Browse Items (1422 total)
Sort by:
-
Reduce Overfitting and Improve Deep Learning Models Performance in Medical Image Classification
A significant role in clinical treatment and educational tasks is played by clinical image classification. However, the traditional approach has reached its peak in terms of implementation. Additionally, using traditional approaches requires a lot of time and effort to remove and choose arrangement features. The deep learning (DL) model is a new machine learning (ML) technique that has proven effective for various classification problems. To alter image classification problems, the convolutional neural network performs well, with the best results. This chapter discusses the importance and challenges of deep learning models in medical image classification and explains some techniques for reducing overfitting and leveraging model performance during model training. 2024 Taylor & Francis Group, LLC. -
Acculturative stress: Psychological health and coping strategies
There is an increasing shift in focus from the causes of immigration to the consequences of immigration, a major aspect being the stress triggered by the myriad changes and challenges experienced during the process of moving into a different culture and settling in. The main aim of this chapter is to introduce the reader to the concept of acculturative stress in detail. The author has gathered the content by doing a keyword search of relevant terms on Google Scholar and choosing articles that provide insight into acculturation, acculturative stress, and psychological health. The chapter will delve into how the different strategies of acculturation are associated with the level of acculturative stress experienced and consequent mental health problems as well as strategies to manage or reduce acculturative stress. 2023, IGI Global. All rights reserved. -
Regression Approach for Predictive Analysis in Cognitive Decline
Cognitive decline refers to the deterioration of cognitive abilities, including memory, thinking, and reasoning, often associated with aging or neurological disorders like Alzheimer's disease. Machine learning (ML) methods can be used for predicting cognitive decline. Techniques such as Generative Adversarial Networks (GANs), feed-forward neural networks, supervised, and unsupervised learning process and analyse data patterns to forecast cognitive changes. By analyzing large datasets, ML algorithms can identify subtle cognitive shifts and predict future decline, enabling early intervention and personalized healthcare strategies. These diverse ML methods provide valuable tools for understanding, detecting, and potentially mitigating cognitive decline, advancing our ability to address cognitive health challenges. Some of these methods have been discussed later. In this research paper, a model to predict cognitive decline using principles of logical regression is proposed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Towards connected government services: A cloud software engineering framework
Cloud computing technologies are being used highly successfully in large-scale businesses. Therefore, it is useful for governments to adopt cloud-driven multi-channel, and multiple devices to offer their services such as e-tax, e-vote, e-health, etc. Since these applications require open, flexible, interoperable, collaborative, and integrated architecture, service-oriented architecture approach can be usefully adopted to achieve flexibility and multi-platform and multi-channel integration. However, its adoption needs to be systematic, secure, and privacy-driven. In this context, micro services architecture (MSA), a direct offshoot of SOA, is also a highly attractive mechanism for building and deploying enterprise-scale applications. This chapter proposes a systematic framework for cloud e-government services based on the cloud software engineering approach and suggests a cloud adoption model for e-government, leveraging the benefits of MSA patterns. The proposed model is based on a set of evaluated application characteristics that, in turn, support emerging IT-based technologies. 2021 by IGI Global. All rights reserved. -
Energy efficiency and conservation using machine learning
This chapter explores the fascinating nexus between machine learning (ML), energy efficiency, and conservation, concentrating on a captivating case study that makes use of the oneAPI framework. Optimizing energy consumption has become crucial due to the increased interest in sustainable practices. By investigating the use of oneAPI in energy efficiency projects, we examine the possibility of ML techniques to overcome this difficulty. We demonstrate how ML algorithms can accurately model and anticipate energy usage patterns through a thorough analysis of real-world data. Additionally, we discuss the importance of feature engineering, algorithm selection, and data pretreatment in creating accurate energy consumption models. The case study emphasizes the wider implications of utilizing ML to support energy-saving initiatives in addition to demonstrating the effectiveness of oneAPI. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Orality, Literacy, and Modernity: A Reading of The Legends of Khasak
What is the relationship between literacy and culture? It is not possible to give a simple answer to this question. Eric Havelock, while commenting on ancient Greek culture and literacy, observes that the classic culture of Greece had attained an advanced stage even before the emergence of Greek script. It continued to exist as an oral culture for a long time (Havelock 1963, 117120). A culture without a script is not uncivilized or underdeveloped. Havelock observes: One can propose with assurance that the pre-Homeric epoch the Dark Age yields for the historian what might be called a controlled experiment in non-literacy. Here, if anywhere, we can study those conditions on which a total culture, and a very complex one, relied for its preservation upon oral tradition alone. (pp. 11718) 2025 selection and editorial matter, E.V. Ramakrishnan and K.C. Muraleedharan; individual chapters, the contributors. -
Exploring digital twins: Attributes, challenges and risks
The recent approach to digitalization and digital transformation is based on the focus of every industry to develop systems and practices for optimizing the operational phase of the product lifecycle and beyond. Digital twins have become the buzzword in the domain of digital transformation. These Digital twins, which are a virtual representation of real-world occurrences such as processes, services, or products offer a new perspective to digitalization. It has emerged from Industry 4.0 and involves a mapping of the real physical world and the virtual world through Digital Twinning. Artificial Intelligence, Cryptography, Blockchain, Big Data technologies, and IoT act as technology enablers for Digital Twins. The capability of Digital Twin is its ability to cater to diverse applications. Within a decade, it has penetrated deeply into every functional aspect of business right from Patient Health Information Systems to remote control and maintenance of satellites/ space stations and to agriculture. This chapter has a focus on the key attributes, challenges, and risk factors that pertain to digital twin technologies and provides adequate examples from diverse sectors. The key challenges of digital twin technologies include Modeling the unknown, Transparency, Interpretability, Interactions with physical assets, Large-scale computation, Physical realism, Future projections, Data management, Privacy, Security and Quality. The four facets of risks related to Digital Twins include restrictions in access to system resources, theft of intellectual property, lack of compliance, and integrity issues in data/information. Hence, additional efforts and a holistic approach towards privacy and security are required to manage these risks. The holistic approach should cover hardware, software, and firmware together with the information that passes between them. Further, it is required to ensure that system, assets and data are adequately protected. Digital Twin technologies provide enormous competitive advantage for an organization, and a more pragmatic approach for mitigation of risks associated with digital twins is required. This would involve co-creation of Digital Twins with clients along with combined extensive knowledge of physical assets, disruptive technologies and appropriate security measures. 2023 Nova Science Publishers, Inc. All rights reserved. -
Ecofriendly Approaches for Ameliorating the Adverse Effects of Cadmium in Plants by Regulating Physiological and Defense Responses: An Overview
Mitigating cadmium stress in agricultural plants becomes extremely critical in order to assure food sufficiency in the scenario of a rapidly growing population. An extensive review of environmentally friendly methods for reducing cadmium toxicity in plants is provided in this chapter, with special attention to a variety of tactics like phytohormones, polyamines, melatonin, mineral ions, nanoparticles, and transgenic techniques. Nanoparticles are capable of changing the distribution of cadmium, activating antioxidant defense mechanisms, and boosting physiological processes that are crucial for plant resilience and growth. Microorganisms greatly increase plant resistance to cadmium stress by modifying phytohormones and regulating defense-related proteins. Phytohormones can increase a plants adaptability to cadmium stress through a number of mechanisms, such as the regulation of gene expression and physiological processes. Melatonin and polyamines provide protection against oxidative stress and heavy metal toxicity, while mineral ions such as silicon, calcium, zinc, iron, and selenium increase plant resistance to cadmium, minimizing pollution-related harm. Transgenic plants that are tolerant to cadmium exhibit enhanced detoxification processes and reduced metal accumulation. These findings provide important insights for long-term plant cadmium mitigation and highlight the significance of interdisciplinary approaches in managing heavy metal stress in agricultural systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A Machine Learning- Based Driving Assistance System for Lane and Drowsiness Monitoring
Lane line detection is a vital component when driving heavy vehicles; this concept follows the path for driving a vehicle to prevent the risk of accidentally entering another lane without the drivers knowledge, which could result in an accident. To detect the lane, use frame masking and Hough line transformation with efficient machine learning algorithms, pre-processed and trained adequately for optimum accuracy as per the provided dataset to spot the white markings on both sides of the lane. Long-distance truck drivers suffer from sleep deprivation, making driving extremely dangerous while tired and they ignore the line markings and wander into the wrong lane. This chapter proposes a portable system that does not require any sensors or interference with the vehicles wiring system; instead, a system that fits on a windshield or any surface to monitor the actions of the driver, using computer vision and feature-extracted datasets within a trained neural network model using cameras. This driver-assisted system can detect drowsiness and give an alarm to wake up the driver by identifying the Region of Interest. These predictions are made based on eye movements, and the algorithm generates a score. The higher the score, the longer the time between alarms. 2024 Taylor & Francis Group, LLC. -
Augmented Reality-Enabled IoT Devices for Wireless Communication
[No abstract available] -
Unleashing the Power of Animation Movies on Sustainable Tourism: A Case Study on Moana
The case study explores the possibilities of implementing the socio-cultural, environmental representation and authenticity of the indigenous community depicted in the movie Moana in real life, thus embracing cultural heritage and identity using a sustainable model of developing tourism. This case study also aims to understand how animation movies like Moana will reflect the causes of climate change and may motivate tourists to choose their destination. Walt Disney Animation Studios 2016 film Moana, which had stunning animation and inspiring themes, captured the attention of viewers worldwide. This case study examines the different factors that led to the movies popularity and its profound effect on spectators, especially regarding indigenous community identity, ecocentrism, and effects of climate because of human intervention. The case explains the movie from different dimensions: culture and heritage, power of determination and hard work, eco-friendly initiatives, climate change, sustainability lessons, and destination choice. With direction by Ron Clements and John Musker, the motion picture Moana depicts the tale of Moana Waialiki, a young Polynesian girl who sets out on a dangerous journey to save her people and find her true identity. The movie displays the rich cultural legacy of the Pacific Islands and is inspired by Polynesian folklore. In this case, we would like to emphasize the value of self-discovery, personal development, and the strength of embracing ones uniqueness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Litigating for Climate JusticeChasing a Chimera?
Across the world, in recent decades, climate litigations have been playing essential roles in shaping domestic policies and legal frameworks on climate change and also in rendering climate justice. There has also been a continuous rise in the development of climate actions, and climate claim litigations by individuals, civil society, and non-state actors. The Indian Supreme Court, High Courts, and the National Green Tribunal have played a significant role in environmental governance by interpreting constitutional and statutory rights to include a right to the environment over the past decades. Nevertheless, with the latest trends in climate litigations, climate challenges have grown across varied climate-related issues, requiring a new judicial approach. In its analysis of climate claims, the justice dispensation mechanism ought to comprehend the shortcomings and be able to generate solutions, similar to those adopted by the courts in the United States, the United Kingdom, and the Netherlands. An analyses of the approach taken by courts in developing nations namely in the Philippines, South Africa, and Pakistan that have compelled governments and corporates to meet their climate commitments are examined. Climate litigation in India has been emerging rapidly over the past decade. As the claims are increasing, the courts and the National Green Tribunal need enhanced capacity building to address climate litigations. This chapter seeks to address the feasibility and implication of equipping courts to address climate litigation. We review the scope of climate litigation and consider the challenges and opportunities to ensure climate justice. This chapter concludes by outlining possible opportunities and challenges in interlinking climate litigation and climate justice in India. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Optimization of Friction Stir Welding Parameters Using Taguchi Method for Aerospace Applications
The current research work investigated the optimization of the input parameters for the friction stir welding of AA3103 and AA7075 aluminum alloys for its applications in aerospace components. Friction stir welding is rapidly growing welding process which is being widely used in aerospace industries due to the added advantage of strong strengths without any residual stresses and minimal weld defects, in addition to its flexibility with respect to the position and direction of welding. Thus, the demand for this type of welding is very high; however, the welding of aluminum alloys is a key aspect for its use in aircraft components, particularly with respect to bracket mounting frames, braces and wing components. Henceforth in the current work, research is focused on optimization of welding of aluminum alloys, viz. AA 3103 and AA 7075; AA 3103 is a non-heat treatable alloy which is having good weldability, while AA 7075 is having higher strength. Therefore, the welding of these aluminum alloys will produce superior mechanical properties. The optimization of input parameters was accomplished in this work based on L9 orthogonal array designed in accordance with Taguchi methodusing which the friction stir welding experiment was conducted. There were nine experimental runs in total after formulating the L9 orthogonal array table in Minitab software. The input parameters which were selected for optimization weretool rotation speed, feed rate, tool pin profile. The output parameters which were optimized were hardness, tensile strength and impact strength. In addition, the microstructure of the fractured surfaces of the friction stir welded joint was analyzed. It was found from the optimization of the process parameters that strong friction stir welded joints for aerospace applications can be produced at an optimized set of parameters of tool rotational speed of 1100rpm, traverse speed of 15mm/min with a FSW tool of triangular pin profile of H13 tool steel material. 2020, Springer Nature Singapore Pte Ltd. -
FinTech in India: A systematic literature review
India is the second-most populous country in the world, with a rapidly growing economy. Its population is highly tech-savvy and has a high level of adoption of digital technologies. The Indian government has taken several initiatives to promote digital transactions and financial inclusion. These initiatives have been instrumental in the growth of fintech in India. Fintech, or financial technology, is transforming the financial sector worldwide. Fintech solutions have led to the creation of new business models, streamlined operations, and enhanced customer experience. India is no exception to this trend, as it has witnessed a significant growth in fintech in recent years. The fintech ecosystem in India is highly diverse, consisting of startups, technology companies, banks, and non-banking financial companies (NBFCs). There are various challenges faced by fintech companies in India, such as lack of access to capital, regulatory hurdles, and competition from established players. This chapter proposal aims to provide a basic literature review on the development of fintech in India. 2023, IGI Global. All rights reserved. -
Deep learning based federated learning scheme for decentralized blockchain
Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors. -
Role of employee resource groups (ERGs) in fostering workforce diversity in information technology (IT) organizations after COVID-19
This chapter discovers how employee resource groups play an important role in fostering organizational diversity within information technology organizations. It examines the activities and practices to improve employee behaviour and also focuses on challenges faced by employees in spite of stress and mental health related issues during the COVID-19 pandemic. The data has been collected from secondary sources. The authors have used desk research and gray literature. The findings showcase increased employee engagement, improvements in diversity and inclusion, and an overall improvement in the inventive and creative skills of employees. It also helps the organization to brand itself better along with better recruitment strategies and practices. The key emphasis of the paper looks at the employees working within information technology organizations and how employee resource groups function to balance, motivate, and empower employees during COVID-19 Pandemic. 2023, IGI Global. All rights reserved. -
Transformative Learning, Community and Leadership for Sustainability Action
[No abstract available] -
Adapting palates: A mapping of food neophobia and neophilia in the shift towards sustainable food consumption
This research explores how two different personality traits-neophilia and neophobia-affect people's eating habits and preferences in the context of sustainable gastronomy tourism. Neophilia, which indicates an openness to trying new culinary experiences, contrasts with neophobia, which is defined as a fear of new foods. Data was collected from 234 gastronomy tourists in Bangalore to examine these dynamics. Smart PLS-SEM 4 was utilized for data analysis. The survey investigated the attitudes and behaviours of participants regarding sustainable food practices that they encountered while engaging in gastronomy tourism. The results show that food neophobia significantly improves people's perceptions of food quality, which further had a statistically significant favourable influence on sustainable consumption; it had no significant effect on post-consumption behaviour. The study highlights how vital gastronomy is to improving experiences, preserving local identity, and drawing tourists-particularly in the rapidly growing category of culinary tourism. 2024, IGI Global. All rights reserved. -
Neuropsychological functions and optimism levels in stroke patients: A cross-sectional study
Neuropsychological abnormalities, as well as behavioural and psychological characteristics, are being examined in patients in order to determine the prevalence of cognitive impairment and other neurovascular riskfactors, including prior strokes. The green light has been given by the institution's human ethics committee for this investigation. In order to conduct the study, the researchers used experimental clinical research techniques. Seventy-five stroke patients ranging in age from 20-70 were the focus of this study. All patients in the hospital had daily clinical examinations and were able to identify the underlying causes of their strokes. The NIMHANS Neuropsychological Battery was administered to all patients between one and six months after the onset of their stroke symptoms. 2023, IGI Global. All rights reserved.