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UVIT view of Centaurus A: A detailed study on positive AGN feedback
Supermassive black holes at the centre of active galactic nuclei (AGNs) produce relativistic jets that can affect the star formation characteristics of the AGN hosts. Observations in the ultraviolet (UV) band can provide an excellent view of the effect of AGN jets on star formation. Here, we present a census of star formation properties in the Northern Star-forming Region (NSR) that spans about 20 kpc of the large radio source Centaurus A hosted by the giant elliptical galaxy NGC 5128. In this region, we identified 352 UV sources associated with Cen A using new observations at an angular resolution of <1.5 arcsec observed with the Ultra-Violet Imaging Telescope (UVIT) onboard AstroSat. These observations were carried out in one far-ultraviolet (FUV; ?mean = 1481 and three near-ultraviolet (NUV; with ?mean of 2196, 2447, and 2792 respectively) bands. The star-forming sources identified in UV tend to lie in the direction of the jet of Cen A, thereby suggesting jet triggering of star formation. Separating the NSR into Outer and Inner regions, we found the stars in the Inner region to have a relatively younger age than the Outer region, suggesting that the two regions may have different star formation histories. We also provide the UVIT source catalogue in the NSR. 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Curvit: An open-source Python package to generate light curves from UVIT data
Curvit is an open-source Python package that facilitates the creation of light curves from the data collected by the Ultra-Violet Imaging Telescope (UVIT) onboard AstroSat, Indias first multi-wavelength astronomical satellite. The input to Curvit is the calibrated events list generated by the UVIT-Payload Operation Center (UVIT-POC) and made available to the principal investigators through the Indian Space Science Data Center. The features of Curvit include: (i) automatically detecting sources and generating light curves for all the detected sources and (ii) custom generation of light curve for any particular source of interest. We present here the capabilities of Curvit and demonstrate its usability on the UVIT observations of the intermediate polar FO Aqr as an example. Curvit is publicly available on GitHub at https://github.com/prajwel/curvit. 2021, Indian Academy of Sciences. -
Predicting Stock Market Trends: Machine Learning Approaches of a Possible Uptrend or Downtrend
This paper delves into a statistical analysis of the stock market, emphasizing the significance of accuracy in stock predictions. Large data sets can be handled by machine learning algorithms, which can also forecast outcomes based on past data and spot intricate patterns in financial data. They assist control risks, automate decision-making procedures, and adjust to changing circumstances. Multi-source data can be combined by ML models to provide a comprehensive picture of market circumstances. They can manage intricate, nonlinear interactions, provide impartial analysis, and lessen human bias. Models are able to adjust to shifting market conditions through ongoing learning and retraining. They must, however, exercise caution when deploying models in real-world situations and ensure that they are validated. Although machine learning has advantages for stock market analysis, it must be carefully evaluated for dangers and validated before being used in practical situations. The traditional machine learning model, Logistic Regression has been used in order to predict stock prices. It focuses on binary classification based on the trend of the stock. Through the model training and evaluation and additional analysis done on the results, this research contributes towards obtaining predictions and studying reasons of a possible uptrend or downtrend to further assist companies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Play therapy as a rehabilitative measure among child survivors of bonded labour
A bonded labour condition is customarily because of relocation of persons due to the situations that are obligatory in nature. Bonded labour, which is characterized by a long-term affiliation between employer and employee, is usually congealed over a loan, and is entrenched complexly in India’s socio-economic culture - a culture that is a creation of class relations, a colonial history, and tenacious scarcity of resources among many citizens. The children living in such conditions face a lot of mistreatment and go through undeniable exploitation. The children present at the facility may or may not work with the parents yet go through a lot of pain, distress and abuse as the journey of cruelty and suffering is just the same as what their parent’s ordeal with. Children as young as 4 Years old are molested, beaten and abused on an average basis traumatizing the children before and after the rescue. These children are not permitted to enroll in schools as they do not have identity proofs or birth certificates. The only way of addressing the subject of emotional, physical and mental turbulence of the child in this perimeter is by enforcing play as a rehabilitative measure to help with past experiences and impending consequences. Play is essential to development because it contributes to the cognitive, physical, social, and emotional well-being of children and youth. Play also offers an ideal opportunity for parents or facilitators to engage fully with the children. Play allows children to use their creativity while developing their imagination, dexterity, and physical, cognitive, and emotional strength. When forms of play like puppetry, art, story-telling etc are added the child conjectures unpleasant feelings which she cannot hide the child is better able to act differently in relation to what he/she is feeling. The study aims to see if play therapy is a technique of rehabilitating child survivors of bonded labour. -
Ocr system framework for modi scripts using data augmentation and convolutional neural network
Character recognition is one of the most active research areas in the field of pattern recognition and machine intelligence. It is a technique of recognizing either printed or handwritten text from document images and converting it to a machine-readable form. Even though there is much advancement in the field of character recognition using machine learning techniques, recognition of handwritten MODI script, which is an ancient Indian script, is still in its infancy. It is due to the complex nature of the script that includes similar shapes of character and the absence of demarcation between words. MODI was an official language used to write Marathi. Deep learning-based models are very efficient in character recognition tasks and in this work an ACNN model is proposed using the on-the-fly data augmentation method and convolution neural network. The augmentation of the data will add variability and generalization to the data set. CNN has special convolution and pooling layers which have helped in better feature extraction of the characters. The performance of the proposed method is compared with the most accurate MODI character recognition method reported so far and it is found that the proposed method outperforms the other method. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Machine Transliteration of Handwritten MODI Script to Devanagari using Deep Neural Networks
The transliteration process involves transcribing words from the source language into the target language that uses a different script. Language and scriptural hurdles can be overcome via transliteration systems. There is a demand for automated transliteration systems due to the existence of several languages and the growing number of multilingual speakers. This study focuses on the Machine Transliteration of handwritten MODI script to Devanagari. MODI script was the official script for Marathi till 1950. Although Devanagari has, since then, taken over as the Marathi languages official script, the MODI script has historical significance as large volumes of its manuscripts are preserved in libraries across different parts of India. However, MODI into Devanagari transliteration is a difficult task because MODI script documents are complex in nature and there is no standard dataset available for the experiment. Machine Transliteration can be approached either as a Natural Language Processing task or as a pattern recognition task. In this research work, the transliteration task is carried out using the pattern recognition technique. The transliteration of MODI script to Devanagari is implemented using Convolutional Recurrent Neural Network (CRNN) based Calamari OCR, which is open-source software. An accuracy of 88.14% is achieved in character level matching of each word in the MODI to Devanagari transliteration process. When considering the entire word matching, the accuracy achieved is 61%. Machine Transliteration of MODI script documents results in the retrieval of large repositories of knowledge from ancient MODI manuscripts. (2024), (Research Institute of Intelligent Computer Systems). All rights reserved. -
Efficient handwritten character recognition of modi script using wavelet transform and svd
MODI script has historical importance as it was used for writing the Marathi language, until 1950. Due to the complex nature of the script, the character recognition of MODI script is still in infancy. The implementation of more efficient methods at the various stages of the character recognition process will increase the accuracy of the process. In this paper, we present a hybrid method called WT-SVD (Wavelet Transform-Singular Value Decomposition), for the character recognition of MODI script. The WT-SVD method is a combination of singular value decomposition and wavelet transform, which is used for the feature extraction. Euclidean distance method is used for the classification. The experiment is conducted using Symlets and Biorthogonal wavelets, and the results are compared. The method using Biorthogonal wavelet feature extraction achieved the highest accuracy The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
A Review of Various Line Segmentation Techniques Used in Handwritten Character Recognition
Segmentation is a very critical stage in the character recognition process as the performance of any character recognition system depends heavily on the accuracy of segmentation. Although segmentation is a well-researched area, segmentation of handwritten text is still difficult owing to several factors like skewed and overlapping lines, the presence of touching, broken and degraded characters, and variations in writing styles. Therefore, researchers in this area are working continuously to develop new techniques for the efficient segmentation and recognition of characters. In the character recognition process, segmentation can be implemented at the line, word, and character level. Text line segmentation is the first step in the text/character recognition process. The line segmentation methods used in the character recognition of handwritten documents are presented in this paper. The various levels of segmentation which include line, word, and character segmentation are discussed with a focus on line segmentation. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Convolutional Autoencoder Based Feature Extraction and KNN Classifier for Handwritten MODI Script Character Recognition
Character recognition is the process of identifying and classifying the images of printed or handwritten text and the conversion of that into machine-coded text. Deep learning techniques are efficiently used in the character recognition process. A Convolutional Autoencoder based technique for the character recognition of handwritten MODI script is proposed in this paper. MODI script was used for writing Marathi until the twentieth century. Though at present, Devnagari is taken over as the official script of Marathi, the historical importance of MODI script cannot be overlooked. MODI character recognition will not be an easy feat because of the various complexities of the script. Character recognition-related research of MODI script is in its initial stages. The proposed method is aimed to explore the use of a deep learning-based method for feature extraction and thereby building an efficient character recognition system for isolated handwritten MODI script. At the classification stage, the features extracted from the autoencoder are categorized using KNN classifier. Performance comparison of two different classifiers, such as KNN and SVM, is also carried out in this work. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data Augmentation for Handwritten Character Recognition of MODI Script Using Deep Learning Method
Deep learning-based methods such as convolutional neural networks are extensively used for various pattern recognition tasks. To successfully carry out these tasks, a large amount of training data is required. The scarcity of a large number of handwritten images is a major problem in handwritten character recognition; this problem can be tackled using data augmentation techniques. In this paper, we have proposed a convolutional neural network-based character recognition method for MODI script in which the data set is subjected to augmentation. The MODI script was an official script used to write Marathi, until 1950, the script is no more used as an official script. The preparation of a large number of handwritten characters is a tedious and time-consuming task. Data augmentation is very useful in such situations. Our study uses different types of augmentation techniques, such as on-the-fly (real-time) augmentation and off-line method (data set expansion method or traditional method). A performance comparison between these methods is also performed. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Handwritten Character Recognition of MODI Script using Convolutional Neural Network Based Feature Extraction Method and Support Vector Machine Classifier
Deep learning based algorithms are used in various pattern recognition tasks, including character recognition. Convolutional Neural Network (CNN) is effectively implemented for character recognition and is one of the best performing deep learning models. CNN can be used for character recognition directly or it can be used for extracting features in the character recognition process. Implementation of a feature extraction method using CNN autoencoder for MODI script character recognition is discussed in the paper. The extracted features are then subjected to Support Vector Machine (SVM) for the purpose of classification. The On-the-fly data augmentation method is used to add variability and generalization of the data set. MODI Script is an ancient Indian script and was used for writing Marathi until 1950. Various libraries and temples in India and abroad have a large collection of MODI documents. Character recognition related research of MODI script is still in infancy and research and development is necessary to extract the information from MODI manuscripts stored in various libraries. The performance of the proposed method, which uses CNN autoencoder as a feature extractor and an SVM based classifier gives very high accuracy and is better compared to the most accurate MODI character recognition method reported so far. 2020 IEEE. -
Feature extraction and classification techniques of modi script character recognition
Machine simulation of human reading has caught the attention of computer science researchers since the introduction of digital computers. Character recognition is the process of recognizing either printed or handwritten text from document images and converting it into machine-readable form. Character recognition is successfully implemented for various foreign language scripts like English, Chinese and Latin. In the case of Indian language scripts, the character recognition process is comparatively difficult due to the complex nature of scripts. MODI script-an ancient Indian script, is the shorthand form for the Devanagari script in which Marathi was written. Though at present, the script is not used officially, it has historical importance. MODI character recognition is a very complex task due to its variations in the writing style of individuals, shape similarity of characters and the absence of word stopping symbol in documents. The advances in various machine learning techniques have greatly contributed to the success of various character recognition processes. The proposed work provides an overview of various feature extraction and classification techniques used in the recognition of MODI script till date and also provides evaluation and comparison of these techniques. 2019, Universiti Putra Malaysia Press. All rights reserved. -
Offline Character Recognition of Handwritten MODI Script Using Wavelet Transform and Decision Tree Classifier
MODI script is derived from the N?gari family of scripts, and it was used for writing Marathi until twentieth century. Though currently not used as an official script, it has historical importance, as a large volume of manuscripts are preserved at various libraries across India. With the use of an appropriate recognition system, the handwritten documents can be transferred into digital media, so that it can be conveniently viewed, edited, or transliterated to other scripts. The research on MODI script is still in the initial stages, and there is a considerable demand for more research in this field. An implementation of wavelet transform-based feature extraction for MODI scripts character recognition is discussed in this paper. The experiment is performed using Daubechies, Haar, and Symlet wavelets, and performance comparison between these different mother wavelets is carried out. Decision tree classifier is used for the classification process, and the results indicate that the feature extraction using Daubechies wavelet yielded better character recognition result. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Character Recognition of MODI Script Using Distance Classifier Algorithms
Machine simulation of human reading is an active research area since the introduction of digital computers. Optical character recognition aims at the recognition of printed or handwritten text from document images and converting the same into a machine-readable form. The focus of this work is handwritten character recognition of MODI Script. A proper recognition system for handwritten documents enables it to be conveniently viewed, edited, and shared via electronic means. The development of a character recognition system for some of the ancient script is still a challenging task due to the complex nature of the script. MODI script is one such script which is the shorthand form of the Devanagari script in which Marathi was written. Though at present MODI script is not an official script, there exists a huge collection of MODI documents in various libraries. In addition, it is observed that scholars and historians are taking serious effort to revive the script. The purposed study based on the implementation of two algorithms for the classification of handwritten MODI script. The algorithms use distance classifier method. The first experiment is done using Euclidean distance classifiers and the second one is with Manhattan distance classifier and the accuracy achieved is 99.28% & 94% respectively. Springer Nature Singapore Pte Ltd 2020. -
The Efficacy of Multi-Component Intervention for Adolescents with Problematic Video Gaming in a Community-Based Setting
Video gaming is a popular leisure activity enjoyed by millions globally, helping with socialisation, interaction, and relieving stress. It may also become a maladaptive coping mechanism to evade distress and negative emotions, leading to problematic usage. Research evidence shows that problematic gaming is associated with different psychosocial issues. Video games can be a way of negative coping and escaping reality, and problematic usage can hide other problems of players in real life. Adolescents are vulnerable to problematic use due to their developmental stages, and those with specific vulnerabilities and disabilities are at greater risk. No one psychotherapy has all the answers, and the multi-component intervention technique might have better treatment utility than a solitary behaviour intervention. The research aims to show the effectiveness of the intervention for problematic video game usage in a community-based setting. The study focuses on adolescents in seventh through ninth grade who were identified as problematic video gamers (not addictive users) from a selected group of schools in Kerala. The study employed an experimental design, encompassing both intervention and control groups, to systematically assess the effects of the experimental manipulation and establish a baseline measurement. The paired t-test results showed no significant decrease in the intervention groups Gaming Addiction Scale at the post-test, but it did lower the addiction scores. By conducting the research, we provide psychological care for adolescents and help them identify and prevent problematic gaming experiences. The research underscores the significance of early identification and prevention of problematic video game usage among adolescents, advocating for a holistic approach incorporating diverse components. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Problematic Gaming Among Adolescents within a Non-Clinical Population: A Scoping Review
Gaming is a pastime activity that has been enjoyed by millions of individuals worldwide for the past few years. The adolescent is in a developmental period that involves significant bio- psychosocial changes, including rapid changes in physical and mental states that make them more vulnerable to addiction. Online Gaming could have a higher risk of developing problematic gaming. Many studies have documented video gaming addiction and not problematic video gaming. Problematic gaming is a condition different from video game addiction. Further research remains needed to synthesise the factors behind problematic video game usage. The purpose of the scoping review is to synthesise the findings related to problematic video by identifying using a search through the following database: JSTOR, ProQuest, APA Psycnet, Ebsco. The research will help detect the early symptoms of addiction and understand the mechanism behind the addictive nature. Through the study, we can provide psychological care for adolescents by educating them and preventing and being aware of problematic gaming usage and experiences. The Electrochemical Society -
Addiction treatment in India: Legal, ethical and professional concerns reported in the media
As per the Magnitude of Substance Use in India 2019 survey report, over 57 million of the Indian population is in need of professional help for alcohol use disorders and around 7.7 million for opioid use disorders. The increasing demand for addiction treatment services in India calls for professionalising every aspect of the field. Frequent human rights violations and various unethical practices in Indian addiction treatment facilities have been reported in the mass media. This study is a content analysis of newspaper reports from January 1, 2016 to December 31, 2019 looking into legal, ethical and professional concerns regarding the treatment of substance use disorders in India. The content analysis revealed various human rights violations, the use of improper treatment modalities, the lack of basic facilities at treatment settings, and the presence of unqualified professionals in practice. Indian Journal of Medical Ethics 2021. -
Social groupwork for promoting psychological well-being of adolescents enrolled in sponsorship programs
Background: The dearth of data on adolescents highlighted in the UN's data disaggregation against the agenda 'no one left behind' calls for research on 'the second decade'. Moreover, India is a country with the world's largest adolescent population, and as such, studies and policies for developing competencies of adolescents are crucial to the country's development; interventions instilling confidence to aspire to a better future in underprivileged adolescents are vital to mitigate inequity. Methods: This intervention study adopted a quasi-experimental design to measure the effectiveness of social groupwork in raising the psychological well-being of adolescents in child sponsorship programs in Kerala. Forty adolescents from a Child Sponsorship Program (CSP) center in Kochi were recruited for the study. Those suggested by the CSP center considering their poor academic performance and behavior problems were allocated to the intervention group and the rest to the comparison group. The intervention was designed in response to the information garnered through a preliminary study and administered to the intervention group (n=20). We conducted pre-test and post-test for both the intervention group and comparison group (n=20). Results: Comparison between pre- and post-measurements carried out using paired sample t-test for the intervention group and comparison group separately gave a p-value of <0.05 for the intervention group and >0.05 for the comparison group. Thus, it was proved that psychological well-being of participants in the intervention group was raised significantly due to the social group work intervention. Conclusions: Applying refined granularity, this research adds data specifically on adolescents enrolled in child sponsorship programs and sets a blueprint for social groupwork to improve their psychological well-being. Proposing a conceptual framework for child sponsorship programs, this study recommends further research in all aspects of its functioning, and interventions at group, family, and community levels, for the well-being and empowerment of marginalized adolescents. 2021 Joseph S and Karalam DSRB. -
Social Work Intervention Research in Child Sponsorship Programs: Enhancing Psychological Well-being of Marginalized Adolescents
The Child Sponsorship Program (CSP) is critical to enhancing the objective and subjective well-being of enrollees. Meanwhile, social work interventions emphasize scientific approaches aimed at empowering marginalized populations. This intervention research (IR) was focused on raising the psychological well-being (PWB) of adolescents in a prominent CSP located in Kochi, Kerala. Preliminary findings from a pilot study underscored the need for intervention, and subsequent Delphi survey results guided the formulation of an intervention strategy. Capitalizing on the transformative power of peer groups, IR implemented a social group work intervention to enhance adolescent PWB in CSP. Using a nonequivalent comparison group interrupted time-series design, the PWB of participants in the intervention group (IG, N = 20) and comparison group (CG, N = 20) was measured and compared. Ryffs PWB scale with 42 items served as the assessment instrument. Descriptive statistics confirmed the normal distribution of baseline data for all participants (N = 40), while repeated measures ANOVA in SPSS 25 validated the alternative hypothesis, indicating significant differences in PWB measures over time within IG and between IG and CG. Additionally, along with statistical evidence of intervention effectiveness, this study used a qualitative design for ongoing evaluation of the intervention process, providing insights for program refinement and demonstrating intervention outcomes. By defining a model for group work intervention among CSP adolescents to improve PWB, this study underscores the important role of social work interventions in empowering marginalized populations. The Author(s) 2024. -
Knowledge society and the era of post-truth: Challenges to democracy
The future of any country in the contemporary era lies in its ability to harness the knowledge potential. The fruits of knowledge society have transformed the terrain of social and political scenario of countries around the world. Democracy as a form of government, to be successful, requires a critically-engaged and politically literate population. Democracy, therefore, requires not only political literacy but also media and digital literacies given the influence of media in our lives. If democracy is viewed as a relationship between knowledge and power, there needs to be a strong distinction between the ideas, the truth of power and the power of truth. The term, 'Post-truth', signifies that objective facts have become less influential in shaping public opinion than appeals to emotion and personal beliefs. The political processes in various democracies seem to have become more managerial and technologically fixated. There has been significant erosion in the ideas of transparency of information and political leadership has become nothing but a propaganda exercise. The paper analyses how the information technology revolution and the surge of new media has impacted the political processes in democracies, and presents the phenomenon of post-truth as a threat to the modern democratic systems. 2019 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore).