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"Case" as "text" in the class: Plethora of pedagogical and structural nuances
The research work undertakes to examine "case, ? "case study, ? and "case pedagogy. ? As the first step, the chapter explores the feel of the Case. This leads to a further investigation and lets out certain malfunctions: gross lapses, loopholes, casualties, and shortcomings. Case then has been subjected to further investigations. Keeping intact the primary concerns of case scan and its explications thereupon, the study takes up the interpretations and intricacies involved in understanding the case. These expose the puzzles involved in the pedagogical exercises in educational institutions. The research with astute expedience does the operation with logical reasoning. The work leads to proper remedial measures and redefines case and case grasp. Using theory of ontology, case is subjected to closer examination. Theory of epistemology further deepens research pursuits to unravel a few more case mysteries. From these, the authors evolve a few keys and tips to study case more effectively. All help readers build up exemplary teaching methods and effective learning concepts. 2022, IGI Global. All rights reserved. -
"The sense" and its manifestations launch new trends in marketing
The chapter investigates the latest developments in marketing and consumer science. The study deepens and strengthens the prospects of marketing. In the process, human sense plays a vital role. The study seeks the assistance of hermeneutics to understand the permutations and combinations generated by a plethora of multifarious conceptual variations in both perception and aesthetics. The "sense" influences marketing of goods and products in business. The proposed configurations relate business functions with aesthetic characteristics. The first part by the research picks the application of senses in the perception level. The second part the researcher chooses sense in the cognition level. The whole study substantiates how Aesthetics and Sense during last decades generate a fast progress in business and marketing. 2024, IGI Global. All rights reserved. -
#MeToo and Times Up: Slippage of Facework in Malayalam Cinema and Industry
#MeToo and Times Up movements, the collective voice of trauma, solidarity and resistance appropriated world over, impacted India a decade since its inception. In the globalised Indian space, the movements evolved/were transplanted in different realms, lashing against democracys tacit silence. Finding resonance in the Indian cinema industry, many women articulated their trauma and protest in a fraternity, broadminded in spirit, patriarchal at heart. An evolutionary reading of #MeToo and Times Up movements as independent/extension of its predecessor with distinctive focus on Malayalam cinema and industry foregrounds riveting issues. Malayalam cinema industry, a case in point is popular for its projected broadminded outlook through films. Discursive readings unravel the inconsistencies and pluralistic divergent voices in this progressive industry, challenging its liberal positioning. With the tag of an industry receptive to ambient changes, how does its films become reflective or mutually inclusive? How does the industrys facework feed into the apparently challenging onscreen representations vis-a-vis the offscreen power hierarchies? Who delineates the boundary-work within the industry and cinema? A complementary reading of the onscreen versus offscreen negotiations problematises the gendered relationships, sexual economies, collective consciousness structured along multiple tangents. Understanding the ongoing confrontation between artistes associations like Association of Malayalam Movie Artistes (AMMA) and Womens Collective in Cinema, negotiation of inclusivity and extension of solidarity, together with the appropriations and assimilations have opened an alternative space to examine the grey areas dotting the silver screen. 2025 selection and editorial matter, Aysha Viswamohan; individual chapters, the contributors. All rights reserved. -
2D Metal-based Electrocatalysts: Properties and Applications
Metallic nanostructures with thickness ranging from a single atom up to 100 nanometers fall under the category of 2D metals. The modified electronic band structure due to quantum confinement effects leads to intriguing electrical and electronic properties. Moreover, the properties can be further altered by variations in their shape, thickness, and lateral size. The exceptionally high surface area to volume ratio of 2D metals and stretchability are beneficial in electrocatalysis. The exposed atoms on the outer surface of 2D metals with low coordination numbers, possess unique properties, forming numerous active sites on the surface. As a result, 2D metals demonstrate a high ability towards the activation of small molecules, including O2, H2, CO2, HCOOH, CH3OH, C2H5OH, etc. This exceptional oxidation reactivity enables 2D metals to be excellent electrocatalysts for hydrogen/oxygen evolution reaction (HER/OER), oxygen reduction reaction (ORR), and oxidation of small molecules (formic acid, methanol, and ethanol) for fuel-cell applications. As the localized surface plasmon resonance (SPRs) is sensitive to the size/shape of plasmonic 2D metals, the optical absorption enabled by SPRs offers additional advantages for photo-electrocatalytic processes. The stability of highly active catalytic 2D metals presents a challenge due to the propensity of metal surfaces with high reactivity to undergo oxidation. Recent developments in the synthesis, properties, and applications of 2D metal nanostructures for electrocatalytic processes are discussed. The challenges and opportunities in the electrocatalytic application of 2D metal nanostructures have been summarized. 2025 Ram K. Gupta. -
2D Photonic Crystal Nano Biosensor with IoT Intelligence
Optical biosensors based on photonic crystals (PCs) offer interesting possibilities for the analysis and identification of bioanalytes. PC is a periodically varying artificial dielectric material that determines the propagation of modes present in the structure. Within dielectric media, there are modes that are selected based on structural perturbations. Changes in the refractive index of biological analytes are used to identify biological samples and are therefore used as sensing media in many applications. Because these PC sensors are designed in the nano range, they have excellent selectivity and sensitivity. The PC is ultra-compact and only small amounts of analyte are required for bioanalyte detection. Quantification of bioanalytes and biochemicals is one of the greatest challenges in the medical and diagnostic fields. However, these electronic devices cannot be directly connected to biological analytes, so the most difficult task is to extract the analyte information and convert it into electronic signals. Optical biosensors offer an attractive way to interrogate the content of bioanalytes because they directly convert biological events into electrical signals. It is also called a self-contained integrated physical medium because of its many applications such as food industry, drug delivery, point-of-care diagnostic sensing devices, and environmental monitoring. Based on the analyte placed on the PC sensor, resonant wavelengths are observed and the measurements are stored in a database. Diseases are identified based on the current users cognitive value, and data is transmitted and monitored over the Internet of Things. 2024 Scrivener Publishing LLC. -
3Rs management: Advances and innovations in waste management and treatment
The increasing industrialisation and fast growth do not only pose problems related to the allocation of resources and powers, but also severely challenge the natural environment. Environmental degradation such as contaminated water, sinking groundwater levels, unhealthy soils, and polluted air has become a harsh reality in many parts of the world. One result of a rapid urbanisation, a slowly reducing gap between urban and rural, changing consumption patterns, and a growing population is the problem of waste. However, although it is the duty of the urban local bodies (ULBs) to address the issue of waste, tight budgets, inefficient organisation, has rendered a situation that has little hope for alleviation in the near future. This chapter aims to understand the concept of waste management through 3Rs. The focus is to identify the contemporary 3Rs practices and also develop advanced strategies for the same. 2024 by IGI Global. -
A Brief Concept on Machine Learning
Machine learning is a subset of AI. Its a research project aimed at gathering computer programscapable of performing intelligent actions based on prior facts or experiences. Most of us utilize various machine learning techniques every day when we use Netflix, YouTube, Spotify recommendation algorithms, and Google and Yahoo search engines and voice assistants like Google Home and Amazon Alexa. All of the data is labeled, and algorithms learn to anticipate the output from the input. The algorithms learn from the datas underlying structure, which is unlabelled. Because some data is labeled, but not all are, a combination of supervised and unsupervised techniques can be used. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A Brief Review onDifferent Machine Learning-Based Intrusion Detection Systems
In the contemporary cybersecurity landscape, the proliferation of complex and sophisticated cyber threats necessitates the development of robust Intrusion Detection Systems (IDS) for safeguarding network infrastructures. These threats make it more challenging to maintain the communitys availability, integrity, and confidentiality. To ensure a secure network, community administrators should implement multiple intrusion detection systems (IDS) to monitor and detect unauthorized and malicious activities. An intrusion detection system examines the networks traffic by analyzing data flowing through computers to identify potential security threats or malicious activities. It alerts administrators when suspicious activities are detected. IDS generally performs two types of malicious activity detection: misuse or signature-based detection, which entails collecting and comparing information to a database of known attack signatures, and anomaly detection, which detects any behavior that differs from the standard activity and assumes it to be malicious. The proposed paper offers an overview of how different Machine Learning Algorithms like Random forest, k - Nearest Neighbor, Decision tree, Support Vector Machine, Naive Bayes, and K- means are used for IDS and how these algorithms perform on different well-known datasets, and Their accuracy and performance are evaluated and compared, providing valuable insights for future work. kNN shows an accuracy of 90.925% for Denial of Service Attacks and 98.244% for User To Root attacks. The SVM algorithm shows an accuracy of 93.051% for Probe attacks and 80.385% accuracy for remote-to-local attacks. According to our implementation, these two algorithms work better than the others. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A case study on a beacon of hope transition: India's renewable energy integration and the Ujwal DISCOM assurance Yojana (UDAY)
This case study examines the transformative impact of the Ujwal DISCOM Assurance Yojana (UDAY) on India's energy landscape, focusing on its role in facilitating renewable energy integration. India's energy sector faced daunting challenges, including financially distressed power distribution companies (DISCOMs) and high aggregate technical and commercial (AT&C) losses. UDAY's financial restructuring and operational efficiency improvements led to remarkable reductions in DISCOMs' debt burdens and AT&C losses, respectively. The policy aligned with India's renewable energy goals, driving DISCOMs to procure renewable energy sources. Consequently, India witnessed significant growth in its renewable energy capacity, environmental benefits through reduced emissions, and economic growth via job creation. This case study offers insights into the challenges faced, technological advancements incentivized, and the long-term sustainability of these reforms. Moreover, it presents broader lessons for energy sector reform and renewable energy integration, both within India and globally. 2024, IGI Global. All rights reserved. -
A Case Study on Zonal Analysis of Cybercrimes Over a Decade in India
Human intelligence has transformed the world through various innovative technologies. One such transformative technology is the internet. The world of the internet, known as cyberspace, though powerful, is also where most crimes occur. Cybercrime is one of the significant factors in cybersecurity, which plays a vital role in information technology and needs to be addressed with high priority. This chapter is a case study where we analyze cybercrimes in India. The data collected from NCRB for 2010 to 2020 are a primary source for the analysis. A detailed analysis of cybercrime across India is done by dividing locations into seven zones: central, east, west, north, south, northeast, and union territories. Cybercrimes reported in each zone are examined to identify which zone requires immediate measures to be taken to provide security. The work also identifies the top ten states which rank high in cybercrime. The main aim of this chapter is to provide a detailed analysis of crimes that occurred and the measures taken to curb them. Along with the primary data, secondary data from CERT-In are also used to provide an analysis of measures taken for handling cybercrime over a decade. The outcome facilitates various stakeholders to better bridge the gap in handling cybercrime incidences, thus helping in incidence prevention and response services as well as security quality management services. 2023 selection and editorial matter, Narasimha Rao Vajjhala and Kenneth David Strang; individual chapters, the contributors. -
A Citation Recommendation System Using Deep Reinforcement Learning
Recommender systems have seen tremendous growth in the last few years due to the emergence of web services like YouTube, Netflix, and Amazon, etc. An excessive amount of data is being utilized to give proper recommendations to the users. The number of research articles getting published every day is increasing exponentially and thus an efficient model is required to provide accurate and relevant recommendations to the research scholars. The proposed Deep Reinforcement Recommender for Citations (DRRC) model uses reinforcement learning to train the available citation network to achieve the most relevant recommendations. The proposed DRRC model outperforms the state-of-the-art models. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
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. -
A Comparative Analysis of Traditional and Machine Learning Forecasting Techniques
Forecasting is the process of making predictions or estimates about future events or conditions based on historical data, trends, and patterns. It involves analyzing past data and using statistical or other quantitative methods to project future outcomes, such as sales figures, market trends, weather patterns, or financial performance. Forecasting can be used in a wide range of fields, including economics, finance, business, weather forecasting, and sports. The accuracy of a forecast depends on the quality of the data, the methods used, and the assumptions made about the future. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
A Comparative Study of Machine Learning Techniques for Credit Card Customer Churn Prediction
A customer is a churner when a customer moves from one service provider to another. Nowadays, with an increasing number of severe competition with inside the market, essential banks pay extra interest on customer courting management. A robust and real-time credit card holders churn evaluation is vital and valuable for bankers to preserve credit cardholders. Much research has been observed that retaining an old customer is more than five times easier compared to gaining a new customer. Hence, this paper proposes a method to predict churns based on a bank dataset. In this work, Synthetic Minority Oversampling Technique (SMOTE) has been used for handling the imbalanced dataset. Credit card customer churn is predicted using random forest, k-nearest neighbor, and two boosting algorithms, XGBoost and CatBoost. Hyperparameter tuning using grid search has been used to increase the accuracy. The experimental result shows Catboost has achieved an accuracy of 97.85% and tends to do better than the other models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A comparative study of text mining algorithms for anomaly detection in online social networks
Text mining is a process by which information and patterns are extracted from textual data. Online Social Networks, which have attracted immense attention in recent years, produces enormous text data related to the human behaviours based on their interactions with each other. This data is intrinsically unstructured and ambiguous in nature. The data involves incorrect spellings and inaccurate grammars leading to lexical, syntactic and semantic ambiguities. This causes wrong analysis and inappropriate pattern identification. Various Text Mining approaches are being used by researchers which can help in Anomaly Detection through Topic Modeling, identification of Trending Topics, Hate Speeches and evolution of the communities in Online Social Networks. In this paper, a comparative analysis of the performance of four classification algorithms, Support Vector Machine (SVM), Rocchio, Decision Trees and K-Nearest Neighbour (KNN) for a Twitter data set is presented. The experimental study revealed that SVM outperforms better than other classifiers, and also classifies the dataset into anomalous and non-anomalous users opinions. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
A comparative study on e-waste management systems in developed and developing countries: Legislative compliances and initiatives
E-waste is an ongoing issue that still lacks a suitable solution, particularly in developing nations. The environment and human health have suffered dramatically as a result of poor recycling practices of waste of electrical and electronic equipment (WEEE), transboundary movement, improper management of e-waste, the lack of environmentally sound management (ESM) programs, and the ineffective EPR (extended producer responsibility) schemes. Although developed nations have implemented efficient legislative frameworks and regulations, emerging nations suffer due to their plans. E-waste management systems differ in developed and developing countries; thus, this study evaluates the differences between the management systems and outlines the areas where the developing nations lack effective e-waste management and the advantages developed countries enjoy. Therefore, the current study results are crucial for comprehending the severe hazard posed by improper management of e-waste and the viability of future research into creating strategies to address these problems of developing nations. 2023, IGI Global. -
A Comparison of Similarity Measures in an Online Book Recommendation System
To assist users in identifying the right book, recommendation systems are crucial to e-commerce websites. Methodologies that recommend data can lead to the collection of irrelevant data, thus losing the ability to attract users and complete their work in a swift and consistent manner. Using the proposed method, information can be used to offer useful information to the user to help enable him or her to make informed decisions. Training, feedback, management, reporting, and configuration are all included. Our research evaluated user-based collaborative filtering (UBCF) and estimated the performance of similarity measures (distance) in recommending books, music, and goods. Several years have passed since recommendation systems were first developed. Many people struggle with figuring out what book to read next. When students do not have a solid understanding of a topic, it can be difficult determining which textbook or reference they should read. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Comprehensive Study on Computer-Aided Cataract Detection, Classification, and Management Using Artificial Intelligence
The day-to-day popularity of computer-aided detection is increasing medical field. Cataract is a main cause of blindness in the entire world. Compared with the other eye diseases, computer-aided development in the area of cataract is remaining underexplored. Several researches are done for automated detection of cataract. Many study groups have proposed many computer-aided systems for detecting cataract, classifying the different type, identification of stages, and calculation of lens power selection prior to cataract surgery. With the advancement in the artificial intelligence and machine learning, future cataract-related research work can undergo very useful achievements in the coming days. The paper studies various recent researches done related to cataract detection, classification, and grading using various artificial intelligence techniques. Various comparisons are done based on the methodology used, type of dataset, and the accuracy of various methodologies. Based on the comparative study, research gap is identified, and a new method is proposed which can overcome the disadvantages and gaps of the studied work. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A comprehensive view of artificial intelligence (ai)-based technologies for sustainable development goals (sdgs)
Agenda 2030, aimed at sustainable and inclusive development through seventeen SDGs formulated by the United Nations (UN), has become a massive challenge for most nations around the world. Many countries are setting a plan of action for achieving carbon neutrality by 2050. Due to this, industries are under immense pressure to mitigate harmful emissions and incorporate SD in their business activities. In the past decade, AI has grown as the dominating technology which influences nearly every aspect of human life, i.e., society, business, environment, etc. This chapter provides a comprehensive view of AI-driven technological applications in achieving SDGs. It provides a snapshot of the emerging relationship between AI applications and sustainable development and how AI could be used to create sustainable business models. Large-scale adoption of AI-driven technologies has enormous potential from the sustainable development perspective. The purpose of this chapter is to map the application of AI-based technological tools and solutions with the various SDGs. Further, this chapter also extends the discussion on AI-based technology as an enabler of or barrier to addressing sustainable development issues. It provides an important insight for policymakers, practitioners, investors, and other stakeholders about the conducive influence of AI on society, governance, and ecology in line with the priorities underlined in the UN SDGs. 2024 Walter de Gruyter GmbH, Berlin/Boston.