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I Dont Play Games: Migrant Workers and Digital Media in Bengaluru
The great impact of media technologies in reordering almost every facet of modern life has been noted by theorists for over a century now, particularly since the idea of the global village imagined by media theorists, and enabled by globalisation and digital technology has become an inescapable reality. The new experience of time and space bears upon various dimensions of life, including the nature of work, the organisation of time and the place of leisure within these rhythms. This article attempts to engage with this very weighty body of scholarship in a modest way, through ethnographic research, to understand how mobile phones and internet technologies structure the experience of everyday life for low-income migrant workers in Bengaluru. The sites include a construction site and a hookah bar, and the study focuses on mobile gaming and the structuring of migrant social networks. 2024 South Asian University. -
IBA Graph Selector Algorithm for Big Data Visualization using Defence Dataset
International Journal of Scientific & Engineering Research Vol.4,Issue 3 pp. 1-5 ISSN No. 2229-5518 -
Identification and structure-activity relationship studies of small molecule inhibitors of the human cathepsin D
Cathepsin D, an aspartyl protease, is an attractive therapeutic target for various diseases, primarily cancer and osteoarthritis. However, despite several small molecule cathepsin D inhibitors being developed, that are highly potent, most of them show poor microsomal stability, which in turn limits their clinical translation. Herein, we describe the design, optimization and evaluation of a series of novel non-peptidic acylguanidine based small molecule inhibitors of cathepsin D. Optimization of our hit compound 1a (IC50 = 29 nM) led to the highly potent mono sulphonamide analogue 4b (IC50 = 4 nM), however with poor microsomal stability (HLM: 177 and MLM: 177 ?l/min/mg). To further improve the microsomal stability while retaining the potency, we carried out an extensive structureactivity relationship screen which led to the identification of our optimised lead 24e (IC50 = 45 nM), with an improved microsomal stability (HLM: 59.1 and MLM: 86.8 ?l/min/mg). Our efforts reveal that 24e could be a good starting point or potential candidate for further preclinical studies against diseases where Cathepsin D plays an important role. 2020 Elsevier Ltd -
Identification of ambulance in traffic videos using image processing techniques
Traffic congestion is one of the commonly faced problems in the Urban areas. To eliminate these problems, there is a need for an Intelligent Transportation System (ITS) that proposes an efficient method to reduce the traffic problems and introduces the priority system for the Emergency vehicles. This paper proposes two frameworks that identify ambulance in traffic videos based on features such as color, siren and text. Frames are extracted from videos to employ methods like multilevel thresholding and region matching. Multilevel thresholding is used for segmenting the ambulance from the other occurring vehicles based on the white color. Region matching for text detection method is employed in the segmented vehicle. Color space thresholding is used for the detection of siren based on red or blue color feature. Optical character recognition (OCR) is employed to extract the text in the frame. Word comparison and Matching detects the ambulance text based on the outcome of OCR. The performance of Framework 1 and Framework 2 are evaluated based on Word accuracy and from the experimental results it is observed that Framework 2 is better from 75% word accuracy. 2018, Institute of Advanced Scientific Research, Inc. All Rights reserved. -
Identification of broken characters in degraded documents
Optical Character Recognition (OCR) deals with the recognition of characters in a text document. Steps like Preprocessing, Segmentation and Recognition are embedded in the OCR machine. When a document is scanned it will be taken into OCR and will recognize the characters. But noisy scanning of documents, low-quality printed documents and thresholding error leads to the generation of broken characters. When these documents are given as inputs into OCR, the recognition becomes a tedious process since the broken characters are misunderstood by the OCR machine. So the broken characters have to be identified and segmented separately. This work aims to enhance the degraded documents with broken characters using image processing techniques. For identifying or recognizing the broken character from the image various techniques like vertical projection profile, horizontal projection profile, chain code, mean based thresholding are used. The lines from the document are separated using line segmentation. Separate characters are extracted using Vertical Projection Profile and Horizontal Projection Profile. The character is identified using chain coding. The broken characters are found from them using Mean-based Thresholding and is merged using Heuristic information. The proposed method achieves an accuracy of 92.88% and also performs well for color image documents as well as black and white image documents also because of the effective preprocessing. 2018 Intelligent Network and Systems Society. -
Identification of coronary artery stenosis based on hybrid segmentation and feature fusion
Coronary artery disease has been the utmost mutual heart disease in the past decades. Various research is going on to prevent this disease. Obstructive CAD occurs when one or more of the coronary arteries which supply blood to myocardium are narrowed owing to plaque build-up on the arteries inner walls, causing stenosis. The fundamental task required for the interpretation of coronary angiography is identification and quantification of severity of stenosis within the coronary circulation. Medical experts use X-ray coronary angiography to identify blood vessel/artery stenosis. Due to the artefact, the image has less clarity and it will be challenging for the medical expert to find the stenosis in the coronary artery. The solution to the problem a computational framework is proposed to segment the artery and spot the location of stenosis in the artery. Here the author presented an automatic method to detect stenosis from the X-ray angiogram image. A unified Computational method of Jerman, Level-set, fine-tuning the artery structure, is developed to extract the segmented artery features and detect the arterys stenosis. The current experimental outcomes illustrate that this computational method achieves average specificity, sensitivity, Accuracy, precision and F-scores of 95%, 97.5%, 98%, 97.5% and 97.5%, respectively. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm
Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset's attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent. 2023 S. Krishnan et al. -
Identification of emission-line stars in transition phase from pre-main sequence to main sequence
Pre-main-sequence (PMS) stars evolve into main-sequence (MS) phase over a period of time. Interestingly, we found a scarcity of studies in existing literature that examine and attempt to better understand the stars in PMS to MS transition phase. The purpose of this study is to detect such rare stars, which we named as 'transition phase' (TP) candidates-stars evolving from the PMS to the MS phase. We identified 98 TP candidates using photometric analysis of a sample of 2167 classical Be (CBe) and 225 Herbig Ae/Be (HAeBe) stars. This identification is done by analysing the near-and mid-infrared excess and their location in the optical colour-magnitude diagram. The age and mass of 58 of these TP candidates are determined to be between 0.1-5 Myr and 2-10.5 M?, respectively. The TP candidates are found to possess rotational velocity and colour excess values in between CBe and HAeBe stars, which is reconfirmed by generating a set of synthetic samples using the machine learning approach. 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Identification of interstitial lung diseases using deep learning
The advanced medical imaging provides various advantages to both the patients and the healthcare providers. Medical Imaging truly helps the doctor to determine the inconveniences in a human body and empowers them to make better choices. Deep learning has an important role in the medical field especially for medical image analysis today. It is an advanced technique in the machine learning concept which can be used to get efficient output than using any other previous techniques. In the anticipated work deep learning is used to find the presence of interstitial lung diseases (ILD) by analyzing high-resolution computed tomography (HRCT) images and identifying the ILD category. The efficiency of the diagnosis of ILD through clinical history is less than 20%. Currently, an open chest biopsy is the best way of confirming the presence of ILD. HRCT images can be used effectively to avoid open chest biopsy and improve accuracy. In this proposed work multi-label classification is done for 17 different categories of ILD. The average accuracy of 95% is obtained by extracting features with the help of a convolutional neural network (CNN) architecture called SmallerVGGNet. 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Identification of language in a cross linguistic environment
World has become very small due to software internationationalism. Applications of machine translations are increasing day by day. Using multiple languages in the social media text is a developing trend. Availability of fonts in the native language enhanced the usage of native text in internet communications. Usage of transliterations of language has become quite common. In Indian scenario current generations are familiar to talk in native language but not to read and write in the native language, hence they started using English representation of native language in textual messages. This paper describes the identification of the transliterated text in cross lingual environment. In this paper a Neural network model identifies the prominent language in the text and hence the same can be used to identify the meaning of the text in the concerned language. The model is based upon Recurrent Neural Networks that found to be the most efficient in machine translations. Language identification can serve as a base for many applications in multi linguistic environment. Currently the South Indian Languages Malayalam, Tamil are identified from given text. An algorithmic approach of Stop words-based model is depicted in this paper. Model can be also enhanced to address all the Indian Languages that are in use. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Identification of misconceptions about corona outbreak using trigrams and weighted TF-IDF model
Misconceptions of a particular issue like health, diseases, politics, government policies, epidemics and pandemics have been a social issue for a number of years, particularly after the advent of social media, and often spread faster than true truth. The engagement with social media like Twitter being one of the most prominent news outlets continuing is a major source of information today, particularly the information distributed around the network. In this paper, the efficacy of Misconception Detection System was tested on Corona Pandemic Dataset extracted from Twitter posts. A Trigram and a weighted TF-IDF Model followed by a supervised classifier were used for categorizing the dataset into two classes: one with misconceptions about COVID-19 virus and the other comprising correct and authenticated information. Trigrams were more reliable as the functional words related to coronavirus appeared more frequently in the corpus created. The proposed system using a combination of trigrams and weighted TF-IDF gave relevant and a normalized score leading to an efficient creation of vector space model and this has yielded good performance results when compared with traditional approaches using Bag of Words and Count Vectorizer technique where the vector space model was created only through word count. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Identification of negative comments from positive sentences through data analysis
Social media has become a part, where people say what they think. It has made remarkable condition for individuals to impart their thoughts to the world. More consumers are writing their reviews which help people to make decisions about the quality and whether they should purchase the product. With respect to distinguishing perspectives out of this immense pool of conclusions, it turns into a laborious task and doing it physically is in every practical sense impossible. When we want to purchase things the best way to choose the finest product is to rely upon the opinions of others who already purchased those items. Sentiment analysis is utilized to choose whether the author's view is positive, negative, or neutral towards a specific item. This paper provides a review of apple mobile phone where we find polarity of a product based on scoring. We also worked on identifying negative comments in a positive sentence. We found the count of different polarity of words from overall positive feedbacks and stored the negative words so that we can identify which feature of the product is not acceptable and should be work with. We are representing our final result using wordcloud where we can detect which features has flaws. IAEME Publication. -
Identification of new classical Ae stars in the Galaxy using LAMOST DR5
We report the first systematic study to identify and characterize a sample of classical Ae stars in the Galaxy. The spectra of these stars were retrieved from the A-star catalogue using the Large sky Area Multi-Object fibre Spectroscopic Telescope (LAMOST) survey. We identified the emission-line stars in this catalogue from which 159 are confirmed as classical Ae stars. This increases the sample of known classical Ae stars by about nine times from the previously identified 21 stars. The evolutionary phase of classical Ae stars in this study is confirmed from the relatively small mid- and far-infrared excess and from their location in the optical colour-magnitude diagram. We estimated the spectral type using MILES spectral templates and identified classical Ae stars beyond A3, for the first time. The prominent emission lines in the spectra within the wavelength range 3700-9000 are identified and compared with the features present in classical Be stars. The H ? emission strength of the stars in our sample show a steady decrease from late-B type to Ae stars, suggesting that the disc size may be dependent on the spectral type. Interestingly, we noticed emission lines of Fe ii, O i, and Paschen series in the spectrum of some classical Ae stars. These lines are supposed to fade out by late B-type and should not be present in Ae stars. Further studies, including spectra with better resolution, is needed to correlate these results with the rotation rates of classical Ae stars. 2021 2020 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Identification of potential ZIKV NS2B-NS3 protease inhibitors from Andrographis paniculata: An insilico approach
Andrographis paniculata is a widely used medicinal plant for treating a variety of human infections. The plant's bioactives have been shown to have a variety of biological activities in various studies, including potential antiviral, anticancer, and anti-inflammatory effects in a variety of experimental models. The present investigation identifies a potent antiviral compound from the phytochemicals of Andrographis paniculata against Zika virus using computational docking simulation. The ZIKV NS2B-NS3 protease, which is involved in viral replication, has been considered as a promising target for Zika virus drug development. The bioactives from Andrographis paniculata, along with standard drugs as control were screened for their binding energy using AutoDock 4.2 against the viral protein. Based on the higher binding affinity the phytocompounds Bisandrographolide A (-11.7), Andrographolide (-10.2) and Andrographiside (-9.7) have convenient interactions at the binding site of target protein (ZIKV NS2B-NS3 protease) in comparison with the control drug. In addition, using insilico tools, the selected high-scoring molecules were analysed for pharmacological properties such as ADME (Absorption, Distribution, Metabolism, and Excretion profile) and toxicity. Andrographolide was reported to have strong pharmacodynamics properties and target accuracy based on the Lipinski rule and lower binding energy. The selected bioactives showed lower AMES toxicity and has potent antiviral activity against zika virus targets. Further, MD simulation studies validated Bisandrographolide A & Andrographolide as a potential hit compound by exhibiting good binding with the target protein. The compounds exhibited good hydrogen bonds with ZIKV NS2B-NS3 protease. As a result, bioactives from the medicinal plant Andrographis paniculata can be studied in vitro and in vivo to develop an antiviral phytopharmaceutical for the successful treatment of zika virus. Communicated by Ramaswamy H. Sarma. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Identification of Superclusters and Their Properties in the Sloan Digital Sky Survey Using the WHL Cluster Catalog
Superclusters are the largest massive structures in the cosmic web, on tens to hundreds of megaparsec scales. They are the largest assembly of galaxy clusters in the Universe. Apart from a few detailed studies of such structures, their evolutionary mechanism is still an open question. In order to address and answer the relevant questions, a statistically significant, large catalog of superclusters covering a wide range of redshifts and sky areas is essential. Here, we present a large catalog of 662 superclusters identified using a modified friends-of-friends algorithm applied on the WHL (Wen-Han-Liu) cluster catalog within a redshift range of 0.05 ? z ? 0.42. We name the most massive supercluster at z ? 0.25 as the Einasto Supercluster. We find that the median mass of superclusters is ?5.8 1015 M ? and the median size ?65 Mpc. We find that the supercluster environment slightly affects the growth of clusters. We compare the properties of the observed superclusters with the mock superclusters extracted from the Horizon Run 4 cosmological simulation. The properties of the superclusters in the mocks and observations are in broad agreement. We find that the density contrast of a supercluster is correlated with its maximum extent with a power-law index, ? ? ?2. The phase-space distribution of mock superclusters shows that, on average, ?90% of part of a supercluster has a gravitational influence on its constituents. We also show the mock halos average number density and peculiar velocity profiles in and around the superclusters. 2023. The Author(s). Published by the American Astronomical Society. -
Identifying explosive behavioral trace in the CNX nifty index: A quantum finance approach
The fnancial markets are found to be fnite Hilbert space, inside which the stocks are displaying their wave-particle duality. Te Reynolds number, an age old fluid mechanics theory, has been redefned in investment fnance domain to identify possible explosive moments in the stock exchange. CNX Nify Index, a known index on the National Stock Exchange of India Ltd., has been put to the test under this situation. Te Reynolds number (its fnancial version) has been predicted, as well as connected with plausible behavioral rationale. While predicting, both econometric and machinelearning approaches have been put into use. Te primary objective of this paper is to set up an efcient econophysics' proxy for stock exchange explosion. Te secondary objective of the paper is to predict the Reynolds number for the future. Last but not least, this paper aims to trace back the behavioral links as well. 2018 Bikramaditya Ghosh, Emira Kozarevic. -
Identifying Social-Cognitive Factors Influencing Aggression in Adolescents: A Cross-Sectional Indian Study
Adolescence is a critical period during which the likelihood of experiencing self-regulation failures like aggressive outbursts is increased. Recent Indian studies on adolescents have reported an increasing incidence of aggressive acts during this time of transition, which is a threat to the adolescent, the victim and society in general. This study focuses on the social-cognitive perspective, implying that aggression is a social behaviour that is largely affected by ones beliefs about the acceptability of aggression and the degree of cognitive and effortful control they have over their emotions. Such beliefs are likely to be influenced by emotion socialisation, wherein parents and peers act as key agents. With this perspective, the current study, through a mediational model, explains the social-cognitive factors predicting aggressive behaviour in adolescents. This is a cross-sectional, descriptive study carried out on a sample of 475 adolescent students from the Delhi-NCR region recruited through purposive sampling. The data were collected through self-report questionnaires from schools and colleges. The model was tested using SPSS AMOS and was found to be a good fit for the data. The findings of this study are crucial from a risk and intervention perspective. It emphasises the need to build socially and emotionally competent students who not only have the skills needed to succeed but also nurture healthy social relationships and maintain positive mental health through adaptive emotion regulation skills. 2024 Department of Psychology, University of Allahabad. -
Identifying the population of T-Tauri stars in Taurus: UVoptical synergy
With the third data release of the Gaia mission, Gaia DR3 with its precise photometry and astrometry, it is now possible to study the behavior of stars at a scale never seen before. In this paper, we developed new criteria to identify T-Tauri stars (TTS) candidates using UV and optical color-magnitude diagrams (CMDs) by combining the GALEX and Gaia surveys. We found 19 TTS candidates and five of them are newly identified TTS in the Taurus molecular cloud (TMC), not cataloged before as TMC members. For some of the TTS candidates, we also obtained optical spectra from several Indian telescopes. We also present the analysis of distance and proper motion of young stars in the Taurus using data from Gaia DR3. We found that the stars in Taurus show a bimodal distribution with distance, having peaks at 130.17-1.241.31 pc and 156.25-5.001.86 pc. The reason for this bimodality, we think, is due to the fact that different clouds in the TMC region are at different distances. We further showed that the two populations have similar ages and proper motion distribution. Using the Gaia DR3 CMD, we showed that the age of Taurus is consistent with 1Myr. 2023, Indian Academy of Sciences. -
IDENTITIES AT THE DINNER TABLE: COMMENSALITY, SELF-PERCEPTION, AND RELATIONSHIPS IN ANNE CHERIANS A GOOD INDIAN WIFE
Food studies is rapidly gaining ground as a multidisciplinary area of research. Within it, literary food studies brings an interdisciplinary perspective as works of literature are viewed through the lens of food that is informed by frameworks and concepts that are rooted in a variety of fields including cultural anthropology, sociology, and more. one such concept that is in focus here is that of commensality that is associated with food and food practices. Commensality, drawing from notions of conviviality, refers to the practice of sharing a table and consuming food together. Deeper meanings of communal identities come to the fore in this social practice, leading it to shape how identities are understood and projected. Commensality can be a complex site of belonging and alienation depending on the context, and this paper seeks to explore the representation of the same in Anne Cherians A Good Indian Wife (2008). Leila, the titular Indian wife in the novel, moves to the US from India after her marriage to Neel and grapples with finding her place in the foreign land. With this displacement comes the endeavor to reaffirm her new identity, which now includes the role of being a wife and the aspect of being an immigrant. Neel also deals with complicated feelings towards the projection of his identity. With food playing a crucial role in the everyday experiences of their lives, commensality becomes a point of enquiry into how they see themselves and how their relationships with each other and themselves evolve through the course of the narrative. 2024 Nayana George. -
Identity in Consumption: Reading Food and Intersectionality in Anita Desai's Fasting, Feasting
With the resurging interest in Food Studies, this rapidly emerging field of study has seen multiple disciplines adding in their distinct flavours that truly make this an area to savour. Literary food studies, in particular, has become a relevant field of study with the understanding that food in literature always plays a symbolic role, as food in literature is never depicted for the sustenance of the literary characters. This paper seeks to explore the novel Fasting, Feasting (1999) by Anita Desai through the lens of food and foodways to explicate how the characters interact with the culinary arena, and ultimately, interact with each other and themselves. These interactions will serve as crucial insights into their identities, particularly their intersectional gender identities considering the facets of nationality, class, and the like. A special focus will also be rendered on the notion of marginalisation seen in the text, of which gender is a crucial deciding factor. The title of the novel hints at consumption-at both its presence and absence-which will prove as the gateway to the interactions of the characters with food in the novel to examine who it is that gets to feast while who are forced to starve. 2022 Aesthetics Media Services. All rights reserved.