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I Dont Play Games: Migrant Workers and Digital Media in Bengaluru
The great impact of media technologies in reordering almost every facet of modern life has been noted by theorists for over a century now, particularly since the idea of the global village imagined by media theorists, and enabled by globalisation and digital technology has become an inescapable reality. The new experience of time and space bears upon various dimensions of life, including the nature of work, the organisation of time and the place of leisure within these rhythms. This article attempts to engage with this very weighty body of scholarship in a modest way, through ethnographic research, to understand how mobile phones and internet technologies structure the experience of everyday life for low-income migrant workers in Bengaluru. The sites include a construction site and a hookah bar, and the study focuses on mobile gaming and the structuring of migrant social networks. 2024 South Asian University. -
Migrant labour and mobile sensibilities
[No abstract available] -
Effect of corporate restructuring on shareholder's value in the information technology sector /
International Review of Research in Emerging Markets and the Global Economy, Vol.1, Issue 1, pp.182-188, ISSN No: 2311-3200. -
Revenue recognition from gift cards: What and how? /
Perspectiva: A Case Research Journal, Vol.5, pp.77-81, ISSN No: 2394-9961. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
A review on serverless architectures-Function as a service (FaaS) 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. Universitas Ahmad Dahlan. -
Decent Work Deficit: A Challenge on the Women Empowerment in Indian Agricultural Sector
Women play a crucial role in Indian agriculture, but they also confront several obstacles that reduce their productivity and prevent them from fully engaging in the sectors development. The majority of women in India are employed in agriculture, which is one of the sectors that contributes most to the GDP and is essential to the economic development of the nation. Although women continue to have a significant and recognized role in agriculture, their function is frequently overlooked. Women make up about 75% of the full-time labor force on Indian farms. The nation wont develop unless its women farmers are empowered. Only through decent work labour the agriculture sector will be developed which will help in the empowermentof women agricultural Labourers in India. So the government should take all steps to implement the decent work concept of ILO in the Indian agricultural sector. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Ecotourism a Sustainable Development Approach: A Case Study of Bandipur Forest
Bandipur Tiger Reserve is geographically speaking, it is an ecological confluence since the Western and Eastern Ghats intersect here, making this region unique and exceptional in terms of its flora and fauna. The community land areas of all the border settlements as well as the nearby notified and unnotified forests have been included in the buffer of this tiger reserve. The scrub jungle along the park's eastern boundaries is made up of stunted trees, scattered bushes, and open grassland patches. The Eco-tourism activity is run in the two Ranges of Bandipur (54 km2) and GS Betta (28 km2), covering a total area of 82.00 km2, or around 9.40% of the Reserve's total size. From the above analysis, it could be concluded that the government should provide that there are administrative facilities, halting facilities, etc. just next to National Highway 67, which cuts through the eco-tourism region. Additionally, the village community people agree that the regions where some Private Tourist Resorts have situated border the Kundu Range's Eco-tourism area. The Reserve benefits from having almost year-round operations. The usual methods of stopping poaching, such as arresting and prosecuting offenders, have obviously failed; conservation education aiming at altering local attitudes will greatly reduce the ongoing threats to the integrity of biological systems in the Bandipur forest. Operationalizing sustainable ecotourism within protected areas ultimately relies on management and operations that maximize the industry's potential positive advantages while minimizing its negative ones. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Development and Validation of Emotion Recognition Software in the Indian Population
Though written extensively, recent debates on universality of emotions have shown that age, gender, and ethnicity have greater implications in the ability to identify expressions from faces. Facial emotion recognition deficits have been consistently shown in psychiatric conditions, which necessitates the need to construct a culturally sensitive tool. Fourteen actors depicted emotions such as happy, sad, anger, fear, surprise, disgust, and neutrality. From a total of 126 images, participants rated in terms of intensity and accuracy. Final software was developed with 28 images, and mean accuracy and reaction time were obtained. Friedmans significance test revealed a significant effect of emotion on its different dimensions. This study helped establish a culturally sensitive emotion recognition tool with the Indian population, which can be used in mental health settings for screening purposes and aid in developing rehabilitation modules. 2020, National Academy of Psychology (NAOP) India. -
Exploring Advances in Machine Learning and Deep Learning for Anticipating Air Quality Index and Forecasting Ambient Air Pollutants: A Comprehensive Review with Trend Analysis
India and the rest of the world are growing more and more worried about polluted atmosphere on a daily basis. A comprehensive prevision and prognostication of air quality parameters is vital due to the major harm that air pollution causes to both the environment and public health, causing concern on a global scale. In-depth analyses of the methods for predicting ambient air pollutants, like carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter with diameters less than 10? (PM10) and less than 2.5? (PM2.5), and ozone (O3), are provided in this work in tandem with the modeling of the Air Quality Index (AQI).To further enhance the anticipated precision and applicability of these models, the assessment additionally employs trend analysis to determine precedents and new trends in air quality. This paper offers insights into recent advances in algorithms using deep learning and machine learning for anticipating AQI and forecasting pollutant concentrations by combining current research in this topic. In order to inform policy decisions and measures aimed at reducing air pollution and its adverse effects on public health, trend analysis integration affords a more thorough comprehension of the dynamics of air quality. 2024 IEEE. -
Data Mining Techniques to Enhance Customer Segmentation and Targeted Marketing Strategies
The retail industry is facing an ever-increasing challenge of effectively identifying and targeting its customers. Using traditional segmentation techniques to fully capture the intricate and ever-changing character of customer behavior is difficult. This project will examine sales data from a general shop using an assortment of data mining technologies in order give insights into customer habits and purchasing trends. Retail sales records builds the dataset. K-means clustering, association rule mining, and regency, the frequency, and monetary (RFM) analysis will all be employed to look into the data. This study contributes to create something of focused marketing strategies and consumer segmentation by identifying high-value and atrisk clients. Association rule mining illuminates consumer taste and actions by identifying hidden patterns and correlations in large datasets. These discoveries extend the scope of our comprehension of consumer purchasing habits and offer data for more targeted advertising initiatives. Additionally, the K-means clustering algorithm divides customers according to their purchasing habits and behavior, allowing profound knowledge to enhance marketing and sales strategies. Findings from the research will give an extensive awareness of customer behavior and purchasing dynamics, which will improve the efficacy of the general store's marketing and sales campaigns. The most effective technique for exploiting insights from sales data will be discovered by contrasting the outcomes of RFM analysis, K-means clustering, and association rule mining. This work promises to make substantial improvements to data mining and buyer behavior research algorithms, and it has the capacity to be implemented across an extensive selection of corporate restrictions intended to improve their sales strategies. 2024 IEEE.