154 20 16481 https://archives.christuniversity.in/files/original/adf23e898312d0e3ba048b987cd195eb.jpg 75c911ec6b7c23030c943a1b610c90b0 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 A name given to the resource 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 A name given to the resource Improved Collaborative Filtering using Evolutionary Algorithm based Feature Extraction Subject The topic of the resource Computer Science Description An account of the resource International Journal of Computer Applications Vol.64,No.20, pp.20-26 ISSN No. 0975-8887 Creator An entity primarily responsible for making the resource Deepa Anand Publisher An entity responsible for making the resource available International Journal of Computer Applications Language A language of the resource English 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 A name given to the resource 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 A name given to the resource Feature Extraction for Collaborative Filtering: A Genetic Programming Approach Subject The topic of the resource Computer Science Description An account of the resource International Journal of Computer Science Issues, Vol. 9, Issue, 5, No. 1, pp. 348-354, ISSN No. 1694-0814 Creator An entity primarily responsible for making the resource Deepa Anand Publisher An entity responsible for making the resource available International Journal of Computer Science Issues Date A point or period of time associated with an event in the lifecycle of the resource 2012 Language A language of the resource English 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 A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter 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 A name given to the resource 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 An account of the resource 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. Creator An entity primarily responsible for making the resource Deepa S.; Gokila S. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-139, pp. 277-289. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <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> <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> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23674512 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 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 A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers 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 A name given to the resource Specialized CNN Architectures for Enhanced Image Classification Performance Subject The topic of the resource CIFAR-10 Dataset; Convolutional Neural Networks; Custom Architectures; Image Classification; Model Comparison Description An account of the resource 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. Creator An entity primarily responsible for making the resource Deepa S.; Jayapriya J.; Vinay M.; Kalpana P.; Suganthi J.; Loveline Zeema J. Source A related resource from which the described resource is derived 2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/NMITCON62075.2024.10699080" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NMITCON62075.2024.10699080</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207439676&doi=10.1109%2FNMITCON62075.2024.10699080&partnerID=40&md5=374f28bd5a9cff70da91fa7d936a0e59" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207439676&doi=10.1109%2fNMITCON62075.2024.10699080&partnerID=40&md5=374f28bd5a9cff70da91fa7d936a0e59</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835037289-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource 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 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 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 A name given to the resource 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 A name given to the resource Deep Belief Network-Based User and Entity Behavior Analytics (UEBA) for Web Applications Subject The topic of the resource Anomaly detection; deep belief network (dbn); federated identity management; user and entity behavior analytics Description An account of the resource 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. Creator An entity primarily responsible for making the resource Deepa S.; Umamageswari A.; Neelakandan S.; Bhukya H.; Sai Lakshmi Haritha I.V.; Shanbhog M. Source A related resource from which the described resource is derived International Journal of Cooperative Information Systems, Vol-33, No. 2 Publisher An entity responsible for making the resource available World Scientific Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S0218843023500168" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S0218843023500168</a> <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> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2188430 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 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 A name given to the resource 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 A name given to the resource Exploratory Architectures Analysis of Various Pre-trained Image Classification Models for Deep Learning Subject The topic of the resource deep learning; image classification; neural network Description An account of the resource 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. Creator An entity primarily responsible for making the resource Deepa S.; Zeema J.L.; Gokila S. Source A related resource from which the described resource is derived Journal of Advances in Information Technology, Vol-15, No. 1, pp. 66-78. Publisher An entity responsible for making the resource available Engineering and Technology Publishing Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <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> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183348264&doi=10.12720%2Fjait.15.1.66-78&partnerID=40&md5=835724298bb9f8c31d2fdd872094d8e3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183348264&doi=10.12720%2fjait.15.1.66-78&partnerID=40&md5=835724298bb9f8c31d2fdd872094d8e3</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 17982340 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 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 A name given to the resource 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 A name given to the resource The influence of social environment on children of a commercial sex worker Subject The topic of the resource Children of commercial sex worker; environmental variance; siblings Description An account of the resource 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. Creator An entity primarily responsible for making the resource Deepa V.; Manjula M. Source A related resource from which the described resource is derived Indian Journal of Social Psychiatry, Vol-36, No. 4, pp. 310-316. Publisher An entity responsible for making the resource available Wolters Kluwer Medknow Publications Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <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> <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> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9719962; CODEN: ISPSE Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepa V., Department of Psychology, SDM College (Autonomous), Dakshina Kannada, India; Manjula M., Department of Psychology, Christ (Deemed To Be University), Karnataka, Bengaluru, India 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 A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers 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/. Creator An entity primarily responsible for making the resource Deepa, B.G.; Ayshwarya, B.; Senthil, S. Title A name given to the resource Concernment of Feature Selection Using Classification Algorithms and Developing the Web Frame for Breast Cancer Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies;Volume;396 SIST;pp.371-385 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-8096-9_28" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-8096-9_28</a> <a href="https://www.scopus.com/pages/publications/86000447026?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/86000447026?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepa B.G., Department of Computer Science, CHRIST University, Bangalore, India; Ayshwarya B., Computer Science, Kristu Jayanti College, Bangalore, India; Senthil S., Department of Computer Science, CHRIST University, Bangalore, India Description An account of the resource Breast cancer is invasive cancer and it is the most common cancer diagnosed in women. The survival rate of breast cancer patients is increasing due to timely detection, better empathy about the disease, and new tailored approach for the treatment. Even hormonal imbalance, environmental factors, gene mutation, and lifestyle are also the reasons for breast cancer. Stages of breast cancer majorly depend on the size of the tumor as well as the spreading of cancer to the lymph nodes. An instinctive disease detection system and computer-aided diagnosis will help the medical practitioners in early prediction of breast cancer using machine learning algorithms. In this paper, Random Forest for ranking the features by assigning the weights and selection of features using support vector machine and Nae Bayes are used. The Breast Cancer Wisconsin Dataset from the UCI Repository has been taken for examination purposes. Features selected from support vector machine and Naive Bayes have been tested by using seven different classifiers: logistic regression, random forest, K-nearest neighbor, support vector classifier, linear support vector classifier, Gaussian Naive Bayes, and decision tree. Based on the experimental results with 7030 and 8020 splits, 7030 is obtained with the best accuracy. Support vector machine with 12 features resulted in an accuracy of 97.66% and Nae Bayes with 17 features resulted in an accuracy of 96.49% with the improved results as compared to without feature selection. As support vector machine resulted with best accuracy with 12 features, by using these 12 features, web application for the prediction of breast cancer has been developed using Web framework using Python Flask, PyCharm IDE, and the instance has been executed virtually in the Amazon EC2 cloud Platform. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Amazon EC2; Breast cancer; Feature selection; Machine learning; Nae Bayes; PyCharm; Python Flask; Random forest; Support vector machine Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981978095-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter 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/. Creator An entity primarily responsible for making the resource Deepa, B.G.; Deepa, S.; Kavitha, S.; Lepakshi, Vijayalakshmi A. Title A name given to the resource Exploring Generative Adversarial Networks: Core Concepts, Innovations, and Future Implications in AI Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Interpretable and Trustworthy AI: Techniques and Frameworks;pp.111-145 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003587781-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003587781-6</a> <a href="https://www.scopus.com/pages/publications/105023184382?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023184382?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepa B.G., Christ University, Bengaluru, Karnataka, India; Deepa S., Christ University, Bengaluru, Karnataka, India; Kavitha S., Christ University, Bengaluru, Karnataka, India; Lepakshi V.A., Reva University, Bengaluru, Karnataka, India Description An account of the resource [No abstract available] Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-100358778-1; 978-103296063-0; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Deepa, B.G.; Lakshmi, Dara Vijaya; Gondkar, Surekha R.; Dandu, Ravi; Gondkar, Raju Ramakarishna Title A name given to the resource Investigating the Role of Intelligent HR Systems in Enhancing the Relationship Between Employee Engagement and Performance: A Computational Perspective for Economic Development Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Indian Journal of Information Sources and Services;Volume;15;Issue;3;pp.362-369 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.51983/ijiss-2025.IJISS.15.3.41" target="_blank" rel="noreferrer noopener">https://doi.org/10.51983/ijiss-2025.IJISS.15.3.41</a> <a href="https://www.scopus.com/pages/publications/105020288300?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020288300?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepa B.G., Department of Computer Science, CHRIST (Deemed to be a University), Yeshwanthpur Campus, Bangalore, India; Lakshmi D.V., School of Business and Management, CHRIST (Deemed to be a University), Yeshwanthpur Campus, Bangalore, India; Gondkar S.R., Electronics and Communication BMS Institute of Technology and Management, Bangalore, India; Dandu R., Department of Computer Science, Christ University, Bangalore, India; Gondkar R.R., Department of Computer Science, CHRIST (Deemed to be University), Yeshwanthpur Campus, Bangalore, India Description An account of the resource This study examines the impact of Human Capital Management Practices (HCMPs), specifically employment opportunities, training, and rewards, on employee performance. Work engagement is a mediating component in this analysis. The results indicate that HCMPs enhance employee performance by increasing work engagement. This demonstrates the strategic role that HR plays in developing a motivated workforce that propels business success. Engaging employees is essential for every business to succeed in providing its clients with high-quality services; engaged employees treat customers royally and talk about the success of their organization. According to research, engaged workers are 87% less likely to quit than disengaged ones. They tend to maintain regular attendance without absenteeism, treat their work with care, and contribute to the business's growth. As a result, they are often rewarded for their dedication and commitment. The manager must communicate with employees, listen to their concerns, and reward them for improved work. Employee engagement, on the other hand, improves performance and helps the company expand. It is bothering because employees who are less engaged are more likely to leave than those who are more engaged. When workers are engaged, they work hard, do better at their jobs, and stick with the company for years. To measure the level of engagement, research indicates that companies with poor employee engagement experienced an average operational income decrease of almost 32%, created a foundation for engagement to occur easily, set goals and tracked employee engagement, and encouraged continued high engagement levels with routine public recognition for employees who are engaged. The primary task is to break the culture of dependency on leaders and develop teamwork with visibility to accountability and engagement. This empowers employees to solve their issues and provides opportunities for coaching and mentoring. High employee engagement results in high organizational performance. The Research Publication. Subject The topic of the resource and Organization Culture; Economic Development; Employee Engagement; Employee Satisfaction; Human Capital Management Practices (HCMPs); Motivation; Organizational Performance; Quality Publisher An entity responsible for making the resource available The Research Publication Relation A related resource ISSN: 22316094; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online 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 A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter 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/. Creator An entity primarily responsible for making the resource Deepa, B.G.; Senthil, S.; Suhaas, K.P.; Poshith, L.; Sai Shashank, S. Title A name given to the resource Adversarial networks in image generation: A detailed approach to manage datasets and to analyze discriminator and generator losses using GANs Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Quantum Computing;Volume;5;pp.137-161 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1515/9783111354934-008" target="_blank" rel="noreferrer noopener">https://doi.org/10.1515/9783111354934-008</a> <a href="https://www.scopus.com/pages/publications/105038227414?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038227414?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepa B.G., Department of Computer Science, Christ University, Bengaluru, India; Senthil S., School of Computer Applications, Dayananda Sagar University, Bengaluru, India; Suhaas K.P., Department of ISE, The National Institute of Engineering, Mysore, India; Poshith L., School of CSA, REVA University, Bengaluru, India; Sai Shashank S., School of CSA, REVA University, Bengaluru, India Description An account of the resource Image production has been transformed by generative adversarial networks (GANs), which have made unprecedented realism and diversity possible. Still, there are significant hurdles in managing datasetsdatasets managing and analyzing lossesloss analysis. This book chapter focusses on dataset administration and loss analysis, while providing a thorough method for using adversarial networks for image production. A thorough approach for selecting and preparing datasets, while maintaining optimal GAN performance is put forth by researchers. The proposed research approach enables the effective training of GANs, resulting in high-quality image generationhigh-quality image generation. Experimental results demonstrate the efficacy of the current method, showcasing improved image realism and diversity. The suggested strategy also presents a fresh way to examine discriminator and generator lossesgenerator losses, offering new perspectives on the convergence and stability of GANs. This study advances the field of GAN-based image productionGAN-based image production and offers professionals and academics who wish to use adversarial networks a priceless tool. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin. Subject The topic of the resource discriminator; GANs (generative adversarial nets); generator; image generation; pomegranate image; random noise Publisher An entity responsible for making the resource available Walter de Gruyter GmbH Relation A related resource ISSN: 29400112; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Deepa, B.G.; Velmurugan, R.; Narender, M.; Suhaas, K.P. Title A name given to the resource A data-driven approach to predicting breast cancer recurrence with hybrid machine learning models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Advances in Computational Design;Volume;11;Issue;1;pp.79-96 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.12989/acd.2026.11.1.079" target="_blank" rel="noreferrer noopener">https://doi.org/10.12989/acd.2026.11.1.079</a> <a href="https://www.scopus.com/pages/publications/105031064136?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031064136?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepa B.G., Department of Computer Science, Christ University, Bangalore, India; Velmurugan R., Department of Computer Science, Kristu Jayanti (Deemed to be University), Bangalore, India; Narender M., Department of Computer Science and Engineering, The National Institute of Engineering, Mysore, India; Suhaas K.P., Department of Computer Science and Engineering, The National Institute of Engineering, Mysore, India Description An account of the resource Breast cancer recurrence is one of the most significant medical concern, and accurate recurrence models can assist in early intervention and treatment planning. Breast cancer recurrent remains as one of the most critical concern for patients prognosis and treatment planning. Accuracy Predicting individual recurrence risk is crucial for the development of precise therapy, specialy for those patients with high-risk profiles. In the study proposes a hybrid machine learning approach that uses the computational modeling and the medical information to predict the recurrence of breast cancer in a patient. The dataset contains the medical and patient information like the age, tumor size, lymph node involvement, malignancy degree, location, irradiation status and recurrence class. This proposed approach begins with the process of data processing, handling the missing data values, features normalization and encoding of categorical variable into numerical format. The dataset is divided into two parts the training set and the testing set and the two selected models random forest and logistic regression models are trained independently. The predictions form both the model is stacked and a logistic regression meta-model is trained on these combined predictions. The evaluation of the model was conducted using the metrics such as accuracy, precision, recall, and F1 score. The designed hybrid model was able to achieve the accuracy of 97.66% with the precision, recall and F1 score all reaching around 98.15%. This study highlights the potential of hybrid machine learning techniques, improving the accuracy and reliability of machine learning models for breast cancer recurrence prediction. This development model can serve as a valuable tool for the medical industry to support decision making and assist in personalized treatment decisions, offering early detection of recurrence. This can enhance the treatment of a patient by supporting early detection and patients outcomes through targeted therapy. Copyright 2026 Techno-Press Subject The topic of the resource breast cancer; logistic regression; machine learning; random forest; recurrence Publisher An entity responsible for making the resource available Techno-Press Relation A related resource ISSN: 23838477; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online 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 A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter 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/. Creator An entity primarily responsible for making the resource Deepa, S.; Loveline Zeema, J.; Vinay, M.; Jayapriya, J.; Sundaravadivazhagan, B. Title A name given to the resource The Establishment of Quantum Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Quantum Computing and Artificial Intelligence: The Industry Use Cases;pp.207-224 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394242399.ch7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394242399.ch7</a> <a href="https://www.scopus.com/pages/publications/105001358558?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001358558?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepa S., Department of Computer Science, Christ University, Bangalore, India; Loveline Zeema J., Department of Computer Science, Christ University, Bangalore, India; Vinay M., Department of Computer Science, Christ University, Bangalore, India; Jayapriya J., Department of Computer Science, Christ University, Bangalore, India; Sundaravadivazhagan B., University of Technology and Applied Sciences, Oman Description An account of the resource The establishment of quantum networks marks an important revolution in quantum information science. This chapter delivers an exploration of the basics, building blocks, architecture, challenges, and future directions of quantum networks. The chapter starts with an introduction to quantum networks, the importance of quantum networks, and potential applications. Quantum networks influence the principles of quantum mechanics, including quantum entanglement and superposition, to facilitate secure communication, quantum computing, and quantum key distribution. The objective and scope of the chapter are defined, providing the first step for a comprehensive analysis. Essential concepts in quantum information science are conferred in the second section. Readers are familiarized to quantum mechanics and its significance to quantum networks. The quantum entanglement and superposition are explained, as they form the basis for several quantum message protocols. Quantum computing and quantum key distribution are the integral components of quantum networks and are also explored. The building blocks of quantum networks are covered in the next section. The Quantum hardware including quantum bits, quantum gates, and quantum processors is also discussed. The purpose of quantum communication channels, such as quantum optical fibers, quantum links, and satellite-based quantum communication, is inspected. These components are essential for transmitting as well as processing quantum information reliably and efficiently. The architecture of quantum networks is detailed in the subsequent section. Various network topologies, ranging from point-to-point connections to quantum indigenous area networks, quantum metropolitan area networks, and quantum wide area networks, are explored in this section. The functions of quantum network nodes and the use of quantum repeaters, quantum switches, and quantum memories are explained. Furthermore, quantum network protocols, including quantum teleportation, routing and switching, error correction, and key distribution, are discussed in detail. The establishment of quantum networks presents a number of challenges, which are addressed in the fifth section. Quantum noise, essential obstacles to quantum communication, are studied. Approaches for quantum error correction are explored. The considerations for network scalability, security, synchronization, management, and monitoring are also discussed. Solutions to these challenges are essential for the effective deployment of quantum networks. The current state of quantum network progress is presented in the sixth section. Investigational quantum networks and real-world implementations are also discussed. Case studies and success stories highlight the practical applications and potential impact of quantum networks. Future directions and emerging trends in quantum networks are outlined in the seventh section. Exploration includes the interoperability of quantum networks, the expansion of a global quantum internet, the development of quantum networks worldwide, quantum cloud computing, quantum sensor networks, and the incorporation of quantum machine learning into network operations. These improvements have the potential to reshape industries and scientific fields, providing a concrete path for transformative technologies. In conclusion, this chapter delivers an outline of the establishment of quantum networks. It covers the fundamentals, building blocks, architecture, challenges, and future directions of quantum networks. By investigating into these topics, this chapter aims to encourage researchers and engineers to explore the enormous opportunities presented by quantum networks and contribute to their improvement and realization. 2025 Scrivener Publishing LLC.. Subject The topic of the resource quantum communication; quantum cryptograhy; quantum key; quantum LAN; Quantum networks; quantum switch Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139424239-9; 978-139424236-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource All Open Access; Gold Open Access 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Deepa, S.; Sheetal; Alli, A.; Siddalingappa, Rashmi Title A name given to the resource Enhancing Image Classification Performance through Hybrid Self-Supervised Learning Strategies Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived SSRG International Journal of Electronics and Communication Engineering;Volume;12;Issue;7;pp.90-101 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14445/23488549/IJECE-V12I7P108" target="_blank" rel="noreferrer noopener">https://doi.org/10.14445/23488549/IJECE-V12I7P108</a> <a href="https://www.scopus.com/pages/publications/105012894771?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012894771?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepa S., Department of Computer Science, Christ University, Bengaluru, India; Sheetal, Department of Computer Applications, Presidency College, Bengaluru, India; Alli A., Department of Computer Applications, Presidency College, Bengaluru, India; Siddalingappa R., Department of Computer Science, Christ University, Bengaluru, India Description An account of the resource Image classification is a cornerstone of computer vision, with the applications spanning healthcare, autonomous driving and security. The dependence on large labeled datasets for supervised learning poses significant challenges, particularly in specialized fields where the labeled data is scarce and expensive to obtain. Self-supervised learning (SSL) has emerged as a promising paradigm, enabling models to learn useful representations from unlabelled data by designing pretext tasks that generate pseudo-labels. SSL faces limitations in handling complex data distributions and achieving robust generalization. This paper explores hybrid self-supervised learning strategies that combine multiple SSL techniques, such as contrastive learning, masked image modeling, and clustering, to enhance image classification performance and reduce dependence on labeled data. This study proposes a comprehensive framework that integrates data augmentation, feature extraction, and hybrid learning mechanisms, evaluated on the CIFAR-100 dataset. The experimental results demonstrate that hybrid SSL approaches achieve significant improvements in performance. The combination of SimCLR and masked image modeling (MAE) achieves a Top-1 accuracy of 77.8% on the clean test set and 71.4% on the domain-shifted set, and self-distillation with contrastive learning (DINO) achieves the highest Top-1 accuracy of 78.4% on the clean test set and 72.1% on the domain-shifted set. Advanced data augmentation techniques, such as CutMix and RandAugment, additionally enhance model robustness, with SwAV (contrastive clustering) achieving 76.5% Top-1 accuracy on the clean test set and 70.1% on the domain-shifted set. The findings highlight the effectiveness of hybrid SSL methods in addressing the challenges of limited labelled data, offering valuable insights for future research and applications in image classification. 2025 Seventh Sense Research Group. Subject The topic of the resource Computer Vision; Contrastive Clustering; Hybrid SSL; Image classification; Multi-Modal; Self-Supervised Publisher An entity responsible for making the resource available Seventh Sense Research Group Relation A related resource ISSN: 23488549; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Deepa, S.; Siddalingappa, Rashmi; Kalpana, P.; Loveline, Zeema.J.; Vinay, M.; Jayapriya; Suganthi, J.J.; Priya Stella Mary, I. Title A name given to the resource A Comparative Analysis of L1, L2, and L1L2 Regularization Techniques in Neural Networks for Image Classification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Engineering Trends and Technology;Volume;73;Issue;10;pp.107-116 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14445/22315381/IJETT-V73I10P108" target="_blank" rel="noreferrer noopener">https://doi.org/10.14445/22315381/IJETT-V73I10P108</a> <a href="https://www.scopus.com/pages/publications/105022820131?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022820131?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepa S., Department of Computer Science, Christ University, Bengaluru, India; Siddalingappa R., York St Jhon University, London, United Kingdom; Kalpana P., Department of Computer Science, Christ University, Bengaluru, India; Loveline Z.J., Department of Computer Science, Christ University, Bengaluru, India; Vinay M., Department of Computer Science, Christ University, Bengaluru, India; Jayapriya, Department of Computer Science, Christ University, Bengaluru, India; Suganthi J.J., Department of Computer Science, Christ University, Bengaluru, India; Priya Stella Mary I., Department of Computer Science, Christ University, Bengaluru, India Description An account of the resource The research examines how L1, L2, and L1L2 weight regularization methods affect neural network performance, generalization, and sparsity using the CIFAR 10 dataset. A Convolutional Neural Network (CNN) trained with the same environment for each regularization method to evaluate test accuracy, weight sparsity, and computational speed. The study shows that L1 regularization produces sparse weights, which makes models more interpretable, and L2 regularization helps prevent overfitting while improving model generalization. The combination of L1L2 regularization enables individual image classification methods to reach test accuracy. The results indicate that the weight regularization plays a vital role in creating neural networks that are both stable and efficient. They are interpretable, and L2 regularization improves generalization and reduces overfitting. The combined L1L2 regularization achieves the balance between sparsity and performance, leading to higher test accuracy compared to individual techniques for image classification. The research results demonstrate that weight regularization stands as an essential factor for Creating Neural Networks that are robust, efficient, and interpretable, thus helping to enhance Deep Learning model performance. 2025 Seventh Sense Research Group. Subject The topic of the resource Convolutional Neural Networks (CNN); Deep Learning; Generalization; L1 Regularization; L1L2 Regularization; L2 Regularization; Overfitting; Weight Regularization Publisher An entity responsible for making the resource available Seventh Sense Research Group Relation A related resource ISSN: 23490918; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online 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 A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers 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 A name given to the resource Performance Analysis of Deep Learning Pretrained Image Classifiation Models Subject The topic of the resource Convolutional Neural Networks (CNNs); Deep Learning; Image classification Description An account of the resource 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. Creator An entity primarily responsible for making the resource Deepa; Alli A.; Sheetal; Prasad Km S. Source A related resource from which the described resource is derived IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AIKIIE60097.2023.10390378" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIKIIE60097.2023.10390378</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187267406&doi=10.1109%2FAIKIIE60097.2023.10390378&partnerID=40&md5=3a50958178d7e3e3228e9fc91e838a08" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187267406&doi=10.1109%2fAIKIIE60097.2023.10390378&partnerID=40&md5=3a50958178d7e3e3228e9fc91e838a08</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835031646-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource 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 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 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 A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter 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 A name given to the resource Hereditary factor-based multi-featured algorithm for early diabetes detection using machine learning Subject The topic of the resource Classification algorithm; Decision trees; Diabetes detection; Multi-featured algorithm; Prediction Description An account of the resource 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. Creator An entity primarily responsible for making the resource Deepajothi S.; Juliana R.; Aruna S.K.; Thiagarajan R. Source A related resource from which the described resource is derived Artificial Intelligent Techniques for Wireless Communication and Networking, pp. 235-253. Publisher An entity responsible for making the resource available wiley Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781119821809.ch15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781119821809.ch15</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148085821&doi=10.1002%2F9781119821809.ch15&partnerID=40&md5=3da47231aabcdb719a732a2e49e6f8d5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148085821&doi=10.1002%2f9781119821809.ch15&partnerID=40&md5=3da47231aabcdb719a732a2e49e6f8d5</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-111982180-9; 978-111982127-4 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 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 A name given to the resource 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 A name given to the resource An intelligent inventive system for personalised webpage recommendation based on ontology semantics Subject The topic of the resource Ontologies; Personalised; Semantic strategy; Web search; Webpage recommendation system Description An account of the resource 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. Creator An entity primarily responsible for making the resource Deepak G.; Ahmed A.; Skanda B. Source A related resource from which the described resource is derived International Journal of Intelligent Systems Technologies and Applications, Vol-18, No. 45689, pp. 115-132. Publisher An entity responsible for making the resource available Inderscience Publishers Date A point or period of time associated with an event in the lifecycle of the resource 2019-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJISTA.2019.097751" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJISTA.2019.097751</a> <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> Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Relation A related resource ISSN: 17408865 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 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 A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers 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 A name given to the resource Descriptive Answer Evaluation using NLP Processes Integrated with Strategically Constructed Semantic Skill Ontologies Subject The topic of the resource E-Learning; Keyword Extraction; Natural Language Processing; Online Evaluation; Ontology Description An account of the resource 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) Creator An entity primarily responsible for making the resource Deepak G.; Ayush Kumar A.; Priyadarshini S.; Singh D. Source A related resource from which the described resource is derived CEUR Workshop Proceedings, Vol-3335, pp. 64-76. Publisher An entity responsible for making the resource available CEUR-WS Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147696836&partnerID=40&md5=44a8dbf4219e99a4043c52da94fd57d5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147696836&partnerID=40&md5=44a8dbf4219e99a4043c52da94fd57d5</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 16130073 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource 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 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 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 A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers 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 A name given to the resource ATRSI: Automatic Tag Recommendation for Videos Encompassing Semantic Intelligence Subject The topic of the resource Bag of Words model; CoSim Rank; Lin similarity; RNN Description An account of the resource 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. Creator An entity primarily responsible for making the resource Deepak G.; Iyer K.; Shukla S. Source A related resource from which the described resource is derived Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 594-599. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICWITE59797.2024.10503160" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICWITE59797.2024.10503160</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192544182&doi=10.1109%2FICWITE59797.2024.10503160&partnerID=40&md5=78a332a57e52ca909315b37872d04331" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192544182&doi=10.1109%2fICWITE59797.2024.10503160&partnerID=40&md5=78a332a57e52ca909315b37872d04331</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038328-7 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource 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 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