308
20
16488
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Faculty Publications
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Pandey, Ananya; Joseph, Jipson
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Emerging trends in usage of drone technology: Exploring the ethico-legal frontiers
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01-01-2025
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Innovations and Developments in Unmanned Aerial Vehicles;pp.171-195
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<a href="https://doi.org/10.4018/979-8-3693-8462-6.ch008" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-8462-6.ch008</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004338661?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004338661?origin=resultslist</a>
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Pandey A., Christ University, India; Joseph J., Christ University, India
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As an offshoot of technological advancement, the integration of autonomous Artificial Intelligence (AI) in the usage of drone technology has entirely changed the current landscape. Usage of drones in commercial activities, military services, emergency operations has expanded the horizons of human life. But together with this evolution there arise several critical ethico-legal concerns such as privacy and security issues, accountability, and environment impact making its real-world implication a significant concern in the current times. These issues need to be addressed in purview of policy framework and existing guidelines concerning the ethical usage of drone technology. Keeping in view the above, this chapter explores the emerging trends in drone usage through the lens of ethico-legal perspective across various sectors. It also highlights the complexities of its usage with regard to its rapid advancement. Furthermore, the global governance perspective considering responsible use of drones among policy makers and stakeholders is widely examined. 2025, IGI Global Scientific Publishing. All rights reserved.
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IGI Global
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ISBN: 979-836938464-0; 979-836938462-6;
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English
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Book chapter
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Restricted Access; Hardcopy may be available in the library
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online
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Book Chapter
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Emerging world of the metaverse: An Indian perspective
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[No abstract available]
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Bali A.; Aditya A.
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Intelligent Communication Networks: Research and Applications, pp. 71-86.
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CRC Press
Date
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2024-01-01
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<a href="https://doi.org/10.1201/9781003303114-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003303114-4</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192307921&doi=10.1201%2F9781003303114-4&partnerID=40&md5=3bc2f7b76589582ba1d8b95ae069cb1d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192307921&doi=10.1201%2f9781003303114-4&partnerID=40&md5=3bc2f7b76589582ba1d8b95ae069cb1d</a>
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ISBN: 978-100330311-4; 978-103230021-4
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Online
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English
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Book chapter
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Bali A., Christ University, Bangalore, India; Aditya A., Christ University, Bangalore, India
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Articles
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Emission line star catalogues post- Gaia DR3: A validation of Gaia DR3 data using the LAMOST OBA emission catalogue
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Be; Catalogs; Herbig Ae/Be; Methods: data analysis; Stars: emission-line; Stars: variables: T Tauri; Techniques: spectroscopic
Description
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Aims.Gaia Data Release 3 (DR3) and further releases have the potential to identify and categorise new emission-line stars in the Galaxy. We perform a comprehensive validation of astrophysical parameters from Gaia DR3 with the spectroscopically estimated emission-line star parameters from the LAMOST OBA emission catalogue. Method. We compare different astrophysical parameters provided by Gaia DR3 with those estimated using LAMOST spectra. By using a larger sample of emission-line stars, we performed a global polynomial and piece-wise linear fit to update the empirical relation to convert the Gaia DR3 pseudo-equivalent width to the observed equivalent width, after removing the weak emitters from the analysis. Results. We find that the emission-line source classifications given by DR3 is in reasonable agreement with the classification from the LAMOST OBA emission catalogue. The astrophysical parameters estimated by the esphs module from Gaia DR3 provides a better estimate when compared to gspphot and gspspec. A second degree polynomial relation is provided along with piece-wise linear fit parameters for the equivalent width conversion. We notice that the LAMOST stars with weak H? emission are not identified to be in emission from BP/RP spectra. This suggests that emission-line sources identified by Gaia DR3 are incomplete. In addition, Gaia DR3 provides valuable information about the binary and variable nature of a sample of emission-line stars. 2022 EDP Sciences. All rights reserved.
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Shridharan B.; Mathew B.; Bhattacharyya S.; Robin T.; Arun R.; Kartha S.S.; Manoj P.; Nidhi S.; Maheshwar G.; Paul K.T.; Narang M.; Himanshu T.
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Astronomy and Astrophysics, Vol-668
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EDP Sciences
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2022-01-01
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<a href="https://doi.org/10.1051/0004-6361/202244353" target="_blank" rel="noreferrer noopener">https://doi.org/10.1051/0004-6361/202244353</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145350689&doi=10.1051%2F0004-6361%2F202244353&partnerID=40&md5=68016d64db20063f4d16f6391f6f9b1b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145350689&doi=10.1051%2f0004-6361%2f202244353&partnerID=40&md5=68016d64db20063f4d16f6391f6f9b1b</a>
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All Open Access; Green Open Access; Hybrid Gold Open Access
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ISSN: 46361; CODEN: AAEJA
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Online
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English
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Article
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Shridharan B., Department of Physics and Electronics, Christ (Deemed to Be University), Hosur Main Road, Bangalore, India; Mathew B., Department of Physics and Electronics, Christ (Deemed to Be University), Hosur Main Road, Bangalore, India; Bhattacharyya S., Department of Physics and Electronics, Christ (Deemed to Be University), Hosur Main Road, Bangalore, India; Robin T., Department of Physics and Electronics, Christ (Deemed to Be University), Hosur Main Road, Bangalore, India; Arun R., Indian Institute of Astrophysics, Koramangala, Bangalore, India; Kartha S.S., Department of Physics and Electronics, Christ (Deemed to Be University), Hosur Main Road, Bangalore, India; Manoj P., Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai, India; Nidhi S., Department of Physics and Electronics, Christ (Deemed to Be University), Hosur Main Road, Bangalore, India; Maheshwar G., Indian Institute of Astrophysics, Koramangala, Bangalore, India; Paul K.T., Department of Physics and Electronics, Christ (Deemed to Be University), Hosur Main Road, Bangalore, India; Narang M., Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai, India; Himanshu T., Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai, India
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Conference Papers
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Faculty Publications- Conference Papers
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Emoji Sentiment Analysis of User Reviews on Online Applications Using Supervised Machine Learning
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Artificial neural network; K-Nearest neighbors; Logistic regression; Machine learning; Naive bayes; Natural language processing; Random forest; Sentiment analysis; Supervised
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Analyzing the sentiment behind emojis can provide valuable insights into the emotional context and user sentiment associated with textual content. To conduct a comparative analysis of diverse supervised machine learning models that can achieve the highest level of accuracy in Emoji Sentiment Analysis is the purpose of this research. Five machine learning models used in this research are K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Logistic Regression, Naive Bayes, and Random Forest. The experimental process resulted in ANN and KNN models giving an accuracy of 92%. The ANN model shows its proficiency in effectively managing large datasets. ANN also supports fault tolerance. The KNN model refrains from conducting calculations during the training phase and only constructs a model when a query is executed on the dataset. This characteristic makes KNN particularly well-suited for data mining. Both ANN and K-NN excelled in the experimental study due to these distinctive attributes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Swetha Cordelia A.; Kokatnoor S.A.
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Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 257-267.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-97-0975-5_23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-0975-5_23</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197304422&doi=10.1007%2F978-981-97-0975-5_23&partnerID=40&md5=b53045be74522d33924d6a75255134cc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197304422&doi=10.1007%2f978-981-97-0975-5_23&partnerID=40&md5=b53045be74522d33924d6a75255134cc</a>
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Restricted Access
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ISSN: 23673370; ISBN: 978-981970974-8
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Online
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English
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Conference paper
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Swetha Cordelia A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India
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Articles
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EMONET: A Cross Database Progressive Deep Network for Facial Expression Recognition
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Cross database; Deep neural network; Emonet; Facial expression recognition; Progressive resizing
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Recognizing facial features to detect emotions has always been an interesting topic for research in the field of Computer vision and cognitive emotional analysis. In this research a model to detect and classify emotions is explored, using Deep Convolutional Neural Networks (DCNN). This model intends to classify the primary emotions (Anger, Disgust, Fear, Happy, Sad, Surprise and Neutral) using progressive learning model for a Facial Expression Recognition (FER) System. The proposed model (EmoNet) is developed based on a linear growing-shrinking filter method that shows prominent extraction of robust features for learning and interprets emotional classification for an improved accuracy. EmoNet incorporates Progressive- Resizing (PR) of images to accommodate improved learning traits from emotional datasets by adding more image data for training and Validation which helped in improving the model's accuracy by 5%. Cross validations were carried out on the model, this enabled the model to be ready for testing on new data. EmoNet results signifies improved performance with respect to accuracy, precision and recall due to the incorporation of progressive learning Framework, Tuning Hyper parameters of the network, Image Augmentation and moderating generalization and Bias on the images. These parameters are compared with the existing models of Emotional analysis with the various datasets that are prominently available for research. The Methods, Image Data and the Fine-tuned model combinedly contributed in achieving 83.6%, 78.4%, 98.1% and 99.5% on FER2013, IMFDB, CK+ and JAFFE respectively. EmoNet has worked on four different datasets and achieved an overall accuracy of 90%. 2020. All Rights Reserved.
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Thiruthuvanathan M.M.; Krishnan B.
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International Journal of Intelligent Engineering and Systems, Vol-13, No. 6, pp. 31-41.
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Intelligent Network and Systems Society
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2020-01-01
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<a href="https://doi.org/10.22266/ijies2020.1231.04" target="_blank" rel="noreferrer noopener">https://doi.org/10.22266/ijies2020.1231.04</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100673488&doi=10.22266%2Fijies2020.1231.04&partnerID=40&md5=5b8a1a7783439d9c0a4d637b180208bf" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100673488&doi=10.22266%2fijies2020.1231.04&partnerID=40&md5=5b8a1a7783439d9c0a4d637b180208bf</a>
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All Open Access; Bronze Open Access
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ISSN: 2185310X
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Online
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English
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Article
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Thiruthuvanathan M.M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India
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https://archives.christuniversity.in/files/original/dd42fa199090c472e529e9b9c3f3d1f9.pdf
a19c35b2cd9af99b5b879b430aaf03f8
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MPHIL
Mphil
Mphil Thesis
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EMOTICONS AND THE NON-VERBAL COMMUNICATION: WITH REFERENCE TO FACEBOOK
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Chen Yuet Wei Amanda
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2012
Source
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Media Studies
Description
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In the recent years, the use of emoticons in text-based and computer-mediated communications has gained a lot of popularity. Though emoticons (a combination of punctuation marks and letters) first began as a representation of facial expression, they have over the years been transformed to now include graphical representations of a variety of items (both static and animated). The usage of emoticons and their interpretation differ from one person to another, depending on factors such as gender, age and culture. Facebook is a platform where people across the globe communicate, share opinions and connect with each other. The researcher, thus, seeks to understand whether emoticons have the ability to infuse the text-based computer-mediated- communications on Facebook with the richness and authenticity of face-to-face interactions, and to arrive at an understanding of how these different groups use and interpret emoticons. A sample size of 139 was selected using the snowball sampling technique. The methods of primary data collection included surveys in the form of questionnaires that were distributed online. A quantitative analysis of the collected data was conducted using SPSS. The study revealed that age, gender and location do have a bearing on the patterns of usage and interpretation of emoticons. It also showed that emoticons cannot provide the text-based computer-mediated- communications on Facebook with the richness and authenticity of face-to-face interactions.
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Emotion Detection Using Machine Learning Technique
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Deep learning; Facial emotion recognition (FER); Facial expressions; Semi-supervised learning; Traditional classification methods
Description
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Face Emotion Recognition (FER) is an emerging and crucial topic today; since much research has been done in this field, there are still many things to explore. In daily life, where people dont have time to fill out feedback, emotion detection plays an important role, which helps to know customer feedback by analyzing expressions and gestures. Analyzing current studies in emotion recognition demonstrates notable advancements made possible by deep learning. A thorough overview of facial emotion recognition (FER) is provided in this publication. The literature cited in this study is taken from various credible research published in the last 10years. This study has built a model for emotion recognition using photos or a camera. The paper is based on the concepts of Machine Learning (ML), Deep Learning (DL), and Neural Networks (NN). A range of publicly available datasets have been used to evaluate evaluation metrics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Shukla S.; Lucas Y.
Source
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Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 547-557.
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Springer Science and Business Media Deutschland GmbH
Date
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-97-0975-5_48" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-0975-5_48</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197320284&doi=10.1007%2F978-981-97-0975-5_48&partnerID=40&md5=3870fdc5766525f82c1cc7d370dbfc50" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197320284&doi=10.1007%2f978-981-97-0975-5_48&partnerID=40&md5=3870fdc5766525f82c1cc7d370dbfc50</a>
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Restricted Access
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ISSN: 23673370; ISBN: 978-981970974-8
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Online
Language
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English
Type
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Conference paper
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Shukla S., Christ University, Bengaluru, India; Lucas Y., Christ University, Bengaluru, India
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Faculty Publications
Article
Faculty Publications -Articles
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Ahmad, Akram; Singh, Vaishali; Upreti, Kamal
Title
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Emotion Recognition Through Facial Expressions: A Machine Learning Perspective in Mobile Multimedia
Date
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01-01-2025
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Journal of Mobile Multimedia;Volume;21;Issue;1;pp.87-112
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<a href="https://doi.org/10.13052/jmm1550-4646.2114" target="_blank" rel="noreferrer noopener">https://doi.org/10.13052/jmm1550-4646.2114</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003940451?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003940451?origin=resultslist</a>
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Ahmad A., Department of Computer Science, Maharishi University of Information Technology, Lucknow, India; Singh V., Department of Computer Science, Maharishi University of Information Technology, Lucknow, India; Upreti K., Christ University, Delhi NCR Campus, Ghaziabad, India
Description
An account of the resource
Facial expression-based emotion detection is very attractive because of the possibilities in security systems, mental health monitoring, and human-computer interaction. Even with the progress in accuracy in real-world settings, issues such as the lack of balanced datasets and the inability to differentiate between faint or superimposed emotions continue to plague it. This study aims to bridge these constraints by developing a CNN-based model that would be able to recognize face emotions reliably and be utilized in real-time situations, such as webcam integration. The Affect Net dataset, which is a comprehensive collection of over a million facial photos labeled with the seven major emotions of anger, disgust, fear, happiness, neutrality, sadness, and surprise, was used to train the proposed model. Other pre-processing data techniques used include grayscale conversion, normalization, scaling, and data supplementation to increase the robustness of the model. Using metrics like accuracy and loss trends for evaluation, the model demonstrated efficiency stability at around the 30th training phase. When the model is compared to existing models, this proposed model can attain the competitive level of accuracy up to approximately 60%. It also has the potential to run in real applications through its webcam integration. While the model can differentiate between various clear-cut emotions, it becomes ineffective at identifying subtle emotions, which include Fear and Neutral majorly because of unbalanced data and the subtleness of these expressions. 2025 River Publishers.
Subject
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advanced machine learning; Emotion detection; facial expressions; feature extraction; human-computer interaction
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River Publishers
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ISSN: 15504646;
Language
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English
Type
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Article
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All Open Access; Gold Open Access
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online
-
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Articles
Article
Faculty Publications -Articles
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Emotion Regulation and Psychological Well-being as Contributors Towards Mindfulness Among Under-Graduate Students
Subject
The topic of the resource
Emotion regulation; Mental health; Mindfulness; Psychological well-being
Description
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Emotion regulation is generally described as the ability of an individual not only to manage emotions effectively but also to respond effectively to the emotional experience. It has also been viewed as a crucial aspect for psychological well-being. It is a psychological state which means more than just being free from stress and not having any other psychological disorder reported by the individual. At the same time, students with higher emotion regulation and psychological well-being are expected to be more attentive and able to observe, describe and participate in the present moment, effectively, with non-judgmental awareness, which is in turn defined as mindfulness. Hence, it has been expected that participants with higher emotion regulation and psychological well-being would also report higher levels of mindfulness. Therefore, the present empirical investigation has been conducted with an objective of assessing the level of emotion regulation, psychological well-being and mindfulness among under-graduate students. Additionally, it was also expected that all the said variables would be positively correlated and emotion regulation and psychological well-being would predict mindfulness positively for under-graduate students. For this purpose, ex post facto research design was adopted, and standardized tools pertaining to emotion regulation, psychological well-being and mindfulness were administered on a sample of 104 under-graduate students. The results of correlation statistics revealed that emotional regulation (r=0.27; p<0.01) and psychological well-being (r=0.21; p<0.01) are the positive and significant correlates of mindfulness. Additionally, statistical outcomes of stepwise multiple regression analysis confirm that emotion regulation and psychological well-being are the significant predictors of mindfulness and contribute collectively towards a 11% variance towards the same. 2020, Springer Nature Switzerland AG.
Creator
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Tasneem S.A.; Panwar N.
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Human Arenas, Vol-5, No. 2, pp. 279-297.
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Springer Science and Business Media B.V.
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2022-01-01
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<a href="https://doi.org/10.1007/s42087-020-00144-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42087-020-00144-4</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100738242&doi=10.1007%2Fs42087-020-00144-4&partnerID=40&md5=03a67764bbddcede0ed2f2832a35608a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100738242&doi=10.1007%2fs42087-020-00144-4&partnerID=40&md5=03a67764bbddcede0ed2f2832a35608a</a>
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ISSN: 25225804
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English
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Article
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Tasneem S.A., P.G. Department of Psychology, Christ (Deemed-To-Be-University), Bangalore, India; Panwar N., P.G. Department of Psychology, Christ (Deemed-To-Be-University), Bangalore, India
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Sharanya, R.; Singh, Shivangi; Jose, Deepa V
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Emotion Trajectory Analysis and Model Comparison for Hate Speech and Radicalization Detection in Code-Mixed Platforms
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01-01-2025
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2025 17th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2025;pp.1378-1383
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<a href="https://doi.org/10.1109/CICN67655.2025.11368245" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CICN67655.2025.11368245</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034677241?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034677241?origin=resultslist</a>
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Sharanya R., Deemed to Be University, Dept. of Computer Science Christ, Bangalore, India; Singh S., Deemed to Be University, Dept. of Computer Science Christ, Bangalore, India; Jose D.V., Deemed to Be University, Dept. of Computer Science Christ, Bangalore, India
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The growing presence of multilingual and codemixed content on social media creates major challenges for automated emotion recognition and mental health support. In this work, we introduce an emotion-aware computational framework that processes code-mixed Indian language comments and predicts user emotions with high accuracy, followed by context-aware support suggestions. Our dataset comes from the AI4Bharat IndicNLP corpus [14] and the Dravidian-CodeMix sentiment dataset [15], featuring a variety of multilingual user comments. To maintain linguistic consistency, we translate the raw texts into English using Google Translator and then preprocess them through normalization, tokenization, and stopword removal. We use three advanced transformer-based models, DistilBERT (six emotions), DistilRoBERTa (seven emotions), and RoBERTa GoEmotions (27+ emotions), to categorize the emotions in the comments. We compare predictions across the models and select the most reliable label for each text, which is further verified through manual checks with human annotators. This process results in a curated dataset labeled with emotions and enriched with model provenance. With this dataset, we train a Logistic Regression classifier using TF-IDF features to create an efficient, explainable prediction pipeline. The system classifies emotions and provides tailored suggestions based on emotional states, improving user support in online interactions. Experimental results show the robustness of the pipeline and its ability to adapt to various code-mixed inputs. This study offers an integrated dataset-model-suggestion framework that advances emotion recognition in multilingual contexts and supports the creation of practical emotion-aware digital systems. 2025 IEEE.
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Code-Mixed Social Media; Computational Social Science; Early Detection; Emotion Dynamics; Hate Speech Detection; Natural Language Processing (NLP); Radicalization; Temporal Emotion Trajectories
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833158733-8;
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English
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Conference paper
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online
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Thomas, Aksa Maria; Kirubanand, V.B.
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Emotion-Aware Sign Language Recognition Using CNN and UNET Architectures
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01-01-2025
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Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025;
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<a href="https://doi.org/10.1109/ICCIST67338.2025.11438577" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCIST67338.2025.11438577</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037453582?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037453582?origin=resultslist</a>
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Thomas A.M., CHRIST (Deemed to be University), Department of Computer Science, India; Kirubanand V.B., CHRIST (Deemed to be University), Department of Computer Science, India
Description
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This paper proposes an AI-based system for the recognition of sign language with the detection of emotions for more expressive communication among speech-impaired and hearing individuals and others. Traditional sign language systems focus mainly on the aspect of hand gestures and neglect the signs for emotions that add meaning and context. In order to overcome the limitation, the project proposes a system that utilizes Convolutional Neural Nets (CNNs) for the recognition of hand signs and UNET for the segmentation of the picture so that the area of the hands can be discriminated from the background. Facial Emotion Recognition (FER) is also incorporated in order to detect signs such as happiness, sadness, or anger. Overall, the parts together constitute a multimodal recognition system that can read signs and emotions and produce more natural and expressive outputs. The paper delves into architecture, dataset challenges, and implementation concepts with publicly available databases such as RWTH-PHOENIX-Weather 2014T. The combined approach can enhance inclusivity and access in learning, communication, and assistive technology. 2025 IEEE.
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Convolutional Neural Network (CNN); Deep Learning; Facial Emotion Recognition (FER); Human-Computer Interaction (HCI); Sign Language Recognition (SLR); UNET
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833159676-7;
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English
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Conference paper
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online
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Thomas, Aksa Maria; Kirubanand, V.B.
Title
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Emotion-Aware Sign Language Recognition Using CNN and UNET Architectures
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01-01-2025
Source
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Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025;
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<a href="https://doi.org/10.1109/ICCIST67338.2025.11438577" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCIST67338.2025.11438577</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037453582?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037453582?origin=resultslist</a>
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Thomas A.M., CHRIST (Deemed to be University), Department of Computer Science, India; Kirubanand V.B., CHRIST (Deemed to be University), Department of Computer Science, India
Description
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This paper proposes an AI-based system for the recognition of sign language with the detection of emotions for more expressive communication among speech-impaired and hearing individuals and others. Traditional sign language systems focus mainly on the aspect of hand gestures and neglect the signs for emotions that add meaning and context. In order to overcome the limitation, the project proposes a system that utilizes Convolutional Neural Nets (CNNs) for the recognition of hand signs and UNET for the segmentation of the picture so that the area of the hands can be discriminated from the background. Facial Emotion Recognition (FER) is also incorporated in order to detect signs such as happiness, sadness, or anger. Overall, the parts together constitute a multimodal recognition system that can read signs and emotions and produce more natural and expressive outputs. The paper delves into architecture, dataset challenges, and implementation concepts with publicly available databases such as RWTH-PHOENIX-Weather 2014T. The combined approach can enhance inclusivity and access in learning, communication, and assistive technology. 2025 IEEE.
Subject
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Convolutional Neural Network (CNN); Deep Learning; Facial Emotion Recognition (FER); Human-Computer Interaction (HCI); Sign Language Recognition (SLR); UNET
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833159676-7;
Language
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English
Type
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Conference paper
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online
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Emotional Abuse and the Pandemic in India: Implications for Policy, Research, and Practice
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emotional violence; gender roles; mental health; National Family Health Survey; pandemic
Description
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During the COVID-19 outbreak, cases of violence and abuse have increased significantly around the world, necessitating a reevaluation of our relation-ships. Both violence and abuse seek to control and instill fear in the individ-ual, gradually disrupting their overall well-being. Emotional abuse does not receive the same level of attention and social response as other forms of abuse due to its subtle nature. Its effects are as harmful as physical and sexual abuse, with serious consequences for the mental health of individual and their families. The COVID-19 pandemic has brought to light the importance of mental health. With the imposition of lockdown in India, the number of helplines for domestic violence and abuse has skyrocketed. Abuse experien-ces can be seen to be bidirectional; women are not alone in such instances. Many cases, however, go unreported and never reach formal institutions. The National Family Health Survey (2019-2021) reveals the current state of Indian health and nutrition, but emotional abuse (also referred to inter-changeably in this article as emotional violence) only includes responses from women and is no longer included under spousal violence in the most recent edition. This article also includes recommendations and attempts to highlight existing shortcomings in addressing the issue of emotional violence. The articles cited in this article were obtained from electronic databases. Other secondary data sources mentioned include newspaper articles, magazines, census reports, and periodicals. 2024 Springer Publishing Company.
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Maliakkal D.J.; George T.S.
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Partner Abuse, Vol-15, No. 1, pp. 95-115.
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Springer Publishing Company
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2024-01-01
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<a href="https://doi.org/10.1891/PA-2023-0023" target="_blank" rel="noreferrer noopener">https://doi.org/10.1891/PA-2023-0023</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183628709&doi=10.1891%2FPA-2023-0023&partnerID=40&md5=b328668f3035e6752b3026f75be283c4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183628709&doi=10.1891%2fPA-2023-0023&partnerID=40&md5=b328668f3035e6752b3026f75be283c4</a>
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ISSN: 19466560
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Online
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English
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Article
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Maliakkal D.J., Department of Psychology, CHRIST (Deemed to be University), Bengaluru, 560029, India; George T.S., Department of Psychology, CHRIST (Deemed to be University), Bengaluru, 560029, India
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Ghosh, Debaleena; Goyal, Nishant; Halder, Susmita
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Emotional Dysregulation and Emerging Psychopathology in Adolescents
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01-01-2025
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Case Studies in Cognitive Behavioral Intervention: Beyond the Basics;pp.163-185
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<a href="https://www.scopus.com/pages/publications/105026329372?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026329372?origin=resultslist</a>
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Ghosh D., Department of Psychology, Christ University, Bangalore, India; Goyal N., Department of Psychiatry, Central Institute of Psychiatry, Ranchi, India; Halder S., St. Xavier's University, Kolkata, India
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[No abstract available]
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Peter Lang AG
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ISBN: 978-180374722-4; 978-180374721-7;
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English
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Book chapter
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online
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Emotional Inhibition and Personality as Predictors of Anxiety and Depression in Young Adults
Subject
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Anxiety; Depression; Emotional Inhibition; introversion-extroversion; Personality
Description
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Purpose: Anxiety and depression have been major contributors to the global burden of disease, and the impact has been exacerbated following the COVID-19 pandemic. Therefore, the aim of this study was to understand the association between emotional suppression and the introverted-extraverted dimension of personality in young people and anxiety and depression. Method: Participants were 152 Indian females between the age group of 18-25 years who provided basic demographic details and completed three questionnaires via a google form. Findings: Results described a significant negative correlation of anxiety r (152) = .500, p <0.01and depression r(152)=.471, p <0.01 with emotional inhibition. There was also a significant positive correlation of anxiety r (152) = .288,p < 0.01 and depression r(152)= .288, p <0.01 with personality. While Emotional inhibition emerged as a significant negative predictor of anxiety (R2= .250) as well as of depression (R2=.222), personality (R2=.243) emerged as a significant predictor of depression. Conclusion/Value: Contrary to popular belief, the results of this study suggest that anxiety and depression are inversely related to emotional inhibition. It restores the complexity of emotions and the need to investigate their role in various pathologies. These findings provide an initial basis for further investigation into the role of emotional expression and suppression in the Indian population. 2024 RJ4All.
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Dhar A.; Kaur H.
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Youth Voice Journal, Vol-14
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RJ4All Publications
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2024-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189071232&partnerID=40&md5=484f6fe24da3169849900cd49266d268" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189071232&partnerID=40&md5=484f6fe24da3169849900cd49266d268</a>
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ISSN: 20492073
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Online
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English
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Article
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Dhar A., Christ Deemed to be University, Delhi-NCR, India; Kaur H., Panjab University, India
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Emotional Intelligence and Cross-Cultural Adaptation of Indian Students in the Context of Interstate Education
Subject
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Cross-Cultural Adaptation; Cultural Novelty; Emotional Intelligence; Expatriate Adjustment; Indian Students; Interstate Students
Description
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India is known for its cultural diversity based on several factors, such as language, religion, race, and customs. In India, people used to move from one place to another for various purposes, and this was particularly the case with students in pursuit of education. In such situations, cross-cultural adaptation is one of the factors that facilitate their adjustment to new cultures and surroundings. Cross-cultural adaptation is needed when a person has to live in a different cultural setting than their own native place. Being sensitive to others emotions is essential when one lives in a new place. Emotional intelligence helps in that way and influences cross-cultural adaptation. Therefore, the present study was intended to explore the influence of emotional intelligence on cross-cultural adaptation. As many as 332 students, aged 17 to 29, who moved to another state for education, participated in the study. Emotional Intelligence Scale and Cross-Cultural Adjustment Scale were used for data collection. The components of emotional intelligence, such as self-emotional appraisal (SEA) and others emotional appraisal (OEA), were found to significantly influence expatriate adjustment. Furthermore, SEA and OEA have also influenced cultural novelty and the use of emotions (UOE). Students from rural areas were found to have more cross-cultural adaptation in the presence of their friends company compared to urban dwellers. In summary, the current study emphasizes the importance of higher emotional intelligence for better cross-cultural adaptation. 2025 Common Ground Research Networks. All rights reserved.
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Sundaramoorthy J.; Eapen J.C.; Geddam S.; Deepthi D.P.; Pandey V.; Eslavath R.; Yadav R.K.; Govindappa L.
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International Journal of Interdisciplinary Cultural Studies, Vol-20, No. 1, pp. 19-33.
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Common Ground Research Networks
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2025-01-01
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<a href="https://doi.org/10.18848/2327-008X/CGP/v20i01/19-33" target="_blank" rel="noreferrer noopener">https://doi.org/10.18848/2327-008X/CGP/v20i01/19-33</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210960322&doi=10.18848%2F2327-008X%2FCGP%2Fv20i01%2F19-33&partnerID=40&md5=d6485f67f8ef517e791ee2cde9d673c4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210960322&doi=10.18848%2f2327-008X%2fCGP%2fv20i01%2f19-33&partnerID=40&md5=d6485f67f8ef517e791ee2cde9d673c4</a>
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ISSN: 2327008X
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Online
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English
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Article
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Sundaramoorthy J., Department of Psychology, Central University of Punjab, Punjab, Bathinda, India; Eapen J.C., Department of Psychology, Christ University, Karnataka, Bangalore, India; Geddam S., Department of Psychology, Gandhi Institute of Technology and Management, Bangalore, India; Deepthi D.P., Department of Psychology, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women (Autonomous), Chennai, India; Pandey V., Department of Psychology, Central University of Karnataka, Karnataka, Kalaburagi, India; Eslavath R., Department of Liberal Arts, Indian Institute of Technology, Chhattisgarh, Bhilai, India; Yadav R.K., Department of Law, Central University of Punjab, Punjab, Bathinda, India; Govindappa L., Department of Social work, Central University of Karnataka, Karnataka, Kalaburagi, India
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Sundaramoorthy, Jeyavel; Eapen, Jojo Chacko; Geddam, Subhasree; Deepthi, D.P.; Pandey, Vijyendra; Eslavath, Rajkumar; Yadav, Raj Kumar; Govindappa, Lakshmana
Title
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Emotional Intelligence and Cross-Cultural Adaptation of Indian Students in the Context of Interstate Education
Date
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01-01-2025
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International Journal of Interdisciplinary Cultural Studies;Volume;20;Issue;1;pp.19-33
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<a href="https://doi.org/10.18848/2327-008X/CGP/v20i01/19-33" target="_blank" rel="noreferrer noopener">https://doi.org/10.18848/2327-008X/CGP/v20i01/19-33</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85210960322?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85210960322?origin=resultslist</a>
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Sundaramoorthy J., Department of Psychology, Central University of Punjab, Punjab, Bathinda, India; Eapen J.C., Department of Psychology, Christ University, Karnataka, Bangalore, India; Geddam S., Department of Psychology, Gandhi Institute of Technology and Management, Bangalore, India; Deepthi D.P., Department of Psychology, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women (Autonomous), Chennai, India; Pandey V., Department of Psychology, Central University of Karnataka, Karnataka, Kalaburagi, India; Eslavath R., Department of Liberal Arts, Indian Institute of Technology, Chhattisgarh, Bhilai, India; Yadav R.K., Department of Law, Central University of Punjab, Punjab, Bathinda, India; Govindappa L., Department of Social work, Central University of Karnataka, Karnataka, Kalaburagi, India
Description
An account of the resource
India is known for its cultural diversity based on several factors, such as language, religion, race, and customs. In India, people used to move from one place to another for various purposes, and this was particularly the case with students in pursuit of education. In such situations, cross-cultural adaptation is one of the factors that facilitate their adjustment to new cultures and surroundings. Cross-cultural adaptation is needed when a person has to live in a different cultural setting than their own native place. Being sensitive to others emotions is essential when one lives in a new place. Emotional intelligence helps in that way and influences cross-cultural adaptation. Therefore, the present study was intended to explore the influence of emotional intelligence on cross-cultural adaptation. As many as 332 students, aged 17 to 29, who moved to another state for education, participated in the study. Emotional Intelligence Scale and Cross-Cultural Adjustment Scale were used for data collection. The components of emotional intelligence, such as self-emotional appraisal (SEA) and others emotional appraisal (OEA), were found to significantly influence expatriate adjustment. Furthermore, SEA and OEA have also influenced cultural novelty and the use of emotions (UOE). Students from rural areas were found to have more cross-cultural adaptation in the presence of their friends company compared to urban dwellers. In summary, the current study emphasizes the importance of higher emotional intelligence for better cross-cultural adaptation. 2025 Common Ground Research Networks. All rights reserved.
Subject
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Cross-Cultural Adaptation; Cultural Novelty; Emotional Intelligence; Expatriate Adjustment; Indian Students; Interstate Students
Publisher
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Common Ground Research Networks
Relation
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ISSN: 2327008X;
Language
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English
Type
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Article
Rights
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
Dublin Core
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Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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Emotional Intelligence and General Well-Being Among Middle Aged People
Subject
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Psychology
Description
An account of the resource
International Journal of Research in Social Sciences, Vol-2 (4), pp. 454-471. ISSN-2249-2496
Creator
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Padmakumari, Prativadi Radha
Publisher
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International Journal of Research in Social Sciences
Date
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2012
Language
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English
-
https://archives.christuniversity.in/files/original/b67c4c1c38e14c78f1fe332f4e27a5ae.pdf
c12abc1d09e42753f03cb37890d7c28b
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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MPHIL
Mphil
Mphil Thesis
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Emotional intelligence and its influence on job satisfaction of employees within Indian IT and ITES organizations
Creator
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Christy Alex Smitha
Date
A point or period of time associated with an event in the lifecycle of the resource
2015
Source
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Psychology
-
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Emotional intelligence and self esteem in cannabis abusers
Subject
The topic of the resource
Cannabis dependence; Emotional intelligence; Self esteem
Description
An account of the resource
This study was taken up to investigate emotional intelligence and self esteem in cannabis abusers. Cross sectional hospital based study, Study is based on a sample of 200 individuals. 100 Cannabis dependent, diagnosed based on DSM-IV TR was selected from two different hospitals in north India. 100 healthy matched subjects constituted the control group. Assessment was done using MINI, General Health Questionnaire, Indian Adaptation of Emotional Intelligence Scale and Rosenberg Self Esteem Scale. Significant differences were seen in Emotional Intelligence between the Cannabis dependent group and normal control group. The cannabis dependent groups scored significantly low on emotional intelligence in comparison with control group. Further, cannabis dependent group scored significantly lower on score of self esteem than the normal control group. Relationship between emotional intelligence and self esteem was found to be positively correlated. Our study suggests an association between low emotional intelligence, low self esteem and cannabis dependence and the prevention and treatment of cannabis dependence should lay focus on these factors. Journal of the Indian Academy of Applied Psychology.
Creator
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Nehra D.K.; Sharma V.; Mushtaq H.; Sharma N.; Sharma M.; Nehra S.
Source
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Journal of the Indian Academy of Applied Psychology, Vol-38, No. 2, pp. 385-393.
Date
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2012-01-01
Identifier
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<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84860523784&partnerID=40&md5=cd135f3f6df1df73c6192c9a160c4267" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84860523784&partnerID=40&md5=cd135f3f6df1df73c6192c9a160c4267</a>
Rights
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Restricted Access
Relation
A related resource
ISSN: 194247
Format
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Online
Language
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English
Type
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Article
Coverage
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Nehra D.K., Advanced Institute for Management of Stress and life style problems, Srinagar, India; Sharma V., Department of Clinical Psychology, Institute of Human Behaviour and Allied Sciences, Delhi, India; Mushtaq H., Department of Psychiatry, Govt. Medical College, Srinagar, India; Sharma N., Department of Psychology, M.D.U, Rohtak, India; Sharma M., Department of Applied Psychology, Christ University, Bangalore, India; Nehra S., Department of Psychology, M.D.U, Rohtak, India