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An Integrated Approach to Green Cloud Solutions for Energy-Efficient Sustainable IT and Carbon Footprint Reduction
Cloud computing has become a very important part in everyday life, but this has also made a lot of carbon footprint because of the energy consumption in the data centers. The pandemic had affected these emissions, and they quickly came back, which has shown the requirement for sustainable solutions which will help in fighting the increase in carbon footprint. For these problems, the green computing technology will give probable solutions by promoting the technology that would be responsible enough to decrease these effects of harming environment. It will have techniques like smarter system designs, operations that are energy efficient, and smart techniques for optimization. This study explores how the above set principles can reduce the overall digital carbon footprint and help to create economically viable businesses. This approach provides a forward path for technology progress and profitability aligning with the environment sustainability which is a necessary component for business longevity. 2025 IEEE. -
Workplace bullying in the service sector
Context: Bullying is a problem that people, the world over, grapple with. It is manifest in different forms among different sections of people. Despite its prevalence, workplace bullying has not received much attention in scholarly literature in India. It is also not widely acknowledged as a threat to individual and organizational well-being. The purpose of this study is to add to the existing body of literature on the topic and to draw attention to the gravity of the issue. Aims: The primary objectives are to identify if there exist variations in its incidence on the basis of gender and years of experience, to identify the source of negative behavior, and the type of bullying that is most prevalent. Settings and Design: The study is a type of cross-sectional, descriptive study. Subjects and Methods: Data have been collected from a sample of 84 respondents using the Work Harassment Scale. All respondents are white-collar employees of the service sector in the cities of India. The data were analyzed using IBM SPSS v25. Results: The results find that there is no difference in the incidence of bullying on the basis of either gender or years of experience. Moreover, the source of negative behavior is generally one's superiors, and the most prevalent type is 'verbal aggression.' Conclusions: The study concludes with suggestions of steps to be implemented at the national and organizational level, to combat the problem. 2022 Authors. All rights reserved. -
The development and validation of the digital intelligence scale for students
Digital intelligence is increasingly recognized as a vital skill in navigating both academic and personal digital environments, yet existing tools often use multidimensional or adult-oriented frameworks. This study aims to develop and validate the Digital Intelligence Scale for Students (DISS), a unidimensional self-report instrument designed to assess the general digital intelligence of school, college, and university students. The study was conducted in two phases. In the first phase, data was collected from 786 students in India to examine the factor structure of the model. The analysis supported a unidimensional model, indicating that all items measured a single underlying construct. In the second phase, data was collected from 611 students in India to confirm the unidimensional model. Results supported a robust unidimensional structure, with excellent internal consistency (? = 0.954). The DISS was found to be significantly correlated with Internet Skills Scale and Digital Literacy Scale, providing evidence for convergent validity. Divergent validity was assessed using State-Trait Anxiety Inventory and Big Five Personality Inventory. This scale provides a practical framework for evaluating digital readiness in educational settings and guiding interventions. Subsequent studies could validate its relevance across cultural contexts and examine developmental trajectories in digital intelligence. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
STUDY ON ORGANIZATIONAL CULTURE IN RELATION TO HUMAN RESOURCE MANAGEMENT PRACTICES IN HOTEL INDUSTRY IN BANGALORE
This research focuses and examines the relationship between organizational culture and human resource management practices in hotel industry in Bangalore. Organizational culture is palpable in any organization which has a diversified work force. Hotel industry is built with employees with various values and beliefs. These factors reflect in the culture and are related with the various human resource management practices. There is a significant relationship between organizational culture and human resource management practices in the hotel industry in Bangalore. There were eleven dimensions of organizational culture and thirteen dimensions of human resource management practices that were considered for examining the relationship between organizational culture and human resource management practices. The sample for the study was 135 mid-level managers of different category of hotels in the geographical location of Bangalore. Organizational culture survey scale developed by Pareek, U. (2003) and human resource management practices scale developed by Sebastian, S., & Patrick, H. A. (2010) was administered for this study. This research concludes by the finding fact that there is significant relationship between organizational culture and human resource management practices in the hotel industry. The study also shows that the culture in hotel industry is narcissistic. Keywords: Organizational culture, Human resource management practices, hotel industry -
A review of artificial intelligence enhanced cognitive behavioural therapy using the BECK AI BOT for mental health interventions
The integration of artificial intelligence (AI) and cognitive behaviour therapy (CBT) is a revolutionary solution to the global mental health issue, characterized by increasing need and decreased access to treatment. This research investigates the potential of AI-fortified cognitive behavioural therapy technologies, including chatbots, virtual reality, and adaptive learning modules, to enhance the efficacy, accessibility, and individualization of treatment for anxiety, depression, and PTSD. The study evaluates the scalability, ethical issues, and therapeutic efficacy of the therapies by combining peer-reviewed and experimental data. The suggested methodology combines AI-driven conversational therapy with predictive modelling to deliver individualized, real-time mental health treatment. In this study, a conceptual chatbot prototype, designated BECK-AI BOT, was developed to illustrate the applications interface and functionality, enhancing accessibility for both patients and therapists in the future. This study does not present new clinical trial data. All reported symptom-reduction and engagement findings are drawn from previously published studies of existing AI-driven CBT systems (e.g., Woebot, Wysa, Eleos, Limbic). The present work offers a narrative synthesis of current evidence and introduces a conceptual architecture and prototype (BECK-AI BOT), without evaluating it clinically. Notwithstanding these difficulties, problems persist, including a lack of long-term efficacy statistics, cultural sensitivity issues, and moral reservations about over-reliance on AI during emergencies. The argument comes in the form of AI possibly improving, not replacing, human therapists, emphasizing hybrid systems for fair treatment. Future research needs to advance emotional intelligence within AI, which combines AI-driven conversational therapy and predictive modelling to deliver real-time, personalized mental health services. The Author(s) 2026. -
Intensity of hospital waste generation and disposal in the selected hospitals in Kerala, India: an analysis based on hospital ownership
Management of hospital wastes has been considered as an integral part of hospital hygiene and infection control, which in turn depends on the intensity of waste generation and disposal. This study analyses the ownership-wise intensities of hospital waste generated, treated and disposed in the selected hospitals in the state of Kerala, India. These intensities are examined using secondary data collected from four districts of Kerala for the period from 2010 to 2014. The intensity of hospital waste generation is measured on the basis of per bed per kilogram per day and also per patient per kilogram per day basis. The study shows that private hospitals are producing significantly higher amount of waste than government and co-operative hospitals. However, private hospitals are found to be more efficient compared to government hospitals in treating and disposing the hospital waste. It is also found that the co-operative hospitals are well-organized in treating and disposing the liquid waste compared with other hospitals in Kerala. 2023, The Author(s), under exclusive licence to Springer Nature Japan KK, part of Springer Nature. -
CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network
Cloud computing is the need of the twenty-first century with an exponential increase in the volume of data. Compared to any other technologies, the cloud has seen fastest adoption in the industry. The popularity of cloud is closely linked to the benefits it offers which ranges from a group of stakeholders to huge number of entrepreneurs. This enables some prominent features such as elasticity, scalability, high availability, and accessibility. So, the increase in popularity of the cloud is linked to the influx of data that involves big data with some specialized techniques and tools. Many data analysis applications use clustering techniques incorporated with machine learning to derive useful information by grouping similar data, especially in healthcare and medical department for predicting symptoms of diseases. However, the security of healthcare data with a machine learning model for classifying patients information and genetic data is a major concern. So, to solve such problems, this paper proposes a Cloud-Machine Learning (CloudML) Model for encrypted heart disease datasets by employing a privacy preservation scheme in it. This model is designed in such a way that it does not vary in accuracy while clustering the datasets. The performance analysis of the model shows that the proposed approach yields significant results in terms of Communication Overhead, Storage Overhead, Runtime, Scalability, and Encryption Cost. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Perceptive VM Allocation in Cloud Data Centers for Effective Resource Management
Virtual Machine allocation in cloud computing centers has become an important research area. Efficient VM allocation can reduce power consumption and average response time which can benefit both the end users as well as the cloud vendors. This work presents a perceptive priority aware VM allocation policy named P-PAVA algorithm, which takes into account the priority of an application along with its compute, memory and bandwidth requirement. The algorithm performs allocation of the applications based on the priority it gets using a machine learning based prediction model. Furthermore, to reduce the overhead of the allocation algorithm, parallelization is employed before assigning various workloads. To achieve this, the algorithm employs the First fit technique as a baseline for the requests allocation with a criteria as low priority. When compared to the state of the art algorithm for VM allocation for priority aware applications, P-PAVA performs better on several criteria such as average response time, execution time and power consumption. 2021 IEEE. -
Hybrid feature optimization and radial basis function networks for cardiovascular disease prediction
The study addresses the critical challenge of accurately predicting cardiovascular disease (CVD), a leading cause of mortality worldwide, where early diagnosis is crucial for effective intervention. Traditional models often struggle with high-dimensional data, imbalanced classes, and nonlinear feature interactions, limiting prediction reliability. Motivated by these gaps, this research proposes a hybrid methodology integrating Harris Hawks Search (HHS) for feature optimization with Radial Basis Function Networks (RBFN) to enhance CVD risk assessment. The HHS algorithm efficiently selects key predictive features such as chest pain type and number of vessels, reducing dimensionality while preserving vital information. Trained on optimized features, the RBFN classifier achieved superior performance with 92.1% accuracy, high sensitivity, and specificity, surpassing conventional models like Logistic Regression (81.2%) and Random Forest (86.7%). Ablation studies confirm each component's contribution, with significant gains validated statistically (p < 0.05). The hybrid model also offers computational efficiency with training times around 31.7 s. Future work aims to validate this approach on diverse, larger datasets and integrate it into real-time clinical decision support systems, advancing personalized, interpretable, and efficient cardiovascular healthcare tools. 2026 Elsevier Ltd -
Effectiveness of gamification in facilitating microlearning for gen Z
This chapter offers a thorough examination of the uses, advantages, and difficulties of gamification in higher education. In contrast to game-based learning, gamification uses specific game features to improve the learning experience. This chapter investigates the use of gamification to engage and inspire Generation Z (Gen Z) pupils with the goal of enhancing their academic performance. It underlines the necessity for game development that increases motivation and engagement in educational settings and highlights the measurement of student progress based on completed activities. Effective instructional approaches are crucial in a time where there is a constant stream of information and people have short attention spans. A promising approach to overcoming these difficulties in both online and offline education utilizing ICT technologies is offered as gamified microlearning, which combines microlearning and games. 2024, IGI Global. -
Deep learning architectures for multimodal fusion
The advancement in technology during the recent years has provided deep learning technology as an emerging and powerful paradigm which can be used for processing and understanding complex data across various domains. Multimodal fusion is integrating the information that is collected from various sources or modalities, which requires a comprehensive understanding of data like autonomous driving, medical diagnosis, etc. In this chapter, we will explore the various advanced deep learning architectures that have been specially designed based on the multimodal fusion. The various challenges that are being faced in multimodal data, which include heterogeneity, noise, reliability of data, etc. Various deep learning architectures that are built to address the various challenges, like convolutional neural networks, recurrent neural networks, are reviewed, and the suitability of the fusion strategies is highlighted. The various techniques that are used for combining the information from disparate modalities, like early fusion, late fusion, and hybrid approaches, are also discussed with their pros and cons. Various real-time applications in the field of healthcare, multimedia, robotics, etc., are demonstrated based on the impact of the architectures. Finally, the potential of deep learning architecture based on the revolutionary multimodal fusion will be discussed. 2026 Elsevier Inc. All rights reserved. -
Smart and Secured Ways of E-Payment: Design of New Frameworks
The rapid development in technology and the rapid growth of e-commerce have paved the way for changes in the method of payment. It also gave rise to various hazards while making the e-payment when compared to the normal or standard payment methods it is necessary to make a secured payment. Various initiatives are taken by the government for providing a secure payment system which will be more useful for all the commercial activities done through online. Already various e-payment systems are used by the consumers for paying the amount for the materials purchased. The increasing need of foreign exchange with an effective and efficient electronic payment system is required for making the low value payment. The framework that is been used in the global market and also in virtual marketplace require a complete legal structure which should also have impact on the economy of the mediaeval trade. Rapid development of e-commerce during the recent years has made more changes in the financial and non-financial transactions. In e-commerce, the payment gateway plays an important role in the exchange in ensuring that the transactions occur without any disputes and also maintains the security of the system. Most of the payment gateways used in the e-commerce are provided by rusted third party who will provide monetary information. Due to the increased use of e-commerce and online payment system, there is also any increase in security breaches during the past few years. So, it is necessary to build a new framework that will provide a secured platform for the e-payment system through which the consumers can directly connect to their merchants securely. Most of the third-party providers are also asking for the identity of the customers while making the payment which might even have change of loss of person information of the customer. The new framework should contain an improved security and the data collected should be confident, proper authentication method should be used, and availability of the data and integrity of the data should be maintained. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Women at work: The cultural and creative industries
[No abstract available] -
Classification of financial news articles using machine learning algorithms
The opinion helps in determining the direction of the stock market. Information hidden in news articles is an information treasure which needs to be extracted. The present study is conducted to explore the application of text mining in binning the financial articles according to the opinion expressed inside them. It is discovered that using the tri-n-gram feature extraction process in conjugation with Support Vector machines increases the reliability and precision of the binning process. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Reading behind the tweets: A sentiment Clustering Approach
Market sentiment influence crude oil future prices in direct or indirect way. In order to measure the polarity of market sentiment various techniques has been deployed by industry and academia alike. This pilot study successfully introduced two instruments, namely topic modeling and Sentiment clustering, to unearth the prevailing sentiments behind crude oil future pricesThree main conclusions that can be drawn from empirical results are. First, the K-Means clustering algorithm is an effective technique for sentiment clustering compared to Louveian and MDS clustering techniques. Second sentiment polarity-related positive sentiments have shown more variations in comparison to neutral and negative sentiments. Third It is possible to extract the keywords related to essential factors influencing crude oil prices using the LDA technique under topic modeling 2022 IEEE. -
Insurance Data Analysis with COGNITO: An Auto Analysing and Storytelling Python Library
Data pre-processing has taken an enhanced role with the advent of Machine learning. It is a vital element that forms the encore of the data science and business analytics process. Data pre-processing involves generating descriptive statistical summary, data cleaning, and data manipulation based on inputs gained after the initial analysis. Of late, it has been observed that data science practitioners spend 45% to 50% of their time cleaning and processing the data. Much time can be saved if the data transformation process can be automated. The COGNITO framework helps in performing the automated feature engineering and data storytelling of the dataset based on end-user discretion. The present work discusses the process and results obtained when automated feature engineering was performed on an insurance dataset using COGNITO. 2021 IEEE. -
The Intellectual Structure of Application of Artificial Intelligence in Forecasting Methods: A Literature Review using Bibliometric Thematic Analysis
Crude oil is a valuable asset class which forms the nucleus of the energy core of the transport sector for any country. According to report [1], crude oil helps in meeting 93% of energy needs for the transportation sector globally. It has been projected across various forums that crude oil along with coal and natural gas is going to satisfy world energy needs for the forthcoming years. Consequently, it has been observed that fluctuations in crude oil prices tilt the economies of scale across the world. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Designing an artificial intelligence-enabled large language model for financial decisions
Purpose: Artificial intelligence (AI) has profoundly reshaped financial decision-making, introducing a paradigm shift in how institutions and individuals navigate the complex finance landscape. The study evaluates the significant impact of integrating advanced AI and large language models (LLMs) in financial decision analytics. Design/methodology/approach: The study offers FinSageNet, a novel framework designed and tested to harness the potential of LLMs in financial decisions. The framework excels in handling and analyzing large volumes of numerical and textual data through advanced data mining techniques. Findings: FinSageNet demonstrates exceptional text summarization capabilities, outperforming models like FLAN and GPT-3.5 in Rouge score metrics. The proposed model has shown more accuracy than generic models. Originality/value: The study emphasizes the significance of consistently updating models and adopting a comprehensive approach to integrating AI into financial decisions. This study improves our understanding of how artificial intelligence transforms financial analytics and decision-making processes. 2025, Emerald Publishing Limited. -
Stock Market Trend Analysis on Indian Financial News Headlines with Natural Language Processing
Predicting the stock movement in the real-time scenario has been the most challenging and sophisticated in business. This business is affected by several factors from physical to psychological as well as rational to irrational. So far only few aspects have been taken into account while breaking down the conclusion. Implementing sentiment analysis, a subfield of Natural Language Processing (NLP), from the news, social media or financial document, investors decide whether they should invest for the company. The results have shown a significant and a feasible method for predicting the stock market trend with higher accuracy. The current research has mainly focus on finding the sentiment score from the news headlines and finding the hidden trend from it. Further the trading signals are generated based on the prevailing trend and trends are executed by the automated trading system. Using this algorithm, traders can reduce the manual intervention in the buy and sell decisions related to the stock market. 2021 IEEE. -
Framing and control for sustainability of industries
Purpose: The paper attempts to frame the challenge of managing the transition to a sustainable economy by way of a conceptual model consisting of a zero-footprint regulatory regime and a sustainability fund. Design/methodology/approach: A conceptual model of the sustainable industrial revolution has been developed based on the learnings from industries such as originators (mining), farming, pharmaceuticals, pesticides and chemicals and long-lasting artefacts against an overall perspective. Findings: It is suggested to have an institutional structural mechanism in place to ensure that footprint is minimized through recycling including refurbishing, resale or transformation. This includes management of recycling businesses through execution of a zero-waste regulatory regime that will build and use a sustainability fund. Research limitations/implications: The limitations of the paper are arising out of the topic being an issue of gigantic proportions with immense complexity. An attempt has been made to bring out the inescapability and the imperative of a sustainable industrial revolution. Practical implications: This paper presents practical aspects such as collusion between trash and recycling businesses, land use and social aspects of criticality of public support. If implemented, the suggested model can make a paradigm shift in the way firms, industry and governments can handle the challenge of sustainability. Originality/value: The value of this conceptual paper lies in an attempt to extend the learning organization framework to the concept of a regulatory model for sustainability that is not limited to the definition of a firm but stands extended to industries and to the economics, land use and demographics of the planet. 2021, Emerald Publishing Limited.