744 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 Okewumi, Peter Olufeyi; Ojo, Olayinka Anthony; Iwendi, Celestine; Sharma, Vandana; Uwah, Salome Enoshi; Aboutorabi, Negin Title A name given to the resource Predictive Modelling of Microwave Link Failures Using Machine Learning and Deep Learning Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICRITO66076.2025.11241758" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRITO66076.2025.11241758</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029853032?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029853032?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 Okewumi P.O., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Uwah S.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Aboutorabi N., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom Description An account of the resource Microwave radio links play a vital role in keeping mobile networks running, especially when it comes to backhaul-the part of the network that connects base stations to the core. Gradual failure in these links could disrupt services and cost providers a lot in both revenue and customer trust. In this study, we explore how machine learning can help predict such failures before they happen. Network performance data from a mobile network operator in Nigeria was collected, cleaned and used to achieve the purpose of the study. Four algorithms belonging to machine learning (ML) and deep learning (DL) were adopted and used for training the dataset and predicting link failures. Results show that the Long ShortTerm Memory (LSTM - a deep learning model effective for handling time-series data) performed best with prediction accuracy of 92%, distantly followed by others. These findings indicate that the LSTM is better in modelling temporal patterns in network behaviours. This study provides a practical framework for automating microwave link monitoring and maintenance, thereby reducing manual diagnostics, preventing outages, and improving service reliability. The proposed solution supports the integration of predictive intelligence into network operations, enhancing the quality of service and operational efficiency for telecom providers. 2025 IEEE. Subject The topic of the resource Deep Learning; Long Short-Term Memory (LSTM); Machine Learning; Microwave Link Failure; Microwave radio; Quality of Service Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155421-7; 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 Prashant, C.; Acharya, Biswaranjan; Vijaya, P.; Bejoy, B.J.; Raju, G.; Singh, Ajay Vikram Title A name given to the resource Machine Learning Models for Apple Disease Detection With Texture Feature Fusion and Feature Selection 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 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICRITO66076.2025.11241915" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRITO66076.2025.11241915</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029839218?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029839218?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 Prashant C., Christ University, Bengaluru, India; Acharya B., Marwadi University, Department of AI, Ml and Ds, Rajkot, India; Vijaya P., Modern College of Business and Science, Department of Mathematics and Computer Science, Bowshar, Muscat, Oman; Bejoy B.J., Christ University, Bengaluru, India; Raju G., Sahrdaya College of Engineering and Technology, Kerala, Thrissur, India; Singh A.V., Aiit, Amity University, Uttar Pradesh, India Description An account of the resource Computer vision has become an integral part of modern agriculture. One of the key applications of Computer vision is the automatic detection and classification of plant disease from digital images of plant leaves. In this study we evaluate the discriminatory capability of selected texture features and their fusion in identifying plant diseases from leaf images. Further, the performance of four feature selection algorithms is also evaluated. Texture features are extracted from resized raw images. Experiments are carried out with public data sets of Apple plants. Through extensive experimentation, two classifiers - Random forest and XGBoost are chosen for the evaluation. The feature fusion and feature selection resulted in 85% accuracy. The result is promising as the features are extracted from whole leaf images, without any segmentation. 2025 IEEE. Subject The topic of the resource Apple diseases; Boruta; feature fusion; LDA; PCA; Random Forest; ReliefF; texture features; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155421-7; 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 Helen Josephine, V.L.; Rajagopal, Manikandan; Kavitha, S.; Rajendran, Stuthi Title A name given to the resource Enhancing Sign Language Recognition Through LSTM Model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025 - Proceedings;pp.986-989 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSADL65848.2025.10933396" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSADL65848.2025.10933396</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002463659?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002463659?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Helen Josephine V.L., School of Business and Management, Christ University, Karnataka, Bangalore, India; Rajagopal M., School of Business and Management, Christ University, Karnataka, Bangalore, India; Kavitha S., Dayanand Sagar University, Bengaluru, India; Rajendran S., School of Business and Management, Christ University, Karnataka, Bangalore, India Description An account of the resource Sign language recognition is a remarkable task in this project completed through two state-of-the-art methods, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This way, the system is able to quickly process each frame of the webcam with real-time information regarding face, body and posture in order to extract critical values. this research seeks to provide the necessary resources and opportunities for deaf people to be able to communicate effectively, obtain an education and enjoy their lives as much as other human beings This makes it a very important tool for education where the system can convert sign motions into text on-the-fly. The data was collected through a live camera, and key points from face, body, and pose were detected for training the model. Kindergarten used the four categories of vegetables, fruits, colors and animals. There were 40 video sequences of 40 frames with a sign in each. the model tries to fit too much to noisy points of data. However comprehensive the training, after 19 epochs the validation accuracy is an impressive 93%. The oscillations in the truth values of models are indicative of some uncertainty in learning where the accuracy is still to be settled. The graph in general shows that the LSTM based sign language movement classifier has a good capacity to learn and identify sign language movements with high precision. 2025 IEEE. Subject The topic of the resource Education for hearing impaired; GRU; Image/video recognition; LSTM; Machine Learning; Sign Language Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152392-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Reddy, Yashika P; Senthil Vadivu, M.; Jeevanand, E.S. Title A name given to the resource Alzheimer's Disease Detection using Deep Feature Extraction and Explainable Machine Learning 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 the 5th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2026;pp.135-140 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSADL67539.2026.11451882" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSADL67539.2026.11451882</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036599758?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036599758?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 Reddy Y.P., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India; Senthil Vadivu M., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India; Jeevanand E.S., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India Description An account of the resource Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by gradual cognitive decline, posing significant diagnostic challenges that necessitate automated detection systems to aid clinical decision-making. This study presents an explainable machine learning framework for binary dementia classification using deep feature extraction from magnetic resonance imaging. A pretrained ResNet50 convolutional neural network was employed to extract 2048-dimensional feature vectors from 86,437 MRI slices derived from the OASIS1 dataset, encompassing 347 subjects. The dataset was imbalanced, containing 67,222 Non-demented and 19,215 demented slices (combining very mild, mild, and moderate dementia). The aggregated features at the Subject-level were used to train three machine learning classifiers: Logistic Regression, Random Forest, and XGBoost. The XGBoost model achieved the highest accuracy of 77.14, with a precision of 0.84 and a recall of 0.87 for Nondemented cases, demonstrating strong discriminative capability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations highlighted the hippocampus and temporal lobes as key regions influencing predictions, aligning with established Alzheimer's pathology. The study demonstrates the potential of combining deep feature extraction with interpretable machine learning for automated dementia screening. 2026 IEEE. Subject The topic of the resource Alzheimer's disease; Deep Learning; Explainable AI; MRI; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156883-2; 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 Agrawal, Kanchi; Kavitha, R.; Kumar, Dalvin Vinoth Title A name given to the resource Depression Severity Prediction Among Higher Education Students Using Neural Network Model 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 the 5th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2026;pp.908-913 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSADL67539.2026.11451970" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSADL67539.2026.11451970</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036563271?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036563271?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 Agrawal K., Christ University, Department of Statistics and Data Science, Bengaluru, India; Kavitha R., Christ University, Department of Statistics and Data Science, Bengaluru, India; Kumar D.V., Christ University, Department of Statistics and Data Science, Bengaluru, India Description An account of the resource Depression significantly affects students' mental health and academic performance, highlighting the need for effective early detection methods. This study investigates machine learning approaches for automated classification of depression severity using responses from the Patient Health Questionnaire-9 (PHQ-9). Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and hybrid models combining structured PHQ-9 scores with descriptive text responses were evaluated. The experimental results show that the LSTM model achieved the highest classification precision (90%), demonstrating its ability to capture sequential relationships between items in PHQ-9. The findings indicate that sequence-based models are well suited to assess the severity of depression in student populations. Integrating such predictive models into digital mental health screening systems may support the early identification of at-risk students and enable timely, data-driven interventions in academic settings. 2026 IEEE. Subject The topic of the resource Depression Detection; Mental Health Screening; PHQ-9; Student Mental Health; Text Analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156883-2; 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 Ramasamy, Gobi; Varghese, Nisha; Sivalingam, Elangovan; Ramachandran, Selvakumar Title A name given to the resource DermAI: A Deep Learning-Based Mobile Application for Multi-type Skin Cancer 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 2025 11th International Conference on Smart Computing and Communications: Intelligent Technologies for a Sustainable World, ICSCC 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSCC66177.2025.11233655" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSCC66177.2025.11233655</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029616022?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029616022?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 Ramasamy G., Christ University, Department of Computer Science, Bangalore, India; Varghese N., Christ University, Department of Computer Science, Bangalore, India; Sivalingam E., EBay, United States; Ramachandran S., University of Oxford, United Kingdom Description An account of the resource The significance of early skin cancer detection for effective prevention and treatment is underscored by the limitations of traditional manual diagnostic methods used by dermatologists. Leveraging Convolutional Neural Networks (CNNs) and the HAM10000 dataset, this research aims to automate skin cancer classification through dermatoscopic image analysis. The primary objective of the research is an accurate classification system identifying seven specific skin cancer types. The novelty is the deployment of the classification system using a Mobile Application - DermAI. The trained CNN model, spanning 10 epochs, achieved remarkable precision, peaking at a 97.90 percentage test accuracy during the 7th epoch. Evaluation metrics like the confusion matrix confirm its reliability in categorizing lesions, minimizing misclassifications, and validating its efficiency as a diagnostic tool. Transforming the model into TensorFlow Lite format enables seamless integration into mobile platforms, optimizing computational resources. This allows users to access prompt skin cancer classification via an Android application, fostering accessibility to preliminary assessments. Early identification facilitates timely medical intervention, a crucial factor in enhancing prognosis. Through CNNs, TensorFlow Lite, and mobile deployment, this research strives to bridge technology and healthcare accessibility, empowering individuals to proactively manage their skin health based on classification results and initiate timely discussions with healthcare professionals. 2025 IEEE. Subject The topic of the resource CNN; dermatofibroma; intraepithelial carcinoma; melanoma; Skin cancer detection Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152562-0; 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 Sivadevuni, Sreeparnesh Sharma; Naveen, J. Title A name given to the resource Indoor Localization and Tracking with IoT: A Critical Survey of Technologies, Challenges, and Future Trends 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 3rd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2025;pp.644-650 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSCDS65426.2025.11167372" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSCDS65426.2025.11167372</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018466827?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018466827?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 Sivadevuni S.S., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Karnataka, Bangalore, 560001, India; Naveen J., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Karnataka, Bangalore, 560001, India Description An account of the resource Indoor localization and tracking have been important areas of research throughout the past 10 years, driven by the expanding Internet of Things (IoT) technologies. The shortcomings of conventional GPS in indoor environments have called for the development of replacement localization methods. This paper presents a methodical review of IoT-enabled indoor localization techniques covering both well-known technologies such as Bluetooth Low Energy (BLE), Radio-Frequency Identification (RFID), Ultra-Wideband (UWB), and Wi-Fi fingerprinting, as well as newer approaches such as Visible Light Communication (VLC). We critically evaluate these technologies by way of a comprehensive analysis of modern research and case studies, emphasizing significant performance criteria such as accuracy, scalability, and energy efficiency as well as pragmatic concerns such as cost and security. Our work looks at field trends still in development, highlights significant gaps and problems, and integrates the current state of the art. We also stress potential application fields - such as smart homes, healthcare, and industrial automation - that stand to benefit significantly from advances in indoor localization. Finally, we outline future research intended to address current limitations, including the need of higher accuracy in complex environments and more robust security measures. 2025 IEEE. Subject The topic of the resource BLE; Future Trends; Indoor Localization; Internet of Things (IoT); RFID; Smart Environments; Tracking; UWB; VLC; Wi-Fi Fingerprinting Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151250-7; 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 George, James; George, Jossy Title A name given to the resource Innovative Hybrid Models for Predicting Diabetes: CNN-LSTM Hybrid and Calibrated Soft Voting 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 Proceedings of 5th International Conference on Soft Computing for Security Applications, ICSCSA 2025;pp.2078-2083 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSCSA66339.2025.11170763" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSCSA66339.2025.11170763</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018453839?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018453839?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 George J., Christ (Deemed to Be University), India; George J., Christ (Deemed to Be University), India Description An account of the resource This study assesses four ensemble techniques - stacking, soft voting, hard voting, and calibrated soft voting - for predicting diabetes onset using the Pima Indians Diabetes dataset. Traditional single-model methods are contrasted with these advanced ensemble approaches, which integrate multiple models to enhance predictive accuracy. The evaluation included metrics such as accuracy, precision, recall, F1 score, and AUC. The CNN-LSTM model was also examined, achieving an accuracy of 75%, precision of 70%, recall of 69%, and an F1 score of 72%. Among the suggested methods, the calibrated soft vote model was the most effective, with improved performance compared to the rest of the techniques. Upcoming studies will address the combination of these models with real-time monitoring systems and deploying their use across a broad range of datasets and medical conditions. 2025 IEEE. Subject The topic of the resource AUC-ROC Curves; CNN; Confusion Matrix; Deep Learning; Diabetes Prediction; F1 Score; Hard Voting; LSTM; Soft Voting; Stacking Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159491-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 Paul, Aditi; Kumari, Sweety; Navadia, Nipun R; Sinha, Somnath Title A name given to the resource Explainable Intrusion Detection System for Internet of Things-explainability with reliability 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 Soft Computing for Security Applications, ICSCSA 2025;pp.226-232 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSCSA66339.2025.11170796" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSCSA66339.2025.11170796</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018461660?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018461660?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 Paul A., Banasthali Vidhapith, Department of Computer Science, Jaipur, India; Kumari S., Banasthali Vidhapith, Department of Computer Science, Jaipur, India; Navadia N.R., Pace University, New York City, NY, United States; Sinha S., Christ University, Department of Computer Science, Bengaluru, India Description An account of the resource Explainable Artificial Intelligence (XAI) based Intrusion Detection System (IDS) (X-IDS) has transformed the traditional IDS into interpretable and transparent system with the goal of providing interpretable justification for IDS models. XAI is now being used to extract more appropriate features for specific cyber-attacks. The black-box model of ML based IDS is not capable of giving reason for false positive to the cyber defense personnel. XAI tools reduces this abstraction by locally interpreting the model's behaviour at some datapoints along with global interpretability. This article proposes an explainable IDS by using XAI tools. We used SHAP (SHapley Additive exPlanations) to identify the variations in feature importance of selected ML based IDSs and explain the variations of their detection accuracies. Also, we have shown that with same dataset, feature importance varies differently with different ML models. This leads us to the conclusion that specific set of features are required for specific ML models while other can be discarded. The explainability proposed in this study also help to select less set of features to overcome time of execution and cost. 2025 IEEE. Subject The topic of the resource DoS attack; feature importance; Intrusion Detection System; XAI based IDS; XAI for machine learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159491-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 Venugopal, E.; Rikimi; Vinjamuri, Lakshmi Priya; Sachan, Vivek Singh; Samuel, Stephen; Shukhratjon, Ismoilov Title A name given to the resource AI-Powered Solutions for Legal Compliance in Industrial Workspaces a Psychological and Labour Law Perspective 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 Sustainability, Innovation and Technology, ICSIT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSIT65336.2025.11295145" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSIT65336.2025.11295145</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031628417?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031628417?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 Venugopal E., Saveetha School of Law, Saveetha University, Chennai, India; Rikimi, MIT Institute of Design, MIT ADT University, Department of Graphic Design, Maharashtra, Pune, India; Vinjamuri L.P., Technology and Legal Analyst Law College, Dehradun Uttaranchal University, Uttarakhand, India; Sachan V.S., School of Business Management, CSJM University, Kanpur, India; Samuel S., CHRIST (Deemed to be University), Department of Psychology, Karnataka, Bangalore, India; Shukhratjon I., Tashkent State University of Law, Department of LabourLaw, Tashkent, Uzbekistan Description An account of the resource The complicated issue of the ways to ensure the workplaces in the industries meet the legal standards brings together the psychology of the workplaces, the labour law, and the convolution of the AI design. In this dissertation, the author investigates the use of AI-mediated options to deal with regulatory compliance and psychological and legal issues. The risk associated with the fair labour, discrimination, and safety issues can be addressed with the help of predictive analytics, automated compliance, and AI-compliance monitoring. The use of AI can also be expanded to support worker well-being and mental health through the identification of work stressors, burnout prevention, and creation of a physiologically safe workplace. But in the case of AI, there are ethical or legal considerations around the agency of workers, bias in algorithms, as well as privacy or confidentiality of data. Due to these reasons, it is necessary to adopt the strategy approach, where AI and human observation are used to determine the work decisions trade-offs in an observable and just manner to employers and employees alike. This dissertation also added to the contribution of how AI can benefit the responsible design of industrial workplaces that do not fail to achieve ethical standards, hold to psychological sustainability, and adhere to labour laws, evaluating the psychological effects of labour law actors as well as effects on the workplace. 2025 IEEE. Subject The topic of the resource Industrial workspace; Legal compliance Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153549-0; 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 Perera, Anston Peter; Alex, Abel Jo; Bhushan, S. Title A name given to the resource Innovative Power Conversion Solutions for Renewable Energy and Electric Mobility 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 Smart Systems for Applications in Electrical Sciences, ICSSES 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSSES64899.2025.11009310" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSSES64899.2025.11009310</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008339095?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008339095?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 Perera A.P., Christ University, Department of Electrical and Electronics Engineering, Bangalore, India; Alex A.J., Christ University, Department of Electrical and Electronics Engineering, Bangalore, India; Bhushan S., Christ University, Department of Electrical and Electronics Engineering, Bangalore, India Description An account of the resource The global transition to renewable energy sources and electrification demands efficient power conversion systems for applications like hybrid electric vehicles (HEVs) and energy storage systems. This paper introduces a novel Multi-Port Bidirectional DC-DC/DC-AC Converter (MBPC) with high efficiency, compact design, and versatile functionality. The MBPC supports two input and two output ports, enabling energy flow between renewable energy sources, storage systems, and loads. Its efficiency exceeds 95%, with a power density of over 10W/cm2. The innovative design minimizes component count, reducing manufacturing costs by 30% compared to conventional converters. Extensive experimentation validates its ability to handle varying current-voltage profiles in multiple operational modes, including DC-DC and DC-AC conversions. With applications in grid-tied systems and electric vehicles, the MBPC addresses efficiency, cost, and flexibility challenges in modern power systems. This work contributes to advancing renewable energy integration and efficient electrification solutions. 2025 IEEE. Subject The topic of the resource DC-AC converter; DC-DC converter; Emobility; MBPC; renewable energy Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150767-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Arun Raja, A.; Abraham, Aaron; Bhushan, S.; Tejas, R. Title A name given to the resource Improving Voltage Regulation in High-Power Solar Applications 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 Smart Systems for Applications in Electrical Sciences, ICSSES 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSSES64899.2025.11009502" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSSES64899.2025.11009502</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008318338?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008318338?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 Arun Raja A., Christ University, Department of Electronics and Communication Engineering, Bangalore, India; Abraham A., Christ University, Department of Electrical and Electronics Engineering, Bangalore, India; Bhushan S., Christ University, Department of Electrical and Electronics Engineering, Bangalore, India; Tejas R., Christ University, Department of Electrical and Electronics Engineering, Bangalore, India Description An account of the resource This paper presents an advanced solar-powered isolated DC-DC converter optimized for high-power applications, with a focus on precise voltage regulation at the output stage. To mitigate high-voltage stresses typically encountered in single-stage DC-DC converters, a Lossless Active Clamp Flyback circuit is integrated, offering soft switching capabilities and regenerative energy features. The proposed topology is designed using low-voltage devices, enhancing overall system efficiency. A hardware prototype rated at 2 kW has been developed to empirically validate the circuit's performance. Additionally, a novel control algorithm is introduced to further optimize the converter's operational characteristics. The proposed converter is benchmarked against existing solutions, highlighting significant improvements in terms of component count, voltage handling, and energy regeneration. The results demonstrate superior efficiency and robustness, making the system highly suitable for high-power renewable energy applications. Through this innovative approach, the converter offers substantial gains in performance and operational feasibility, especially in scenarios demanding high power density and stringent efficiency standards. 2025 IEEE. Subject The topic of the resource AC-DC; DC-DC converter; LCF; ZCS; ZVS Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150767-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Adesola, Adedeji Edward; Ojo, Olayinka Anthony; Aboutorabi, Negin; Iwendi, Celestine; Sharma, Vandana Title A name given to the resource Predicting Job Risk from Artificial Intelligence in London Using Supervised Machine Learning 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 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/ICTBIG68706.2025.11323513" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323513</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032917186?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032917186?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 Adesola A.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Aboutorabi N., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India Description An account of the resource This study investigates the risk of job automation in London due to artificial intelligence (AI), applying supervised machine learning techniques to identify occupations most at risk. Leveraging a dataset encompassing job-specific features such as primary tasks, industry domains, and associated AI models, the research develops two predictive models. A Random Forest Classifier is used to categorize jobs as low, medium, or high automation risk, while a Linear Regression model estimates the proportion of each occupation's workload likely to be automated. The Random Forest model achieved a high accuracy rate of 97% in classifying job risk, indicating strong predictive capability. Meanwhile, the regression model explained 85% of the variance in the AI workload ratio, highlighting a significant relationship between job attributes and automation potential. These results suggest that job characteristics are reliable indicators of AI impact, particularly in routine, repetitive, and low-skilled roles that are more easily codified and replicated by algorithms. The findings align with broader economic theories such as creative destruction and technological waves, suggesting that AI not only displaces certain roles but also drives structural transformation within the labor market. By focusing on London, this study provides a localized understanding of how AI is reshaping employment patterns. It underscores the growing urgency for strategic workforce re-skilling and adaptive policy frameworks to mitigate negative outcomes and maximize opportunities presented by AI. Ultimately, this research contributes valuable insights into the interaction between AI technologies and employment, helping policymakers, employers, and educators anticipate change and prepare for a more resilient, inclusive labor market. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Automation Risk; Employment Forecasting; Labor Market; Machine Learning; Random Forest Classification 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 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 Katare, Aditi Rajendra; Isaac, Kenin; Jayapandian, N.; Jacob, Geo T Title A name given to the resource Artificial Intelligence Personalization: Opportunities, Risks, and the Need for Ethical Data Practices 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/ICTBIG68706.2025.11323537" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323537</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032858488?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032858488?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 Katare A.R., Christ University, Department of CSE, Bangalore, India; Isaac K., Christ University, Department of CSE, Bangalore, India; Jayapandian N., Christ University, Department of CSE, Bangalore, India; Jacob G.T., Christ University, Department of CSE, Bangalore, India Description An account of the resource The benefits of AI personalization are numerous; however, the question still remains, what are the side effects of this feature? Will it help make shopping and enjoying content more enjoyable and efficient or will it destroy the trust of the users by creating privacy concerns. These are the issues this article has attempted to discuss and investigate. Artificial Intelligence (AI) personalization is a very helpful and useful feature, but there must remain a proper balance between personalization and data collection in order to ensure client comfort. A transparent and honest collection of data is to be expected for almost all the companies and this data is to be used responsibly. Activities like profiling must be regulated and controlled and should not be left unregulated. Despite laws being updated to be more considerate towards the privacy of users, the development of better enforcement is imperative. Regardless of the presence of appropriate laws, it is important that each individual practice the respective ethics while present in the digital universe. This article is discussed about AI personalization issues and its research challenges. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Data Privacy; Deep Learning; Encryption; Personalization 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 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Ramendra; Kshirsagar, Pravin R.; Tak, Tan Kuan; Upreti, Kamal; Gora-Blaszczykowska, Agnieszka; Radhakrishnan, G.V. Title A name given to the resource Enhancing Healthcare Ecosystems Through the Integration of IoT for Patient-Centric Solutions 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/ICTBIG68706.2025.11323576" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323576</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032888071?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032888071?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh R., Raj Kumar Goel Institute of Technology, Ghaziabad, India; Kshirsagar P.R., J D College of Engg &amp; Mgmt, Nagpur, India; Tak T.K., Sinagpore Institute of Technology, Singapore; Upreti K., Christ University, Ghaziabad, India; Gora-Blaszczykowska A., War Studies University, Warsaw, Poland; Radhakrishnan G.V., KIIT, Bhubaneswar, India Description An account of the resource The Internet of Things (IoT) is a newly implemented technology in the field of healthcare and can enhance patient-centrical care and efficiency in the healthcare field. IoT may be used to assist in delivering real-time health data and predictive diagnostics and custom care plans by interconnecting medical equipment, sensors, and information systems. This paper will discuss how the IoT technologies, particularly wearable sensors, cloud-based analytics and smart health architectures are changing the way healthcare is delivered. The article highlights how the data merge on the utilization of clinical and non-clinical sources to aid in remote patient monitoring, resources use optimization, and positive patient outcomes. It also identifies the concerns of the implementation of IoT such as the security risks, data privacy and the failure to connect the devices and systems. To address them, the paper discusses the new structures that integrate blockchain and artificial intelligence and ensure safe implementation of data management and heightened clinical decision-making. The results of various works of 2015-2020 have revealed that IoT applications and patent health care-related tendencies are growing, which implies that the shift towards interconnected and intelligent ecosystems is rapid. The consequences of this digital transformation are not confined to the hospital sphere only, as it is extended to homecare, telemedicine, and state population management. Lastly, IoT will allow healthcare stakeholders to shift their healthcare systems to patient-centered, rather than hospital-centered systems, in which a focus on accessibility, efficiency, and personalization would be placed on healthcare provision. 2025 IEEE. Subject The topic of the resource Healthcare; Interoperability; IoT; Patient-centric; Wearable sensors 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 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 Aboutorabi, Negin; Adesola, Adedeji Edward; Iwendi, Celestine; Ojo, Olayinka Anthony; Oluwaseun, Afeez; Sharma, Vandana Title A name given to the resource Exploring Social Cues and Engagement in Humanoid Robots: A Robosen K1 Case Study Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 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/ICTBIG68706.2025.11323596" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323596</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032842038?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032842038?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 Aboutorabi N., Centre of Intelligence of Things, University of Greater Manchester, Bolton, United Kingdom; Adesola A.E., Centre of Intelligence of Things, University of Greater Manchester, Bolton, United Kingdom; Iwendi C., Centre of Intelligence of Things, University of Greater Manchester, Bolton, United Kingdom; Ojo O.A., Centre of Intelligence of Things, University of Greater Manchester, Bolton, United Kingdom; Oluwaseun A., Centre of Intelligence of Things, University of Greater Manchester, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India Description An account of the resource With the increase adoption of humanoid robots in today's world, the need to understand the ways through which these robots communicate social cues has become indispensable for effective human-robot interaction (HRI) in everyday life. The focus of this study is on the examination of the influence of nonverbal behaviour of Robosen K1 (a humanoid robots) on human perceptions and emotional responses. K1 was programmed to perform expressive full-body movements, due its lack of facial expressions, such as dancing, push-ups, and standing on its head. The research design was a mixed-method approach, which combined behavioural observations from live interactions with data from an online survey. Findings from the study revealed positive emotional reactions from participants, most of which described the robot as 'impressive', 'curious', and 'amusing'. Also, results indicated that 89.8% of participants were favourably disposed to engaging with similar robots in the future. Finally, it was found that the robot's gestures, being highly expressive, contributed to perceived personality traits such as 'playful' and 'friendly'. The study, therefore, concluded that a well-designed non-verbal cues would play critical role in enhancing emotional connection, engagement, and trust in humanoid robots, hence, their importance for successful HRI design. 2025 IEEE. 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 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 Sahoo, Prajnee Samedhini; Bang, Sanjay Title A name given to the resource HALC: An AI-Driven Legal Decision-Making Framework - A Qualitative NVivo Case Study on Tribal Rights 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/ICTBIG68706.2025.11323618" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323618</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032820923?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032820923?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 Sahoo P.S., SVKM's Pravin Gandhi College of Law, Mumbai, India, Christ University, Lavasa Campus, Lavasa, India; Bang S., Christ University, Lavasa Campus, Lavasa, India Description An account of the resource This study proposes a structured human-AI collaboration framework for legal and ethical decision-making, integrating artificial intelligence with human expertise. Unlike fully automated AI systems, it prioritizes transparency, accountability, and ethical oversight. Through expert interviews with legal professionals and AI technologists, we identified key challenges, including bias, lack of explainability, and the need for human validation. Using thematic analysis in NVivo, we developed a stepwise framework that ethically collects data, applies AI-driven analysis, ensures human oversight, and informs policy decisions. This approach enhances human judgment rather than replacing it, with potential applications in law, governance, and public policy. Future research will test and refine this framework. 2025 IEEE. Subject The topic of the resource Accountability; Ethical AI; Explainability; Human-AI collaboration; Legal AI; Transparency 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 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 Chandra, Akkaraju Sailesh; Iyer, Lakshmi Shankar; Helen Josephine, V.L.; Shankar, Nisha Title A name given to the resource Machine Learning Algorithms for Optimizing Blockchain-Based Decentralized Autonomous Organizations 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/ICTBIG68706.2025.11323619" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323619</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032887390?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032887390?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 Chandra A.S., School of Business and Management, CHRIST (Deemed to Be University), India; Iyer L.S., School of Business and Management, CHRIST (Deemed to Be University), India; Helen Josephine V.L., School of Business and Management, CHRIST (Deemed to Be University), India; Shankar N., School of Business and Management, CHRIST (Deemed to Be University), India Description An account of the resource This research investigates the integration of machine learning algorithms within blockchain-based Decentralized Autonomous Organizations (DAOs) to enhance operational efficiency, resource allocation, decision-making, and governance. While DAOs provide a transparent and trustless mechanism for digital collaboration, they face challenges related to scalability, bias, data privacy, and coordination. We propose a novel framework that leverages supervises learning models for predictive analytics, reinforcement learning for autonomous decision-making, and unsupervised learning for anomaly detection in DAO voting and resource usage patterns. The study also addresses security and privacy risks by incorporating federated learning and homomorphic encryption. Our proposed model demonstrates improved throughput, decision accuracy, and fairness, as evidenced by performance benchmarks against traditional DAO implementations. The findings suggest that machine learning can significantly optimize DAO architecture and contribute to a more scalable, democratic, and intelligent decentralized ecosystem. 2025 IEEE. Subject The topic of the resource Anomaly Detection; Blockchain Technology; DAO Governance; Federated Learning; Homomorphic Encryption; Machine Learning; Natural Language Processing; Privacy Preservation; Reinforcement Learning; Resource Allocation 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 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 Iwendi, Celestine; Nwibo, Ezekiel Gabriel; Sharma, Vandana; Lemeke, Collins; Uwah, Salome Enoshi; Ukura, Kumasuun Kimberly Title A name given to the resource Integrating Behavioural Science using the Psycho-Intelligence Framework in Connected Systems 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/ICTBIG68706.2025.11323725" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323725</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032910899?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032910899?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 Iwendi C., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Nwibo E.G., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Lemeke C., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Uwah S.E., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Ukura K.K., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom Description An account of the resource The fast-growing convergence of neuroscience, behaviour computing, and adaptive artificial intelligence (AI) offers the possibility to transform human, machine interaction. This work presents Psycho-Intelligence, a new, closed-loop system that merges electroencephalography (EEG) and inertial motion unit (IMU) signals to adaptively recognise and react to users' cognitive and affective states. Levying low-cost wearable sensors (Muse EEG and MPU-6050), the system has real-time signal acquisition, sophisticated preprocessing, spectral and statistical feature extraction, as well as multimodal fusion features. Dimensionality reduction and feature selection techniques, including Principal Component Analysis and XGBoost gain metrics, enhance learning optimally. Multiple machine learning algorithms like Random Forest, SVM and XGBoost are trained to identify engagement states with high accuracy, warranted by extensive testing through cross-validation, ROC AUC, and F1-scores. The pipeline is incorporated into an adaptive feedback system that can regulate chatbot tone, learning material, or interactive graphics based on detected user states. Statistical validation with linear mixed models confirms the robustness of EEG-derived measurements in engagement prediction. The research establishes a new paradigm for emotionally intelligent AI systems and provides a technical foundation for ethical, real-time psycho-behavioural intelligence for communication networks, education systems, and cognitive health monitoring. 2025 IEEE. Subject The topic of the resource Adaptive Human-AI Interaction; Behavioural Computing; EEG-IMU Fusion; Multimodal Emotion Recognition; Psycho-Intelligence 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 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, Manish; Shanbhog, Manjula; Alapatt, Bosco Paul; Upreti, Kamal; Poonia, Ramesh Chandra Title A name given to the resource A Systematic Review and Meta-Analysis of Pneumonia Diagnosis Using 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 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/ICTBIG68706.2025.11323738" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323738</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032858146?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032858146?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., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Shanbhog M., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Alapatt B.P., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Upreti K., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Poonia R.C., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India Description An account of the resource Pneumonia is an infection that results in inflammation of the lungs and, if not identified in time, can be life-threatening. The most frequent method of diagnosing pneumonia is chest X-rays; the pictures are scrutinized closely. Pneumonia is still a global health burden. The accurate and timely diagnosis is difficult, especially in low-resource settings. X-rays have served as a primary key for the identification of pneumonia for many years. However, with the recent advancement in artificial intelligence technologies, especially deep learning and machine learning, there's now a potential to automatically detect and classify pneumonia using chest x-ray images. This review examines the research from 2019 to 2024 to understand the current trends and future direction in various deep learning and machine learning models. These encompass the convolutional neural networks, transfer learning methods, combined network designs, explainable AI models, and the use of radiomics with conventional machine learning techniques. However, the three significant challenges remain differences in the data, an imbalance between the classes, and a limited ability to apply these methods in real clinical settings. Based on the review, this paper suggests more future research on machine learning techniques for detecting pneumonia. In this work, a new system is also introduced to improve both case identification and the clinical diagnosis process. The proposed model was evaluated using the Key Parameter Indicator (KPI) as a feature and was compared with an earlier model. Finally, recommendations are provided for future research on trustworthiness, clinical usefulness, and multi-modal AI systems. 2025 IEEE. Subject The topic of the resource convolutional neural networks; Deep learning; machine learning; transfer 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