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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. -
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. -
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. -
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. -
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. -
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. -
Trigonometric Cosine, Square, Sawtooth and Triangular Waveforms of Internal Heating Modulations for Three-Component Convection in a Couple Stress Liquid: A Detailed Analysis
In this paper, the main focus is to study the effect of internal heating modulations of sinusoidal and non-sinusoidal waveforms on a three-component convection in a couple stress liquid. This three-component layer is heated from below and salted with two solutes at the bottom. In order to facilitate a solution to the problem, linear case is formulated using the Venezian approach for modulations while the non-linear case is modeled using 7-mode generalized Lorenz equations. With the aim of quantifying the heat and mass transfer, average Nusselt and average Sherwood numbers are computed. The investigation reveals that, internal heating modulations show a stabilizing or destabilizing trend that precisely depends on the modulated frequency and appropriate waveforms. The effect of heat source and sink is recorded on different convection processes. The effect of the pertinent parameters and waveforms on the stability of the system and on heat and mass transfer have been captured via graphs. The results confirm that the heat and mass transfer escalates to its maximum due to the square waveform. In this research paper, six problems involving three types of convection in two different liquids are solved as limiting cases of the problem. 2022, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Digital transactional tools and their optimum use in language learning: An interdisciplinary approach /
IOSR Journal of Humanities and Social Science, Vol.19, Issue 11, pp.230-233, ISSN No: 2279-0845. -
MOOCs: A disruptive teaching-learning process in interdisciplinary boundaries /
International Journal of Language & linguistics, Vol.1, Issue 2, pp.54-61, ISSN No: 2374-8869. -
Signature based key exchange for securing data and user from web data stealing attacks
Due to the immense technological growth, web and its related applications are becoming a major part of everyday life. The growth of the internet and technology not only increases the positive benefits but also increases negative activities such as data theft. As web applications are used frequently for many online services, it is the most common and valuable target for the adversary to host any web vulnerabilities. Data theft or data stealing attacks are quite common in the web and the internet with severe consequences. The private data are generally stored on the system which gives an opportunity for the attacker to steal the data from the storage or during transit. However, apart from stealing the critical data from the user, the attacker also steals the sensitive data from the web applications. This type of attack takes several forms for stealing perilous information from the user and web application. Unfortunately, these attacks are easy to execute as the attacker needs only the internet connection, a web server and technical knowledge which are readily available. Several prevention strategies exist to thwart the user and the application from the web attacks, however, they do not provide the complete solution. This paper presents the signature based key exchange to prevent the user as well as the web application from several variations of data stealing attacks through mutual attestation. The experimental results show that the proposed method prevents the user and application from data theft than any other existing methods. BEIESP. -
Cognitive technology in human capital management: a decision analysis model in the banking sector during COVID-19 scenario
Cognitive technologies are products of the artificial intelligence (AI) domain which execute tasks that only humans used to perform. The impact of cognitive technologies on the management of human capital (HC) has a massive effect in the banking sector. This paper studies the transformation of cognitive technology to human capital management (HCM) in the banking sector during the COVID-19 pandemic. The study draws data from 201 bank employees working in private, public, and foreign banks using a multi-stage sampling method in India. A number of hypotheses were framed and tested using multivariate and regression analyses. The results from the study indicate a significant change in the performances of bank employees statistically during the transformation of cognitive technologies. Cognitive technologies such as payment, product customisation, self-services, workload alleviation, automated back-office function, and a personalised experience significantly contribute to the HCM. 2024 Inderscience Enterprises Ltd. -
Determinants of Loan Repayment Behaviour of Bank Borrowers - A Relative Study with evidence from Bengaluru
The present study aimed to review the various factorsthat influence the credit score and find the effect of credit score and other financial aspects of an individuals loan repayment and whether they had ever defaulted. Primary data was collected through a questionnaire from 516 customers of twelve different banks in Bengaluru city during July-August 2019 using a stratified sampling method. The results revealed that credit score, loan amount, and loan repayment amount did not affect the loan repayment behaviour of individuals. However, the type of loan obtained and the kind of bank the loan is received significantly influence individuals loan repayment behaviour. Home loans, education loans, other loans and the loan obtained from a foreign bank were particularly significant in affecting the loan repayment of individuals. The high beta value for other loans indicates that most individuals who have no other loans have defaulted in repaying their dues. Indian Institute of Finance. -
Towards an Epistemology of Reading: Defining the Process of Reading in Modern Terms
The chaotic space caused by information explosion in present times has made the process and purpose of reading to be always questioned. Technological advancement has made reading appear as a mere mockery at the very outset. But the world still prioritizes knowledge that is acquired through observation, valuation and interpretation. At the time of Big Data, there still persists a sense of agency to define a given information as episteme. The present essay emphasizes on looking at reading as a modern phenomenon by presupposing the epistemological presence at the centre of any rational pursuit. Based on the Kantian precepts on enlightenment, the paper attempts to understand this presence of knowledge by delving into the major disciplines of modern philosophy that help in observing, valuing and interpreting the act of reading in present times. More than laying terms for defining the text within the modern space, the study essentializes reading in a virtually driven algorithmic world. AesthetixMS 2021 -
Effects of dark matter in star formation
The standard model for the formation of structure assumes that there existed small fluctuations in the early universe that grew due to gravitational instability. The origins of these fluctuations are as yet unclear. In this work we propose the role of dark matter in providing the seed for star formation in the early universe. Very recent observations also support the role of dark matter in the formation of these first stars. With this we set observable constraints on luminosities, temperatures, and lifetimes of these early stars with an admixture of dark matter. 2019, Springer Nature B.V. -
Alternate models to dark energy
One of the unresolved questions currently in cosmology is that of the non-linear accelerated expansion of the universe. This has been attributed to the so called Dark Energy (DE). The accelerated expansion of the universe is deduced from measurements of Type Ia supernovae. Here we propose alternate models to account for the Type Ia supernovae measurements without invoking dark energy. 2017 COSPAR -
Dark matter, dark energy, and alternate models: A review
The nature of dark matter (DM) and dark energy (DE) which is supposed to constitute about 95% of the energy density of the universe is still a mystery. There is no shortage of ideas regarding the nature of both. While some candidates for DM are clearly ruled out, there is still a plethora of viable particles that fit the bill. In the context of DE, while current observations favour a cosmological constant picture, there are other competing models that are equally likely. This paper reviews the different possible candidates for DM including exotic candidates and their possible detection. This review also covers the different models for DE and the possibility of unified models for DM and DE. Keeping in mind the negative results in some of the ongoing DM detection experiments, here we also review the possible alternatives to both DM and DE (such as MOND and modifications of general relativity) and possible means of observationally distinguishing between the alternatives. 2017 COSPAR -
Gammaless gamma-ray bursts?
One of the possible resolutions of the compactness problem in gamma-ray bursts (GRBs) is by invoking the Lorentz factors associated with the relativistic bulk motion. This model applies to GRBs where sufficient energy is converted to accelerate the ejected matter to relativistic speeds. In some situations, this may not be a possible mechanism, and as a result, the gamma rays are trapped in the region. In this work, we look at such possible scenarios and where the neutrino pair production process can dominate. As a result, the neutrinos can escape freely. This could give rise to a scenario where the release of neutrinos precedes the gamma-ray emission that is much attenuated. This model can thus possibly explain why fewer GRBs are observed than what is expected. 2023, Indian Association for the Cultivation of Science.