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A critical study on role of social media in Delhi state elections 2013 & 2015 /
Social media is often hailed as an instrument of digital democracy and social change. This perception is deeply rooted in the well acknowledged potential of ICT’s as ‘agents for development and empowerment .It has liberated people from the tyranny of the free flow of information and ideas. The Delhi elections held in 2013 and 2015 made a revolutionary change in India’s political equations. It shows the emergence of a new party Aam AadmiPart, Aam Aadmi Party and its victory. -
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A Critical Study: The Transactional Concept of Coping through Electronic Media during the COVID-19 Pandemic
Introduction: Numerous individuals worldwide experienced grief during the COVID-19 pandemic. Due to the imposed isolation and limited accessibility of external resources, media was used extensively as a coping mechanism in several forms. Purpose: In the fast-moving world with the emergence of technology, this chapter articulates the emerging trends of media and its impact. The study aims to explore how grief is handled and resolved with the help of electronic media. Methodology: The study reviews existing literature to explore media-related coping strategies by applying the Lazarus-Folkman transactional coping theory as a lens. Results: During the COVID-19 pandemic, there was an increase in media usage among individuals. Based on a review of existing research, media-based coping was used for a range of stressors, including isolation, misinformation and time wastage, work-life disruption, and personal loss. Media is a potential source of readily available, accessible, and effective coping. It can be harnessed to support the rising number of individuals whose mental health needs cannot be catered to by the limited number of qualified mental health professionals. Conclusion: Grief can be handled and resolved in different ways with the assistance of the media. The media can also be used to override the taboo that prevents individuals from seeking support to cope with their grief. Researchers and practising mental health professionals can explore the utility of media-based coping mechanisms and formulate plans to use them effectively. 2025 selection and editorial matter, Dr Uzaina, Dr Rajesh Verma with Dr Ruchi Pandey; individual chapters, the contributors. -
A cross-country analysis of the relationship between human capital and foreign direct investment
Purpose: The ZhangMarkusen (Z-M) inverse U-shape theory uses education as a human capital variable to investigate the impact of educational attainment on foreign direct investment (FDI) inflows to a country. The objective of this research is to empirically test this theory in a cross-country framework. Design/methodology/approach: Fixed effect panel regression has been used to test the Z-M hypothesis for 172 countries for the period 19902015. For the purpose of this study, countries were divided into four groups as per the World Bank classification: Low-income economies, lower middle-income countries, upper middle-income economies and high-income economies. Findings: The findings of this study reinforce the proposition that macroeconomic factors are the major determinants of FDI inflows into various countries. The authors find that the size of the market measured by gross domestic product (GDP), the growth potential of the market measured by real GDP growth rate and the availability of infrastructure are the major factors that enhance the attractiveness of a country as an FDI destination. Originality/value: Though the Z-M theory has been empirically tested in cross-country frameworks, no consensus has been reached. Thus, it is interesting to look again at the validity of the Z-M hypothesis using data covering longer and more recent periods. The study includes both macroeconomic and human capital determinants of FDI, so as to arrive at a comprehensive model explaining the FDI flows into various countries. 2021, Emerald Publishing Limited. -
A Cross-sectional Study for Examining Catastrophic Healthcare Expenditure Across Socio-demographic Variables among Employees in a Sedentary Occupation
Health expenditure above a certain threshold level can result in a financial catastrophe by reducing the expenses on necessities. Certain socio-demographic variables have been observed to play a role in influencing catastrophic healthcare expenditure, guiding the present study to examine this scenario for employees in sedentary occupations. A cross-sectional study has been conducted among 370 employees recruited through a random sampling technique. Multinomial logistic regression was used to test the main objective of the study. The factors associated with a higher probability of catastrophic healthcare expenditure were males with increasing age. Years of work experience tend to be associated with a lower likelihood of catastrophic healthcare expenditure. No conclusive evidence could be drawn for BMI, income, marital status and education. 2024 Indian Journal of Community Medicine. -
A Cross-sectional Study on Factors Associated with Sexual Satisfaction Among Non-working Married Women in Bengaluru
Background: Sexual satisfaction is a complex concept influenced by physical, psychological and socio-cultural factors. However, there is a lack of research on what determines sexual satisfaction among non-working married women in India. This gap hinders our understanding of how traditional gender roles, economic dependence and cultural norms affect the sexual well-being of this group. This study aims to explore the factors associated with sexual satisfaction among non-working married women in Bengaluru, India. Materials and Methods: A cross-sectional survey was conducted among 180 non-working married women. Data were collected using the New Sexual Satisfaction Scale, the Psychological Distress Scale, the Subjective Happiness Scale and a self-prepared questionnaire on various factors related to sexual satisfaction. Descriptive statistics and multiple regression were used to analyse the data. Results: Factors significantly associated with non-working womens sexual satisfaction include physical factors (menstrual health difficulties, reproductive health issues and urogenital problems), psychological factors (psychological distress and subjective happiness) and socio-cultural factors (education, knowledge of sexual health at the time of marriage, type of marriage, age, age difference between couples and duration of marital life). Family-related factors (type of family, family pressure for children and exhausting household work) and couple-related characteristics (spouses smoking/drinking patterns and relationship with the spouse) were also significant. Together, these factors explained 78.6% of the variability in sexual satisfaction among non-working married women. Conclusion: The findings highlight the need for health interventions to promote healthy lifestyles and suggest changes in sexual health practices. They also indicate the need for training health professionals to address the sexual health aspects of women. Further longitudinal studies with larger samples are required to better understand the relationship between these predictors and sexual satisfaction. 2025 The Author(s). This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
A Cross-Sectional Study on Mental Health of School Students during the COVID-19 Pandemic in India
The broad objective of the present study is to assess the levels of anxiety and depression of school students during the COVID-19 lockdown phase and their association with students background, stress, concerns and social support. In this regard, the present study follows a novel two stage approach. In the first phase, an empirical survey was carried out, based on multivariate statistical analysis, wherein a group of 273 school students participated in the study voluntarily. In the second phase, a novel Picture Fuzzy FFA (PF-FFA) method was applied for understanding the dynamics of facilitating and prohibiting factors for three categories of focus groups (FG), formulated on the basis of attendance in online classes. Findings revealed a significant impact of anxiety and depression on mental health. Further, PF-FFA examinedthe impact of the driving forces that steered children to attend class as contrasted to the the impact of the restricting forces. 2022 by the authors. -
A Cryptocurrency Price Prediction Study Using Deep Learning and Machine Learning
A cryptocurrency is a network-based computerized exchange that makes imitation and double-spending pretty much impossible. Many cryptocurrencies are built on distributed networks based on blockchain technology, which is a distributed ledger enforced by a network of computers. Thanks to blockchain technology, transactions are secure, transparent, traceable, and immutable. As a result of these traits, cryptocurrency has increased in popularity, especially in the financial industry. This research looks at a few of the most popular and successful deep learning algorithms for predicting bitcoin prices. LSTM and Random Forest outperform our generalized regression neural architecture benchmarking system in terms of prediction. Bitcoin and Ethereum are the only cryptocurrencies supported. The approach can be used to calculate the value of a number of different cryptocurrencies. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A cultural analysis of "Newshour" in shaping the image of Times now news channel /
Television is medium which has the power to influence the people. With the advancement in technology and innovation, this medium became pervasive in nature. This particular study tires to explore how a particular television programme helps in shaping the image of television channel. -
A culturally grounded mental health response to intimate partner violence among tribal women in Wayanad, Kerala: a case study
Purpose This case study aims to develop a culturally grounded mental health intervention for tribal women survivors of intimate partner violence (IPV) in Wayanad, Kerala. It responds to the urgent need for trauma-informed mental health services that resonate with the cultural identities, worldviews and healing traditions of indigenous communities. Design/methodology/approach The intervention was developed through a mixed-methods doctoral research project grounded in Bronfenbrenners Ecological Systems Theory and psychodynamic principles. Data were collected via qualitative interviews with tribal women survivors (n?=?25), quantitative mental health screening and participatory engagement with traditional healers, local leader and Self-Help Group (SHG) facilitators. Thematic analysis and iterative community consultations shaped the design of the intervention. Findings The resulting intervention integrates healing circles, folklore-based psychoeducation, engagement with traditional leaders and SHG-based empowerment. These components reflect indigenous practices, spiritual worldviews and collective resilience strategies. Participants reported increased emotional safety, cultural validation and a sense of solidarity and empowerment. Research limitations/implications The intervention is deeply embedded in the cultural and spiritual frameworks of Wayanads tribal communities. This context-specific design may limit the generalizability of the model to other indigenous groups with differing belief systems, social structures or ritual practices. Some biomedical professionals and institutional stakeholders may resist integrating traditional practices due to concerns about scientific validity or standardization. Negotiating these tensions requires ongoing dialogue and institutional buy-in. Differences in cultural practices, taboos and language even within Wayanads tribal groups (e.g. Paniya vs Kurumba) can affect the uniformity of intervention delivery and reception. Practical implications The model can inform mental health practitioners and NGOs working in indigenous or marginalized contexts by offering a flexible, community-rooted intervention framework adaptable across tribal regions. Social implications By legitimizing indigenous knowledge and promoting collective healing, the intervention strengthens social cohesion, reduces stigma around IPV and empowers tribal women to reclaim agency within their communities. Originality/value This is one of the few interventions in India that explicitly centers tribal worldviews in mental health care for IPV survivors. It demonstrates how formal psychological models can be meaningfully adapted through cultural co-creation. 2025 Emerald Publishing Limited -
A cyber-physical systems and the smart city vision: A comprehensive guide
The process of urban areas' transformation into smart cities with the help of Smart Cyber-Physical Systems (SCPS) is one of the most defining trends of modern urbanism. It requires a multifaceted perspective of smart cities, thereby evaluating the facets of SCPS intently concerning the complexities of their integration in urban structures while exploring their influence that transcends the domains of social sciences and economics, which has become crucial. In this context, smart cities are constructed as integrated systems at the crossroads of the digital and the physical: they sustain, facilitate, and improve the performance of the city's functions and living environment. The importance of technological environments in orientation and close consideration of SCPS reveals the functions in gathering data, immediate analysis, and decision-making processes of urban management. The interconnection of the Internet of Things (IoT), artificial intelligence (AI), and big data analytics considering their impact and the creation of sustainable enhancing the quality of public services. 2026 by IGI Global Scientific Publishing. All rights reserved. -
A cyber-physical systems and the smart city vision: A comprehensive guide
The process of urban areas' transformation into smart cities with the help of smart cyber-physical systems (SCPS) is one of the most defining trends of modern urbanism. It requires a multifaceted perspective of smart cities, thereby evaluating the facets of SCPS intently concerning the complexities of their integration in urban structures while exploring their influence that transcends the domains of social sciences and economics, which has become crucial. In this context, smart cities are constructed as integrated systems at the crossroads of the digital and the physical: they sustain, facilitate, and improve the performance of the city's functions and living environment. The importance of technological environments in orientation and close consideration of SCPS reveals the functions in gathering data, immediate analysis, and decision-making processes of urban management. 2025, IGI Global Scientific Publishing. -
A Data Mining approach on the Performance of Machine Learning Methods for Share Price Forecasting using the Weka Environment
It is widely agreed that the share price is too volatile to be reliably predicted. Several experts have worked to improve the likelihood of generating a profit from share investing using various approaches and methods. When used in reality, these methods and algorithms often have too low of a success rate to be helpful. The extreme volatility of the marketplace is a significant contributor. This article demonstrates the use of data mining methods like WEKA to study share prices. For this research's sake, we have selected a HCL Tech share. Multilayer perceptron's, Gaussian Process and Sequential minimal optimization have been employed as the three prediction methods. These algorithms that develop optimal rules for share market analysis have been incorporated into Weka. We have transformed the attributes of open, high, low, close and adj-close prices forecasted share for the next 30 days. Compare actual and predicted values of three models' side by side. We have visualized 1step ahead and the future forecast of three models. The Evaluation metrics of RMSE, MAPE, MSE, and MAE are calculated. The outcomes achieved by the three methods have been contrasted. Our experimental findings show that Sequential minimal optimization provided more precise results than the other method on this dataset. 2023 IEEE. -
A data-driven approach to predicting breast cancer recurrence with hybrid machine learning models
Breast cancer recurrence is one of the most significant medical concern, and accurate recurrence models can assist in early intervention and treatment planning. Breast cancer recurrent remains as one of the most critical concern for patients prognosis and treatment planning. Accuracy Predicting individual recurrence risk is crucial for the development of precise therapy, specialy for those patients with high-risk profiles. In the study proposes a hybrid machine learning approach that uses the computational modeling and the medical information to predict the recurrence of breast cancer in a patient. The dataset contains the medical and patient information like the age, tumor size, lymph node involvement, malignancy degree, location, irradiation status and recurrence class. This proposed approach begins with the process of data processing, handling the missing data values, features normalization and encoding of categorical variable into numerical format. The dataset is divided into two parts the training set and the testing set and the two selected models random forest and logistic regression models are trained independently. The predictions form both the model is stacked and a logistic regression meta-model is trained on these combined predictions. The evaluation of the model was conducted using the metrics such as accuracy, precision, recall, and F1 score. The designed hybrid model was able to achieve the accuracy of 97.66% with the precision, recall and F1 score all reaching around 98.15%. This study highlights the potential of hybrid machine learning techniques, improving the accuracy and reliability of machine learning models for breast cancer recurrence prediction. This development model can serve as a valuable tool for the medical industry to support decision making and assist in personalized treatment decisions, offering early detection of recurrence. This can enhance the treatment of a patient by supporting early detection and patients outcomes through targeted therapy. Copyright 2026 Techno-Press -
A data/document management system and method /
Patent Number: 202111047326, Applicant: Anil Kumar.
A data/document management system and method comprising a user device (101); internet (102); a server (103); an authentication module (104); a code generation module (105); record management system (106); a processing module (107); a database (108). The method comprises following steps of A) Data /document storing mode and B) data / document fetching mode. The invention provides a high efficient and low, user friendly data / document management system and method. The invention provides a record management system (106) which is connected to the database system (108). -
A data/document management system and method /
Patent Number: 202111047326, Applicant: Anil Kumar.
A data/document management system and method comprising a user device (101); internet (102); a server (103); an authentication module (104); a code generation module (105); record management system (106); a processing module (107); a database (108). The method comprises following steps of A) Data /document storing mode and B) data / document fetching mode. The invention provides a high efficient and low, user friendly data / document management system and method. The invention provides a record management system (106) which is connected to the database system (108). -
A decade of climate change concern in India: Determinants of personal and societal climate concern
Scientists have called for a culturally relevant investigation of factors impacting public climate concern to devise relevant behavioural and policy interventions. Although India will be adversely affected by climate change, there is a shortage of models that track changes in Indian climate concern across time. The study tracked the growth of climate concern from 2006 to 2020 and identifies determinants of personal and societal climate concern. Secondary analyses of survey data from the International Science Survey and World Values Survey (2006-2020, N = 9254), were conducted to predict climate concern across the year, environmental protection versus economic growth preferences, and socio-demographic variables. Within responses from 2020 (N = 3176), the predictive role of anthropogenic climate change beliefs, trust in scientists, adequate government action, collective efficacy, environmental protection preferences, and sociodemographic variables were evaluated to understand personal and societal climate concern. Binary logistic regression found that climate concern increased significantly from 2006 (2.6%) to 2020 (89.5%) and was predicted by education and preferences for environmental protection. Multiple regression results identified personal climate concern as predicted by education, anthropogenic climate change beliefs, trust in scientists, and environmental protection preferences; while government action beliefs and favouring left-wing affiliation predicted societal climate concern. There was mixed support for the political polarization of climate concern. The study shows an increase in Indian climate change concern over the past decade, with personal and societal climate concern being influenced by different psychological characteristics. Important implications for future climate communication research and social policy development are discussed. 2024 by author(s). -
A decade survey on internet of things in agriculture
The Internet of Things (IoT) is a united system comprising of physical devices, mechanical and digital machines, and different hardware components like sensors, actuators, cameras etc., monitored and operated by the software. The combination of devices and systems connected over the internet opens the pathway for development of various applications beneficial in terms of economic growth of a nation. IoT has evolved as a potentially emerging computer technology solving various real-life problems and issues. IoT covers vast group of applications, from warfare to surveillance, from habitat monitoring to energy harnessing, predictive analytics and personalized health care, and so on. Among various fields, agriculture is one important field having maximum scope of implementation and investment. The main aim of this book chapter is to furnish all the details related to applications of IoT in the field of agriculture. This includes the details related to data collection, types of sensors used, deployment details, data access through cloud. It also covers details related to various communication technologies used in IoT such as Bluetooth, LoRaWAN, LTE, 6LowPAN, NFC, RFID etc. And above all, the chapter focuses on the significance of IoT on agronomics, agricultural engineering, crop production and livestock production. This chapter is a decade survey conducted to study the contribution of IoT in the field of agriculture. Around 40 research papers for the duration 2008-2018 are collected from peer reviewed journals and conferences. The collected articles are analyzed to provide relevant information required for the various end users. Springer Nature Switzerland AG 2020. -
A Deep Assessment of ML Based Procedure used as a Classifiers in the Clinical Field
In the unexpectedly evolving panorama of healthcare technology, the mixing of data mining and machine mastering gives exceptional possibilities for the advancement of sickness prediction fashions. This research paper introduces a unique Machine Learning Smart Health Procedure designed to harness the predictive energy of those era for forecasting illnesses. By meticulously reading ancient healthcare facts, which includes affected individual signs and symptoms and effects, this system leverages cutting-edge algorithms which includes Nae Bayes, Support Vector Machines (SVM), and neural networks to expect capacity health problems with accelerated accuracy. This method now not best pursuits to facilitate early and specific evaluation but also strives to noticeably enhance affected individual care and treatment consequences. Through the strategic utility of statistics mining and prediction analysis in the healthcare area, our proposed machine demonstrates the capacity to revolutionize conventional diagnostic techniques, developing a proactive and predictive healthcare model more plausible and effective than ever earlier than. 2024 IEEE. -
A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data
Several economists and social scientists have held a longstanding fascination with the practice of stock market prediction. As the stock market is essentially uncontrollable chaos, many experts believe that trying to predict it is futile. Due to the complexity of the numerous factors, accurate stock price predictions are notoriously difficult to achieve. While the market behaves more like a scale than a voting machine over the long run, its behavior may be predicted with some certainty. Information from Twitter is used into the algorithm. In this proposed method, a convolutional extreme learning machine model with kernel support was introduced (CKELM). To improve feature extraction and data classification, the CKELM model builds on the KELM's hidden layer by adding convolutional and subsampling layers. The convolutional layer and the subsampling layer do not employ the gradient technique to fine-tune their parameters because some designs worked well with random weights. When compared to popular models like CNN and KELM, The proposed model fares quite well, with an accuracy of around 98.3 percent. 2023 IEEE.