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                <text>Order Sum Graph of a Group</text>
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                <text>Algebraic graphs; Center; Domination; Graph spectra; Order sum graphs</text>
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                <text>The concept of the order sum graph associated with a finite group based on the order of the group and order of group elements is introduced. Some of the properties and characteristics such as size, chromatic number, domination number, diameter, circumference, independence number, clique number, vertex connectivity, spectra, and Laplacian spectra of the order sum graph are determined. Characterizations of the order sum graph to be complete, perfect, etc. are also obtained.  2023 University of Baghdad. All rights reserved.</text>
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                <text>Amreen J.; Naduvath S.</text>
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                <text>Baghdad Science Journal, Vol-20, No. 1, pp. 181-188.</text>
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                <text>University of Baghdad</text>
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                <text>&lt;a href="https://doi.org/10.21123/bsj.2022.6480" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.21123/bsj.2022.6480&lt;/a&gt;
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                <text>ISSN: 20788665</text>
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                <text>Amreen J., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Naduvath S., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, India</text>
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                <text>On the Non-Inverse Graph of a Group</text>
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                <text>inverse graph; non-inverse graph</text>
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                <text>Let (G, ?) be a finite group and S = {u G|u u-1}, then the inverse graph is defined as a graph whose vertices coincide with G such that two distinct vertices u and v are adjacent if and only if either u ? v S or v ? u S. In this paper, we introduce a modified version of the inverse graph, called i?-graph associated with a group G. The i?-graph is a simple graph with vertex set consisting of elements of G and two vertices x, y ? are adjacent if x and y are not inverses of each other. We study certain properties and characteristics of this graph. Some parameters of the i?-graph are also determined.   2022 Javeria Amreen et al., published by Sciendo.</text>
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                <text>Amreen J.; Naduvath S.</text>
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                <text>Discussiones Mathematicae - General Algebra and Applications, Vol-42, No. 2, pp. 315-325.</text>
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                <text>&lt;a href="https://doi.org/10.7151/dmgaa.1392" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.7151/dmgaa.1392&lt;/a&gt;
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                <text>ISSN: 15099415</text>
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                <text>Amreen J., Department of Mathematics, CHRIST (Deemed to Be University), Bangalore, 560029, India; Naduvath S., Department of Mathematics, CHRIST (Deemed to Be University), Bangalore, 560029, India</text>
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                <text>Some New Results on Non-inverse Graph of a Group</text>
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                <text>The non-inverse graph corresponding to a group G, is a simple graph with vertices being elements of the group G and there is an edge between two vertices if they are not mutual inverses. In the current paper, we study few properties of the non-inverse graphs of groups. We also discuss the parameters related different types of domination no.s of the non-inverse graph. We further obtain the Laplacian spectra and signless Laplacian spectra of the non-inverse graphs.  2022 American Institute of Physics Inc.. All rights reserved.</text>
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                <text>Amreen J.; Naduvath S.</text>
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                <text>AIP Conference Proceedings, Vol-2516</text>
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                <text>American Institute of Physics Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1063/5.0108434" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1063/5.0108434&lt;/a&gt;
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                <text>Amreen J., Department of Mathematics, CHRIST(Deemed to be University), Bangalore, India; Naduvath S., Department of Mathematics, CHRIST(Deemed to be University), Bangalore, India</text>
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                <text>Some Variations of Domination in Order Sum Graphs</text>
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                <text>Complement of a graph; Domination; Line graphs; Order sum graphs</text>
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                <text>An order sum graph of a group G, denoted by ? os(G), is a graph with vertex set consisting of elements of G and two vertices say a, b? ? os(G) are adjacent if o(a) + o(b) &amp;gt; o(G). In this paper, we extend the study of order sum graphs of groups to domination. We determine different types of domination such as connected, global, strong, secure, restrained domination and so on for order sum graphs, their complement and line graphs of order sum graphs.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Amreen J.; Naduvath S.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-462, pp. 117-125.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>Amritha A.P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, India; Sandeep J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, India</text>
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                <text>Psychological autopsy studies are a method to understand the causes of equivocal deaths. Suicides and homicide-suicide are the result of various events. The understanding of an individual's life before their death, by interviewing the next of kin, provides some insight into the causes. The research articles dealing with psychological autopsy studies through case studies of suicide or homicide-suicide instances, obtained from different search engines, shed light on the mental health, Alcohol use disorder (AUD), and Drug use disorders (DUD) of individuals before death. The primary characteristic of suicides in a population of late twenties to early fifties was romantic relationship conflicts. In contrast, suicides in the teenage years and early twenties were characterized primarily by a lack of familial acceptability and, to a lesser extent, self-acceptance. Simultaneously, there was a high risk of suicidal behavior and commission of acts among the DUD patients, getting treatment without psychiatric diagnosis and therapy. Over time, the psychological autopsy technique has shown to be quite helpful in determining the risk factors for suicidal behavior. The study helps to develop multiple rehabilitation and mental awareness that need to be created among various populations so that the suicide and homicide-suicide rates can be reduced.  2025 Elsevier Ltd and Faculty of Forensic and Legal Medicine</text>
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                <text>Amruth P., Biochemistry and Nutrition Division, ICAR-Central Institute of Fisheries Technology, Kerala, Cochin, 682029, India, Faculty of Marine Sciences, Cochin University of Science and Technology, Kerala, Cochin, 682022, India, Department of Life Sciences, Christ University, Hosur Main Road, Bhavani Nagar, Karnataka, Bangalore, 560029, India; Akshay P., Biochemistry and Nutrition Division, ICAR-Central Institute of Fisheries Technology, Kerala, Cochin, 682029, India, Faculty of Marine Sciences, Cochin University of Science and Technology, Kerala, Cochin, 682022, India; Rosemol Jacob M., Biochemistry and Nutrition Division, ICAR-Central Institute of Fisheries Technology, Kerala, Cochin, 682029, India, Faculty of Marine Sciences, Cochin University of Science and Technology, Kerala, Cochin, 682022, India; Joy J.M., Biochemistry and Nutrition Division, ICAR-Central Institute of Fisheries Technology, Kerala, Cochin, 682029, India, Faculty of Marine Sciences, Cochin University of Science and Technology, Kerala, Cochin, 682022, India, St.Teresa's College (Autonomous), Kerala, Ernakulam, 682011, India; Mathew S., Biochemistry and Nutrition Division, ICAR-Central Institute of Fisheries Technology, Kerala, Cochin, 682029, India</text>
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                <text>Amrutha D.S., Christ (Deemed to be University), India; Ashwini M., Christ (Deemed to be University), India</text>
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                <text>Evaluating the Pertinence of Pose Estimation model for Sign Language Translation</text>
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                <text>deep learning model; landmark detection; machine learning model; pose estimation; SLR models</text>
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                <text>Sign Language is the natural language used by a community that is hearing impaired. It is necessary to convert this language to a commonly understandable form as it is used by a comparatively small part of society. The automatic Sign Language interpreters can convert the signs into text or audio by interpreting the hand movements and the corresponding facial expression. These two modalities work in tandem to give complete meaning to each word. In verbal communication, emotions can be conveyed by changing the tone and pitch of the voice, but in sign language, emotions are expressed using nonmanual movements that include body posture and facial muscle movements. Each such subtle moment should be considered as a feature and extracted using different models. This paper proposes three different models that can be used for varying levels of sign language. The first test was carried out using the Convex Hull-based Sign Language Recognition (SLR) finger spelling sign language, next using a Convolution Neural Network-based Sign Language Recognition (CNN-SLR) for fingerspelling sign language, and finally pose-based SLR for word-level sign language. The experiments show that the pose-based SLR model that captures features using landmark or key points has better SLR accuracy than Convex Hull and CNN-based SLR models. 2023 World Scientific Publishing Europe Ltd.</text>
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                <text>Amrutha K.; Prabu P.</text>
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                <text>International Journal of Computational Intelligence and Applications, Vol-22, No. 1</text>
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                <text>ISSN: 14690268 | CS; 2023-2024; Vol-1; 00263-00264</text>
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                <text>Amrutha K., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, India; Prabu P., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, India</text>
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                <text>Effortless and beneficial processing of natural languages using transformers</text>
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                <text>68T05; 68T50; ALBERTA; Attention model; BERT; GPT3; NLP; RoBERTa; Sequential model encoder-decoder</text>
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                <text>Natural Language Processing plays a vital role in our day-to-day life. Deep learning models for NLP help make human life easier as computers can think, talk, and interact like humans. Applications of the NLP models can be seen in many domains, especially in machine translation and psychology. This paper briefly reviews the different transformer models and the advantages of using an Encoder-Decoder language translator model. The article focuses on the need for sequence-to-sequence language-translation models like BERT, RoBERTa, and XLNet, along with their components.  2022 Taru Publications.</text>
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                <text>Journal of Discrete Mathematical Sciences and Cryptography, Vol-25, No. 7, pp. 1987-2005.</text>
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                <text>Amrutha K., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India</text>
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                <text>This paper reviews different steps in an automated sign language recognition (SLR) system. Developing a system that can read and interpret a sign must be trained using a large dataset and the best algorithm. As a basic SLR system, an isolated recognition model is developed. The model is based on vision-based isolated hand gesture detection and recognition. Assessment of ML-based SLR model was conducted with the help of 4 candidates under a controlled environment. The model made use of a convex hull for feature extraction and KNN for classification. The model yielded 65% accuracy.   2021 IEEE.</text>
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                <text>ISBN: 978-166540467-9</text>
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                <text>Amrutha K., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India; Prabu P., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India</text>
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                <text>A Comparative Study on Indian Sign Language Representation</text>
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                <text>continuous sign language; Indian sign language; isolated sign language; sign language recognition system</text>
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                <text>Communication among people can happen with the help of verbal or nonverbal language. Nonverbal communication is shared only among the hearing and speech impaired and is not common among others. Non-verbal communication is also different for different countries around the world. A solution to remove the gap between verbal and non-verbal communicators is to create an automated language translation model that can effortlessly convert sign language to text or audio. This area has been under research for a long time, but an economical and robust system that can efficiently convert signs into speech still does not exist. This paper focuses on different approaches that were put forward to turn Indian sign language into audio signals. The Sign Language Recognition (SLR) system is classified as isolated and continuous sign language models based on its input.  2021 IEEE.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125007329&amp;amp;doi=10.1109%2FICAECA52838.2021.9675678&amp;amp;partnerID=40&amp;amp;md5=dea4cd7784fceb333711694dc44108d2" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125007329&amp;amp;doi=10.1109%2fICAECA52838.2021.9675678&amp;amp;partnerID=40&amp;amp;md5=dea4cd7784fceb333711694dc44108d2&lt;/a&gt;</text>
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                <text>ISBN: 978-166542829-3</text>
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                <text>Amrutha K., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India; Prabu P., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India</text>
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                <text>Human Body Pose Estimation and Applications</text>
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                <text>2D pose estimation; 3D pose estimation; Body modelling; Holistic Pipeline estimation; MediaPipe; Sign language recognition</text>
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              <elementText elementTextId="183176">
                <text>Human Pose Estimation is one of the challenging yet broadly researched areas. Pose estimation is required in applications that include human activity detection, fall detection, motion capture in AR/VR, etc. Nevertheless, images and videos are required for every application that captures images using a standard RGB camera, without any external devices. This paper presents a real-time approach for sign language detection and recognition in videos using the Holistic pose estimation method of MediaPipe. This Holistic framework detects the movements of multiple modalities-facial expression, hand gesture and body pose, which is the best for the sign language recognition model. The experiment conducted includes five different signers, signing ten distinct words in a natural background. Two signs, 'blank' and 'sad, ' were best recognized by the model.  2021 IEEE.</text>
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                <text>Amrutha K.; Prabu P.; Paulose J.</text>
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                <text>3rd IEEE International Virtual Conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/i-PACT52855.2021.9696513" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/i-PACT52855.2021.9696513&lt;/a&gt;
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                <text>Amrutha K., CHRIST (Deemed to Be University), Dept. of Computer Science, Bengaluru, India; Prabu P., CHRIST (Deemed to Be University), Dept. of Computer Science, Bengaluru, India; Paulose J., CHRIST (Deemed to Be University), Dept. of Computer Science, Bengaluru, India</text>
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