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Visible light active bismuth chromate/curcuma longa heterostructure for enhancing photocatalytic activity
Bismuth chromate nanostructures were fabricated via hydrolysis technique using curcuma longa for enhancing the photocatalytic activity. The analytes have been labelled as Bi2CrO6-C, when prepared without using curcuma longa and Bi2CrO6-G, prepared using curcuma longa extract (Bi2CrO6/Curcuma longa). The as-fabricated catalysts have been confirmed via characterization techniques including X-ray diffraction, Transmission electron microscopy (TEM), and Field emission scanning electron microscopy (FESEM), UVVis. DRS. The as-synthesised analytes have been evaluated their photocatalytic efficiency via photodegradation of an organic pollutant, Methyl Orange (MO). The current research findings imposed the effect of inculcation of a green extract curcuma longa reduces particle size and increases surface area of the material and moreover makes heterostructure with Bismuth chromate and inhibits recombination of photogenerated charges for efficient degradation of the organic pollutant. Bi2CrO6-G demonstrates here enhanced photocatalytic activity as compared to Bi2CrO6-C. Akadiai Kiad Budapest, Hungary 2024. -
Diabetes mellitus prediction using machine learning within the scope of a generic framework
Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting
Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs stepby-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved AUC and ROC values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as accuracy, f1-score, precision, and recall significantly support the need for presented methodologies for qualitative NAFLD prediction modelling. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
Beyond boundaries: Role of artificial intelligence and ChatGPT in transforming higher education
The goal of the proposed chapter is to give readers a thorough understanding of the complex effects of ChatGPT on higher education. It will cover the short-and long-term benefits that ChatGPT offers, as well as limitations that may affect both educators and learners. The chapter will also highlight a wide range of ethical issues and challenges that arise while using ChatGPT. Research in this area is very limited and the literature review reveals that there are benefits as well as limitations of using ChatGPT in the domain of higher education but the fact is that it is going to grow further which makes it an urgent need for the policymakers and stakeholders to explore and understand how ChatGPT should be integrated into higher education to deliver more value to the educators and learners. The proposed chapter will cover the evolution of ChatGPT, its growing popularity and impact, benefits it offers, the associated disadvantages and the road ahead. 2024, IGI Global. -
Predicting a Rise in Employee Attrition Rates Through the Utilization of People Analytics
Modern organizations have a multitude of technological tools at their disposal to augment decision-making processes, with artificial intelligence (AI) standing out as a pivotal and extensively embraced technology. Its application spans various domains, including business strategies, organizational management, and human resources. There's a growing emphasis on the significance of talent capital within companies, and the rapid evolution of AI has significantly reshaped the business landscape. The integration of AI into HR functions has notably streamlined the analysis, prediction, and diagnosis of organizational issues, enabling more informed decision-making concerning employees. This study primarily aims to explore the factors influencing employee attrition. It seeks to pinpoint the key contributors to an employee's decision to quit an organization and develop a futuristic data driven model to forecast the possibility of an employee leaving the organization. The study involves training a model using an employee turnover dataset from IBM analytics, including a total of thirty-five features and approximately one thousand and five hundred samples. Post-training, the model's performance is assessed using classical metrics. The Gaussian Nae Bayes classifier emerged as the algorithm delivering the most accurate results for the specified dataset. It notably achieved the best recall (0.54) indicating its ability to correctly identify positive observations and maintained false negative of merely 4.5%. 2023 IEEE. -
Artificial Intelligence in Disaster Management: A Survey
This paper provides a literature review of cutting-edge artificial intelligence-based methods for disaster management. Most governments are worried about disasters, which, in general, are unbelievable events. Researchers tried to deploy numerous artificial intelligence (AI)-based approaches to eliminate disaster management at different stages. Machine learning (ML) and deep learning (DL) algorithms can manage large and complex datasets emerging intrinsically in disaster management circumstances and are incredibly well suited for crucial tasks such as identifying essential features and classification. The study of existing literature in this paper is related to disaster management, and further, it collects recent development in nature-inspired algorithms (NIA) and their applications in disaster management. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
HR Analytics: An Indispensable Tool for Effective Talent Management
Business organizations have changed tremendously in the way they visualize the human capital of the organization and make all efforts to create a workforce that is productively engaged and is ready to embrace the challenges posed by uncertainty and turbulence in the business environment. This calls for a decision-making approach that is based on observed people behaviours rather than relying on intuition and gut feel. These observed behaviours are reactions or consequences to stimuli and therefore the science of Human Resource Management can be better understood as predicting these dependent variables based on a set of independent variables. This chapter attempts to present a complete framework of HR analytics in terms of concept, need and how it can add immense value to effective talent management in the organizations. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Discrimination between scheduled and non-scheduled groups in access to basic services in urban India
Access to basic services such as water, sanitation, and electricity is a key determinant of an individuals well-being. Nevertheless, access to these services is unequally distributed among different social groups in many countries. India is no exception, with the scheduled castes (SC) and scheduled tribes (ST) being one of the countrys most marginalised and disadvantaged groups. This paper analyses the disparities in access to basic services between scheduled and non-scheduled households, investigates the factors contributing to the unequal access, and suggests policy recommendations. Using data from the National Sample Survey 76th Round, we analyse the access to basic services such as durable housing, improved water and sanitation, and access to electricity. The papers objectives are (a) to investigate the factors impacting the quality of basic service delivery in urban India separately for scheduled and non-scheduled households and (b) to quantify the discrimination between scheduled and non-scheduled households in urban India concerning access to quality of basic services through computing a comprehensive index and by using the Fairlie decomposition approach. The analysis corroborates the finding that systemic discrimination exists between scheduled and non-scheduled households in urban India regarding access to good quality basic services up to an extent of 24%. 2024 The Authors. -
Sampling and Categorization of Households for Research in Urban India
Conventional sampling methodologies for citizens/households in urban research in India are constrained due to the lack of readily available, reliable sampling frames. Voter lists, for example, are riddled with errors and, as such may not be able to provide a robust sampling frame from which a representative sample can be drawn. The JanaBrown Citizenship Index project consortium (Janaagraha, India; Brown University, USA) has conceptualized a unique research design that provides an alternative way on how to identify, categorize and sample households (and citizens within) in a city in a representative and meaningful way. The consortium consists of the Janaagraha Centre for Citizenship and Democracy, based in India, and the Brown Center for Contemporary South Asia, part of Brown University, USA. The methodology was designed to enable systematic data collection from citizens and households on aspects of citizenship, infrastructure and service delivery across different demographic sections of society. The article describes how (a) data on communities that are in the minority, such as Muslims, scheduled castes (SC) and scheduled tribes (ST), were used to categorize Polling Parts to allow for stratified random sampling using these strata, (b) geospatial tools such as QGIS and Google Earth were used to create base maps aligning to the established Polling Part unit, (c) the resulting maps were used to create listings of buildings, (d) how housing type categorizations were created (based on the structure/construction material/amenities, etc.) and comprised part of the building listing process, and (e) how the listings were used for sampling and to create population weights where necessary. This article describes these methodological approaches in the context of the project while highlighting advantages and challenges in application to urban research in India more generally. 2022 Lokniti, Centre For The Study Of Developing Societies. -
Protecting Privacy and Ethics in AI-driven Conversational Systems using Laplace Mechanism
AI-powered analytics and conversational systems designed for data mining and other manipulative tasks raise concerns about who can access data and how to utilize it. This data progression has brought forth significant ethical considerations concerning privacy and ethics. AI-powered conversational agents, such as chatbots and virtual assistants, collect and process vast amounts of personal data to enhance user experience and provide tailored responses. Many industries have been transformed by artificial intelligence (AI)-based analytics systems, and some have unthinkable analytical data in terms of better decision-making, highly personalized customer experiences, and improved operational efficiency. While conversational AI offers convenience for users, this technology is also associated with privacy and data protection threats. This research seeks to understand the ethical issues with AI-driven analytics, focusing on data privacy and ethics in verbal and written interactions. This work will look at the current state and potential threats in depth and demonstrate the implementation of differential privacy using the Laplace mechanism in the query output so that the document has no special meaning and does not distort the released results significantly. Author(s) 2025. -
Climate finance and the transition to a low-carbon economy: Financing a sustainable future
Climate finance is very critical in facilitating the transition toward a low-carbon economy. The chapter then details the challenges and opportunities of mobilization and allocation of climate finance for mitigation and adaptation. It therefore analyzes various sources of climate finance, including public and private investments, multilateral development banks, and specialized climate funds. It also touches on such governance, transparency, and accountability aspects with regards to delivering climate finance effectively. The chapter will fulfill the aim to arm policymakers, investors, and other stakeholders with a range of complexities in climate finance since it hopes to present a case for a sustainable future. The chapter uses a mix of questions, literature review, data analysis, case studies, and expert discussions to provide a deep, informative, and comprehensive analysis. Understanding the challenges and opportunities of climate finance by its stakeholders may then lead to cooperation towards a more sustainable and equitable future. 2025 by IGI Global Scientific Publishing. All rights reserved. -
An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting
Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs stepby-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved AUC and ROC values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as accuracy, f1-score, precision, and recall significantly support the need for presented methodologies for qualitative NAFLD prediction modelling. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
Early Disaster Detection and Monitoring Using Text Analysis and Levy Flight-based Particle Swarm Optimization Algorithm
Disasters can strike unexpectedly and leave a trail of destruction, causing immense suffering and loss of life while disrupting entire communities. These events can be natural, such as floods, earthquakes, hurricanes, wildfires, or man-made, including industrial accidents and technological failures. This study investigates a hybrid approach that uses text analysis, natural language processing, and optimization techniques to identify and monitor disaster-related events. The methodology of this paper involves collecting and analyzing text, focusing on sentiment and keywords associated with disaster-related text. Various aspects of text patterns are examined to enhance the models performance. The proposed model uses a Levy flight-based Particle Swarm Optimization algorithm to select optimal features from a vector set. It uses Text Blob for sentiment analysis, cosine similarity to classify each tweet as a disaster, Count Vectorizer for feature extraction, and XGBoost machine learning algorithm for classification. The significance of this model is that it provides early warning and insight for any disaster based on text analysis and classification. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. -
Enhanced Random Forest-Based Model forFlood Detection andClassification
Flooding is one of the most devastating natural disasters globally, causing extensive damage to infrastructure, the environment, and human lives. With increasing occurrences due to climate change, accurate classification and analysis of flood imagery are essential for early detection, damage assessment, and post-disaster recovery. Reliable flood classification systems are critical for early warning, resource allocation, and mitigation efforts, helping to minimize the impact on affected regions. Remote sensing and computer vision techniques, including the Bag-of-Visual-Words (BOV) model, offer powerful tools for interpreting flood images by categorizing and identifying flooded regions across vast and complex terrains. This paper presents a modification of the standard Random Forest algorithm to enhance the accuracy of image classification within a Bag-of-Visual-Words (BOV) model. The modified Random Forest achieves better adaptability and performance across flood image datasets by introducing flexibility in parameter tuning through custom hyperparameters and automatic grid search. This modification addresses challenges in balancing efficiency and accuracy for classifying high-dimensional image data sets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Access to clean cooking fuel and discrimination between scheduled and non-scheduled groupsacross urban and rural India
Access to clean cooking fuel constitutes a fundamental element of household well-being and national energy security, particularly for marginalized and socio-economically disadvantaged communities. This paper examines the discrimination in access to clean cooking fuel between Scheduled Caste/Scheduled Tribe (SC/ST) and non-SC/ST households in India. Drawing on data from the National Sample Survey Office (NSSO) 78th Round (202021) Multiple Indicator Survey, the study seeks to quantify the extent of this discrimination and analyze the underlying factors contributing to disparities in clean fuel access. Empirical evidence suggests that non-SC/ST households have significantly greater access to clean cooking fuel than SC/ST households. This disparity is primarily explained by socio-economic variables such as income, education, gender, region, and employment status. However, the decomposition analysis reveals that a considerable portion, 22 percent, of the gap remains unexplained, indicating persistent discrimination that cannot be attributed solely to observable characteristics. The study recommends strengthening last-mile delivery of LPG in SC/ST-dominated areas and integrating energy access with housing programs like PMAY. It also advocates for targeted subsidies linked to caste and income data to support recurring fuel costs. Additionally, the paper emphasizes the need for infrastructure improvements, such as separate kitchens and durable housing, to enable sustained adoption of clean cooking fuels. The Author(s), under exclusive licence to Institute for Social and Economic Change 2025. -
Revolutionizing Arrhythmia Classification: Unleashing the Power of Machine Learning and Data Amplification for Precision Healthcare
This paper presents a comprehensive exploration of arrhythmia classification using machine learning techniques applied to electrocardiogram (ECG) signals. The study delves into the development and evaluation of diverse models, including K-Nearest Neighbors, Logistic Regression, Decision Tree Classifier, Linear and Kernelized Support Vector Machines, and Random Forest. The models undergo rigorous analysis, emphasizing precision and recall due to the categorical nature of the dependent variable. To enhance model robustness and address class imbalances, Principal Component Analysis (PCA) and Random Oversampling are employed. The results highlight the effectiveness of the Kernelized SVM with PCA, achieving a remarkable accuracy of 99.52%. Additionally, the paper discusses the positive impact of feature reduction and oversampling on model performance. The study concludes with insights into the significance of PCA and Random Oversampling in refining arrhythmia classification models, offering potential avenues for future research in healthcare analytics. 2024 IEEE. -
Larval descriptions and natural history of two endemic frogs (Amphibia: Anura) from the Western Ghats, India
Western Ghats of India is known for its high anuran diversity; however, the larvae of many anurans are still unknown. Studies on anuran larvae can provide insights into their natural history and evolution, help identify cryptic species and aid in amphibian conservation. In this study, we describe the tadpoles of two poorly known species Indirana bhadrai and Micrixalus candidus from the Western Ghats, India using morphology and molecular techniques and provide details on their natural history. The morphology of the tadpoles reflected their habitats. The tadpole of Indirana bhadrai was semiterrestrial, adapted to wet rocky slopes while the tadpole of Micrixalus candidus was fossorial, found under small rocks and sand in slow-flowing streams. Molecular analysis using the 16S rRNA gene showed 100% identity between tadpoles of Indirana bhadrai, and Micrixalus candidus with their adults respectively. The larval descriptions provided in this study can help understand the ecology of the frogs from the Western Ghats. Copyright 2025 Magnolia Press. -
Diet of the Dattatreya night frog Nyctibatrachus dattatreyaensis from the central Western Ghats, India
The Dattatreya night frog Nyctibatrachus dattatreyaensis, found in the Chandra Drona Parvatha massif, is a stream-dwelling, evolutionarily distinct and globally Endangered species threatened by increasing habitat loss and alteration. We examined the stomach contents of 104 individuals, from ten different streams, of which 42 had prey in their stomachs. The prey items were in 12 orders across 4 classes, mainly dipterans, hymenopterans and lepidopterans. The frog exhibits a passive foraging mode, has a moderate trophic niche breadth (Bst = 0.43), and may have a preference for agile prey. Apart from this, there were plant materials, sand grains and plastic debris found in the stomach contents, with 0.82 mm3 of plastic debris found in eight individuals across three streams. The presence of plastic debris indicates the impact of anthropogenic activities leading to a form of habitat degradation. The data presented indicates the need for immediate and efficient conservation strategies to be put in place for this understudied species. 2025 British Herpetological Society. All rights reserved. -
Transforming Industry 5.0: Real Time Monitoring and Decision Making with IIOT
This chapter explores the transformative potential of Industry 5.0 by leveraging real-time monitoring and decision-making capabilities through the use of IIoT dashboards. It extends in examining how IIoT dashboards enable organizations to gain real-time insights into their operations, facilitating data-driven decision-making and improving overall efficiency. By embracing IIoT dashboards, businesses can effectively transform Industry 5.0, unlocking new levels of productivity, agility, and competitiveness. In this chapter, important challenges such as data integration, data security, scalability, and user experience are identified. It highlights key considerations for implementing IIoT dashboards and offers practical methods for successful adoption of this technology. Remarkable achievements in implementing this technology include applications such as crude oil production with IIoT and edge computing, as well as IIoT-enabled smart agriculture dashboards. Adopting IIoT dashboards may involve initial costs, but they offer long-term benefits and cost-effectiveness, particularly in the era of Industry 5.0 transformation. 2024 selection and editorial matter, C Kishor Kumar Reddy, P R Anisha, Samiya Khan, Marlia Mohd Hanafiah, Lavanya Pamulaparty and R Madana Mohana. -
Review and Design of Integrated Dashboard Model for Performance Measurements
This article presents a new approach for performance measurement in organizations, integrating the analytic hierarchy process (AHP) and objective matrix (OM) with the balanced scorecard (BSC) dashboard model. This comprehensive framework prioritizes strategic objectives, establishes performance measures, and provides visual representations of progress over time. A case study illustrates the methods effectiveness, offering a holistic view of organizational performance. The article contributes significantly to performance measurement and management, providing a practical and comprehensive assessment framework. Additionally, the project focuses on creating an intuitive dashboard for Fursa Foods Ltd. Using IoT technology, it delivers real-time insights into environmental variables affecting rice processing. The dashboard allows data storage, graphical representations, and other visualizations using Python, enhancing production oversight for the company. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.