323 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 Muttungal, Peter V.; George, Jossy P.; Davidson, Benny Godwin J.; Tur, Anna Title A name given to the resource Enhancing Learning and Societal Innovation with Cyber-Physical Systems: Education and Society 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;435 SIST;pp.191-202 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-2182-8_15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-2182-8_15</a> <a href="https://www.scopus.com/pages/publications/105011343624?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011343624?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 Muttungal P.V., Department of Psychology, Christ University, Bangalore, India; George J.P., School of Business and Management, Christ University, Bangalore, India; Davidson B.G.J., School of Business, University of the Fraser Valley, Abbotsford, Canada; Tur A., Acsenda School of Management, Vancouver, Canada Description An account of the resource Cyber-Physical Systems (CPS) are evolving rapidly and can significantly impact education and society. In this paper, we explore the various technologies and applications of CPS in the context of education and social concerns. Through the integration of computation, networking, and physical processes, CPS can enhance educational experiences, foster social inclusion, and address societal challenges. Using CPS in education allows for intelligent learning environments, personalized learning experiences, and real-time feedback systems that meet the diverse needs of students and encourage them to engage in their learning. With real-time monitoring and response systems, CPS can help develop smart communities, improve accessibility for disabled individuals, and enhance public safety. This paper provides a comprehensive analysis of CPS technologies and their applications, highlighting their transformative potential in education and society, motivating further research and policy development. The implementation of CPS can transform education and society, but enhanced security measures must accompany their implementation. Integrating CPS in real-world applications requires safeguarding sensitive data and safeguarding against cyber-threats. This collaboration ensures that diverse perspectives are considered, leading to more comprehensive solutions that address the multifaceted challenges of CPS deployment. It also enhances the effectiveness and sustainability of CPS applications by combining different expertise. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Cyber-physical systems; Education technology; Real-time monitoring; Smart communities; Social inclusion Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981962181-1; 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 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 Natarajan, Arul Kumar; Galety, Mohammad Gouse; Mishra, Nilamadhab; Prabu, M.; Manimaran, A.; Ramasamy, Gobi Title A name given to the resource Enhancing Log File Analysis in Digital Forensics and Incident Response through Machine Learning 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 Cyber Security, Forensics and National Security;pp.378-396 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003497868-17" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003497868-17</a> <a href="https://www.scopus.com/pages/publications/105021606008?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021606008?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 Natarajan A.K., Samarkand International University of Technology, Samarkand, Uzbekistan; Galety M.G., Samarkand International University of Technology, Samarkand, Uzbekistan; Mishra N., VIT Bhopal University, Bhopal, India; Prabu M., Amrita Vishwa Vidyapeetham Erachakulam, Andhra Pradesh, India; Manimaran A., VIT-AP University, Amaravati, India; Ramasamy G., Christ University, Bengaluru, India Description An account of the resource Log file analysis is crucial for identifying and exploring digital security incidents by recording system and network traffic. The growing volume and complexity of log data do not allow traditional analytical methods to be used, which led to the need for the development of more advanced analytical tools. This chapter shows a new method to infer practical information from the log file analysis using machine learning algorithms combined with Python programming. The technique has the following structure: Data preprocessing, Feature extraction, and then using multiple machine learning models such as RandomForestClassifier, Gradient Boosting Classifier, SVM, XGBoostClassifier, and MLPClassifier. Adding Python greatly improves these advanced models' accuracy and efficiency in analyzing log files. The XGBoostClassifier achieved the highest accuracy, which was 0.9198 as precision, and it indicates good applicability to complicated log data compared to another model in our test. This section compares the machine learning models using the UNSWNb15 dataset, which provides a broad range of network traffic data. The chapter contains some visualizations of flagship results and a detailed discussion about the results, discussing the challenges and limitations of the proposed approach. It also suggests future research directions. The results also typify the specifics of how Python and machine learning can be disrupted to develop digital forensics incident response practicability, bringing forth such innovations that cater to solving the cyber world's rapidly transitioning threat landscapes and tooling up valued scientific knowledge in the domain. 2026 selection and editorial matter, Vinay Aseri, Sumit Kumar Choudhary, and Adarsh Kumar; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104041196-4; 978-104041198-8; 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 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 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 Hundekari, Sheela; Onumajuru, Onyekachi Kelvin; Multani, Danish; Oluwakemi, Olutoye Timothy; Upreti, Kamal Title A name given to the resource Enhancing Low-Power VLSI Design through AI-Based Simulation and Optimization 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 - 4th International Conference on Smart Technologies, Communication and Robotics 2025, STCR 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/STCR62650.2025.11018967" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/STCR62650.2025.11018967</a> <a href="https://www.scopus.com/pages/publications/105008422182?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008422182?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 Hundekari S., Pimpri Chinchwad University, Dept. of Computer Science, Pune, India; Onumajuru O.K., University of Hertfordshire, Dept. of Computer Science, United Kingdom; Multani D., Ust Global Solutions, Haryana, Gurgaon, India; Oluwakemi O.T., Olabisi Onabanjo University, Electrical and Electronics Engineering, Ago-Iwoye, Nigeria; Upreti K., Christ University, Dept. of Computer Science, Delhi NCR, India Description An account of the resource AI and ML techniques have dramatically influenced rapid developments in low-power VLSI design with fast advancements in device simulations and power optimization strategies. AI-based simulation tools are now used for accurate modeling of power consumption, improving thermal analysis, and quickening design iterations through the detection of inefficiencies and optimization of energy consumption. In fact, this work focuses on some AI-enabled methods of power reduction techniques such as voltage scaling, clock gating, and leakage current minimization with respect to a sustainable VLSI design. Moreover, a synthetic dataset is created to mimic the actual power consumption trend in VLSI circuits so that predictive modeling and regression techniques can be used for power estimation. Different regression models are used to check the predictive accuracy, and it was found that the highest R2 score was 0.85 by Linear Regression, while the worst was achieved by Decision Tree Regression at 0.50. Results of the correlation analysis and models by machine learning clearly indicate that the frequency and operating voltage are the major contributors to consumption power, while gate counts have a relatively insignificant contribution. Introduction of AI in VLSI simulation enables the enhancement of power efficiency while maintaining sustainability outcomes by optimizing energy usage and cost reduction in terms of computation. 2025 IEEE. Subject The topic of the resource Design-optimization; Low-Powered VLSI; Machine Learning Models; Power Consumption; Sustainability; Synthetic Data Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-835035753-0; 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 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 Abraham, Jerin; Upreti, Kamal Title A name given to the resource Enhancing Lung Cancer Detection Accuracy: Implementing Smote for Balanced Learning 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 2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.138-143 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC363308.2025.10956812" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC363308.2025.10956812</a> <a href="https://www.scopus.com/pages/publications/105003905445?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003905445?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 Abraham J., Christ (Deemed to be University), Department of Computer Science, Ghaziabad, India; Upreti K., Christ (Deemed to be University), Department of Computer Science, Ghaziabad, India Description An account of the resource This research goal is to forecast lung cancer using machine learning, and addressing the dataset's class imbalance is a top priority. The data that was initially gathered was extremely unbalanced, with 87.38% of instances being of the minority class of lung cancer and only 12.62% being non-cancer cases. To address this imbalance, minority over-sampling through self-generated SMOTE (Synthetic Minority Over-sampling Technique) was implemented wherein there were 64.85% cases of lung cancer and 35.15% of non-lung cancer cases after deduplication. Logistic regression (LR), Gaussian naive Bayes, Support Vector Machine (SVM), Bernoulli naive Bayes, K nearest neighbors (KNN), Random Forest (RF), multi-layer perceptron, and extreme gradient boosting are among the machine learning methods that were tested. The best test performance was shown by the Random Forest and Extreme Gradient Boosting methods that achieved an accuracy of 97.3% followed by K Nearest Neighbors at 95.95%, and Multi-Layer Perceptron at 93.24%. This highlights the necessity of data balance and the ways in which these methods can improve the efficacy of predictive models for lung cancer. As such, this addition contributes to the dearly needed critical knowledge which may be a stepping stone for innovation within the domains of diagnosis and treatment medicine through machine learning. 2025 IEEE. Subject The topic of the resource Class Imbalance; classification; Lung cancer Prediction; Machine Learning; SMOTE Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152749-5; 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 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 Alphine, P.J.; Alapatt, Bosco Paul; George, Jossy P Title A name given to the resource Enhancing Malware Detection Through Hybrid Deep Learning Techniques 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 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.478-483 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICICV64824.2025.11085589" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICV64824.2025.11085589</a> <a href="https://www.scopus.com/pages/publications/105013072560?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013072560?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 Alphine P.J., CHRIST University, School of Sciences, India; Alapatt B.P., CHRIST University, School of Sciences, India; George J.P., CHRIST University, School of Sciences, India Description An account of the resource The detection of malware needs superior methods than basic signature detection because it remains vital to cybersecurity. This research examines malware classification through the deep learning approach by analyzing Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and develops a new BiGRU + CNN hybrid model. The main purpose is to achieve better detection performance through reduced numbers of false alarms. The research employs executable file feature data while implementing preprocessing methods together with fivefold cross-validation validation to establish strong model reliability. Experimental findings show CNN along with LSTM and GRU attains excellent recall values yet produces elevated erroneous positive predictions. The proposed BiGRU + CNN model delivers superiority over single-model architecture as it reaches 96.06% accuracy alongside 96.13% precision and 99.92% recall and 97.99% F1-score. The obtained results show that this integration has better malware detection capabilities thereby demonstrating its potential for cybersecurity applications. 2025 IEEE. Subject The topic of the resource BiGRU-CNN; Cybersecurity; Deep Learning; Malware Detection Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151175-3; 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 Enhancing Medical Decision Support Systems withtheTwo-Parameter Logistic Regression Model Subject The topic of the resource Liu estimator; Mean squared error; Multicollinearity; Ridge estimator; Two-parameter logistic regression Description An account of the resource The logistic regression model is an invaluable tool for predicting binary response variables, yet it faces a significant challenge in scenarios where explanatory variables exhibit multicollinearity. Multicollinearity hinders the models ability to provide accurate and reliable predictions. To address this critical issue, this study introduces innovative combinations of Ridge and Liu estimators tailored for the two-parameter logistic regression model. To evaluate the effectiveness of the combination of ridge and Liu estimators under the two-parameter logistic regression, a real-world dataset from the medical domain is utilized, and Mean Squared Errors are employed as a performance metric. The findings of our investigation revealed that the ridge estimator, denoted as k4, outperforms other Liu estimators when multicollinearity is present in the data. The significance of this research lies in its potential to enhance the reliability of predictions for binary outcome variables in the medical domain. These novel estimators offer a promising solution to the multicollinearity challenge, contributing to more accurate and trustworthy results, ultimately benefiting medical practitioners and researchers alike. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Joshi H.; Chakraborty A. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 195-202. 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-0975-5_17" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-0975-5_17</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197244049&doi=10.1007%2F978-981-97-0975-5_17&partnerID=40&md5=f6ddcb6e05742c0b3768b7b61e0fd06f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197244049&doi=10.1007%2f978-981-97-0975-5_17&partnerID=40&md5=f6ddcb6e05742c0b3768b7b61e0fd06f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981970974-8 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 Joshi H., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, India; Chakraborty A., Department of Statistics and Data Science, CHRIST (Deemed to be 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 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 Srinivasan, S.; Vallipriya, R.; Joseph, Jipson Title A name given to the resource Enhancing Mental Health and Treatment Adherence in HIV/AIDS Patients Through Strengthened Social 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 Revolutionizing HIV Treatment With AI;pp.193-232 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-2863-8.ch007" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-2863-8.ch007</a> <a href="https://www.scopus.com/pages/publications/105040300390?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040300390?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 Srinivasan S., Department of Humanities and Social Sciences, Graphic Era University, Dehradun, India; Vallipriya R., Department of Biochemistry and Biotechnology, Pavendar Bharathidasan College of Arts and Science, Bharathidasan University, Tiruchirappalli, India; Joseph J., School of Law, Christ University, Bangalore, India Description An account of the resource The study explores how social networks and support enhance the health and treatment adherence of HIV patients. HIV/AIDS affects not only physical health but also leads to social stigma, discrimination, and emotional challenges. A strong social network improves the quality of life, reduces psychological issues like anxiety and depression, and promotes overall well-being. Strengthening network sustainability for vulnerable HIV/AIDS patients can enhance emotional support, self-care, and treatment adherence. Conducted in Pudukottai District, Tamil Nadu, the study involved 120 samples, with data collected through the Positive Network and NGO support. The findings offer policy implications, assist healthcare providers in managing affected individuals effectively, and highlight the role of NGO services in improving mental health outcomes and creating a supportive environment for PLHIV. Copyright 2026, IGI Global Scientific Publishing. Publisher An entity responsible for making the resource available IGI Global Scientific Publishing Relation A related resource ISBN: 979-833732865-2; 979-833732863-8; 979-833732864-5; 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 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 Moozhippurath, Bineesh; Natarajan, Jayapandian Title A name given to the resource Enhancing Metabolomics Pathway Prediction with Sequential Graph Convolutional Network 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 2025 IEEE 14th International Conference on Communication Systems and Network Technologies, CSNT 2025;pp.733-737 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CSNT64827.2025.10967932" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSNT64827.2025.10967932</a> <a href="https://www.scopus.com/pages/publications/105004556930?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004556930?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 Moozhippurath B., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Natarajan J., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India Description An account of the resource Metabolomics is a powerful tool for the understanding of biological systems by analysis of metabolites and their related pathways. Prediction of metabolic pathways is still one of the most challenging tasks because of the complexity of molecular structures and graph-structured metabolomics data. This article presents a robust framework using Graph Convolutional Networks (GCNs) to address the challenges. The methodology proposed includes first preprocessing through metabolite identification by mass spectrometry, and then it utilizes feature extraction through the RDKit library. The objective of the research is aim to metabolic pathway prediction using machine learning algorithm. Complex patterns and relationships are captured from the SMILES representation through the molecular graphs constructed and passed on for the GCN model to learn structured data. ReLU activation functions have been employed within a three-layer sequential GCN architecture that enables it to deliver highly accurate results while ensuring that they are understandable as well. The proposed sequential GCN Model was evaluated on the KEGG dataset with an accuracy of 98.00%, precision of 92.10%, and recall of 93.02%. The performance of these metrics is well beyond traditional approaches such as KNN, ensemble logistic regression, and other GCN based approaches. Thus, this work brings GCN based approaches closer to revolutionizing metabolic pathway prediction and the advancement of the metabolomics field. 2025 IEEE. Subject The topic of the resource Biochemical; Deep learning; Graph Convolutional Networks; Machine Learning; Metabolomics Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153193-5; 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 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 Josephine, Helen V.L.; Rajagopal, Manikandan; Gayatri, S.; Lakshmi, K. Title A name given to the resource Enhancing Mobile Application Security Through Android Threat 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 Procedia Computer Science;Volume;252;pp.154-164 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.procs.2024.12.017" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2024.12.017</a> <a href="https://www.scopus.com/pages/publications/85219166914?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85219166914?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 Josephine H.J.V.L., Business Analytics, School of Business and Management, Chrisut University, Bangalore, 560074, India; Rajagopal M., School of Business and Management, Chrisut University, Bangalore, 560029, India; Gayatri S., School of Computer Science and Application, Reva University, Bangalore, India; Lakshmi K., School of Business and Management, Christ Univeristy, Bangalore, 560074, India Description An account of the resource The Android application market has grown significantly, offering customers an ever-growing range of features to suit a variety of purposes. Users are exchanging more and more sensitive data thanks to the widespread usage of mobile applications, therefore safeguarding personal information is crucial. But this boom has also opened the door for a corresponding rise in cybersecurity risks, especially for malware and adware that target mobile devices. It is imperative to categorise mobile applications into distinct groups such as malware, adware, and benign in order to fortify the mobile ecosystem. This project's primary objective is to create and apply cutting-edge machine learning algorithms that can precisely categorise mobile apps into groups including adware, malware, and benign apps. This will necessitate investigating various machine learning strategies and ensemble methods to improve classification accuracy and robustness. Multiple machine learning models were developed based on feature importance, utilizing various machine learning techniques. The evaluation metrics showcase the effectiveness of the final model, especially the Tuned XGBoost model. While achieving a high overall accuracy of 92.51%, the findings highlight the importance of considering diverse features beyond traditional flow-based ones, providing a more robust and complete perspective on mobile network security. 2025 The Authors. Published by Elsevier B.V. Subject The topic of the resource Classification; Cyber Security; Random Forest; SHAP; SMOTE; Supervised Mahcine learning; XG-Boost Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 18770509; 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 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 Jain, Yogesh Kumar; Tak, Tan Kuan; Upreti, Kamal; Kumar, Nishant; Kshirsagar, Pravin R. Title A name given to the resource Enhancing Mobile DeFi Transactions Through Blockchain Adoption: A Pythagorean Fuzzy AHP Study 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 Basic and Applied Sciences;Volume;14;Issue;2;pp.678-690 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14419/6726x143" target="_blank" rel="noreferrer noopener">https://doi.org/10.14419/6726x143</a> <a href="https://www.scopus.com/pages/publications/105011030185?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011030185?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 Jain Y.K., School of Management, IILM University, Greater Noida, India; Tak T.K., Engineering Cluster, Singapore Institute of Technology, Singapore; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Kumar N., School of Business Management, Christ University, Bengaluru, India; Kshirsagar P.R., J D College of Engineering & Management, Maharashtra, Nagpur, India Description An account of the resource Blockchain technology has emerged as a pivotal enabler for innovation in mobile decentralized finance (DeFi) ecosystems. This study ememploys the Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) to identify and rank critical enablers driving blockchain adoption for enhancing mobile DeFi transactions. A structured three-stage methodology evaluates twenty-four sub-criteria across operational, managerial, and strategic dimensions. Results emphasize managerially focused factorssuch as reduced foreign exchange (FX) transfer costs and open-source adaptabilityas the most critical enablers, followed by operational drivers like transparency. Sensitivity analysis confirmed the ro-robustness of these findings. The results offer actionable insights for fintech practitioners, digital strategists, and policymakers seeking to optimize blockchain-based mobile financial platforms. By contributing to improved financial inclusion, operational efficiency, and regulatory alignment, the study supports broader welfare enhancement in the digital economy. The proposed PF-AHP framework provides an empirical decision-making tool to guide innovation and strategic planning in financial technology services. Yogesh Kumar Jain et al. Subject The topic of the resource Blockchain; Cross-Border Transactions; Defi; Digital Finance; Fintech Strategy; PF-AHP Publisher An entity responsible for making the resource available Science Publishing Corporation Inc. Relation A related resource ISSN: 22275053; 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 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 Enhancing mobility management in 5G networks using deep residual LSTM model Subject The topic of the resource 5G networks; Handover optimization; LSTM; Mobility management; Resource Allocation Description An account of the resource Mobility management is an essential component of 5G networks to provide mobile users with seamless connectivity and efficient cell transition. However, increasing user mobility, device density, and the diversity of service requirements all pose significant challenges to achieving optimal mobility management. This article describes a novel method for improving mobility management in 5G networks that employs a deep residual Long Short-Term Memory model. Deep learning and LSTM, a type of recurrent neural network, are used in the proposed model to identify temporal dependencies and patterns in user mobility data. The model learns to predict future user locations and mobility patterns by training on historical mobility data, allowing for proactive resource allocation and handover decisions. We incorporate residual connections into the LSTM architecture, inspired by the residual learning framework, to address the inability of traditional LSTM models to capture complex temporal dynamics. This allows the model to effectively incorporate long-term dependencies and improves prediction accuracy. Furthermore, we incorporate the mLSTM model into the mobility management framework of 5G networks. The model continuously obtains real-time user location updates and predicts future user positions, allowing for proactive handover decisions. The network can optimize resource allocation, reduce handover latency, and improve user experience by leveraging anticipated mobility patterns. We test the proposed method by simulating it extensively with real-world mobility traces. The results show that the mLSTM model accurately predicts user mobility and outperforms conventional methods in transition performance. The model is not affected by changing network conditions, user mobility patterns, or service specifications. 2024 Elsevier B.V. Creator An entity primarily responsible for making the resource Baz A.; Logeshwaran J.; Natarajan Y.; Patel S.K. Source A related resource from which the described resource is derived Applied Soft Computing, Vol-165 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.asoc.2024.112103" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.asoc.2024.112103</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202155517&doi=10.1016%2Fj.asoc.2024.112103&partnerID=40&md5=8e04a5aa78f775d9789c9dffbe538692" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202155517&doi=10.1016%2fj.asoc.2024.112103&partnerID=40&md5=8e04a5aa78f775d9789c9dffbe538692</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 15684946 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 Baz A., Department of Computer and Network Engineering, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia; Logeshwaran J., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Natarajan Y., Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Tamil Nadu, Coimbatore, 641062, India; Patel S.K., Department of Computer Engineering, Marwadi University, Gujarat, Rajkot, 360003, 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 Enhancing Mobility: A Smart Cane with Integrated Navigation System and Voice-Assisted Guidance for the Visually Impaired Subject The topic of the resource audio feedback; Blind mobility; haptic feedback; Navigation; Smart Cane; Ultrasonic sensor; vibration motor; voice control Description An account of the resource Blindness is a condition which affects many people, and for the affected people, quality of life can take a big hit. Most blind people already use walking sticks to feel the terrain in front of them as they move around and navigate using touch and sound. However, they cannot judge distances to objects until the cane actually hits the object. In some cases, the contact with the cane may damage the object. Hence, it may be better to have some early warning system so that there is less likelihood of causing damage. This paper presents the design and development of a 'Smart Cane' aimed at enhancing mobility and safety for visually impaired individuals. The cane incorporates ultrasonic sensors to detect objects in the user's surroundings. When an object is detected within a specified distance range, the cane provides haptic feedback through a bidirectional vibration motor, alerting the user to its presence. The microcontroller-based system processes data from both sensors and efficiently manages power consumption to ensure extended battery life. The device's design includes user-friendly controls and an ergonomic enclosure to offer ease of use and protection for the electronic components. Further, there is built-in navigation via online Map API. With the convenience of navigating oneself without external assistance, the 'Smart Cane' demonstrates great potential to improve the independence and confidence of visually impaired individuals in navigating their environments safely. 2024 IEEE. Creator An entity primarily responsible for making the resource Muktha D.S.; Niveditha G.; Pinto N.A.; Sinha S. Source A related resource from which the described resource is derived Proceedings - 2024 13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024, pp. 1124-1129. 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/CSNT60213.2024.10546111" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSNT60213.2024.10546111</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196767291&doi=10.1109%2FCSNT60213.2024.10546111&partnerID=40&md5=ae03094e8cb81e78fe8b11ea347c2dfc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196767291&doi=10.1109%2fCSNT60213.2024.10546111&partnerID=40&md5=ae03094e8cb81e78fe8b11ea347c2dfc</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835030546-3 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 Muktha D.S., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India; Niveditha G., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India; Pinto N.A., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India; Sinha S., 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 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 Enhancing Movie Genre Classification through Emotional Intensity Detection: An Improvised Machine Learning Approach Subject The topic of the resource AI/ML; Face perception; Facial emotion recognition; Image processing; Mental health (MH); Software evaluation Description An account of the resource Movie Genre Classification through Emotion Intensity is a computer vision technique used to identify facial emotion through a sequential neural network model and to get the genre of the movie with it. This paper delves into latest advancements in Emotion Detection, particularly emphasizing neural network models and leveraging face image analysis algorithms for emotion recognition. Grenze Scientific Society, 2024. Creator An entity primarily responsible for making the resource Mehta M.; Sinha S.; Singhal N.; Sudhakar T. Source A related resource from which the described resource is derived 15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024, Vol-2, pp. 3913-3919. Publisher An entity responsible for making the resource available Grenze Scientific Society 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="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209106157&partnerID=40&md5=de537fc376291b13ae7d20185b3e78b6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209106157&partnerID=40&md5=de537fc376291b13ae7d20185b3e78b6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-833130057-9 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 Mehta M., Christ (Deemed to be University), Bangalore, India; Sinha S., Christ (Deemed to be University), Bangalore, India; Singhal N., Christ (Deemed to be University), Bangalore, India; Sudhakar T., Christ (Deemed to be 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 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 Ara, Affreen; Rekha, V. Title A name given to the resource Enhancing Music Emotion Recognition with LSTM: Evaluating Various Embedding Techniques 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;6;pp.293-303 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14445/23488549/IJECE-V12I6P123" target="_blank" rel="noreferrer noopener">https://doi.org/10.14445/23488549/IJECE-V12I6P123</a> <a href="https://www.scopus.com/pages/publications/105009850939?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009850939?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 Ara A., Department of Computer Science and Engineering, Christ University, Karnataka, India; Rekha V., Department of Computer Science and Engineering, Christ University, Karnataka, India Description An account of the resource The study investigates the application of Long Short-Term Memory (LSTM) networks for emotion classification in music lyrics. It focuses on the comparative effectiveness of various word embedding techniques. It evaluates the performance of static embeddings (GloVe, Word2Vec, FastText) versus contextual embeddings (BERT, Distil BERT) across three datasets: MER Lyrics, Mood Lyrics, and Combined Lyrics. Additionally, the study examines the role of stylistic and content-based features in enhancing classification accuracy. The results demonstrate that contextual embeddings considerably outperform static embeddings, achieving accuracy rates of up to 98% compared to 60% for static approaches. Moreover, combining multiple lyric datasets leads to improved model generalization. The findings show the potential of transformer-based models for advancing music emotion recognition. Future research will focus on optimizing large embedding models using techniques such as pruning, quantization, and distillation to enhance computational efficiency. 2025 Seventh Sense Research Group. Subject The topic of the resource BERT; Embedding; Emotion classification and Emotion; LSTM 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 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 Enhancing Natural Gas Price Prediction: A Machine Learning and Explainable AI Approach Subject The topic of the resource Feature Engineering; Machine Learning Algorithms; Model Evaluation; Natural Gas; Predictive Analysis Description An account of the resource This research includes an innovative approach to refine natural gas price predictions by employing advanced machine learning techniques, including Random Forest, Linear Regression, and Support Vector Machine algorithms. Against the backdrop of natural gas's increasing influence in the energy sector, both environmentally and economically, the study adopts a robust methodology using a comprehensive dataset from Kaggle. Through rigorous data preprocessing, feature engineering, and model training, the chosen algorithms are optimized to capture complex patterns within the data, demonstrating the potential to significantly enhance forecast precision. The application of these techniques aims to extract meaningful insights, providing stakeholders in the natural gas market with more accurate and reliable predictions, there by contributing to a deeper understanding of market dynamics and informed decision- making. 2024 IEEE. Creator An entity primarily responsible for making the resource Singh K.; Mahajan J.; Singh V.P.; Paswan A.S.; Saxena A. 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/TQCEBT59414.2024.10545057" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545057</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204456145&doi=10.1109%2FTQCEBT59414.2024.10545057&partnerID=40&md5=3946dd63d268db5e91bba072c4a62d6a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204456145&doi=10.1109%2fTQCEBT59414.2024.10545057&partnerID=40&md5=3946dd63d268db5e91bba072c4a62d6a</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 Singh K., Christ University, India; Mahajan J., Christ University, India; Singh V.P., Ewec, United Arab Emirates; Paswan A.S., Christ University, India; Saxena A., Christ University, 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 Enhancing Network Security with Comparative Study of Machine Learning Algorithms for Intrusion Detection Subject The topic of the resource Intrusion detection; K-nearest neighbors (KNN); Logistic regression (LR); Machine learning (ML); Random forest (RF) Description An account of the resource With the ever-increasing network systems and dependency on digital technologies, ensuring the security and integrity of these systems is of paramount importance. Intrusion detection systems (IDS) play a major role in sheltering such systems. Intrusion detection systems are technologies that are designed to monitor network and system activities and detect suspicious, unauthorized, malicious behavior. This research paper conducts a comprehensive comparative analysis of three popular machine learning algorithmsK-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR)in the context of intrusion detection using the renowned NSL-KDD dataset. Preprocessing techniques are applied, and the dataset is split for rigorous evaluation. The findings of this research highlight the effectiveness of Random Forest in detecting intrusions, showcasing its potential for real-world network security applications. This study contributes to the field of intrusion detection and offers valuable insights for network administrators and cybersecurity professionals to enhance network protection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Thachil M.; Siby A.; Kumar K.P.; Chowdary C.R.; Karthikeyan H. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 345-354. 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-0975-5_31" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-0975-5_31</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197295025&doi=10.1007%2F978-981-97-0975-5_31&partnerID=40&md5=dc18ab91e4b4da17d9b6d5470551a212" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197295025&doi=10.1007%2f978-981-97-0975-5_31&partnerID=40&md5=dc18ab91e4b4da17d9b6d5470551a212</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981970974-8 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 Thachil M., Computer Science & Engineering, CHRIST University, Bangalore, India; Siby A., Computer Science & Engineering, CHRIST University, Bangalore, India; Kumar K.P., Computer Science & Engineering, CHRIST University, Bangalore, India; Chowdary C.R., Computer Science & Engineering, CHRIST University, Bangalore, India; Karthikeyan H., Computer Science & Engineering, 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 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 Desai, Shivasagar; Pavithra, G.; Kulkarni, Manasa; Nireeksha Title A name given to the resource Enhancing Network Topology with ONOS, P4 Runtime, and BMV2 Switches 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 2025 9th International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CSITSS67709.2025.11294483" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSITSS67709.2025.11294483</a> <a href="https://www.scopus.com/pages/publications/105031412957?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031412957?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 Desai S., Dayanand Sagar College of Engineering, Bangalore, India; Pavithra G., Dayanand Sagar College of Engineering, Bangalore, India; Kulkarni M., CHRIST University, Bangalore, India; Nireeksha, Dayanand Sagar College of Engineering, Bangalore, India Description An account of the resource The increasing complexity of modern networks demands advanced solutions for efficient and adaptive topology management. Traditional networking approaches, characterized by their rigid and hardware-centric architectures, often fall short in addressing the dynamic requirements of contemporary networks. This paper introduces a novel approach to network topology management by leveraging the Open Network Operating System (ONOS), P4 Runtime, and BMV2 switches. ONOS, a scalable and distributed SDN controller, provides centralized control and a global view of the network, while P4 Runtime offers a protocol-independent interface to manage programmable data planes. BMV2, a versatile software switch, emulates the behavior of P4-programmable hardware, allowing for the development and testing of custom packet processing pipelines. This research lays the groundwork for future developments in programmable networks by bringing out the potential of combining SDN with P4-based data plane programmability to meet the evolving demands of modern network environments. 2025 IEEE. Subject The topic of the resource BMV2 Switches; Mininet; Network Topology; ONOS Controller; P4 Language; P4 Runtime Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158894-6; 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 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 Enhancing neurocognitive skills for effective leadership and decision-making Description An account of the resource In today's dynamic workplace, human resource development and management (HRDM) professionals face multifaceted challenges requiring advanced cognitive abilities. This book chapter explores the critical interplay between leadership skills, decision-making, and executive functions (EFs) in HRDM. It sheds light on their pivotal role in shaping workplace dynamics and organizational outcomes. Focusing on skills such as emotional intelligence, cognitive flexibility, and continuous learning, the chapter delves into their neurocognitive underpinnings, particularly within the prefrontal cortex. It discusses strategies for enhancing EFs, including reflective practice, empathy training, and mindfulness, and emphasizes the concept of neuroplasticity in fostering continuous learning and adaptation within HRDM. By integrating insights from neuroscience into HR practices, the chapter offers valuable guidance for HR professionals seeking to optimize organizational performance, enhance leadership qualities, and drive effective decision-making processes. 2024 by IGI Global. All rights reserved. Creator An entity primarily responsible for making the resource Jeni A.; Reddy K.J. Source A related resource from which the described resource is derived Cognitive Behavioral Neuroscience in Organizational Settings, pp. 208-226. Publisher An entity responsible for making the resource available IGI Global 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/10.4018/979-8-3693-1858-4.ch012" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-1858-4.ch012</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200703139&doi=10.4018%2F979-8-3693-1858-4.ch012&partnerID=40&md5=c2303eca90b14b85e0a9be0fcd5021a2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200703139&doi=10.4018%2f979-8-3693-1858-4.ch012&partnerID=40&md5=c2303eca90b14b85e0a9be0fcd5021a2</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-836931859-1; 979-836931858-4 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 Jeni A., Christ University, India; Reddy K.J., Christ University, 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 Helen Josephine, V.L.; Gupta, Sharad; Joy, Rosewine; Sharma, Manjari Title A name given to the resource Enhancing Online Education Through Sentiment Analysis and Complex Systems Modelling 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 Northeast Journal of Complex Systems;Volume;7;Issue;1;Article No.;11; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.63562/2577-8439.1094" target="_blank" rel="noreferrer noopener">https://doi.org/10.63562/2577-8439.1094</a> <a href="https://www.scopus.com/pages/publications/105005998452?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005998452?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 Helen Josephine V.L., Business Analytics, School of Business and Management, Christ University, Bangalore, India; Gupta S., Christ University, Bangalore, India; Joy R., Business Analytics, School of Business and Management, Christ University, Bangalore, India; Sharma M., Christ University, Bangalore, India Description An account of the resource This research explores the application of sentiment analysis through the lens of complex systems modelling to enhance the quality of online certification courses, with a particular focus on global platforms such as Coursera. The COVID-19 pandemic catalyzed significant growth in online learning, creating an urgent need for adaptive and student-centric approaches to ensure relevance and effectiveness. Leveraging unstructured textual data from student reviews of courses, this study integrates methodologies from systems science, computer science, and education to address real-world challenges in online education. By employing both lexiconbased (SentiWordNet and VADER) and supervised machine learning techniques (Multinomial Naive Bayes, Support Vector Machine, and Stochastic Gradient Descent), the research conducts a detailed sentiment analysis to identify patterns, emergent behaviours, and feedback loops inherent in course design and delivery. Findings reveal that Support Vector Machine achieves the highest accuracy at 97.3%, offering insights that guide iterative improvements in course content and pedagogical strategies. The study demonstrates how interdisciplinary approaches to sentiment analysis can inform responsive education environments, aligning with broader societal goals of accessibility, inclusivity, and quality in online learning ecosystems. 2025, Binghamton University Libraries. All rights reserved. Subject The topic of the resource Complex Systems Modelling; Machine Learning Techniques; Online Certification Courses; Sentiment Analysis; Student-Centric Education Publisher An entity responsible for making the resource available Binghamton University Libraries Relation A related resource ISSN: 25778439; 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 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 Singh, Nidhi; Arya, Prabha; Dadwal, Sapna; Upreti, Shitiz; Poonia, Ramesh Chandra; Jain, Rituraj Title A name given to the resource Enhancing operational efficiency in cloud computing through computational intelligence for environmental sustainability 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 Cognitive Cloud Computing: Building Intelligent Systems for Tomorrow;pp.289-306 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003569251-15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003569251-15</a> <a href="https://www.scopus.com/pages/publications/105024634821?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024634821?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 Singh N., School of Management, G D Goenka University, Haryana, Sohna Gurugram, India; Arya P., School of Management, G D Goenka University, Haryana, Sohna Gurugram, India; Dadwal S., Department of Management Studies, DPG Institute of Technology and Management, Gurugram, India; Upreti S., Manav Rachna International Institute of Research & Studies (MRIIRS), Haryana, Faridabad, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University, Bengaluru, India; Jain R., Department of Information Technology, Marwadi University, Rajkot, India Description An account of the resource In the era of generative artificial intelligence technologies, an increasing number of organizations are adopting solutions such as ChatGPT, DeepSeek, Gemini, and Bard, and there has been a significant increase in computationally intensive operations that impact data center operations, as these require a high CPU- and GPU-based infrastructure and a large amount of electricity to run. In this data-driven environment, there is a need to look for and identify a holistic solution to address the concern of managing cloud operations with sustainable practices that can help not only reduce the carbon footprint but also be environment and pocket friendly. Organizations have started adopting smart intelligent technologies and sensors that track and help minimize and prescribe solutions to address the high usage of cloud resources. In this study, we have tried to check the impact of the role of such technologies in optimizing energy consumption, reducing the carbon footprint, and enabling eco-friendly, sustainable practices within cloud operations. While going through the study, you will find that the adoption of new technological solutions that leverage sensors and artificial intelligence-based smart technologies has a profound impact on long-term energy efficiency gains. The study highlights the importance of leveraging computational intelligence-based solutions to drive long-term environmental benefits, while also emphasizing the role of sustainable solutions, such as renewable energy integration and collaborative partnerships, in achieving the desired outcomes. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. Publisher An entity responsible for making the resource available Taylor and Francis Relation A related resource ISBN: 978-104054278-1; 978-103294165-3; 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