Manu Vasudevan Unni

@claretcollege.edu.in

Assistant Professor, Department of Management
St. Claret College

Manu Vasudevan Unni


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https://researchid.co/manuvasudevanunni
I am a triple Master Graduate from France and India, with a proven record of academics and industrial experience, my research interests are widely focused on consumer behaviour, branding, strategy, international marketing and business.

RESEARCH, TEACHING, or OTHER INTERESTS

Marketing, Business and International Management, Business, Management and Accounting
27

Scopus Publications

165

Scholar Citations

6

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Introduction to Digital Literacy in Early Childhood Education
    Shivendu Kumar Rai, B. Suresh Kumar, Rajib Mandal, Shyam Kumar Anand, Manu Vasudevan Unni, Bharti, V. Bhoopathy
    Building Digital Literacy in Early Childhood Education, 2026
    Digital literacy has become an essential skill in ECE since young children are using technology more and more in their daily lives. This chapter talks about digital literacy for young children and why it is important for their cognitive, social, and language growth. It looks at digital skills that are right for each stage of development, teaching methods for using technology in play-based learning, and the roles of teachers and parents in helping kids have good digital experiences. The chapter also talks about important problems, such as how to limit screen time, how to make sure everyone has equal access, and how to stay secure online. It also talks about moral issues that are important for responsible digital involvement. This chapter stresses how careful and balanced use of digital tools may encourage creativity, communication, and critical thinking in young children, which is the first step towards becoming a responsible and confident digital citizen.
  • AI and Machine Learning in K–12 and Higher Education
    Roseline Jesudas, Irfan Abdul Karim Shaikh, M. Janani, C. Nithiya, Manu Vasudevan Unni, Naveen Nandal, V. Bhoopathy
    Multilingual and Multicultural Identities in Education Teacher Training and Awareness, 2026
    This chapter looks at how storytelling, role-playing, and simulation might work together as teaching methods to improve intercultural competency in multilingual classrooms. As language and cultural diversity grows more common in schools, teachers need flexible solutions that help students communicate, comprehend other cultures, and learn in a way that includes everyone. The chapter illustrates how a combination of theory and classroom practice fosters culturally responsive learning settings that validate students' linguistic identities, diminish communication barriers, and promote global citizenship. The conversation also brings up problems like teachers not being ready, language barriers, and tests that are too hard. In the end, it calls for a comprehensive and inclusive teaching style that helps students feel comfortable and sensitive in many cultural settings.
  • Research on Digital Privacy: Current State and Future Prospects
    Manu Vasudevan Unni, Jeevananda S, Jacob Joseph K, Krishnakishore S V
    2025 3rd International Conference on Sustainable Computing and Smart Systems Icscss 2025, 2025
    The introduction of digital technologies has had a profound impact on the marketing sector over the past two decades. As a result, there has been a rise in the importance of studying and talking about consumers' right to privacy. It's generally accepted that digitalization has had a significant effect on organizations and consumers. However, academics, businesspeople, and lawmakers are currently tackling the significant difficulties posed by consumer privacy worries. This article surveys the literature on privacy and digital marketing technology, keeping in mind the rapid pace at which the area is developing. This research summarizes the papers presented in a special issue on the topic of digital technology and personal privacy. Academics and professionals in the domains of communication, commerce, pricing, and product customization can learn a lot from the novel viewpoints presented in this publication, which are highlighted by the study. The findings of this study have implications for corporate and government leaders and should inspire more investigation from academia.
  • Fuzzy Logic and Genetic Algorithm Framework for Modelling Sustainable Customer Behaviour and Product Pricing in E-Commerce
    Surjit Victor, Divya H, Jalaja Enamala, S. Baskaran, Manu Vasudevan Unni, S. Subha
    2025 IEEE 6th Global Conference for Advancement in Technology Gcat 2025, 2025
    E-commerce dynamic product pricing frequently puts profit maximisation ahead of sustainable customer preferences, which results in lost opportunities to match corporate strategy with environmental responsibility. The hybrid fuzzy logic–genetic algorithm (FL–GA) framework proposed in this study simulates sustainable consumer behaviour and adjusts product pricing appropriately. While the genetic algorithm maximises prices to strike a balance between profitability and the likelihood of a sustainable purchase, fuzzy logic evaluates sustainability affinity, price sensitivity, and purchasing habits to capture the inherent uncertainty in customer purchase decisions. The system's efficiency has been shown by experiments conducted on e-commerce datasets sourced from Kaggle, which yielded an 18.6% increase in profit margin, a 92.4% likelihood to make sustainable purchases, and an R2 score of 0.982. These results outperformed baseline models such as support vector regression, random forest, and linear regression. The findings demonstrate that pricing strategies that incorporate sustainability considerations can produce better environmental and commercial results. This framework offers e-commerce platforms a scalable, data-driven strategy to gain a competitive edge and advance their worldwide environmental objectives.
  • Enhancing Recruitment Efficiency in HRM through Intelligent Resume Screening and Job Matching Using Fuzzy Logic and Ensemble Learning
    Kiruthiga V, Kalpana Deshmukh, V S Narayana Tinnaluri, M. Priyadharsini, Manu Vasudevan Unni, S. Ramya
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking Gcwcn 2025, 2025
    The increasing number of job applications on digital recruitment platforms is a problem for human resource managers, who have to balance efficiency with equity in candidate evaluation. Conventional resume screening systems frequently show restricted interpretability and inadequate matching precision. The proposed study introduces a hybrid recruitment model that combines fuzzy logic and ensemble learning to provide intelligent and explainable candidate-job matching. Fuzzy logic integrates recruiter selection patterns through interpretable membership functions and rule sets, while the ensemble architecture utilises models like Random Forest, Gradient Boosting, and XGBoost to improve predicting accuracy. The system was subjected to extensive evaluation using curated Kaggle recruitment datasets, benchmarked against established baseline models. Experimental results demonstrated substantial performance enhancements, with nDCG@10 = 0.964, Precision@5 = 0.948, Recall@5 = 0.931, MAP = 0.957, AUC = 0.981, and RMSE = 0.082, outperforming conventional methodologies. The suggested approach automates extensive screening while ensuring openness, allowing HR experts to track decisions to comprehensible rules. The suggested study integrates powerful machine learning with human-aligned reasoning to improve the efficiency and lack of trust in AI-driven recruitment platforms. Its use could optimise talent acquisition processes, reduce bias, and enhance recruitment results across several industry sectors.
  • Predictive Stress Management for Higher Education Faculty Using DCNN and LSTM Models
    C Lakshmi, P. Jayasaradadevi, Vijayalakshmi Chintamaneni, Rajasekar P, Santhiya Parivallal, Manu Vasudevan Unni
    3rd International Conference on Integrated Circuits and Communication Systems Icicacs 2025, 2025
    Teachers in higher education, who are already under a lot of strain from both classroom instruction and administrative duties, need to find ways to alleviate the stress that they experience so that they may be more productive and effective in their roles. The commitment of teachers and the quality of education as a whole are profoundly impacted by their well-being. Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) models are utilised in this research to present a predictive stress management methodology. Included in the methodology are steps for preparing data, extracting features, and training the model. In the preprocessing phase, extensive changes were made to the text data, and TF-IDF was used to effectively extract features and weight terms. With an average accuracy of 93.50%, the model outperformed standalone CNN and LSTM models thanks to its hybrid CNN-LSTM architecture. According to the results, the suggested method effectively reduces problems caused by stress in educators. This study highlights the model's ability to enhance prediction tasks, which can help institutions implement effective stress management strategies.
  • Dynamic Pricing Strategy using Reinforcement Learning and NLP-based Sentiment Analysis of Social Media Data
    Akana Chandra Mouli Venkata Srinivas, Munawar Yusuf Sayed, Yashwant Waykar, Sandesh R, Sweta Priya, Manu Vasudevan Unni
    Proceedings of the 6th International Conference on Smart Electronics and Communication Icosec 2025, 2025
    To improve classification accuracy in social media sentiment analysis, good preprocessing is essential. Pretreatment of data is a crucial part of any sentiment analysis pipeline since it enhances the number of occurrences that are correctly identified. An improved method for handling multi-step NLP problems, RLAP is presented in this paper. It is based on LLM. It represents these tasks as a MDP in our framework, and an LLM is an environment variable within this model. The TF-IDF method is used to extract useful textual information, which allows for more accurate sentiment classification. To guarantee that RLAP is a robust and general sable model, it is tested extensively on three separate natural language processing tasks utilising diverse social media datasets. The outcomes show that RLAP surpasses current models in accuracy and consistency, achieving an impressive 97.55% success rate. This demonstrates how effective it is to manage complicated NLP operations by integrating reinforcement learning concepts with sophisticated language models. To sum up, RLAP is a successful and versatile approach to sentiment analysis, especially in social media settings, and it highlights the significance of combining smart model planning with efficient preprocessing methods.
  • The Role of HR in Sustainable and Ethical Supply Chain Management using a Residual Neural Network Approach
    Zulfugarova Seljan Rauf, M. Kathiravan, P. Vanitha, R. Melba Kani, R. Sethumadhavan, Manu Vasudevan Unni
    International Conference on Intelligent Systems and Computational Networks Iciscn 2025, 2025
    Green supply chain management (GSCM) and green human resource management (GHRM) are new OM and HRM concepts. GSCM and GHRM improve organizational sustainability, according to HRM and SCM researchers. HRM and SCM integration is hindered by a large collaboration gap. Our approach requires preprocessing, feature extraction, and model training. During preprocessing, data normalization creates a new range. Principal Component Analysis (PCA) simplifies feature extraction by removing irrelevant or superfluous components. ABiMultiResRNN trains the model. With 92.08% accuracy, the ABiMultiResRNN model outperformed sophisticated models like ResNet and BiLSTM. GSCM and GHRM should be better integrated to promote sustainability, according to this study. ABiMultiResRNN accuracy suggests it could promote sustainable management in organizations.
  • The Role of Machine Learning in Threat Detection System
    L. Shammi, Sujatha Kamepalli, Radha Ranjan, Manu Vasudevan Unni, J. A. Baskar, V. Bhoopathy
    Deep Learning Innovations for Securing Critical Infrastructures, 2025
    Conventional detection approaches frequently fall behind the ever-changing complexity and frequency of cybersecurity threats. The use of machine learning (ML) has revolutionized threat detection in many different fields, including physical security systems, fraud detection, and network security. This chapter explores the use of ML models for threat identification, prediction, and mitigation, shedding light on important techniques, problems, and practical applications. It delves further into the topic by looking at potential trends, ethical concerns, and the necessity of a comprehensive strategy to protect vital systems and data. This chapter provides real-world examples of ML's application in threat detection. It explains the process and the challenges of these strategies. More and more, ML is being used in security contexts, which raises serious concerns about data privacy, model bias, and explainability. Emerging concepts such as federated learning, explainable AI, and edge computing are covered in this chapter, along with future advancements in ML-driven threat detection.
  • Ensemble Learning and Behavioral Analysis for Advanced Cybersecurity in Evolving Digital and IoT Ecosystems
    Ketan Rathor, Sujit Kumar Sadhukhan, Mohammad Shahid Kamal, Prasanthi Vallurupalli, Manu Vasudevan Unni, Gourav Kalra
    Proceedings of the 6th International Conference on Smart Electronics and Communication Icosec 2025, 2025
    The exponential growth of digital and IoT ecosystems has made cybersecurity an urgent matter of concern. Despite the many advantages, there are also serious security risks associated with the coming together of cloud computing, artificial intelligence, and networked gadgets. Operations are disrupted, sensitive data is compromised, and user trust is eroded by threats including data breaches, ransomware, and vulnerabilities targeting the IoT. The protection of these intricate digital infrastructures is becoming more difficult as the attack surface is being widened by the increasing number of IoT devices. This work addresses these challenges by assessing scalar and normalization function preprocessing procedures and applying a univariate chi-squared test for effective feature selection. They provide an EML strategy that combines the best features of EML with static analysis to tackle cybersecurity issues in the IoT ecosystem. This approach takes advantage of a large feature space to improve cybersecurity systems' detection capabilities. The experimental results show that the suggested method successfully identifies threats in large-scale, dynamic digital and IoT contexts with detection accuracies ranging from 97.3% to 99%. These results show that sophisticated machine learning methods may greatly enhance cybersecurity resilience and performance, making them crucial for protecting contemporary digital and IoT ecosystems.
  • Hybrid Machine Learning and Image Processing Model for HRM-Driven Resume Screening and Shortlisting in Higher Education
    Amit Joshi, Subha S, Saumya Sharma, K. Kalaivani, Manu Vasudevan Unni, Agilesh Saravanan.R
    2025 IEEE International Conference on Communication Networks and Computing Cnc 2025, 2025
  • Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles
    Manu Vasudevan Unni, Jeevananda S., Jacob Joseph Kalapurackal, Saba Fatma
    Indonesian Journal of Electrical Engineering and Computer Science, 2024
  • Enhancing Student Placement Predictions with a Trans-CLSTM Based Recommendation System
    Neerav Nishant, Alkawati Magadum, Vivekanand Pandey, D Suganthi, Manu Vasudevan Unni, S Anusuya
    1st International Conference on Electronics Computing Communication and Control Technology Iceccc 2024, 2024
  • Effectiveness of Digital Marketing Strategies in the Food Delivery Business Based on Hybrid BiGRU-CRF Approach
    Meenakshi, Vijayalakshmi P, Prabakaran V, Rudresh S, S. Devichandrika, Manu Vasudevan Unni
    2nd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2024, 2024
  • Digital Marketing Strategy and Consumer Behavior Analysis Through Deep Q-Network (DQN) Models
    Irfan Abdul Karim Shaikh, K Abirami Devi, Pooja Deshmukh, Mallothu Rajaram, Manu Vasudevan Unni, Arti Bhatt
    4th IEEE International Conference on Mobile Networks and Wireless Communications Icmnwc 2024, 2024
  • Safeguarding E Commerce and Preventing Financial Fraud with AdaBoost CNN Cybersecurity Approach
    V. Malathi, Shammi L, Manu Vasudevan Unni, Akabarsaheb Babulal Nadaf, S. Rukmani Devi, Thangam E
    International Conference on Distributed Systems Computer Networks and Cybersecurity Icdscnc 2024, 2024
  • Analyzing Customer Psychological and Behavioral Attributes in Corporate Social Responsibility in Supply Chain Management Using a Multilayer Perceptron Approach
    Kuldeep Agnihotri, Deepthi Kamidi, Radha Ranjan, Manu Vasudevan Unni, Ramesh Vijaya Babu K, N. Jayanthi
    2nd International Conference on Emerging Research in Computational Science Icercs 2024, 2024
  • Fostering Synergy: Exploring the Intersection of Operations and Human Resources Management through an RF-SVM Approach
    Keyurkumar M Nayak, Renjith Krishnan K, Surekha Dehu Khetree, Manu Vasudevan Unni, A. Vignesh, S. Ramya
    2nd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2024, 2024
  • Manta Ray Foraging Optimizer with Deep Learning based Malicious Activity Detection for Privacy Protection in Social Networks
    Manu Vasudevan Unni, S Jeevananda, K Jacob Joseph, Saba Fatma
    Proceedings of 2023 IEEE Technology and Engineering Management Conference Asia Pacific Temscon Aspac 2023, 2023
  • The Performance Impact of Human Resources Recruitment System for Business Process Management Using K-Means and SVM
    Sameer Yadav, Shikha Mann, Renjith Krishnan K, Dinesh Chandra Dobhal, Manu Vasudevan Unni, Manoj S
    International Conference on Self Sustainable Artificial Intelligence Systems Icssas 2023 Proceedings, 2023
  • Research on Factors Affecting Consumer Purchasing Behavior on E-commerce Website During COVID-19 Pandemic based on RBF-SVM Network
    Sameer Yadav, Ranjana Singh, E. Manigandan, Manu Vasudevan Unni, S Bhuvaneswari, Nitin Girdharwal
    2nd International Conference on Automation Computing and Renewable Systems Icacrs 2023 Proceedings, 2023
  • Automation using Artificial Intelligence in Business Landscape
    Manu Vasudevan Unni, S Rudresh, Bh Rashmi, K Renjith Krishnan, Rohit Kar, S. Devichandrika
    2nd International Conference on Automation Computing and Renewable Systems Icacrs 2023 Proceedings, 2023
  • Implementation of Machine Learning and Data Science in the Process of Making Financial Decisions
    Chandrasekar Venkatachalam, Anamika Kumari, K. Soujanya, Subharun Pal, B Prabu Shankar, Manu Vasudevan Unni
    2nd International Conference on Automation Computing and Renewable Systems Icacrs 2023 Proceedings, 2023
  • Effect of VR Technological Development in the Age of AI on Business Human Resource Management
    Manu Vasudevan Unni, Rudresh S, Rohit Kar, Rashmi Bh, Vasu V, Johnsy Mary Johnson
    Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems Icears 2023, 2023
  • Analysis of Digital Twins Implementation in Smart City using Big Data and Deep Learning
    Chinmaya Kumar Nayak, S. Karunakaran, P. Yamunaa, S. Kayalvili, Mohit Tiwari, Manu Vasudevan Unni
    Proceedings of the 7th International Conference on Intelligent Computing and Control Systems Iciccs 2023, 2023

RECENT SCHOLAR PUBLICATIONS

  • Building Digital Literacy in Early Childhood Education
    B Manditereza, T Jita, Z Nhase, LJ Maraisane, B Hadebe-Ndlovu
    IGI Global , 2026
    2026
  • AI and Machine Learning in K–12 and Higher Education
    R Jesudas, IAK Shaikh, M Janani, C Nithiya, MV Unni, N Nandal, ...
    Multilingual and Multicultural Identities in Education: Teacher Training and … , 2026
    2026
  • Introduction to Digital Literacy in Early Childhood Education
    SK Rai, BS Kumar, R Mandal, SK Anand, MV Unni, V Bhoopathy
    Building Digital Literacy in Early Childhood Education, 1-28 , 2026
    2026
  • Hybrid Machine Learning and Image Processing Model for HRM-Driven Resume Screening and Shortlisting in Higher Education
    A Joshi, S Subha, S Sharma, K Kalaivani, MV Unni
    2025 IEEE International Conference on Communication Networks and Computing … , 2025
    2025
  • Enhancing Recruitment Efficiency in HRM through Intelligent Resume Screening and Job Matching Using Fuzzy Logic and Ensemble Learning
    V Kiruthiga, K Deshmukh, VSN Tinnaluri, M Priyadharsini, MV Unni, ...
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN … , 2025
    2025
  • Fuzzy Logic and Genetic Algorithm Framework for Modelling Sustainable Customer Behaviour and Product Pricing in E-Commerce
    S Victor, H Divya, J Enamala, S Baskaran, MV Unni, S Subha
    2025 IEEE 6th Global Conference for Advancement in Technology (GCAT), 1-7 , 2025
    2025
  • Ensemble Learning and Behavioral Analysis for Advanced Cybersecurity in Evolving Digital and IoT Ecosystems
    K Rathor, SK Sadhukhan, MS Kamal, P Vallurupalli, MV Unni, G Kalra
    2025 6th International Conference on Smart Electronics and Communication … , 2025
    2025
  • Dynamic Pricing Strategy using Reinforcement Learning and NLP-based Sentiment Analysis of Social Media Data
    ACMV Srinivas, MY Sayed, Y Waykar, S Priya, MV Unni
    2025 6th International Conference on Smart Electronics and Communication … , 2025
    2025
  • Research on Digital Privacy: Current State and Future Prospects
    MV Unni, S Jeevananda, SV Krishnakishore
    2025 3rd International Conference on Sustainable Computing and Smart Systems … , 2025
    2025
  • An Empirical Study Using Meta-Analysis to Learn How Social Media Affects Organic Consumer Goods Purchase Behaviour
    RS Unni, Manu Vasudevan
    2025
  • Impact of Customer Based Brand Equity on Brand Preference and Purchase Intention
    RS Unni Manu Vasudevan
    2025
  • Hybrid and Advanced Technologies: Proceedings of the International Conference on Hybrid and Advanced Technologies (ICHAT 2024), April 26-28, 2024, Ongole, Andhra Pradesh, India …
    SPJ Christydass, N Nurhayati, S Kannadhasan
    CRC Press , 2025
    2025
    Citations: 4
  • Digital marketing strategy to increase sales conversion on e-commerce platforms based on LSTM-RNN model
    N Akram, S Victor, D Raghava, MV Unni, P Gottumukkala, S Garg
    Hybrid and Advanced Technologies, 358-363 , 2025
    2025
    Citations: 1
  • Enhancing enterprise human resource management: Predicting workers' stress for improved workplace satisfaction using a hybrid deep transfer learning approach
    DP Singh, K Gupta, N Singh, MV Unni, S Jayasudha, A Chauhan
    Hybrid and Advanced Technologies, 328-333 , 2025
    2025
    Citations: 3
  • Exploring the influence of work environment factors on personality traits, job satisfaction, and turnover intentions among employees: A ResNet analysis
    S Singh, SK Bharti, R Pujar, D Pilli, D Indoria, MV Unni
    Hybrid and Advanced Technologies, 340-345 , 2025
    2025
  • Predictive Stress Management for Higher Education Faculty Using DCNN and LSTM Models
    C Lakshmi, P Jayasaradadevi, V Chintamaneni, S Parivallal, MV Unni
    2025 3rd International Conference on Integrated Circuits and Communication … , 2025
    2025
    Citations: 6
  • The Role of HR in Sustainable and Ethical Supply Chain Management using a Residual Neural Network Approach
    ZS Rauf, M Kathiravan, P Vanitha, RM Kani, R Sethumadhavan, MV Unni
    2025 International Conference on Intelligent Systems and Computational … , 2025
    2025
    Citations: 1
  • The Role of Machine Learning in Threat Detection System
    L Shammi, S Kamepalli, R Ranjan, MV Unni, JA Baskar, V Bhoopathy
    Deep Learning Innovations for Securing Critical Infrastructures, 333-348 , 2025
    2025
  • Analyzing Customer Psychological and Behavioral Attributes in Corporate Social Responsibility in Supply Chain Management Using a Multilayer Perceptron Approach
    K Agnihotri, D Kamidi, R Ranjan, MV Unni, N Jayanthi
    2024 International Conference on Emerging Research in Computational Science … , 2024
    2024
    Citations: 2
  • Digital Marketing Strategy and Consumer Behavior Analysis Through Deep Q-Network (DQN) Models
    IAK Shaikh, KA Devi, P Deshmukh, M Rajaram, MV Unni, A Bhatt
    2024 4th International Conference on Mobile Networks and Wireless … , 2024
    2024
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Developing interpretable models and techniques for explainable AI in decision-making
    NJ Jagannathan, NDN Labhade-Kumar, NR Rastogi, NMV Unni, ...
    The Scientific Temper 14 (04), 1324-1331 , 2023
    2023
    Citations: 47
  • Does Digital and Social Media Marketing Play a Major Role in Consumer Behaviour?
    MV Unni
    International Journal of Research in Engineering, Science and Management 3 … , 2020
    2020
    Citations: 22
  • Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles
    MV Unni, S Jeevananda, JJ Kalapurackal, S Fatma
    Indonesian Journal of Electrical Engineering and Computer Science 35 (1 … , 2024
    2024
    Citations: 9
  • Effect of VR technological development in the age of AI on business human resource management
    MV Unni, S Rudresh, R Kar, R Bh, V Vasu, JM Johnson
    2023 Second International Conference on Electronics and Renewable Systems … , 2023
    2023
    Citations: 8
  • The Paradigm Shift in the Indian Education System during COVID19: Impact, Opportunities and Trends
    MVU Sidhi Menon U
    International Journal of Engineering and Management Research 10 (4), 1-10 , 2020
    2020
    Citations: 8
  • Predictive Stress Management for Higher Education Faculty Using DCNN and LSTM Models
    C Lakshmi, P Jayasaradadevi, V Chintamaneni, S Parivallal, MV Unni
    2025 3rd International Conference on Integrated Circuits and Communication … , 2025
    2025
    Citations: 6
  • Fostering synergy: Exploring the intersection of operations and human resources management through an RF-SVM approach
    KM Nayak, SD Khetree, MV Unni, A Vignesh, S Ramya
    2024 2nd International Conference on Intelligent Data Communication … , 2024
    2024
    Citations: 6
  • Analysis of Digital Twins implementation in smart city using big data and deep learning
    CK Nayak, S Karunakaran, P Yamunaa, S Kayalvili, M Tiwari, MV Unni
    2023 7th international conference on intelligent computing and control … , 2023
    2023
    Citations: 6
  • Research on Factors Affecting Consumer Purchasing Behavior on E-commerce Website During COVID-19 Pandemic based on RBF-SVM Network
    S Yadav, R Singh, E Manigandan, MV Unni, S Bhuvaneswari, ...
    2023 2nd International Conference on Automation, Computing and Renewable … , 2023
    2023
    Citations: 5
  • The Performance Impact of Human Resources Recruitment System for Business Process Management Using K-Means and SVM
    S Yadav, S Mann, DC Dobhal, MV Unni
    2023 International Conference on Self Sustainable Artificial Intelligence … , 2023
    2023
    Citations: 5
  • Hybrid and Advanced Technologies: Proceedings of the International Conference on Hybrid and Advanced Technologies (ICHAT 2024), April 26-28, 2024, Ongole, Andhra Pradesh, India …
    SPJ Christydass, N Nurhayati, S Kannadhasan
    CRC Press , 2025
    2025
    Citations: 4
  • Crypto-Currencies: Can Investors Rely on them as Investment Avenue?
    MVU Rudresh S
    2022
    Citations: 4
  • Enhancing enterprise human resource management: Predicting workers' stress for improved workplace satisfaction using a hybrid deep transfer learning approach
    DP Singh, K Gupta, N Singh, MV Unni, S Jayasudha, A Chauhan
    Hybrid and Advanced Technologies, 328-333 , 2025
    2025
    Citations: 3
  • Implementation of Machine Learning and Data Science in the Process of Making Financial Decisions
    C Venkatachalam, A Kumari, K Soujanya, S Pal, BP Shankar, MV Unni
    2023 2nd International Conference on Automation, Computing and Renewable … , 2023
    2023
    Citations: 3
  • Sparrow Search Optimization with Ensemble of Machine Learning Model for Customer Retention Prediction and Classification
    RJT Nirmalraj, M Rajeswari, SH Krishna, B Haralayya, R Narayanamurthy, ...
    2023 7th International Conference on Trends in Electronics and Informatics … , 2023
    2023
    Citations: 3
  • A Review on COVID-19 Outbreak: Marketing and Corporate Social Responsibility (CSR)
    MVU Rudresh S
    2022
    Citations: 3
  • Unleashing the efficacy of AI for smart banking: A demographic survey of Indian private banks
    BH Rashmi, DV Vidyashree, MRK Kuncha, MMV Unni, P Antony, P Vinod
    Ann. For. Res 65 (1), 2868-2883 , 2022
    2022
    Citations: 3
  • Does The Indian Education System Require A Paradigm Shift?
    SMU Manu Vasudevan Unni
    International Journal for Science and Advance Research In Technology 6 (4 … , 2020
    2020
    Citations: 3
  • Analyzing Customer Psychological and Behavioral Attributes in Corporate Social Responsibility in Supply Chain Management Using a Multilayer Perceptron Approach
    K Agnihotri, D Kamidi, R Ranjan, MV Unni, N Jayanthi
    2024 International Conference on Emerging Research in Computational Science … , 2024
    2024
    Citations: 2
  • Digital Marketing Strategy and Consumer Behavior Analysis Through Deep Q-Network (DQN) Models
    IAK Shaikh, KA Devi, P Deshmukh, M Rajaram, MV Unni, A Bhatt
    2024 4th International Conference on Mobile Networks and Wireless … , 2024
    2024
    Citations: 2