Browse Items (11855 total)
Sort by:
-
Vigilance and surveillance reinforced using mathematical approaches in object tracking techniques
Visual tracking is crucial to the study of object recognition and has been utilized in a variety of realistic settings, such as robotics, traffic monitoring, self-driving automobiles, forensics, and more. This research concentrates on techniques for counting the total number of individuals entering or exiting a space under the watchful eye of a camera. The techniques described here can detect the number of persons in a scene, both for a single individual and for many passers in front of the camera. With the aid of surveillance that use the centroid concept, an effective solution has been devised for monitoring. Secondly, in this study, object tracking methods utilising deep learning are also reviewed and analysed. This study also compares the effectiveness of various algorithms on the LaSOT, VOT2015, VOT2016, VOT2017 and OTB2015 tests. 2024, Taru Publications. All rights reserved. -
Can Hubble tension be eased by invoking a finite range for gravity?
The estimation of the Hubble constant in the past few decades has increasingly become more accurate with the advancement of new techniques. However, its value seems to depend on the epoch at which the measurements are made. The Planck estimate of the Hubble constant from the observations of the cosmic microwave background radiation in the early universe is about 67km\s\Mpc, whereas that obtained from the distance indicators at the current epoch is ?73-74km\s\Mpc. This discrepancy between the two groups of measurement is termed as the Hubble tension which has gained much attention in the past few decades with growing significance as measurements from both, the early and the late universe, studies continue to produce results with increasing precision. In this work, we propose a modification to gravity by considering a finite range gravitational field as an alternate explanation for this discrepancy in the value of the Hubble constant. 2024 World Scientific Publishing Company. -
Long Term X-Ray Spectral Variations of the Seyfert-1 Galaxy Mrk 279
We present the results from a long term X-ray analysis of Mrk 279 during the period 2018-2020. We use data from multiple missions - AstroSat, NuSTAR and XMM-Newton, for the purpose. The X-ray spectrum can be modeled as a double Comptonization along with the presence of neutral Fe K? line emission, at all epochs. We determined the sources X-ray flux and luminosity at these different epochs. We find significant variations in the sources flux state. We also investigate the variations in the sources spectral components during the observation period. We find that the photon index and hence the spectral shape follow the variations only over longer time periods. We probe the correlations between fluxes of different bands and their photon indices, and found no significant correlations between the parameters. 2024. National Astronomical Observatories, CAS and IOP Publishing Ltd. -
Investigation on the analysis of integration of IoT and AI technologies with information security for advanced education 4.0
This research examines the integration of emerging technologies in the form of the Internet of Things and Artificial Intelligence in driving forward to the educational application of Education 4.0. The systematic meta-analysis study provides evidence in the transformative capability of these technologies regarding attendance, performance, and learning pathway. The systems implementation was in the form of IoT sensors to capture and record student attendance, while the use of Artificial Intelligence based on machine learning models such as Support Vector Machine, Artificial Neural Network, k-Nearest Neighbors, and Decision Tree generated a personalized recommendation for the academic improvement or sports activity to be participated as an extracurricular activity. The performance evaluation of these models was illustrated for accuracy to correctly predict student responses related to the provided recommendations. The findings of implementation suggest the systems significant impacts given the augmented performance achievement with respect to academics and sports is the result of the implementation. It was measured comparing students performance before and after system implementation to capture the interpretation of student improvement regarding the use of the implemented system. The findings indicated that the systems implementation contributed to the increase in academic improvement from 65% to 75% and sports performance from 55% to 70% depending on student response to the provided academical or extracurricular recommendations. Such findings confirm an overall improvement in performance based on the use of the presented system. Taru Publications. -
Lignocellulosic biomass for biochar production: A green initiative on biowaste conversion for pharmaceutical and other emerging pollutant removal
Lignocellulosic waste generation and their improper disposal has accelerated the problems associated with increased greenhouse gas emissions and associated environmental pollution. Constructive ways to manage and mitigate the pollution associated with lignocellulosic waste has propelled the research on biochar production using lignocellulose-based substrates. The sustainability of various biochar production technologies in employing lignocellulosic biomass as feedstock for biochar production not only aids in the lignocellulosic biomass valorization but also helps in carbon neutralization and carbon utilization. Functionalization of biochar through various physicochemical methods helps in improving their functional properties majorly by reducing the size of the biochar particles to nanoscale and modifying their surface properties. The usage of engineered biochar as nano adsorbents for environmental applications like dye absorption, removal of organic pollutants and endocrine disrupting compounds from wastewater has been the thrust areas of research in the past few decades. This review presents a comprehensive outlook on the up-to-date research findings related to the production and engineering of biochar from lignocellulosic biomass and their applications in environmental remediation especially with respect to wastewater treatment. Further a detailed discussion on various biochar activation methods and the future scope of biochar research is presented in this review work. 2024 Elsevier Ltd -
Analysing the impact of the taxation law amendment of 2019 on corporate taxation in India
The Taxation Law (Amendment) Act, 2019 in India has brought major changes in the taxation revenue as well as in legal provisions. The actual ground reality of the Amendment on a microeconomic level is unknown, but a correlation analysis on macroeconomic indicators show that there is a high positive correlation between the corporate tax revenue and the GDP growth. The author also interlinks the effects of tax cuts on the economy with privatization and how it can mitigate the risks of tax evasion. There is a generalized misconception with privatization that it leads to a significant loss in taxation revenue. The study shows that in fact, privatization helps to expand the earnings of the Government by widening the taxation structure and slab, which the author has found through statistics. It is high time to have strong regulatory measures to prevent tax evasion by encouraging more corporate entities to become a part of the tax base. Indian Institute of Finance. -
Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges
In the age of digital transactions, credit cards have emerged as a prevalent form of payment in smart cities. However, the surge in online transactions has heightened the challenge of accurately discerning legitimate from fraudulent activities. This paper addresses this crucial concern by introducing a pioneering system for detecting fraudulent credit card transactions, particularly within highly imbalanced datasets, in the realm of cybersecurity. This paper proposes a hybrid model to effectively manage imbalanced data and enhance the detection of fraudulent transactions. This paper emphasizes the efficacy of the hybrid approach in proficiently identifying and mitigating fraudulent activities within highly imbalanced datasets, thereby contributing to the reduction of financial losses for both merchants and customers in smart cities. As cybersecurity in smart cities evolves, this paper underscores the significance of ensemble learning and cross-validation techniques in optimizing credit card transaction analysis and fortifying the security of digital payment systems. 2024, Taru Publications. All rights reserved. -
Environmental and Sustainable Development Policies to Address the Pollution Catastrophe in India
Although the environment, crops, water, air, food and fiber, control the weather, and supply oxygen, its air, water, and soil are polluted too. Humans have altered about 75% of the earth, reducing wildlife and nature's space and harming the environment. Industrialisation, urbanisation, population growth, and globalisation have affected people and the environment. This study aims to investigate the environmental and sustainable development-focussed policies to address the pollution catastrophe. The study is a content analysis of prominent online newspaper media reports from January 1, 2020, to November 30, 2022, on legal, environmental, and sustainable issues to reduce pollution and advocate an Indian environmental and sustainable development policy. Since pollution and environmental degradation pose significant threat to humanity, ecosystems, and sustainable living are at risk. Despite national and international legislative and regulatory actions, the environment remains a significant issue. An environmental strategy that encourages sustainable development for future generations is the need of the times. It was found that there were legal and environmental offenses, the management of unscientific treatment procedures, the lack of fundamental education about existing court orders, and fatality-induced health problems. Therefore, India needs an environmental and sustainable development policy to limit environmental concerns' fatality and protect the earth from pollution. 2024 - IOS Press. All rights reserved. -
Synthesis, characterization and application of rare earth (Lu3+) doped zinc ferrites in carbon monoxide gas sensing and supercapacitors
The novel rare earth (Lu) doped zinc ferrite nanoparticles, synthesized via a solution combustion approach, exhibit exceptional sensitivity to carbon monoxide (C.O.), a capability studied for the first time. The successful detection of C.O. by these nanoparticles underscores their potential as efficient gas sensors. Structural and morphological characterization confirmed the creation of single-phase zinc ferrite nanoparticles, utilizing various standard and advanced modern probes. To assess the gas sensing capabilities, the nanoparticles were exposed to carbon monoxide gas, revealing an outstanding gas response of 80 % at 300 C, with a response against 20,000 parts per million by volume (PPMv) of carbon monoxide. These results indicate the promising applicability of Lu-doped zinc ferrite nanoparticles in C.O. gas sensing applications. Furthermore, the supercapacitance performance of the synthesized nanoparticles was investigated. Electrodes fabricated from Lu-doped zinc ferrite nanoparticles (Lu 0, 0.3, 0.5, and 0.7) were examined in a 3 M K.O.H. electrolyte using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (E.I.S.). The electrochemical properties of all nanoparticles exhibited good Faradaic behaviour, with the Lu 0.7 electrode achieving a high specific capacitance of 280 F/g at a current density of 0.25 A/g. This highlights the prominent electrochemical stability and potential applications of Lu-doped zinc ferrite nanoparticles in energy storage devices. Overall, the comprehensive investigation of the gas sensing and super capacitance performance of Lu-doped zinc ferrite nanoparticles demonstrates their versatility and potential for various technological applications, including gas sensing and energy storage. These findings pave the way for further research and development in utilizing rare earth-doped ferrite nanoparticles for advanced functional materials. 2024 Elsevier Ltd and Techna Group S.r.l. -
Enhancing power conversion efficiency in five-level multilevel inverters using reduced switch topology
This paper presents extensive research on improving the power conversion efficiency of five-level multilevel inverters (MLIs) by utilizing a reduced switch topology. MLIs have received an abundance of focus because of their ability to generate high-quality output waveforms and have better harmonic outcomes than traditional two-level inverters. The high number of switches in MLIs, on the other hand, can result in increased power losses and lower overall efficiency. In this paper, a novel reduced switch topology for five-level MLIs, which is having five switches is proposed with the aim of minimizing power losses while preserving superior performance due to lesser number of switches. To achieve efficient power conversion, the proposed topology employs advanced pulse width modulation control strategies and optimized switching patterns. The simulation results show that the minimized switch topology improves the power conversion efficiency of the five-level MLI, resulting in lower losses and better overall system performance. The total harmonic distortion (THD) value of the output current has been reduced to 7.12% and the efficiency has been achieved to 96.92%. The findings of this investigation help to advance MLI technology, allowing for more efficient and reliable power conversion in a variety of applications such as renewable energy systems, electric vehicles, and industrial drives. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Sustainable Spending in Destinations : Factors Discouraging Tourists
Purpose: This study investigated the factors influencing tourists sustainable spending behavior in tourism destinations aimed to provide insights for policymakers and businesses to promote sustainability. The research objectives included identifying key factors affecting sustainable spending, assessing their impact, and examining their implications. Methodology: A quantitative approach utilizing confirmatory factor analysis (CFA) was employed to analyze data collected from tourists visiting destinations. Findings: The major findings revealed six significant factors: Perceived costs, availability, awareness, convenience, hedonism & benefits, and culture, elucidating the complex interplay shaping sustainable spending behavior. Theoretical Implications: The importance of perceptions, awareness, and cultural norms in understanding tourists spending decisions has been found to be value added to the literature. A comprehensive measurement technique has been produced in the literature. Practical Implications: This study offered insights for marketers to address perceived cost concerns, enhance availability, and promote awareness of sustainable options. Policymakers could use these findings to formulate targeted policies and incentives to encourage sustainable spending. This research also contributed to advancing the understanding of sustainable tourism spending and provided actionable insights to promote sustainability in line with the United Nations Sustainable Development Goals (UNSDGs). 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Complete analysis of differential cross section in 7 Li + ? ? ? 6 Li + n at astrophysical energies
We have carried out complete analysis of differential cross section in 7 Li + ? ? 6 Li + n using model-independent theoretical formalism. A complete analysis of the reaction involves measurements of not only one state of linear polarization of the photon but also another state of linear polarization inclined to the first at 45? and two states of circular polarization of the photon. An analytical study of the differential cross section including all the photon polarization states is carried out at near-threshold energies of interest to Big Bang Nucleosynthesis. 2024 IOP Publishing Ltd -
Digital twins' readiness anditsimpacts on supply chaintransparency andsustainable performance
Purpose: Production systems occupy geographically dispersed organizations with limited visibility and transparency. Such limitations create operational inefficiencies across the Supply Chain (SC). Recently, researchers have started exploring applications of Digital Twins Technology (DTT) to improve SC operations. In this context, there is a need to provide comprehensive theoretical knowledge and frameworks to help stakeholders understand the adoption of DTT. This study aims to fulfill the research gap by empirically investigating DTT readiness to enable transparency in SC. Design/methodology/approach: A comprehensive literature survey was conducted to develop a theoretical model related to Supply Chain Transparency (SCT) and DTT readiness. Then, a questionnaire was developed based on the proposed theoretical model, and data was collected from Indian manufacturers. The data was analyzed using Confirmatory Factor Analysis (CFA) and Structural EquationModelling (SEM) to confirm the proposed relationships. Findings: The findings from the study confirmed a positive relationship between DTT implementation and SCT. This study reported that data readiness, perceived values and benefits of DTT, and organizational readiness and leadership support influence DTT readiness and further lead to SCT. Originality/value: This study contributes to the literature and knowledge by uniquely mapping and validating various interactions between DTT readiness and sustainable SC performance. 2024, Emerald Publishing Limited. -
Deep Convolutional Neural Network Driven Interpolation Filter for High Efficiency Video Coding
Research in video coding has gained significant importance in recent years, driven by the increasing demand for multimedia transmission. High Efficiency Video Coding (HEVC) has emerged as a prominent standard in this field. Interpolation is a crucial aspect of HEVC, particularly when using fixed half-pel interpolation filters derived from traditional signal processing techniques. In recent times, there has been an exploration of interpolation filters that are based on Convolutional Neural Networks (CNNs). Conventional signal processing techniques are used in traditional HEVC methods to employ fixed half-pel interpolation filters. Recent advancements have delved into the application of Convolutional Neural Networks (CNNs) to enhance interpolation performance. Our proposed method utilises a sophisticated CNN architecture specifically crafted to extract valuable features from low-resolution image patches and accurately predict high-resolution images. The network consists of multiple layers of CNN blocks, which utilise 1 and 3 convolutional kernels to enable efficient and thorough feature extraction through parallel processing. This architecture improves computational efficiency and greatly enhances prediction accuracy The suggested interpolation filter shows a 2.38% enhancement in bitrate savings, as evaluated by the BD-rate metric, specifically in the low delay P configuration. This highlights the potential of deep learning techniques in improving video coding efficiency. 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). -
Synthesis and electrochemical studies of 1,1-binaphthyl-2,2-diol for aqueous rechargeable lithium-ion battery applications
The constant increase in the utilization of lithium-ion batteries (LIBs) in various field applications, including electrical vehicles and electronic devices, has led researchers to focus on their multiple path developments to obtain new electrode materials. The practical development of these electrode materials, based on organic and inorganic moieties, is challenging for various groups of LIB scientists. The concept of organic electrode materials is highly competitive with inorganic electrode materials because of the accessibility of more active sites with structural diversity, high energy and power density, environmental friendliness potential sustainability, and low cost. Herein, 1,1-binaphthyl-2,2-diol (BINOL) is investigated as an organic electrode material that contains two hydroxyl groups that act as active centers. The oxidative coupling process is employed to synthesize BINOL and so obtained product was characterized by using FT-IR, 1H-NMR and MASS techniques. The electrochemical investigations were carried out using sat. Li2SO4 electrolytic medium at three-electrode cell system. The Cyclic voltammetry (CV) has provided information on the anodic behavior of the material and its stability studied at different scan rates. The battery performance of the cell BINOL | Sat. Li2SO4 | LiMn2O4 by galvanostatic charge-discharge potential limit (GCPL) shows 197/171mAhg?1 specific capacity and 90% columbic efficiency. The electrochemical kinetic obtained by potentiostatic electrochemical impedance spectroscopy (PEIS) shows a semi-infinite diffusion process. 2024 Elsevier B.V. -
Synthesis and Characterization of WO3 Nanostructures by the Solvothermal Method for Electrochromic Applications
In this study, a tungsten trioxide (WO3) thin film was deposited by direct current (DC) sputtering onto a fluorine-doped tin oxide (FTO) substrate as the seed layer at an oxygen partial pressure of 8 10?4mbar. A simple solvothermal method involving tungsten hexacarbonyl (W(CO)6), ethanol (C2H5OH), and hydrochloric acid (HCl) was used to synthesize vertically stacked nanoscale WO3 hierarchical structures on WO3 seed-layered FTO. After the deposition process, the FTO samples with nanostructures were subjected to annealing in air at 400C for 4 h. After annealing, the surface morphology, structural characteristics, and optical and electrochromic properties of the grown nanostructures were investigated using scanning electron microscopy (SEM), x-ray diffraction (XRD), Raman spectroscopy, UVvisible spectroscopy, and electrochemical analysis. From the XRD analysis, all the diffraction patterns were ascribed to a monoclinic phase. The SEM analysis showed that films grown with 5?L HCl had a nanoflower structure compared to the films grown with 0?L HCl and 20?L HCl. The nanoflower-structured films showed a higher cathodic peak current (?2.22mA), diffusion coefficient (5.43 10?9 cm2/s), and coloration efficiency (23.6 cm2/C). The increased electrochromic characteristics were attributed to the nanostructured films, which enhanced the diffusion of H+ ions by providing a large surface area during the charge transfer process. The Minerals, Metals & Materials Society 2024. -
Optimal design of controller for automatic voltage regulator performance enhancement: a survey
For regulating the Synchronous Generator (SG) output voltage, the Automatic Voltage Regulator (AVR) system is a significant device. This work propounds a survey on Optimization Algorithms (OAs) utilized for tuning the controller parameters on the AVR system. A device wielded for adjusting the SGs Terminal Voltage (TV) is named AVR. A Controller is utilized for improving stability and getting a superior response by mitigating maximum Over Shoot (OS), reducing Rise Time (RT), reducing Settling Time (ST), and enhancing Steady State Error (SSE) since output voltage has a slower response and instability. The controllers utilized here are Proportional-Integral-Derivative (PID), Intelligent Controller (IC), along with Fraction Order PID (FOPID). Owing to the occurrence of time delays, nonlinear loads, variable operating points, and others, OAs are wielded for tuning the controller. (a) Particle Swarm Optimization (PSO), (b) Genetic Algorithm (GA), (c) Gray Wolf Optimizer (GWO), (d) Harmony Search Algorithm (HSA), (e) Artificial Bee Colony (ABC), (f) Teaching Learned Based Optimization (TLBO), et cetera are the various sorts of OA. For enhancing the TV response along with stability, various OAs were tried by researchers. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification
The majority of people affected by Parkinsons disease (PD) are middle-aged and older. The condition causes a variety of severe symptoms, including tremors, limited flexibility, and slow movements. As Parkinsons disease develops with changing symptoms and growing severity, the importance of computer-aided diagnosis based on algorithms cannot be highlighted. Gait recognition technology appears to be a potential path for Parkinson's disease identification since it captures unique properties of a persons walking pattern without requiring active participation, providing stability and non-intrusiveness. To begin,the median filter is used to remove noise from the input images received during data collection. This paper describes a new method for finding local and global features in gait images to assess the severity of Parkinsons disease.Local features are extracted using a stacked autoencoder, and global features are obtained using an Improved Convolutional Neural Network (ICNN). The Enhanced Sunflower Optimisation (ESO) technique is used to improve the CNN model's performance by optimizing hyperparameters such as batch size, learning rate, and number of convolutional layers. To classify PD severity, a modified bidirectional LSTM (MBi-LSTM) classifier receives input in the form of a combination of local and global features. The proposed model's performance is completely evaluated with the GAIT-IT and GAIT-IST datasets, which include key measures such as accuracy, precision, recall, and the F-measure. This study improves the diagnosis of Parkinsons disease by introducing a non-intrusive real-time monitoring system capable of early detection and prevention. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Predicting nitrous oxide contaminants in Cauvery basin using region-based convolutional neural network
Nitrous oxide (N2O) in riverbeds affects hydrological processes by contributing to the greenhouse effect, indicating poor water quality, disrupting biogeochemical cycling, and linking to eutrophication. Elevated N2O levels signal environmental issues, impacting aquatic life and necessitating precise forecasting for effective environmental management and reduced greenhouse gas emissions. Precisely forecasting nitrous oxide (N2O) emissions from riverbeds is paramount for effective environmental management, given its significant potency as a greenhouse gas. This study focuses on the difficulties related to spatial feature extraction and modeling accuracy in predicting N2O in riverbeds in Tamil Nadu. To address the obstacles, the research suggests utilizing the Deep Learning Based Prediction of Nitrous Oxide Contaminants (DL-PNOC), which studies the N2O contaminants in water using Region-based Convolutional Neural Network (RCNN) for spatial feature extraction, to predict nitrous oxide contaminants. The study is centered on the Cauvery River Basin located in Tamil Nadu, where the emission of N2O is a matter of environment. The outcomes encompass the specialized N2O contaminant model for riverbeds and the implementation of RCNN achieves precise N2O forecasting. The DL-PNOC approach combines a contaminant model with RCNN deep learning techniques to capture spatial characteristics and predict N2O pollutants accurately. Furthermore, using the River Bed Dynamics Simulator reinforces the dependability of the findings. The DL-PNOC approach has exhibited encouraging results, as evidenced by the following metrics: a high IoU of 88.66%, precision of 88.96%, recall of 90.03%, F1 score of 89.22%, and low RMSE and MAE values of 9.14% and 7.59%, respectively. The findings highlight the efficacy of the DL-PNOC approach in precisely forecasting N2O pollutants in river sediments. 2024 Elsevier B.V. -
Relationship between Digital Learning, Digital Literacy and Academic Performance of Higher Education Students: Moderated Mediation Role of Critical Thinking
In today's rapidly evolving educational landscape, digital technologies have become increasingly prevalent, transforming how students access and engage with information. This study explores the relationships among digital learning, digital literacy, and academic performance in higher education, focusing on the moderating and mediating role of critical thinking. The adoption of digital learning platforms, such as online courses and virtual classrooms, has expanded educational access and flexibility. However, concerns regarding their effectiveness persist. Digital literacy, encompassing the ability to navigate digital tools and critically evaluate information, is crucial in this context. This research investigates how students' digital literacy levels influence their academic achievement and examines the mediating role of critical thinking in this relationship. Critical thinking is hypothesized to mediate the effects of digital literacy on academic performance and the impact of digital learning on critical thinking skills. Additionally, the study assesses whether critical thinking moderates the prime relationship between digital learning and academic performance. This descriptive, cross-sectional study employs structured questionnaires to gather primary data from 384 students enrolled in undergraduate, postgraduate, professional, and research programs at private universities in Bangalore, India. The findings indicate that the academic program significantly influences students' perceptions of digital literacy, digital learning, critical thinking, and academic performance, while demographic factors do not. Digital learning alone has a slight negative effect on academic performance, but digital literacy acts as a positive mediator, mitigating this impact. However, critical thinking does not significantly moderate the relationship between digital learning and academic performance. 2024, Iquz Galaxy Publisher. All rights reserved.