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Migration in India: Questions of social exclusion /
International Research Journal Of Social Sciences, Vol.4, Issue 4, pp.87-91, ISSN No: 2319-3565. -
Migration to bangalore : A study of return migration of IT professionals
Bengaluru, Karnataka, is widely known as India s Silicon Valley and an important centre for Information Technology (IT). It is also one of the fastest growing cities in India. Migration to the city has contributed to its growth and this study has focussed on the phenomenon of return migration with specific reference to the return migration of IT professionals to Bengaluru. The main newlineobjectives of the study were to explore some of the factors underlying return migration in general and return migration to Bengaluru in particular, to examine the reasons underlying the decision to migrate, to examine the process of return migration, to describe transnationalism in the context of this return migration and to observe the experience of being back home This study used mixed methods and adopted concurrent triangulation to analyse and interpret the data in the study of the research problem. The framework adopted by Lee was also used to understand the factors that influence the decision to migrate within a broader theoretical framework to study return migration. The researcher explored the factors that influenced the decision to return which were an outcome of the push factors operating from the place of origin and the pull factors operating in the place of destination, India and Bengaluru in this case, both sets of which relate to the socio-cultural, economic and political realms. The newlineresearcher examined the factors that operated as push factors in the host country as well as those that operated as pull factors upon professionals working in the IT sector in the context of return migration to Bengaluru.According to the study, the pull factors being exercised from the city of newlineBengaluru were more relevant in terms of the forces underlying the decision to move. Bengaluru s reputation as a technopole or a high tech cluster has contributed largely to the way in which the migrants have been drawn to the city. -
Identifying Social-Cognitive Factors Influencing Aggression in Adolescents: A Cross-Sectional Indian Study
Adolescence is a critical period during which the likelihood of experiencing self-regulation failures like aggressive outbursts is increased. Recent Indian studies on adolescents have reported an increasing incidence of aggressive acts during this time of transition, which is a threat to the adolescent, the victim and society in general. This study focuses on the social-cognitive perspective, implying that aggression is a social behaviour that is largely affected by ones beliefs about the acceptability of aggression and the degree of cognitive and effortful control they have over their emotions. Such beliefs are likely to be influenced by emotion socialisation, wherein parents and peers act as key agents. With this perspective, the current study, through a mediational model, explains the social-cognitive factors predicting aggressive behaviour in adolescents. This is a cross-sectional, descriptive study carried out on a sample of 475 adolescent students from the Delhi-NCR region recruited through purposive sampling. The data were collected through self-report questionnaires from schools and colleges. The model was tested using SPSS AMOS and was found to be a good fit for the data. The findings of this study are crucial from a risk and intervention perspective. It emphasises the need to build socially and emotionally competent students who not only have the skills needed to succeed but also nurture healthy social relationships and maintain positive mental health through adaptive emotion regulation skills. 2024 Department of Psychology, University of Allahabad. -
A novel two-tier feature selection model for Alzheimers disease prediction
The interdisciplinary research studies of artificial intelligence in health sector is bringing drastic life saving changes in the healthcare domain. One such aspect is the early disease prediction using machine learning and regression algorithms. The purpose of this research is to improve the prediction accuracy of Alzheimers disease by analysing the correlation of unexplored Alzheimer causing diseases. The work proposes Chi square-lasso ridge linear (Chi-LRL) model, a new two-tier feature ranking model which recognizes the significance of including diabetes, blood pressure and body mass index as potential Alzhiemer predictive parameters. The newly added predictive parameters of Alzheimers disease were statistically verified along with the conventional prediction parameters using chi-square method (Chi) as Tier 1 and an embedded model of lasso, ridge and linear (LRL) Regression for feature ranking as Tier 2. The performance of the proposed Chi-LRL model with selected features were then analysed using machine learning algorithms for performance analysis. The result shows a noticeable performance by selecting eleven significant features and a 4.5% increase in the prediction accuracy of Alzheirmer disease. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Design of a Decision Making Model for Integrating Dark Data from Hybrid Sectors
The research on Dark data, from its definition to identification and utilization is a widely identified and encountered research problem since 2012 when Gartner defined Dark data as every possible information that an organization collects, process, analyze and store throughout regular business activities, but usually fails to make use of the stored information for other suitable purposes. The presence of dark data and its impact has been experienced by every sector, these data occupy large storage and remain unused. In this paper, we analyze Dark Data and proposed a design model to utilize dark data from multiple sectors and providing a solution to any critical situation a person might be in. For eg: Multiple cash transactions from an organizational bank account in a hospital successively over a period of 2-3 days may indicate a health emergency of any particular employee from that organization. Thus we are considering institutional data, medical data, and banking data in which machine learning algorithms can contribute huge changes in the current system and can help the decision-makers to make better decisions. The paper also proposes a few techniques and methods for the conversion of unstructured dark data to structured one and some extraction techniques for data using NLP and Machine Learning. Grenze Scientific Society, 2022. -
A novel AI model for the extraction and prediction of Alzheimer disease from electronic health record
Dark data is an emerging concept, with its existence, identification, and utilization being key areas of research. This study examines various aspects and impacts of dark data in the healthcare domain and designs a model to extract essential clinical parameters for Alzheimer's from electronic health records (EHR). The novelty of dark data lies in its significant impact across sectors. In healthcare, even the smallest data points are crucial for diagnosis, prediction, and treatment. Thus, identifying and extracting dark data from medical data corpora enhances decision-making. In this research, a natural language processing (NLP) model is employed to extract clinical information related to Alzheimer's disease, and a machine learning algorithm is used for prediction. Named entity recognition (NER) with SpaCy is utilized to extract clinical departments from doctors' descriptions stored in EHRs. This NER model is trained on custom data containing processed EHR text and associated entity annotations. The extracted clinical departments can then be used for future Alzheimer's diagnosis via support vector machine (SVM) algorithms. Results show improved accuracy with the use of extracted dark data, highlighting its importance in predicting Alzheimer's disease. This research also explores the presence of dark data in various domains and proposes a dark data extraction model for the clinical domain using NLP. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
A Hybrid Approach for Predictive Maintenance Monitoring of Aircraft Engines
The realm of aircraft maintenance involves predictive maintenance, which utilizes historical data and machine parts' performance to anticipate the need for maintenance activities. The primary focus of this paper is to delve into the application of predictive maintenance of aircraft gas turbine engines. Our methodology involves assigning a randomly chosen deterioration value and monitoring the change in flow and efficiency over time. By carefully analyzing these factors, we can deduce whether the engines are at fault and whether their condition will deteriorate further. The ultimate objective is to identify potential engine malfunctions early to prevent future accidents. Recent years have witnessed the emergence of multiple machine learning and deep learning algorithms to predict the Remaining Useful Life (RUL) of engines. The precision and accuracy of these algorithms in assessing the performance of aircraft engines are pretty promising. We have incorporated a hybrid model on various time series cycles to enhance their efficacy further. Employing data collected from 21 sensors, we can predict the remaining useful life of the turbine engines with greater precision and accuracy. 2024 IEEE. -
Sustainable Technologies for Recycling Process of Batteries in Electric Vehicles
The effective management of batteries has always been a key concern for people because of the imposing challenges posed by battery waste on the environment. This paper explores strategic perspectives on the sustainable management of batteries incorporating modern techniques and scientific methodologies giving batteries a second-life application. A paradigm shift towards the legitimate use of the batteries by the introduction of round economy for battery materials and simultaneously checking the biological impression of this fundamental innovation area. 2023 IEEE. -
Colorimetric and theoretical investigation of coumarin based chemosensor for selective detection of fluoride
Coumarin based Sensor 1 has been designed and synthesized to recognize fluoride ion visually with high selectivity and sensitivity over other anionic analytes through color change from very faint yellow to pink in acetonitrile. The probable binding phenomenon in solution phase has been explained by 1H NMR study of sensor 1 with different concentration of fluoride ions. The binding constant of the sensor 1 with fluoride has been determined as 3.9 104 M?1 and the lower detection limit 6.5 M of the sensor 1 towards fluoride, which has made the sensor 1 as a promising backbone for selective detection of fluoride. For the practical application, test strips based on sensor 1 were fabricated, which could act as a convenient and efficient naked eye F?test kits. The experimentally observed absorption maxima along with its binding nature with fluoride ions also have been supported through theoretical calculations using density functional theory (DFT) calculations. 2022 -
Dual-mode chemosensor for the fluorescence detection of zinc and hypochlorite on a fluorescein backbone and its cell-imaging applications
Fluorescein coupled with 3-(aminomethyl)-4,6-dimethylpyridin-2(1H)-one (FAD) was synthesized for the selective recognition of Zn2+ over other interfering metal ions in acetonitrile/aqueous buffer (1 : 1). Interestingly, there was a significant fluorescence enhancement of FAD in association with Zn2+ at 426 nm by strong chelation-induced fluorescence enhancement (CHEF) without interrupting the cyclic spirolactam ring. A binding stoichiometric ratio of 1 : 2 for the ligand FAD with metal Zn2+ was proven by a Jobs plot. However, the cyclic spirolactam ring was opened by hypochlorite (OCl?) as well as oxidative cleavage of the imine bond, which resulted in the emission enhancement of the wavelength at 520 nm. The binding constant and detection limit of FAD towards Zn2+ were determined to be 1 104 M?1 and 1.79 ?M, respectively, and the detection limit for OCl? was determined as 2.24 ?M. We introduced here a dual-mode chemosensor FAD having both the reactive functionalities for the simultaneous detection of Zn2+ and OCl? by employing a metal coordination (Zn2+) and analytes (OCl?) induced chemodosimetric approach, respectively. Furthermore, for the practical application, we studied the fluorescence imaging inside HeLa cells by using FAD, which demonstrated it can be very useful as a selective and sensitive fluorescent probe for zinc. 2022 The Royal Society of Chemistry. -
INTERFACING PRIMAL RELIGION OF THE HAMAI (ZELIANGRONG), CHRISTIANITY, HERAKA, AND TINGKAO RAGWANG CHAPRIAK
This chapter explores the intertwinement between four religious traditions, namely (1) Characheng (primal religion of the Hamai) and its offshoots, (2) Heraka, (3) Tingkao Ragwang Chapriak (TRC), and (4) Christianity in contemporary Hamai (Zeliangrong) communities. The influence of the primal Hamai religion on Christianity is unquestionable, and at the same time, these two traditions hold sway over Heraka and TRC in varying degrees. The impacts of the interaction are at the levels of consciousness, belief systems, practices, and values. The chapter brings out the asymmetric encounter between reformed religious traditions (Heraka) of the Hamai and the proselytisation of Christianity in the Hamai communities that had led to the extinction of the primal religion of the former. Remarkably, Heraka and TRC are counter-proselytising movements against Christianity based on the primal belief system and synthesis of Christian and Hindu belief systems. For this purpose, the research employs comparative and dialogical approaches to explore and analyse the interconnection among the above religions. It argues that the current forms of Christianity, Heraka, and TRC in Hamai tribes are unique in themselves, and at the same time, they are also cyclically inspired by one another in the process of their encounters. 2025 selection and editorial matter, Maguni Charan Behera; individual chapters, the contributors. -
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Leadership management strategies and organisational practices with respect to the hotel sector of rainbow tourism group limited
This was a research exercise which sought to explore leadership management strategies and organisational practices taking place in the hotel sector of Rainbow Tourism Group Limited (RTGL). RTGL is one of Zimbabwe s biggest hotel and tourism sector. The hotel portfolio are comprised of Rainbow Towers and Conference Centre, Bulawayo Rainbow, Victoria Falls Rainbow, Kadoma Rainbow and Conference Centre, A Zambezi River Lodge and Ambassador Hotel. The six hotels have a combined total of 886 rooms, with the largest number of 304 rooms being in the five star hotel, Rainbow Towers and Conference Centre and while the rest in the three star group hotels. Other operations outside Zimbabwe are Hotel Edinburg, the Savoy Hotel and a hotel in Mozambique. The problem statement of the research study was to examine the role of organisational culture in shaping the leadership strategies in hotel and catering sector, organizational leadership and their effectiveness in helping to achieve organizational objectives. The following was newlinethe set of objectives that the research sought to achieve. Firstly, the research sought to determine and analyze the different types of leadership strategies adapted by the hotel sector of Rainbow Tourism Group Limited in the hotel sector a Case Study. The second newlineobjective was to determine the environment forces affecting the acceptance and newlineassimilation of the mentioned strategies. Thirdly the research sought to ascertain the newlineefficacy of mentioned strategies in attaining these organisational strategies in the hotel newlinesector of RTGL. The fourth objective was to suggest if any alternate strategies will be newlinerequired to enhance leadership effectiveness in Hotel Sector of the RTGL. Finally the newlineresearch sought to develop a Leadership Model that can be used in the hotel industry. -
PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications
Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time for recommending the related and latest version of Apps. To overcome this issues, a hybrid Apps recommendation framework which is considering the version of the mobile Apps is proposed. This novel framework named 'Probabilistic Evolution based Version Recommendation Model (PEVRM)' integrates the principles of Probabilistic Matrix Factorization (PMF) with Version Evolution Progress Model (VEPM). With the help this novel recommendation algorithm, the mobile users easily identify the specific Apps for particular task based on its version progression. At same time, this framework helps in resolving cold start problems of new users. Evaluations of this framework utilize a benchmark dataset, i.e., Apple's iTunes App Store3, for revealing its promising performance. 2013 IEEE. -
Prediction of Stock Prices using Prophet Model with Hyperparameters tuning
As part of the data analytical process, predicting and time - series are crucial. In academics and financial research, anticipating share prices is a prominent and significant subject. A share market would be an uncontrolled environment for anticipating shares since there are no fundamental guidelines for evaluating or anticipating share prices there. As a result, forecasting share prices is a difficult time-series issue. fundamental, technical, time series predictions and analytical strategies are just a few of the various techniques and approaches that machine learning uses to execute stock value predictions. This article implements the stock price prediction, Researchers compared the model of the prophet with the tuned model of the prophet. By utilizing the tuning of hyperparameters using parameter grid search to improve the performance of the model accuracy for the best prediction. The findings of the study demonstrated that tuned model of the prophet with hyperparameters tuning which results in model accuracy and based on the experimental findings mean squared error (MSE) and mean absolute percentage error (MAPE) has significant improvement. 2022 IEEE. -
Towards an Improved Model for Stability Score Prediction: Harnessing Machine Learning in National Stability Forecasting
In our increasingly interconnected world, national stability holds immense significance, impacting global economics, politics, and security. This study leverages machine learning to forecast stability scores, essential for understanding the intricate dynamics of country stability. By evaluating various regression models, our research aims to identify the most effective methods for predicting these scores, thus deepening our insight into the determinants of national stability. The field of machine learning has seen remarkable progress, with regression models ranging from conventional Linear Regression (LR) to more complex algorithms like Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting (GB). Each model has distinct strengths and weaknesses, necessitating a comparative analysis to determine the most suitable model for specific predictive tasks. Our methodology involves a comparative examination of models such as LR, Polynomial Regression (PR), Lasso, Ridge, Elastic Net (ENR), Decision Tree (DT), RF, GB, K-Nearest Neighbors (KNN), and SVR. Performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared (R2) assess each model's predictive accuracy using a diverse dataset of country stability indicators. This study's comprehensive model comparison adds novelty to predictive analytics literature. Our findings reveal significant variations in the performance of different regression models, with certain models exhibiting exceptional predictive accuracy, as indicated by high R2 values and low error metrics. Notably, models such as LR, SVR, and Elastic Net demonstrate outstanding performance, suggesting their suitability for stability score prediction. 2024 IEEE. -
Comparative Analysis of Machine Learning Models in Predicting Academic Outcomes: Insights and Implications for Educational Data Analytics
In the evolving landscape of educational research, the predictive analysis of student performance using data science has garnered significant interest. This study investigates the influence of diverse factors on academic outcomes, ranging from personal demographics to socioeconomic conditions, to enhance educational strategies and support mechanisms. We employed a diverse ml models to analyze a information containing academic records and socioeconomic information. The models tested include Logistic Regression, Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVC), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, and Decision Trees. The process involved comprehensive data preprocessing, exploratory analysis, model training, and evaluation based on metrics such as precision, recall, accuracy, and F1 score. The results indicate that ensemble methods, specifically RF and GB, demonstrate superior efficacy in accurately predicting categories of student performance such as 'Enrolled,' 'Graduated,' and 'Dropped Out.' These models excelled in handling the complex interplay of varied predictors affecting student success. The results further underline the potential of advanced ensemble ML techniques in significantly outperforming the prediction accuracy in the academic domain, hence facilitating the tailoring of educational interventions to foster improved engagement and better outcomes for students. This has provided a comparative analysis of the methods that guide the future application of predictive analytics in education. 2024 IEEE. -
Occupational stress: A pre and post COVID-19 perspective on teaching personnel in higher education institutions of India
The COVID-19 as a catalytic phenomenon exposes many loop holes in the socio-economic sustainability of business and society. It exposed people to different occupational stressors and anxiety. This study was focused on occupational stress of teaching personnel in higher education institutions (HEIs), in both pre and post COVID-19 scenario. Descriptive analysis shows work autonomy, career progression, community membership, work conditions, and freedom to use own judgements are major stressors to to HEI teachers in the post COVID-19 scenario. Inferential evaluation has confirmed that Job security, social service, and creativity are major concerns to HEI teachers. They experience limitations to try their own ways of doing job. 2021 Ecological Society of India. All rights reserved. -
Stakeholders' pedagogical preferences for teaching 'marketing' in management education
This study has been realized that there is a dire need for re-thinking, particularly obvious for matters of assessment and its relation to the current focus on teaching marketing. A descriptive design of the research was used where convenient sampling has been followed for data collection. In order to achieve the purpose, it was decided to collect independent opinions of students, teachers, and professionals. Analysis has been done through descriptive statistics and Spearman's rank correlation. As a result, a significant difference between the stakeholders' perceptions about the pedagogy for teaching marketing in management education was identified. 2021 Ecological Society of India. All rights reserved. -
Data-Driven Decision Making in the VUCA Context: Harnessing Data for Informed Decisions
Data-driven decision making (DDDM) has evolved from being a strategic advantage to a necessity for organizations aiming to thrive in the dynamic business contexts. It is about using data as a tool to enhance strategic thinking, scenario planning, and adaptation in rapidly changing environments. It involves leveraging data and analytics to navigate the challenges of volatility, uncertainty, complexity, and ambiguity. By embracing DDDM, organizations can enhance their decision-making processes, gain a competitive edge, and navigate the challenges of volatility, uncertainty, complexity, and ambiguity with greater confidence. However, successful implementation requires addressing challenges, fostering a data-driven culture, and continually adapting best practices to meet the evolving demands of the VUCA environment. This chapter discusses how organizations leverage DDDM in VUCA context to support effective and rapid decision making aligned with organizations vision. Particularly, it would offer insights to transit from volatility to vision, uncertainty to understanding, complexity to clarity, and ambiguity to agility. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.