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Living with Coronavirus outbreak in India
The present paper focuses on living with coronavirus outbreak in India. This piece emphasizes on various policies adopted by the government of India to face the coronavirus crisis. It brings into perspective what financial strides the economy is going through, the mental health of the citizens, and the current situation of health care in the country. The current commentary reflects the learnings from COVID-19, the role of defined governmental policies, and support in surviving such an unforeseen situation. 2020 American Psychological Association. -
Load Balancing Strategy for Large Scale Software Defined Networks
Programmability has left its mark on every facet of business, with technology playing newlinean integral role. Social networking industry trends underscore technology s ubiquity in newlinenearly every business transaction. Traditional networks grapple with numerous challenges, rendering them ill-equipped to process and handle the demands of the modern newlinelandscape effectively. The lack of programming in these networks leads to stagnation, newlineinhibiting their ability to evolve or enhance performance. The advent of Software Defined Networks (SDN) has introduced increased flexibility into conventional networks, newlineopening avenues for creating innovative services. newlineSDN technology addresses challenges in large-scale networks, offering solutions for newlinehigh throughput, virtualization, fault detection, and load balancing, providing effective network management. The rapid expansion of network services and applications newlinein SDN environments demands sophisticated load-balancing solutions that adapt to newlinedynamic traffic patterns and varying service requirements. This study presents a pioneering algorithm, the Dynamic Load Balancing Algorithm (DLBA), which utilizes the newlineProgramming Protocol-independent Packet Processors (P4) language. The algorithm is newlinespecifically crafted to tackle the issues associated with optimizing traffic distribution in newlinethe data plane of SDN. newlineP4 programming language, recognized as one of the most robust languages, addresses newlinethe limitations of traditional networking, enhancing programmability and agility by newlinedistributing the load across the network. The research implements a novel quotDynamic newlineLoad Balancing Algorithmquot using the P4 language to instill dynamism and achieve load newlinebalance in large-scale networks. The P4-based implementation showcases dynamicity, scalability, flexibility, and adaptability. This research commences with thoroughly newlineexamining existing load-balancing algorithms implemented using the P4 language, followed by a comparative analysis between these algorithms and DLBA. -
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Load balancing with availability checker and load reporters (LB-ACLRs) for improved performance in distributed systems
Distributed system has quite a lot of servers to attain increased availability of service and for fault tolerance. Balancing the load among these servers is an important task to achieve better performance. There are various hardware and software based load balancing solutions available. However there is always an overhead on Servers and the Load Balancer while communicating with each other and sharing their availability and the current load status information. Load balancer is always busy in listening to clients' request and redirecting them. It also needs to collect the servers' availability status frequently, to keep itself up-to-date. Servers are busy in not only providing service to clients but also sharing their current load information with load balancing algorithms. In this paper we have proposed and discussed the concept and system model for software based load balancer along with Availability-Checker and Load Reporters (LB-ACLRs) which reduces the overhead on server and the load balancer. We have also described the architectural components with their roles and responsibilities. We have presented a detailed analysis to show how our proposed Availability Checker significantly increases the performance of the system. 2014 IEEE. -
Load shedding using GA and ACO in smart gird environment
Increasing pressure on the utilities to accommodate energy efficiency, load management and progress in advanced technology has led to transformations for existing grid into a smarter grid. Creating awareness among the end-users to participate in load management programs instead of capacity addition is the best solution for maintaining the stability in the grid. Load shedding is a strategy under load management in which load connected to the smart grid is individually controlled via two- way communication. In this paper, a Smart Load shedding approach is developed based on load prioritization. The required amount of load to be shed under lack of sufficient generation level is optimized by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithms. The proposed approach is implemented using a real time feeder data from the substation, India. The results reflect the effectiveness of proposed algorithms taken into practical applications. -
Loan Default Prediction Using Machine Learning Techniques and Deep Learning ANN Model
Loan default prediction is a critical task in the financial sector, aimed at assessing the creditworthiness of borrowers and minimizing potential losses for lending institutions. Online loans continue to reach the public spotlight as Internet technology develops, and this trend is expected to continue in the foreseeable future. In this paper, the authors proposed loan default loan prediction system based on ML and DL models. This work makes use of the information on loan defaults provided by Lending Club. The dataset is preprocessed by applying various data preprocessing techniques and preprocessed dataset is generated. Later, we proposed four ML algorithms decision tree, random forest, logistic regression, K-NN and Feed forward neural network. The experimental results shown that proposed feed forward neural network achieved good accuracy for loan default prediction with an accuracy of 99%. 2023 IEEE. -
Local community involvement in wildlife resorts: Issues and Challenges
The Global Code of Ethics for Tourism Article 5 states that tourism should be a beneficial activity for host countries and communities (UNWTO). The code also emphasises on equitable distribution (between host countries and communities) of the economic and sociocultural benefits generated by tourism activities. The tourism resorts and accommodation sector have to involve local communities in socio-economic activities and priority should be given to local manpower. A wildlife resort has vast opportunities to involve local communities in their day to day operation by purchasing local products, promoting local festivals, providing employment opportunities to locals, and involving local communities in decision-making. Wildlife resorts can also promote local culture, create environment awareness among local people, provide educational support to the local children, and support development of infrastructure and medical facilities for the locals. Though local communities can be involved in various activities of wildlife resorts, it is essential to address the issues and challenges that hinder wildlife resorts from doing so. This paper attempts to determine the issues and challenges faced by wildlife resorts in involving local communities in their day to day operations and suggests ways and means to overcome those challenges. The scope of the study covered selected wildlife resorts in Karnataka. The targeted respondents of the research survey were resort managers and data were collected using open-ended questions to understand real-time issues and challenges involving local communities in resort activities. The data were then analysed using thematic text analysis. The findings from the study will help explore means of providing a better framework which will help wildlife resorts overcome issues and challenges involving local communities. The Author(s) 2017. -
Local post-hoc interpretable machine learning model for prediction of dementia in young adults
Dementia is still the prevailing brain disease with late diagnosis. There is a large increase in dementia disease among young adults. The major reason is over indulgence of young adults on social media resulting in denial of disease and delayed clinical diagnosis. Dementia is preventable and curable if diagnosed at an early stage, however, no attempts are being made to mitigate dementia in young adults. Today artificial intelligence (AI) based advanced technology with real-life consultations in clinical or remote setups are proved beneficial and is used to detect dementia. Most AI-based test is dependent on computer-aided diagnosis (CAD) tools and uses non-invasive imaging technology such as magnetic resonance imaging (MRI) data for disease diagnosis. In this paper, a local post-hoc interpretable machine learning (LPIML) model for prediction of dementia in young adults is proposed. The performance parameters are computed and compared based on accuracy, specificity, precision, F1 score and recall. The proposed work yields 98.87% training accuracy on original images and 99.31% training accuracy on morphologically enhanced images. The performance results are intrinsic and intuitive in learning the prediction results of individual case. The adoption of the proposed work will accelerate the diagnosis process in the era of digital healthcare. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Localization Method for Camera Networks in Surveillance System
The significance of prevention and mitigation of critical issues especially in the homeland security has been increasing day by day. Emergence of autonomous video analytics tools greatly helped in the prevention of security threats. The recognition of video analytics for anomaly detection based on a set of unsupervised approaches has many fundamental technical challenges. This entails autonomous object localization and tracking technique especially in the presence of occlusion. This paper focuses on deriving a solution for the object detection and tracking in a heterogeneous camera network. The object tracking method is mainly based on Kalman filter whereas frame difference algorithm is used for object localization. This detection and tracking solution is expected to significantly reduce the effect of occlusion while tracking the anomaly. The organisation of the thesis is done into various chapters. The first chapter contains an introduction to the video surveillance system and the need for an unsupervised approach. This chapter also states the objective of the research. The solution overview gives high level solution architecture of the proposed system. The second chapter focus on the literature overview in which the citation from different papers in the field of video analytics, Kalman filter implementation and camera configuration has been referred. Chapter 3 provides the methodology in which a brief introduction to the basic algorithms used in the solution, the Kalman filter and the frame difference algorithm, are discussed. This is followed by the solution architecture of the proposed system. Chapter 4 shows the Matlab implementation of the mentioned algorithms. In Chapter 5, the results of the implementation are discussed. Chapter 6 talks about the summary of the work done and conclusion. This chapter also includes the future enhancements suggested. -
Locating Indian universities in knowledge societies: A critique
Knowledge societies characterize a defining feature of the present era. Veering away from their initial connotation of scientific temper and reasoning, today, they assume a new meaning in which the basis of economy, polity, and social action is knowledge. In the post-capitalist, post-industrial societies, knowledge has become the foundation of industrial productivity and social wellbeing. The crux of knowledge production has been shifting from the traditional disciplinary contexts promoted by academic interests in the universities to its applications for better productivity and wellbeing. Nevertheless, productivity and usefulness are accorded an epistemological appeal in defining what counts as knowledge. In this context, the present paper discusses the changes in knowledge production and dissemination processes in knowledge societies and their implications for universities in India. 2019 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore). -
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Log-Base2 of Gaussian Kernel for Nuclei Segmentation from Colorectal Cancer H and E-Stained Histopathology Images
Nuclei Segmentation is a very essential and intermediate step for automatic cancer detection from H and E stained histopathology images. In the recent advent, the rise of Convolutional Neural Network (CNN), has enabled researchers to detect nuclei automatically from histopathology images with higher accuracy. However, the performance of automatic nuclei segmentation by CNN is fraught with overfitting, due to very less number of annotated segmented images available. Indeed, we find that the problem of nuclei segmentation is an unsupervised problem, because still now there is no automatic tool available which can make annotated images (nuclei segmented images) accurately, to the best of our knowledge. In this research article, we present a Logarithmic-Base2 of Gaussian (Log-Base2-G) Kernel which has the ability to track only the nuclei portions automatically from Colorectal Cancer H and E stained histopathology images. First, Log-Base2-G Kernel is applied to the input images. Thereafter, we apply an adaptive Canny Edge detector, in order to segment only the nuclei edges from H and E stained histopathology images. Experimental results revealed that our proposed method achieved higher accuracy and F1 score, without the help of any annotated data which is a significant improvement. We have used two different datasets (Con-SeP dataset, and Glass-contest dataset, both contains Colorectal Cancer histopathology images) to check the effectiveness and validity of our proposed method. These results have shown that our proposed method outperformed other image processing or unsupervised methods both qualitatively and quantitatively. 2023 SPIE. -
Logistic growth and SIR modelling of coronavirus disease (COVID-19) outbreak in India: Models based on real-time data
The logistic growth model and the Susceptible-Infectious-Recovered (SIR) framework are utilized for the mathematical modelling of the Coronavirus disease (COVID-19) outbreak in India. Karnataka, Kerala and Maharashtra, three states of India, are selected based on the pattern of the disease spread and the prominence in being affected in India. The parameters of the models are estimated by utilizing real-time data. The models predict the ending of the pandemic in these states and estimate the number of people that would be affected under the prevailing conditions. The models classify the pandemic into five stages based on the nature of the infection growth rate. According to the estimates of the models it can be concluded that Kerala is in a stable situation whereas the pandemic is still growing in Karnataka and Maharashtra. The infection rate of Karnataka and Kerala are lesser than 5% and reveal a downward trend. On the other hand, the infection rate and the high predicted number of infectives in Maharashtra calls for more preventive measures to be imposed in Maharashtra to control the disease spread. The results of this analysis provide valuable information regarding the disease spread in India. 2020, International Information and Engineering Technology Association. -
Long memory investigation during demonetization in India
Long-range dependence (LRD) in financial markets remains a key factor in determining whether there is market memory, herding traces, or a bubble in the economy. Usually referred to as 'Long Memory', LRD has remained a key parameter even today since the mid-1970s. In November 2016, a sudden and drastic demonetization measure took place in the Indian market, aimed at curbing money laundering and terrorist funding. This study is an attempt to identify market behavior using long-range dependence during those few days in demonetization. Besides, it tries to identify nascent traces of bubble and embedded herding during that time. Auto Regressive Fractionally Integrated Moving Average (ARFIMA) is used for three consecutive days around the event. Tick-by-tick data from CNX Nifty High Frequency Trading (CNX Nifty HFT) is used for three consecutive days around demonetization (approximately, 5000 data points from morning trading sessions on each of the three days). The results show a clear and profound presence of herd behavior in all three data sets. The herd intensity remained similar, indicating a unique mixture of both 'Noah Effect' and 'Joseph Effect', proving a clear regime switch. However, the results on the event day show stable and prominent herding. Mandelbrot's specified effects were tested on an uncertain and sudden financial event in India and proved to function perfectly. Bikramaditya Ghosh, Saleema J. S., Aniruddha Oak, Manu K. S., Sangeetha R., 2020. -
Long run relationship between macroeconomic indicators and Indian sectoral indices
Investors and fund managers continuously strive to find new ways to diversify their portfolio and minimise risk exposure. The study aims to find out whether the macroeconomic indicators exert the same influence on stock prices across the entire stock market or varies across different sectors. The impact of macroeconomic indicators would not be the same on all the sectors. This paper provides empirical evidence of macroeconomic indicators such as crude oil prices, interest rates, foreign currency rates, money supply and inflation rates having a varied impact on Nifty50 index and each of the select sectoral stock indices namely, Nifty Bank, Nifty IT and Nifty financial services. The sample period runs from Jan 2009 to Jan 2019. The study employs the Error Correction Mechanism to study whether the macroeconomic indicators have the same impact across sectoral stock indices in the long run. The findings show that variations in macroeconomic variables do not trigger the same response from all the sectoral stock indices. While most of the variables chosen have a significant influence on Nifty50 index and NiftyIT; Nifty financial services and Nifty Bank remain unaffected by changes in few major macroeconomic variables or show opposite reaction than the other sectors. The findings of the study have significant implications for long term investors and investment managers for building a diversified portfolio and thereby protecting themselves from financial losses during adverse market conditions. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
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. -
Long-term Optical and ?-Ray Variability of the Blazar PKS 1222+216
The ?-ray emission from flat-spectrum radio quasars (FSRQs) is thought to be dominated by the inverse Compton scattering of the external sources of photon fields, e.g., accretion disk, broad-line region (BLR), and torus. FSRQs show strong optical emission lines and hence can be a useful probe of the variability in BLR output, which is the reprocessed disk emission. We study the connection between the optical continuum, H? line, and ?-ray emissions from the FSRQ PKS 1222+216, using long-term (?2011-2018) optical spectroscopic data from Steward Observatory and ?-ray observations from Fermi Large Area Telescope (LAT). We measured the continuum (F C,opt) and H? (F H? ) fluxes by performing a systematic analysis of the 6029-6452 optical spectra. We observed stronger variability in F C,opt than F H? , an inverse correlation between the H? equivalent width and F C,opt, and a redder-when-brighter trend. Using discrete cross-correlation analysis, we found a positive correlation (DCF ? 0.5) between the F ??ray>100 MeV and F C,opt (6024-6092 light curves with a time lag consistent with zero at the 2? level. We found no correlation between the F ??ray>100 MeV and F H? light curves, probably dismissing the disk contribution to the optical and ?-ray variability. The observed strong variability in the Fermi-LAT flux and F ??ray>100 MeV ? F C,opt correlation could be due to the changes in the particle acceleration at various epochs. We derived the optical-to-?-ray spectral energy distributions during the ?-ray flaring and quiescent epochs that show a dominant disk component with no variability. Our study suggests that the ?-ray emission zone is likely located at the edge of the BLR or in the radiation field of the torus. 2022. The Author(s). Published by the American Astronomical Society. -
Long-term optical and infrared variability characteristics of Fermi blazars
We present long-term optical and near-infrared flux variability analysis of 37 blazars detected in the ?-ray band by the Fermi Gamma-Ray Space Telescope. Among them, 30 are flat spectrum radio quasars (FSRQs) and 7 are BL Lac objects (BL Lacs). The photometric data in the optical (BVR) and infrared (JK) bands were from the Small and Moderate Aperture Research Telescope System acquired between 2008-2018. From cross-correlation analysis of the light curves at different wavelengths, we did not find significant time delays between variations at different wavelengths, except for three sources, namely PKS 1144-379, PKS B1424-418, and 3C 273. For the blazars with both B- and J-band data, we found that in a majority of FSRQs and BL Lacs, the amplitude of variability (?m) in the J band is larger than that in B band, consistent with the dominance of the non-thermal jet over the thermal accretion disc component. Considering FSRQs and BL Lacs as a sample, there are indications of ?m to increase gradually towards longer wavelengths in both, however, found to be statistically significant only between B and J bands in FSRQs. In the B-J v/s J-colour magnitude diagram, we noticed complicated spectral variability patterns. Most of the objects showed a redder when brighter (RWB) behaviour. Few objects showed a bluer when brighter (BWB) trend, while in some objects both BWB and RWB behaviours were noticed. These results on flux and colour characteristics indicate that the jet emission of FSRQs and BL Lacs is indistinguishable. 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Longitudinal study on noncommunicable diseases using machine learning
This longitudinal case study thoroughly explores the intricate connection between body mass index (BMI) and four key factors: physical health, psychological well-being, lifestyle choices, and the impact of diet on health. Through the analysis of longitudinal data, notable trends emerge, revealing an increase in risk factors for noncommunicable diseases (NCDs) and unhealthy behaviors over time. This highlights the combined impact of these interconnected factors on health outcomes and the risk of developing NCDs like heart disease, diabetes, and cancer. Leveraging machine learning, the study effectively identifies individuals at elevated risk for NCDs and dispels common health misconceptions, underscoring the significance of holistic wellness approaches. Serving as a beacon for the next generation, this study provides insights that contribute to shaping a healthier future. 2025 selection and editorial matter, Arun Kumar Rana, Vishnu Sharma, Sanjeev Kumar Rana, and Vijay Shanker Chaudhary; individual chapters, the contributors. All rights reserved. -
Looking at psychological well-being through the lens of identity among adolescent girls: An exploration
Purpose: This research endeavours to delve into the intricate dimensions of adolescent girls' psychological well-being and identity, aiming to shed light on their interplay and identify key predictors of psychological well-being. The study, conducted with a sample of adolescent girls, seeks to enrich our understanding of the multifaceted nature of their developmental experiences. Psychological well-being is attained by achieving a state of balance affected by both challenging and rewarding life events and a stable sense of identity. Approach: The present research is an ex-post facto research falling in the area of quantitative research design. Data has been collected on 348 adolescents, purposely recruited from different schools of Delhi NCR. The age range of the respondents was 15 to 17 years. Findings: The results reveal that psychological well-being is being predicted by identity processes among adolescent females. The different dimensions of identity processes are found to be explaining almost 19% variance in the regression model. Commitment has been found to have a ? value of 0.197 (t= 3.511; p<.01), in-depth exploration has a ?= 0.161 (t= 2.867; p<.01), and reconsideration of commitment has a ?= 0.314 (t= 6.294; p<.01). Value: By addressing the objectives of this research, valuable insights may be received by educators, mental health professionals, and policymakers to better support and enhance the well-being of adolescent girls through having a stable sense of identity. 2024 RESTORATIVE JUSTICE FOR ALL.