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Migrants and homelessness: Life on the streets in urban India
There has been division and segregation of urban spaces and homeless migrants highlighting urbanization's contradiction. Despite being unseen, they make up a sizable portion of urban population. Being homeless in the city is a case of minimum citizenship, devoid of the right to the city, and is subject to ongoing violence. The expanding claim for the citizen's right to various public areas like pathways, pavements, and parks challenges their very existence on the streets and sidewalks (where they live). How do they perceive the hatred and disinterest of the wealthier classes? What uneasiness does the politics being played out in the name of locals against migrants create? The study will also examine the country's approach to the homeless, including their access to housing and sources of income. The Indian government has taken some steps to address homelessness among migrant workers, including providing financial assistance and setting up temporary shelters. Still, more needs to be done to address the root causes of this issue and ensure that migrant workers have access to safe and affordable housing, healthcare, and employment opportunities. This chapter plans to investigate issues, including how frequently migrants who are homeless experience violence and humiliation. It would analyze the macromicro paradox of the dynamics of migration. As the invisibleness and neglect of migrants frequently coincide with a widespread belief that migration must be reduced, this has obvious policy implications and implications for the inclusive growth model. In addition to that, this chapter has analyzed the country's policies on the homeless and their livelihood. The data was collected from secondary sources, and extensive study was conducted on various literature available from multiple databases. 2024 Elsevier Inc. All rights are reserved. -
Mind and Nature: Study on Mental Health, Nature Connectedness, Pro-Nature Conservation Behaviors and Geographical Green Cover among Indian Adults
For centuries the relation between mind and nature has been represented through literature, songs and cultural traditions. However with increasing urgency of the climate crisis and the corresponding growing distance between humans and nature, we find very limited scientific work exploring their relationship, which could perhaps help re-bridge the connection between the two. A significant, yet not directly observable, and often overlooked impact of the climate crisis is its impact on mental health. This study looks at this relationship in the Indian context, through a relatively unexplored perspective, by investigating the effects of nature connectedness (NC), pro-nature conservation behaviours (ProCoB) and geographical green cover (GGC) on mental health (MH) among middle-aged adults residing in India, and the existing inter-relationships. 180 middle-aged Indian adults, selected through purposive and snowball sampling, from across 21 states and 2 Union Territories (UTs), were administered questionnaires through a Google form. Their data was collected and scored, and the GGC was calculated for each state/ UT from the India State of Forest Report 2021. Correlation and Regression analysis were conducted on the scores using SPSS. A positive and statistically significant correlation exists between the variables NC, ProCoB and MH; NC, MH and GGC; and NC and ProCoB. NC and ProCoB predict MH. Gardening also predicts MH. The findings are new and contribute to the field of Environmental Psychology. It provides a scientific basis for the often romanticized relationship between man and nature as found in literature. It has great implications for the future, such as increasing awareness and understanding, and planning interventions to improve both environment and wellbeing. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Mind of a portfolio investor: Which strategies should I use as a basis for my investment decisions
It is smart for investors to plan for a drop that may be accompanied by a recession in the late stages of a bull market. The authors examine a variety of passive and active strategies, as well as their success in different crises. However, while choosing the best of strategies in the worst of circumstances, investors must be cautious in defining 'best.' It's critical to comprehend not only the long-term performance but also the whole cost of putting various preventive measures in place. The authors analyse popular strategies like technical analysis, fundamental analysis, relying on financial news, seeking professional advice, tips from trade experts, and self-intuition while making portfolios. Our findings indicate that every investment is unique. Some defensive methods will be more effective than others in each case. As a result, diversification across several viable strategies may be the wisest course of action. 2023, IGI Global. All rights reserved. -
Mindful movement: VR-enhanced yoga and exercise for well-being
Investigating virtual reality (VR)-enhanced meditative movement reveals a promising strategy for improving mental and physical health. Virtual reality technology offers immersive, individualized experiences that effectively reduce tension, manage anxiety, and control pain, making it a valuable addition to conventional therapies. Research also demonstrates its efficacy in boosting motivation, maintaining an exercise regimen, and reducing stress. VR is an essential tool for treating mental health conditions such as anxiety and PTSD in clinical settings, with the potential to serve diverse populations. The significance of VR-enhanced mindful movement for overall well-being rests in its holistic approach, personalized experiences, and potential to revolutionize how individuals approach mental and physical health. With the ultimate goal of integrating VR into healthcare practices to enhance lives, a call to action includes additional research, ethical guidelines, accessibility efforts, and keeping abreast of emerging developments. 2024, IGI Global. All rights reserved. -
Mindfulness and social cognitive processing
This chapter explores the intricate interplay between social cognitive processing and mindfulness, highlighting their reciprocal relationship. Present-moment awareness defines mindfulness, which influences and is influenced by social interactions. It promotes prosocial behaviour, compassion, and empathy by enhancing social cognition. The symbiotic relationship also impacts individuals, cultivating a harmonious sense of self. The multifunctionality of mindfulness is apparent in various contexts, as it provides benefits such as reduced tension and enhanced team relations. Longitudinal studies underscore the enduring positive effects, emphasizing improved well-being over time. The potential of mindfulness to affect positive social change is considerable, as it can foster a society that is more interconnected, empathetic, and mindful of social issues. 2024, IGI Global. -
Mindfulness-based interventions: Applications in Western and Indian psychology
This chapter examines mindfulness-based interventions (MBIs) in Western and Indian psychology. It begins with a summary of MBIs, their historical origins, and their theoretical frameworks. The empirical evidence then examines the efficacy of MBIs, emphasizing their impact on both Western and Indian psychology. It probes deeper into the specific applications of MBIs in each cultural context, focusing on adapting mindfulness practices to accommodate cultural differences. Case studies demonstrate the efficacy of MBIs in reducing anxiety, depression, and work-related stress. The significance of cultural sensitivity and inclusiveness, in addition to the implications of MBIs for psychological practice and future research directions, is discussed. It highlights the significance of MBI and provides recommendations for future advancements. It provides an overview of the applications of MBIs, emphasizing their potential to improve well-being, reduce psychological distress, and promote a more inclusive approach to psychological practice. 2024, IGI Global. All rights reserved. -
Minimizing the waste management effort by using machine learning applications
Waste management is a process of collecting, transporting, disposing, and monitoring waste materials generated by human activities. It is an essential part of maintaining public health, hygiene, and environmental sustainability. Waste management systems can be designed to handle different types of waste, such as household waste, industrial waste, hazardous waste, and medical waste. The increasing amount of waste has become a major issue for the development of sustainable communities. Machine learning can help solve this problem by allowing scientists to analyze and reduce waste. This chapter aims to provide a comprehensive overview of the various aspects of waste management using machine learning. The chapter covers the various aspects of waste disposal, generation, transportation, and collection. It also explores machine learning's potential in this area, such as data analysis and prediction. It additionally compiles case studies about how machine learning has been utilized in this field. 2024, IGI Global. -
Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix
Early detection of any sort of cancer, particularly lung cancer, which is one of the worlds most lethal illnesses, can save many lives. Life expectancy can be improved and the degree of mortality reduced by adopting the early forecast. While there are different methods like X-ray and CT scans to detect lung cancer cells, CT images resulted as more favored. The 2D images are used for more accurate medical results, such as CT scans. The proposed approach here will address how to interpret the CT images for the Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix. This research will explore how the image conversion can be achieved through different methods of image processing to obtain better results from CT images. The Confusion Matrix helps to estimate inequality in a picture pattern. After the evaluation of the processed images by Confusion Matrix, a final accuracy with a result of 93% is obtained. 2023 Scrivener Publishing LLC. -
Misuse of Internet Among School Children: Risk Factors and Preventative Measures
The Internet has been one of the most transformative and rapidly growing technologies. In recent years, it has improved the quality of life in areas such as communication, education, recreation. On the contrary, there are growing concerns about the use of the Internet that have created adverse consequences in the areas of social life, interpersonal relationships, family environment, and school activities. School-going children were vulnerable to such unhealthy outcomes due to readily available high-speed Internet and ease of access to different Internet platforms, which resulted in risky behaviours, decreased academic performance, poor nutrition, decreased sleep quality, and a high incidence of inter-social conflicts. While the majority of the research has focused on the adolescent population in terms of problematic Internet use, only a few studies have identified the vulnerabilities of school-going children in the same context. The research also confirmed that the risk factors for problematic Internet use start as early as middle childhood. Heightened risky use of the Internet was observed in children with neurodevelopmental concerns. This study explores risk factors associated with problematic Internet use among school-going children, identifying relevant warning signs followed with preventative measures. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Mitigating Mental Health Burden of Youth During COVID-19 Through Resilience and Hope: Evidences from India and Germany
In the global crisis caused by the COVID-19 pandemic, young professionals and graduating students experience considerable psychological adversity due to the uncertainty surrounding their futures. Given the positive psychological outcomes and the potential to alleviate stress, we examine the role of resilience and hope in causing a substantial variance in the stress response to anticipation of crisis among Indians living in India and Germany. Resilience, hope, crisis apprehension, and the psychological response to the COVID-19 pandemic were measured among participants from India and Germany (n = 650) via an online survey using non-probability convenient sampling. Parallel mediation and conditional indirect effects showcase the differential roles of resilience and hope among socio-culturally similar but geographically divergent groups. Hope mediates the effect of pandemic-led crisis apprehension on perceived stress among those residing in India; resilience operates to mitigate stress among those from Germany. Findings highlight the contradistinctive role of resilience and hope in reducing stress and imply an urgent need for promotion of ameliorative practices. Resilience effectively mitigates the psychological burden of the COVID-19 crisis and can be promoted to reskill individuals; however, elevating hope in a crisis obligates prudence. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
ML Algorithms and Their Approach on COVID-19 Data Analysis
This chapter begins with characterizing Supervised Learning and Unsupervised learning and investigates Machine Learning algorithms in every one of the sub domains of Regression, Classification, Clustering, and so forth. It also talks about the engineering of calculations like Linear Regression, Logistic Regression, K-Means, K Nearest Neighbors, Hierarchical, DB Scan, Decision Tree, Random Forest Regression, and Random Forest classifier. Utilization of every algorithm to investigate the dataset will be displayed by carrying out it on renowned dataset model, and output of each piece of code is displayed with their preview. This section likewise takes care of the issue of predicting the future number of COVID-19 cases and the precision behind each model or algorithm is shown and investigated utilizing different measurements dependent on situation or issue articulation, for example, either issue is on forecast or order. This chapter does not focus on the solution of COVID-19 data analysis or expectation, rather it will be followed and will task different models dependent on need with conclusive target being clear comprehension of the Machine Learning algorithms and its execution in Python. 2023 Scrivener Publishing LLC. -
ML in drug delivery-current scenario and future trends
Machine learning (ML) has enabled transformative applications and emerged as a domain-agnostic decision-making tool as a virtue of its rapid democratization. The authors believe that a systematic assortment of important publications on this issue is indispensable in this context. In terms of data ingestion, data curation, data preprocessing, data handling, and model cross-validation, this review gathers together several studies that have demonstrated a minimum ML framework approach. In general, ML models are described as black-box models, with limited information supplied about their transparency. The authors propose techniques based on the US Food and Drug Administration (FDA)'s current good ML practice (GMLP) in order to improve the ML framework and minimize the aforementioned gap, especially for data. Considering this, the conversation around a model's logic and interpretability are additionally provided. Explicitly, the authors explore the challenges and constraints that ML execution confronts throughout the development of pharmaceuticals. In this context, a structural approach in statistics is presented to allow the scientist to assess the quality of data and incorporate important ideas and techniques that would be implemented in modern ML. The data analytics tetrahedron proposed here can be applied to data of any size. To further contextualize, selected case studies capturing good practices are highlighted to provide pharmaceutical scientists, pharmaceutical ML enthusiasts, readers, reviewers, and regulatory authorities an exposure to fundamental and cuttingedge techniques of ML and data science with respect to chemistry, manufacture, and control (CMC) of drug products. In addition, the authors believe that leveraging ML within CMC procedures can assist in improving decision-making, increasing quality, and enhancing the speed of pharmaceutical product development. IOP Publishing Ltd 2023. All rights reserved. -
Mobile Apps for Enhanced Bleisure Tourism Experiences: Exploring the Prospects and Challenges
Mobile applications play a pivotal role in enabling and enhancing bleisure travel experiences. These apps offer solutions for communication, itinerary planning, transportation booking, and leisure discovery, reflecting the evolving expectations of modern travelers for efficiency, flexibility, and customized experiences. Despite their benefits, challenges such as data privacy concerns and information overload persist. Looking ahead, the future of bleisure travel is poised for further transformation through advances in mobile technology, including augmented reality and artificial intelligence. However, a research gap exists in understanding the full spectrum of mobile apps catering to bleisure tourists' needs. This chapter aims to address this gap by classifying mobile apps for bleisure tourism, exploring their advantages, and identifying challenges and opportunities for innovation. By doing so, it seeks to contribute to a deeper understanding of the role of mobile technology in shaping the landscape of bleisure tourism in the digital age. 2024 by IGI Global. All rights reserved. -
Mobile apps in bleisure tourism: Enhancing travel experience, work-life balance, and destination exploration
This study aims to achieve four primary objectives: first, to evaluate how mobile apps improve travel productivity and efficiency by streamlining logistics and simplifying planning for both business and leisure activities; second, to investigate how these apps support the integration of work and leisure by providing tools for remote work, task management, and peer communication; third, to explore how mobile apps enhance the quality and authenticity of bleisure experiences by helping travelers discover new places and immerse themselves in local culture; and finally, to construct a comprehensive framework for mobile apps in bleisure tourism for use by multiple stakeholders, including travelers, travel companies, the hospitality industry, employers, local tourism boards, and app developers. This study highlights the significance of mobile technology in optimizing the bleisure travel experience. 2024 by IGI Global. All rights reserved. -
Mobile-Based Indian Currency Detection Model for the Visually Impaired
According to surveys held in 2019, India holds the largest population standing just after China, but when it comes to visually impaired people, India ranks number one. There are approximately 37 million people across India who are suffering from visual impairment. Special care and measures are taken to help these people live a peaceful life as any other citizen of India, but with the demonetization that happened in the recent years, the Indian economy was replaced with newer currency notes as an attempt to stop black money and fight corruption. Even though the objectives were clear and attainable, with the newer currency notes, the visually impaired people are facing various problems, as there is no provision for them to actually check the currency as the notes are not equipped with Braille system and the sizes of each and every currency is also the same in many cases. To counteract this problem, a mobile-based Indian currency detection model would be a better solution as it enables a visually impaired person to identify the value of specific currency he is holding. The mobile-based Indian currency detection model is the proposed model which will be using image processing for feature extraction and a basic CNN (convolutional neural network) for identification of currency with the given feature inputs. This model is being made into a mobile-based application so as to enable a visually impaired person to check for any possible frauds as fast as possible. 2020, Springer Nature Switzerland AG. -
Modeling destination competitiveness: The unfamiliar shift for destination rebranding, restructuring, and repositioning with DMOs
Tourism is a tactical economic practice across the globe, but the urban and provincial transformations in the industry are strongly contemplated in the light of an unfamiliar shift in tourism business. This chapter discusses an integrated concept with a framework relating systematic approach of managing the destination and its competitiveness. An investigation on the impact on tourism and the recent narrative of national, regional, and local planning approach directs towards efficient destination management organizations (DMO) in practice for future development. This has proceeded by the formation of a competitive approach, emphasizing on the DMO roles and responsibilities helpful for a destination management during an unfamiliar business trend. Modeling destination competitiveness demands an absolute mechanism through destination rebranding, restructuring, and repositioning with DMOs for enabling competency. 2018, IGI Global. -
Modeling requirements with diabetes using supervised machine learning techniques
Diabetes is characterized by either insufficient or inefficient insulin production by the body. High blood glucose levels result from this, which over time can harm a number of tissues and organs in the body. Diabetes can be brought on by a specific age, obesity, inactivity, insufficient physical activity, inherited diabetes, lifestyle, poor diet, hypertension, etc. This chapter explores modeling requirements with diabetes using supervised machine learning techniques. 2023, IGI Global. All rights reserved. -
Modelling the role of institutional support in shaping the social behaviour of business administration students
The relevance and scope of teaching social responsibility and ethical behaviour to business students has been widely discussed among academicians worldwide (Giacalone & Thompson, 2006). Presently all business schools emphasize teaching social responsibility to the students. But the effectiveness of this education on the student's social responsibility was not evaluated in the past. This study tries to fill this gap by conducting an empirical study on the effectiveness of social responsibility projects undertaken by undergraduate business students for their overall development. The study hypothesized that the course support and institutional support would influence the student's perception of social responsibility, which in turn affects the student's academic performance. For this purpose, the study was conducted among 450 students who have undergone a social responsibility course. The path analysis method was used to test the hypothesized model. Further, the study also evaluated the moderation effect of gender on this model. The study's major finding indicated that the social responsibility course and the organizational support positively impacted students' social responsibility perceptions, which, in turn, influenced students' academic performance. The study suggests that business institutions should emphasize social responsibility initiatives. 2024 Nova Science Publishers, Inc. -
Modernization of Rural Electric Infrastructure
In the recent digital era, the energy sector in India is truly challenging. But some way or another digital technology has the potential to change the scenario of energy supply in industry. One of the important developments in this decade is the application of Artificial Intelligence (AI). This technology will help us to control smart software and optimize our decision-making and operations. We cannot ignore the need of energy to become sustainable after the introduction of the Internet of Things (IoT). Smart grid technology in IoT is used to detect even minute changes in electricity supply and demand. These two technologies (AI and IOT) jointly provide us a magical tool to improve operational performance in the energy industry. In rural areas, there is a lack of electricity infrastructure supply and demand technologies. A large portion electricity supply is shifting from manufacturing industry to rural areas. They are using grid technology to transform electricity and the load is highly variable. From the demand side, lack of infrastructure and industrial equipment affect consumer devices. An increasing need for electricity in all aspects presents a significant challenge to utilization and cost efficiency. An important issue for the delivery of electricity to rural areas is the infrastructure and administrative policies and regulations. Power plants need to be constructed in rural areas to supply the electricity. This is the modernization of a rural electricity infrastructure. In modernization techniques, smart grid technology can be used to meet low carbon emission and cost-efficiency. It will be interconnected with the traditional grid architecture of electricity energy. Based on recent research, the smart grid should be robust and agile and it might dynamically optimize the grid operations, energy-efficient resources, and so on. Without affecting the nature of village environments, an alternate technology, such as the consumption of solar energy, can also be mutually considered in order to utilize renewable energy. In this chapter we focus on the comparison of traditional and modern technology used for the supply and demand of electricity in rural areas, issues on the implementation of modern technologies, research and development in modernization of electric power systems, and so on. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Modified Non-local Means Model for Speckle Noise Reduction in Ultrasound Images
In the modern health care field, various medical imaging modalities play a vital role in diagnosis. Among the modalities, Medical Ultrasound Imaging is the most popular and economic modality. But its vulnerability to multiplicative speckle noise is challenging, which obscure accurate diagnosis. To reduce the influence of the speckle noise, various noise filtering models have been proposed. But while filtering the noise, these filters exhibit limitations like high computational complexity and loss of detailed structures and edges of organs. In this article, a novel Non-local means (NLM)-based model is proposed for the speckle reduction of Ultrasound images. The design parameters of the NLM filter are obtained by applying the Grey Wolf Optimization (GWO) to the input image. The optimized parameters and the noisy image are passed to the NLM filter to get the denoised image. The efficiency of this proposed method is evaluated with standard performance metrics. A comparative analysis with existing methods highlights the merit of the proposal. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.