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Serverless Architecture - A Revolution in Cloud Computing
Emergence of cloud computing as the inevitable IT computing paradigm, the perception of the compute reference model and building of services has evolved into new dimensions. Serverless computing is an execution model in which the cloud service provider dynamically manages the allocation of compute resources of the server. The consumer is billed for the actual volume of resources consumed by them, instead paying for the pre-purchased units of compute capacity. This model evolved as a way to achieve optimum cost, minimum configuration overheads, and increases the application's ability to scale in the cloud. The prospective of the serverless compute model is well conceived by the major cloud service providers and reflected in the adoption of serverless computing paradigm. This review paper presents a comprehensive study on serverless computing architecture and also extends an experimentation of the working principle of serverless computing reference model adapted by AWS Lambda. The various research avenues in serverless computing are identified and presented. 2018 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. -
A Survey on Domain-Specific Summarization Techniques
Automatic text summarization using different natural language processing techniques (NLP) has gained much momentum in recent years. Text summarization is an intensive process of extracting representative gist of the contents present in a document. Manual summarization of structured and unstructured text is a tedious task that involves immense human effort and time. There are quite a number of successful text summarization algorithms for generic documents. But when it comes specialized for a particular domain, the generic training of algorithms does not suffice the purpose. Hence, context-aware summarization of unstructured and structured text using various algorithms needs specific scoring techniques to supplement the base algorithms. This paper is an attempt to give an overview of methods and algorithms that are used for context-aware summarization of generic texts. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Tangent Search Long Short Term Memory with Aadaptive Reinforcement Transient Learning based Extractive and Abstractive Document Summarization
Text summarization is the process of creating a shorter version of a longer text document while retaining its most important information. There have been a number of methods proposed for text summarization, but the existing method does not provide better results and has a problem with sequence classification. To overcome these limitations, a tangent search long short term memory with adaptive reinforcement transient learning-based extractive and abstractive document summarization is proposed in this manuscript. In abstractive phase, the features of the extractive summary are extracted and then the optimal features are selected by Adaptive Flamingo Optimization (AFO). With these optimal features, the abstractive summary is generated. The proposed method is implemented in python. For extractive text summarization, the proposed method attains 42.11% ROUGE-1 Score, 23.55% ROUGE-2 score and 41.05% ROUGE-L score using Gigaword. Additionally, 57.13% ROUGE-1 Score, 28.35% ROUGE-2 score and 52.85% ROUGE-L score using DUC-2004 dataset. For abstractive text summarization the proposed method attains 47.05% ROUGE-1 Score, 22.02% ROUGE-2 score and 48.96% ROUGE-L score using Gigaword. Also, 35.13% ROUGE-1 Score, 20.35% ROUGE-2 score and 35.25% ROUGE-L score using DUC-2004 dataset. 2023, Modern Education and Computer Science Press. All rights reserved. -
The double-edged sword of ChatGPT: fostering and hindering creativity in postgraduate academics in Bengaluru
Purpose: This research examines the complex relationship between usage of Chat Generative Pre-Trained Transformer (ChatGPT) amongst student and their creativity, learning and assessment using empirical data collected from postgraduate students. In addition, the study explores the students intrinsic motivation for usage to understand student categories. This research seeks to provide further insights into this artificial intelligence tool in enhancing the educational ecosystem for all stakeholders concerned. Design/methodology/approach: The target population of this research the students of post-graduation in diverse fields of science and management. A five-point Likert scale-structured questionnaire adapted from earlier literature relevant to the research questions was adopted for data collection. The data were collected for twomonths, resulted in 403 usable responses. Ethical considerations of assurance of confidentiality to the participants were strictly adhered to. Structured equation modelling (SEM) was employed to explore the relationships between the constructs of the study for the assessment of latent relationships. SmartPLS 4 was used to explore these relationships. Findings: Usage has a negative impact on a students creativity, but increased usage of ChatGPT encourages a students adoption due to its perceived usability. Pedagogical applications of ChatGPT aid students as a learning tool but require controlled usage under supervision. Originality/value: This study is innovative in the context of postgraduate students, where very little evidence of creativity exists. Through this research, the authors illuminate how ChatGPT use affects academic performance, benefiting educators as a tool but for evaluation and assessment, policymakers and students. Thefindings of the study provide implications that help to create effective digital education strategies for stakeholders. 2024, Emerald Publishing Limited. -
The concept of entrepreneurial ability-evidence from women in MSMEs of Karnataka state
The Indian women entrepreneurs have come a long way today from the traditional deep-rooted view of the Indian society and are predominantly found in the MSME sectors of India. To understand their growth and advancements, a proper understanding of their entrepreneurial ability with respect to their performance is of paramount importance. The objective of this study is to explore the factors of women entrepreneurial ability which impacts the successful performance of the women entrepreneurs in MSMEs of Karnataka state in India. A theoretical framework model of entrepreneurial ability developed for the study is tested with a primary data collected through a survey-questionnaire method from a sample size of 427 women entrepreneurs using a random sampling method, factor analysis and Pearson correlations. Overall the results of this study support the contention: the perceived business performances of women entrepreneurs have a significant influence on their entrepreneurial ability. Copyright 2020 Inderscience Enterprises Ltd. -
Exploring Female Narratives of Sexual Intimacy and the Social Suppression of Desire
Exploring the construction of sexual identities by women, this research attempts to provide an experiential understanding of sexual intimacy in young adulthood through critical narrative analysis of the accounts of ten unmarried cis-gender women, located in the postmodern feminist paradigm and drawing from contemporary psychoanalytic tradition. The study highlighted the lack of discourse on female desire and pleasure (that is not fetishised, penalised, or ostracised) in the hetero-patriarchal socio-cultural fabric of India and how it manifests in the sense of shame, guilt, and self-doubt in the navigation of sexual intimacy. The social matrix, including the influence of family and partner dynamics and cultural and generational differences, was observed to play a prominent role in the evolution of individual perceptions of sexual intimacy. Analysing the narratives through a feminist lens foregrounded the predominance of male satisfaction and pleasure, the sense of obligation towards male partners, the infringement of boundaries and compromise, and the performativity in sexual experiences, thus calling attention to the female struggle of realising and practising sexual agency. The research indicates the need to critically examine the pervasive phallocentrism in the experience of sexual intimacy and the marginalisation of female sexual desire due to the suppression of female sexuality in the patriarchal hierarchy of power distribution. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Emotional Intelligence as a Predictor of Police Operational Stress: A Pilot Study
The present study examined the relationship between police operational stress and emotional intelligence. The study also observed the difference in operational stress and emotional intelligence concerning gender, rank, education, and marital status. The sample included 80 police officers from Bangalore, India. The operational police stress questionnaire developed by McCreary and Thompson (2006) and emotional intelligence scale developed by Hyde et al. (2002) were used to measure police operational stress and emotional intelligence, respectively. Independent sample t-test and Cohens d indicated that differences in gender, rank, education, and marital status had no significant effect on police operational stress. Gender differences had a significant effect on the emotional intelligence factors, empathy, and self-motivation. Differences in rank had a significant effect on empathy, self-motivation, emotional stability, managing relationships, integrity, value orientation, and commitment. Differences in marital status had a significant effect on value orientation. Correlation analysis showed that operational stress had a significant negative relationship with emotional intelligence and its factors such as self-motivation, emotional stability, value orientation, and altruistic behavior. Regression analysis showed emotional intelligence and its factor, emotional stability, as significant predictors of police operational stress. 2021, Society for Police and Criminal Psychology. -
Fine-Tuned Deep Contextual BERT for Enhanced Aspect-based Sentiment Analysis: A Comparative Study on Laptop Reviews
Sentiment analysis entails the care full analysis, conduction of interpretation and conclusion of subjective texts even as an evaluation. In the business context, the companies' strategies towards growth makes use of both level of experience of consumers, market reach, social media, opinion and reputation of the brand. The different levels of performing the analysis includes the analysis at the document, phrase, and aspect levels. The sentiment which targets the polarity on some components of texts is often recognized by various Natural language processing (NLP) tasks for example aspect level sentiment analysis. This study presents the fine-tuned deep contextual BERT (FTDC BERT) aiming at improving the accuracy of sentiment polarization prediction. We look at different types of models including the LSTM based and the attention based and the BERT based models and where they performed on the laptop dataset. The fine-tuned and pre-trained BERT model exceeded all benchmarks and gave the most accurate work at 84.48%. This remarkable achievement testifies to the capability of the model in adapting its structure to varying degrees of sentiment contained in laptop reviews. Based on the comparative analysis, different models have different degree of success which indicates that sentiment has to be modelled separately for every set of data. This paper describes interesting areas of the future inline sentiment analysis for researchers and practitioners. 2025 IEEE. -
Understanding psychoneuroimmunology in pediatric development: A focus on neurodivergent populations
Psychoneuroimmunology can be defined as the study of interactions between behavior, neural and endocrine function, and immune processes. The research suggests that the mind and body have a bidirectional connection. These interactions bring many chemical changes in the body. Both Positive and negative emotional states, creating different neuroimmune responses in the body, and thus influence health and recovery from illness. The bi-directional influence of psychology and immunology is especially evident in the pediatric population. Children's quality of life is quite important to define their overall health. Children's quality of life includes child's social, physical, emotional functioning and their family support. Quality of physical well-being (self-care, exercise), interpersonal relationships, emotionally balanced state, and mental construction positively influence psychoneuroimmunology. This book chapter explores the most significant factors that enhance children's psychoneuroimmunology to strengthen their quality of Life. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Stitching the threads of change: Malayalam actor Indrans as the multifaceted tailor
While many figures in Malayalam cinema have entered the industry through elite artistic networks, a parallel history is shaped by individuals whose artistic journeys emerge from domains of labour that have remained under-recognized within dominant cinematic narratives. Among them is the National Award-winning actor Indrans, whose humble beginnings as a costume designer and tailor constitute a framework for his foray into the film industry and a key component of his distinctive identity. Drawing on a reflective in-person interview with the actor, this conversation explores how Indrans consistently affirms and mobilizes his tailoring background, not as a past remaining in the periphery but as a vital source of embodied knowledge, aesthetic sensitivity and cultural authorship. The conversation foregrounds the continuities between material craft and screen performance, revealing how his artisanal expertise informs his minimalist acting style and creation of characters. Through evaluating his labour within broader shifts in the monetary systems of costume designing and holding an acting career in Malayalam cinema, the conversation situates Indrans as a unique prism, a befitting case in point for revisiting class, skill versus talent and performative authorship from the Kerala context, India, whose four decades of positioning in the Malayalam film industry become a repertoire of data, as an individual who possesses extensive experience and knowledge in the industry, and has witnessed the shifts for nearly half a century. An account of his journey invites a re-examination of the hierarchical structure of cinematic labour, where backstage artisanal work substantially impacts and optimizes on-screen performance. 2025 Intellect Ltd. -
ML-Based Fall Risk Prediction to Substitute Personal Assistance for Hospitalized Elderly: Integrating Geriatric Assessment and E-Health Records
Geriatric assessment serves as a holistic evaluation tool, encompassing various aspects of the elderly individual's health, including physical function, cognition, and psychosocial factors. Integration of CGA data with EHRs allows for a comprehensive analysis of the individual's health status and medical history, providing valuable insights into their risk factors for falls. The ML-based predictive model developed in this study utilizes these integrated data sources to identify patterns and trends associated with fall occurrences among hospitalized elderly patients. By analysing various variables, including mobility indicators, medication usage, and previous fall history, the model can generate accurate predictions of fall risks for individual patients. This ML-driven approach has the potential to significantly improve patient safety and quality of care by enabling healthcare providers to pre-emptively identify and address fall risks among hospitalized elderly individuals, thereby reducing the reliance on constant personal assistance while ensuring optimal patient outcomes. 2025 by IGI Global Scientific Publishing. -
Striving for Decent Work: Assessing Kerala's Progress Toward SDG Labour Targets
This Article delves into Goal 8 of the Sustainable Development Goals, which is pivotal in ensuring universal access to quality employment, thereby diminishing unemployment and, consequentially, alleviating widespread poverty. It underscores the global consensus on essential facets of equitable employment opportunities, encompassing job availability and social security for workers. Drawing upon data primarily sourced from the State Statistical Office in Kerala, this paper examines the myriad aspects and indicators of decent work and presents the latest empirical findings. Our analysis reveals that a glut in labour supply coupled with scant employment opportunities escalates unemployment rates, notably affecting women and youth. This challenge is further exacerbated by structural inefficiencies and socioeconomic disparities. To counter these issues, the study proposes several strategic interventions. Enhancing educational and vocational training for women not only equips them with necessary skills but also fosters an inclusive workforce. Additionally, bolstering the capabilities of labour inspectorates can ensure adherence to labour standards and promote decent work conditions. Effective management of the Decent Work phenomenon is crucial and requires comprehensive policy frameworks that integrate these elements. By implementing these strategies, there is potential to significantly transform the labour market dynamics, promoting sustainable economic growth and social equity. This paper aims to contribute to the discourse on employment policies, advocating for informed decisions that are pivotal in achieving the targets set forth in Goal 8 of the Sustainable Development Agenda. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
The Devastating Impact of Greenhouse Gas Emissions: A Looming Threat to Sustainable Development in India
This study investigates the trends in greenhouse gas emissions across various sectors in India, focusing on carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). By estimating and evaluating the emissions and removals from key sectors, including industrial, agricultural, and other significant subsectors, this research aims to comprehensively analyse the types and sources of greenhouse gases emitted by Indias industries. The study conducts a detailed trend analysis within multiple subsectors, identifying significant patterns and discrepancies over different time periods. A comparative analysis between the industrial and agricultural sectors highlights the differences in emission sources and levels. Furthermore, this research delves into the measures and initiatives undertaken by the Indian government to mitigate greenhouse gas emissions and promote sustainable development. This includes policies, regulations, and programs to reduce emissions and enhance environmental sustainability. The findings of this study are expected to contribute to a deeper understanding of sector-specific emissions in India, offering valuable insights into the effectiveness of current policies and suggesting areas for improvement. This research aims to inform and influence policy-making for enhanced environmental sustainability in India by comprehensively evaluating emissions trends and government actions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Humour as a tool for brand recall. Understanding the concept through five star advertisements /
Advertisements are considered as one of the effective tool of promoting a service or product. Though it has high impact on people it uses different appeals to attract the viewers. One of the effective appeals used by advertisers is humour. Humour has been used widely in advertisements since years. Everyone likes humour and the concept behind using humour in advertisements is clearly based on the people’s psychology. The researcher tries to find out whether humour is used as a tool for brand recall or not by using different qualitative and quantitative methods. -
Pratixa: A Cognitive Framework for Behavioral Decision-Making and Its Mathematical Formalization
The present study introduces pratixa, an internal cognitive structure that functions as a reference architecture guiding human decision-making. Pratixa is a dynamic, event-sensitive archive of anticipated outcomes of behavior, learned event-behavior-outcome associations, and adaptive behavioral responses, drawing on the theories from decision science, psychology, and behavioral adaptation. Past experiences shape pratixa, and iterative learning reinforces it. It supports predictive mental representations by enabling individuals to anticipate the outcomes of their own behavioral responses and adjust those responses when discrepancies arise between anticipated and actual outcomes. Pratixa supports anticipatory learning and real-time correction, making it a future-oriented cognitive structure for decision making. It matures in a spiral progression, from null pratixa, where no prior event-behavior-outcome associations exist, through quixotic pratixa, characterized by illusory or arbitrary associations, to realistic pratixa, where causal relationships are adequately approximated. This spiral maturation reflects how individuals adapt through experiential learning and reinforcement, transitioning from effortful reasoning to increasingly automatic and context-sensitive decision-making. By positioning decision-making within this evolving structure, pratixa offers a distinct perspective on predictive cognition in complex and ambiguous contexts, with implications for strategic foresight, behavioral economics, and adaptive behavioral decision making. The study also proposes a mathematical formulation to represent how this reference architecture evolves through reinforcement-based learning and guides decision-making, providing a computational basis for modeling human foresight and adaptation. 2025 John Wiley & Sons Ltd. -
The double-edged sword of ChatGPT: fostering and hindering creativity in postgraduate academics in Bengaluru
Purpose: This research examines the complex relationship between usage of Chat Generative Pre-Trained Transformer (ChatGPT) amongst student and their creativity, learning and assessment using empirical data collected from postgraduate students. In addition, the study explores the students intrinsic motivation for usage to understand student categories. This research seeks to provide further insights into this artificial intelligence tool in enhancing the educational ecosystem for all stakeholders concerned. Design/methodology/approach: The target population of this research the students of post-graduation in diverse fields of science and management. A five-point Likert scale-structured questionnaire adapted from earlier literature relevant to the research questions was adopted for data collection. The data were collected for twomonths, resulted in 403 usable responses. Ethical considerations of assurance of confidentiality to the participants were strictly adhered to. Structured equation modelling (SEM) was employed to explore the relationships between the constructs of the study for the assessment of latent relationships. SmartPLS 4 was used to explore these relationships. Findings: Usage has a negative impact on a students creativity, but increased usage of ChatGPT encourages a students adoption due to its perceived usability. Pedagogical applications of ChatGPT aid students as a learning tool but require controlled usage under supervision. Originality/value: This study is innovative in the context of postgraduate students, where very little evidence of creativity exists. Through this research, the authors illuminate how ChatGPT use affects academic performance, benefiting educators as a tool but for evaluation and assessment, policymakers and students. Thefindings of the study provide implications that help to create effective digital education strategies for stakeholders. 2024, Emerald Publishing Limited. -
Bibliometric Insights into the Nexus of Digital HR, Innovation, and Sustainability: Toward a Smart Workforce
HR professionals use AI, blockchain, cloud computing, big data analytics, and Metaverse to optimize the workforce as technology advances. These technologies boost corporate value, employee performance, and smart workforce development. Metaverse improves virtual reality training, 3D simulations, and wearable self-tracking technologies. Cloud computing simplifies simulations and collaborative mixed reality for employees. AI tools usage increases an organization's staff efficiency. Smart workforce tactics and workplace technologies improve success and human experience management, especially in virtual, remote, and collaborative work contexts. Many companies have failed to integrate Metaverse in the workplace despite advances in digital technologies. A Biblioshiny analysis- based systematic assessment of human capital management automation systems addresses this gap. This study examines smart workforce requirements and future automation trends at the organizational, managerial, and individual levels. Additionally, this study allows for the creation of a self-sustaining virtual HR system. 2025 Scrivener Publishing LLC. All rights reserved. -
Exploring the intersection: Technological innovations and green management in enhancing sustainable organizational performance
Organizational changes in recent times have indicated a shift toward using innovative approaches to solve environmental concerns in both processes and products. This is in response to calls from stakeholders for a more comprehensive approach to business operations that puts profit, the environment, and people first-also known as the triple bottom line. In line with the resource-based view (RBV) framework, organizations can effectively address environmental challenges and meet social responsibilities. Embracing technologies such as AI, ML, and automation in their green practice operations unlocks new opportunities, new markets, and partnerships. This shift strengthens their reputations and goodwill among stakeholders. Although there is extant literature on green initiatives and organizational success, there is a dearth of thorough research that examines green management innovations and technological techniques in their entirety. This study attempts to close this gap by defining several aspects of green management and examining how green initiatives and technology affect future growth coupled with organizational performance and success. This study employs bibliometric analysis to conduct a systematic literature evaluation, focusing on a dataset (n = 1026) spanning the years 2005 to 2024. The results reveal (i) the two dominant clusters are, first, sustainability, sustainable development, innovations, digital technologies, and supply chain management, and, second, sustainability, performance assessment, and industrial performance, while a third cluster-sustainability, industrial performance, and innovation-reveals a growing interest among the research community; (ii) productivity and the impact of publications, authors, and countries prominently crusading on the theme; and (iii) thematic clusters that open the scope for further research. 2026 Shivakami Rajan, L.R. Niranjan. All rights reserved. -
Analysis and optimization of uplink spectral efficiency in massive multiple-input and multiple-output
Fifth Generation (5G) specifications aims for data rate of 1 Gbps in high mobility and 10 Gbps in low mobility conditions, 15-30 bps/Hz of spectral efficiency with less than 1 milli second (ms) latency reduction. Massive multiple-input and multiple-output (Massive MIMO) is one of the promising technologies in 5G standard which offers a high spectral efficiency improvement. This work focus on the uplink scenario spectral efficiency in a Massive MIMO simulation network based on third generation partnership project (3GPP) and long term evolution (LTE) document of 5G. This work analyzes the spectral efficiency metric by simulating the 5G Massive MIMO network. Then, the research identified major constraint parameters; number of user antennas, K, number of base station antennas, M, transmission power, P, channel bandwidth, B, and coherence time, Tau_C and pilot time Tau_P which plays a significant role in varying this metric. The authors focus on improving the spectral efficiency by passing these constraint parameters through different meta-heurestic optimization algorithms, such as, convex optimization solver, White shark optimization (WSO) and Particle swarm optimization (PSO). The results show an overall, 1-10 percent of improvement of the parameter wnen compared with other research articles. The maximum value achieved is 49.84 bps/Hz, which is three times higher as per to the 3GPP and International Telecommunication Unioin (ITU) release document. 2022 Institute of Advanced Engineering and Science. All rights reserved.