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The 90-h workweek controversy at L&T: leadership, culture and the future of work in India
Learning outcomes Upon completion of this case, students will be able to understand the implications of psychological contract theory in organizational contexts. It will allow them to analyse how leadership communication affects internal trust and public perception and critically evaluate the reputational consequences of symbolic leadership behaviour. Case overview/synopsis S.N. Subrahmanyan, Chairman and Managing Director of Larsen & Toubro (L&T), Indias leading engineering conglomerate, made a public statement encouraging employees to work 90?h a week and use Sundays to achieve global excellence. Although intended as a motivational message, the statement triggered nationwide backlash. Employees, industry leaders and the public interpreted the comment as reflecting an outdated, unsustainable work culture, sparking debates around worklife balance and generational shifts in employee values. Internally, the comment created anxiety and ambiguity regarding the companys expectations, especially among younger professionals. The case will allow students to examine how leadership communication can reshape psychological contracts, explore generational tensions in the workplace and evaluate how organizations should respond to reputational challenges while preserving performance culture. The case dilemma centres on whether L&T should clarify, retract or reinforce the chairmans statement. Complexity academic level This case is appropriate for upper undergraduate- and graduate-level programs in organizational behaviour, strategic human resource management, business ethics and leadership studies. Potential programs include, BBA, MBA HR and industrial and organizational psychology. Supplementary Material Teaching notes are available for educators only. Subject code CSS 6: Human Resource Management. 2025 Emerald Publishing Limited -
The (Pk,I) Transformation Graph of a Graph
A study on graphs that are derived from graphs based on the intersection of all k-paths of a graph G is initiated. The (Pk,I) transformation graph of paths, cycles, Pn?pK1 and Cn?pK1 is discussed. Also, certain structural properties of the newly constructed graph are also discussed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
THAYIL, JEET (1959-)
[No abstract available] -
Texture-Based DNN for Pneumonia in Thorax X-Rays
This paper introduces an innovative methodology for identifying pneumonia in thoracic X-ray images through the application of neural network classifiers. In our experiment, we employed a comprehensive training regimen involving multiple neural network classifiers, each trained on distinct sets of texture features meticulously extracted from thoracic X-ray images. Four different gray-level matrices and a neighboring gray-tone difference matrix (NGTDM) were used to generate these input features, guaranteeing a reliable depiction of the textural properties found in the X-ray pictures. We carried out an extensive evaluation utilizing a number of performance criteria to gauge the trained classifiers' efficacy. Classifying the thoracic X-ray pictures into two groups pneumonia and healthy state was the assignment assigned to the classifiers. A thorough study of the classifiers' performance was provided by our assessment measures, which comprised accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). The experimental findings showed that the suggested method accomplished a remarkable 91% overall test categorization accuracy, which was encouraging. This degree of precision highlights how well our approach works to accurately diagnose pneumonia from thoracic X-ray images. Furthermore, the consistent performance across different metrics highlights the robustness and generalizability of the proposed strategy. 2025, Iquz Galaxy Publisher. All rights reserved. -
Textual and media-based self-learning modules: Support for achievement in algebra and geometry
Owing to the importance of a subject like mathematics in the teaching and learning of science, self-learning often poses a challenge to the educator. The objective of this study is to analyse the enhancement of the textual and the media form of self-learning modules to teach algebra and geometry to eighth graders considering their retention levels. A pre-Test post-Test single-group quasi experimental design was tested and tried out on 49 participants of a school. The 20 modules of self-learning material covering content in the topics of algebra and geometry in the textual and media-Assisted forms of self-learning were administered over three months. The findings of the study revealed the ability of media-Assisted self-learning modules to enhance achievement in the post-Test when compared to the pre-Test. The textual-Assisted learning modules were able to enhance significant difference in the achievements in geometry, but not of algebra. The delayed post-Test results were found to indicate an improved achievement in mathematics. 2022 IGI Global. All rights reserved. -
Textile tourism and the challenges of the indigenous handloom sector in Northern Kerala
This study investigates the functioning of indigenous handloom enterprises and their relationship with textile tourism. It also explores the regional textile industry and the challenges weavers encounter in promoting their goods to visitors and exporters. Data was collected using a purposive sampling method, and a structured questionnaire was administered to 120 weavers from four textile weaving centers in Kozhikode, Kerala. The most significant obstacle for weavers and independent producers is the lack of direct communication with customers and the limited access to information provided by manufacturers, corporations, and gallery owners. Firstly, handcrafted items are becoming more accessible and affordable; secondly, the interest of the younger generation is gradually fading, which reduces the number of skilled professionals. The result of this study provides insight into how Khadi Textiles has the potential to contribute positively to socioeconomic achievements and enhance weaver's and destination image. 2024, IGI Global. All rights reserved. -
Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches
Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general mass.The primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. 2023 IEEE. -
Text summarization using residual-based temporal attention convolutional neural network
To address the computational complexity and limited to large data Enhanced Residual based Temporal Attention Convolutional Neural Network (ERTACNN) with Improved Initialization strategy-based Aquila Optimization Algorithm (IIAOA) is proposed. Initially the document is pre-processed to get structured data and given to feature extraction. Then the features are selected with Aquila Optimization Algorithm to remove redundant or unrelated features from high-dimensional data, from which the entropy values are calculated and given to proposed classifier. In this classification, the temporal attention mechanism is combined with classifier to compute attention weight and accompanied with important time points for classifying the documents. Finally, the proposed method is implemented in python and evaluated against existing works which achieves 70.34, 55.6 and 72.4 Recall Oriented Understudy for Gisting Evaluation (ROUGE) score than existing approaches. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management. -
Text Summarization Using Combination of Sequence-To-Sequence Model with Attention Approach
In daily life, we come across tons and tons of information which can be related to news articles or any kind of social media posts or customer reviews related to product. It is difficult to read all the content due to time constraint. Being able to develop the software that can identify and automatically extract the important information. There are two types of summarization methods. Extractive text summarization is the method where it picks the important content from the source text and gives same in the form of short summary, and on the other hand, abstractive summarization is the technique where it gets the context of the source text, and based on that context, it regenerates small and crisps summary. In this paper, we use the concept of neural network with attention layer to deal with abstractive text summarization that generates short summary of a long piece of text using review dataset. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Text Summarization Techniques for Kannada Language
Text Summarization is summarizing the original text document into a shorter description. This short version should retain the meaning and information content of the original text document. A concise summary can help humans quickly understand a large original document better in a short time. Summarization can be used in many text documents, such as reviews of books, movies, newspaper articles, content, and huge documents. Text summarization is broadly classified into extractive Text Summarization (ETS) and Abstractive Text Summarization (ATS). Even though more research works are carried out using extractive methods, meaningful summaries can be attained using abstractive summary techniques, which are more complex. In Indian languages, very few works are carried out in abstract summarization, and there is a high need for research in this area. The paper aims to generate extractive and abstractive summaries of the text by using deep learning and extractive summaries and comparisons between them in the Kannada language. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Text Mining-A Comparative Review of Twitter Sentiments Analysis
Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media net-works. Objective: This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Methods: Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. Results: The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Conclusion: Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a mar-ginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Nae Bayes, Multinomial Nae Bayes, Multinomial Nae Bayes with Bagging, Logistic Regression and a similar enhancement is observed with Ada-Boost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.. 2024 Bentham Science Publishers. -
Text extraction from video images
Video data contains beneficial textual information such as scene text and caption text. The different types of videos like movies, news videos, and TV programs video etc. are created by various video frames based on its purpose. In a country like India, there are only fewer studies has done on text extraction from video data especially in south Indian languages like Malayalam, Telugu, Kannada, and Tamil. The extracted text has many useful applications in video indexing, video key searching and assisting visually challenged people. Malayalam news channel named Mathrubhumi News videos data are considered for the proposed study. It is very beneficial to Kerala people as it is one of the most media-centric regions in the world. In this proposed paper, a new method for text extraction experiments. The anticipated method extracts 13 different features for classifying the image consists of text or not. Both spatial and frequency domain features are extracted to classify. The different types of classification techniques are used to validate the algorithm. Simple Logistic, J48 and Random Forest classification techniques are giving a good result when compared to other methods. Results are encouraging, the average success rate found to be 98%. Research India Publications. -
Testing the Diversifying Asset Hypothesis between Clean Energy Stock Indices and Oil Price
In theory, geopolitical risk and political uncertainty can directly affect energy markets. Fluctuations lead to the cost of clean energy sources as they compete with traditional energy. The purpose of this study is to analyse financial integration and test the diversifying asset hypothesis between clean energy indices, specifically the Clean Energy Fuels (CLNE), Nasdaq Clean Edge Green Energy (CELS), S&P Global Clean Energy (SPGTCLEN), TISDALE Clean Energy (TCEC.CN), Wilderhill (ECO) and West Texas Intermediate (WTI) stock indices, over the period from 1 January 2018 to 23 November 2023. Analysing the results reveals a scenario where most of the clean energy indices show cointegration with each other, indicating long-term relationships that reflect common trends in the clean energy sector. However, the relative independence of the WTI suggests that Oil still acts as an important and potentially diversifying external factor for investors focused on sustainable energy. Structural breaks in 2021 and 2022 in several indices point to significant events that have altered market dynamics, possibly including changes in environmental policies, technological innovations and the impacts of the COVID-19 pandemic. The cointegration evidence and structural breaks provide valuable information for building investment portfolios. Investors can consider the WTI to diversify portfolios dominated by clean energy assets, taking advantage of Oils relative independence. On the other hand, the high correlation between clean energy indices suggests that, within this sector, diversification options are more limited, requiring careful analysis of the specific characteristics of each index and the macroeconomic forces affecting them. 2024, Econjournals. All rights reserved. -
Testing The Causal Link Between Perceived Fee Fairness and Student Loyalty-Empirical Assessment in the Context of Service Marketing in Higher Education
Journal of Global Management Outlook, Vol-1 (5), pp. 43-55. ISSN-2277-3789 -
Testing of long run association between crude oil and gold commodities: An empirical study in India /
Test engineering & Management, Vol.82, pp.2902-2906, ISSN No: 0193-4120. -
Testing for the Bidirectional Relationship Between FDI in Services and Trade in Services: Evidence from Emerging Economies
We examine the two-way links between foreign direct investments (FDI) in services and trade in services for 26 emerging economies from 2003 to 2015 using sectoral and sectoral disaggregated FDI data. Within a multivariate framework, we use panel unit root tests, recently developed heterogeneous panel cointegration and panel vector error correction model (VECM). Our results confirmed the cointegrating relationship between trade in services, FDI in services, financial services FDI and nonfinancial services FDI. We find the existence of long-run unidirectional causality from trade in services to FDI in services. However, the disaggregated analysis shows a bidirectional link between nonfinancial services FDI and trade in services in the short run. Still, there is no causality between financial services FDI and trade in services both in the short run and long run. The result also shows the evidence of unidirectional causality running from trade in services to nonfinancial services FDI in the long run. It implies that sectoral decomposition matters in the FDItrade nexus in emerging economies. JEL Codes: G20, F14, G20, F23 2022 Indian Institute of Foreign Trade. -
Test case reduction and SWOA optimization for distributed agile software development using regression testing
Regression testing is a well-established practice in software development, but its position and importance have shifted in recent years as agile approaches have grown in popularity, emphasizing the fundamental role of regression testing in preserving software quality. In previous techniques, the challenge to address is determining the number and size of clusters and optimization to stabilize the cost and efficacy of the strategy. To overcome all the existing drawbacks; this research study proposes test case reduction and Support-based Whale Optimization Algorithm (SWOA) for distributed agile software development using regression testing. The purpose of this research study is to look into regression testing strategies in agile development teams and to find out what they are optimum clustered test cases. The proposed strategy is divided into two stages: prioritization as well as selection. Prioritization and selection are carried out once the test instances have been retrieved and grouped. The test case clusters are sorted and prioritized in this stage to ensure that the most critical instances are chosen first. During this stage, the test case clusters undergo sorting and prioritization to guarantee that the most essential cases are selected initially. Second, the SWOA is used to choose test cases with a greater frequency of failure or coverage criterion. The results of the assessment metrics show that the proposed approach outperforms other current regression testing strategies substantially. Based on experimental findings, our proposed approach betters existing methods in terms of information performance. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Test case reduction and SWOA optimization for distributed agile software development using regression testing
Regression testing is a well-established practice in software development, but its position and importance have shifted in recent years as agile approaches have grown in popularity, emphasizing the fundamental role of regression testing in preserving software quality. In previous techniques, the challenge to address is determining the number and size of clusters and optimization to stabilize the cost and efficacy of the strategy. To overcome all the existing drawbacks; this research study proposes test case reduction and Support-based Whale Optimization Algorithm (SWOA) for distributed agile software development using regression testing. The purpose of this research study is to look into regression testing strategies in agile development teams and to find out what they are optimum clustered test cases. The proposed strategy is divided into two stages: prioritization as well as selection. Prioritization and selection are carried out once the test instances have been retrieved and grouped. The test case clusters are sorted and prioritized in this stage to ensure that the most critical instances are chosen first. During this stage, the test case clusters undergo sorting and prioritization to guarantee that the most essential cases are selected initially. Second, the SWOA is used to choose test cases with a greater frequency of failure or coverage criterion. The results of the assessment metrics show that the proposed approach outperforms other current regression testing strategies substantially. Based on experimental findings, our proposed approach betters existing methods in terms of information performance. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Tertiary Packaging Issues and Their Influence on Repurchase Intention and Loyalty of Customers Towards E-Retailers
his thesis investigates the significance of tertiary packaging in the context of e-retail business and its influence on customer preference, repurchase intention, and loyalty towards e-retailers. With the rapid growth of e-retail, it is expected to become the dominant form of retail worldwide, surpassing traditional brick and mortar establishments. While e-retail offers convenience and flexibility to customers, intensifying competition among e- retailers raises concerns about how they will effectively manage it. As e- retailers resort to increased marketing efforts to attract customers, certain aspects, including tertiary packaging, may be inadvertently overlooked. Tertiary packaging plays a critical role in the e-retail process, and this study analyses its impact on customer perception and satisfaction. By exploring the issues related to tertiary packaging that affect customers, this research aims to provide insights to e-retailers for developing a more efficient and sustainable tertiary packaging model. The anticipated outcomes of this research are expected to enhance e-retailers' ability to attract customers, increase repurchase intention, and foster loyalty towards the e-retailer, ultimately contributing to their long-term success in the evolving e-commerce landscape. -
