324 20 16481 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 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 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/. 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 Description An account of the resource [No abstract available] 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 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 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 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 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 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 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 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 Small and Medium OEMs' Adoption of IIoT Technologies Subject The topic of the resource Business support; Enhancing IIoT adoption; Industry Internet of Things manufacturer; Operational support; Small and Medium Original Equipment Manufacturers (SME OEMs); Strategic partnerships Description An account of the resource Small and Medium Original Equipment Manufacturers (SME OEMs) face challenges of high initial costs, lack of skilled workforce, data security concerns, and limited infrastructure for IIoT implementation. This research explores the crucial factors influencing the successful integration of Industry Internet of Things (IIoT) technologies into products and processes of SME OEMs. The study investigates the impact of IIOT Manufacturers' operational and business support, training effectiveness, and awareness of benefits on SME OEMs' adoption intention of IIoT solutions. A survey was conducted among 263 firms operating in 103 different equipment manufacturing operations across 67 cities, representing 11 industry sectors. The participants were SME OEMs, and data were collected to assess the influence of various factors on their willingness to adopt IIoT technologies. The study revealed significant insights into adopting IIoT solutions among SME OEMs. Training provided by IIoT manufacturers was found to have the most substantial impact on the adoption intention. Moreover, awareness of benefits and business and operational support had an equal and notable influence on the adoption intention of SME OEMs. These findings underline the importance of effective training programs and comprehensive support from IIoT manufacturers in facilitating successful IIoT integration. The study's outcomes emphasize the value of fostering strategic partnerships between Small and Medium Original Equipment Manufacturers and Industry IoT Manufacturers. Such collaborations can be pivotal in enhancing IIoT adoption rates among SME OEMs, enabling them to stay competitive in the fast-paced market. 2024 IEEE. Creator An entity primarily responsible for making the resource Despande P.; Kumar Chandar S.; Elangovan N. Source A related resource from which the described resource is derived Proceedings - 2024 8th International Conference on Inventive Systems and Control, ICISC 2024, pp. 656-661. 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/ICISC62624.2024.00115" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICISC62624.2024.00115</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206078510&doi=10.1109%2FICISC62624.2024.00115&partnerID=40&md5=d09e23a67937bbabf33a18d76657f90e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206078510&doi=10.1109%2fICISC62624.2024.00115&partnerID=40&md5=d09e23a67937bbabf33a18d76657f90e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038657-8 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 Despande P., Christ University, School of Business & Management, Karnataka, Bangalore, India; Kumar Chandar S., Christ University, School of Business & Management, Karnataka, Bangalore, India; Elangovan N., Christ University, School of Business & Management, 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 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 Kulshrestha, Reenish; Poonia, Ramesh Chandra Title A name given to the resource Enhancing Smart Home Security Through Dynamic Authentication for Voice-Activated Commands 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;1786 LNNS;pp.681-693 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-14757-8_47" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-14757-8_47</a> <a href="https://www.scopus.com/pages/publications/105039565926?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039565926?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 Kulshrestha R., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Poonia R.C., CHRIST (Deemed to be University), Karnataka, Bangalore, India Description An account of the resource The proliferation of voice-activated IoT devices in smart environments has introduced significant security challenges, particularly around unauthorized access and command spoofing. In response, this paper proposes a unique multi-factor authentication framework that combines dynamically generated code words with smartphone-based verification to ensure secure voice command execution. Unlike conventional fixed-passphrase systems, our method generates unique session-based code words and requires real-time confirmation via a trusted mobile device, offering increased resistance against replay and impersonation attacks. The proposed framework is designed to integrate seamlessly with existing virtual assistants and IoT ecosystems, and its applicability extends beyond smart homes to include connected vehicles and industrial IoT systems, offering a scalable and secure authentication solution. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Cybersecurity; Dynamic authentication; Dynamic code word generation; Internet of Things (IoT); Logging and auditing; Smart home systems Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303214756-1; 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 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 social cognition in individuals with ADHD: An eastern approach Description An account of the resource With the increasing prevalence of ADHD in the global front, it is essential to explore different effective methods for providing support and intervention. The difficulties with social cognition are reflected in their limitations in emotional self-regulation, emotion recognition, and empathy. Though several interventions exist for ADHD, many at times, the effectiveness of eastern approaches are overlooked due to the limited awareness about its nature. Research suggests that systematic and regular practice of yoga helps to improve attention, control emotion, and reduce restlessness among them. Several asanas are found to be especially helpful for managing ADHD symptoms including cobra (bhujangasana) pose, cat-cow pose (bitilasana marjaryasana), downward-facing dog (adho mukha shvanasana), tree pose (vrikshasana), mountain pose (tadasana), among many others. The chapter gives a comprehensive summary on the application of yoga techniques on the improvement of social cognition in individuals with ADHD. 2024, IGI Global. Creator An entity primarily responsible for making the resource Rajan A.V.; Alexander A.E. Source A related resource from which the described resource is derived Principles and Clinical Interventions in Social Cognition, pp. 62-69. 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/10.4018/9798369312650.ch004" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/9798369312650.ch004</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190935199&doi=10.4018%2F9798369312650.ch004&partnerID=40&md5=fd6ca8fa8bde834583251108cc1c46d2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190935199&doi=10.4018%2f9798369312650.ch004&partnerID=40&md5=fd6ca8fa8bde834583251108cc1c46d2</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-836931266-7; 979-836931265-0 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 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 Rajan A.V., Christ University, India; Alexander A.E., 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 Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique Subject The topic of the resource Battle Royale Optimization; BRO; COCOMO model; cost drivers; cost estimation; machine learning; QEMRT; Quantum Ensemble Meta-Regression Technique; software Description An account of the resource This research suggests a unique method for improving software cost estimates by combining Battle Royale Optimisation (BRO) and Quantum Ensemble Meta-Regression Technique (QEMRT) with COCOMO cost driver characteristics. The strengths of these three strategies are combined in the suggested strategy to increase the accuracy of software cost estimation. The COCOMO model is a popular software cost-estimating methodology that considers several cost factors. BRO is a metaheuristic algorithm that mimics the process of the fittest people being selected naturally and was inspired by the Battle Royale video game. The benefits of quantum computing and ensemble learning are combined in the machine learning approach known as QEMRT. Using a correlation-based feature selection technique, we first identified the most important COCOMO cost drivers in our study. To get the best-fit model, we then used BRO to optimize the weights of these cost drivers. To further increase the estimation's accuracy, QEMRT was utilized to meta-regress the optimized model. The suggested method was tested on two datasets for software cost estimating that are available to the public, and the outcomes were compared with other cutting-edge approaches. The experimental findings demonstrated that our suggested strategy beat the other approaches in terms of accuracy, robustness, and stability. In conclusion, the suggested method offers a viable strategy for improving the accuracy of software cost estimation, which might help software development organizations by improving project planning and resource allocation. 2023 IEEE. Creator An entity primarily responsible for making the resource Shoran P.; Sinha A.; Mahmood H.R.; Sharma V.; Jha P.; Kumar B.; Alkhayyat A. Source A related resource from which the described resource is derived 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 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/ICCCNT56998.2023.10307113" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCNT56998.2023.10307113</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179851350&doi=10.1109%2FICCCNT56998.2023.10307113&partnerID=40&md5=fe37e1fdafcf79f36c4c54068f74e349" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179851350&doi=10.1109%2fICCCNT56998.2023.10307113&partnerID=40&md5=fe37e1fdafcf79f36c4c54068f74e349</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835033509-5 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 Shoran P., Dr. Akhilesh das Gupta Institure of Technology and Management, India; Sinha A., Ignou, Department of Cs and It, New Delhi, India; Mahmood H.R., Fast Nuces Cfd Campus, Punjab, Pakistan; Sharma V., Christ University, Department of Computer Science, Delhi NCR, India; Jha P., Amity University Jhrakhand, Department of Computer Science An Engineering, Ranchi, India; Kumar B., Amity University Jhrakhand, Department of Computer Science An Engineering, Ranchi, India; Alkhayyat A., The Islamic University, College of Technical Engineering, Najaf, Iraq 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 stochastic optimization: investigating fixed points of chaotic maps for global optimization Subject The topic of the resource Chaos optimization algorithms; Chaotic maps; Global solutions; Newton's method; Random number generation; Stochastic optimization Description An account of the resource Chaotic maps, despite their deterministic nature, can introduce controlled randomness into optimization algorithms. This chaotic map behaviour helps overcome the lack of mathematical validation in traditional stochastic methods. The chaotic optimization algorithm (COA) uses chaotic maps that help it achieve faster convergence and escape local optima. The effective use of these maps to find the global optimum would be possible only with a complete understanding of them, especially their fixed points. In chaotic maps, fixed points repeat indefinitely, disrupting the map's characteristic unpredictability. While using chaotic maps for global optimization, it is crucial to avoid starting the search at fixed points and implement corrective measures if they arise in between the sequence. This paper outlines strategies for addressing fixed points and provides a numerical evaluation (using Newton's method) of the fixed points for 20 widely used chaotic maps. By appropriately handling fixed points, researchers and practitioners across diverse fields can avoid costly failures, improve accuracy, and enhance the reliability of their systems. 2024 Institute of Advanced Engineering and Science. All rights reserved. Creator An entity primarily responsible for making the resource Rani G.S.; Jayan S.; Alatas B.; Rajamanickam S. Source A related resource from which the described resource is derived Indonesian Journal of Electrical Engineering and Computer Science, Vol-35, No. 3, pp. 1817-1834. 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/ijeecs.v35.i3.pp1817-1834" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijeecs.v35.i3.pp1817-1834</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197866435&doi=10.11591%2Fijeecs.v35.i3.pp1817-1834&partnerID=40&md5=88a3227017c3a058582de21828895ef6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197866435&doi=10.11591%2fijeecs.v35.i3.pp1817-1834&partnerID=40&md5=88a3227017c3a058582de21828895ef6</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 25024752 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 Rani G.S., Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India; Jayan S., Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India; Alatas B., Department of Software Engineering, Firat University, Elazig, Turkey; Rajamanickam S., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Santhosh Kumar, S.; Manjunatha Swamy, C.; Balamurugan, M. Title A name given to the resource Enhancing Stock Market Forecasting with LLMs, Sentiment Analysis and Technical Indicators 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 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/InC465408.2025.11256269" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC465408.2025.11256269</a> <a href="https://www.scopus.com/pages/publications/105030333276?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030333276?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 Santhosh Kumar S., Christ University, Dept. of CSE, Bangalore, India; Manjunatha Swamy C., Christ University, Dept. of CSE, Bangalore, India; Balamurugan M., Christ University, Dept. of CSE, Bangalore, India Description An account of the resource Forecasting stock market trends remains a complex and demanding endeavor due to the intricate and dynamic nature of financial markets. This study explores the combination of sentiment analysis with technical indicators to improve the accuracy of stock price predictions. The research incorporates stock market and news data spanning from January 2015 to June 2023, ensuring a well-aligned and comprehensive dataset. Data was sourced using the Google News API and the Bombay Stock Exchange (BSE), followed by rigorous preprocessing, which involved handling missing values and standardizing sentiment scores for accuracy and consistency. To analyze sentiment, tools like VADER, TextBlob, and the Gemini-1.5-Flash API were employed, with sentiment scores aggregated at the stock level. Simultaneously, key technical indicators including the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, and Exponential Moving Averages were derived from stock price patterns. These diverse data points were integrated to predict 14-day closing stock prices, leveraging the Gemini1.5-Flash model for forecasting. The model s performance was assessed using various error metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). The results indicated strong predictive accuracy for stable stocks while pointing out challenges in forecasting highly volatile stocks. Ultimately, the findings suggest that combining sentiment analysis with technical indicators strengthens stock market trend predictions, offering a solid foundation for future advancements in real-time financial analytics. 2025 IEEE. Subject The topic of the resource Gemini-1.5-flash; multimodal data; sentiment analysis; Stock market prediction; technical indicators Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152118-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications 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 Jain, Yogesh Kumar; Kumar Mannepalli, Praveen; Kumar, Santosh; Malhotra, Amit; Choudhary, Deepak Kumar; Saharan, Mohit Title A name given to the resource Enhancing Stock Market Price Prediction with Advanced Machine Learning Techniques: A Comparative 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 2025 International Conference on Next Generation of Green Information and Emerging Technologies, GIET 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/GIET65294.2025.11234804" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/GIET65294.2025.11234804</a> <a href="https://www.scopus.com/pages/publications/105030995215?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030995215?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; Kumar Mannepalli P., Chandigarh University, Department of Computer Science and Engineering, Mohali, India; Kumar S., ERA University, Computer Science, Uttar Pradesh, Lucknow, India; Malhotra A., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Ghaziabad, India; Choudhary D.K., Muzaffarpur Institute of Technology, Department of Computer Science and Engineering, Bihar, Muzaffarpur, India; Saharan M., Vidya University, Meerut, India Description An account of the resource The non-linearity and intrinsic volatility of financial markets make accurate stock price prediction an important but challenging undertaking. This research proposes a Gated Recurrent Unit (GRU)-based model to forecast the stock prices of Tata Consultancy Services (TCS) using 18 years of historical data sourced from Yahoo Finance, comprising features such as Date, Open, High, Low, Close, Adjusted Close, and Volume. The methodology includes data preprocessing steps such as feature selection using Recursive Feature Elimination (RFE), normalization with standard scaling, and data splitting into 70% training and 30% testing sets. The proposed GRU model was evaluated and benchmarked against existing models including Long Short-Term Memory (LSTM), Linear Regression (LR), and Decision Tree (DT), using performance metrics such as Root Mean Squared Error (RMSE) and R2 score. Experimental outcomes revealed that the GRU model achieved the best performance with an RMSE of 0.045, outperforming LSTM (38.19), LR (8.66), and DT (5.22). The study's findings have important implications for algorithmic trading and well-informed investment choices, since the GRU model effectively captures temporal trends in stock data while minimizing prediction mistakes. 2025 IEEE. Subject The topic of the resource Machine Learning; price prediction; Stock Market Prediction; Yahoo Finance data Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 978-166545806-1; 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