346 20 16488 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 Nanjundan, Preethi; Mishra, Suraj; George, Jossy P.; Dash, Bibhuti Bhusan Title A name given to the resource Explainable AI in Healthcare: A Hybrid CNN-ViT Approach for Pneumonia Detection Using SHAP 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 Communications in Computer and Information Science;Volume;2730 CCIS;pp.286-308 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-12232-2_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-12232-2_20</a> <a href="https://www.scopus.com/pages/publications/105027520190?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027520190?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 Nanjundan P., Department of Data Science, CHRIST University, Bangalore, India; Mishra S., Department of Data Science, CHRIST University, Bangalore, India; George J.P., Department of Data Science, CHRIST University, Bangalore, India; Dash B.B., School of Computer Applications, KIIT Deemed to be University, Bhubaneswar, India Description An account of the resource The adoption of Artificial Intelligence (AI) in healthcare has improved diagnostic accuracy, particularly in medical imaging. However, the opaque nature of deep learning models raises concerns about interpretability in high-stakes applications such as pneumonia diagnosis. This study proposes an Explainable AI (XAI) framework that integrates a Hybrid CNN-ViT architecture with SHAP (SHapley Additive Explanations) for pneumonia detection from chest X-rays. Our approach achieves competitive diagnostic performance (94% accuracy) while enhancing transparency by highlighting clinically relevant features such as lobar consolidations and ground-glass opacities. By grounding explanations in established radiological findings, the framework supports clinical trust and regulatory compliance. This work contributes to bridging the gap between AI performance and medical accountability, positioning explainable deep learning as a trustworthy tool for real-world healthcare deployment. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource CNN-ViT; Explainable AI; Medical Imaging; Pneumonia Detection; SHAP Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303212231-5; 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 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 Explainable AI Method for Cyber bullying Detection Subject The topic of the resource CyberBullying; ExplainableAI; LIME; Machine Learning Description An account of the resource People of all ages and genders are using social media platforms to engage themselves in all sorts of activities. People create profiles on online social networks in order to communicate with one another in this virtual environment. Hundreds or thousands of friends and followers are split across many profiles. Along with the virtual communication in this social media life, cyber-crimes also creep in many distinguished forms to grab user's information and emotionally degrade them with harassment and arrogant behavior. A set of machine learning methods are proposed and used to detect such a bullying behavior. Along with the detection of such an act, the model should also provide the logical reasoning of the evidence extracted. The explain ability of the models classification will give us a view of the way towards portraying a suspect as a bullier. This paper illustrates a machine learning model that works on a twitter data set to suggest the tweets as category bullying or non-bullying. LIME a tool to predict the interpretability of the model is used to depict the performance of model and provides explainability. 2022 IEEE. Creator An entity primarily responsible for making the resource Pawar V.; Jose D.V.; Patil A. Source A related resource from which the described resource is derived 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022 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 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICMNWC56175.2022.10031652" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICMNWC56175.2022.10031652</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148301272&doi=10.1109%2FICMNWC56175.2022.10031652&partnerID=40&md5=caf7a3087ba4573a26a075336d3ecfa2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148301272&doi=10.1109%2fICMNWC56175.2022.10031652&partnerID=40&md5=caf7a3087ba4573a26a075336d3ecfa2</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166549111-2 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 Pawar V., CHRIST(Deemed to Be University), Department of Computer Science, Bengaluru, India, CMR Institute of Technology, Department of Computer Applications, Bengaluru, India; Jose D.V., CHRIST(Deemed to Be University), Department of Computer Science, Bengaluru, India; Patil A., CMR Institute of Technology, Department of Computer Applications, 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 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 Gunjan, Abhishek; Bhattacharyya, Siddhartha Title A name given to the resource Explainable artificial intelligence enhanced quantum-inspired spider monkey optimization for a constrained portfolio optimization proble 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 Machine Intelligence;Volume;7;Issue;2;Article No.;108; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s42484-025-00338-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42484-025-00338-5</a> <a href="https://www.scopus.com/pages/publications/105022056733?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022056733?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 Gunjan A., Christ (Deemed to be University), Bangalore, India; Bhattacharyya S., VSB Technical University of Ostrava, Ostrava, Czech Republic, Algebra Bernays University, Zagreb, Croatia Description An account of the resource Optimizing portfolios has consistently posed significant challenges while being an extensively researched subject in finance and accounting. This process requires selecting and distributing appropriate assets in alignment with a set of specified objectives. This nonlinear constraint issue is not effectively solvable using traditional methods. This paper investigates the use of spider monkey optimization, ageist spider monkey optimization, and a newly proposed enhanced spider monkey optimization technique for portfolio optimization problems. The explainability of the spider monkey optimization has been improved without compromising the optimization results. It has been observed that the proposed technique marginally enhances the results of spider monkey optimization and can improve trust and risk management in the portfolio optimization problem. Furthermore, a quantum-inspired version of the proposed method is also implemented, and the results are compared using three benchmarked datasets from Dow Jones, BSE, and NASDAQ. Experimental results obtained using these benchmark datasets demonstrate that the newly introduced technique within the quantum-inspired framework marginally outperforms all other methods in the classical and quantum-inspired domains. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. Subject The topic of the resource Ageist spider monkey optimization; Explainable AI; Portfolio optimization; Quantum-inspired metaheuristics; Spider monkey optimization (SMO) Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 25244906; 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 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 Rengasamy, Priyadharshini; R, Rajesh Title A name given to the resource Explainable artificial intelligence framework for wind turbine fault detection using random forest Extreme gradient boosting hybrid model 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 Results in Engineering;Volume;28;Issue;;Article No.;107446; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.rineng.2025.107446" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.rineng.2025.107446</a> <a href="https://www.scopus.com/pages/publications/105017424887?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017424887?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 Rengasamy P., Department of Statistics and Data Science, CHRIST (Deemed to be University), Dharmaram Post, Karnataka, Bangalore, 560029, India; R R., Department of Statistics and Data Science, CHRIST (Deemed to be University), Dharmaram Post, Karnataka, Bangalore, 560029, India Description An account of the resource Though wind energy has great promise for clean energy generation in India, operational inefficiencies and underutilization still present major obstacles. Although installed wind capacity exceeds 51. 3 GW, actual power generation is still significantly lower than predicted mostly because of weak fault detection and maintenance techniques. Existing machine learning (ML) methods offer high accuracy but typically lack transparency in their forecasts, therefore making it difficult for engineers to correctly interpret and act on model outputs. This research aims to develop an understandable and high-performance anomaly detection model using real-time SCADA data from an Indian wind plant. This research aims to develop an understandable and high-performance anomaly detection model using real-time SCADA data from an Indian wind plant. A hybrid ensemble approach integrating Random Forest and XGBoost is proposed, combined with Local Interpretable Model-Agnostic Explanations (LIME) to provide local interpretability of predictions. The model was trained and evaluated on actual SCADA data using SelectKBest for feature selection, SMOTE for handling class imbalance, and RandomizedSearchCV for hyperparameter optimization. The tuned hybrid model achieved outstanding performance, with an accuracy of 0.9995, F1-score of 0.9995, and minimal error rates (MAE and MSE = 0.00052). LIME-based interpretability highlighted key features driving predictions, with Nacelle Temperature and Gearbox Temperature consistently emerging as critical indicators of turbine braking events, underscoring the importance of thermal variables in fault diagnosis. The findings suggest that interpretable machine learning not only enhances root cause analysis but also supports proactive maintenance, particularly by emphasizing improvements to cooling systems to reduce thermal failures. By providing transparent and reliable insights, the proposed solution enables wind farm operators to make informed, timely decisions, thereby improving turbine reliability and energy yield. The framework is practical, explainable, and well-suited for deployment in smart wind farms, aligning with the United Nations Sustainable Development Goals, including SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 12 (Responsible Consumption and Production) 2025 The Author(s) Subject The topic of the resource Anomaly detection; Explainable AI(XAI); LIME (local interpretable model-agnostic explanations); Machine learning; Predictive maintenance; Random forest; SCADA data; SDG 12; SDG 7; SDG 9; SMOTE; Sustainable energy; Wind turbine generator Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 25901230; 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; Green 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 Najmusseher; Nizar Banu, P.K.; Azar, Ahmad Taher; Kamal, Nashwa Ahmad Title A name given to the resource Explainable artificial intelligence in epilepsy management: Unveiling the model interpretability 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 Explainable AI in Healthcare Imaging for Medical Diagnoses: Digital Revolution of Artificial Intelligence;pp.175-211 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-23979-3.00008-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-23979-3.00008-7</a> <a href="https://www.scopus.com/pages/publications/105011234107?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011234107?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 Najmusseher, Department of Computer Science, CHRIST (Deemed to be University), Central Campus, Karnataka, Bangalore, India; Nizar Banu P.K., Department of Computer Science, CHRIST (Deemed to be University), Central Campus, Karnataka, Bangalore, India; Azar A.T., College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia, Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt; Kamal N.A., Faculty of Engineering, Cairo University, Giza, Egypt Description An account of the resource The field of epileptic seizure classification has witnessed significant advancements in the use of electroencephalogram (EEG) data for accurate and timely diagnoses. This study introduces a comprehensive framework for EEG-based seizure classification, encompassing data preprocessing and the application of machine learning techniques, specifically the supervised learning classifier known as Extreme Gradient Boosting (Xgboost). Machine learning methods have shown promising accuracy in binary classification tasks, particularly in distinguishing between seizure and healthy EEG signals. However, the need for a robust explanation of these results and decision-making processes is imperative for technical verification and clinical validation, especially for potential clinical applications. Explainable Artificial Intelligence (XAI) emerges as a critical component in addressing this need. In this chapter, we propose and discuss a binary classification model that leverages Xgboost to classify EEG signals as either Seizure or normal, a crucial aspect in epilepsy diagnosis. XAI techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive Explanations) are incorporated to elucidate the model's predictions. LIME offers localized interpretability by creating surrogate models for individual predictions, revealing the essential EEG features influencing each classification decision. Conversely, SHAP provides a global perspective on feature importance, shedding light on the collective impact of EEG features on classification outcomes. The synergy between LIME and SHAP enhances our understanding of the model's predictions and the intricate nuances within EEG data. This research highlights the transformative potential of LIME and SHAP in EEG-based seizure classification. The integration of XAI techniques not only enhances the transparency and interpretability of the model but also empowers clinicians and researchers to make more informed decisions, ultimately improving patient care and outcomes in epilepsy management. By bridging the gap between complex EEG data and actionable insights, this study marks a significant paradigm shift in the application of XAI techniques in medical diagnostics. It paves the way for a new era in epilepsy diagnosis and management, where advanced machine learning models guided by LIME and SHAP play a crucial role in revolutionizing healthcare practices. 2025 Elsevier Inc. All rights reserved. Subject The topic of the resource EEG; Feature importance; LIME; Machine learning; Seizure classification; SHAP; XAI Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044323979-3; 978-044323978-6; 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 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 Explainable Artificial Intelligence: Frameworks for Ensuring the Trustworthiness Subject The topic of the resource and Transparency; Autonomy; Control; Data Privacy; Fairness; Reliability; Safety and Security Description An account of the resource The growing computer power and ubiquity of big data are allowing Artificial Intelligence (AI) to gain widespread adoption and applicability in a wide range of sectors. The absence of an explanation for the conclusions made by today's AI algorithms is a significant disadvantage in crucial decision-making systems. For example, existing black-box AI systems are vulnerable to bias and adversarial assaults, which can taint the learning and inference processes. Explainable AI (XAI) is a recent trend in AI algorithms that gives explanations for their AI conclusions. Many contemporary AI systems have been shown to be vulnerable to undetectable assaults, biased against underrepresented groups, and deficient in user privacy protection. These flaws damage the user experience and undermine people's faith in all AI systems. This study proposes a systematic way to tie the social science notions of trust to the technology employed in AI-based services and products. 2024 IEEE. Creator An entity primarily responsible for making the resource Shama U.; Sharma V.; Gupta V. Source A related resource from which the described resource is derived TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 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/TQCEBT59414.2024.10545063" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545063</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204449341&doi=10.1109%2FTQCEBT59414.2024.10545063&partnerID=40&md5=7514240432d4d683ddf10df45d0ca3f3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204449341&doi=10.1109%2fTQCEBT59414.2024.10545063&partnerID=40&md5=7514240432d4d683ddf10df45d0ca3f3</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038427-7 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 Shama U., Christ University, Banglore, India; Sharma V., Christ University, Banglore, India; Gupta V., Christ University, Banglore, 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 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 Sahu, Premananda; Kumar, Ashwani; Jain, Rituraj; Upreti, Kamal; Yadav, Dileep Kumar; Radhakrishnan, G.V. Title A name given to the resource Explainable Hybrid Deep Learning Framework with Multimodal Inputs for Diabetic Retinopathy Detection 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 An-Najah University Journal for Research - A (Natural Sciences);Volume;40;Issue;3;pp.319-332 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.35552/anujr.a.40.2.2617" target="_blank" rel="noreferrer noopener">https://doi.org/10.35552/anujr.a.40.2.2617</a> <a href="https://www.scopus.com/pages/publications/105038182686?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038182686?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 Sahu P., School of Computer Science Engineering, Lovely Professional University, Punjab, India; Kumar A., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Yadav D.K., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India Description An account of the resource Diabetic Retinopathy (DR) is a leading cause of vision loss, making accurate and interpretable detection critical. This study proposes a hybrid interpretable machinedeep learning framework that integrates multimodal data for enhanced DR severity classification. The model combines unstructured fundus images from EyePACS, Messidor, and APTOS with structured clinical and lifestyle variables such as age, sex, HbA1c, BMI, blood pressure, and diabetes duration. Fundus images undergo preprocessing through resizing, normalization, augmentation, and noise reduction, while clinical data are imputed, normalized, and one-hot encoded. For feature extraction, EfficientNetV2, ResNet50, and Swin Transformer are applied to images, and XGBoost, LightGBM, and TabNet to clinical data. Features are fused via concatenation and attention, followed by classification using Logistic Regression, Random Forest, and MLP. Explainability is provided by Grad-CAM for imaging data and SHAP/LIME for clinical data, supporting clinical interpretability. The proposed model outperformed unimodal baselines, achieving 99.34% accuracy, 98.5% precision, 98.0% recall, 99.0% specificity, 98.2% F1-score, and 0.99 AUC-ROC, with a 10% gain over ResNet50 alone. Performance improvements included a 9% increase in recall and 8% in F1-score, alongside excellent calibration. Confusion matrix analysis confirmed balanced severity detection, and clinicians validated the interpretability outputs. This framework demonstrates robust accuracy, generalization, and clinical applicability for DR screening. 2026, An-Najah National University. All rights reserved. Subject The topic of the resource Diabetic Retinopathy; Explainability; Eyepacs; Fundus Image; Grad-Cam; Lime; Shap Publisher An entity responsible for making the resource available An-Najah National University Relation A related resource ISSN: 17272114; 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 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 Sureshkumar, T.; Meena Suguanthi, G.; Rekha, N.; Vadhana Kumari, S.; Devi, A. Sindhu; Atabek, Rashidkhon Uulu Title A name given to the resource Explainable Information Retrieval Techniques in Academic Search Engines 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.390-400 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.51983/ijiss-2025.IJISS.15.3.44" target="_blank" rel="noreferrer noopener">https://doi.org/10.51983/ijiss-2025.IJISS.15.3.44</a> <a href="https://www.scopus.com/pages/publications/105020302481?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020302481?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 Sureshkumar T., Librarian National College (Autonomous), Affiliated to Bharathidasan University, Tamil Nadu, Tiruchirappalli, India; Meena Suguanthi G., School of Management, Sri Krishna College of Engineering and Technology, Coimbatore, India; Rekha N., Department of School of Commerce, Accounts and Finance College: Christ University, Bangalore, India; Vadhana Kumari S., Department of Computer Science and Engineering and Business Systems, Vimal Jyothi Engineering College, Chemperin, Kerala, Kannur, India; Devi A.S., Department of AIDS, New Prince Shri Bhavani College of Engineering and Technology, Chennai, India; Atabek R.U., Faculty of Business Administration, Turan International University, Namangan, Uzbekistan Description An account of the resource Due to the rapid increase in scholarly publications globally, researchers rely heavily on specialized academic search engines to gather pertinent information. However, the algorithms used in many of these systems create a black box that breaches transparency, significantly eroding user trust and interpretability. Thanks to XIR, Explainable Information Retrieval, this issue has become a thing of the past. Users can now receive easily understood rationales for why documents were retrieved and how the documents were ranked. This work examines the various XIR techniques integrated into academic search tools, assesses their application methods, and analyzes how effectively they enhance users' understanding, satisfaction, decision-making, and information processing. The paper also formulates a central proposal, which incorporates important elements of XIR, and highlights remnant problems that require deeper analysis. The Research Publication, www.trp.org.in. Subject The topic of the resource Academic Search Engines; Explainable AI; Information Retrieval; Interfaces for Trust; Transparency; Trustworthy AI; User Trust; XAI; XAI 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 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 Paul, Aditi; Kumari, Sweety; Navadia, Nipun R; Sinha, Somnath Title A name given to the resource Explainable Intrusion Detection System for Internet of Things-explainability with reliability 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 Proceedings of 5th International Conference on Soft Computing for Security Applications, ICSCSA 2025;pp.226-232 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSCSA66339.2025.11170796" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSCSA66339.2025.11170796</a> <a href="https://www.scopus.com/pages/publications/105018461660?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018461660?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 Paul A., Banasthali Vidhapith, Department of Computer Science, Jaipur, India; Kumari S., Banasthali Vidhapith, Department of Computer Science, Jaipur, India; Navadia N.R., Pace University, New York City, NY, United States; Sinha S., Christ University, Department of Computer Science, Bengaluru, India Description An account of the resource Explainable Artificial Intelligence (XAI) based Intrusion Detection System (IDS) (X-IDS) has transformed the traditional IDS into interpretable and transparent system with the goal of providing interpretable justification for IDS models. XAI is now being used to extract more appropriate features for specific cyber-attacks. The black-box model of ML based IDS is not capable of giving reason for false positive to the cyber defense personnel. XAI tools reduces this abstraction by locally interpreting the model's behaviour at some datapoints along with global interpretability. This article proposes an explainable IDS by using XAI tools. We used SHAP (SHapley Additive exPlanations) to identify the variations in feature importance of selected ML based IDSs and explain the variations of their detection accuracies. Also, we have shown that with same dataset, feature importance varies differently with different ML models. This leads us to the conclusion that specific set of features are required for specific ML models while other can be discarded. The explainability proposed in this study also help to select less set of features to overcome time of execution and cost. 2025 IEEE. Subject The topic of the resource DoS attack; feature importance; Intrusion Detection System; XAI based IDS; XAI for machine learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159491-6; 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 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 Explainable IoT Forensics: Investigation on Digital Evidence Subject The topic of the resource Cyber Security; Explainable Forensics; Internet of things (IoT); IoT Applications; IoT Forensics Description An account of the resource This research examines the relevance of digital forensics in the field of Internet of Things and describes how different forensics tools and software are used to investigate cybercrimes. It emphasizes the importance of IoT Forensics and how it's used to tackle cybercrimes. It also discusses on the challenges faced by IoT forensics and gives an insight into the recent advancements in the field. It gives a walkthrough about how digital forensics investigation is done in 'data stolen' or 'data deleted' scenario. An outline of research potential and problems in IoT forensics is given in this chapter. The main details of IoT forensics are described. In all stages of a forensic investigation, issues linked to IoT are highlighted along with the potential that IoT presents for forensics. An illustration of an IoT forensics case is given with appropriate analytics. A brief research overview is provided, with information on the important research directions and a review of relevant articles. Future research proposals are included in the chapter's conclusion. 2023 IEEE. Creator An entity primarily responsible for making the resource Gopinath A.; Kumar K.P.; Shehan Saleem K.M.; John J. Source A related resource from which the described resource is derived Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications 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/InC457730.2023.10262942" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC457730.2023.10262942</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174694086&doi=10.1109%2FInC457730.2023.10262942&partnerID=40&md5=f03dc274bec50be93b32a97b9d5c8c5e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174694086&doi=10.1109%2fInC457730.2023.10262942&partnerID=40&md5=f03dc274bec50be93b32a97b9d5c8c5e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835033577-4 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 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 Gopinath A., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Kumar K.P., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Shehan Saleem K.M., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; John J., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, 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 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 Mudassir Alam, N.; Batra, Umang; George, Shiju Title A name given to the resource Explainable Temporal Knowledge Graph Reasoning for Geopolitical Risk Assessment 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 Lecture Notes in Networks and Systems;Volume;1928 LNNS;pp.140-152 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-22917-5_12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-22917-5_12</a> <a href="https://www.scopus.com/pages/publications/105039515933?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039515933?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 Mudassir Alam N., Christ University, Karnataka, Bangalore, India; Batra U., Christ University, Karnataka, Bangalore, India; George S., Christ University, Karnataka, Bangalore, India Description An account of the resource The paper examines the application of the Graph Neural Networks (GNNs) to the ICEWS14 temporal knowledge graph to predict geopolitical events and relationship. We suggest a new architecture that involves negative sampling and temporal encoding trick that enhances performance on temporal link prediction. Our model combines a central temporal-conscious attention GNN with numerous domain-specific GNN sub-models that are taught on economics trends, political choices, and business interrelations and it additionally incorporates knowledge graphs, sentiment analysis of the people, and market tendencies data to provide comprehensive consulting assistance. The framework has an AUCROC of 0.9234, test set, indicating that the framework is more likely to detect both positive and negative links. Exploring the high-confidence predictions, we identify the regularities of the political enforcement action, whereas the low-confidence ones assist in suspecting the improbable or counterfeit connections. Such explanations are useful in designing early warning systems, risk-assessment monitors, and decision-support frameworks in foreign affairs and think tank consultancy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Event Prediction; Geopolitical Forecasting; Graph Neural Networks; ICEWS14; Negative Sampling; Temporal Knowledge Graph Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303222916-8; 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 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 Explaining Autism Diagnosis Model Through Local Interpretability Techniques - A Post-hoc Approach Subject The topic of the resource anchor; explainability; lime; ml interpretability; shap; xai Description An account of the resource In this era of machine learning and deep learning algorithms dominating the Artificial Intelligence (AI) world, the trustworthiness of these black box models is still questionable. Life-caring sectors like healthcare and banking make use of these black box models as assistance in critical decision-making processes, but the degree of reliability of these decisions is still uncertain. This is because these black box models will not reveal the causation of the predicted outcome. However, creating an interpretable model that can explain the internal workings of these black box models can provide some reliable insights and trustable justifications for the predicted outcome. This study aimed to create an interpretable model for autism diagnosis which can give some trustable explanations for its predicted outcome. Using local interpretability methods such as LIME, SHAP, and Anchors the predicted outcome for each instance is explained well with some standard visual representations. As a result, this study developed an interpretable autism diagnosis model with an accuracy rate of 91.37% and with good local model explanations. 2023 IEEE. Creator An entity primarily responsible for making the resource Sujana D.S.; Augustine D.P. Source A related resource from which the described resource is derived 2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 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/ICDSAAI59313.2023.10452575" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSAAI59313.2023.10452575</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187798451&doi=10.1109%2FICDSAAI59313.2023.10452575&partnerID=40&md5=f2a075f0e0a1452551c7349b322ef9a4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187798451&doi=10.1109%2fICDSAAI59313.2023.10452575&partnerID=40&md5=f2a075f0e0a1452551c7349b322ef9a4</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034891-0 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 Sujana D.S., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Augustine D.P., Christ (Deemed to Be University), Department of Computer Science, 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 Explaining the intention to uptake COVID-19 vaccination using the behavioral and social drivers of vaccination (BeSD) model Subject The topic of the resource BeSD; COVID-19; Trust in the healthcare; Vaccination uptake; Vaccine acceptance scale; Vaccine communication strategy Description An account of the resource Background: The World Health Organization (WHO) has proposed a tool to measure behavioral and social drivers (BeSD) of vaccination uptake intentions of people across all countries. This study tests BeSD model to predict people's intentions to uptake COVID-19 vaccination in rural India. Methods: An online cross-sectional survey was developed for the purpose based on the components of the BeSD model, i.e., confidence, motivation, and behavioral intention. A convenient sampling technique was used to collect samples, amounting to a total of 625, from rural Bengaluru, in the Karnataka state of India. Structural equation modelling (SEM) was applied to examine the proposed model. All respondents for the survey were in the age category of 1868 years with a mean age of 35 years. Findings: The results showed that 85% of COVID-19 vaccine uptake intentions can directly or indirectly be attributed to the government's vaccine communication strategy, perceived threats about the vaccine, and their trust in the healthcare sector. The dimensions of the vaccine acceptance scale (motivation factors) act as a mediator between these factors and COVID-19 vaccination uptake (the behavioral factor). Conclusion: The study demonstrates that the BeSD framework is an efficient model for predicting the COVID-19 vaccination uptake in India. 2022 The Authors Creator An entity primarily responsible for making the resource Alagarsamy S.; Mehrolia S.; Pushparaj U.; Jeevananda S. Source A related resource from which the described resource is derived Vaccine: X, Vol-10 Publisher An entity responsible for making the resource available Elsevier Ltd 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/10.1016/j.jvacx.2021.100140" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jvacx.2021.100140</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122560280&doi=10.1016%2Fj.jvacx.2021.100140&partnerID=40&md5=3799e214bd19c7237e6e07bbd364f4ca" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122560280&doi=10.1016%2fj.jvacx.2021.100140&partnerID=40&md5=3799e214bd19c7237e6e07bbd364f4ca</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 25901362 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 Alagarsamy S., Manipal Academy of Higher Education, Dubai Campus, United Arab Emirates; Mehrolia S., School of Business and Management, Christ University, Bangalore, India; Pushparaj U., Department of Anaesthesiology, St. John's Medical College Hospital, Bangalore, India; Jeevananda S., School of Business and Management, Christ University, 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 PHD PhD PhD Thesis Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Relation A related resource 61000067 Title A name given to the resource Explanations for anomalies in semi strong from of efficient market hypithesis Subject The topic of the resource Management Studies Description An account of the resource The tradition notion of efficient market described in the academic literature is quite strong and probably is unrealistic because of following factors: short selling, short covering, fresh buying and profit booking. After all, science is just a human idea about phenomena which can change over time. In real markets, traders can cause markets to be inefficient. According to verifiability newlinetheory of meaning, most of statements are meaningless such as prices reflect earning .in order to make a statement meaningful that statements have to be tested with all ideas related to it. Researcher is testing EMH under various conditions to make meaningful. In this study, researcher is testing so called semi strong under market condition and firm size which is based upon market capitalization. National stock exchange provides classification of companies based newlineupon market capitalization. This is an event study to study stock return to earning newlineannouncements in recession and post recession periods. In other words, testing so called semi strong under market conditions and firm size of market capitalization such as large cap, mid cap and small cap. Researcher uses AARs, CAARS and T-test to study the impact of earning announcements on stock returns. Result of this study shows its onerous to accept semi strong so Fama received the Nobel prize in economics for what? Creator An entity primarily responsible for making the resource Chetan, K S Source A related resource from which the described resource is derived Author's Submission Publisher An entity responsible for making the resource available Christ(Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2017-01-01 Contributor An entity responsible for making contributions to the resource Kumar, D N S Rights Information about rights held in and over the resource Open Access Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource PhD Identifier An unambiguous reference to the resource within a given context <a href="http://hdl.handle.net/10603/325478" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/325478</a> https://archives.christuniversity.in/files/original/8ec41380e2a0f67220ebc39f6f35a95e.pdf 186c07eac996623a9f9acbbab614ad0e 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 Thesis PhD PhD Thesis Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Explanations for anomalies in semi-strong form of efficient market hypothesis / Subject The topic of the resource Management Creator An entity primarily responsible for making the resource K.S, Chetan. - 1140004 Publisher An entity responsible for making the resource available CHRIST (Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2017-12-22 Language A language of the resource English Type The nature or genre of the resource PhD 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 Exploration and Analysis of Seizure Spikes Through Spectral Domain Transformation Subject The topic of the resource Correlation analysis; EEG; Epilepsy; Frequency spectrum; Magnitude spectrum; Seizure; Spectral transformation; T-SNE Description An account of the resource Seizure detection is the most crucial area of investigation when it comes to understanding brain disorders. This proposed research study embarked on an automated model for epileptic seizure diagnosis by means of different kinds of Spectral transformation using EEG inputs from seizure sufferers and healthy subjects. This automated model accommodates non-invasive brain electrical activity monitoring. This method aims to facilitate the analysis and identification of epileptic seizure states since, monitoring and diagnosing such brain electrical activity is a complex task due to its numerous divisions and underlying features. The primary objective of this research study is to distinguish between EEG-based seizures and healthy individuals. To achieve this goal, a combination of spectral transformation and EEG analysis techniques is utilized. These techniques include examining the frequency spectrum, magnitude spectrum, correlation, and T-Distributed Stochastic Neighboring Embedding (T-SNE) analysis. This analysis yields valuable insights from EEG data, refining the input data and making it more suitable for prediction and identification. The models performance is evaluated using two distinct datasets: real-time EEG data from individuals experiencing epileptic seizures and EEG data from healthy subjects. These datasets are sourced from the Bangalore EEG Epilepsy Dataset (BEED), India and the BONN epilepsy dataset from the UCI repository. In a comparative study of spectral transformation methods, including Complex Fast Fourier Transform (CFFT) and Real-Valued Fast Fourier Transform (RFFT), it is discovered that reducing the data dimension by using feature extraction is not the optimal approach. This simplification leads to the loss of valuable information. Therefore, preserving the full spectrum of EEG characteristics is crucial for gaining valuable insights into brain neuronal functions, ultimately enabling more accurate seizure prediction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Najmusseher; Nizar Banu P.K.; Azar A.T.; Kamal N.A.; Alzahrani A. Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies, Vol-394 SIST, pp. 99-113. 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/10.1007/978-981-97-3980-6_9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-3980-6_9</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208914333&doi=10.1007%2F978-981-97-3980-6_9&partnerID=40&md5=c74d128040a709d05380518b2c84ec50" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208914333&doi=10.1007%2f978-981-97-3980-6_9&partnerID=40&md5=c74d128040a709d05380518b2c84ec50</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21903018; ISBN: 978-981973979-0 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 Najmusseher, Department of Computer Science, CHRIST (Deemed to Be University) Central Campus, Karnataka, Bangalore, India; Nizar Banu P.K., Department of Computer Science, CHRIST (Deemed to Be University) Central Campus, Karnataka, Bangalore, India; Azar A.T., College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518, Egypt, Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia; Kamal N.A., Faculty of Engineering, Cairo University, Giza, Egypt; Alzahrani A., Department of Computer Engineering and Science, Faculty of Computer Science and Information Technology, Al-Baha University, Al-Baha, Saudi Arabia 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 Exploration of activation energy and binary chemical reaction effects on nano Casson fluid flow with thermal and exponential space-based heat source Subject The topic of the resource Activation energy; Binary chemical reaction; Exponential heat source; Nanofluids; Nanoparticles; Nonlinear radiation Description An account of the resource Purpose: The purpose of this paper is to explore the effects of binary chemical reaction and activation energy on nano Casson liquid flow past a stretched plate with non-linear radiative heat, and also, the effect of a novel exponential space-dependent heat source (ESHS) aspect along with thermal-dependent heat source (THS) effect in the analysis of heat transfer in nanofluid. Comparative analysis is carried out between the flows with linear radiative heat process and non-linear radiative heat process. Design/methodology/approach: A similarity transformation technique is utilised to access the ODEs from the governed PDEs. The manipulation of subsequent non-linear equations is carried out by a well-known numerical approach called RungeKuttaFehlberg scheme. Obtained solutions are briefly discussed with the help of graphical and tabular illustrations. Findings: The effects of various physical parameters on temperature, nanoparticles volume fraction and velocity fields within the boundary layer are discussed for two different flow situations, namely, flow with linear radiative heat and flow with non-linear radiative heat. It is found that an irregular heat source/sink (ESHS and THS) and non-linear solar radiation play a vital role in the enhancement of the temperature distributions. Originality/value: The problem is relatively original to study the effects of activation energy and binary chemical reaction along with a novel exponential space-based heat source on laminar boundary flow past a stretched plate in the presence of non-linear Rosseland radiative heat. 2019, Emerald Publishing Limited. Creator An entity primarily responsible for making the resource Gireesha B.J.; Archana M.; Mahanthesh B.; Prasannakumara B.C. Source A related resource from which the described resource is derived Multidiscipline Modeling in Materials and Structures, Vol-15, No. 1, pp. 227-245. Publisher An entity responsible for making the resource available Emerald Group Holdings Ltd. 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/10.1108/MMMS-03-2018-0051" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/MMMS-03-2018-0051</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057578294&doi=10.1108%2FMMMS-03-2018-0051&partnerID=40&md5=d792296fbd47a401cb9361d34a7969dd" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057578294&doi=10.1108%2fMMMS-03-2018-0051&partnerID=40&md5=d792296fbd47a401cb9361d34a7969dd</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 15736105 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 Gireesha B.J., Department of Mathematics, Kuvempu University, Shankaraghatta, India; Archana M., Kuvempu University, Shankaraghatta, India; Mahanthesh B., Department of Mathematics, Christ University, Bangalore, India; Prasannakumara B.C., Department of Mathematics, Government First Grade College, Koppa, 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 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 Tiwari, Ashish Kumar; SK, Vaishnu; Karthikeyan, Mano Chitra; Narayanan, Mohankumar; Garg, Mohit; Arockiam, Antony Joseph Velanganni; Manoharan, Natesan; Manoharan, Vishnu Varma; Anjali, R.; Menon, Revathi S; Nallasivam, Jothi L.; Biju, V.M. Title A name given to the resource Exploration of aldazine Schiff bases as promising bioactive agents: A synergistic approach using DFT, ADME, antibacterial and cytotoxicity analysis 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 Journal of Molecular Structure;Volume;1349;Issue;;Article No.;143754; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.molstruc.2025.143754" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.molstruc.2025.143754</a> <a href="https://www.scopus.com/pages/publications/105015062281?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015062281?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 Tiwari A.K., Department of Chemistry, National Institute of Technology, Tamil Nadu, Tiruchirappalli, 620015, India; SK V., Department of Chemical Engineering, Birla Institute of Technology and Science, Rajasthan, Pilani, 333031, India; Karthikeyan M.C., Molecular Oncology Laboratory, Department of Biochemistry, School of Life Science, Bharathidasan University, Tamil Nadu, Tiruchirappalli, 620024, India; Narayanan M., Marine Pharmacology and Toxicology Laboratory, Department of Marine Science, Bharathidasan University, Tamil Nadu, Tiruchirappalli, 620024, India; Garg M., Department of Chemical Engineering, Birla Institute of Technology and Science, Rajasthan, Pilani, 333031, India; Arockiam A.J.V., Molecular Oncology Laboratory, Department of Biochemistry, School of Life Science, Bharathidasan University, Tamil Nadu, Tiruchirappalli, 620024, India; Manoharan N., Marine Pharmacology and Toxicology Laboratory, Department of Marine Science, Bharathidasan University, Tamil Nadu, Tiruchirappalli, 620024, India; Manoharan V.V., Department of Chemistry, National Institute of Technology, Tamil Nadu, Tiruchirappalli, 620015, India; Anjali R., Department of Chemistry, National Institute of Technology, Tamil Nadu, Tiruchirappalli, 620015, India; Menon R.S., Department of Chemistry, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India; Nallasivam J.L., Department of Chemistry, National Institute of Technology, Tamil Nadu, Tiruchirappalli, 620015, India; Biju V.M., Department of Chemistry, National Institute of Technology, Tamil Nadu, Tiruchirappalli, 620015, India Description An account of the resource A straightforward method for synthesizing four new asymmetric Aldazine Schiff base derivatives using aromatic aldehydes and hydrazine precursors was successfully demonstrated under moderate conditions. These compound are designated as follows: 1-((E)-(((E)-2-ethoxy benzylidene) hydrazineylidene) methyl)naphthalene-2-ol (2-EHMN) (L1), 1-((4-ethoxy benzylidene) hydrazineylidene) methyl) naphthalene-2-ol (4-EHMN) (L2), 1-((2?hydroxy-4-methoxybenzylidene) hydrazineylidene) methyl) naphthalene-2-ol (HMHMN) (L3), and 1-((2?chloro-6-hydroxyybenzylidene) hydrazineylidene) methyl) naphthalene-2-ol (CHHMN) (L4). The compounds obtained were analyzed via FT-IR, 1H-/13CNMR spectroscopy, HRMS spectrometry techniques, and elemental analysis. Infrared (IR) spectroscopy, UVVis spectroscopy, and accurate melting point determination all contribute to the improved study of synthesised compounds. A comprehensive solubility analysis was conducted for all synthesized compounds, demonstrating their solubility in dichloromethane (DCM), tetrahydrofuran (THF), and dimethylformamide (DMF). Thermoanalytical studies of all the ligands were also examined and compared. Furthermore, a single-crystal X-ray diffraction (SCXRD) analysis of L1 was conducted using a single-crystal diffractometer, with unit cell calculations and data collection performed using MoK? radiation (? = 0.7107 . Density functional theory (DFT) computations were used to optimise the structures of molecules and assess reactivity, durability, and electronic characteristics of the developed ligands. Molecular docking of L1, L2 and L3 has been done in different proteins, which gives precise results to show the activity for cytotoxicity and antibacterial studies. In silico, the ADME process calculations showed that the synthesised compounds have favourable drug-like features. In vitro antibacterial (L2 and L3) and cytotoxicity (L1) tests were also performed to assess their efficacy as therapeutic agents. 2025 Elsevier B.V. Subject The topic of the resource Aldazine; Antibacterial studies; Azine; Computational studies (DFT and MEP); Cytotoxicity studies Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 222860; CODEN: JMOSB 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 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 Exploration of carbon nano dots in hydro carbon soot and carbon black Subject The topic of the resource Aromaticity and environmental pollutant; Carbon nanodots; Carbon nanostructure; Hydrocarbon soot Description An account of the resource Hydrocarbon soot, a prime component of particulate matter pollution, poses a great threat to the environment. In this study, we put forth a novel way of harnessing carbon nanodots from the soot particulates thereby converting an environmentally perilous component to an innocuous entity suitable for many applications such as biomedical tracers, gas detectors etc. Large scale production of pure carbon nanodots (PCN) was achieved via direct catalyst free thermal decomposition of kerosene and diesel. Nanostructure of carbon black and graphite is also investigated for comparative studies. In UV-Vis spectra, absorptions at 233, 232 and 229 nm are attributed to ?-?? transition of the C=C bonding. XRD of the samples shows a highly intense peak at ?24 and a slightly broadened peak around 42 due to (002) and (010) reflections of graphitic planes respectively. In IR spectra, peaks at 3431 and 1047 cm-1 were assigned to O-H and C-O stretching vibrations respectively. The band observed at 1619 cm-1 manifests the skeletal vibrations from graphitic domains and hence indicates the presence of crystalline graphitic carbon. The absorption bands at 2920 and 2850 cm-1 arise because of the existence of aliphatic groups in the soot sample. 2017, International Congress of Chemistry and Environment. All rights reserved. Creator An entity primarily responsible for making the resource Mohan Anu N.; Manoj B. Source A related resource from which the described resource is derived Research Journal of Chemistry and Environment, Vol-21, No. 2, pp. 1-6. Publisher An entity responsible for making the resource available International Congress of Chemistry and Environment Date A point or period of time associated with an event in the lifecycle of the resource 2017-01-01 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-s2.0-85012267316&partnerID=40&md5=bbea2648605486ea749452f92d5c0cfa" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012267316&partnerID=40&md5=bbea2648605486ea749452f92d5c0cfa</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9720626 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 Mohan Anu N., Department of Physics, Christ University, Bangalore, Karnataka, India; Manoj B., Department of Physics, Christ University, Bangalore, Karnataka, 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 Exploration of Chemical Reaction Effects on Entropy Generation in Heat and Mass Transfer of Magneto-Jeffery Liquid Subject The topic of the resource chemical reaction; cross diffusion; entropy generation; heat source/sink; Jeffery fluid; MHD Description An account of the resource In many chemical engineering processes, a chemical reaction between a foreign mass and the fluid does occur. These processes find relevance in polymer production, oxidation of solid materials, ceramics or glassware manufacturing, tubular reactors, food processing, and synthesis of ceramic materials. Therefore, an exploration of homogeneous first-order chemical reaction effects on heat and mass transfer along with entropy analysis of Jeffrey liquid flow towards a stretched isothermal porous sheet is performed. Fluid is conducting electrically in the company of transverse magnetic field. Variations in heat and mass transfer mechanisms are accounted in the presence of viscous dissipation, heat source/sink and cross-diffusion aspects. The partial differential equations system governing the heat transfer of Jeffery liquid is reformed to the ordinary differential system through relevant transformations. Numerical solutions based on Runge-Kutta shooting method are obtained for the subsequent nonlinear problem. A parametric exploration is conducted to reveal the tendency of the solutions. The present study reveals that the Lorentz force due to magnetism can be used as a key parameter to control the flow fields. Entropy number is larger for higher values of Deborah and Brinkman numbers. It is also established that the concentration species field and its layer thickness of the Jeffery liquid decreases for a stronger chemical reaction aspect. To comprehend the legitimacy of numerical results a comparison with the existing results is made in this exploration and alleged an admirable agreement. 2018 Walter de Gruyter GmbH, Berlin/Boston 2018. Creator An entity primarily responsible for making the resource Mohapatra R.; Mahanthesh B.; Gireesha B.J.; Mishra S.R. Source A related resource from which the described resource is derived International Journal of Chemical Reactor Engineering, Vol-16, No. 9 Publisher An entity responsible for making the resource available De Gruyter Date A point or period of time associated with an event in the lifecycle of the resource 2018-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1515/ijcre-2018-0005" target="_blank" rel="noreferrer noopener">https://doi.org/10.1515/ijcre-2018-0005</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054328107&doi=10.1515%2Fijcre-2018-0005&partnerID=40&md5=675c95fa876d616f6cbd63209d503cc1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054328107&doi=10.1515%2fijcre-2018-0005&partnerID=40&md5=675c95fa876d616f6cbd63209d503cc1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 15426580 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 Mohapatra R., Department of Mathematics, Siksha - Anusandhan University, Bhubaneswar, Odisha, India; Mahanthesh B., Department of Mathematics Christ (Deemed to Be University), Karnataka, Bengaluru, 560029, India; Gireesha B.J., Department of Studies and Research in Mathematics, Kuvempu University, Shimoga, Karnataka, India; Mishra S.R., Department of Mathematics, Siksha - Anusandhan University, Bhubaneswar, Odisha, India