Manu Vasudevan Unni
@claretcollege.edu.in
Assistant Professor, Department of Management
St. Claret College
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
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Keyurkumar M Nayak, Renjith Krishnan K, Surekha Dehu Khetree, Manu Vasudevan Unni, A. Vignesh, and S. Ramya
IEEE
Since its inception a century ago, HRM has seen significant changes in both theory and practice, with the most notable changes occurring during the past two decades. As a result of a number of factors, both internal and external to the organization, human resource management is no longer seen as merely a maintenance function with minimal financial impact; instead, it is seen by many in the academic and professional communities as the source of sustained competitive advantage in today's global economy. The first stages of the proposed method include preprocessing, feature selection, and model training. It is crucial to apply pre-processing and representation to text input before feeding it to classifiers in order to optimize the performance of machine learning models. Data mining and knowledge -based authentication rely heavily on feature selection. LDA-BERT models are then trained after feature selection has taken place. The proposed method outperforms LDA and BERT, two widely used alternatives. Using the offered method, the system was able to obtain a maximum accuracy of 96.31%.
Chandrasekar Venkatachalam, Anamika Kumari, K. Soujanya, Subharun Pal, B Prabu Shankar, and Manu Vasudevan Unni
IEEE
The tension that results from people's decisions being affected by external factors is becoming too common. Despite the fact that researchers hope to shed light on stress-coping mechanisms in order to improve decisionmaking processes, it is crucial that they first comprehend the dynamics between the state induced by a stressful environment and the manner in which decisions are made under such conditions. The purpose of this study was to investigate whether or not acute stress affects financial decision-making, with a focus on whether or not stress has a greater impact on positive or negative decisions. When participants were under stress, the reflection effect was more pronounced in their decisions compared to when they were in the no-stress control phase. This indicates that stress affects risk taking, which may amplify irrational tendencies when making decisions. Under disruptive stress, decisionmakers revert to automated reactions to risk, which is consistent with dual-process theories. Preprocessing, feature selection, and model training are all used in the suggested method. Preprocessing makes use of techniques like abbreviation extension and the elimination of numerals and connections. LDA and LR are utilized for feature selection. The proposed model's efficacy is measured with GRU-CNN. The proposed approach is compared with two established approaches, such as GRU and CNN.
Manu Vasudevan Unni, S Rudresh, Bh Rashmi, K Renjith Krishnan, Rohit Kar, and S. Devichandrika
IEEE
The integration of Artificial Intelligence (AI) with automation has sparked a remarkable transformation in the contemporary business landscape, promising elevated efficiency and quality. However, this convergence encounters multifaceted challenges, notably in the adoption of recent AI techniques such as deep learning, reinforcement learning, and natural language processing. These techniques, while potent, grapple with challenges in data quality, interpretability, and ethical considerations. In this study, we aim to delineate the intricate interplay between AI and automation, illuminating their collective potential to augment operational efficiency and confer a competitive advantage. Through a comprehensive review, we will explore the effective integration of these technologies, navigating hurdles such as data bias, system compatibility, and human-machine collaboration. Here, the primary research objective is to provide insights on optimizing the outcomes by synergizing AI and automation while addressing the inherent challenges, ultimately fostering sustainable and impactful implementations in organizational frameworks.
Sameer Yadav, Ranjana Singh, E. Manigandan, Manu Vasudevan Unni, S Bhuvaneswari, and Nitin Girdharwal
IEEE
The proposed model is able to forecast online purchases by creating a connection between site visitors’ behaviors and the information to which they are exposed. Many modeling challenges arise when attempting to predict purchases made on the Internet. Models of online purchase need to consider the heterogeneity of users because online retailers reach a wide variety of people in a variety of competitive settings. The low probability of making a purchase online also reduces the predictive and explanatory power of models. Customers must complete the product configuration, fill up their personal information, and confirm the order with their payment information before making a purchase. The proposed strategy requires the processing of data, the selection of features, and the training of a model. Natural language processing and normalization are examples of preprocessing that can be used to data collected from e-commerce websites. Specifically, the Particle Swarm Optimization (PSO) technique is employed to select the features. Models are trained using an RBF-SVM hybrid approach. The proposed methodology outperforms SVM and RBF, two popular current methods.
Rohit Kar, Renjith Krishnan K, Philcy Antony, Rashmi BH, Padmapriya S, and Manu Vasudevan Unni
IEEE
The fields of humanities, psychology, and sociology are where the word “job satisfaction” originated. According to psychology, it is a condition in which a worker experiences his circumstances emotionally and responds by experiencing either pleasure or suffering. It is regarded as a variable in various sociological categories pertaining to how each employee assesses and thinks about his work. Because a satisfied employee contributes to and builds upon an organization's success, job satisfaction is intimately tied to an employee's performance and the quality of the work they do. As a result, job satisfaction directly correlates to an organization's success. The proposed strategy incorporates data preprocessing, feature selection, and model training. The missing value is a common feature of data preparation. Feature selection is chosen using the ANOVA F-Test Filter, the Chi-Square Filter, and the full Data Set Construction procedure. The model's efficacy can be evaluated with the help of CNN-BiGRU. The proposed technique is compared to two more models: BiGRU and CNN. It has been shown that our proposed technique outperforms two other models.
Sameer Yadav, Shikha Mann, Renjith Krishnan K, Dinesh Chandra Dobhal, Manu Vasudevan Unni, and Manoj S
IEEE
In this proposed approach to take a critical look at the literature on human resource management and diversity management. The proposed study describes the primary concerns and desired outcomes of diversity management and assess the current level of diversity management practices in enterprises with respect to human resources. The findings show that bias and inequality persist in many workplaces despite global efforts from HR departments. There has been a dismissal of the value of diversity in all its forms. The findings highlight a knowledge gap in the area of human resource management's potential for improving organizations' handling of diversity. Data preprocessing, feature extraction, and model training were all utilized in this proposed approach. Normalization of fault detection and fault identification are achieved by data preprocessing. The data will be extracted after a preliminary processing step. The proposed system employed PCA and K-means for feature extraction. The training process will be assessed using support vector machine, k-means, and k-SVM. The accuracy of the proposed approach improves to around 98.42%.
Chinmaya Kumar Nayak, S. Karunakaran, P. Yamunaa, S. Kayalvili, Mohit Tiwari, and Manu Vasudevan Unni
IEEE
Digital twins are becoming more relevant for business and academic users due to advances in IoT, AI, and Big Data. Due to global urbanization, pollution, public safety, traffic congestion, and other challenges have arisen. New technologies make cities smarter to keep up with growth. In the Internet of Things (IoT) age, many sensing devices acquire and/or produce a broad range of sensory data over long periods of time for a variety of businesses and applications. The use case determines the device’s data stream volume and speed. The efficacy of the analytics process used to analyze these streams of data to learn, predict, and act determines IoT’s worth as a business paradigm changer and quality-of-life technology. This study introduces Deep Learning (DL), a family of advanced machine learning techniques, to enhance IoT analytics and teaching. Introducing new results, challenges, and research opportunities. This study may assist academics and newbies comprehend how to use DL to smart cities. Analyzing and summarizing major IoT DL research initiatives. Check out smart IoT devices with DL embedded into their AI. Ultimately, the study will identify issues and suggest additional research. Each chapter concludes with experimental findings and the newest literature review.
R. J. T Nirmalraj, M. Rajeswari, Somanchi Hari Krishna, Bhadrappa Haralayya, Rajadurai Narayanamurthy, and Manu Vasudevan Unni
IEEE
Customer Relationship Management (CRM) system is majorly used to allow organizations to obtain new customers, establishes a continuous relationship with them and rises customer retention for gaining more profits. CRM systems use machine learning (ML) algorithms for analyzing customers' behavioural and personal data to give organizations a competitive advantage by rising customer retention rates. Therefore, this study designs a Sparrow Search Optimization with Ensemble of Machine Learning Models for Customer Retention Prediction and Classification (SSOEML-CRPC) technique. The presented SSOEML-CRPC technique aims to classify the possibility of customer churn. To attain this, the SSOEML-CRPC technique follows data normalization approach to uniformly scale the data. Next, the SSO algorithm is employed for the choice of optimal features. For classifying customer churn, ensemble of ML models namely back propagation neural network (BPNN), adaptive neuro fuzzy inference system (ANFIS), and extreme gradient boosting (XGBoost) models. The experimental result analysis of the SSOEML-CRPC technique is well studied on benchmark datasets and the results can be investigated in terms of several aspects. The experimentation outcomes illustrate the better outcomes of the SSOEML-CRPC technique over other existing models.
Manu Vasudevan Unni, Rudresh S, Rohit Kar, Rashmi Bh, Vasu V, and Johnsy Mary Johnson
IEEE
Human resource management (HRM) strategies are increasingly using AI and other AI-based technologies for managing employees in both local and foreign enterprises. An exciting new field of study has emerged in the last decade on topics like the media interaction of AI and robotics, the possessions of AI acceptance on independence and consequences, and the evaluation of AI-enabled HRM practices due to the proliferation of AI-based implementations in the HRM function. The use of these technologies has influenced the way work is organized in both domestic and global corporations, presenting new possibilities for better resource management, faster decision-making, and more creative issue resolution. Research on AI-based solutions for HRM is scarce and dispersed, despite a growing interest in academia. Human resource management (HRM) roles and human-AI interactions in major multinational corporations disseminating such advances need more study. As computing and networking infrastructure has advanced rapidly, so has the era of artificial intelligence. Now that in the age of AI, virtual reality technology has found many applications beyond gaming. Human resource management has emerged as a hot topic, with interest coming from both large businesses and government agencies. Many studies have been conducted on HRM in the business world, but in order to stay up with the trends, HRM must be constantly updated. This article does a demand analysis, and sets up and tests a fully-featured VR business human resource management system, all against the backdrop of the age of artificial intelligence and the present popularity of VR technology.