113
20
11858
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Title
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Book Chapter
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Faculty Publications- Book Chapter
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Doctoral Research by Youth: Analyzing the Role of Socio-Demographic Variables on Flourishing and Grit
Subject
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Academic achievement; Demographic variables; Doctoral attrition; Doctoral education; Flourishing; Grit
Description
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The study examines the importance of socio-demographic variables like age, gender, family environment, and relationship with parents and friends in deter-mining non-cognitive traits such as flourishing and grit, during the tenure of doctoral research. The cross-sectional correlational study comprises 400 Ph.D. scholars from a Central University in India, who were given a personal data sheet, the Flourishing Scale and the Grit Scale, for assessment. The results of the F-test showed that flourishing was significantly related to age, family environment and relationship with friends, and grit was significantly related to family environment and relationship with friends. Analysis using Pearson correlation found a weak correlation between flourishing and the three subscales of grit, namely ambition, consistency of interest, and perseverance of effort. Findings suggest that the socio-demographic variables are important contributors in the long-term goal-oriented behaviors and that flourishing and grit are two related but not correlated variables that influence completion and attrition of the doctoral research. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
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Deb S.; Sunny A.M.; Thomas S.; Deb S.; Ashra F.G.; Sujith V.S.; Paul D.
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Handbook of Youth Development: Policies and Perspectives from India and Beyond, pp. 531-544.
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Springer Nature
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2023-01-01
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<a href="https://doi.org/10.1007/978-981-99-4969-4_30" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-4969-4_30</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208344294&doi=10.1007%2F978-981-99-4969-4_30&partnerID=40&md5=0d0cc66655283d0f98dfc6bd99f109c0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208344294&doi=10.1007%2f978-981-99-4969-4_30&partnerID=40&md5=0d0cc66655283d0f98dfc6bd99f109c0</a>
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ISBN: 978-981994969-4; 978-981994968-7
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Online
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English
Type
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Book chapter
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Deb S., Department of Applied Psychology, Pondicherry University (A Central University), Puducherry, India; Sunny A.M., PGCE Teacher Trainee, UCL Institute of Education, London, United Kingdom; Thomas S., Teacher of Psychology, St. Georges College, Weybrigde, United Kingdom; Deb S., Department of Psychology, CHRIST (Deemed to be University), Bengaluru, India; Ashra F.G., Department of Applied Psychology, Pondicherry University (A Central University), Puducherry, India; Sujith V.S., Department of Applied Psychology, Pondicherry University (A Central University), Puducherry, India; Paul D., Department of Applied Psychology, Pondicherry University (A Central University), Puducherry, India
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Title
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Articles
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Dublin Core
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Title
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Happiness, Meaning, and Satisfaction in Life as Perceived by Indian University Students and Their Association with Spirituality
Subject
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Happiness; Meaning in life; Satisfaction; Spirituality; University students
Description
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The present study aims to examine the association between various dimensions of psychological well-being (subjective happiness, satisfaction, and meaning in life), spirituality, and demographic and socioeconomic background of university students. A total of 414 postgraduate students were selected from three different schools, viz. science, management, and social sciences/humanities of Pondicherry University (A Central University), Puducherry, India, following multistage cluster sampling method. One semi-structured questionnaire and four standardized psychological scales, viz. subjective happiness scale, satisfaction with life scale, meaning in life questionnaire, and spirituality attitude inventory, were used for data collection after checking psychometric properties of the scales. The results show that a positive significant correlation between spirituality and subjective happiness exists. Spirituality is also correlated with meaning in life and satisfaction with life scale. Statistically, no significant gender difference was observed with respect to subjective happiness, meaning, and satisfaction in life as well as spirituality although the mean score of female students was more in all the four psychological domains. Non-integrated students are found to be happier than integrated students, and statistically it was significant. Positive interpersonal relationship and congenial family environment were probed to be facilitating factors for positive mental health of university students. There is a severe need to address students mental health by every educational institution through multiple programs. 2019, Springer Science+Business Media, LLC, part of Springer Nature.
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Deb S.; Thomas S.; Bose A.; Aswathi T.
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Journal of Religion and Health, Vol-59, No. 5, pp. 2469-2485.
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Springer
Date
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2020-01-01
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<a href="https://doi.org/10.1007/s10943-019-00806-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10943-019-00806-w</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064255087&doi=10.1007%2Fs10943-019-00806-w&partnerID=40&md5=410e70e57bcc1ded3bafba2dd8ae9378" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064255087&doi=10.1007%2fs10943-019-00806-w&partnerID=40&md5=410e70e57bcc1ded3bafba2dd8ae9378</a>
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ISSN: 224197; PubMed ID: 30963363; CODEN: JRHEA
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Online
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English
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Article
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Deb S., Department of Applied Psychology, Pondicherry University, Puducherry, India; Thomas S., Department of Psychology, CHRIST (Deemed to be University), Bangalore, India; Bose A., Department of Applied Psychology, Pondicherry University, Puducherry, India; Aswathi T., Department of Applied Psychology, Pondicherry University, Puducherry, India
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Title
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Articles
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Title
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Level of green computing based management practices for digital revolution and New India /
Subject
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Green Information Technology
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International Journal of engineerig And Advanced Technology, Vol.8, Issue 3, pp.133-136, ISSN No: 2249-8958.
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Debabrata Samanta and Suplab Kanti Podder
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<a href="https://www.ijeat.org/wp-content/uploads/papers/v8i3S/C10270283S19.pdf" target="_blank" title="Level of green computing based management practices for digital revolution and New India" rel="noreferrer noopener">https://www.ijeat.org/wp-content/uploads/papers/v8i3S/C10270283S19.pdf</a>
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Blue Eyes Intelligence Engineering & Sciences Publication
Date
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2019-12-30
Language
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English
Type
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Journal Article
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Title
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Conference Papers
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Title
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Facial Expression Recognition with Transfer Learning: A Deep Dive
Subject
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CK+; DenseNet; HCI; Imagenet; Transfer Learning
Description
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In the realm of affective computing, where the nuanced interpretation of facial expressions plays a pivotal role, this research presents a comprehensive methodology aimed at refining the precision of facial expression recognition on the CK+ (Cohn-Kanade Extended) dataset. Our method uses the robust DenseNet121 architecture that has been pretrained on the 'imagenet' dataset, and it leverages transfer learning on the foundational CK+ dataset. The model deftly handles issues with overfitting, normalization, and feature extraction that are present in facial expression detection on CK+. Our approach not only achieves an overall accuracy of 98%, with a 5.86% accuracy enhancement over the base paper on the CK+ dataset, but also shows remarkable precision, recall, and F1-score values for individual emotion classes. It is noteworthy that emotions such as anger, contempt, and disgust have precision rates that reach 100%. The study highlights the model's noteworthy input to affective computing and presents its possible real-world uses in emotion analysis on CK+ and human-computer interaction. 2024 IEEE.
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Debroy M.; Thomas K.T.; Mathew L.
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TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/TQCEBT59414.2024.10545031" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545031</a>
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ISBN: 979-835038427-7
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Online
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English
Type
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Conference paper
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Debroy M., Christ(Deemed to Be University), Dept. of Data Science, Lavasa, Pune, India; Thomas K.T., Christ(Deemed to Be University), Dept. of Data Science, Lavasa, Pune, India; Mathew L., Christ(Deemed to Be University), Dept. of Data Science, Lavasa, Pune, India
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Articles
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Title
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Pandemic, theatre and performance: Democratizing the subalterns through the Theatre of the Oppressed
Subject
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COVID-19 pandemic; democratization; forum theatre; legislative theatre; marginalization; Theatre of the Oppressed
Description
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The presented work analyses Theatre of the Oppressed (TO) methods impacting the pandemic. It follows the WHO timeline, when the COVID-19 pandemic had cast a dark shadow, making sustenance difficult for the marginalized section of Indian society. TO methods, though reflected, adapted and accommodated exhaustively in Indian applied theatre over the last four decades, offered a fresh, collective, democratic space during the pandemic. Forum theatre (FT) and legislative theatre (LT) praxis rendered a platform for activism, awareness and emancipation of the subalterns during the pandemic. Thus, TO renewed psycho-social dialogue and critical, creative, experimental space during this time. The applicability of such methods facilitating social change is gauged using Boals spect-actorship and Freires conscientization. The article looks forward to the TO signposts to serve as nodal points for further scholarly discussion and study on democratizing the disenfranchised population through FT and LT during the pandemic. 2023 Intellect Ltd Article. English language. All Rights Reserved.
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Debsarma A.; Sarkar S.
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Applied Theatre Research, Vol-11, No. 2, pp. 175-186.
Publisher
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Intellect Ltd.
Date
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2023-01-01
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<a href="https://doi.org/10.1386/ATR_00083_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1386/ATR_00083_1</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184753261&doi=10.1386%2FATR_00083_1&partnerID=40&md5=7a7c73cad49fdc2d3adf92aa2a6a8e03" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184753261&doi=10.1386%2fATR_00083_1&partnerID=40&md5=7a7c73cad49fdc2d3adf92aa2a6a8e03</a>
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ISSN: 20493010
Format
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Online
Language
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English
Type
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Article
Coverage
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Debsarma A., Christ University, India; Sarkar S., Christ University, India
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Title
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Conference Papers
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Faculty Publications- Conference Papers
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Title
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Sentiment Analysis for Online Shopping Reviews Using Machine Learning
Subject
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Accuracy; Amazon reviews; BART; BERT; GPT; Machine learning; Online shopping; Sentiment analysis
Description
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Everyday shoppers need reliable and insightful reviews of e-commerce websites to enhance their shopping experience. This research study explores sentiment analysis on Amazon reviews. It utilizes them as a diverse repository of customer opinions by unlocking their embedded sentiments, thereby recognizing their pivotal role in guiding potential buyers. Sentiment misinterpretations may result from many machine learning models that have trouble comprehending the context of Amazon reviews, particularly regarding subtle wordings, sarcasm, or irony. Additionally, these models can have biases that skew sentiment analysis results, mainly when working with a diverse set of Amazon review datasets. To overcome these, three machine learning models, namely, Bidirectional Encoder Representations from Transformers (BERT), Bidirectional and Auto-Regressive Transformers (BART), and Generative Pre-trained Transformers (GPT) are used in this study. During the experimental research, it was observed that BERT gave the highest accuracy of 90% when compared with BART (70%) and GPT (84%) models. BERTs bidirectional contextual comprehension at identifying subtleties in language provides a thorough and realistic representation of the sentiments of Amazon users, which is why the model gave the highest accuracy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Dedeepya M.; Kokatnoor S.A.; Kumar S.
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Lecture Notes in Networks and Systems, Vol-856 LNNS, pp. 453-465.
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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/10.1007/978-981-97-7571-2_35" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7571-2_35</a>
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ISSN: 23673370; ISBN: 978-981977548-4
Format
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Online
Language
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English
Type
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Conference paper
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Dedeepya M., CHRIST University, Bengaluru, India; Kokatnoor S.A., CHRIST University, Bengaluru, India; Kumar S., CHRIST University, Bengaluru, India
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Title
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Articles
Article
Faculty Publications -Articles
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Developing the assessment questions automatically to determine the cognitive level of the e-learner using NLP techniques
Subject
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Blooms Taxonomy; Learning Management System; Multiple Choice Questions; Named Entity Recognition; Natural Language Processing
Description
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The key objective of the teaching-learning process (TLP) is to impart the knowledge to the learner. In the digital world, the computer-based system emphasis teaching through online mode known as e-learning. The expertise level of the learner in learned subjects can be measured through e-assessment in which multiple choice questions (MCQ) is considered to be an effective one. The assessment questions play the vital role which decides the ability level of a learner. In manual preparation, covering all the topics is difficult and time consumable. Hence, this article proposes a system which automatically generates two different types of question helps to identify the skill level of a learner. First, the MCQ questions with the distractor set are created using named entity recognizer (NER). Further, based on blooms taxonomy the Subjective questions are generated using natural language processing (NLP). The objective of the proposed system is to generate the questions dynamically which helps to reduce the occupation of memory concept. 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Deena G.; Raja K.; Nizar Banu P.K.; Kannan K.
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International Journal of Service Science, Management, Engineering, and Technology, Vol-11, No. 2, pp. 95-110.
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IGI Global
Date
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2020-01-01
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<a href="https://doi.org/10.4018/IJSSMET.2020040106" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/IJSSMET.2020040106</a>
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ISSN: 1947959X
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Online
Language
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English
Type
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Article
Coverage
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Deena G., Sathyabama University, Chennai, Tamil Nadu, India; Raja K., Dhaanish Ahmed College of Engineering, Chennai, India; Nizar Banu P.K., CHRIST (Deemed to be University), Bangalore, Karnataka, India; Kannan K., AudiSankara College of Engineering and Technology, Gudur, Nellore, India
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https://archives.christuniversity.in/files/original/38404c84f306ae032ab8b89455e0c660.jpg
3a7de802cb277fef001b0870821eda5b
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Articles
Article
Faculty Publications -Articles
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Title
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Group Movie Recommendations via Content Based Feature Preferences
Subject
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Computer Science
Description
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International Journal of Scientific & Engineering Research Vol. 4, Issue 2, pp.1-5 ISSN No. 2229-5518
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Deepa Anand
Publisher
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International Journal of Scientific & Engineering Research
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English
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<a href="https://www.ijser.org/onlineResearchPaperViewer.aspx?Group-Movie-Recommendations-via-Content-Based-Feature-Preferences.pdf" target="_blank" title="Click Here" rel="noreferrer noopener">https://www.ijser.org/onlineResearchPaperViewer.aspx?Group-Movie-Recommendations-via-Content-Based-Feature-Preferences.pdf</a>
-
https://archives.christuniversity.in/files/original/adf23e898312d0e3ba048b987cd195eb.jpg
75c911ec6b7c23030c943a1b610c90b0
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Articles
Article
Faculty Publications -Articles
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Title
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Improved Collaborative Filtering using Evolutionary Algorithm based Feature Extraction
Subject
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Computer Science
Description
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International Journal of Computer Applications Vol.64,No.20, pp.20-26 ISSN No. 0975-8887
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Deepa Anand
Publisher
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International Journal of Computer Applications
Language
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English
-
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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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Feature Extraction for Collaborative Filtering: A Genetic Programming Approach
Subject
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Computer Science
Description
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International Journal of Computer Science Issues, Vol. 9, Issue, 5, No. 1, pp. 348-354, ISSN No. 1694-0814
Creator
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Deepa Anand
Publisher
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International Journal of Computer Science Issues
Date
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2012
Language
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English
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Title
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Book Chapter
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
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Title
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Comparative Analysis of Different Machine Learning Prediction Models for Seasonal Rainfall and Crop Production in Cultivation
Subject
The topic of the resource
Crop prediction; Linear model; Machine learning; Rain fall; Regression model
Description
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Agriculture is one of the strengths of India, from the last few years, gradually the agriculture growth is going downwards in other side the population growth is upwards. Reason for agricultural downward growth depends on so many parameters. The rainfall is one of the main parameters which affects the crop yield. Because of this, the farmers are also facing the loss. If they know this information in prior, the farmers can plan accordingly the type of crop suited for the particular season and it helps the farmer to get good profit out of it. Machine learning scientific and statistical methods are used for predicting the rain fall and crop yield. Kharif and Rabi are two seasons taken for analysis. The regressor predicting models are constructed to predict the seasonal rainfall and crop yield. This study primarily focuses on seasonal crop production prediction, which is dependent on rainfall. The different types of machine learning regression method are used to achieve better results. The performance of comparison models is evaluated using different metrics. Finally, the linear regression and Bayesian linear regression models comparatively produce the best result in terms of accuracy for rainfall prediction. The boosted decision tree regression model is achieving the better result for crop prediction. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Deepa S.; Gokila S.
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Lecture Notes on Data Engineering and Communications Technologies, Vol-139, pp. 277-289.
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Springer Science and Business Media Deutschland GmbH
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2023-01-01
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<a href="https://doi.org/10.1007/978-981-19-3015-7_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-3015-7_20</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138159983&doi=10.1007%2F978-981-19-3015-7_20&partnerID=40&md5=9c4e9c76643c050c57e4bfa4268b3066" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138159983&doi=10.1007%2f978-981-19-3015-7_20&partnerID=40&md5=9c4e9c76643c050c57e4bfa4268b3066</a>
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ISSN: 23674512
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Online
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English
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Book chapter
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Deepa S., School of Sciences, CHRIST (Deemed to be University), Bengaluru, India; Gokila S., Department of Computer Applications, Hindustan Institute of Technology and Science, Chennai, India
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Conference Papers
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Title
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Specialized CNN Architectures for Enhanced Image Classification Performance
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CIFAR-10 Dataset; Convolutional Neural Networks; Custom Architectures; Image Classification; Model Comparison
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Image classification is one of the important tasks in computer vision, with a greater number of applications from facial recognition, medical imaging, object recognition and many more. Convolutional Neural Networks (CNNs) have developed as the foundation for image all classification tasks, showcasing the capacity to learn the hierarchical features automatically. In this study proposed three custom CNN models and its comprehensive analysis for the image classification tasks. The models are evaluated using CIFAR-10 dataset to assess the performance and efficiency. The experimental results shows that the proposed custom CNN Model-3 performance is better than the other two models. Our findings demonstrate that Model 3, featuring with the global average pooling, achieves the highest overall accuracy of 94 % with competitive computational efficiency. This suggests that global average pooling is the valuable technique for balanced and accurate image classification. 2024 IEEE.
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Deepa S.; Jayapriya J.; Vinay M.; Kalpana P.; Suganthi J.; Loveline Zeema J.
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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/NMITCON62075.2024.10699080" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NMITCON62075.2024.10699080</a>
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ISBN: 979-835037289-2
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Online
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English
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Conference paper
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Deepa S., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Vinay M., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Kalpana P., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Suganthi J., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Loveline Zeema J., Christ University, Department of Computer Science, Karnataka, Bangalore, India
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Articles
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Deep Belief Network-Based User and Entity Behavior Analytics (UEBA) for Web Applications
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Anomaly detection; deep belief network (dbn); federated identity management; user and entity behavior analytics
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Machine learning (ML) is currently a crucial tool in the field of cyber security. Through the identification of patterns, the mapping of cybercrime in real time, and the execution of in-depth penetration tests, ML is able to counter cyber threats and strengthen security infrastructure. Security in any organization depends on monitoring and analyzing user actions and behaviors. Due to the fact that it frequently avoids security precautions and does not trigger any alerts or flags, it is much more challenging to detect than traditional malicious network activity. ML is an important and rapidly developing anomaly detection field in order to protect user security and privacy, a wide range of applications, including various social media platforms, have incorporated cutting-edge techniques to detect anomalies. A social network is a platform where various social groups can interact, express themselves, and share pertinent content. By spreading propaganda, unwelcome messages, false information, fake news, and rumours, as well as by posting harmful links, this social network also encourages deviant behavior. In this research, we introduce Deep Belief Network (DBN) with Triple DES, a hybrid approach to anomaly detection in unbalanced classification. The results show that the DBN-TDES model can typically detect anomalous user behaviors that other models in anomaly detection cannot. 2024 World Scientific Publishing Company.
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Deepa S.; Umamageswari A.; Neelakandan S.; Bhukya H.; Sai Lakshmi Haritha I.V.; Shanbhog M.
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International Journal of Cooperative Information Systems, Vol-33, No. 2
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World Scientific
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2024-01-01
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<a href="https://doi.org/10.1142/S0218843023500168" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S0218843023500168</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187297463&doi=10.1142%2FS0218843023500168&partnerID=40&md5=d95481829b249e3544ecbf7dfc84104e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187297463&doi=10.1142%2fS0218843023500168&partnerID=40&md5=d95481829b249e3544ecbf7dfc84104e</a>
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ISSN: 2188430
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Online
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English
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Article
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Deepa S., Department of Computer Science and Engineering, Srm Institute of Science and Technology, Ramapuram Campus, Tamil Nadu, Chennai, India; Umamageswari A., Department of Computer Science and Engineering, Srm Institute of Science and Technology, Ramapuram Campus, Tamil Nadu, Chennai, India; Neelakandan S., Department of Computer Science and Engineering, R.M.K Engineering College, Kavaraipettai, Tamil Nadu, Chennai, India; Bhukya H., Department of Computer Science and Engineering (Networks), Kakatiya Institute of Technology and Science, Telangana, Warangal, India; Sai Lakshmi Haritha I.V., Department of Information Technology, Mlr Institute of Technology, Telangana, Hyderabad, India; Shanbhog M., Department of Sciences, Christ (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, 201003, India
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Articles
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Exploratory Architectures Analysis of Various Pre-trained Image Classification Models for Deep Learning
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deep learning; image classification; neural network
Description
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The image classification is one of the significant applications in the area of Deep Learning (DL) with respective to various sectors. Different types of neural network architectures are available to perform the image classification and each of which produces the different accuracy. The dataset and the features used are influence the outcome of the model. The research community is working towards the generalized model at least to the domain specific. On this gesture the contemporary survey of various Deep Learning models is identified using knowledge information management methods to move further to provide optimal architecture and also to generalized Deep Learning model to classify images narrow down to the sector specific. The study systematically presents the different types of architecture, its variants, layers and parameters used for each version of Deep Learning model. Domain specific applications and limitations of the type of architecture are detailed. It helps the researchers to select appropriate Deep Learning architecture for specific sector. 2024 by the authors.
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Deepa S.; Zeema J.L.; Gokila S.
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Journal of Advances in Information Technology, Vol-15, No. 1, pp. 66-78.
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Engineering and Technology Publishing
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2024-01-01
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<a href="https://doi.org/10.12720/jait.15.1.66-78" target="_blank" rel="noreferrer noopener">https://doi.org/10.12720/jait.15.1.66-78</a>
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All Open Access; Gold Open Access
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ISSN: 17982340
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Online
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English
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Article
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Deepa S., Department of Computer Science, Christ University, Bangalore, India; Zeema J.L., Department of Computer Science, Christ University, Bangalore, India; Gokila S., Department of Computer Applications, Hindustan Institute of Technology and Science, Chennai, India
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Articles
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Faculty Publications -Articles
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Title
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The influence of social environment on children of a commercial sex worker
Subject
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Children of commercial sex worker; environmental variance; siblings
Description
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The case study aims to understand the influence of social environment on the course of life of children of a commercial sex worker. The participants of the study were two sons of a commercial sex worker who grew up in different environments. The older sibling who is 19 years of age (case 1) lives with his mother, whereas the younger sibling who is 17 years of age (case 2) lives in a hostel distant from everyday influence of a brothel. The study adopts multiple case study design and in-depth interviews were conducted to gather data. The obtained data were subjected to thematic analysis. Each case was analyzed individually, and then cross comparison of the themes derived was carried out. The themes derived on analyzing case 1 were social categorization, mercenary activity, substance aficionado, complacency in life, and compliance with life while the themes derived on analyzing case 2 were disgust toward commercial sex work, feeling of precariousness, antipathy toward home environment, irrational thoughts and anticipation of a better future. The only overlapping issue that emerged in both cases was being protective about their mother. It was concluded that environmental variance contributes to the difference in experience and perception of the situation and society. Indian Journal of Social Psychiatry. All Rights Reserved.
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Deepa V.; Manjula M.
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Indian Journal of Social Psychiatry, Vol-36, No. 4, pp. 310-316.
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Wolters Kluwer Medknow Publications
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2020-01-01
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<a href="https://doi.org/10.4103/ijsp.ijsp_8_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.4103/ijsp.ijsp_8_20</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150779721&doi=10.4103%2Fijsp.ijsp_8_20&partnerID=40&md5=ca341c951107e6f68792b12c9565f320" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150779721&doi=10.4103%2fijsp.ijsp_8_20&partnerID=40&md5=ca341c951107e6f68792b12c9565f320</a>
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ISSN: 9719962; CODEN: ISPSE
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Online
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English
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Article
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Deepa V., Department of Psychology, SDM College (Autonomous), Dakshina Kannada, India; Manjula M., Department of Psychology, Christ (Deemed To Be University), Karnataka, Bengaluru, India
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Conference Papers
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Faculty Publications- Conference Papers
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Title
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Performance Analysis of Deep Learning Pretrained Image Classifiation Models
Subject
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Convolutional Neural Networks (CNNs); Deep Learning; Image classification
Description
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Convolutional Neural Networks (CNNs) is revolutionized in the field of computer vision, with the high accuracy and capability to learn features from raw data. In this research work focused on a comparative analysis of two popular CNN architectures, VGG16 and VGG19. The CIFAR dataset consists of 60,000 images, each with a resolution of 32x32 and it's belong to one of the 10 classes. Experimental results are compared with VGG16 and VGG19 in terms of their accuracy and training time, and to identify any differences in their ability to learn features from the CIFAR-10 dataset. The results of this research can aid in directing the choice of appropriate architectures for image classification tasks as well as the advantages of optimisation strategies for enhancing the efficiency of deep learning models. In order to enhance the performance of these structures, more optimisation methods and datasets may be investigated in subsequent research. 2023 IEEE.
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Deepa; Alli A.; Sheetal; Prasad Km S.
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IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
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Institute of Electrical and Electronics Engineers Inc.
Date
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2023-01-01
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<a href="https://doi.org/10.1109/AIKIIE60097.2023.10390378" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIKIIE60097.2023.10390378</a>
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ISBN: 979-835031646-9
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Online
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English
Type
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Conference paper
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Deepa, Christ University, Department of Computer Science, Bengaluru, India; Alli A., Presidency College, Department of Computer Applications, Bengaluru, India; Sheetal, Presidency College, Department of Computer Science and Engineering, Bengaluru, India; Prasad Km S., Rao Bahadur y Mahabaleswarappa Engineering College, Department of Computer Applications, Karnataka, Ballari, India
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Book Chapter
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Hereditary factor-based multi-featured algorithm for early diabetes detection using machine learning
Subject
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Classification algorithm; Decision trees; Diabetes detection; Multi-featured algorithm; Prediction
Description
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Today's advent in the medical industry have given numerous chances to improve the quality of detection and reporting the diseases at the early stages for a better diagnosis. Modern day datasets generate fruitful information for timely and periodic monitoring of patients' health conditions. Such information is hidden to a naked eye or hidden in multiple track records of highly affected population. Diabetes mellitus is one such disease which is predominant among a global population which ultimately leads to blindness and death in some cases. The model proposed in this system attempts to design and deliver an intelligent solution for predicting diabetes in the early stages and address the problem of late detection and diagnosis. Intensive research is carried out in many tropical countries for automating this process through a machine learning model. The accuracy of machine learning algorithms is more than satisfactory in the detection of Type 2 diabetes from the dataset of PIMA Indians Diabetes Dataset. An additional feature of hereditary factor is implemented to the existing multiple objective fuzzy classifiers. The proposed model has improved the accuracy to 83% in the training and tested datasets when compared to NGSA model of prediction. 2022 Scrivener Publishing LLC.
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Deepajothi S.; Juliana R.; Aruna S.K.; Thiagarajan R.
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Artificial Intelligent Techniques for Wireless Communication and Networking, pp. 235-253.
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wiley
Date
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2022-01-01
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<a href="https://doi.org/10.1002/9781119821809.ch15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781119821809.ch15</a>
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ISBN: 978-111982180-9; 978-111982127-4
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Online
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English
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Book chapter
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Deepajothi S., Dept. of CSE, Gurunanak Institute of Technology, Hyderabad, India; Juliana R., Dept. of IT, Loyola ICAM College of Engineering and Technology, Chennai, India; Aruna S.K., Dept. of CSE, Christ (Deemed to be University) School of Engineering and Technology, Bangalore, India; Thiagarajan R., Dept. of CSE, Prathyusha Engineering College, Thiruvallur, India
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Articles
Article
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An intelligent inventive system for personalised webpage recommendation based on ontology semantics
Subject
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Ontologies; Personalised; Semantic strategy; Web search; Webpage recommendation system
Description
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Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users web usage data. An overall accuracy of 87.73% is achieved by the proposed approach. Copyright 2019 Inderscience Enterprises Ltd.
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Deepak G.; Ahmed A.; Skanda B.
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International Journal of Intelligent Systems Technologies and Applications, Vol-18, No. 45689, pp. 115-132.
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Inderscience Publishers
Date
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2019-01-01
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<a href="https://doi.org/10.1504/IJISTA.2019.097751" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJISTA.2019.097751</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061298512&doi=10.1504%2FIJISTA.2019.097751&partnerID=40&md5=ed644d47c5eb3c9bc994e25cf6d17c67" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061298512&doi=10.1504%2fIJISTA.2019.097751&partnerID=40&md5=ed644d47c5eb3c9bc994e25cf6d17c67</a>
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All Open Access; Bronze Open Access
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ISSN: 17408865
Format
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Online
Language
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English
Type
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Article
Coverage
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Deepak G., Department of Computer Science and Engineering, Faculty of Engineering, Christ University, Bangalore, 560074, India; Ahmed A., RSA Business Unit, Dell EMC, Bangalore, 560048, India; Skanda B., Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, 560001, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Title
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Descriptive Answer Evaluation using NLP Processes Integrated with Strategically Constructed Semantic Skill Ontologies
Subject
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E-Learning; Keyword Extraction; Natural Language Processing; Online Evaluation; Ontology
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The world is moving towards an online methodology of education. One of the key challenges is the assessment of questions which do not have a definite answer and have several correct answers. To solve this problem, and for quality evaluation of descriptive answers online, an automatic evaluation methodology is proposed in this work. A language model is modelled from the expected answer key, and entity graphs are generated from the ontology modelled using the input answer to be evaluated. Natural Language Processing (NLP) techniques like Stemming, Summarization, and Polarity Analysis are integrated in this work with Ontologies for the efficient evaluation of descriptive answers. Several challenges which come across evaluating descriptive answers are discussed in this chapter, and they have been solved in order to obtain a dynamic and robust evaluating system. Finally, the system is evaluated using a user-feedback methodology comprising a panel of 100 students and 100 professors. 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
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Deepak G.; Ayush Kumar A.; Priyadarshini S.; Singh D.
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CEUR Workshop Proceedings, Vol-3335, pp. 64-76.
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CEUR-WS
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2022-01-01
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ISSN: 16130073
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Online
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English
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Conference paper
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Deepak G., Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India; Ayush Kumar A., National Institute of Technology, Tiruchirappalli, India; Priyadarshini S., CHRIST(Deemed to be University), Bangalore, India; Singh D., Birla Institute of Technology and Science, Pilani, India
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Conference Papers
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Faculty Publications- Conference Papers
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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/.
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ATRSI: Automatic Tag Recommendation for Videos Encompassing Semantic Intelligence
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Bag of Words model; CoSim Rank; Lin similarity; RNN
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There is a requirement for an automatic semantic-oriented framework for Web video tagging in the epoch of Web 3.0, as Web 3.0 is much denser, intelligent, but more cohesive compared to Web 2.0. This paper proposes the ATRSI framework which is the Automatic Tag Recommender framework which encompasses the semantic-oriented Artificial Intelligence that outgrows the dataset by making the use of informative terms using TF-IDF and bag of words model to build the intermediate semantic network which is further organized using an Lin similarity measure and is optimized using red deer optimization by encompassing the entities from the World Wide Web to focused crawling. RNN is a classifier that is used for the classification of the dataset, it is a strong deep-learning classifier. Semantic-oriented Intelligence is achieved using the CoSim rank and Morisita's overlap index. The bag of lightweight graphs is obtained from the semantic network which is an intermediate knowledge representation mechanism that is further embedded in the intrinsic model. A semantically consistent system for video recommendation, ATRSI outperforms the other baseline models in terms of average accuracy, average precision and F-measure for a variety of recommendations. 2024 IEEE.
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Deepak G.; Iyer K.; Shukla S.
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Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 594-599.
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/ICWITE59797.2024.10503160" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICWITE59797.2024.10503160</a>
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ISBN: 979-835038328-7
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Online
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English
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Conference paper
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Deepak G., Manipal Institute of Technology Bengaluru, Manipal Academy of Higer Education, Department of Cse, Manipal, India; Iyer K., Manipal Institute of Technology Bengaluru, Manipal Academy of Higer Education, Department of Cse, Manipal, India; Shukla S., School of Engineering, Christ (Deemed to Be University), Department of Cse, Kengeri Campus, Bengaluru, India