737 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 Kumar, Manoj; Sharan, Mudita; Shoran, Preety Title A name given to the resource Explainable AI for Heart Disease prediction: A Clinical Transparency Route Experiment 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 International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDISS68238.2025.11320656" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDISS68238.2025.11320656</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033597851?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033597851?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 Kumar M., Department of Computer Sciences, CHRIST University, Bangalore, India; Sharan M., Department of Computer Sciences, CHRIST University, Bangalore, India; Shoran P., Department of Computer Sciences, CHRIST University, Bangalore, India Description An account of the resource In this paper, a proposeable explainable machine learning procedure on estimating the danger of heart attack will be proposed with a stacked ensemble of XGBoost, Random Forest, and Multi-layered perceptron (MLP). The data set of UCI Heart Disease was preprocessed by normalization, imputation, and SMOTE to address the imbalance problem and the variables were optimized with the help of the feature engineering. The model performance was measured using accuracy, precision, recall, F1-score and ROC-AUC. In order to make the results more interpretable, Explainable AI were applied with SHAP and LIME, and the most relevant risk factors including troponin, cholesterol, and blood pressure were indicated.. In this paper, it is shown that ensemble learning in XAI can yield plausible, interpretable, and clinically practical data to complement enhanced cardiovascular diagnostics. 2025 IEEE. Subject The topic of the resource Clinical Decision Support; Ensemble Learning; Ex explainable AI; Heart Attack Prediction; Random Forest; SHAP; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155641-9; 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 Sharma, Madhvi; Verma, Tanya; Kamal, Arif; Keskar, Yogesh; Upreti, Kamal Title A name given to the resource Towards Net-Zero Hotels: AI-Enabled Energy Transition and Demand Response Models 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 International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDISS68238.2025.11320661" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDISS68238.2025.11320661</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033649380?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033649380?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 Sharma M., Amity School of Architecture and Planning, Amity University, Rajasthan, Jaipur, India; Verma T., Dean School of Architecture and Design, K R Mangalam University, Haryana, Gurugram, India; Kamal A., Architecture Section, Aligarh Muslim University, Aligarh, India; Keskar Y., Amity school of Architecture and Planning, Rajasthan, Jaipur, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India Description An account of the resource This paper presents an AI and machine learning-driven approach to support energy transition and demand-side management in hotel operations, aiming for net-zero energy goals in hot and arid regions. The proposed framework leverages predictive modeling to forecast HVAC energy consumption based on key environmental and operational factors such as temperature, humidity, occupancy, and thermostat settings. Among the tested models, LightGBM demonstrated superior performance in terms of accuracy and computational efficiency. Additionally, a demand response strategy was simulated, where small thermostat adjustments during high-load periods led to significant energy savings without affecting guest comfort. The results highlight the effectiveness of integrating AI-powered decision systems into hospitality energy management, contributing to climate-resilient, sustainable hotel infrastructure. 2025 IEEE. Subject The topic of the resource AI in sustainability; demand response; hotel energy management; HVAC prediction; LightGBM; machine learning; Net-zero energy; smart hospitality systems Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155641-9; 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 Varshini, R. Loga; Ragesh, K.; Ramkumar, K.; Vel, R. Arun Shai Saravana; Kumar, R. Bharani Raj; Saravanan, M. Title A name given to the resource A Verilog-Based Design Framework for Real-Time Edge Detection in Image Processing 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 International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDISS68238.2025.11320708" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDISS68238.2025.11320708</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033585274?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033585274?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 Varshini R.L., Department of ECE, Sri Eshwar College of Engineering, Tamilnadu, Coimbatore, India; Ragesh K., Department of ECE, Sri Eshwar College of Engineering, Tamilnadu, Coimbatore, India; Ramkumar K., School of Engineering and Technology, Christ (Deemed to be University), Bengaluru, India; Vel R.A.S.S., Department of ECE, Sri Eshwar College of Engineering, Tamilnadu, Coimbatore, India; Kumar R.B.R., Department of ECE, Sri Eshwar College of Engineering, Tamilnadu, Coimbatore, India; Saravanan M., Department of ECE, Sri Eshwar College of Engineering, Tamilnadu, Coimbatore, India Description An account of the resource This article details the design process of a real-time image processing system developed in Verilog. The design has proven highly effective in real-time image acquisition, buffering, and processing, with a focus on hardware and performance optimization. The principal modules are a line buffer for image frame storage, a convolution engine featuring edge detection filters such as Sobel and Prewitt, and a control unit responsible for data flow and synchronization. The architecture facilitates the transmission of image data from a camera, with processed images transmitted via VGA/HDMI interfaces. Focus is placed on attaining low latency, high throughput, and optimal utilization of FPGA platform resources. The technology is particularly relevant for autonomous systems, medical imaging, industrial automation, and surveillance, where real-time edge detection is crucial for decision-making. 2025 IEEE. Subject The topic of the resource Edge Detection; FPGA; Hardware Acceleration; Hardware Design; High-Throughput Architecture; Image Processing; Line Buffer; Low Latency; Parallel Processing; Real-Time Image Processing; Real-Time Processing; Verilog; VGA/HDMI Output Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155641-9; 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 Kumar, Deepanshu; Poonia, Ramesh Chandra; Shanbhog, Manjula Title A name given to the resource Stacked Ensemble Method of Multi Class Malware Detection Using PE Header and Section Attributes 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 International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDISS68238.2025.11320719" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDISS68238.2025.11320719</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033671810?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033671810?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 Kumar D., CHRIST University, Bengaluru, India; Poonia R.C., CHRIST University, Bengaluru, India; Shanbhog M., CHRIST University, Bengaluru, India Description An account of the resource Malware has now become sophisticated. The type of attacks has changed, too. To identify and remove them is now a great challenge. This paper presents a machine learning model for malware detection in windows. The Malware is detected based on the static collection of features, which includes the Portable Executable (PE) Header and Section data. Several classifiers were trained on a balanced dataset, including Logistic Regression, K-Nearest Neighbour, Support Vector Machine, Multi-Layer Perceptron, XGBoost, and Stacked Ensemble. The proposed stacking method utilises SVM, MLP, and XGBoost, with XGBoost serving as the meta-learner. The model delivered the best performance when compared with all the baseline models for an accuracy of 96.25% and an AUC of 0.9978. 2025 IEEE. Subject The topic of the resource classification; cybersecurity; Ensemble learning; machine learning; Malware detection; PE fields Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155641-9; 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 Joy, Salna; Rajesh, Siddharth; Neethu, P.S.; Sumanth, S.; Sagar; Swarup, M.; Dhananjay, R. Title A name given to the resource Towards Smarter Transit Systems: An Artificial Intelligence based IoT Approach 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 International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDISS68238.2025.11320770" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDISS68238.2025.11320770</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033619976?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033619976?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 Joy S., Department of Computer Science and Engineering, New Horizon College of Engineering, Bengaluru, India; Rajesh S., Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, India; Neethu P.S., Department of AIML and Data Science, School of Engineering and Technology, Christ University, Bengaluru, India; Sumanth S., Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, India; Sagar, Department of Electrical and Electronics Engineering, New Horizon College of Engineering, Bengaluru, India; Swarup M., Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, India; Dhananjay R., Department of Electrical and Electronics Engineering, New Horizon College of Engineering, Bengaluru, India Description An account of the resource Transportation today is paramount, and difficulties such as unreliable bus schedules and overcrowding are still found due to inadequate managerial practices. While cities are confronted with rapid urbanization and population growth, public transit remains a strong reliance of the middle class, especially in India. Individuals are subsequently subjected to overcrowded, and unreliable modes of transit, which lead them to seek private solutions that ultimately leads to increased private vehicle usage, which is directly related to more congestion and pollution. Therefore, utilising an IoT/machine learning based solution which provides commuters with updated bus locations and occupancy via their mobile phones to make more informed travel decisions, thus reducing wait times is proposed. Accurately tracking the buses via gps, is beneficial for providing timely information, where sensors are used for estimating occupancy based on passenger counts. The traffic prediction provided to users is generated from a Random Classifier machine learning model that would otherwise improve commuting efficiency and urban mobility. The model is found to have 98% accurate on cross-validation and 99% on test data, while the average F1-score over various traffic situations is 0.99. The described solution assists transit users by providing up to date service information improving the passengers quality of travel, heightened their sense of safety, and creates a more integrated urban experience, which promotes long-term sustainable development to meet the interconnectedness challenges cities confront with rapid urban expansion. 2025 IEEE. Subject The topic of the resource IoT; Machine Learning; Random Forest; Real-time seat tracking; Traffic prediction model Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155641-9; 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 Ajayraj, C.; Ms, Asha; Pk, Sathish Title A name given to the resource Prediction of Facial Emotions using Deep Learning and Machine Learning Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSAAI65575.2025.11011786" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSAAI65575.2025.11011786</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007976191?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007976191?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 Ajayraj C., Christ University, Department of CSE, Bangalore, India; Ms A., Christ University, Department of CSE, Bangalore, India; Pk S., Christ University, Department of CSE, Bangalore, India Description An account of the resource Facial expression prediction has gained considerable attention in recent years, particularly because of its applications in human-computer interaction. This paper compares a wide range of deep learning and machine learning models for the prediction of face emotion using the CK+ dataset proposed by Cohn-Kanade. The dataset is characterized by seven classes of emotions represented by the labels, namely surprise, happiness, disgust, anger, sadness, fear, and contempt, on 784 training, 98 validation, and 99 testing images. To further improve model performance, preprocessing techniques were employed that enhanced data efficiency. To increase variability in the data and reduce overfitting, all images were scaled to a 48*48 pixel resolution, pixel values were scaled to be between 0 and 1 for uniformity and the following data augmentation techniques were implemented: 10-degree rotation, horizontal flip, 0.15 zoom. The five models that were tested were CNN, SVM, VGG16, InceptionV3 and VGG19. The results demonstrate the high accuracy achieved by the CNN model which showed an accuracy of 98.98%, 99% and 99% in training, validation and test respectively. The SVM classifier got an accuracy of 99%. Both InceptionV3 and VGG19 on the other hand achieved competitive testing accuracy values of 90.91% and 97.98% respectively, while VGG16 got tested and reached an accuracy of 85.86%. 2025 IEEE. Subject The topic of the resource CK+Dataset; Deep Learning; Emotion Prediction; Machine Learning; Model Comparison; Pre-processing Techniques Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153755-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 Athulya, S.; Reena, Melbin J. Title A name given to the resource AI-Driven Continuous Learning Analysis and Blockchain Validation: A Review on Innovations in Digital Education 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 Data Science and Business Systems, ICDSBS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSBS63635.2025.11032007" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSBS63635.2025.11032007</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011047046?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011047046?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 Athulya S., Department of Computer Science and Engineering, Christ University, Bangalore, India; Reena M.J., Department of Computer Science and Engineering, Christ University, Bangalore, India Description An account of the resource The rapid evolution of digital education has necessitated innovative approaches to enhance learning experiences, provide personalized insights, and ensure the credibility of achievements. This study analyses the challenges in AI-based continuous learning analysis for students and how we can securely share certificates through blockchain technology. The amalgamation of artificial intelligence and blockchains can create a secure, open, and trustworthy environment that schools, students, and employers can make use of. It is important to protect student data along with technological advancements. In response to the dynamic landscape of digital education, novel approaches are essential to enrich learning experiences, offer personalized insights, and maintain the credibility of achievements. This research reviews existing AI-based framework that facilitates ongoing learning assessment for students while ensuring secure certificate sharing through blockchain technology. By amalgamating artificial intelligence and blockchain, a robust ecosystem emerges that fosters transparent, efficient, and secure interactions among educational institutions, learners, and employers. Drawing from these evaluations, the framework suggests customized learning paths, thus amplifying the effectiveness of learning journeys. The objective of this study is to analyze the existing methods and to suggest the latest technologies in education, which will help the education sector to keep track of technological advancements and be in the race. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Blockchain; data privacy; Edtech; Personalized learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158560-0; 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 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 Gamya, K.; Saxena, Surabhi; Singhal, Neha Title A name given to the resource Comparative Analysis of Machine Learning Algorithms for Effective Crop Recommendation 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 1st IEEE International Conference on Data Science and Intelligent Network Computing, ICDSINC 2025;pp.886-890 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSINC66221.2025.11448153" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSINC66221.2025.11448153</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037428427?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037428427?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 Gamya K., Department of Computer Science, Christ University, Bengaluru, India; Saxena S., Department of Computer Science, Christ University, Bengaluru, India; Singhal N., Department of Computer Science, Christ University, Bengaluru, India Description An account of the resource The global call for sustainable farming necessitates a move away from traditional crop selection methods. These conventional approaches, often relying on farmer intuition, are imprecise and scale poorly in the face of complex environmental variables. Machine Learning (ML) models offer a robust, data-driven solution. By analyzing multifaceted data-spanning soil chemistry, weather patterns, precipitation trends, and historical yield performance-ML models can significantly enhance decision-making, optimize resource utilization, and improve overall crop outcomes. This paper delivers an extensive comparative review of key ML algorithms employed for crop recommendation, including Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN). We also explore the critical role of Explainable AI (XAI) in building model transparency. Our study evaluates these models on the metrics of accuracy, interpretability, and computational overhead. The research also investigates hybrid methods that integrate deep learning with conventional ML to enhance predictive power. Our comparative findings highlight the strengths and weaknesses of each model, concluding that ANN and XAI-based approaches demonstrate the highest accuracy and adaptability for diverse agricultural conditions. We also identify significant challenges, such as data imbalances and the absence of real-time data, and discuss future trends like the integration of IoT, remote sensing, and federated learning, which will be key to making precision farming scalable and accessible. 2025 IEEE. Subject The topic of the resource AI in Farming; Crop Recommendation; Machine Learning; Precision Agriculture; Soil Analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156524-4; 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 Samal, Sonali; Champatiray, Chiranjibi Title A name given to the resource Image Data Driven Lung Cancer Classification using Deep Learning and Optimization Algorithm 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 1st IEEE International Conference on Data Science and Intelligent Network Computing, ICDSINC 2025;pp.53-57 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSINC66221.2025.11448185" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSINC66221.2025.11448185</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037444824?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037444824?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 Samal S., Department of Computer Science and Engineering, Alliance School of Advanced Computing, Alliance University, Bengaluru, India; Champatiray C., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, Christ University, Bengaluru, India Description An account of the resource Lung cancer continues to be a major contributor to global cancer mortality, underscoring the importance of early detection and accurate diagnosis. This work introduces an integrated framework that leverages deep learning in combination with Bayesian optimization to achieve robust lung cancer classification. Convolutional Neural Networks (CNNs) are employed for feature extraction and image analysis, while Bayesian optimization is applied to automatically fine-tune critical hyperparameters, thereby improving accuracy and minimizing training overhead. The methodology focuses on the analysis of computed tomography (CT) images to distinguish between different lung cancer categories. By addressing the limitations of manual hyperparameter selection, the proposed framework enhances the efficiency and reliability of deep learning models in medical imaging. The outcomes of this study highlight its potential contribution to computer-aided diagnosis, offering clinicians an effective decision-support tool for precise and timely lung cancer detection. 2025 IEEE. Subject The topic of the resource Batch Size Selection; CNN; Hyperparameter Tuning; Learning Rate Optimization Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156524-4; 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 Varalakshmi, Ch.; Ranka, Monica; Christina, Sowmya; Sucharitha, M. Martha; Basha, Md. Shaik Amzad Title A name given to the resource Deploying a Multi-Model Forecasting System for Bitcoin Prices: Bridging Statistical Forecasting and Deep Learning Innovations 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 3rd International Conference on Data Science and Information System, ICDSIS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSIS65355.2025.11070535" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSIS65355.2025.11070535</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011820377?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011820377?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 Varalakshmi C., Andhra Loyola College, Department of MBA, Vijayawada, India; Ranka M., Dayananda Sagar College of Arts Science and Commerce, 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; Basha M.S.A., GITAM (Deemed to Be University), GITAM School of Business, Hyderabad, India Description An account of the resource In this study, we investigate and compare several forecasting models for predicting Bitcoin market prices using historical data sourced from Nasdaq Data Link (formerly Quandl) spanning from 2016 to 2025. Our analysis evaluates traditional time series methods - such as ARIMA and Holt Winters exponential smoothing - alongside modern machine learning and deep learning techniques including LSTM, Prophet, XGBoost, SVR, Random Forest, and GRU. Performance was assessed via metrics such as RMSE, MAE, MAPE, sMAPE, directional accuracy, and R-squared. Our experiments reveal that while classical methods (e.g., ARIMA and Holt Winters) exhibit large estimation errors and limited explanatory capacity, advanced neural network architectures - particularly the GRU - demonstrate superior accuracy with an RMSE of 2,505.84, MAE of 1,760.93, MAPE of 2.79%, and an R-squared of 0.99. The best-performing model (GRU) was deployed as a web application on PythonAnywhere, providing real-time forecasts through an interactive dashboard. This deployment not only validates the predictive efficacy of the GRU model but also offers a practical tool for investors and financial analysts to monitor and predict Bitcoin price movements using reliable Nasdaq data. 2025 IEEE. Subject The topic of the resource bit coin price prediction; data link; deep learning; deployment; nasdaq Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154265-8; 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 Sandeep, C.S.; Yadav, Sameer; Kumar, B. Rajesh; Baranidharan, S.; Vijayaraj, T.; Lakineni, Prasanna Kumar Title A name given to the resource A Novel Network-Based Digital Payment Fraud Detection using OP-ELM Network Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 3rd International Conference on Data Science and Information System, ICDSIS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSIS65355.2025.11070833" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSIS65355.2025.11070833</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011822281?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011822281?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 Sandeep C.S., Jawaharlal College of Engineering and Technology, Department of Electronics and Communication Engineering, Palakkad, India; Yadav S., University of Allahabad, Department of Commerce and Business Administration, Prayagraj, India; Kumar B.R., Dhanalakshmi Srinivasan College of Engineering, Department of Computer Science and Engineering, Coimbatore, India; Baranidharan S., Christ University, School of Business and Management, Bangalore, India; Vijayaraj T., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Mechanical Engineering, Chennai, India; Lakineni P.K., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vaddeswaram, India Description An account of the resource Internet and Industry 4.0 have helped banks and other financial organizations enhance procedures and decrease fraud. Digital payment techniques have helped internet buying skyrocket. Industry 4.0 promotes process optimization, ecosystem collaboration, and growth by integrating digital systems with physical and IoT devices. Unfortunately, digital payment cybercrime has grown rapidly, causing large annual financial losses. Because of this, fraud detection systems must be constantly improved. The suggested TLELM approach includes preprocessing, feature selection, and model training. Preprocessing involves standardizing data, eliminating outliers, and handling null or missing values. The CSO technique selects relevant features by optimizing selection. A new approach combining TL and ELM improves DPFD procedures. The new metaheuristic TL excels at combinatorial optimization. TLELM efficacy was examined using multiple datasets. The recommended method was compared to top-tier algorithms for binary and multiclass data categorization. Experimental data shows that TLELM outperforms other models with 99.37% accuracy. This study found that TLELM can detect online payment fraud. The method optimizes fraud detection and classification accuracy using TL and ELM. Add more real-world datasets to strengthen robustness and make additional improvements to handle future fraud methods. 2025 IEEE. Subject The topic of the resource competitive swarm optimization (CSO); digital payment fraud detection (DPFD); extreme learning machine (ELM); single layer feedforward neural network (SLFN); transformer learning (TL) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154265-8; 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 Subha, B.; Asrani, Deepak; Burgula, Kezia Rani; Acharjee, Purnendu Bikash; Paul, P. Mano; Suganthi, D. Title A name given to the resource Smart Facial Expression Analysis: Fuzzy Extreme Learning Machine in Emotion Detection 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 3rd IEEE International Conference on Data Science and Network Security, ICDSNS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSNS65743.2025.11168498" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSNS65743.2025.11168498</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019043585?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019043585?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 Subha B., Kristu Jayanti College, Department of Professional, Management Studies, Bengaluru, India; Asrani D., Bn College of Engineering and Technology, Department of Computer Science and Engineering, Lucknow, India; Burgula K.R., Vasavi College of Engineering, Hyderabad, India; Acharjee P.B., Christ University, Department of Computer Science, Bengaluru, India; Paul P.M., Dayananda Sagar Academy of Technology and Management, Department of Computer Science and Engineering (Artificial Intelligence), Bengaluru, India; Suganthi D., Saveetha College of Liberal Arts and Sciences, Simats, Department of Computational Intelligence, Thandalam, Chennai, India Description An account of the resource The immense academic and economic potential of facial emotion recognition (FER) has made it a crucial field in computer vision and artificial intelligence. Because of the fundamental role that facial expressions play in interpersonal communication; face photographs are vital for analysing human emotions within the context of Smart Facial Expression Analysis. This research provides a successful pipeline for emotion identification and examines FER methods that rely just on face pictures. Preprocessing, segmentation, feature extraction, and training the model are the steps that make up the suggested method's organised procedure. Face detection using the Viola-Jones technique is the first step in the preprocessing phase. Four rectangular characteristics are used for segmentation, with greyscale conversion being a necessity. In order to train a fuzzy -ELM model, feature extraction uses HOS and LBP. Emotions are better understood with this method. The suggested fuzzy-ELM approach outperforms two state-of-the-art models, ELM and CNN. With an accuracy of 98.33 %, the experimental findings show a substantial improvement in precision. A dependable and high-performing method for emotion recognition using just facial imaging, these findings highlight the usefulness of the suggested approach for Smart Facial Expression Analysis. 2025 IEEE. Subject The topic of the resource facial expression recognition (FER); histogram of oriented gradients (HOG); local binary patterns (LBP) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153679-4; 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 Rani, M.; Arul Mary Rexy, V.; Halle, Priyanka D; Kaushal, Jyoti; Chauhan, Amit; Basha, H. Anwer Title A name given to the resource A Systematic Approach for Predicting Cybersecurity Attacks in IoT using CNN-LSTM with HABCABO 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 3rd IEEE International Conference on Data Science and Network Security, ICDSNS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSNS65743.2025.11168604" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSNS65743.2025.11168604</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019062459?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019062459?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 Rani M., Kumaraguru College of Technology and Business School, Coimbatore, India; Arul Mary Rexy V., Simats Kuthambakkam, Saveetha College of Liberal Arts and Sciences, Department of Commerce, Chennai, India; Halle P.D., Skn Sinhgad Institute of Technology and Science, Department of Information Technology, Lonavala, Pune, India; Kaushal J., Geetanjali Institute of Technical Studies, Department of Computer Science and Engineering, Udaipur, India; Chauhan A., School of Sciences, Christ (Deemed to Be University), Department of Life Sciences, Bengaluru, India; Basha H.A., Saveetha Institute of Medical and Technical Sciences, Saveetha College of Liberal Arts and Sciences, Department of Computer Science, Chennai, India Description An account of the resource IoT has transformed how devices work together. Now, billions of connected devices may share data across smart homes, energy systems, and environmental monitoring. In Internet of Things ecosystems, rapid IoT expansion has made them very vulnerable, which makes them easy targets for cyberattacks. Hackers can break into IoT devices that don't have enough protection to stop services, steal data, and invade privacy. This paper shows how to use deep learning using CNNs and LSTM networks and the HABCABO optimization algorithm to deal with these new dangers. After careful sequencing, scaling, and noise reduction, filter-based feature selection uses statistical methods to keep the most important information. To get the best detection, the CNN-LSTM model is trained with features that are carefully regulated. The suggested model is more accurate than CNN and LSTM approaches, with an accuracy rate of 98.04 %. These results show that the model can find and stop IoT cybersecurity threats. In conclusion, CNN-LSTM and HABCABO are strong and smart ways to make sure that IoT infrastructure is safe and reliable right now. 2025 IEEE. Subject The topic of the resource cybersecurity attacks; internet of things (IOT); long short-term memory (LSTM) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153679-4; 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 Kumar, Santosh; Johri, Methily; Kumar, Kishan; Awasthi, Yashmita; Kakkar, Barkha; Nandan, Akash Title A name given to the resource Using Machine Learning Sentiment Analysis to Evaluate Students Learning Impact 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 ICDT 2025 - 3rd International Conference on Disruptive Technologies;pp.21-24 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDT63985.2025.10986651" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDT63985.2025.10986651</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105006466382?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006466382?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 Kumar S., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Johri M., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Kumar K., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Awasthi Y., School of Commerce, Finance and Account, Christ University, Karnataka, Bengaluru, India; Kakkar B., School of Management, Institute of Technology &amp; Science, Uttar Pradesh, Ghaziabad, India; Nandan A., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India Description An account of the resource For educational experiences and results to be improved, learning impact assessment is essential. Students' emotional reactions, which are crucial to their involvement and understanding, are frequently missed by traditional evaluation techniques. Through a review of student feedback, conversations, and course ratings, this study investigates the use of machine learning-based sentiment analysis to assess the impact of learning. Performance evaluations were conducted on a number of sentiment categorization models, including Nae Bayes, Support Vector Machines (SVM), Logistic Regression, Random Forest, Long Short-Term Memory (LSTM), and BERT. With an accuracy of 91.7%, the results show that BERT performs better than other models and offers more accurate sentiment classification. Accuracy and insights are further improved by combining textual, auditory, and visual signals in multi-modal sentiment analysis. The results show how sentiment analysis may be used to track feedback in real time facilitating adaptive learning techniques to raise student interest. Future studies should concentrate on expanding sentiment analysis applications to traditional and hybrid learning contexts, integrating multi-modal data, and ethical implications. 2025 IEEE. Subject The topic of the resource Adaptive Learning; Educational Data Mining; Learning Impact Evaluation; Machine Learning; Sentiment Analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151958-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications 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 Manasa, K.R.; Naik, Pramod Kumar; Shashi Kumar, D. Title A name given to the resource Pattern Reconfigurable Antennas for Wireless Applications: A Review of Design Techniques and Advances 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 Electrical, Electronics, and Computer Science with Advance Power Technologies - A Future Trends, ICE2CPT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICE2CPT66440.2025.11340437" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICE2CPT66440.2025.11340437</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033355611?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033355611?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 Manasa K.R., Dayananda Sagar University, Dept. of ECE, Harohalli, India; Naik P.K., Dayananda Sagar University, Dept. of AI and Robotics, Harohalli, India; Shashi Kumar D., Christ University, Dept. of ECE, Bangalore, India Description An account of the resource This literature survey investigates advances in pattern reconfigurable antennas that take advantage of Meta-surface (MS) technology over other techniques. The study explores how these reconfigurable antennas are transforming next-generation communication systems, addressing critical applications such as 5G/6G networks and smart wireless environments. Key research observations include the ability of MS-based designs to achieve dynamic beam steering, crucial to meeting the diverse requirements of future communication systems. The paper also identifies challenges such as design complexity, power efficiency, integration with existing systems, and scalability for practical deployments. By highlighting these advances and addressing open challenges, this survey aims to provide information on the potential of MS-enabled reconfigurable antennas to shape the future of wireless communication technologies. 2025 IEEE. Subject The topic of the resource Beam steering; Metasurface; Pattern reconfigurable; planar antennas Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151499-0; 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 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 Jaisree, M.K.; Madhumitha, P.; Umamaheswari, D.; Loveline Zeema, J. Title A name given to the resource Exploring Conditional Generative Models for Sketch-to-Image Translation: cGAN, cVAE, and Conditional Diffusion Models 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 3rd International Conference on Emerging Applications of Material Science and Technology, ICEAMST 2025;pp.1177-1182 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICEAMST67459.2025.11335717" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEAMST67459.2025.11335717</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033674265?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033674265?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 Jaisree M.K., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Madhumitha P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Umamaheswari D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Loveline Zeema J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India Description An account of the resource Creating realistic facial pictures from hand-drawn sketches is of significant utility in forensic investigations because eyewitness drawings are frequently the only visual leads for suspect identification. Turning a hand-drawn sketch into a realistic image is a difficult task. This is because sketches lack detailed information, they are abstracted, and ambiguous. Most of the conventional image creation and generation techniques tend to lose facial structure, identity, and realism. This makes it a great area for generative AI. This paper is a comparative analysis of three generative models: Conditional GANs, Conditional VAEs, and Conditional Diffusion Models. We evaluate these models on the sketch-to-image synthesis problem using the CUHK Face Sketch Dataset. We recognize and compare how every model handles the challenge of generating images from sketches of faces, with an emphasis on producing realistic images, maintaining identity and diversity. The paper demonstrates the advantages and disadvantages of each approach. It also offers insights into their usefulness for forensic applications and suggests directions for future improvements through combined or specialized generative structures. 2025 IEEE. Subject The topic of the resource Conditional Diffusion model; Conditional GAN; Conditional VAE; CUHK dataset; Forensic application; Sketch-to-Image synthesis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158707-9; 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 Jaisree, M.K.; Madhumitha, P.; Umamaheswari, D.; Loveline Zeema, J. Title A name given to the resource Exploring Conditional Generative Models for Sketch-to-Image Translation: cGAN, cVAE, and Conditional Diffusion Models 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 3rd International Conference on Emerging Applications of Material Science and Technology, ICEAMST 2025;pp.1177-1182 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICEAMST67459.2025.11335717" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEAMST67459.2025.11335717</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033674265?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033674265?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 Jaisree M.K., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Madhumitha P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Umamaheswari D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Loveline Zeema J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India Description An account of the resource Creating realistic facial pictures from hand-drawn sketches is of significant utility in forensic investigations because eyewitness drawings are frequently the only visual leads for suspect identification. Turning a hand-drawn sketch into a realistic image is a difficult task. This is because sketches lack detailed information, they are abstracted, and ambiguous. Most of the conventional image creation and generation techniques tend to lose facial structure, identity, and realism. This makes it a great area for generative AI. This paper is a comparative analysis of three generative models: Conditional GANs, Conditional VAEs, and Conditional Diffusion Models. We evaluate these models on the sketch-to-image synthesis problem using the CUHK Face Sketch Dataset. We recognize and compare how every model handles the challenge of generating images from sketches of faces, with an emphasis on producing realistic images, maintaining identity and diversity. The paper demonstrates the advantages and disadvantages of each approach. It also offers insights into their usefulness for forensic applications and suggests directions for future improvements through combined or specialized generative structures. 2025 IEEE. Subject The topic of the resource Conditional Diffusion model; Conditional GAN; Conditional VAE; CUHK dataset; Forensic application; Sketch-to-Image synthesis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158707-9; 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 Ksheersagar, Shripad Kamlakar; Pandian, P. Pal; Deshmukh, Monali; Chutke, Sravanthi; Bombatkar, Ankita Nivrutti; Ali, M. Imadad Title A name given to the resource Blockchain-Driven Architecture for Decentralized Energy Transactions in Smart Grids 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 Proceedings of 4th International Conference on Electronics and Renewable Systems, ICEARS 2026;pp.914-919 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICEARS67481.2026.11416484" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEARS67481.2026.11416484</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035727197?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035727197?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 Ksheersagar S.K., Kavikulguru Institute of Technology &amp; Science (KITS), Ramtek, India; Pandian P.P., Christ University, Mechanical &amp; Automobile Engineering, Bangalore, India; Deshmukh M., International Institute of Information Technology, Pune, India; Chutke S., Anurag University, ECE, School of Engineering, Hyderabad, India; Bombatkar A.N., Dr. D Y Patil Institute of Technology, Electronics &amp; Telecommunications Engineering, Pune, India; Ali M.I., Santhiram Engineering College, Cse (AIML), Nandyal, India Description An account of the resource The versatility of blockchain technology enables its capabilities to protect decentralized energy trading and transform modern smart grids by removing all dependencies on centralized utility operators and eliminating vulnerabilities that stem from data tampering, pricing manipulation, and single-point failures. The purpose of this paper is to present a fully virtualized and software-implemented architecture of a blockchain that incorporates a lightweight Proof-of-Authority (PoA) consensus model, dynamic pricing smart contract(s), and a multi-layer energy ledger, tailored specifically for seamless peer-to-peer energy trading. The proposed energy trading model is built using an entirely virtualized architecture and is validated through simulation, as opposed to previously proposed models that are based on expensive consensus mechanisms and require hardware-assisted metering. The proposed model delivers significant improvements (37.4% reduction in transaction latency, 52.8% improved throughput, and 41.6% lower computational overhead) when compared to traditional Proof-of-Work and DAG-based models. The smart contract engine ensures energy-pricing fluctuations remain stable, and the system as a whole achieves 95.2% transaction validity, all while preserving ledger immutability, user anonymity, and high scale performance. The results achieved from this innovative software-defined architecture ensure its decentralized smart-grid deployments and high scalability exceed market expectations. 2026 IEEE. Subject The topic of the resource Blockchain; Decentralized Energy Trading; Distributed Ledger; Energy Transactions; Peer-to-Peer Power Exchange; Proof-of-Authority; Security; Smart Contracts; Smart Grid Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154881-0; 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 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 Bhushan, S.; Kumar, Rishikesh; Hariharan, A.; Chithrabhanu, Arun; Suresh, K. Title A name given to the resource DC-DC and DC-AC Converters with Bi-Directional Capabilities for EV Applications 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 Proceedings of 4th International Conference on Electronics and Renewable Systems, ICEARS 2026;pp.570-577 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICEARS67481.2026.11416534" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEARS67481.2026.11416534</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035738638?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035738638?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 Bhushan S., Christ (Deemed to Be University), Dept. of Eee, Bangalore, India; Kumar R., Christ (Deemed to Be University), Dept. of Eee, Bangalore, India; Hariharan A., Christ (Deemed to Be University), Dept. of Eee, Bangalore, India; Chithrabhanu A., Christ (Deemed to Be University), Dept. of Eee, Bangalore, India; Suresh K., Christ (Deemed to Be University), Dept. of Eee, Bangalore, India Description An account of the resource Abstract: This paper presents the design and implementation of a compact bidirectional DC-DC converter coupled with a DC-AC inverter for electric vehicle (EV) motor-drive applications. Both propulsion and regenerative braking modes are made possible by the suggested architecture, which facilitates smooth power transfer between a 48 V battery and a series- wound AC motor. Additionally, the inverter provides controlled AC stimulation for a dependable motor operation, while a high- efficiency bidirectional DC-DC converter controls battery power flow during acceleration and recovers energy during braking. In order to maintain dynamic stability under changing load circumstances, an Arduino Nano microcontroller uses a proportional-integral (PI) control method to regulate motor speed and current. Cross-conduction losses are decreased and MOSFET switching safety is improved by customized PWM pulse generation with dead-time insertion. 2026 IEEE. Subject The topic of the resource Bidirectional Converter; Electric Vehicle; Inverter; Motor Drive; Proportional Integral Control; Pulse Width Modulation; Regenerative Braking Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154881-0; 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 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 Suresh, K. Title A name given to the resource Reinforcement Learning-Driven Energy Management for Battery-Supercapacitor Hybrid Storage in Electric Vehicles 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 Proceedings of 4th International Conference on Electronics and Renewable Systems, ICEARS 2026;pp.941-945 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICEARS67481.2026.11416618" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEARS67481.2026.11416618</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035742630?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035742630?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 Suresh K., Christ Deemed to Be University, Department of Electrical and Electronics Engineering, Karnataka, Bangalore, 560074, India Description An account of the resource The fast growth of the electric vehicles (EVs) market has increased the requirements towards high power transients, efficiency, and reliability on automotive onboard energy management systems by extending battery lifetime. Pure battery storage systems are similarly subject to frequent peak power demands during rapid acceleration and regenerative braking, and thus suffer from rapid aging. Aiming at this issue, in this paper, an AI-based EMS for a battery-supercapacitor HESS in EVs is developed. Dynamic driving conditions are handled by an RL-based power splitting control strategy which dynamically divides power between lithium-ion battery and supercapacitor in this context. The battery stress is to be minimized with the stabilization of the DC-link voltage and traction power demand. System modeling and validation is carried out in MATLAB/Simulink with the use of typical urban drive cycles. Simulation results show that, compared with a rule-based control of the EMS, our proposed AI-enabled EMS can decrease battery peak current by 38.6%, enhance energy efficiency by 11.2%, and increase cycle life by around 27%. The deviation of the DC-link voltage is limited within 1.8% and such control can be used to reduce total system response time in rapid load transition by 22%. Comparison results reveal that the optimal management framework has better adaptability and stability when compared to the corresponding one under different loads and driving conditions, which are promising for next generation EVs energy management issues. 2026 IEEE. Subject The topic of the resource Affordable and Renewable Energy; Battery - Supercapacitor; Electric Vehicles; Energy Management Strategy; Hybrid Energy Storage System; Power Split Control; Reinforcement Learning; Resilience Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154881-0; 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