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RayleighBard magnetoconvection with asymmetric boundary condition and comparison of results with those of symmetric boundary condition
The paper concerns two RayleighBard magnetoconvection problems, one in a mono-nanofluid (H2OCu) and the other in a hybrid nanofluid (H2OCuAl2O3) bounded by asymmetric boundaries. A minimal FourierGalerkin expansion is used to obtain the generalized Lorenz model (GLM) which is then reduced to an analytically solvable GinzburgLandau equation using the multiscale method. The results of asymmetric boundaries are extracted by using the Chandrasekhar function with appropriate scaling of the Rayleigh number and the wave number. The solution of the steady-state version of the GLM is used to estimate the Nusselt number analytically, and the unsteady version is solved numerically to estimate the time-dependent Nusselt number and also to study regular, chaotic, and periodic convection. Streamlines are plotted and analyzed in both steady and unsteady states. The analytical expression for the HopfRayleigh number, rH , coincides with the value predicted using the bifurcation diagram. This number determines the onset of chaos. For r?> rH , one observes chaotic motion with spells of periodic motion in between. For r?< rH , one sees non-chaotic motion (regular motion). It is found that by increasing the strength of the magnetic field, we can prolong the existence of regular motion by suppressing the manifestation of chaos. The Lorenz attractor is a signature of chaos since it is found that the attractor appears only for r?> rH . The magnitude of the influence of the asymmetric boundary on rH is between those of the two symmetric boundary conditions with the freefree isothermal boundary being the one that most favors chaotic motion: A result also seen in the context of regular convection. 2023, Akadiai Kiad Budapest, Hungary. -
Impact of boundary conditions on Rayleigh-Bard convection: stability, heat transfer and chaos
The paper compares the results of Rayleigh-Bard convection problem for rigid-rigid-isothermal, rigid-free-isothermal and free-free isothermal boundaries. A minimal Fourier-Galerkin expansion yields the generalized-Lorenz-model whose scaled version is reduced to the Stuart-Landau-model using the multiscale-method. Nusselt number is estimated for both steady and unsteady regimes. Regular, chaotic, and periodic natures of the solution are studied using the Hopf-Rayleigh-number and by means of a bifurcation diagram. The linear and weakly-nonlinear-analyses reveal that the onset of regular and chaotic motions in the case of rigid-free-isothermal boundaries happens later than that of free-free isothermal boundaries but earlier than rigid-rigid-isothermal boundaries. It is shown that the scaled-Lorenz-model possesses all the features of the classical-Lorenz-model. Beyond the value of the Hopf-Rayleigh-number, we observe chaotic-motion between two consecutive spells of periodic motion. It is found that one can also have a window of periodicity for all three boundaries. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Optimized task group aggregation-based overflow handling on fog computing environment using neural computing
It is a non-deterministic challenge on a fog computing network to schedule resources or jobs in a manner that increases device efficacy and throughput, diminishes reply period, and maintains the system well-adjusted. Using Machine Learning as a component of neural computing, we developed an improved Task Group Aggregation (TGA) overflow handling system for fog computing environments. As a result of TGA usage in conjunction with an Artificial Neural Network (ANN), we may assess the models QoS characteristics to detect an overloaded server and then move the models data to virtual machines (VMs). Overloaded and underloaded virtual machines will be balanced according to parameters, such as CPU, memory, and bandwidth to control fog computing overflow concerns with the help of ANN and the machine learning concept. Additionally, the Artificial Bee Colony (ABC) algorithm, which is a neural computing system, is employed as an optimization technique to separate the services and users depending on their individual qualities. The response time and success rate were both enhanced using the newly proposed optimized ANN-based TGA algorithm. Compared to the present works minimal reaction time, the total improvement in average success rate is about 3.6189 percent, and Resource Scheduling Efficiency has improved by 3.9832 percent. In terms of virtual machine efficiency for resource scheduling, average success rate, average task completion success rate, and virtual machine response time are improved. The proposed TGA-based overflow handling on a fog computing domain enhances response time compared to the current approaches. Fog computing, for example, demonstrates how artificial intelligence-based systems can be made more efficient. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
AN IOT-BASED COMPUTATIONAL INTELLIGENCE MODEL TO PERFORM GENE ANALYTICS IN PATERNITY TESTING AND COMPARISON FOR HEALTH 4.0
Parental comparison and parenthood testing are essential in various legal and medical scenarios. The accuracy and reliability of these tests heavily rely on the gene analysis algorithms used. However, analyzing the quality of succession data are quite challenging due to the presence of detrimental characteristics. To address this issue, we propose using machine learning-based algorithms such as clustering (Correlation-based) and Classification (Modified Naive Bayesian) to separate these characteristics from the parent-child gene array. This progression helps to identify, validate, and select tools, techniques for scrutinizing indecent sequences, leading to accurate and reliable results. In this paper, we present an IoT-based intelligence tool for parental comparison that uses a secure gene analysis algorithm. The model employs multiple sensors and devices to collect genetic data, which is then securely processed and analyzed using contemporary algorithms. The suggested model uses advanced techniques such as encryption and decryption to ensure the privacy and confidentiality of the genetic information. Our experimental consequences reveal that the proposed model is reliable, secure, and provides accurate results. The model has the potential to be used in various legal and medical contexts where the security and reliability of genetic data are critical. 2023 Little Lion Scientific. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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.