12 20 16488 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Devika Menon, M.K.; Rodrigues, Joseph; Herbert, E.S.; Prince Anthony Sylvester, J. Title A name given to the resource Performance Analysis of Pulse Shaper-Generated Gaussian Pulses for Effective V2V Communication Using Microstrip Patch Antenna Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE International Conference on Vehicular Technology and Transportation System, ICVTTS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICVTTS67119.2025.11296476" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICVTTS67119.2025.11296476</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032086974?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032086974?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 Devika Menon M.K., CHRIST (Deemed to be University), Bengaluru, India; Rodrigues J., CHRIST (Deemed to be University), Bengaluru, India; Herbert E.S., CHRIST (Deemed to be University), Bengaluru, India; Prince Anthony Sylvester J., CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource This research presents a comparative analysis of shaped fifth and seventh derivative Gaussian pulses as excitation sources for microstrip patch antennas in V2V communication at 3.69 GHz. The antenna was modeled and simulated using CST Studio Suite, and its performance was evaluated based on return loss, directivity, radiation efficiency, and total efficiency. An Ultra-Wideband pulse shaper was also designed to ensure FCC compliance, reduce sidelobe levels, and minimize interference. Results show that while the fifth derivative pulse yields slightly better return loss, the seventh derivative pulse offers improved frequency alignment, radiation efficiency, and gain, making it more suitable for dynamic vehicular environments. Overall, integrating pulse shaping with higher-order Gaussian pulses enhances the safety, reliability, and interference resilience of V2V communication systems. 2025 IEEE. Subject The topic of the resource Gaussian pulses; microstrip patch antenna; pulse shaping; Ultra-Wideband; Vehicle-to-Vehicle communication Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156522-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Uthaman, Ujwal; Dsilva, Alan Chris; Johny, Danny; Parag Jose, C.; Venkataswamy, R.; Wang, Yong Title A name given to the resource Digital Twinning of an Electric Two-Wheeler for Real-Time Battery State Estimation and Augmented Reality Visualization Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE International Conference on Vehicular Technology and Transportation System, ICVTTS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICVTTS67119.2025.11296457" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICVTTS67119.2025.11296457</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032070394?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032070394?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 Uthaman U., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Dsilva A.C., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Johny D., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Parag Jose C., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Venkataswamy R., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Wang Y., Binghamton University, New York, United States Description An account of the resource This paper describes the design and structure of a digital twin system for an electric two-wheeler. It enables real-time battery state estimation and augmented reality visualization. Using digital twin technology, the system improves battery monitoring, rider experience, and operational efficiency through real-time simulation and optimization. The structure includes a physical sensing layer, an ESP32-based processing unit, cloud-based data management using ThingSpeak, and an AR front-end. It tackles key challenges in estimating the state of LiFePO4 batteries, such as voltage plateau behavior, temperature sensitivity, and accuracy of algorithms. The system also considers the limits of microcontroller-based sensing and data interfacing, ensuring reliable performance in real-time. 2025 IEEE. Subject The topic of the resource Augmented Reality; battery management system; Digital twin; electric two-wheeler; ESP32; Internet of Things; LiFePO4 battery; Model Viewer; range estimation; State of Charge; State of Health; ThingSpeak Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156522-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Alappat, Steven J.; Pranav, Sai; Parag Jose, C.; Wang, Yong Title A name given to the resource Controller Area Network (CAN) Sniffing and Mobile App Based Control of Vehicular Subsystems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE International Conference on Vehicular Technology and Transportation System, ICVTTS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICVTTS67119.2025.11296447" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICVTTS67119.2025.11296447</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032107894?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032107894?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 Alappat S.J., Christ (Deemed to be University), Kengeri, Bangalore, India; Pranav S., Christ (Deemed to be University), Kengeri, Bangalore, India; Parag Jose C., Christ (Deemed to be University), Kengeri, Bangalore, India; Wang Y., Binghamton University, New York, United States Description An account of the resource Modern vehicles use Controller Area Network instead of the traditional wire to wire connections for enabling simultaneous data exchange among all the electronic control units (ECUs). CAN lacks inherent security features making it susceptible to attacks like spoofing and unwanted command injections. This project makes a low budget and scalable system that helps in CAN sniffing and enabling remote control of a vehicle through a mobile application. A CAN sniffing tool is used to map the data form the car. An Arduino board, wifi shield, mcp2515 and an On-Board Diagnostics tool (OBD II) are connected in a setup in order to inject the messages into the car. The connections support both monitoring and message injection allowing users to control in vehicular functions. The proposed setup offers a tool for diagnosing, testing and vehicular controlling by highlighting cybersecurity implications in automotive networks. 2025 IEEE. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156522-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Swetha, S.; Prakash, Gladis Sam; Thomas, Jestin; Kalpana, P.; Jose, Teena; Sumathi, P. Title A name given to the resource Synthetic Data Augmentation for Robust Solar Flare Classification: A Comparative Analysis of Conditional DCGAN, VAE, and Diffusion Models 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 2nd International Conference on Visual Analytics and Data Visualization, ICVADV 2026;pp.896-901 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICVADV67766.2026.11470543" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICVADV67766.2026.11470543</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037458658?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037458658?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 Swetha S., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Prakash G.S., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Thomas J., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Kalpana P., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Jose T., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Sumathi P., Vyasa College, Department of Computer Science, Tamil Nadu, Salem, India Description An account of the resource Solar flares are extremely dangerous to the ground and space-based resources. Solar flares have to be classified properly and in good time to offer protection to assets in both the environments. Deep Learning-based flares have been divided into 3 classes (C, M and X). The main problems with this kind of classification are that high impact M- or X-class solar flares are extremely rare, and cannot be well sampled, thus leading to a very unbalanced sample. This paper exemplifies a comparative analysis of three models of Conditional Generative Models (cDCGAN models, cVAE models and cDDPM) to produce realistic images of solar flares considering each of the low frequency and high-impact solar flare types. The research question will be how such models can be evaluated in terms of their capacity to create realistic, class-specific images (magnetograms, and EUV) and time-series data which could be used to create class-balanced training samples. The initial experiments make use of the cDCGAN, cVAE, cDDPM architecture and considering the generation of class-conditional solar flare images portray high levels of stability (convergence was stated in less than 600 epochs in the case of the cDCGAN, cVAE models and cDDPM) and the generation of images which could be considered as practically indistinguishable to real life images. The results indicate that cDDPM may be a possible solution to a high-fidelity production of solar features. We measured the efficiency of these models in quantitative terms (popular metrics, like the Frhet Inception Distance, Structural Similarity Index) in a manner that we could determine the best manner of training model based solar flare classification systems using realistic data. This research is aligned with Sustainability Development Goals 9- Industry, Innovation and Infrastructure with focus on verticals 9.1 and 9.5. 2026 IEEE. Subject The topic of the resource cDCGAN; Class Imbalance; Conditional Diffusion Model; Conditional Generative Models; cVAE; Data Augmentation; SDG Goal 9; Solar Flare Classification; Synthetic Data Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159237-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Saxena, Anushka; Swaraj Khan, P. Title A name given to the resource From Nodes to Notables a Graph Theoretic Framework for Uncovering Emerging Influencers 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 2nd International Conference on Visual Analytics and Data Visualization, ICVADV 2026;pp.1542-1547 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICVADV67766.2026.11469862" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICVADV67766.2026.11469862</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037465760?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037465760?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 Saxena A., Christ University, Dept. of Statistics and Data Science, Bengaluru, India; Swaraj Khan P., Bengaluru, India Description An account of the resource Identification of new effects in social networks is important for effective digital marketing, information dissemination and community engagement. This article introduces a new graph-theoretical framework designed to systematically reveal impressively by analyzing structural network properties. Our function benefits from the main centrality matrix - including degrees, beach, proximity and self vector center - to evaluate the effect of users in the complex network. In addition, we use social identity algorithms such as the Luven and label suggestions to identify opinions of opinions and important contacts in networking groups. The processing of data affecting recent progress in Graph Neural Network (GNNS) is integrated to limit the impressive identity in order to detect the treatment and the fine effect pattern. Experimental results accurately demonstrate the efficiency of our hybrid approaches in influencing pinpointing, leading to actionable insight into targeted marketing campaigns and the effect of impact monitoring is increased. The challenges related to interpretation and dynamic network situations are discussed for future research. 2026 IEEE. Subject The topic of the resource Centrality Measures; Community Detection; Graph Theory; Influencer Detection; Information Diffusion; Social Network Analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159237-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Athul Ghosh, P.; Chidambaram, S. Title A name given to the resource Hyperspectral Image Classification using Archimedes Optimized 3D CNN layers Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 5th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2025;pp.151-157 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICUIS67429.2025.11380606" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICUIS67429.2025.11380606</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034832266?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034832266?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 Athul Ghosh P., Christ University, School of Engineering and Technology, Department of Ece, Bangalore, India; Chidambaram S., Christ University, School of Engineering and Technology, Department of Ece, Bangalore, India Description An account of the resource Background: Hyperspectral Image (HSI) classification involves analyzing images captured across numerous spectral bands to identify and categorize materials or objects. By exploiting spectral information, HSI enables precise detection of materials based on their unique spectral signatures. This extensive spectral information allows for accurate identification of materials and land cover types, making HSI classification essential for various applications. This research developed an optimization-based Deep Learning (DL) framework for effective classification of HSI. The methodology begins with a preprocessing step using Minimum Noise Fraction (MNF), a technique that leverages the noise covariance structure of the dataset to reduce dimensionality while preserving the most informative spectral features. To enhance classification performance, a novel model is proposed, known as Archimedes Optimization Algorithm-3D Convolutional Neural Network (AOA based 3D CNN). Archimedes Optimization Algorithm (AOA) is applied to fine-tune learning rate of 3D Convolutional Neural Network (3D CNN), thereby optimizing training convergence and enhancing accuracy. Result: The proposed AOA based 3D CNN approach exhibits outstanding performance, achieving 95.6% accuracy, 95.9% TPR, 94.1% TNR, 94.9% PPV, a Kappa coefficient of 95.4%, and an F1-score of 95.5%.Keywords: These results highlight the model's effectiveness, adaptability and robustness for HSI classification. 2025 IEEE. Subject The topic of the resource deep learning; HSI classification; Hyperspectral images; MNF; optimization Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155284-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Thomas, Shiju; Govind, Sabeen; Varghese, Bindiya; Benny, Diljith; Krishna, Addapalli Title A name given to the resource A High-Capacity Reversible Data Hiding on Grayscale Images Using Threshold-Based Clustering Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Conference on Trends in Engineering Systems and Technologies, ICTEST 2025 - Proceedings; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTEST64710.2025.11042854" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTEST64710.2025.11042854</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010385678?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010385678?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 Thomas S., Rajagiri College of Social Sciences, Department of Computer Science, Cochin, India; Govind S., Rajagiri College of Social Sciences, Department of Computer Science, Cochin, India; Varghese B., Rajagiri College of Social Sciences, Department of Computer Science, Cochin, India; Benny D., Rajagiri College of Social Sciences, Department of Computer Science, Cochin, India; Krishna A., Christ University, Computer Science and Engineering, Bengaluru, India Description An account of the resource In the digital age, safeguarding confidential information from unauthorized access is critical. Data transmission occurs in various formats, including text, images, and videos, and while advancements have enhanced the efficiency and convenience of these processes, vulnerabilities in communication channels remain a concern. The proposed scheme secures communication by hiding secret data within a carrier image, visually making it undetectable to potential attackers. Steganography is challenging when transmitting huge quantities of secret data because there is a negotiation between data capacity and imperceptibility. The mastered framework uses grayscale images as a medium for the conveyance of high-capacity secret data. First, the threshold value is computed from the histogram and the statistical mode value. The threshold calculated in the initial step will be a crucial data embedding parameter in the proposed scheme. Then the carrier image is interpolated to increase its size to double. The expanded image is then segmented into 2X2 blocks, the block undergoes a segmentation function to decide whether the block value is above the threshold or below the threshold. Finally, the secret data is embedded into the pixel block based on the threshold value of the block. The secret data and the input image are extracted separately on the receiving end. Standard metrics, Peak Signal to Noise Ratio (PSNR), and Structure Similarity Index Metric (SSIM) are used to evaluate the efficiency of the proposed scheme. The values above 42 and 36 obtained for PSNR for low payload and high payload show the effectiveness of the mastered scheme. Statistical measures, such as mean, standard deviation, and entropy, are used to validate the efficiency of the scheme in ensuring data integrity and security during transmission. 2025 IEEE. Subject The topic of the resource bits per pixel; cryptography; interpolation; least significant bit; steganography Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150537-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Gupta, Vansh; George, Jossy; Chanti, S. Title A name given to the resource Volatility Prediction in the Indian Share Market Using Sentiment Analysis 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 - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.382-390 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00076" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00076</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037459775?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037459775?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 Gupta V., CHRIST (Deemed to be University), Bengaluru, India; George J., CHRIST (Deemed to be University), Bengaluru, India; Chanti S., CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource In addressing the challenge of accurate volatility prediction in the Indian share market, the study explores the performance of deep learning-based models using sentimentdriven features. A demo model was deployed using data from five major Nifty 50 stocks-RELIANCE, HDFCBANK, INFOSYS, ITC, and MARUTI-for the financial years 2020 to 2023. We compared the results of traditional ARIMA model and standalone LSTM and its hybrid variants: LSTM + CNN and LSTM+RNN. Sentiment scores were gathered from financial news using FinBERT and NLTK, and combined with stock price data to generate time-series features. While all models demonstrated promising results, the LSTM+RNN hybrid model consistently achieved the lowest MAE and RMSE, indicating improved learning of temporal dependencies. The standalone LSTM and LSTM+RNN models also showed positive results for Sharpe ratio and Maximum drawdown indicating strong economic significance of the models. The study emphasizes the potential of hybrid LSTM architectures in modeling market volatility driven by investor sentiment. Limitations included limited dataset size, exclusion of other volatility factors, and overfitting in early hybrid GARCH trials. Future work aims to expand data coverage, integrate hybrid GARCH models more effectively, and explore additional market indicators. This research highlights a scalable and effective approach for sentiment-informed volatility forecasting in financial domains. 2025 IEEE. Subject The topic of the resource ARIMA; Deep learning; FinBERT; Hybrid LSTM + RNN and LSTM + CNN; Indian share market; LSTM; MAE; RMSE; Sentiment analysis; Stock market; Volatility prediction Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Haresh, Simon Mathew; Chalissery, Benita Jose; Newbegin, M. Title A name given to the resource Soft Voting Ensemble for Heart Disease Detection Using RF, CatBoost, and XGBoost 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 - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.358-365 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00072" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00072</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037444599?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037444599?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 Haresh S.M., Christ (Deemed to be) University, Department of Computer Science, Bangalore, India; Chalissery B.J., Christ (Deemed to be) University, Department of Computer Science, Bangalore, India; Newbegin M., Christ (Deemed to be) University, Department of Computer Science, Bangalore, India Description An account of the resource Global health reports constantly list heart disease as a major threat to human health. With the help of reliable information and early detection methods the disease's impact can be reduced. The study suggests an RCX(Random Forest, CatBoost, XGBoost) Ensemble machine learning method to figure out how likely a person is to get heart disease. Data for the system came through the UCI Repository containing records that were from Cleveland,Switzerland,Veterans Affairs(VA),Hungarian and statlog medical centers. As the raw datasets were not usable for performing operations, Data was put through preprocessing stages-which had involved removing missing values,making all labels uniform(standardizing them) and employing the SMOTE Technique for artificially creating new minority samples to balance the dataset. To explore an effective approach possible,models including Logistic Regression, Naive Bayes, Ran- dom Forest,CatBoost and gradient boosting(XGBoost) were trained and tested on the datasets. Using the Recursive Feature Elimination (RFE) method 8 out of 14 relevant features were selected. The models were improved through hyperparameter tuning and Among all models used the best results had come from a soft voting ensemble that combined Random Forest, CatBoost and XGBoost. The RCX ensemble model which was developed, has been shown to give accuracy and ROC AUC results of 91.18% and 96.21% respectively, showing stronger results compared to individual models. Metrics and Indicators like accuracy, precision, recall, F1-Score and the ROC AUC were used for comparing performance during testing periods. The data was visualized by using Python libraries such as matplotlib and seaborn for confusion matrices, heatmaps,and bar graphs for better understanding of the data at a glance. 2025 IEEE. Subject The topic of the resource Ensemble Learning; Heart Disease; Machine Learning; RCX Ensemble; SMOTE; Soft Voting Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Das, Vishal; George, Jossy; Kumar, Pawan Title A name given to the resource Early Detection of Plant Diseases Using IoT Sensors and Machine Learning Algorithms 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 - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.344-350 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00070" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00070</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037448966?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037448966?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 Das V., CHRIST (Deemed to be University), India; George J., CHRIST (Deemed to be University), India; Kumar P., CHRIST (Deemed to be University), India Description An account of the resource Agriculture is one of the most important and necessitates of the world. This paper is a study to detect plant diseases using IoT sensors and ML Algorithms for early detection of plant diseases using IoT sensors and machine learning algorithms. A primary dataset from Indian Agricultural Research Institute (ICAR) was used for the research. The dataset comprised the following features: temperature, humidity, soil moisture, leaf wetness, and dew point. Five different machine learning algorithms were explored for the implementation: Logistic Regression, Random Forest, XGBoost, CatBoost, and LightGBM. Upon comparative analysis, it was found that the LightBGM model performed the best with an accuracy of 93.4 % using cross-validation, implying remarkable performance for real-time plant disease monitoring. 2025 IEEE. Subject The topic of the resource CatBoost; Gradient Boosting; Internet of Things (IoT); Machine Learning; Plant Disease Detection; Precision Agriculture Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Suhan Krishna, M.G.; George, Jossy; Chanti, S. Title A name given to the resource Smart Street Lighting System Using AI, Computer Vision, and IoT for Energy Efficiency and Sustainability Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.339-343 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00069" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00069</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037455542?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037455542?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 Suhan Krishna M.G., CHRIST (Deemed to be University), India; George J., CHRIST (Deemed to be University), India; Chanti S., CHRIST (Deemed to be University), India Description An account of the resource In pursuit of smart cities, optimum utilization of energy without any compromise on public security is a requirement. Traditional lighting systems work with minimal flexibility but result in enormous wastage of energy. The proposed model dynamically controls lighting intensity based on real-time pedestrian and environmental information. The model is capable of saving wasteful utilization of electricity by integrating innovative technologies without any compromise on public security and visibility levels. The system requires the deployment of IoT-enabled cameras at strategic points on the highways to monitor the movement and traffic of pedestrians and automobiles. The system is capable of detecting and recognizing moving objects in real-time through computer vision technologies. Based on movement and density patterns, the system determines the optimal level of brightness to be used over each street lighting segment. This adaptive lighting method ensures the utilization of energy only where it is required and minimizes the energy consumption substantially without any compromise on security. Overall, this AI-driven smart lighting solution is a cost-effective and scalable solution for cities to become intelligent and resource-efficient. 2025 IEEE. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Benny, Sandra; Das, Deepthi; Rajesh Kanna, R. Title A name given to the resource Security and Privacy in Biometric Authentication: Advancements and Risks Across Platforms 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 - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.254-259 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00056" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00056</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037440350?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037440350?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 Benny S., Christ University Computer Science, Bengaluru, India; Das D., Christ University Statistics and Data Science, Bengaluru, India; Rajesh Kanna R., Christ University Computer Science, Bengaluru, India Description An account of the resource Biometric identification systems like fingerprint, face, iris, and voice recognition have revolutionized user authentication with more convenience and increased security over conventional password based authentication. Nonetheless, the fact that biometric features are immutable introduces unprecedented issues around privacy, spoofing, and database compromise because once biometric data leaks out, it cannot be withdrawn or substituted. This work advances empirical testing with analytical analysis to test biometric authentication for face, fingerprint, and iris modalities. Models based on convolutional neural networks (CNNs) were used and evaluated on benchmarked datasets, and findings indicated that face and iris recognition performed nearly perfectly with zero false acceptance and rejection rates in controlled environments, while fingerprint recognition performed poorly because of dataset size and quality constraints. The conclusions identify the significance of data preparation and variation in ascertaining the reliability of biometric information. In an effort to cross-verify the experiments further, case studies of Aadhaar, Apple Face ID, and Biostar 2 also clarified the threats of central storage, spoofing, and regulatory loopholes. The research concludes by suggesting privacy preserving frameworks, encryption, and multimodal methods for securing the future of biometric authentication. 2025 IEEE. Subject The topic of the resource Biometric authentication; Face recognition; Fingerprint recognition; Iris recognition; Privacy and security Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Adithiya, Dhanush; Jayabalan, Bhuvana; Beaulah Soundarabai, P.; Augustin, Peter Title A name given to the resource Evaluating Generalization and Robustness of U-Net Based Image Steganography 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 - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.178-183 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00045" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00045</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037437357?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037437357?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 Adithiya D., Christ (Deemed to be) University, Department of Computer Science, Bangalore, India; Jayabalan B., Christ (Deemed to be) University, Department of Computer Science, Bangalore, India; Beaulah Soundarabai P., Christ (Deemed to be) University, Department of Computer Science, Bangalore, India; Augustin P., Christ (Deemed to be) University, Department of Computer Science, Bangalore, India Description An account of the resource This paper investigates the effectiveness and generalization ability of U-Net based image steganography models across multiple datasets, with comparisons to the classical Least Significant Bit (LSB) substitution method. Models were trained on STL-10, CIFAR-10, and Stanford Cars datasets and evaluated both in-distribution and on out-of-distribution internet images. Results show that the STL-10 model consistently achieved the best trade-off between imperceptibility and recovery quality, while the CIFAR-10 model failed to generalize due to its low resolution and limited diversity. Baseline experiments confirmed that LSB achieves extremely high PSNR and SSIM at low payloads, but suffers sharp increases in Bit Error Rate (BER) under higher payloads or even mild distortions such as JPEG compression and Gaussian noise. By contrast, the U-Net model provided more stable recovery and greater robustness, highlighting the advantages of learned feature embeddings over handcrafted substitution. These findings underscore the importance of dataset diversity and robustness testing in developing practical steganographic systems for real-world deployment. 2025 IEEE. Subject The topic of the resource Deep Learning; Generalization; Image Steganography; Robustness; U-Net Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ealias, Aleena; Jayabalan, Bhuvana; Priya, Mohana Title A name given to the resource AI-Based Risk Profiling of Online Human Trafficking Victims: A Multimodal Framework for Proactive 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 Proceedings - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.50-55 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00027" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00027</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037443495?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037443495?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 Ealias A., Christ University, Department of Computer Science, Bengaluru, India; Jayabalan B., Christ University, Department of Computer Science, Bengaluru, India; Priya M., Christ University, Department of Computer Science, Bengaluru, India Description An account of the resource Human trafficking is still a major worldwide crime that is made easier by digital channels, including social media, job advertisements, and websites that offer escort services. This study offers a thorough examination of artificial intelligence (AI) approaches to human trafficking detection and prevention, with a focus on identifying the most susceptible groups. From authorship attribution and geolocation extraction to social network analysis and multimodal detection, we analyze around 20 scholarly papers that include text, photos, audio, and network data. Lack of victim-focused profiling, data scarcity, bias in AI, and ethical deployment issues are some of the main research needs. In response, we provide a conceptual AI system that uses publicly available signals to evaluate individual vulnerability by combining network analysis, natural language processing, and weakly supervised learning. This work emphasizes the importance of ethically grounded AI systems to assist NGOs, law enforcement, and policymakers proactively identify at-risk individuals and prevent exploitation before it occurs. 2025 IEEE. Subject The topic of the resource ethical AI; Human trafficking detection; multimodal AI; social network analysis; victim risk profiling; weakly supervised learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jayaraj, Jishnu Plavinchottil; Ashok Immanuel, V.; Chandra, J. Title A name given to the resource Personalized Explainable Transformer Models for Student Performance 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 - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.44-49 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00026" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00026</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037468909?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037468909?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 Jayaraj J.P., CHRIST University, Department of Computer Science, Bangalore, India; Ashok Immanuel V., CHRIST University, Department of Computer Science, Bangalore, India; Chandra J., CHRIST University, Department of Computer Science, Bangalore, India Description An account of the resource The research presents a unified framework for forecasting student academic achievement using a transformer-based architecture supported by Explainable Artificial Intelligence techniques. The research is motivated by the need to combine predictive accuracy of transformer models with interpretability in student performance prediction. The framework applies an adapted Feature Tokenizer Transformer to the UCI Student Performance dataset and integrates SHAP and LIME methods to generate instance level, human readable explanations for each prediction. These explanations can help educators design targeted interventions. A Random Forest regressor is included as a baseline for comparison. The experiment results showed that the Random Forest performed slightly better than the Feature Tokenizer Transformer, which could be due to the small size of the dataset and certain features having a strong impact on the results. Nevertheless, the results show that modern deep learning models combined with personalized explainability offer a practical foundation for scalable solutions in more complex educational datasets, which helps connect high performing prediction models with actionable insights, contributing to the development of interpretable, data driven student support systems. 2025 IEEE. Subject The topic of the resource Explainable AI; LIME; SHAP; Student Performance Prediction; Transformer Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Payankan, Gavin George; Singal, Neha; Saxena, Surabhi Title A name given to the resource Latency Reduction and Input Prediction for Cloud Gaming Clients 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 - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.30-34 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00023" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00023</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037433918?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037433918?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 Payankan G.G., Christ University, Dept. of Computer Science, Bengaluru, India; Singal N., Christ University, Dept. of Computer Science, Bengaluru, India; Saxena S., Christ University, Dept. of Computer Science, Bengaluru, India Description An account of the resource Cloud gaming enables access to high-quality games on thin clients by streaming rendered content from remote servers, but network-induced latency remains a critical barrier to responsive gameplay. This paper presents a browser-based system that profiles user input in real-time, employs a lightweight machine learning model to predict actions, and dynamically compensates for lag by speculative input. Our solution reduces perceived lag by up to 25% and maintains a 94%+ prediction accuracy, fully within a free-tier cloud environment. Compared to traditional infrastructure-based approaches, our method imposes no proprietary hardware requirements and offers platform-wide scalability. 2025 IEEE. Subject The topic of the resource browser-based; clientside; cloud gaming; input prediction; latency; real-time systems Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Bhavya, A.; Srivastava, Kamakshi; Jayapandian, N. Title A name given to the resource Enhanced Autism Prediction using Hybrid Machine Learning 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 2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTBIG68706.2025.11323994" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323994</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032907036?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032907036?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 Bhavya A., CHRIST (Deemed to Be University), Department of CSE, India; Srivastava K., CHRIST (Deemed to Be University), Department of CSE, India; Jayapandian N., CHRIST (Deemed to Be University), Department of CSE, India Description An account of the resource Autism Spectrum Disorder (ASD) is a condition where individuals face challenges in neurological development and have verbal, non-verbal, learning and behavioral disorders. Even though this condition is identifiable in the first few years in the children's life, many remain undiagnosed until later. This leads to long term developmental issues and delayed interventions. This is what makes the early detection critical for improving development in children. Despite that, traditional diagnosis approaches like behavioral checklists and pre structured interviews rely on the clinician's expertise and are time consuming and have a risk of inconsistency. This study entails and addresses the above problem by proposing a machine learning based multi model to automate early detection in toddlers aged 12 to 36 months. In the initial stage, the traditional classification algorithms like Logistic Regression, SVM are evaluated with high accuracy, F1 score. Then, hybrid models are developed by combining Gradient Boosting as the anchor model with other high performing algorithms, to overcome the limitation of single classification models. These hybrid models help to overcome the limitations of the individual classifiers. Finally, the best-performing hybrid model is enhanced further by Hyperparameter tuning, Feature selection and Cross validation. The outcome of this research will be a hybrid model, combining machine learning algorithms with the best scores, ensuring high accuracy and low false positives. This aims to help in the detection of ASD in early stages in toddlers. 2025 IEEE. Subject The topic of the resource Autism; Cross-validation; Deep Learning; Gradient Boosting; Machine Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157981-4; 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 Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Bhavya, A.; Srivastava, Kamakshi; Jayapandian, N. Title A name given to the resource Enhanced Autism Prediction using Hybrid Machine Learning 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 2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTBIG68706.2025.11323994" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323994</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032907036?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032907036?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 Bhavya A., CHRIST (Deemed to Be University), Department of CSE, India; Srivastava K., CHRIST (Deemed to Be University), Department of CSE, India; Jayapandian N., CHRIST (Deemed to Be University), Department of CSE, India Description An account of the resource Autism Spectrum Disorder (ASD) is a condition where individuals face challenges in neurological development and have verbal, non-verbal, learning and behavioral disorders. Even though this condition is identifiable in the first few years in the children's life, many remain undiagnosed until later. This leads to long term developmental issues and delayed interventions. This is what makes the early detection critical for improving development in children. Despite that, traditional diagnosis approaches like behavioral checklists and pre structured interviews rely on the clinician's expertise and are time consuming and have a risk of inconsistency. This study entails and addresses the above problem by proposing a machine learning based multi model to automate early detection in toddlers aged 12 to 36 months. In the initial stage, the traditional classification algorithms like Logistic Regression, SVM are evaluated with high accuracy, F1 score. Then, hybrid models are developed by combining Gradient Boosting as the anchor model with other high performing algorithms, to overcome the limitation of single classification models. These hybrid models help to overcome the limitations of the individual classifiers. Finally, the best-performing hybrid model is enhanced further by Hyperparameter tuning, Feature selection and Cross validation. The outcome of this research will be a hybrid model, combining machine learning algorithms with the best scores, ensuring high accuracy and low false positives. This aims to help in the detection of ASD in early stages in toddlers. 2025 IEEE. Subject The topic of the resource Autism; Cross-validation; Deep Learning; Gradient Boosting; Machine Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157981-4; 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 Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jiby Jose, E.; Biswas, Priyanka Title A name given to the resource Next Gen Text Mining in English Literature: A Machine Learning Approach for Narrative and Stylistic Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTBIG68706.2025.11323969" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323969</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032841648?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032841648?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 Jiby Jose E., CHRIST (Deemed to Be University), Bangalore, India; Biswas P., CHRIST (Deemed to Be University), Bangalore, India Description An account of the resource This paper presents a research work that is novel in nature, based on a new machine learning framework that focuses on a computational analysis in the field of English literature. This research uniquely aims at focusing on an intersection based on stylistic patterns and narrative structures. The newly proposed model is termed the 'Next Gen Text Mining Framework', which leverages transformer-based models (such as GPT, BERT), along with sentiment trajectory-based modeling, network analysis, and clustering algorithms for extracting stylistic features and latent semantics from the text on a large scale. A meticulously used re-processing type pipelining framework modifies and prepares the data for the model ingestion. The multi-modal approach used in this framework enables the experimental analysis of different models across various diverse corpora in the literature that demonstrate stability, superior accuracy, and robustness when compared to the traditional models, in tasks like the authorship attribution, sentimental trajectory mapping, and the theme-based classification. The proposed framework bridges the gap between distant and close reading practices, enhancing the pedagogically based engagements and translating the computational insights into interpretative forms for research and teaching. This research highlights a scalable and replicable framework model that is a transformative tool when a large-scale inquiry in literature is considered and sets a foundation towards multi-modal, future, cross-lingual, and multidisciplinary-based applications. 2025 IEEE. Subject The topic of the resource Computational Literary Studies; Machine Learning; Narrative Analysis; Stylistic Analysis; Text Mining; Transformer Models Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157981-4; 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 Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kansal, Vandna; Upreti, Kamal; Singh, Jaspreet; Shanbhog, Manjula; Poonia, Ramesh Chandra Title A name given to the resource A Diabetes Detection Framework Based on Datadriven Predictive Technologies Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTBIG68706.2025.11323965" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323965</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032835679?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032835679?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 Kansal V., Christ (Deemed to Be University), Department of Computer Science, Delhi NCR, Ghaziabad, India; Upreti K., Christ (Deemed to Be University), Department of Computer Science, Delhi NCR, Ghaziabad, India; Singh J., Christ (Deemed to Be University), Department of Computer Science, Delhi NCR, Ghaziabad, India; Shanbhog M., Christ (Deemed to Be University), Department of Computer Science, Delhi NCR, Ghaziabad, India; Poonia R.C., Christ (Deemed to Be University), Department of Computer Science, Delhi NCR, Ghaziabad, India Description An account of the resource Diabetes is a chronic disease spreading worldwide with major health challenges. It is not only caused by medical factors but other factors too such as genetic, demographics and lifestyle factors. With traditional or manual diagnosis methods, timely diagnosis becomes challenging due to complex and fragmented datasets. Recent advancements in machine learning (ML) models have greatly enhanced the efficiency and accuracy in disease diagnosis and risk evaluation. This review synthesizes the findings from the recent studies in the field of diabetes, major contributions and limitations, identifies the directions for the future work. This review has included the articles from three databases: Scopus, IEEE Xplore and PubMed; published between 2017 and 2025. The study has employed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model for the review process. The scope of this work includes datadriven predictive technologies for diabetes detection, risk of complications and disease progression. It also sheds light on ongoing challenges such as data imbalance, limited interpretability, and population generalizability, while pointing to future opportunities in explainable AI and more personalized approaches to diabetes care. The review highlights that hybrid or ensemble models performing better than classical single models for risk prediction. 2025 IEEE. Subject The topic of the resource artificial neural network; diabetes; diabetes prediction; ensemble model; machine learning; random forest; social determinants of health (SDH) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157981-4; 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 Format The file format, physical medium, or dimensions of the resource online