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                <text>Color image segmentation based on improved sine cosine optimization algorithm</text>
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                <text>Color image segmentation; Enhanced sine cosine algorithm; Metaheuristics algorithm; Particle swarm optimization; PSNR</text>
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                <text>Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE L*a*b* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signalnoise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA.  2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</text>
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                <text>Mookiah S.; Parasuraman K.; Kumar Chandar S.</text>
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                <text>Soft Computing, Vol-26, No. 23, pp. 13193-13203.</text>
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                <text>&lt;a href="https://doi.org/10.1007/s00500-022-07133-5" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s00500-022-07133-5&lt;/a&gt;
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                <text>ISSN: 14327643</text>
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                <text>Mookiah S., Department of Computer Science, JP College of Arts and Science, Tenkasi, India; Parasuraman K., Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, Tirunelveli, 627012, India; Kumar Chandar S., School of Business and Management CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India</text>
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                <text>The use of self-protective measures to prevent COVID-19 spread: an application of the health belief model</text>
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                <text>COVID-19; health belief model; self-protective measures; SmartPLS</text>
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                <text>This study uses a health belief model to examine the preventive behavioral orientation or self-protective measures adopted by people in the face of the current COVID-19 pandemic. A total of 603 participants were selected from the city of Bangalore, India. The data was collected through an online survey with participants age varying between 17 and 54 and mean as 23 years (SD = 4.32). The findings revealed that perceived barrier has significant negative impact, while perceived threat, perceived consequences, perceived benefits, community and individual self-efficacy, and general health cues have a positive influence on an individuals intention to follow self-protective measures against COVID-19. Based on the constructs of the health belief model, this study proposes multiple health-related interventions to reduce the spread of COVID-19.  2022 Informa UK Limited, trading as Taylor &amp;amp; Francis Group.</text>
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                <text>Moorthy V.; Alagarsamy S.; Mehrolia S.; Jeevananda S.</text>
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                <text>Psychology, Health and Medicine, Vol-28, No. 10, pp. 2977-2992.</text>
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                <text>&lt;a href="https://doi.org/10.1080/13548506.2022.2162935" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1080/13548506.2022.2162935&lt;/a&gt;
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                <text>ISSN: 13548506; PubMed ID: 36582073; CODEN: PHMEF</text>
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                <text>Moorthy V., School of Business and Management, Christ University, Bangalore, India; Alagarsamy S., School of Business, Manipal Academy of Higher Education - Dubai Campus, International Academic City, United Arab Emirates; Mehrolia S., School of Business and Management, Christ University, Bangalore, India; Jeevananda S., School of Business and Management, Christ University, Bangalore, India</text>
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                <text>Investigating the Impact of Emotional Contagion on Customer Attitude, Trust and Brand Engagement: A Social Commerce Perspective</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="110836">
                <text>Attitude; Brand engagement; Emotion Contagion; Emotional Contagion; Social Media Content; Trust</text>
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                <text>Social Commerce networks are a powerful platform for spreading positive and negative emotional contagion, which is affecting users from different perspectives, i.e., psychology, attitude, buying decision. Emotional contagion is the phenomenon of having a person's emotions and behaviours directly trigger similar emotions or behaviour in other people. This research proposes a model to analyze the factors influencing emotional contagion that, in turn, impact consumer's attitudes, trust, and brand engagement. This study used a survey approach using a structured questionnaire. Primary data was collected from 174 social media users who shop online. The proposed model was tested using multiple regression analysis. The results demonstrated that effective content, visual or text, triggers customers' emotional contagion, influencing customer attitude and trust leading to brand engagement. The research study's findings can be used for deciding on content strategies of advertisements pertaining to social commerce.  2022 Academy of Taiwan Information Systems Research. All rights reserved.</text>
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                <text>Moorthy V.; Kiran P.; Banerjee J.; Krishna Kishore S.V.</text>
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                <text>International Journal of Electronic Commerce Studies, Vol-13, No. 3, pp. 99-118.</text>
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                <text>Academy of Taiwan Information Systems Research</text>
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                <text>&lt;a href="https://doi.org/10.7903/ijecs.2049" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.7903/ijecs.2049&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141283125&amp;amp;doi=10.7903%2Fijecs.2049&amp;amp;partnerID=40&amp;amp;md5=f89acec463f9fb1525a699f28f914b22" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141283125&amp;amp;doi=10.7903%2fijecs.2049&amp;amp;partnerID=40&amp;amp;md5=f89acec463f9fb1525a699f28f914b22&lt;/a&gt;</text>
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              <elementText elementTextId="110843">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 20739729</text>
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                <text>Moorthy V., Christ University, Bangalore, India; Kiran P., Westminster International University, Tashkent, Uzbekistan; Banerjee J., Amity University, Kolkata, India; Krishna Kishore S.V., Christ University, Bangalore, India</text>
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                <text>Comparing Influence of Depression and Negative Affect on Decision Making</text>
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          <element elementId="49">
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                <text>Balloon Analog Risk Task; Depression; Iowa Gambling Task; Mood induction; Value-based decision making</text>
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                <text>The current study aimed to explore differential value-based decision-making patterns across three groupsindividuals diagnosed with mild-to-moderate depression, a healthy matched control group, and a negative mood induction group. In the current study, drug- and therapy-nae individuals diagnosed with first episode of mild-to-moderate depression (n = 40), healthy individuals matched on age, gender, and education (n = 40), and healthy individuals with no current, past, or family history of any psychiatric conditions in a negative mood-induced state (n = 40) were administered the IOWA Gambling Task (IGT) and the Balloon Analog Risk Task (BART). Results indicated that individuals with depression showed heightened punishment sensitivity on both the IGT and the BART (p &amp;lt; 0.05 on the BART and p &amp;lt; 0.05 on the IGT), andperformed poorly on the IGT indicating poor and slow learning (p &amp;lt; 0.01). A similar, less severe, pattern was observed in the negative mood induction group. Individuals with mild-to-moderate depression performed poorly on tasks of value-based decision making. The significance of process factors in decision making, such as reward and punishment sensitivity, valuation of outcomes and learning, was highlighted in this study. The study also demonstrated how a negative affective state, without the other clusters of depressive symptomatology, can also lead to a less severe, but impaired decision making.  2023, The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India.</text>
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                <text>Moosath H.; Rangaswamy M.</text>
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                <text>Psychological Studies, Vol-68, No. 3, pp. 310-318.</text>
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                <text>&lt;a href="https://doi.org/10.1007/s12646-023-00719-5" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s12646-023-00719-5&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158110945&amp;amp;doi=10.1007%2Fs12646-023-00719-5&amp;amp;partnerID=40&amp;amp;md5=1832f756790b2d4bf3e8a1694023b698" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158110945&amp;amp;doi=10.1007%2fs12646-023-00719-5&amp;amp;partnerID=40&amp;amp;md5=1832f756790b2d4bf3e8a1694023b698&lt;/a&gt;</text>
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                <text>ISSN: 332968</text>
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                <text>Moosath H., Department of Psychology, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Rangaswamy M., Department of Psychology, CHRIST (Deemed to be University), Karnataka, Bangalore, India</text>
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                <text>Lung cancer prediction with advanced graph neural networks</text>
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                <text>Graph attention network; Graph neural network; Lung cancer; Machine learning; Prediction</text>
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                <text>This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions.  2025 Institute of Advanced Engineering and Science. All rights reserved.</text>
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                <text>Moozhippurath B.; Natarajan J.</text>
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                <text>Indonesian Journal of Electrical Engineering and Computer Science, Vol-37, No. 2, pp. 1077-1084.</text>
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                <text>ISSN: 25024752</text>
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                <text>Moozhippurath B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Natarajan J., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India</text>
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                <text>WELL-BEING AND PROSPERITY: Multidirectional Disciplinary Interactions with Religion</text>
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          <element elementId="49">
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              <elementText elementTextId="105173">
                <text>and well-being; charitable giving; circularity; linearity; political participation; religion; sustainable development</text>
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                <text>Despite significant advancements in science and technology, religion continues to influence human lives. The twentieth-century perspectives from social sciences, influenced by the secular hypothesis, mainly highlight the negative influence of religion on human progress and practically ignore its influential and positive impact on various fields of knowledge/disciplines. In this paper, we have examined literature from politics, economics, and psychology to understand religions impact on these disciplines and vice versa. We find that religions contribution to human society in the 20th and 21st centuries has been mostly positive, especially in education, healthcare, social justice, economic growth, ethics, and initiatives for eradicating inequality and injustice. For instance, religion provides effective coping measures and strategies when humans face uncertainties and catastrophes and facilitate comfort, confidence, and emotional wellness. Further, we realised that (i) the contemporary research literature in social sciences generally highlights the interaction between religion and various fields of knowledge in a unidirectional way i.e., religion influencing disciplines and not how disciplines influence religion, and (ii) that it fails to reveal a more complex multidirectional and circular relationship between religion and social sciences. This paper proposes ways to bring together social scientists and religious scholars to facilitate the much-needed discussion on the multidirectional relationship between religion and social sciences, thereby paving the way toward the well-being of individuals and social transformation.  2022 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore),.</text>
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                <text>Morais M.; Karakunnel J.J.; Perumbilly S.; Yeon G.; Rassendren G.</text>
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                <text>Journal of Dharma, Vol-47, No. 3, pp. 263-282.</text>
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                <text>Dharma Research Association</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139153732&amp;amp;partnerID=40&amp;amp;md5=25b9e2d23b0fc0e428f675ccb204964a" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139153732&amp;amp;partnerID=40&amp;amp;md5=25b9e2d23b0fc0e428f675ccb204964a&lt;/a&gt;</text>
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              <elementText elementTextId="105180">
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                <text>ISSN: 2537222</text>
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              <elementText elementTextId="105183">
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                <text>Morais M., Department of Economics, CHRIST University, Bangalore, India; Karakunnel J.J., Department of Economics, CHRIST University, Bangalore, India; Perumbilly S., Department of Marriage and Family Therapy, Southern Connecticut State University, United States; Yeon G., Christ University, Bangalore, India; Rassendren G., Department of Economics, CHRIST University, Bangalore, India</text>
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                  <text>Reviews</text>
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              <elementText elementTextId="195693">
                <text>A review on anti-cancer plants of India</text>
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          <element elementId="49">
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              <elementText elementTextId="195694">
                <text>Cancer; Catharanthus roseus; Curcuma longa; medicinal plants; Phyllanthus emblica; Zingiber officinale</text>
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                <text>India has a high level of endemism and a diverse range of floral species. Cancer is one of the most significant challenges facing global health today. The indigenous peoples and residents who live in India have, for a very long time, made use of specific medicinal plants to fight cancer. This practice is still prevalent today. Several different drugs may be utilized in the treatment of cancer. Because of the potential drawbacks associated with such treatments and the development of drug resistance, the quest for new therapies that are both safer and much more effective is still the most challenging field of study. Several cancer medicines used today come from natural sources. We're returning to our old ways because medicinal plants are a good, natural way to make medicines that prevent cancer without causing major side effects. Within the scope of this study, a few herbs traditionally used to treat cancer are looked at to see what they might be good for. The cytotoxicity of these plants, the processes that lead to them, and the different compounds they make were looked into. This study has tried to focus on how these plants fight cancer. The Author(s).</text>
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                <text>Morris C.C.; Ramyashree C.S.; Kruthika P.; Pappuswamy M.; Chaudhary A.; Meyyazhagan A.; Anand A.V.; Balasubramanian B.</text>
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                <text>Plant Science Today, Vol-10, pp. 1-7.</text>
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                <text>Horizon e-Publishing Group</text>
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                <text>Morris C.C., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Ramyashree C.S., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Kruthika P., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Pappuswamy M., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Chaudhary A., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Meyyazhagan A., Department of Life Sciences, Christ (Deemed to be University), Bangalore, 560029, India; Anand A.V., Department of Human Genetics and Molecular Biology, Bharathiar University, Coimbatore, 641046, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Sciences, Sejong University, Seoul, 05006, South Korea</text>
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                <text>The learning and teaching experience can be improved by using approaches that are not obtrusive to perform a comprehensive student engagement analysis throughout the classroom. In these modern times, when courses are conducted online, it is vital to accurately measure the levels of participation that each individual student has. It is crucial and essential to provide assistance to educators so that they may annotate and comprehend the signifcant learning rate of the students. A system that can perceive data and transpire it into information automates the learning and teaching experience in a classroom. In this study, videos are collected from online and ofand#64258;ine classes that have one single student per frame or many students per frame and are analysed for emotions and behavioural engagement through a multimodal system. newlineLarge amounts of video data processing call for an increase in the hardware resources newlineas well as the time required for processing images. This is particularly true in a newlineclassroom setting, where there are a large number of frames to analyse each and every minute in order to handle classroom involvement detection. Hierarchical Video newlineSummarization is used as a preliminary step on the videos to detect important frames newlinethat have the sum of all the information in the local neighborhood. These key frames newlineserve as important information units that provide details of facial emotions and behavioural aspects. The local maxima estimation based on the frst derivative provides summative information about the local neighborhood. The key frames serve newlineas an input for face detection and emotional analysis. In this research, the method newlinecan perform video summarization on a varied category of videos and with different newlineresolutions. Face detection in a temporal environment have not been trivial. Though there are methods that can identify multiple faces with varied sizes in a frame, it is still a current research topic to address false localization of faces in a frame.</text>
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                <text>K, Balachandran.</text>
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                <text>&lt;a href="http://hdl.handle.net/10603/465366" target="_blank" rel="noreferrer noopener"&gt;http://hdl.handle.net/10603/465366&lt;/a&gt;</text>
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                <text>A Deterministic Key-Frame Indexing and Selection for Surveillance Video Summarization</text>
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                <text>Adaptive threshold; Similarity index; Surveillance video Summarization</text>
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                <text>Video data is voluminous and impacts the data storage devices as there are CCTV surveillance videos being created every minute and stored continuously. Due to this increase in data there is a need to create semantic information out of the frames that are being stored. Video Summarization is a process that continuously monitors changes and helps in reducing the number of frames being stored. This work enables summarization to be carried out based on selecting threshold-based system that can select key-frames ideally suit for storage and further analysis. Initially a Global threshold based on Otsus method is carried out for all frames of a surveillance video and based on the set threshold a retrospective comparison is done on each frame based on statistical methods to converge on determining the keyframes. A similarity index is generated based on the iterative comparison of frames based on global and local threshold comparison. The local threshold is indexed based on Analysing Method Patterns to Locate Errors(AMPLE), An-derbergs D(AbD), Cohens Kappa(CK), Tanimoto Similarity(TS), Tversky feature contrast model(TFCM), Pearson coefficient of mean square contingency(Pmsc). The Global threshold is updated each time a keyframe is selected based on the comparison of local and global threshold. The results are compared with five surveillance videos and six methods to identify keyframes Selection Rate is the metric used for calculating the performance.  2019 IEEE.</text>
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                <text>Moses T.M.; Balachandran K.</text>
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              <elementText elementTextId="188265">
                <text>Abnormal behavior; Indexing and clustering; Semantics; Sparse dictionary; Video Summarization</text>
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                <text>In today's world data represented in the form of a video are prolific and has increased the requisite of storage devices unconditionally. These video sets takes up a huge space for amassing data and takes a long time to ascertain the content that requires a higher cognitive process for content search and retrieval. The efficient method for storing video data is to remove high-degree redundancies and for creating an index of important events, objects and a preview video based on vital key-frames. These requirements imbibes the need to build algorithms that can concise the necessity of space and time for video and adequate approaches are to be developed to solve the needs of summarization. The three effective attributes for a semantic summarized video system are Un-supervision, efficient and dynamically scalable system that can help in reducing time and space complexities. Dimensionality reduction based on sub space analysis helps in plummeting the multidimensional data into a low-dimensional data to enable faster feature extraction and summarization. In this paper we have made a study and description related to several summarization methodologies for video's that are available.  2017 IEEE.</text>
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                <text>2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies, ICAMMAET 2017, Vol-2017-January, pp. 1-6.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICAMMAET.2017.8186684" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICAMMAET.2017.8186684&lt;/a&gt;
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                <text>Moses T.M., Computer Science and Engineering, Faculty of Engineering, Christ University, Bengaluru, India; Balachandran K., Computer Science and Engineering, Faculty of Engineering, Christ University, Bengaluru, India</text>
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            <name>Language</name>
            <description>A language of the resource</description>
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                <text>English</text>
              </elementText>
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            <name>Type</name>
            <description>The nature or genre of the resource</description>
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                <text>Review</text>
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            <name>Coverage</name>
            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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                <text>Mrinalini Kalyani A.K., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India; Rajeev R., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India; Benny L., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India; Cherian A.R., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India; Varghese A., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India</text>
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