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A Comparative Analysis of Machine Learning Algorithms for Image Classification: Evaluating Performance
Image classification plays a crucial role in various applications, and selecting the most effective machine learning algorithm is essential for achieving accurate results. In this study, we conducted a comparative analysis of several well-known supervised machine learning techniques, including logistic regression, support vector machine (SVM), k-nearest neighbours (kNN), nae Bayes, decision trees, random forest, AdaBoost, and artificial neural networks (ANN). To assess the performance of these algorithms, we utilised different fonts of the English alphabet as our dataset and performed the analysis using the R programming language. We evaluated the algorithms based on standard performance criteria, such as the area under the Receiver Operating Characteristic curve (ROC), accuracy, F1 score, precision, and recall. Our research findings demonstrated that the classification performance varied depending on the training size of the dataset. Notably, as the training size increased, neural networks exhibited superior performance compared to other machine learning techniques. Consequently, we conclude that neural networks and SVM are the most effective algorithms for image classification based on our study. By conducting this comprehensive analysis, we contribute valuable insights into selecting appropriate machine learning algorithms for image classification tasks. Our findings emphasise the significance of considering the training dataset size and highlight the advantages of neural networks and SVM in achieving high classification accuracy. This study provides valuable guidance for practitioners and researchers in choosing the most suitable machine learning algorithm for image classification, considering their specific requirements and dataset characteristics. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Student engagement in community development: A strategy for whole-person development
Student engagement in community development has been closely linked to enhanced learning outcomes and whole-person development. The Centre for Social Action (CSA) at Christ University emerged as a student-led student-driven initiative to promote volunteerism and engagement in community development that enabled the student community to identify and work on development initiatives. The objective of the chapter is to examine the factors that influence student engagement in community development initiatives and explore the factors that motivate them to volunteer. It also looks at their perception of the benefits for the two main stakeholders in the process, namely the students themselves and the community that they work with. This chapter uses a qualitative framework, and the data is collected through in-depth interviews and focus group discussions with current and past volunteers with CSA. The participants in the study have been selected using purposive sampling techniques and represent students who have worked on the major initiatives undertaken by CSA, namely the activity centre and the various social awareness and sensitization initiatives. The interviews and the focus group discussions have been conducted on virtual meeting platforms, and the data has been analyses thematically. The research design lends itself to a rich exploration of student perception of their engagement in community development, their motivation, the benefits that they perceive of their engagement with the activities for both themselves and other stakeholders, and how it ties into the construct of whole-person development at the individual level and whole-person education at a broader level. 2024 Nova Science Publishers, Inc. -
Sustainable environment protection and waste management in higher education institutions: A case study
Sustainable environment protection and waste management are global concerns that major cities are grappling with, and it is the most important environmental factor that higher educational institutions in India are considering right now. "Parivarthana, "-the recycling unit of the Centre for Social Action at Christ University, Bengaluru, is evolved as a paradigm for all higher education institutions in terms of long-term environmental protection through waste management and the efforts of student volunteers. The student volunteers have been successful in sensitizing all members of the University to the importance of environmental conservation, and there is rising evidence of accountability among all members regarding waste management. The basic sense of social responsibility of student volunteers toward environmental sustainability is the primary focus of this chapter. Student volunteers who have a higher sense of social responsibility have a better attitude toward their studies, which leads to higher academic accomplishment and a desire to take action to address environmental challenges. Their environmental awareness, climate change, the need to reduce greenhouse gas emissions, efficient use of natural resources, waste management, and sustainable consumerism have served as an example for students of other higher educational institutions. A qualitative method with a case study approach was applied for this research with In-depth interviews with all stakeholders of the unit. The objectives include the participants' understanding of the prevailing process of waste management in the unit, the relation between waste management and Climate change, and the role of the student volunteers and other stakeholders of the Higher Educational Institutions in bringing a model to Sustainable Development and Global Climate Change. 2024 Nova Science Publishers, Inc. -
Sustainable Waste Management and Womens Empowerment
Waste management is a problem faced by major cities. Rural migration to urban areas created unplanned residential areas and high population density, and temporary living structures have a direct impact on poor waste management systems in urban areas. From September 2020 until February 2021, a case study was conducted (the first lockdown period of the COVID-19 Pandemic) among women members from an urban slum in Bangalore with objectives to understand the prevalent process of waste management and comprehend the association between womens empowerment and sustainable waste management in a slum community. The purposive sampling technique was applied to select 10 women members of the slum community for this community-based participatory research as co-researchers from the slum community, along with all stakeholders. The results show that the women members could implement the immediate plans on waste management, including educating their neighbours on waste management, to ensure that a large part of the society they are living in is aware of it. The women members demonstrated their motivation and willingness in their actions in the slum neighbourhood concerning sustainable waste management. They applied their participatory activities to empower other women in the area by focussing on every stretch of the slum and educating on the management of waste. All the actions by the women members in the urban slum community and the stakeholders of waste management in that community intend to support the quality of life and strengthen the resilience to climate change through sustainable waste-management and are reflected in SDG 3, SDG 5, SDG 11, and SDG 13. 2024 CRC Press. -
Nanobiosensors for COVID-19
Coronavirus Disease (COVID-19) is an internationally recognized public health emergency. The disease, which has an incredibly high propagation rate, was discovered at the end of December 2019 in Wuhan, Hubei Province, China. The virus that causes COVID-19 is referred to as severe acute respiratory illness. Real-time reverse transcriptase (RT)-PCR assay is the primary diagnostic practice as a reference method for accurate diagnosis of this disease. There is a need for strong technology to detect and monitor public health. Early notification on signs and symptoms of the disorder is important and may be managed up to a few extents. To analyze the early signs and side effects of COVID-19 explicit techniques were applied. Sensors have been used as one of the methods for detection. These sensors are cost effective. These sensors will combine with a systematic device. It is utilized to detect the chemical compound and combined with a biological component. It is detected through physiochemical detector. Nanomaterials represent a robust tool against COVID-19 since they will be designed to act directly toward the infection, increase the effectiveness of standard antiviral drugs, or maybe to trigger the response of the patient. In this paper, we investigate how nanotechnology has been used in the improvement of nanosensor and the latest things of these nanosensors for different infections. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Impact of social media on consumer decisiveness in the food and grocery sector
Consumers are currently inclined to acknowledge online information but purchase food and grocery products offline. Also, the buyer's decision is coherent with the factors like income, age, social media influences, cost of products, etc. The chapter studies the Influence of Social Media on Consumer Decision-making in the food and Grocery Sector. As per the findings, the effectiveness of marketing tools and techniques has a homogeneous effect on all GenX, GenY, GenZ. Contrary to expectation, Gen X was most influenced by offers. Social media equally influenced all generations to make purchases, irrespective of their incomes. Post Covid there is a shift in consumption habits disregarding generations and income brackets of all the participants. 2023 by IGI Global. All rights reserved. -
Implementation Strategies for Green Computing
In this chapter, we look at how renewable energy sources can be integrated into the planning, design, and construction of long-term sustainability in green buildings. When it comes to establishing a framework for environmentally friendly building, there are two primary schools of thought. One is related to the use of conventional architecture and low-energy construction material. The fundamental focus of green building design is on using renewable energy solutions for the purpose of managing energy protection. When referring to a green building, either sustainable construction or green construction may be used instead. To guarantee a structure will last for its intended purpose and the environment will not be harmed in the process, sustainable construction practices should be included from the start. Additionally, the economics of renewable energy are presented in this chapter with eco-friendly construction practices that make use of renewable energy sources. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa. -
Navigating the Future of Intelligent and Smart Manufacturing A Comprehensive Bibliometric Analysis (2012-2024)
Intelligent and smart manufacturing is at the forefront of a digital revolution in the manufacturing industry. This chapter reveals how key technologies have developed and converged during the period 2012-2024 by analysing 1046 articles from the Web of Science database. Literature was compiled into ten distinct clusters: Convolutional Neural Networks (CNN), Technologies, Internet of Things (IoT), Services (Applications), Gateway, Industry 4.0, Heuristic, Cutting Force, Remote Maintenance, and Fabrication. Each cluster is measured by its degree, modularity, density, citation analysis metrics and help in predicting the impact and about their interrelations in research within these domains. Further, the major focus is on Industry 4.0 principles and the evolving research landscape that promptly helps in findings and the rapid adoption of advanced computational methods. With this analysis, the most influential authors, articles, and journals will provide insight for building collaborative networks and intelligent manufacturings intellectual structure. This study nonetheless delivers an overview of previously published research; in addition, it also illustrates trends and openings that provide a significant underpinning to support subsequent academic endeavours and their application in practice. 2025 selection and editorial matter, Alka Chaudhary, Vandana Sharma, and Ahmed Alkhayyat individual chapters, the contributors. -
IoT in schools: Revolutionizing education through smart technology
The Internet of Things (IoT) is rapidly transforming various sectors, and education is no exception. This chapter explores the potential and applications of IoT technology in the education sector, shedding light on how it can revolutionize teaching, learning, and overall school management. By seamlessly integrating smart devices and applications into the classroom environment, IoT creates an interconnected and efficient learning ecosystem. The discussion covers the current state of IoT adoption in education, highlighting the benefits, challenges, and future prospects of this technological integration. 2025 selection and editorial matter, Adesh Kumar, Surajit Mondal, Gaurav Verma, and Prashant Mani; individual chapters, the contributors. -
Engaged institution model: A faculty perspective
This paper attempts to build the engaged institution model from faculty perspective. Data was collected from 200 faculty members across disciplines, who were engaged in community engagement and social responsibility activities in one or the other ways. On analysis of the data, it was found that Instruction and Research, Facilitator, Scholarship factors contribute towards community engagement activities in higher educational institutions and that these factors contribute towards Faculty engagement, Student engagement and Community Engagement. All these factors create Engagement institution model. This work has an implications on theory, practice and policy. Service learning, as a pedagogical tool if implemented in HEIs can effectively bring all the influencing factors together and can help in creating an engaged institution. 2024, IGI Global. All rights reserved. -
Dietary Plants, Spices, and Fruits in Curbing SARS-CoV-2 Virulence
Patients with coronavirus disease 2019 (COVID-19) infection can suffer from a variety of neurological disorders; therefore, there is a demand to investigate specific treatments. As a part of this endeavor, academic databases related to clinical, neuropathological, and immunological biomarkers were examined for searching promising drugs to treat neurological disorders in the COVID-19 group. Also, the neuroprotective potential of herbs for patients with post-COVID-19 has been evaluated using PubMed, MEDLINE, Scopus, EMBASE, Google Scholar, EBSCO, Web of Science, Cochrane Library, WHO database, and ClinicalTrials.gov. The terms used for the Boolean search were Indian herbs and neuroprotective potential, post-COVID-19 symptoms, and so on. Based on our knowledge, nervous system immunity is an inherent characteristic of the nervous system because it is highly immunologically active. It was found that patients infected with COVID-19 often experience neurological symptoms such as muscle pain, headaches, confusion, dizziness, and loss of smell and taste. The most commonly used herbs for neurological disorders are Bacopa monnieri, Mucuna pruriens, Withania somnifera, Acorus calamus, Phyllanthus emblica, Blumea balsamifera, Asparagus racemosus, Cannabis sativa, Convolvulus prostratus, Swertia chirata, Vitex negundo, Nyctanthes arbor-tristis Linn, Centella asiatica, Curcuma longa, Ocimum tenuiflorum. It is widely recognized that herbal drugs have the potential for treating neurological diseases such as Parkinsons, Alzheimers, and cerebrovascular diseases in COVID-19 patients. However, clinical trials are still limited. The suitability of drugs depends on the investigation of biomarkers and pathobiological mechanisms. Thus, it is necessary to use modern scientific approaches and technologies to conduct comprehensive mechanistic studies to understand the therapeutic potential of herbs for neurological disorders associated with the SARS-CoV-2 infection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Remote work realities: A comprehensive study on individual choices and task accomplishments
The global work landscape has undergone a paradigm shift with the widespread adoption of telecommuting, a transformation further accelerated by the COVID- 19 pandemic. This research delves into the intricate dynamics of telecommuting, focusing on the impact of individual characteristics on the choice to work remotely in the post- pandemic era. It reveals that gender significantly influences telecommuting preferences, while age and years of experience do not show a discernible impact. Beyond individual factors, the study examines how telecommuting attitudes affect task accomplishment, highlighting substantial effects on goal attainment and underscoring the need to understand work arrangement's complexities. Additionally, the research explores the diverse strategies employed by employers and employees, illustrating successful approaches to remote work. Ultimately, this study navigates the evolving telecommuting landscape, offering insights into challenges and achieve-ments, and provides a foundation for future research on telecommuting's impact on work practices and individual well- being. 2024, IGI Global. All rights reserved. -
Exploring the Roots of Customer Adaptability Through the Landscape of Physical Retail Environments
This research delves into the intricate dynamics of customer adaptability within the realm of brick-and-mortar retail, examining the pivotal role of managing customer knowledge capabilities. As traditional retail spaces evolve in response to technological advancements and shifting consumer behaviors, understanding the antecedents of customer agility becomes paramount. Drawing on insights from customer knowledge management literature and retail studies, this paper elucidates the nuanced interplay between customer adaptability and the strategies employed to harness and leverage customer knowledge. Through a comprehensive analysis of the contextual landscape of physical retail environments, we uncover the underlying factors that influence customer adaptability. By illuminating these connections, this study offers valuable insights for practitioners and scholars alike, contributing to a deeper understanding of how organizations can effectively navigate the evolving retail landscape to enhance customer experiences and foster sustainable growth. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Clinical Intelligence: Deep Reinforcement Learning for Healthcare and Biomedical Advancements
Deep reinforcement learning (DRL) is showing a remarkable impact in the healthcare and biomedical domains, leveraging its ability to learn complex decision-making policies from raw data through trial-and-error interactions. DRL can effectively extract the characteristic information in the environment, propose effective behavior strategies, and correct errors that occurred during the training process. Targeted toward healthcare professionals, researchers, and technology enthusiasts, this chapter begins with notable applications of DRL in healthcare, including personalized treatment recommendations, clinical trial optimization, disease diagnosis, robotic surgery and assistance, mental health support systems, chronic disease management and scheduling, and a few more. It also delves on challenges such as data privacy, interpretability, regulatory compliance, validation, and the need for domain expertise to ensure safe and effective deployment. Next, the chapter seamlessly transitions into DRL algorithms contributing to the biomedical field which are gaining traction due to their potential to provide timely and personalized interventions. Over time, the research community has proposed several methods and algorithms within the field of deep reinforcement learning that help agents learn optimal policies from rich data. Healthcare data is often complex, high-dimensional, and unstructured, such as medical images, genomics data, and patient records. The healthcare-suitable DRL algorithms such as Q-learning, SARSA, Bayesian, actor-critic, reinforcement learning (RL), Deep-Q-Networks (DQN), and Monte Carlo Tree Search (MCTS) are highlighted. In addition, the section offers guidelines for the application of DRL to healthcare and biomedical problems, aiming at providing indications to the designer of new applications in order to choose among different RL methods. Furthermore, a case study is included to fully realize the revolutionary benefits of DRL in healthcare environments, aiming to bridge the gap between theory and practice. The case study presents a remarkable impact on categories such as precision medicine, dynamic treatment regime, medical imaging, diagnostic systems, control systems, chat-bots and advanced interfaces, and healthcare management systems. 2024 Scrivener Publishing LLC. -
Introduction: Tourism at a crossroads
[No abstract available] -
Experimental investigation of tribocorrosion
This chapter discusses various techniques available for evaluation of tribocorrosion behavior of industrial components, their applications, and limitations. Numerous influential factors of tribocorrosion, their mechanisms, and their characteristics have been discussed at length. Further, a case study of tribocorrosion behavior of aluminum-based in situ metal matrix composites have been deliberated comprehensively. 2021 Elsevier Inc. All rights reserved. -
Integrating deep learning in an IoT model to build smart applications for sustainable cities
These days, many CS experts focus their efforts on IoT. IoT is an emerging & cutting-edge technology that enables many items, including vehicles and home appliances, to connect and cooperate via mechanisms like machine to machine communication, big data, and AI. It has found use in a wide range of settings, from smart homes and cities, to healthcare and agriculture, to factory automation. Smart cities are becoming smarter, cars are getting more features, and health and fitness devices are getting more sophisticated thanks to the internet of things. Many problems that are directly relevant to the IoT's development have yet to be resolved. The exponential development of IoT has given birth to new problems, including concerns about personal data and security. There is need of a comprehensive approach that tackles the scalability, security, efficiency, and privacy concerns raised by the widespread deployment of IoT. 2023, IGI Global. -
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. -
Successful footprints of ChatGPT deployments in the education sector: Pros outweigh cons by embracing ethics and etiquette
Artificial intelligence (AI) is essential in all aspects of life. One crucial area to examine is the integration of artificial intelligence in education. The true essence is in providing individuals with the necessary knowledge, skills, and values needed to have a fulfilling and meaningful life. Education must adapt to equip students with essential abilities to navigate life's challenges while upholding integrity in a dynamic world. Artificial intelligence (AI), shown by technologies such as ChatGPT, exhibits significant promise in educational environments. This chapter explores personalized learning enabled by artificial intelligence (AI). Furthermore, intelligent tutoring systems are also analyzed. The text delves into various facets of the educational system where ChatGPT might offer help. Additionally, offering explanations for the prohibition of Chat GPT in many countries and educational institutions. The discussion has focused on how AI affects the socio-economic gap in the education sector. 2024, IGI Global. All rights reserved.