443 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 Letters Letter Faculty Publications-Letters Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A machine learning model to predict suicidal tendencies in students Description An account of the resource [No abstract available] Creator An entity primarily responsible for making the resource Mukku L.; Thomas J. Source A related resource from which the described resource is derived Asian Journal of Psychiatry, Vol-79 Publisher An entity responsible for making the resource available Elsevier B.V. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.ajp.2022.103363" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ajp.2022.103363</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144018882&doi=10.1016%2Fj.ajp.2022.103363&partnerID=40&md5=db6739754dbc82fc0e2cfdf065fedb26" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144018882&doi=10.1016%2fj.ajp.2022.103363&partnerID=40&md5=db6739754dbc82fc0e2cfdf065fedb26</a> Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Relation A related resource ISSN: 18762018; PubMed ID: 36481568 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Letter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mukku L., CHRIST (Deemed to be University), Kengeri Campus, Bengaluru, 560074, India; Thomas J., CHRIST (Deemed to be University), Kengeri Campus, Bengaluru, 560074, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Letter Faculty Publications-Letters Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mukku, Lalasa; Burri, Vikas Title A name given to the resource Unsupervised digital counselling: Addressing the safety gap of large language models as mental health tools 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 Asian Journal of Psychiatry;Volume;116;Issue;;Article No.;104824; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.ajp.2025.104824" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ajp.2025.104824</a> <a href="https://www.scopus.com/pages/publications/105026880741?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026880741?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 Mukku L., CHRIST (Deemed to be University), Bangalore, 560074, India; Burri V., Colorado State University, Fort Collins, 80523, United States Description An account of the resource [No abstract available] Subject The topic of the resource Artificial intelligence; Counselling; Large language models; Machine learning; Psychiatry; Therapy Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 18762018; Language A language of the resource English Type The nature or genre of the resource Letter Rights Information about rights held in and over the resource 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 Mukku, Lalasa; Burri, Vikas Reddy; Lamani, Manjunath Ramanna Title A name given to the resource Mul-Sensis: Multilingual Sentiment Analysis Framework for Emotion Detection 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 Smart Innovation, Systems and Technologies;Volume;454 SIST;pp.237-247 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-07837-7_19" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-07837-7_19</a> <a href="https://www.scopus.com/pages/publications/105036731464?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036731464?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 Mukku L., Christ (Deemed-to-be) University, Bengaluru, India; Burri V.R., Colorado State University, Fort Collins, CO, United States; Lamani M.R., Moodlakatte Institute of Technology, Kundapura, India Description An account of the resource Sentiment analysis is a pivotal Natural Language Processing (NLP) task that enables the extraction of actionable insights from textual data, particularly social media. With the rise of public discourse on platforms like Twitter, analyzing sentiment trends has become crucial for decision-making in domains such as policy implementation, feedback evaluation, and public opinion monitoring. Mul-Sensis employs a hybrid approach combining transformer-based models with classical machine learning algorithms to enhance sentiment classification. The system integrates advanced preprocessing techniques to address linguistic complexities like sarcasm, idiomatic expressions, and domain-specific nuances. A robust hybrid annotation approach, incorporating both human expertise and machine-assisted methods, ensures high-quality, bias-free sentiment labeling. This study contributes a scalable, interpretable, and domain-agnostic framework for sentiment analysis, offering valuable insights for policymakers, researchers, and industries relying on textual data analytics. The findings highlight the transformative potential of hybrid and ensemble-based NLP approaches for understanding public sentiment across diverse cultural and linguistic contexts. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource BERTweet; Ensemble learning; Hybrid transformers; Mul-Sensis; TextBlob; VADER Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-303207836-0; 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 Mukku, Lalasa; Burri, Vikas; Lamani, Manjunath Ramanna Title A name given to the resource A Hybrid Deep Learning Model Using U-Net and Vision Transformer for Artificial Intelligence Powered Cervical Stenosis Diagnosis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1772 LNNS;pp.243-253 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-14044-9_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-14044-9_20</a> <a href="https://www.scopus.com/pages/publications/105030823221?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030823221?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 Mukku L., CHRIST (Deemed to be University), Bangalore, India; Burri V., Colorado State University, Fort Collins, United States; Lamani M.R., Moodlakatte Institute of Technology, Kundapura, India Description An account of the resource This study presents a deep learning-based approach for the classification of cervical stenosis using MRI spine images, integrating multiple phases such as preprocessing, segmentation, feature extraction, and classification. A U-Net-based segmentation model effectively delineates key anatomical structures, including the spinal canal, intervertebral discs (IVDs), and neural foramen, improving feature extraction and classification accuracy. Furthermore, ResNet-50 is employed for feature map generation, leveraging deep hierarchical representations to extract meaningful spatial patterns from MRI slices. For classification, a Vision Transformer (ViT)-based model is utilized, taking advantage of its self-attention mechanism to capture both local and global dependencies within MRI images. Unlike conventional CNN-based models, ViT processes MRI scans as patches, enabling a more context-aware analysis of stenotic regions. The model is trained using an 80%20% train-test split and evaluated using standard performance metrics, achieving an accuracy of 92.60%, precision of 90.16%, recall of 95.43%, and an F1-score of 91.56%. These results indicate that the ViT model outperforms traditional CNN-based classifiers in cervical stenosis detection, ensuring higher sensitivity and specificity in real-world clinical applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource artificial intelligence; Cervical stenosis; deep learning; MRI; spine degeneration Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303214043-2; 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 Mukku, Lalasa; Burri, Vikas; Lamani, Manjunath Ramanna Title A name given to the resource Artificial Intelligence-Driven Lumbar Stenosis Diagnosis: A Deep Learning Pipeline for MRI-Based Segmentation and Classification 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 4th World Conference on Applied Intelligence and Computing, AIC 2025;pp.240-244 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AIC66080.2025.11211995" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIC66080.2025.11211995</a> <a href="https://www.scopus.com/pages/publications/105023638615?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023638615?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 Mukku L., CHRIST University, Department of Computer Science and Engineering, Bangalore, India; Burri V., Colorado State University, CS, Fort Collins, United States; Lamani M.R., Moodlakatte Institute of Technology, Department of Computer Science and Engineering, Karnataka, Kundapura, India Description An account of the resource Lumbar spinal stenosis is a prevalent musculoskeletal disorder that requires accurate diagnosis through magnetic resonance imaging (MRI). However, manual interpretation of MRI images is time-consuming and subject to inter-observer variability. This study proposes an automated deep learning-based pipeline for lumbar stenosis identification, integrating advanced methodologies for preprocessing, segmentation, feature extraction, and classification. The pipeline consists of Super-Resolution Convolutional Neural Network (SRCNN) for MRI image enhancement, SegNet for segmentation of the spinal canal, intervertebral discs (IVDs), and neural foramen, Convolutional Block Attention Module (CBAM) for feature refinement, and Swin Transformer for final classification. The proposed method was evaluated on a publicly available multicenter lumbar spine MRI dataset, comprising 218 patient studies with 447 MRI series. Model performance was assessed using accuracy, recall, precision, and F1-score, achieving 95.2% accuracy, 89.82% recall, 92.3% precision, and an F1-score of 96.12%. The results demonstrate that SRCNN enhances MRI quality for improved segmentation, CBAM strengthens feature extraction, and Swin Transformer effectively classifies stenosis cases. This study highlights the efficacy of AI-driven methodologies in lumbar spine MRI analysis, offering a potential computer-aided diagnosis (CAD) tool for clinical applications. Future work may focus on optimizing model efficiency and improving generalization across diverse imaging protocols. 2025 IEEE. Subject The topic of the resource artificial intelligence; disc degeneration; Lumbar spine; SegNet; spinal stenosis; swin transformer Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152613-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mukku, Lalasa; Lamani, Manjunath Ramanna; Hegde, Lavanya; Mahapurush, Prathima; Mahapurush, Shivanandaswamy Title A name given to the resource Precise cervical cancer cell boundary denoising and segmentation with adaptive wavelet-spectral enhancement 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 International Journal of Advances in Intelligent Informatics;Volume;12;Issue;1;pp.136-164 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.26555/ijain.v12i1.2267" target="_blank" rel="noreferrer noopener">https://doi.org/10.26555/ijain.v12i1.2267</a> <a href="https://www.scopus.com/pages/publications/105034406727?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034406727?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 Mukku L., CHRIST (Deemed to be University), Bangalore, 560074, India; Lamani M.R., Moodlakatte Institute of Technology, Kundapura, 576217, India; Hegde L., GOVERNMENT SKSJTI, Bangalore, 560001, India; Mahapurush P., SKSVMACET Gadag, India; Mahapurush S., Moodlakatte Institute of Technology, Kundapura, 576217, India Description An account of the resource Accurate segmentation of cell nuclei in cervical cytology images is crucial for automated cervical cancer screening, yet existing methods struggle with blurred boundaries, noise-induced degradation, and topologically implausible predictions. The current research proposes Cell-Seg Tool, a novel triplet-branch diffusion AI tool that synergistically integrates three innovations to address these limitations. The Wavelet-Enhanced Contour Refinement Branch employs a learnable multi-scale discrete wavelet transform with adaptive coefficient attention to dynamically enhance boundary features across horizontal, vertical, and diagonal orientations. The Adaptive Spectral Noise Suppression module performs dual-domain processing using DCT-based filtering and uncertainty-guided fusion, coupled with bidirectional anchor semantic feedback to couple cross-branch information. The Topology-Aware Hybrid Loss integrates a focal Tversky loss, a persistent homology loss, a directional boundary loss, a skeleton completeness loss, and a diffusion-noise MSE loss for multi-objective optimization. Comprehensive experiments on multiple datasets demonstrate superior performance, achieving 94.45% Dice coefficient and 19.2% reduction in boundary localization error compared to state-of-the-art methods. Unlike prior work that applies these techniques independently, this work demonstrates that their adaptive, synergistic integration within a diffusion-based framework yields substantial improvements in boundary accuracy and topological correctness. 2026 The Author(s). Subject The topic of the resource Adaptive wavelet-spectral; Cell boundary denoising; Cervical cancer; Cervical cells; Segmentation enhancement Publisher An entity responsible for making the resource available Universitas Ahmad Dahlan Relation A related resource ISSN: 24426571; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications 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 Mukku, Lalasa; SivaChitraLakshmi, S. Title A name given to the resource Lalasa Quantum Computing Method: A Unique Quantum Convolutional Neural Network Architecture 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 2nd International Conference on Electrical, Electronics, Communication and Computers, ELEXCOM 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ELEXCOM67950.2025.11451378" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ELEXCOM67950.2025.11451378</a> <a href="https://www.scopus.com/pages/publications/105036473501?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036473501?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 Mukku L., Department Of Computer Science and Engineering, CHRIST(Deemed to be University), Bengaluru, 560074, India; SivaChitraLakshmi S., Department Of Computer Science and Engineering(Emerging Technologies), SRM Institute of Science and Technology, Vadapalani, chennai, 600026, India Description An account of the resource A novel Lalasa Quantum Computing Method architecture is presented in this paper for classification of medical images, aiming to enable efficient early detection of cancer. The proposed framework integrates a custom preprocessing pipeline that removes specular reflections using a SWIN Transformer and segments regions of interest via an Enhanced Gaussian Mixture Model. The quantum classification module employs amplitude encoding to map classical image data into quantum states, enabling structured feature extraction through a sequence of quantum convolutional layers with trainable variational circuits. The model was implemented using IBM Qiskit and trained on the publicly available Intel & Mobile ODT Cervical Cancer Screening dataset. Experimental results show a high overall classification accuracy of 98.58%, with moderate performance on class-specific F1-scores, recall, and precision. These results demonstrate the feasibility and effectiveness of quantum-classical hybrid models for medical image analysis, particularly in high-dimensional, low-sample scenarios. The study sets stage for future advancements in quantum machine learning applications in healthcare, with potential extensions involving real quantum hardware deployment and multiclass classification improvements. 2025 IEEE. Subject The topic of the resource artificial intelligence; cervical cancer; Quantum computing; quantum machine learning; Quantum neural networks Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159078-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mukku, Lalasa; Thomas, Jyothi Title A name given to the resource EGMM: removal of specular reflection with cervical region segmentation using enhanced Gaussian mixture model in cervix images 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 Multimedia Tools and Applications;Volume;84;Issue;9;pp.6273-6294 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11042-024-19035-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11042-024-19035-9</a> <a href="https://www.scopus.com/pages/publications/105001087239?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001087239?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 Mukku L., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, India; Thomas J., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, India Description An account of the resource Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance.Before segmenting the cervical region, specular reflection removal is an efficient one. Because, cervical cancer can be found using a visual check with acetic acid, which turns precancerous and cancerous areas whiteand these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-whiteareas and should therefore be removed. So, in this paper, specular reflection removal with segmentingthe cervix region ina colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican Axolotl Optimization (AMAO) algorithm. The performance of the proposed approach is analyzed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Subject The topic of the resource Cervical region; Colposcopy; Enhanced Gaussian mixture model; Mexican axolotl optimization; Specular reflection Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 13807501; CODEN: MTAPF Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource 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 Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mukku, Lalasa; Thomas, Jyothi Title A name given to the resource AMAA-GMM: adaptive Mexican axolotl algorithm based enhanced Gaussian mixture model to segment the cervigram images 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 International Journal of Reasoning-based Intelligent Systems;Volume;18;Issue;1;pp.33-40 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJRIS.2026.150612" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJRIS.2026.150612</a> <a href="https://www.scopus.com/pages/publications/105025190162?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025190162?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 Mukku L., CHRIST (Deemed to be University), Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India; Thomas J., CHRIST (Deemed to be University), Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India Description An account of the resource Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance. Before segmenting the cervical region, specular reflection removal is an efficient approach. Cervical cancer can be found using a visual check with acetic acid that turns precancerous and cancerous areas white and these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-white areas and should therefore be removed. So, in this paper, specular reflection removal with segmenting the cervix region in a colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican axolotl optimisation (AMAO) algorithm. The performance of the proposed approach is analysed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods. Copyright 2026 Inderscience Enterprises Ltd. Subject The topic of the resource adaptive Mexican axolotl optimisation; AMAO; deep learning; EGMM; enhanced Gaussian mixture model; Gaussian mixture models; machine learning; metaheuristics; segmentation Publisher An entity responsible for making the resource available Inderscience Publishers Relation A related resource ISSN: 17550556; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource 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 Mukku, Lalasa; Thomas, Jyothi Title A name given to the resource A Novel Cross-Validation Fusion Model Combining Vision Transformer and DenseNet161 for Enhanced Cervical Lesion Classification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1276;pp.437-448 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-2697-7_32" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-2697-7_32</a> <a href="https://www.scopus.com/pages/publications/105008675698?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008675698?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 Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India Description An account of the resource Cervical cancer is fourth most common cancer in women across the world with highest impact in low- and middle-income countries. World Health Organization sent out a call for all UN nations to work toward the elimination of cervical cancer. Deep learning and artificial intelligence have been the go-to solutions for medical image analysis for diagnosis and prognosis. This paper aims to classify lesions in a colposcope captured cervix image with help of artificial intelligence models. To further advance automated cervical lesion classification, the study proposes a novel hybrid model that combines the complementary strengths of a vision transformer and DenseNet architecture. The paper also addresses ongoing challenges, such as interference from specular reflection areas and the difficulty in distinguishing between different lesion grades due to subtle visual differences. The proposed cross-validation decision fusion strategy aims to improve the reliability and robustness of the classification process. The results of the study affirm that deep learning and fusion technologies will steer the future direction of research in medical image analysis. DenseNet model has performed with an accuracy score of 0.695, sensitivity of 0.912, specificity of 0.979 and F1 score of 0.9100. These metrics are significantly improved versions of state of the art used in this study for comparative analysis. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Cervical cancer; Deep learning; Fusion; Lesion; Transformer Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981962696-0; 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 Mukku, Lalasa; Thomas, Jyothi; Vullam, Radha; Shoba, K. Title A name given to the resource Comparative Performance Analysis of Segmentation Methods in Cervigram Images Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;5588 LNNS;pp.359-370 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-1918-4_25" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-1918-4_25</a> <a href="https://www.scopus.com/pages/publications/105010093881?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010093881?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 Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India; Vullam R., Government Maternity Hospital, Tirupati, India; Shoba K., KIDWAI Memorial Institute of Oncology, Bangalore, India Description An account of the resource One of the most common cancers of the lower female reproductive tract is cervical cancer and it is a major contributor of mortality in developing nations. Screening tests include image analysis of pap smear and colposcope pictures. In image analysis, machine learning techniques can be employed to analyze and interpret images of the cervix through segmentation and extraction of characteristics for the classification of cervix images. K-means algorithm and Gaussian mixture model are popular segmentation algorithms used in cervix region-of-interest extraction. In the context of deep network learning, segmentation means the use of deep convolution networks to accurately identify different objects or regions in an image. R-CNN and Deeplab architectures are among the most frequently employed models in deep learning for automated cervix image processing. In this paper, we have systematically reviewed machine and deep learning models popularly employed in cervical cancer identification through colposcope images. Four carefully chosen models were deployed, and their performance was comparatively analyzed. This research can be a foundation for scientists looking to develop new models for the classification and segmentation of cervical cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Cervigrams; Deeplab V3; GMM; K-means; Segmentation; Specular reflection Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981961917-7; 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 Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhancing Mobility: A Smart Cane with Integrated Navigation System and Voice-Assisted Guidance for the Visually Impaired Subject The topic of the resource audio feedback; Blind mobility; haptic feedback; Navigation; Smart Cane; Ultrasonic sensor; vibration motor; voice control Description An account of the resource Blindness is a condition which affects many people, and for the affected people, quality of life can take a big hit. Most blind people already use walking sticks to feel the terrain in front of them as they move around and navigate using touch and sound. However, they cannot judge distances to objects until the cane actually hits the object. In some cases, the contact with the cane may damage the object. Hence, it may be better to have some early warning system so that there is less likelihood of causing damage. This paper presents the design and development of a 'Smart Cane' aimed at enhancing mobility and safety for visually impaired individuals. The cane incorporates ultrasonic sensors to detect objects in the user's surroundings. When an object is detected within a specified distance range, the cane provides haptic feedback through a bidirectional vibration motor, alerting the user to its presence. The microcontroller-based system processes data from both sensors and efficiently manages power consumption to ensure extended battery life. The device's design includes user-friendly controls and an ergonomic enclosure to offer ease of use and protection for the electronic components. Further, there is built-in navigation via online Map API. With the convenience of navigating oneself without external assistance, the 'Smart Cane' demonstrates great potential to improve the independence and confidence of visually impaired individuals in navigating their environments safely. 2024 IEEE. Creator An entity primarily responsible for making the resource Muktha D.S.; Niveditha G.; Pinto N.A.; Sinha S. Source A related resource from which the described resource is derived Proceedings - 2024 13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024, pp. 1124-1129. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CSNT60213.2024.10546111" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSNT60213.2024.10546111</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196767291&doi=10.1109%2FCSNT60213.2024.10546111&partnerID=40&md5=ae03094e8cb81e78fe8b11ea347c2dfc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196767291&doi=10.1109%2fCSNT60213.2024.10546111&partnerID=40&md5=ae03094e8cb81e78fe8b11ea347c2dfc</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835030546-3 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Muktha D.S., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India; Niveditha G., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India; Pinto N.A., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India; Sinha S., CHRIST(Deemed to Be University), Department of Computer Science, Bangalore, India https://archives.christuniversity.in/files/original/00c6a37774233bc5827a739eac650c9b.pdf 90e12250e53f8e544506c3754e20a9f7 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 Thesis PhD PhD Thesis 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 Antecedents of psychological capital and its consequences on affective commitment and psychological wellbeing / Subject The topic of the resource Psychology Creator An entity primarily responsible for making the resource Mukti Clarence - 1750071 Publisher An entity responsible for making the resource available CHRIST (Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-02 Language A language of the resource English Type The nature or genre of the resource PhD 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 Mukunthan, K.; Alenchery, Alex Stanley; Sharma, Dinesh; Deepa, B.G.; Loveline Zeema, J. Title A name given to the resource Comparative Analysis of GANs and Diffusion Models for Hyperspectral Image Classification 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 Smart Innovation, Systems and Technologies;Volume;479 SIST;pp.403-414 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-18974-5_35" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-18974-5_35</a> <a href="https://www.scopus.com/pages/publications/105039047437?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039047437?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 Mukunthan K., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; Alenchery A.S., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; Sharma D., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; Deepa B.G., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; Loveline Zeema J., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India Description An account of the resource Hyperspectral imaging, which is obtained across numerous spectral bands, presents difficulties in classification due to its high dimensionality and intricate nature. This study provides a comparison of Generative Adversarial Networks (GANs) and Diffusion models regarding the classification of the Indian Pines, Pavia University, and Salinas Datasets, utilizing Multi-Layer Perceptron and Random Forest classifiers. The findings indicate the GANs combined with Random Forest outperform Diffusion models, attaining accuracies of 88%, 96% and 95% respectively. This approach may not outperform the top models, such as HTD-2D-3D-PCNN, but is simpler in structure and more computational efficient. Key recommendations would be real-time processing, edge device optimization, and applications customized to agriculture and urban planning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Computational Complexity; Diffusion Models; Hyperspectral Imaging; Multi-Layers Perceptron; Spectral Analysis; Spectral Overlap Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-303218973-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predicting the financial behavior of Indian salaried-class individuals Subject The topic of the resource behavioral finance; Bengaluru; conservatism; financial decision-making; financial selfefficacy; herding; India Description An account of the resource COVID-19 has caused not only unprecedented health crises but also economic crises among individuals across the world. White-collar (salaried-class) employees with a fixed salary face financial insecurity due to job loss, pay cuts and uncertainty in retaining a job. This study examines the financial behavior of Indian white-collar salariedclass investors to their cognitive biases. In addition, the mediating effect of financial self-efficacy on cognitive biases and financial behavior is examined. Respondents were given structured questionnaires (google forms) through emails and WhatsApp for data collection. SPSS and R-PLS are used to analyze the data. Conservatism (r = -.603, p < 0.05) and herding bias (r = -.703, p < 0.05) have a significant negative correlation with financial behavior. Financial self-efficacy has a significant positive correlation (r =.621. p < 0.050). Conservatism and herding predicted 60.5% and 62.2% of the variance, respectively. The direct and indirect paths between conservatism bias, financial self-efficacy, and financial behavior are significant. The paths between herding, financial self-efficacy and financial behavior are also significant. Ankita Mulasi, Jain Mathew, Kavitha Desai, 2022. Creator An entity primarily responsible for making the resource Mulasi A.; Mathew J.; Desai K. Source A related resource from which the described resource is derived Investment Management and Financial Innovations, Vol-20, No. 1, pp. 26-37. Publisher An entity responsible for making the resource available LLC CPC Business Perspectives Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.21511/imfi.20(1).2023.03" target="_blank" rel="noreferrer noopener">https://doi.org/10.21511/imfi.20(1).2023.03</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148227348&doi=10.21511%2Fimfi.20%281%29.2023.03&partnerID=40&md5=c3552dd08ae79147d12aafd938f97a63" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148227348&doi=10.21511%2fimfi.20%281%29.2023.03&partnerID=40&md5=c3552dd08ae79147d12aafd938f97a63</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 18104967 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mulasi A., School of Business and Management, CHRIST (Deemed to be University), India; Mathew J., School of Business and Management, CHRIST (Deemed to be University), India; Desai K., Department of Professional Studies, CHRIST (Deemed to be University), India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource PHD PhD PhD Thesis 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/. Relation A related resource 61000330 Title A name given to the resource Impact of Behavioural Biases, Emotional Intelligence and Financial Literacy on Financial Behaviour Subject The topic of the resource Management Studies Description An account of the resource Behavioural finance is the integration of finance and psychology. Finance pertains to the administration of finances by an individual, while psychology involves the examination of the mind and human conduct. Behavioural finance provides a reasonable explanation for the abnormalities observed in financial markets and the irrational decisions made by investors. The foundation of behavioural finance lies in the irrational choices made by individuals, which elucidates the reasons for investors frequently encountering different biases, emotional filters, and a lack of knowledge when making financial decisions. The primary challenges stem from three critical deficiencies: the investors' inability to express their cognitive and emotional biases, insufficient financial knowledge, and an inability to regulate emotions during financial decision-making. This study investigates the influence of investors' financial literacy, emotional intelligence, and behavioural bias on their financial conduct. Studies have shown that behavioural biases negatively impact financial behaviour. Financial literacy and emotional intelligence have been recognised as critical elements that significantly impact the financial behaviour of salaried class investors. Creator An entity primarily responsible for making the resource Mulasi, Ankita Source A related resource from which the described resource is derived Author's Submission Publisher An entity responsible for making the resource available Christ(Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Contributor An entity responsible for making contributions to the resource Mathew, Jain Rights Information about rights held in and over the resource Open Access Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource PhD Identifier An unambiguous reference to the resource within a given context <a href="http://hdl.handle.net/10603/574361" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/574361</a> Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mulavarickal, Godwin; Pundhir, Shiv Kumar Singh Title A name given to the resource Leadership strategies for change and innovation 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 Strategic Management, Regulatory Challenges, and Global Governance of MedTech;pp.91-124 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-1205-7.ch005" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-1205-7.ch005</a> <a href="https://www.scopus.com/pages/publications/105007743542?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007743542?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 Mulavarickal G., Christ University, India; Pundhir S.K.S., Christ University, India Description An account of the resource Organizational innovation and change development depends heavily on leadership activities. This chapter investigates how leaders should integrate charismatic and authentic methods with transformational concepts to create the best possible leadership practice for long-term organizational success and innovation promotion. The chapter also demonstrates that leadership success will emerge from blending different leadership styles since any one approach alone is inadequate for solving current organizational challenges. Strong leaders who learn to unite multiple leadership approaches gain enhanced abilities to encourage their personnel while promoting innovative initiatives. 2025, IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833731207-1; 979-833731205-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource JP-DAP: An Intelligent Data Analytics Platform for Metro Rail Transport Systems Subject The topic of the resource Big data analytics; Hadoop; intelligent transport systems; machine learning; metro rail; TestDFSIO Description An account of the resource This paper deals with an intelligent data analytics platform-Jaison-Paul Data Analytics Platform (JP-DAP)-for metro rail transport systems. JP-DAP is intended to ensure smooth functioning, improved customer experience, ridership forecasting, and efficient administration of metro rail transportation systems by integrating and analysing its many data sources. It consists of a middleware which is built on the top of a Hadoop Distributed File System (HDFS) and Spark framework, along with a set of open-source software tools like Apache Hive, Pandas, Google TensorFlow and Spark ML-lib for real-time and legacy data processing. The benchmarking of JP-DAP was conducted using TestDFSIO and have found that it performs well according to industry standards. The specific use case for this project is Kochi Metro Rail Limited (KMRL). The analysis of Automated Fare Collection data from KMRL on JP-DAP framework have produced descriptive statistics visualisation of inflow and outflow analysis, travel patterns during weekdays and weekends, origin-destination matrix, etc.. Moreover JP-DAP framework is capable of producing short term passenger flow predictions using SVR machine learning algorithm with linear, radial basis function and polynomial kernels. Our experiments have shown that SVR linear kernel gives the most accurate results with the least errors in predicting the next day's passenger count using the previous five weekdays data. The station usage (one-to-all) prediction using Long Short-Term Memory (LSTM) is also integrated to this framework. The visualisation as well as analytical outcomes of JP-DAP framework have also been made available to the external world using a rich set of REST APIs and are projected on to a web-dashboard. 2000-2011 IEEE. Creator An entity primarily responsible for making the resource Mulerikkal J.; Thandassery S.; Dixon K D.M.; Rejathalal V.; Ayyappan B. Source A related resource from which the described resource is derived IEEE Transactions on Intelligent Transportation Systems, Vol-23, No. 7, pp. 9146-9156. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TITS.2021.3091542" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TITS.2021.3091542</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113196240&doi=10.1109%2FTITS.2021.3091542&partnerID=40&md5=1491387c02b153ac72ea7f197f0f88fd" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113196240&doi=10.1109%2fTITS.2021.3091542&partnerID=40&md5=1491387c02b153ac72ea7f197f0f88fd</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 15249050 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mulerikkal J., The Department of Computer Science, Rajagiri School of Engineering and Technology, Kochi, 682039, India; Thandassery S., The Department of Computer Science, Rajagiri School of Engineering and Technology, Kochi, 682039, India, The Department of Computer Science, Christ-Deemed to Be University, Bengaluru, 560029, India; Dixon K D.M., The Department of Computer Science, Jyothi Engineering College, Thrissur, 679531, India; Rejathalal V., The Department of Computer Science, Jyothi Engineering College, Thrissur, 679531, India; Ayyappan B., The Department of Information Technology, Rajagiri School of Engineering and Technology, Kochi, 682039, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mullick, Tabassum; Gnana Prakasi, O.S.; Yogish, Deepa Title A name given to the resource Comparative Analysis of Neural Network Models for Indian Sign Language Hand Gesture Recognition Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/InC465408.2025.11256235" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC465408.2025.11256235</a> <a href="https://www.scopus.com/pages/publications/105030344482?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030344482?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 Mullick T., Christ University, Bengaluru, India; Gnana Prakasi O.S., Christ University, Bengaluru, India; Yogish D., Christ University, Bengaluru, India Description An account of the resource The recognition of sign language is a crucial element in filling communication gaps that exist in the population. As inclusive communication technologies become more popular, there has been a significant push to develop trustworthy systems for translating sign language into written or visual form. The use of hand gestures and body movements is a fundamental aspect of sign languages, which are commonly used by those who are deaf. The lack of proficiency in sign language makes communication difficult for most people. A project was undertaken to convert Indian Sign Language (ISL) into spoken language through research. The paper presents a comparison of various neural network models. Using OpenCVgenerated real-time images and MediaPipe, it is possible to identify hand movements and collect ISL gesture data in realtime. In the study, it was demonstrated that ResNet50 is 92 per cent accurate in real-time recognition when compared to other models. This work aims to promote inclusivity and communication skills among people who may not have the ability to hear or speak fluently. Adding face recognition to future work may improve accuracy and enable continuous sign language recognition, providing more dynamic and real-time translation capabilities. 2025 IEEE. Subject The topic of the resource CNN; ISL; LSTM; MediaPipe; OpenCV; ResNet50; VGG16; YOLOv5 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152118-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Restrained geodetic domination of edge subdivision graph Subject The topic of the resource Edge subdivision; Extreme vertices; Geodetic domination; Geodetic number; Restrained geodetic domination Description An account of the resource For a connected graph G = (V,E), a set S subset of V (G) is said to be a geodetic set if all vertices in G should lie in some u-v geodesic for some u,v S. The minimum cardinality of the geodetic set is the geodetic number. In this paper, the authors discussed the geodetic number, geodetic domination number, and the restrained geodetic domination of the edge subdivision graph. 2022 World Scientific Publishing Company. Creator An entity primarily responsible for making the resource Mulloor J.J.; Sangeetha V. Source A related resource from which the described resource is derived Discrete Mathematics, Algorithms and Applications, Vol-14, No. 5 Publisher An entity responsible for making the resource available World Scientific Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S1793830922500021" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S1793830922500021</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113530489&doi=10.1142%2FS1793830922500021&partnerID=40&md5=c3e71529cb4c0272c004bb2a7c2c925d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113530489&doi=10.1142%2fS1793830922500021&partnerID=40&md5=c3e71529cb4c0272c004bb2a7c2c925d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17938309 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mulloor J.J., Department of Mathematics, Christ (Deemed to Be University), Bengaluru, 560029, India; Sangeetha V., Department of Mathematics, Christ (Deemed to Be University), Bengaluru, 560029, India