Engineering Technology, 2025
    The level of service provided by disseminating industrial management was difficult to match by the customer or multivalent based on embedded data stores now in usage. An Embedded Cloud Database Service (ECDT) approach is proposed as a solution to the problems. To enhance the actual-time performance and dependability to transaction processing, ECDT architecture should be first built, and a Dual-Timing Transaction Control (DTC) mechanism was proposed. Then, a different network sniff-timing calculation method was presented to increase the effectiveness of the ECDT method, and a cloud services middleware component was built to carry out dynamic access control and real-time DB search. In comparison to MySQL and Berkeley DB, the total transaction processing time could be slashed by 45.5% and 36.7%, correspondingly. The DNS2 algorithm could also function with average demand and time change and use less energy. Finally, the results of the numerical and commercial experiments show that the DTC technique could improve the real period precision of transmission operations in a condition where reading consistency was guaranteed, and the data transfer speed could be 20 MB/s.
  • An Intelligent Decision Support Framework for Competitiveness Optimization in Indian Shipping and Port Management
    P. Baiju, Kabirdoss Devi
    2025 IEEE 5th International Conference on ICT in Business Industry and Government Ictbig 2025, 2025
    Data-driven intelligence is essential for Indian ports to compete by minimizing the usage of resources, maximizing operational efficiency, and making more informed decisions in ever-evolving sea conditions. The current research formulates an Intelligent Decision Support Framework (IDSF) based on Reinforcement Learning (RL) and Analytic Hierarchy Process (AHP) to achieve adaptive optimization in port and shipping operations. AHP layer produces understandable weights by carefully ranking competitiveness factors such as berth utilization, logistics expense, and ship turnaround time. The best-working practices, including historical and real-time data, are iteratively learned by the RL agent, designated as an MDP, by taking these weights as input. Experimental verification with actual data from Indian ports proves that the proposed method has an efficiency accuracy of 97.6%, a decrease in operation delays by 22.4%, and a significant increase in competitiveness indices in comparison with traditional optimization procedures. The platform promises scalability, flexibility, and openness, while at the same time offering a revolutionary technique for maritime decision intelligence and sustainable port management.
  • Machine Learning-Based Predictive Analysis for Assessing Operational Efficiency in Indian Port Management Systems
    P. Baiju, Kabirdoss Devi
    2025 IEEE 5th International Conference on ICT in Business Industry and Government Ictbig 2025, 2025
    Port operations in India are becoming increasingly complicated as vessel arrivals, cargoes, and weather conditions change, necessitating the use of data-driven decision-making to enhance efficiency. Existing systems based on rule-based logic and disconnected deep learning models suffer from irrelevant, unchangeable thresholds, poor explainability, and isolated data connections, resulting in inaccurate stay time and berth assignment. The proposed machine learning-based predictive analysis platform uses AIS, manifest data, meteorological inputs, and operational logs to estimate vessel berth usage, delay class, and berth productivity. Using 2019-2024 Indian port datasets for training, it achieves an MAE of 4.1 hours, an 18% improvement over the old system, increases the delay classification F1-score from 0.68 to 0.79, and provides a 9.4% gain in berth productivity. These advantages include more accurate prediction, increased decisionmaking transparency, and quantifiable operational efficiency gains in various maritime conditions.
  • The Role of the Board of Directors in Corporate Governance: A Case Study of Byju’s
    Kabirdoss Devi, V. Vardhini
    Approaches to Global Sustainability Markets and Governance, 2025
  • Enhancing Stock Trading Performance in Chennai: A Reinforcement Learning and Sentiment Analysis Approach for Real-Time Decision-Making
    S. Karthick, Kabirdoss Devi
    Icdt 2025 3rd International Conference on Disruptive Technologies, 2025
    Trading in stocks is facilitated by both technical and sentiment factors that are not well captured in routine models. The work presented investigates a reinforcement learning model integrated with sentiment analysis for improved stock trading in Chennai. The model analyses live data of stock and sentiment and makes changes to the trades being made dynamically. Training and finally testing the model, a dataset that consisted of stock prices and input sentiment data was used. Different results illustrate an average profit of 12.5% and a maximum drawdown of 5.2% with a Sharpe ratio of 1.8 to outperform previous methods. Profitable trades perfectly solved by the model to a success rate of 78%, and it had great improvements from success rates of 54%, 62 %, and 69 % in the years 2021, 2022, and 2023 respectively. In conclusion, the proposed model's performance suggests that the integration of sentiment analysis and reinforcement learning translates to large improvements in stock trading strategies.
  • Predictive Analytics Framework for Strategic Decision-Making in Retail and Customer Churn Management
    T.V. Ambuli, Sampath. K, S. Venkatesan, V. Ramu, Kabirdoss Devi
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking Gcwcn 2025, 2025
    Customer relationship management continues to pose a substantial obstacle in the prediction of client attrition, particularly in sectors like telecommunications and e-commerce. This study introduces a hybrid prediction system that is reliable and incorporates XGBoost and MLP models using a soft-voting ensemble approach. When assessed on benchmark datasets, the proposed model achieved exceptional results, surpassing its competitors in terms of metrics such as 98.26% accuracy, 98.31% precision, 98.04% recall, 98.17% F1-score, and 0.9924 AUC. Comparing the ensemble method to previous models reveals that it strikes a satisfactory balance between interpretability and non-linear feature representation. This model is an excellent choice for enterprise-level attrition prediction systems due to its SHAP-based interpretability, real-time inference capacity, and tolerance to noisy data. This method is both adaptable and scalable, offering practical insights and predictive potential in a variety of customer-centric domains.
  • Deep Learning for Stock Market Prediction: A Comparative Study of LSTM and Transformer-Based Models
    S. Venkatesan, Kabirdoss Devi, T.V. Ambuli, Sampath K., V. Ramu
    2025 IEEE International Conference on Blockchain and Distributed Systems Security Icbds 2025, 2025
    Stock market prediction involves complicated and dynamic tasks because financial data shows stochastic behavior. The research details an evaluation of the predictive abilities between the Long Short-Term Memory (LSTM) and Transformer-based deep learning models in stock market analysis. Stock market data from the past was employed to test both models for their capability to extract pattern information and generate trustworthy predictive estimations. When applied to stock market predictions the Transformer achieved better results than LSTM since it attained 97.9% accuracy while LSTM only achieved a prediction accuracy of 94.2%. The Transformer-based approach demonstrated superior performance, achieving a lower Mean Absolute Error (MAE) of 8.97, highlighting its capability to better model long-range dependencies in financial data. The research results demonstrate that Transformer models exceed LSTM models in their capacity to handle complex market dependencies and market trend patterns. The study adds value to present-day attempts that use state-of-the-art deep learning strategies to enhance algorithmic trading systems as well as financial business decision systems.
  • AI-Driven Learning Behavior Analysis for Adaptive Content Delivery and Enhanced Productivity
    Sampath. K, Murugan Kandaswamy, V. Ramu, Kabirdoss Devi, S. Venkatesan
    2025 IEEE International Conference on Blockchain and Distributed Systems Security Icbds 2025, 2025
    The modern importance of adaptive learning platforms continues to grow because most platforms currently fail to achieve both dynamic behavior analysis and real-time personalization. An artificial intelligence system that evaluates learner conduct through a two-part approach where reinforcement learning works with decision tree classifier methods. The system tracks live student interactions to modify information distribution according to the current patterns of learner participation. The analysis relied on behavioral data from OpenEd, which included a diverse set of interactions for both training and assessment purposes. The use of the proposed model led to accuracy enhancements, reaching 96.3%, along with improvements in content relevance to 89.5% and increased learner retention to 41.7%, surpassing the existing systems from 2022 to 2024. The implemented TensorFlow and Python for its simulation runs through a cloud-based education simulation environment. The model demonstrates better delivery adaptability and learner involvement than basic regulationbased systems. It demonstrates that educational platforms containing AI behavior modeling show the potential to upgrade learning effectiveness together with real-time productivity improvements in instructional settings.
  • An Intelligent Decision Support System for Human Resource Planning Using Fuzzy Logic and AHP Techniques
    Kabirdoss Devi, V. Ramu, K. Sampath, S. Venkatesan, Murugan Kandaswamy
    2025 5th International Conference on Emerging Research in Electronics Computer Science and Technology Icerect 2025, 2025
    Human Resource Planning (HRP) is an important part of running a business that makes sure there are enough people with the relevant abilities available when required. Because people are subjective and it's hard to judge qualitative elements, traditional HRP approaches typically don't make decisions that are very accurate. This study suggests a new and unified technique that combines Fuzzy Logic with the Analytic Hierarchy Process (AHP) to create an intelligent Decision Support System (DSS) that makes human resource planning more reliable and accurate. The method employs fuzzy sets to show how unclear and vague human judgment may be, and it also ranks numerous HR factors, including as skill levels, performance, experience, and organizational needs, in order of priority. The system helps you be ready for recruiting, assigning staff, and succession in a manner that is orderly, scalable, and adaptable. This used a real-world HR dataset to evaluate the created DSS, and it showed huge improvements in how effectively it planned and how consistently it made judgments. The proposed model is useful for HR in making better judgments in circumstances that are hard to understand and convoluted.
  • Smart HR Dashboard for Remote Workforce Using Real-Time Analytics and Automated Reporting Tools
    V. Ramu, S. Venkatesan, Kabirdoss Devi, Murugan Kandaswamy, Sampath. K
    2025 IEEE 5th International Conference on ICT in Business Industry and Government Ictbig 2025, 2025
    The direct challenge of managing a remote workforce is minimal monitoring performance, keeping employees engaged, and analytics regarding the human resource at the appropriate time. There is no real-time and predictive intelligence in the traditional dashboard, and this becomes a problem in the process of decision-making. The current work has envisaged such a Smart HR Dashboard with real-time analytics, automated report generation, along with predictive modeling using machine learning. The system records active employee engagement and processes it via an NLP sentiment analysis mechanism and gives automatic report generation via a rule-based engine. The prediction of attrition and productivity showed an accuracy of 94.6% and 92.1%, respectively, while the AUC-ROC was 0.947. It resulted in 3.2 seconds of data refreshing, 1.8 seconds of report creation, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 9. 2 \%}$</tex> cloud synchronization. The proposed solution showed an incremental performance increase of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2 - 3 7 \%}$</tex> improvement over the HR metrics as compared to the existing systems. Smart HR Dashboard, therefore, provides a scalable and responsive remote workforce management solution that offers proactive insights and real-time views to HR professionals.
  • Evaluating Retail Investor Responses to AI-Based Trading Tools in Chennai's Stock Market
    S. Karthick, Kabirdoss Devi
    Proceedings of the 2024 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2024, 2024
  • Exploring Sustainable Consumer Behavior Among Women Educators in West Cochin Using ML and Social Media Analytics
    Silliya Stanly, Kabirdoss Devi
    Proceedings of the 2024 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2024, 2024
  • AI-Powered Employee Performance Evaluation Systems in HR Management
    K. Sampath, Kabirdoss Devi, T.V. Ambuli, S. Venkatesan
    Proceedings of International Conference on Circuit Power and Computing Technologies Iccpct 2024, 2024
  • Deep Learning for Predictive Maintenance Revolutionizing Asset Management in Finance
    Kabirdoss Devi, K. Sampath, S. Venkatesan, T.V. Ambuli, S. Kumaran
    International Conference on Distributed Systems Computer Networks and Cybersecurity Icdscnc 2024, 2024
  • Enhancing Team Productivity with Synergistic Work Patterns using Genetic Algorithms
    P.C. Saranya, Kabirdoss Devi, K. Sampath, V. Vardhini, S. Kumaran
    2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2024 Proceedings, 2024
  • Data-Driven Decisions: Integrating Machine Learning into Human Resource and Financial Management
    S.Venkatesan, T.V Ambuli, Kabirdoss Devi, K.Sampath, S.Kumaran
    Proceedings of International Conference on Circuit Power and Computing Technologies Iccpct 2024, 2024
  • AI-Driven Financial Management Optimizing Investment Portfolios through Machine Learning
    T.V Ambuli, S. Venkatesan, K. Sampath, Kabirdoss Devi, S. Kumaran
    Proceedings of International Conference on Circuit Power and Computing Technologies Iccpct 2024, 2024
  • Secure IoT-Based Communication Protocols for Smart Cities
    Kabirdoss Devi, Srinivas Jangirala, Naseer Ali Hussien, Jamal K. Abbas, Sarmad Jaafar Naser, Vinayagam. S
    Proceedings of 9th International Conference on Science Technology Engineering and Mathematics the Role of Emerging Technologies in Digital Transformation Iconstem 2024, 2024
  • AI-Enabled Predictive Maintenance for Industrial Equipment in the Era of IoT
    Muthu Raman Subbiah, Kabirdoss Devi, Dhanamalar M, Ashraf Mohammed Shareef, Taha Raad AI-Shaikhli, Sudheer Nidamanuri
    Proceedings of 9th International Conference on Science Technology Engineering and Mathematics the Role of Emerging Technologies in Digital Transformation Iconstem 2024, 2024
  • A contemporaneous sample on the consumer brand preferences for air conditioners in west Chennai
    Kabirdoss Devi, A.N. Durga Anupama, Mr Rafic, Ali Arashshahin, Kazemi, et al.
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Social media as the next trend in social business marketing social media as the next trend in solar business marketing
    International Journal of Innovative Technology and Exploring Engineering, 2019

RECENT SCHOLAR PUBLICATIONS

  • An Intelligent Decision Support Framework for Competitiveness Optimization in Indian Shipping and Port Management
    P Baiju, K Devi
    2025 IEEE 5th International Conference on ICT in Business Industry … , 2025
    2025
  • Smart HR Dashboard for Remote Workforce Using Real-Time Analytics and Automated Reporting Tools
    V Ramu, S Venkatesan, K Devi, M Kandaswamy
    2025 IEEE 5th International Conference on ICT in Business Industry … , 2025
    2025
  • Machine Learning-Based Predictive Analysis for Assessing Operational Efficiency in Indian Port Management Systems
    P Baiju, K Devi
    2025 IEEE 5th International Conference on ICT in Business Industry … , 2025
    2025
    Citations: 1
  • Predictive Analytics Framework for Strategic Decision-Making in Retail and Customer Churn Management
    TV Ambuli, S Venkatesan, V Ramu, K Devi
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN … , 2025
    2025
  • AI-Driven Learning Behavior Analysis for Adaptive Content Delivery and Enhanced Productivity
    M Kandaswamy, V Ramu, K Devi, S Venkatesan
    2025 IEEE International Conference on Blockchain and Distributed Systems … , 2025
    2025
    Citations: 1
  • Deep Learning for Stock Market Prediction: A Comparative Study of LSTM and Transformer-Based Models
    S Venkatesan, K Devi, TV Ambuli, V Ramu
    2025 IEEE International Conference on Blockchain and Distributed Systems … , 2025
    2025
  • An Intelligent Decision Support System for Human Resource Planning Using Fuzzy Logic and AHP Techniques
    K Devi, V Ramu, K Sampath, S Venkatesan, M Kandaswamy
    2025 5th International Conference on Emerging Research in Electronics … , 2025
    2025
  • The Role of the Board of Directors in Corporate Governance: A Case Study of Byju's
    K Devi, V Vardhini
    Developments in Corporate Governance: Emerging Market Perspective, 89 , 2025
    2025
  • TRAINING NEEDS ANALYSIS AT MONTRA ELECTRIC BRIDGING SKILL GAPS TO ENHANCE EMPLOYEE PERFORMANCE AND ORGANIZATIONAL EFFICIENCY
    DKD Yokesh A
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025
  • TRAINING NEEDS ANALYSIS AT MONTRA ELECTRIC BRIDGING SKILL GAPS TO ENHANCE EMPLOYEE PERFORMANCE AND ORGANIZATIONAL EFFICIENCY
    DKD Yokesh A
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025
  • ANALYZING THE EFFECTIVENESS OF MOVING AVERAGE AND BOLLINGER BANDS IN TRADING STRATEGIES
    DKD Saravanan S
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025
  • COMPETITIVE ANALYSIS OF FINANCIAL PRODUCTS OFFERED BY LEADING NBFC’s
    DKD Sankeerthana R
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025
  • MARKET ANALYSIS OF FRESH & HONEST’S BUSINESS MODEL COFFEE MAKER RENTALS AND COFFEE BEAN SALES
    DKD Roshan M
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025
  • ASSESSING AWARENESS OF PRIME MINISTER'S STARTUP SCHEMES AMONG STUDENTS IN CHENNAI'S HIGHER EDUCATION INSTITUTES
    DKD Ragaveni. B
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025
  • Comparative Analysis of Customer Satisfaction and Service Quality in Public Sector Banks at Tambaram, Chennai
    DKD Poojana. K
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025
  • CRITICAL REVIEW OF RECRUITMENT AND SELECTION METHODS IN FINANCIAL SERVICE INDUSTRY
    DKD Leo Leninn J
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025
  • ROLE OF GOAL-BASED FINANCIAL PLANNING IN ACHIEVING LONG-TERM FINANCIAL SECURITY
    DKD Lavanya D
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025
  • Financial Literacy and Its Impact on Savings and Investment Decisions Among Migrant Laborers in Dubai
    DKD Keerthana.K
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025
  • ANALYZING FINANCIAL EFFICIENCY AND STABILITY OF FIN -SERVE FIRMS
    DKD John Ashwin H
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025
  • ROLE OF EMPLOYEE ENGAGEMENT IN DRIVING CUSTOMER SATISFACTION IN SERVICE INDUSTRY
    DKD Joel Samraj D
    International Advanced Research Journal in Science, Engineering and … , 2025
    2025

MOST CITED SCHOLAR PUBLICATIONS

  • WITHDRAWN: Real time data analysis of face mask detection and social distance measurement using Matlab
    S Meivel, KI Devi, SU Maheswari, JV Menaka
    Materials Today: Proceedings , 2021
    2021
    Citations: 44
  • AI-powered employee performance evaluation systems in HR management
    K Sampath, K Devi, TV Ambuli, S Venkatesan
    2024 7th International Conference on Circuit Power and Computing … , 2024
    2024
    Citations: 31
  • Effects of Evolving Applications of IoT in the Education Sector
    K Devi, J Sabitha, J Sathish Kumar
    Digital Technologies for Smart Business, Economics and Education: Towards a … , 2023
    2023
    Citations: 31
  • AI-Driven Financial Management Optimizing Investment Portfolios through Machine Learning
    TV Ambuli, S Venkatesan, K Sampath, K Devi, S Kumaran
    2024 7th International Conference on Circuit Power and Computing … , 2024
    2024
    Citations: 19
  • Design and analysis of normally-off GaN-HEMT using β-Ga2O3 buffer for low-loss power converter applications
    A Revathy, JV Kumar, P Murugapandiyan, M Wasim, KN Devi, ...
    Micro and Nanostructures 182, 207643 , 2023
    2023
    Citations: 16
  • Remote sensing analysis of the lidar drone mapping system for detecting damages to buildings, roads, and bridges using the faster cnn method
    S Meivel, KI Devi, AS Subramanian, G Kalaiarasi
    Journal of the Indian Society of Remote Sensing 53 (2), 327-343 , 2025
    2025
    Citations: 11
  • WITHDRAWN: A survey report of air polluting data through cloud IoT sensors
    KI Devi, S Meivel, KR Kumar, J Vijayamenaka
    Materials Today: Proceedings , 2021
    2021
    Citations: 10
  • A review: smart farming using IoT in the area of crop monitoring
    KK Devi, J Premkumar, K Kavitha, P Anitha, MS Kumar, ...
    Annals of the Romanian Society for Cell Biology 25 (5), 3887-3896 , 2021
    2021
    Citations: 10
  • Data-driven decisions: Integrating machine learning into human resource and financial management
    S Venkatesan, TV Ambuli, K Devi, K Sampath, S Kumaran
    2024 7th International Conference on Circuit Power and Computing … , 2024
    2024
    Citations: 8
  • An efficient data collection tool for crop recommendations model using Robotic Process Automation
    KK Devi, JP Kumar
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023
    Citations: 8
  • WITHDRAWN: Computational analysis and intelligence for mediating effect of investment knowledge on investment intention and investment behaviors
    K Sathiyamurthi, K Devi, AN Raj
    Materials today: proceedings , 2021
    2021
    Citations: 8
  • Seismic Vulnerability Assessment of Excisting Buildings: It’s Importance
    K Devi, N Naroem
    International Journal of Innovative Technology and Exploring (IJITEE) 4 (9 … , 2015
    2015
    Citations: 8
  • Growth of zinc oxide crystals by accelerated evoporation technique from supersaturated solutions
    M Saraswathi, A Claude, KN Devi, P Sevvanthi
    Growth 4 (4), 1343-1349 , 2012
    2012
    Citations: 7
  • Digital technologies for smart business, economics and education: Towards a promising future
    A Omrane, G Patra, S Datta
    Springer Nature , 2023
    2023
    Citations: 5
  • WITHDRAWN: Real time analysis of unmask face detection in human skin using tensor flow package and IoT algorithm
    S Meivel, KI Devi, TM Selvam, SU Maheswari
    Materials Today: Proceedings , 2021
    2021
    Citations: 5
  • Design of iot based garbage segregation for automatic smart trash bin using ni labview
    K Devi, D Dharini, L Vandhana
    Int. J. Innov. Technol. Exploring Eng 8 (11), 725-729 , 2019
    2019
    Citations: 5
  • Adapting Floating Solar Power Projects: A Study of Sustainability and Economic Viability in Tamil Nadu, India
    V Vardhini, K Devi
    3rd International Conference on Reinventing Business Practices, Start-ups … , 2024
    2024
    Citations: 4
  • Deciphering the Indian Start-up Landscape: A Spot-light on Chennai's Ecosystem
    K Devi¹, DJ Bibiyana, K Sampath
    Proceedings of the 3rd International Conference on Reinventing Business … , 2024
    2024
    Citations: 4
  • Social media as the next trend in social business marketing social media as the next trend in solar business marketing
    V Vardhini, P Raja, K Devi
    International Journal of Innovative Technology and Exploring Engineering 8 … , 2019
    2019
    Citations: 4
  • Growth and characterization of novel thiourea based NTS, MTS And NMTS crystals
    A Claude, KN Devi, S Karthick, P Sevvanthi, K Sudha, M Saraswathi, ...
    Int. J. ChemTech Res 5, 512-521 , 2013
    2013
    Citations: 4