324 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 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 Patient Safety and Efficiency in Intravenous Therapy: A Comprehensive Analysis of Smart Infusion Monitoring Systems Subject The topic of the resource Air embolism; Bioelectronics; Healthcare; IoT; IV fluid; Sensors; Smart hospitals; Smart IV infusion monitoring Description An account of the resource Intravenous (IV) fluids, comprising vitamin-rich solutions, are administered to address patient electrolyte imbalances and dehydration through IV infusion therapy. Infusion pumps are integral for precise medication dosage delivery in this common medical procedure, generally posing low risks. These fluids are stored in polypropylene bags connected to patients through tubes. However, when the IV bag empties, the patients blood may flow backward into the IV tube due to higher blood pressure, known as diffusion, potentially leading to complications like air embolism-life-threatening if air enters the bloodstream through the IV line, obstructing blood flow to vital organs. Smart IV Bags emerged as a solution to mitigate such risks, eliminating the need for manual IV bag monitoring while preventing reverse blood flow. This research comprehensively assesses various IoT-enabled IV Bag monitoring systems, comparing their strengths, weaknesses, and unique features. Key evaluation criteria include component efficiency, real-world applicability, accuracy, latency, and technical specifications. The aim is to provide an objective evaluation of each Smart Intravenous Liquid Monitoring System to inform future developments in this field. A systematic approach ensures the selection of systems that best meet specific requirements in diverse healthcare environments. 2024 Scrivener Publishing LLC. Creator An entity primarily responsible for making the resource Sreekumar K.; Reddy T.P.; Prathap B.R. Source A related resource from which the described resource is derived Internet of Things in Bioelectronics: Emerging Technologies and Applications, pp. 171-200. Publisher An entity responsible for making the resource available wiley Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394241903.ch9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394241903.ch9</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208344297&doi=10.1002%2F9781394241903.ch9&partnerID=40&md5=18a103b20828d8910a2353416f859e14" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208344297&doi=10.1002%2f9781394241903.ch9&partnerID=40&md5=18a103b20828d8910a2353416f859e14</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-139424190-3; 978-139424187-3 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 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 Sreekumar K., Department of CSE, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Reddy T.P., Department of CSE, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Prathap B.R., Department of CSE, CHRIST (Deemed to be University), Karnataka, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Patient Well-Being in Healthcare Through the Integration of IoT and Neural Network Subject The topic of the resource Healthcare; IoT systems; machine learning; patient monitoring; user-friendly interfaces Description An account of the resource This study analyses the revolutionary integration of Internet of Things (IoT) structures in healthcare through a complete examination of outstanding case research. The first case study focuses on real-time patient fitness monitoring in a clinic setting. The suggested device utilizes an Internet of Things-ready device that has many sensors, including oxygen, pressure, and temperature sensors. The issues of forecasting patient health in advance are handled with the deployment of machine learning models, notably Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM). The second case study analyses IoT's effect on patient-precise medication identification and remote fitness monitoring, uncovering issues associated with accessibility, pricing, and human interfaces. Proposed alternatives, which incorporates greater education, increased accessibility, and user-pleasant interfaces with robust technical assistance, have been evaluated with 30 patients over a three-month duration. The results reveal a great growth in impacted person health, along with heightened attention of periodic health monitoring. The results highlight how IoT technologies may transform healthcare procedures by offering pro-active solutions for patients' well-being. This study offers insightful information that may be used to solve practical issues, promote patient-centered solutions, and broaden the scope of the healthcare period. A significant step towards a patient-centered and technologically advanced healthcare environment, the successful outcomes validate the capacity for sustained innovation, cooperation, and improvement in the integration of IoT systems for optimal patient care. 2024 IEEE. Creator An entity primarily responsible for making the resource Singh J.; Shelke N.A.; Upreti K.; Divakaran P.; Lingareddy N.; Deepika S. Source A related resource from which the described resource is derived Proceedings - 2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024, pp. 241-246. 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/INNOCOMP63224.2024.00047" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/INNOCOMP63224.2024.00047</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205541532&doi=10.1109%2FINNOCOMP63224.2024.00047&partnerID=40&md5=79f06348ccc77dcfcf2efb12099cc7c6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205541532&doi=10.1109%2fINNOCOMP63224.2024.00047&partnerID=40&md5=79f06348ccc77dcfcf2efb12099cc7c6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835037647-0 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 Singh J., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Shelke N.A., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Upreti K., CHRIST (Deemed to Be University), Department of Computer Science, Ghaziabad, India; Divakaran P., Himalayan University, Itanagar, India; Lingareddy N., Vignan Institute of Technology and Science, Department of CSE, Deshmukhi, India; Deepika S., Sreyas Institute of Engineering and Technology, Department of CSE, Hyderabad, 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 Enhancing performance of WSN by utilising secure QoS-based explicit routing Subject The topic of the resource Cryptography; Digital signature; QoS; Quality of service; Wireless sensor network; WSN Description An account of the resource Wireless sensor networks (WSN) are infrastructure less and self-configured a wireless network that allows monitoring the physical conditions of an environment. Many researchers focus on enhancing the performance of WSN in order to provide effective delivery of data on the network, but still results in lower quality of services like energy consumption, delay and routing. We tackle this problem by introducing a new routing algorithm, QoS-based explicit routing algorithm which helps in transmitting the data from source node to destination node on WSN. We also involve clustering process in WSN based on genetic algorithm and particle swarm optimisation (GA and PSO) algorithm. We proposed identity-based digital signature (IBDS) and enhanced identity-based digital signature (EIBDS) that involves reduction of computation overhead and also increasing resilience on the WSN. We also use advanced encryption standard (AES), for ensuring the security between nodes and avoid hacking of data by other intruders. Copyright 2020 Inderscience Enterprises Ltd. Creator An entity primarily responsible for making the resource Kantharaju H.C.; Narasimha Murthy K.N. Source A related resource from which the described resource is derived International Journal of Computer Aided Engineering and Technology, Vol-13, No. 45689, pp. 101-124. Publisher An entity responsible for making the resource available Inderscience Publishers Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJCAET.2020.108107" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJCAET.2020.108107</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088097261&doi=10.1504%2FIJCAET.2020.108107&partnerID=40&md5=accd5bc13a9ba30222ed052943d376f9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088097261&doi=10.1504%2fIJCAET.2020.108107&partnerID=40&md5=accd5bc13a9ba30222ed052943d376f9</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17572657 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 Kantharaju H.C., Department of Computer Science and Engineering, Vemana Institute of Technology, Koramangala, Bengaluru, 560034, India; Narasimha Murthy K.N., Faculty of Engineering, Christ University, Bengaluru, 560074, 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 Fay, F.; Praveenraj, D. David Winster; Muhamedale, Hasssan; Subramani, K.; Anita Sofia Liz, D.R.; Nithya, J. Title A name given to the resource Enhancing Personalization in Search Engines Through Behavioral Profiling 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;4;pp.192-200 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.51983/ijiss-2025.IJISS.15.4.22" target="_blank" rel="noreferrer noopener">https://doi.org/10.51983/ijiss-2025.IJISS.15.4.22</a> <a href="https://www.scopus.com/pages/publications/105026252765?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026252765?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 Fay F., College of Engineering Technique, Al-Farahidi University, Baghdad, Iraq; Praveenraj D.D.W., School of Business and Management, Christ University, Bangalore, India; Muhamedale H., Department of Computers Techniques Engineering, College of Technical Engineering, Islamic University of Najaf, Najaf, Iraq, Department of Computers Techniques Engineering, College of Technical Engineering, Islamic University of Najaf of Al Diwaniyah, Al Diwaniyah, Iraq; Subramani K., Department of Operations, Vignana Jyothi Institute of Management, Hyderabad, India; Anita Sofia Liz D.R., Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Tamil Nadu, Chennai, India; Nithya J., Department of Information Technology, K.S. Rangasamy College of Technology, Tamil Nadu, Tiruchengode, India Description An account of the resource With the development of search engines, people demand more contextual, relevant, and important results according to their needs and preferences. The current paper will examine the enhancement of search engine personalization through behavioral profiling, which involves capturing user interaction data, such as search histories, clicks, and other similar data, to understand user interests and intentions. The behavioral profiling promotes the ability to adjust the results to the requirements of mutual changes in user behavior and apply machine learning algorithms and advanced data mining techniques. We describe the key aspects of the successful behavioral profiling systems, such as user modeling, data collection frameworks, and privacy boundaries of the data protection. The paper will address the points mentioned by providing behavioral profiling to enhance user satisfaction and effective search and engagement. It will discuss the predictive relevance ranking's triple impacts on socioeconomic gains: time, energy costs, and attention time. We also discuss the ethical issues of user data collection, and the invitation implies achieving the appropriate compromise between individualization and privacy. By the case studies and comparisons, we affirm that the behavioral personalization greatly improves the accuracy of the search when the methods are either static or generic. This study enhances the design of a smart, convenient search engine by cultivating actionable, individual-sensitive recency search. It aims to smoothly aid personalized interactions in real time, inspiring advancement in context-sensitive retrieval systems. The Research Publication,. Subject The topic of the resource Behavioral Profiling; Information Retrieval; Machine Learning; Personalization; Search Engines; User Behavior; User Modeling 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 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 photocatalytic performance through surfactant-assisted electrochemical synthesis: Surface modification of hierarchical ZnO morphologies with Ag/ZnWO4 nanoparticles Subject The topic of the resource Electrochemical synthesis; Malachite green; Photocatalysis; ZnO; ZnWO4 Description An account of the resource This study presents the synthesis of surface-decorated CTAB-capped ZnO nanoparticles doped with Ag/ZnWO4 through a surfactant-assisted electrochemical synthesis approach. The development of surface-decorated composites is of considerable interest for enhancing photocatalytic efficiency. We report the synthesis of pristine, binary, and surface-decorated ZnO catalysts, specifically Zn, Zn/Ag, Zn/ZnWO4, and Zn/Ag/ZnWO4. Various methods for physicochemical characterization have been utilized to verify the catalysts' structural, optical, and morphological properties. The results demonstrate the successful surfactant capping and metal doping. The synthesized nanoparticles have been tested for their photocatalytic performance against Malachite Green, an environmentally harmful organic dye, across various reaction conditions. Scavenger studies reveal that the photodegradation process is primarily driven by superoxide and hydroxyl radicals and, to a lesser extent, by photogenerated holes. The decrease in electron-hole pair recombination in the Zn/Ag/ZnWO4 photocatalyst results in an enhanced degradation of Malachite Green when exposed to visible light. 2024 Elsevier B.V. Creator An entity primarily responsible for making the resource Bharadwaj R.; R M.; K R S.D.; Pinheiro D.; Manickam S. Source A related resource from which the described resource is derived Journal of Molecular Structure, Vol-1306 Publisher An entity responsible for making the resource available Elsevier B.V. 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.molstruc.2024.137835" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.molstruc.2024.137835</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186403544&doi=10.1016%2Fj.molstruc.2024.137835&partnerID=40&md5=76eabb955cb070911e1a6bfc3ee37fb1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186403544&doi=10.1016%2fj.molstruc.2024.137835&partnerID=40&md5=76eabb955cb070911e1a6bfc3ee37fb1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 222860; CODEN: JMOSB 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 Bharadwaj R., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; R M., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; K R S.D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Pinheiro D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Manickam S., Petroleum and Chemical Engineering, Faculty of Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan, BE1410, Brunei Darussalam 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 Tanni, Nikita; Elappila, Manu; George, Tessa; Premjith, Nithin Title A name given to the resource Enhancing Pneumonia Diagnosis with Convolutional Neural Networks: A Comprehensive Evaluation 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 Lecture Notes in Networks and Systems;Volume;5589 LNNS;pp.597-609 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-1687-9_42" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-1687-9_42</a> <a href="https://www.scopus.com/pages/publications/105010198719?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010198719?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 Tanni N., CHRIST University, Bengaluru, India; Elappila M., CHRIST University, Bengaluru, India; George T., CHRIST University, Bengaluru, India; Premjith N., CHRIST University, Bengaluru, India Description An account of the resource Pneumonia, a significant health concern globally, presents unique challenges in diagnosis and treatment due to its diverse ethology and impact on respiratory function. The potential of augmentation techniques and Convolutional Neural Networks, for automated pneumonia detection is explored in this study. Employing a transfer learning approach with VGG16, DenseNet, and our proposed model achieves outstanding accuracy (95%) and robust performance metrics. The research explores augmentation techniques to enhance the precision and accuracy of the model, emphasizing the importance of data augmentation in improving classification accuracy. A comparative analysis with related models highlights advancements in automated pneumonia detection, showcasing the efficacy of our proposed model. The models ability to correctly identify pneumonia from chest X-ray pictures is demonstrated by the results, suggesting that medical image analysis could benefit from practical implementation of this model. Future directions include expanding the dataset, exploring alternative architectures, and integrating explanation techniques to enhance model interpretability. This research contributes to the advancement of artificial intelligence in healthcare, offering a promising approach for accurate and efficient pneumonia diagnosis, thus addressing critical challenges in respiratory medicine. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Accuracy; Advance healthcare; Automated classification; Convolutional Neural Networks (CNNs); DenseNet; Machine learning; VGG16 Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981961686-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 power conversion efficiency in five-level multilevel inverters using reduced switch topology Subject The topic of the resource Efficiency; Inverter; Pulse width modulation; Reduced switch count; Total harmonic distortion Description An account of the resource This paper presents extensive research on improving the power conversion efficiency of five-level multilevel inverters (MLIs) by utilizing a reduced switch topology. MLIs have received an abundance of focus because of their ability to generate high-quality output waveforms and have better harmonic outcomes than traditional two-level inverters. The high number of switches in MLIs, on the other hand, can result in increased power losses and lower overall efficiency. In this paper, a novel reduced switch topology for five-level MLIs, which is having five switches is proposed with the aim of minimizing power losses while preserving superior performance due to lesser number of switches. To achieve efficient power conversion, the proposed topology employs advanced pulse width modulation control strategies and optimized switching patterns. The simulation results show that the minimized switch topology improves the power conversion efficiency of the five-level MLI, resulting in lower losses and better overall system performance. The total harmonic distortion (THD) value of the output current has been reduced to 7.12% and the efficiency has been achieved to 96.92%. The findings of this investigation help to advance MLI technology, allowing for more efficient and reliable power conversion in a variety of applications such as renewable energy systems, electric vehicles, and industrial drives. 2024, Institute of Advanced Engineering and Science. All rights reserved. Creator An entity primarily responsible for making the resource Ezhilvannan P.; Ramasamy D.; Subramanian S.K.; Krishnan S. Source A related resource from which the described resource is derived Bulletin of Electrical Engineering and Informatics, Vol-13, No. 3, pp. 1495-1503. Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science 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/10.11591/eei.v13i3.6884" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/eei.v13i3.6884</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196480092&doi=10.11591%2Feei.v13i3.6884&partnerID=40&md5=b1e3a4b1437bc4cafa34c8584d2df5b1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196480092&doi=10.11591%2feei.v13i3.6884&partnerID=40&md5=b1e3a4b1437bc4cafa34c8584d2df5b1</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 20893191 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 Ezhilvannan P., Department of Electrical and Electronics Engineering, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Tirupati, India; Ramasamy D., Department of Electrical and Electronics Engineering, Panimalar Engineering College, Chennai, India; Subramanian S.K., Department of Electrical and Electronics Engineering, S.A. Engineering College, Chennai, India; Krishnan S., Department of Electrical and Electronics Engineering, 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 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 Velmurugan, R.; Suresh, K.; Thilagaraj, T.; Sharma, Meenakshi; Saranya, D. Title A name given to the resource Enhancing Prognostication in Colorectal Cancer with Integrated Machine Learning for Improved Survival Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025;pp.1681-1686 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICMSCI62561.2025.10894577" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICMSCI62561.2025.10894577</a> <a href="https://www.scopus.com/pages/publications/105000463509?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000463509?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 Velmurugan R., Kristu Jayanti College, Autonomous, Department of Computer Science (PG), Bangalore, India; Suresh K., Christ (Deemed to be University), Department of Computer Science, Bangalore, India; Thilagaraj T., Moodlakatte Institute of Technology, Department of Artificial Intelligence and Machine Learning, Karnataka, Kundapura, India; Sharma M., Global Group of Institutes, Punjab, Amritsar, India; Saranya D., Sri Eshwar College of Engineering, Department of Information Technology, Coimbatore, India Description An account of the resource Machine learning methods are recently used to predict patient survival in colorectal cancer using such models as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), VGG16, and Support Vector Machines (SVM). Taking advantage of a combination of CT, MRI scan images, and clinical records with drug recommendations, the study also checks to see how these models compare for distinguishing between patients in terms of their illness course-whether they are going to get better or worse over time. The results reveal VGG16 has better accuracy than CNN, RNN and SVM; as the highest-performing model tested, it also demonstrates superior precision, recall and F1-score. The research findings also validate these proposed models as they compare favorably with existing literature. This presents a promising proposition: a new, revolutionary approach to using artificial intelligence to boost prognostic accuracy. 2025 IEEE. Subject The topic of the resource Colorectal cancer; Machine learning; Personalized treatment; Prognostication; Survival outcomes Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150982-8; 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 Letters Letter Faculty Publications-Letters 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 Psychotherapy Research: The Critical Need for Detailed Reporting of Intervention Protocols Description An account of the resource [No abstract available] Creator An entity primarily responsible for making the resource Davidar R.S.; Ballal D. Source A related resource from which the described resource is derived Indian Journal of Psychological Medicine Publisher An entity responsible for making the resource available SAGE Publications Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2025-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1177/02537176241313127" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/02537176241313127</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216188291&doi=10.1177%2F02537176241313127&partnerID=40&md5=81c162d7c31f289cdcd6b53dac85db21" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216188291&doi=10.1177%2f02537176241313127&partnerID=40&md5=81c162d7c31f289cdcd6b53dac85db21</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2537176 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 Letter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Davidar R.S., Dept. of Psychology, Christ University, Karnataka, Bangalore, India; Ballal D., Dept. of Psychology, Centre for Advanced Research and Development, Christ University, Karnataka, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications 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 Davidar, Rebecca Suganthi; Ballal, Divya Title A name given to the resource Enhancing Psychotherapy Research: The Critical Need for Detailed Reporting of Intervention Protocols 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 Indian Journal of Psychological Medicine;Volume;48;Issue;1;pp.90-91 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1177/02537176241313127" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/02537176241313127</a> <a href="https://www.scopus.com/pages/publications/85216188291?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85216188291?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 Davidar R.S., Dept. of Psychology, Christ University, Karnataka, Bangalore, India; Ballal D., Dept. of Psychology, Centre for Advanced Research and Development, Christ University, Karnataka, Bangalore, India Publisher An entity responsible for making the resource available SAGE Publications Ltd Relation A related resource ISSN: 2537176; Language A language of the resource English Type The nature or genre of the resource Letter 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 Haneefa, K.V.; Singh, Harmandeep Title A name given to the resource Enhancing Quick Commerce Service Experience Through AI Marketing: An Empirical Investigation 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 Leveraging AI-Powered Marketing in the Experience-Driven Economy;pp.203-228 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-9561-5.ch007" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-9561-5.ch007</a> <a href="https://www.scopus.com/pages/publications/105004515941?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004515941?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 Haneefa K.V., Christ University, Bangalore, India; Singh H., Christ University, Bangalore, India Description An account of the resource This study examines the impact of AI-driven marketing strategies on quick commerce service experience (QCSE). Specifically, it investigates how personalization, smart search, and dynamic pricing influence consumers' perceptions of service experience in ultra-fast delivery platforms. Using partial least squares structural equation modelling (PLS-SEM) on survey data from 427 quick commerce users in India, the study finds that personalization has the strongest positive effect on QCSE, followed by dynamic pricing, while smart search has a weaker yet significant impact. The research validates QCSE as a higher-order formative construct comprising app design, security, fulfilment, and service support dimensions. The findings contribute to signaling theory by demonstrating how AI-driven marketing features serve as signals of platform experience perceptions. For practitioners, the results highlight the importance of AI-powered personalization and pricing strategies in enhancing service experiences. 2025 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836939563-9; 979-836939561-5; 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 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 rainwater harvesting and groundwater recharge efficiency with multi-dimensional LSTM and clonal selection algorithm Subject The topic of the resource Clonal selection algorithm; Multi-dimensional LSTM; Rainwater harvesting; Resource optimization; Sustainability; Water management; Water scarcity mitigation Description An account of the resource Rainwater harvesting stands out as a promising solution to alleviate water scarcity and alleviate pressure on conventional water reservoirs. This work introduces a pioneering strategy to elevate the efficiency of rainwater harvesting systems through the fusion of Multi-Dimensional Long Short-Term Memory (LSTM) networks and the Clonal Selection Algorithm (CSA). The Multi-Dimensional LSTM networks serve to model intricate temporal and spatial rainfall patterns, enabling precise predictions regarding the optimal times and locations for rainwater abundance. This insight is pivotal in refining the design and operation of rainwater harvesting setups. Drawing inspiration from the immune system, the Clonal Selection Algorithm is employed to optimize site selection and resource allocation, ensuring the maximal utilization of harvested rainwater. The adaptability and robustness of CSA prove invaluable in tackling the dynamic nature of rainfall patterns. This research endeavor is dedicated to enhancing groundwater levels and optimizing its sources through the implementation of efficient harvesting techniques. By delving into innovative methodologies, it aims to contribute significantly to sustainable water management practices and ensure a reliable supply of groundwater for various societal needs. The experiments are conducted to study the effectiveness of rainwater harvesting systems, where the proposed method achieves increased efficiency, thereby reducing dependence on conventional water sources and contributing to sustainable water management practices. The proposed CSA-LSTM model demonstrates superior performance compared to ACO-ANN and PSO-BPNN, achieving higher training, testing, and validation accuracies while exhibiting lower training, testing, and validation losses. Additionally, CSA-LSTM showcases excellent site suitability, high resource utilization, and robustness to changes, with a fast response time, emphasizing its potential for efficient and effective applications. 2024 Elsevier B.V. Creator An entity primarily responsible for making the resource Raghava Rao N.; Pokkuluri Kiran S.; Amena I T.; Senthilkumar A.; Sivakumar R.; Ashok Kumar M.; Velusamy S. Source A related resource from which the described resource is derived Groundwater for Sustainable Development, Vol-25 Publisher An entity responsible for making the resource available Elsevier B.V. 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.gsd.2024.101167" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.gsd.2024.101167</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189021911&doi=10.1016%2Fj.gsd.2024.101167&partnerID=40&md5=6d346877ff14cf448e7e80b8efab687d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189021911&doi=10.1016%2fj.gsd.2024.101167&partnerID=40&md5=6d346877ff14cf448e7e80b8efab687d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2352801X 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 Raghava Rao N., Department of Information Technology, Institute of Aeronautical Engineering, Dundigal, Hyderabad, 500043, India; Pokkuluri Kiran S., Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Vishnupur, AP, Bhimavaram, India; Amena I T., Department of Civil Engineering, Deogiri College of Engineering and Management (DIETMS), Maharashtra, Aurangabad, India; Senthilkumar A., School of Science and Information Technology, Skyline University Nigeria, Kano State, Nigeria; Sivakumar R., Department of Statistics and Data Science, CHRIST(Deemed to be University), Bangalore, India; Ashok Kumar M., Department of Computer Science and Software Engineering, Skyline University Nigeria, Kano, Nigeria; Velusamy S., Department of Civil Engineering, Kongu Engineering College, Perundurai, 638060, 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 red wine quality prediction through Machine Learning approaches with Hyperparameters optimization technique Subject The topic of the resource Grid search; Hyperparameter tuning; Machine learning classifiers; Red wine; Regressors Description An account of the resource In light of the intricacy of the winemaking process and the wide variety of elements that could affect the taste and quality of the finished product, predicting red wine quality is difficult. ML methods have been widely used in forecasting red wine quality from its chemical characteristics in recent years. This Paper evaluated the comparison of classification and regression methods to predict the quality of red wine and performed the initial data analysis and exploratory data analysis on the data. This study implemented different Classifiers and Regressors that were trained and tested. Contrasted and Comparative analysis of the accuracies of eight models with hyperparameter tuning optimization, including Logistic Regression, Gradient Boosting, Extra Tree, Ada Boost, Random Forest, Support Vector Classifier, and Decision Tree, Knn and measured the classification report with F1, Accuracy, and Recall Scores. For Imbalance data, SMOTE Classifier was used. This study performed the Cross-validation technique, such as Grid search and with the best hyperparameters tuning. The study's findings demonstrated that the Gradient Boosting technique accurately predicted red wine quality. This research shows the promising results of Gradient Boosting for predicting red wine quality and adds important context to the usage of machine learning classifiers for this challenge. 2023 IEEE. Creator An entity primarily responsible for making the resource Amzad Basha M.S.; Desai K.; Christina S.; Sucharitha M.M.; Maheshwari A. Source A related resource from which the described resource is derived 2023 2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 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/ICEEICT56924.2023.10157719" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEEICT56924.2023.10157719</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165756417&doi=10.1109%2FICEEICT56924.2023.10157719&partnerID=40&md5=10ebee4274e6253fb6d090659a62ed73" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165756417&doi=10.1109%2fICEEICT56924.2023.10157719&partnerID=40&md5=10ebee4274e6253fb6d090659a62ed73</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039763-5 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 Amzad Basha M.S., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Desai K., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Christina S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Maheshwari A., School of Commerce, Finance, Christ (Deemed to Be University), Department of Commerce, Ghaziabad, 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 Tiwari, Satyam; Kujur, Lawrence; Shanbhog, Manjula Title A name given to the resource Enhancing Regional Language Proficiency in Large Language Models Through Translated Datasets 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;1741 LNNS;pp.332-347 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-12827-0_26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-12827-0_26</a> <a href="https://www.scopus.com/pages/publications/105027588525?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027588525?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 S., Christ (Deemed to Be University), School of Science, NCR, Delhi, India; Kujur L., Christ (Deemed to Be University), School of Science, NCR, Delhi, India; Shanbhog M., Christ (Deemed to Be University), School of Science, NCR, Delhi, India Description An account of the resource Although Large Language Models (LLMs) have made significant progress in Natural Language Processing the lack of high-quality training data frequently limits their ability to perform well in regional languages. To improve LLM competency this study methodically translates an English dataset into the low-resource language of Bhojpuri. On this new dataset we apply a structured translation methodology and then refine an LLM that has already been trained. The models capacity to produce contextually relevant and culturally appropriate responses in Bhojpuri has significantly improved according to a comparison of its performance before and after fine-tuning. Our findings show that this translation-centric approach provides a practical and affordable way to enhance the usefulness and inclusivity of LLMs increasing the effectiveness and accessibility of these potent AI tools for underrepresented linguistic groups globally. For linguistic groups that are marginalized globally. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Dataset Translation; Fine-tuning; Large Language Models; Machine Translation; Natural Language Processing; Regional Languages Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303212826-3; 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 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 Avin, Lydia Mary; Vinodha, D.; Jenefa, J.; Sambandam, Rakoth Kandan; Vetriveeran, Divya; Swamy, C. Manjunatha Title A name given to the resource Enhancing Reliability and Accuracy in Wind Energy Forecast Using CNN-LSTM 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 Smart Innovation, Systems and Technologies;Volume;114 SIST;pp.41-52 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4718-7_4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4718-7_4</a> <a href="https://www.scopus.com/pages/publications/105014492026?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105014492026?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 Avin L.M., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Vinodha D., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Jenefa J., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Sambandam R.K., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Vetriveeran D., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Swamy C.M., Christ (Deemed to Be University), Karnataka, Bengaluru, India Description An account of the resource The global shift to sustainable energy is increasing the demand for wind energy. Accurate forecasting becomes crucial for renewable energy systems to function effectively in terms of resource allocation, grid management, and overall reliability. The need for wind energy is growing as a result of the worlds transition to sustainable energy. For renewable energy systems to operate efficiently in terms of resource allocation, grid management, and overall reliability, accurate forecasting becomes essential. It is challenging for current forecasting methods to correctly predict the dynamic nature of wind energy demand. For utilities and grid managers, the inherent variability and unpredictability in wind energy generation pose serious issues. The forecasting models that are now in use are challenged by the variable and sporadic character of wind energy generation. This makes it more difficult to integrate wind energy into the electrical grid effectively and increases the risk of grid instability and inefficient resource utilization. This research addresses these challenges by proposing a hybrid forecasting model that integrates the strengths of Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). By capturing both spatial and temporal dependencies in wind data, the hybrid model aims to enhance accuracy and reliability in wind energy forecasts. The precise forecasting of wind energy is made more difficult by shifting weather patterns, changing environmental factors, and shifting patterns of energy usage. Improving the forecasting models accuracy and dependability in the renewable energy industry requires addressing these difficulties. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Forecasting; Hybrid models; Wind energy Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981964717-0; 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 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 Retailer Auctions and Analyzing the Impact of Coupon Offers on Customer Engagement and Sales Through Machine Learning Subject The topic of the resource Classification; Coupon Campaign; Coupon Code; Customer Behavior; Machine Learning; Prediction Description An account of the resource Systems that use coupons have been used extensively to boost customer interaction on platforms having a digital component. We use causal machine learning techniques to determine the effect of an advertising intervention, especially a discount offer, on the bids of a shop. Discount shopping coupons are a popular tactic for increasing sales. The largest challenge for dealers is accurately anticipating the wants of their customers, and here is where they always struggle. Machine learning algorithms have been utilized by researchers to address a variety of problems. Selecting the right coupon is a challenging undertaking because every customer's behavior differs depending on the deal. Due to categorical data adjustments being necessary due to the majority of characteristics having missing values, the situation is made more difficult. The dataset is used to classify the dataset, and machine learning algorithms like logistic regression, random forest and SVM model, decision tree and naive bayes models are used to determine the correctness of the classification. 2023 IEEE. Creator An entity primarily responsible for making the resource Jayadharshini P.; Sharon Roji P.C.; Lalitha K.; Santhiya S.; Keerthika S.; Abinaya N. Source A related resource from which the described resource is derived 2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 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/ICCEBS58601.2023.10448900" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCEBS58601.2023.10448900</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189175983&doi=10.1109%2FICCEBS58601.2023.10448900&partnerID=40&md5=7df96b71b8c147943257e21c02ec41d7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189175983&doi=10.1109%2fICCEBS58601.2023.10448900&partnerID=40&md5=7df96b71b8c147943257e21c02ec41d7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039458-0 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 Jayadharshini P., Department of Artificial Intelligence, Kongu Engineering College, Tamilnadul, Erode, India; Sharon Roji P.C., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Karnataka, Bangalore, India; Lalitha K., School of Engineering and Technology, Christ(Deemed to Be) University, Department of Cse, Bengaluru, India; Santhiya S., Department of Artificial Intelligence, Kongu Engineering College, Tamilnadul, Erode, India; Keerthika S., Department of Artificial Intelligence, Kongu Engineering College, Tamilnadul, Erode, India; Abinaya N., Kongu Engineering College, Department of Artificial Intelligence, Tamilnadul, Erode, 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 Singh, Hitesh; Mehndiratta, Vandana; Poonia, Ramesh Chandra Title A name given to the resource Enhancing Road Safety and Efficiency Through IoT-Enabled Car-to-Car Communication 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 Lecture Notes in Networks and Systems;Volume;1122;pp.375-383 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7426-5_29" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7426-5_29</a> <a href="https://www.scopus.com/pages/publications/85218187499?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218187499?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 H., CHRIST (Deemed to be University), NCR, Delhi, India; Mehndiratta V., CHRIST (Deemed to be University), NCR, Delhi, India; Poonia R.C., CHRIST (Deemed to be University), NCR, Delhi, India Description An account of the resource Today's vehicles have been revolutionized by integrating Internet of Things (IoT) technology, which facilitates communication between cars, known as car-to-car (C2C) communication. This paper explores the potential of IoT-enabled C2C communication systems to improve security and efficiency by creating dynamic, real-time data exchange between vehicles. Through a comprehensive review of existing literature and technological advances, this study leads to an understanding of how IoT-based C2C communication can reduce incidents, reduce traffic accidents, and create a more peaceful driving environment. It also highlights the potential impact of C2C communications on transportation and policy development. This paper highlights the potential of IoT-enabled C2C communications as a revolutionary technology for the automotive industry, promoting road safety and better vehicle management. The findings highlight the importance of regulatory frameworks, data processing, and stakeholder collaboration for the successful deployment of communications systems of IoT-based C2C networks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Car-to-Car (C2C) communication; Internet of Things; Road safety; Traffic efficiency Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981977425-8; 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 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 Varghese, Aleena; Sudhakar, T.; Joy, Helen K. Title A name given to the resource Enhancing Satellite Imagery with GAN Based Cloud Removal 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 2nd International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECCC65144.2025.11063844" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECCC65144.2025.11063844</a> <a href="https://www.scopus.com/pages/publications/105012027174?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012027174?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 Varghese A., CHRIST University, Department of Computer Science, Bangalore, India; Sudhakar T., CHRIST University, Department of Computer Science, Bangalore, India; Joy H.K., CHRIST University, Department of Computer Science, Bangalore, India Description An account of the resource Satellite imaging is one of the most common uses for applications agricultural, urban planning and environmental monitoring to mention a few. Unfortunately, even the best-laid plans for aerial photography can be decimated by one thing: cloud cover. The novel way of cloud extraction from the satellite data that is demonstrated in this article, use a Generative Adversarial Network (GAN). For the betterment of cloud removal, ResNet based discriminator and a UNet-based generator are utilized in the suggested approach. To accurately train the networks, a new technique was also developed to introduce noise that resembles natural cloud patterns. The PSNR score, as a qualitative and quantitative index card that uses the PyTorch- based GAN methodology to verify different performances in traditional methods based on EuroSat. 2025 IEEE. Subject The topic of the resource Cloud Removal; Generative Adversarial Networks (GANs); Image Reconstruction; Peak Signal-to-Noise Ratio (PSNR); Perlin Noise; ResNet; Satellite Imagery; UNet Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152162-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 Security and Resource Optimization in IoT Applications with Blockchain Inclusion Subject The topic of the resource Blockchain; Hybrid ABC-WOA; IoT; Resource Optimization; Security Enhancement; Time & Energy Consumption Description An account of the resource The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data-driven applications. However, optimizing the allocation of resources within IoT networks is a pressing challenge. This research explores a novel approach to resource optimization, combining blockchain technology with enhanced security measures, while addressing the critical concerns of time and energy consumption. In this study, we propose a resource allocation framework that leverages the transparency and immutability of blockchain to enhance data integrity and security in IoT applications. The blockchain-based method is utilized to identify the malicious users in the IoT applications. The proposed method is implemented in MATLAB and performance is evaluated by performance metrics such as the probability of detection, false alarm probability, average network throughput, and energy efficiency. The proposed method is compared by existing methods such as Friend or Foe and Tidal Trust Algorithm. To further optimize this process, we introduce a Hybrid Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), a powerful optimization technique designed to minimize time delays and energy consumption in IoT environments. Our findings demonstrate the effectiveness of the proposed approach in achieving resource efficiency, reducing time and conserving energy within IoT networks. 2023 IEEE. Creator An entity primarily responsible for making the resource Malik R.; Dua S.; Shoran P.; Nain A. Source A related resource from which the described resource is derived 2023 2nd International Conference on Futuristic Technologies, INCOFT 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/INCOFT60753.2023.10425679" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/INCOFT60753.2023.10425679</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187403520&doi=10.1109%2FINCOFT60753.2023.10425679&partnerID=40&md5=b31fdd490ff19cebbb419b8f8d2340a0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187403520&doi=10.1109%2fINCOFT60753.2023.10425679&partnerID=40&md5=b31fdd490ff19cebbb419b8f8d2340a0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835030884-6 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 Malik R., School of Computer Applications, Lovely Professional University, Phagwara, India; Dua S., School of Computer Applications, Lovely Professional University, Phagwara, India; Shoran P., School of Science, Christ University, Noida, India; Nain A., School of Computer Applications, Lovely Professional University, Phagwara, 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 Helen Josephine, V.L.; Rajagopal, Manikandan; Kavitha, S.; Rajendran, Stuthi Title A name given to the resource Enhancing Sign Language Recognition Through LSTM 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 4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025 - Proceedings;pp.986-989 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSADL65848.2025.10933396" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSADL65848.2025.10933396</a> <a href="https://www.scopus.com/pages/publications/105002463659?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002463659?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., School of Business and Management, Christ University, Karnataka, Bangalore, India; Rajagopal M., School of Business and Management, Christ University, Karnataka, Bangalore, India; Kavitha S., Dayanand Sagar University, Bengaluru, India; Rajendran S., School of Business and Management, Christ University, Karnataka, Bangalore, India Description An account of the resource Sign language recognition is a remarkable task in this project completed through two state-of-the-art methods, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This way, the system is able to quickly process each frame of the webcam with real-time information regarding face, body and posture in order to extract critical values. this research seeks to provide the necessary resources and opportunities for deaf people to be able to communicate effectively, obtain an education and enjoy their lives as much as other human beings This makes it a very important tool for education where the system can convert sign motions into text on-the-fly. The data was collected through a live camera, and key points from face, body, and pose were detected for training the model. Kindergarten used the four categories of vegetables, fruits, colors and animals. There were 40 video sequences of 40 frames with a sign in each. the model tries to fit too much to noisy points of data. However comprehensive the training, after 19 epochs the validation accuracy is an impressive 93%. The oscillations in the truth values of models are indicative of some uncertainty in learning where the accuracy is still to be settled. The graph in general shows that the LSTM based sign language movement classifier has a good capacity to learn and identify sign language movements with high precision. 2025 IEEE. Subject The topic of the resource Education for hearing impaired; GRU; Image/video recognition; LSTM; Machine Learning; Sign Language Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152392-3; 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