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REMAP: Determination of the inner edge of the dust torus in AGN by measuring time delays
Active galactic nuclei (AGN) are high luminosity sources powered by accretion of matter onto super-massive black holes (SMBHs) located at the centres of galaxies. According to the Unification model of AGN, the SMBH is surrounded by a broad emission line region (BLR) and a dusty torus. It is difficult to study the extent of the dusty torus as the central region of AGN is not resolvable using any conventional imaging techniques available today. Though, current IR interferometric techniques could in principle resolve the torus in nearby AGN, it is very expensive and limited to few bright and nearby AGN. A more feasible alternative to the interferometric technique to find the extent of the dusty torus in AGN is the technique of reverberation mapping (RM). REMAP (REverberation Mapping of AGN Program) is a long term photometric monitoring program being carried out using the 2 m Himalayan Chandra Telescope (HCT) operated by the Indian Institute of Astrophysics, Bangalore, aimed at measuring the torus size in many AGN using the technique of RM. It involves accumulation of suitably long and well sampled light curves in the optical and near-infrared bands to measure the time delays between the light curves in different wavebands. These delays are used to determine the radius of the inner edge of the dust torus. REMAP was initiated in the year 2016 and since then about one hour of observing time once every five days (weather permitting) has been allocated at the HCT. Our initial sample carefully selected for this program consists of a total of 8 sources observable using the HCT. REMAP has resulted in the determination of the extent of the inner edge of the dusty torus in one AGN namely H0507+164. Data accumulation for the second source is completed and observations on the third source are going on. We will outline the motivation of this observational program, the observational strategy that is followed, the analysis procedures adopted for this work and the results obtained from this program till now. 2019 Societe Royale des Sciences de Liege. All rights reserved. -
Determination of the size of the dust torus in H0507+164 through optical and infrared monitoring
The time delay between flux variations in different wavelength bands can be used to probe the inner regions of active galactic nuclei (AGNs). Here, we present the first measurements of the time delay between optical and near-infrared (NIR) flux variations in H0507+164, a nearby Seyfert 1.5 galaxy at z = 0.018. The observations in the optical V-band and NIR J, H, and Ks bands carried over 35 epochs during the period 2016 October to 2017 April were used to estimate the inner radius of the dusty torus. From a careful reduction and analysis of the data using cross-correlation techniques, we found delayed responses of the J, H, and Ks light curves to the V-band light curve. In the rest frame of the source, the lags between optical and NIR bands are found to be 27.1-12.0 +13.5 d (V versus J), 30.4-12.0 +13.9 d (V versus H) and 34.6-9.6 +12.1 d (V versus Ks). The lags between the optical and different NIR bands are thus consistent with each other. The measured lags indicate that the inner edge of dust torus is located at a distance of 0.029 pc from the central ultraviolet/optical AGN continuum. This is larger than the radius of the broad line region of this object determined from spectroscopic monitoring observations thereby supporting the unification model of AGN. The location of H0507+164 in the ?-MV plane indicates that our results are in excellent agreement with the now known lag-luminosity scaling relationship for dust in AGN. 2018 The Author(s). -
Quasar catalogue for the astrometric calibration of the forthcoming ILMT survey
Quasars are ideal targets to use for astrometric calibration of large scale astronomical surveys as they have negligible proper motion and parallax.The forthcoming 4-m International Liquid Mirror Telescope (ILMT) will survey the sky that covers a width of about 27?. To carry out astrometric calibration of the ILMT observations, we aimed to compile a list of quasars with accurate equatorial coordinates and falling in the ILMT stripe. Towards this, we cross-correlated all the quasars that are known till the present date with the sources in the Gaia-DR2 catalogue, as the Gaia-DR2 sources have position uncertainties as small as a few milli arcsec (mas). We present here the results of this cross-correlation which is a catalogue of 6738 quasars that is suitable for astrometric calibration of the ILMT fields. In this work, we present this quasar catalogue. This catalogue of quasars can also be used to study quasar variability over diverse time scales when the ILMT starts its observations. While preparing this catalogue, we also confirmed that quasars in the ILMT stripe have proper motion and parallax lesser than 20 masyr- 1 and 10 mas, respectively. 2020, Indian Academy of Sciences. -
Review on Emerging Internet of Things Technologies to Fight the COVID-19
The Internet of Things (IoT) has been gaining attention in various disciplines ranging from agriculture, health, industries and home automation. When a pandemic first breaks out early detection, isolating the infected, and tracing the contacts are the most important challenges. IoT protocols like Radio-frequency identification (RFID), Wireless Fidelity (WiFi), Global Positioning System (GPS) are gaining popularity for providing solutions to these challenges. IoT based applications in the health sector are benefitting COVID-19 (coronavirus disease of 2019) patients during this pandemic situation. This article explores and reviews the various Internet of Things enabled technologies and applications used in screening, contact tracing, and surveillance. IoT based telemedicine processes are very useful during the pandemic COVID-19. The purpose of this paper is to deliver an overall understanding of the existing and proposed technologies of IoT based solutions to make the situations better during COVID-19. 2020 IEEE. -
An analogical study of the narrative techniques used in the film Paradesi (2013) an adaptation of Tamil translation (Yerium Panikkadu) of the novel 'Red Tea' /
International Journal Of Humanities and Social Science Invention, Vol.5, Issue 3, pp.1-6, ISSN: 2319-7722 (Online) 2319-7714 (Print). -
Understanding the emerging integrated marketing communication strategies used in marketing Tamil films /
IOSR Journal Of Humanities And Social Science, Vol.21, Issue 2, Ver. V, pp.33-37, ISSN: e-ISSN: 2279-0837, p-ISSN: 2279-0845. -
The Effect of Prediction on Employee Engagement Organizational Commitment and Employee Performance Using Denoised Auto Encoder and SVM Based Model
The purpose of human resources is to ensure that the appropriate people are hired for open positions at appropriate times, that the system receive the necessary training, and that their performance is monitored and their perspective skills are secure through the use of evaluation methods. Despite the importance of this data to decision-makers, it can be difficult to glean useful insights from large datasets. Data mining has made it possible for human resources experts to automate the hitherto tedious task of manually processing enormous data sets. Finding almost perfect outcomes is the main goal of data mining, which is to discover hidden knowledge in data patterns and trends. The proposed method goes as follows: preprocessing is done by data cleaning and data normalization, feature selection using correlation and information theoretic ranking criteria. The last step in training and evaluating the model is using AE-SVM, which stands for Auto Encoder Support Vector Machine. The suggested model is more effective and performs better than two existing models: Support Vector Machine and AE-CNN. The suggested approach attains an accuracy rate of 94%. 2024 IEEE. -
Non-destructive classification of diversely stained capsicum annuum seed specimens of different cultivars using near-infrared imaging based optical intensity detection
The non-destructive classification of plant materials using optical inspection techniques has been gaining much recent attention in the field of agriculture research. Among them, a near-infrared (NIR) imaging method called optical coherence tomography (OCT) has become a well-known agricultural inspection tool since the last decade. Here we investigated the non-destructive identification capability of OCT to classify diversely stained (with various staining agents) Capsicum annuum seed specimens of different cultivars. A swept source (SS-OCT) system with a spectral band of 1310 nm was used to image unstained control C. annuum seeds along with diversely stained Capsicum seeds, belonging to different cultivar varieties, such as C. annuum cv. PR Ppareum, C. annuum cv. PR Yeol, and C. annuum cv. Asia Jeombo. The obtained cross-sectional images were further analyzed for the changes in the intensity of back-scattered light (resulting due to dye pigment material and internal morphological variations) using a depth scan profiling technique to identify the difference among each seed category. The graphically acquired depth scan profiling results revealed that the control specimens exhibit less back-scattered light intensity in depth scan profiles when compared to the stained seed specimens. Furthermore, a significant back-scattered light intensity difference among each different cultivar group can be identified as well. Thus, the potential capability of OCT based depth scan profiling technique for non-destructive classification of diversely stained C. annum seed specimens of different cultivars can be sufficiently confirmed through the proposed scheme. Hence, when compared to conventional seed sorting techniques, OCT can offer multipurpose advantages by performing sorting of seeds in respective to the dye staining and provides internal structural images non-destructively. 2018 by the authors. Licensee MDPI, Basel, Switzerland. -
Digital Forensics Investigation for Attacks on Artificial Intelligence
The new research approaches are needed to be adopted to deal with security threats in AI based systems. This research is aimed at investigating the Artificial Intelligence (AI) attacks that are malicious by design. It also deals with conceptualization of the problem and strategies for attacks on Artificial Intelligence (AI) using Digital Forensic tools. A specific class of problems in Adversarial attacks are tampering of Images for computational processing in applications of Digital Photography, Computer Vision, Pattern Recognition (Facial Mapping algorithms). State-of-the-art developments in forensics such as 1. Application of end-to-end Neural Network Training pipeline for image rendering and provenance analysis, 2. Deep-fake image analysis using frequency methods, wavelet analysis & tools like - Amped Authenticate, 3. Capsule networks for detecting forged images 4. Information transformation for Feature extraction via Image Forensic tools such as EXIF-SC, Splice Radar, Noiseprint 5. Application of generative adversarial Networks (GAN) based models as anti-Image Forensics [8], will be studied in great detail and a new research approach will be designed incorporating these advancements for utility of Digital Forensics. The Electrochemical Society -
Predictive Analytics for Stock Market Trends using Machine Learning
Navigating the intricacies of stock market trends demands a novel approach capable of deciphering the web of financial data and market sentiment. This research embarks on a transformative journey into the realm of machine learning, where we harness the power of data to forecast stock market trends with increased precision and accuracy. Commencing with an exploration of stock market dynamics and the inherent limitations of traditional forecasting techniques, this paper takes a bold step into the future by embracing the potential of machine learning. The study begins with an in-depth analysis of data preprocessing, unraveling the complexity of feature selection and engineering, setting the stage for a data-driven odyssey. As our exploration progresses, we dive into the deployment of diverse machine learning algorithms, including linear regression, decision trees, random forests, and the formidable deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These algorithms act as our guiding lights, revealing intricate patterns concealed within historical stock price data. Our journey reaches new heights as we recognize the significance of augmenting predictive models with external data sources. Incorporating elements like news sentiment analysis and macroeconomic indicators enriches our understanding of the market landscape, enhancing the predictive capabilities of our models. We also delve into the crucial aspects of model evaluation, guarding against overfitting, and selecting appropriate performance metrics to ensure robust and reliable predictions. The research reaches its zenith with a meticulous analysis of real-world case studies, providing a comparative perspective between machine learning models and traditional forecasting methods. The results underscore the remarkable potential of machine learning in predicting stock market trends more accurately. 2023 IEEE. -
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A Study on Enhancing E-Governance Applications Through Semantic Web Technologies
International Journal of Web Technology, Vol-1 (2), pp. 53-59. ISSN-2278-2389 -
THE MEDIATING EFFECT OF HEEDFUL INTERRELATING ON SELF DETERMINATION AND THRIVING AT WORK AMONG UNIVERSITY FACULTY MEMBERS; [EL EFECTO MEDIADOR DE LA INTERRELACI ATENTA EN LA AUTODETERMINACI Y EL PROSPERAR EN EL TRABAJO ENTRE LOS MIEMBROS DEL PROFESORADO UNIVERSITARIO]; [O EFEITO MEDIADOR DA INTER-RELAO CUIDADA NA AUTODETERMINAO E NO PROSPERO NO TRABALHO ENTRE MEMBROS DO FACULDADE UNIVERSITIA]
Objective: The objective of this study is to empirically examine the mediating effect of heedful interrelating on the direct effect of self-determination and thriving at work among university faculty members. Theoretical Framework: The organismic human integration philosophy forms the theoretical underpinning for the study. The conceptual model is built by integrating self-determination theory (SDT) with the theory of heedful interrelating. Method: Following an explanatory research design, data from 396 university faculty members PAN India was used to test the conceptual model with the PLS-SEM bootstrapping technique. Results and Discussion: The findings validate a significant direct influence of self-determination on thriving at work. Furthermore, there exists a significant mediation effect of heedful interrelating between self-determination and thriving at work. Through causal mediation, it is interpreted that self-determined and autonomously motivated behaviors, stemming from the satisfaction of universal basic psychological needs of autonomy, competence, and relatedness, play a pivotal role in fostering heed-based behavior within an individual. Research Implications: This empirical study validated the organismic integration theory of human nature in the academic sector through the positive direct effect. Implications for the sample of university faculty members suggest the use of heedful interrelating during group tasks through the dimensions of contributing, representing, and sub-ordinating. Originality/Value: This study makes significant original theoretical contributions to the SDT literature and to the SDT puzzle, firstly, by adding heed as a novel indicator to self-determination theorys relatedness dimension and secondly, by validating the role of heedful interrelating in bridging the dialectic gap within the self-determination theory. 2024 ANPAD - Associacao Nacional de Pos-Graduacao e Pesquisa em Administracao. All rights reserved. -
Personal fableness and perception of risk behaviors among adolescents
Adolescence is a crucial period where one tends to identify who they are as an individual. However, as a teenager is struggling to find his/her place in this world, it is also a time where they are prone to engaging in risk behaviors, which tend to have an extreme psychological impact. The objective was to explore the experiences of an adolescent who engages in risk behaviors and to understand their level of personal fables. The study was a qualitative design with content analysis with semi-structured interviews of ten male adolescents aged 16-18 years. The major findings of the study indicated that adolescents pattern of thinking revolves around the fact that they are invincible and invulnerable. Furthermore, adolescents are aware of the risks they are putting themselves through and how in the process they are hurting others. The implications of the study are to conduct more life skill programs in schools; greater awareness has to be created on the impact and harmful effects of such behaviors. 2018, Indian Journal of Public Health Research and Development. All rights reserved. -
Influence of employees' perception on the use of flexible work arrangements
The study aims to explore the factors that influence the perception of employees on the usability of flexible work arrangements and to predict whether those factors induce them to opt for such flexible practices. The data was collected from 239 Indian employees working across different sectors of the country. The study employed a quantitative approach for data collection by using a structured questionnaire consisting of close-ended questions. The data was analyzed using factor analysis, binomial logistic regression and Analysis of Variance on SPSS Statistics 25. The study identified five major factors that influenced the employees perception about using flexible work options. Among them two factors namely, FWA perquisites and FWA anxiety were found significant in predicting the employees use of flexible work options. Further, it was found that married employees recognized strong benefits from using flexible options. This study contributes to the existing literature by unveiling the mindset of Indian employees towards flexible work arrangement and suggests that the employers, society and the government should create favorable environment for deploying flexible work practices. 2020 IJSTR. -
Bacterial biofilm inhibition activity of ethanolic extract of hemidesmus indicus
Multi-drug resistance is one of the biggest nightmares in the field of healthcare today. Adding on to this, some bacteria like Staphylococcus aureus and Pseudomonas aeruginosa have the ability to form biofilms. These essentially are large colonies of bacteria that are held together by polysaccharides and other biomolecules which in turn facilitate in their adherence to solid substrate both natural and synthetic. This further creates a life-threatening implication leading to nosocomial infections like pneumonia, Urinary tract infections (UTI), etc. increasing the co-morbidities and mortality of critically-ill patients. The combination of antimicrobial resistance, ability to form biofilms and threat of nosocomial infections calls for a need to investigate newer, safer alternatives. Plant based medicaments have been used for centuries and they are a great alternative to synthetic drugs. In the present study, ethanolic extracts of Hemidesmus indicus was evaluated against clinically-important multi-drug resistant organisms. Percentage biofilm inhibition of plant extracts of Hemidesmus indicus by crystal violet assay method. Triplicate analysis was done and data obtained was statistically interpreted using Microsoft Excel. Alcoholic extracts of Hemidesmus indicus exhibited significant biofilm inhibitory activity against the common bacteria Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Bacillus subtilis. Further, isolation of the chief active constituent responsible for Anti-biofilm activity is in process. 2020, National Institute of Science Communication and Information Resources (NISCAIR). All rights reserved. -
Role of Artificial Intelligence in Neuroimaging for Cognitive Research
Artificial intelligence (AI)-based solutions are used in most of our daily activities. AI has been adapted and it has found various applications. Cognitive research is one area where AI has been applied to understand the hidden patterns in the data. Neuroimaging techniques investigate the neural basis of cognitive processes like perception, attention, memory, language, reasoning, decision-making, and problem-solving. The irregularities in the cognitive process lead to cognitive disabilities and diseases. Neuroimaging techniques, including magnetic resonance imaging (MRI), functional MRI (fMRI), electroencephalography (EEG), and positron emission tomography (PET), along with other data-gathering techniques, are studied to identify cognitive disorders. The imaging techniques generate large amounts of complex data. AI methods, including machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision, are applied and used to analyse and interpret the data generated by various imagining techniques. Numerous techniques have been designed, developed, and proposed to handle the neuroimaging data for cognitive research with the help of AI techniques. AI techniques include ML algorithms like decision trees, random forest, support vector machine (SVM), principal component analysis (PCA), and DL algorithms, including convolution neural networks (CNNs), long short-term memory (LSTM), and generative adversarial networks (GANs). Recent advancements in the field of neuroimages use AI techniques to preprocess, process, and analyse the data generated by various neuroimaging modalities. This chapter provides an in-depth analysis and summary of various AI techniques for processing neuroimages for cognitive disorders. 2024 selection and editorial matter, Anitha S. Pillai and Bindu Menon; individual chapters, the contributors. -
AI-Based Feature Extraction Approaches for Dual Modalities of Autism Spectrum Disorder Neuroimages
High-dimensional data, lower detection accuracy, susceptibility to manual errors, and the requirement of clinical experts are some drawbacks of conventional classification models available for Autism Spectrum Disorder (ASD) detection. To address these challenges and explore the affiliated information from advanced imaging modalities such as Magnetic Resonance Imaging (MRI) in structural MRI (sMRI) and resting state-functional MRI (rs-fMRI), the study applied an Artificial Intelligence (AI) approach. In this context, AI is used to automate the feature extraction process, which is crucial in the interpretation of medical images for diagnosis. The work aims to apply AI-based techniques to extract the features and identify the impact of each feature in the Autism diagnosis. The morphometric features were extracted using sMRI images and rs-fMRI scans were employed to fetch functional connectivity features. Surface-based, region-based, and seed-based analyses are performed for the whole brain, followed by feature selection techniques such as Recursive Feature Elimination (RFE) with correlation, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and graph theory are implemented to extract and distinguish features. The effectiveness of the extracted features was measured as classification accuracy. Support Vector Machine (SVM) with RFE is the best classification model, with 88.67% accuracy for high-dimensional data. SVM is a supervised learning model that outperforms other classification models due to its capability to handle high-dimensional data with a larger feature set. Medical imaging modalities provide detailed insights and visual differences related to various cognitive conditions that must be recognized accurately for efficient diagnosis. The study presented an empirical analysis of various Feature extraction approaches and the significance of the extracted features in high-dimensional data scenarios for Autism classification. 2024 Meenakshi Malviya Chandra J and Nagendra N. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
A comprehensive investigation on machine learning techniques for diagnosis of down syndrome
Down Syndrome is a chromosomal disease which causes many physical and cognitive disabilities. Down Syndrome patients are more vulnerable than any other patient. Medical experts started knowing it now with keen awareness. In recent years it has become a field of interest for many researchers, medical experts and social organisation. For the researchers it is an area of interest where very little work is done and a lot to be explored. Machine Learning consists of different processing levels like pre-processing, segmentation, feature selection and classification. Each level contains a vast set of techniques like filters, segmentation algorithms and classifiers. Machine Learning is one of the most popular algorithm, which is used to automate the decision making process with higher rate of accuracy in less time with least error rate. Machine Learning proved its significance with highest rate of accuracy in decision making and problem solving in almost all the fields but automated decision making in medical science is still a challenge. This paper reviews the different works done in the field of Down Syndrome using Machine Learning applied on different medical images, and the techniques like pre-processing, segmentation, feature selection and classification. The aim of this research work is to analyse and identify the Machine Learning methodologies that works efficiently to detect Down Sundrome. 2017 IEEE. -
A Systematic Review on Prognosis of Autism Using Machine Learning Techniques
Quality of life (QoL) and QoL predictors have become crucial in the pandemic. Neurological anomalies are at the highest level of QoL threats. Autism is a multisystem disorder that causes behavioural, neurological, cognitive, and physical differences. Recent studies state that neurological disorders can result in dysfunction of the brain or whole nervous system which may cause other symptoms of Autism. The paper focuses on reviewing various Machine Learning techniques used for diagnosing Autism at an early age with the help of multiple datasets. The study of brain Magnetic Resonance Imaging (MRI) provides astute knowledge of brain structure that helps to study any minor to significant changes inside the brain that have emerged due to the disorder. Early diagnosis leads to a healthy life by getting timely treatment and training. "Early diagnosis of autism spectrum disorder" is an objective and one of the prime goals of health establishments worldwide. The research paper aims to systematically review and find which machine learning algorithms are efficient for the prognosis of autism. The Electrochemical Society