142 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 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 Attention Based Meta-Module to Integrate Cervigrams with Clinical Data for Cervical Cancer Identification Subject The topic of the resource attention; cervical cancer; Deep learning; EfficientNet; feature integration Description An account of the resource Cervical cancer remains a significant burden on public health, particularly in developing countries, where its malignancy and mortality rates are alarmingly high. Early diagnosis stands as a pivotal factor in effectively treating and potentially curing the cervical cancer. This study introduces a novel approach of meta module based on recurrent gate architecture designed to enhance the classification of cervix images efficiently. This innovative framework incorporates a meta module capable of dynamically selecting image modalities most pertinent attributes. Furthermore, it integrates clinical data with extracted image features and employs a range of EfficientNet architectures (B0-B5) for image classification. Our results indicate that the EfficientNet B5 architecture outperforms its counterparts, achieving an AUC (Area Under the Curve) score of 55.1 and an F1-Score of 75.1. Overall, this work represents a crucial step towards improving the early detection of cervical cancer, which in turn can lead to more effective treatment strategies and, ultimately, better outcomes for patients worldwide. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. Creator An entity primarily responsible for making the resource Mukku L.; Thomas J. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 286-295. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH 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/10.1007/978-3-031-64813-7_30" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-64813-7_30</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200692709&doi=10.1007%2F978-3-031-64813-7_30&partnerID=40&md5=545245aa0af99f5a209eaab737f97bd0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200692709&doi=10.1007%2f978-3-031-64813-7_30&partnerID=40&md5=545245aa0af99f5a209eaab737f97bd0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-303164812-0 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India https://archives.christuniversity.in/files/original/c03651daa8a49c681d403e0a507bebc4.jpg 0b6dfeeadbd8dbf97f5aef738454f6b9 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 Patents Subject The topic of the resource Patent Description An account of the resource Patent Patent Patents 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 Attention based sentence classification using term frequency-inverse document frequency and n-gram / Subject The topic of the resource Computer Science Description An account of the resource Patent Number: 202241048840, Applicant: Nagendra N. Attention Based Sentence Classification Using Term Frequency-Inverse Document Frequency and N-Gram On an e-commerce platform, products sold have critical and varying reviews from buyers which are difficult to analyze considering the hugenumber of reviews. The proposed model aspect extraction helps in categorizing sentence accurately. Creator An entity primarily responsible for making the resource J, Chandra. Source A related resource from which the described resource is derived <a href="https://drive.google.com/file/d/1Iz2PiEFIcSY8bIeIZNqR7LGIQnm95LUp/view" target="_blank" title="Attention based sentence classification using term frequency-inverse document frequency and n-gram /" rel="noreferrer noopener">https://drive.google.com/file/d/1Iz2PiEFIcSY8bIeIZNqR7LGIQnm95LUp/view</a> Publisher An entity responsible for making the resource available Intellectual Property India Date A point or period of time associated with an event in the lifecycle of the resource 2022-05-22 Language A language of the resource English Type The nature or genre of the resource Patent Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Attention to Economic Factors and Its Response to Foreign Portfolio Investment: An Evidence from Indian Capital Market Subject The topic of the resource Equity and debt market; FPI; India; Macroeconomic factors Description An account of the resource Stock market consists of a variety of investors. Among these, Foreign Portfolio Investors (FPIs) is a key investment influx. These investments can change or fluctuate due to several macroeconomic factors which can cause a shift in the dynamics of the markets in India. This paper examines the factors influencing for foreign portfolio investment in long run as well as short run. The sample comprises of 120 monthly observations on Foreign Portfolio Investment (FPIs) and Macro economic variables such as Oil prices (OP), Gross Domestic Product (GDP), Interest Rate (IR), Exchange rate of Indian Rupee with USD (ER), Inflation (CPI), Nifty Index (NSEI), 10year Bond Prices (BP) and Index of Industrial production (IIP) over a period of 10years, spanning from January 2013 to November 2022. The study employed Autoregressive Distributed Lag model (ARDL) to establish the long run association with error correction models. The result indicates that there is long run association between the Foreign Portfolio Investment and macro-economic variables. Among this, NSEI, IIP and ER played a significant role to determine FPI investments in the long run, whereas in the short run, FPI was impacted by ER and NSEI significantly. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. Creator An entity primarily responsible for making the resource Pachiyappan S.; Kandral A.; Shylaja H.N.; Raj V J.P.; Vellayan S. Source A related resource from which the described resource is derived Studies in Systems, Decision and Control, Vol-440, pp. 659-676. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH 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/10.1007/978-3-031-42085-6_57" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-42085-6_57</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182481537&doi=10.1007%2F978-3-031-42085-6_57&partnerID=40&md5=25fe81828c44045f80e41d45eb2a833d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182481537&doi=10.1007%2f978-3-031-42085-6_57&partnerID=40&md5=25fe81828c44045f80e41d45eb2a833d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21984182 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Pachiyappan S., School of Business and Management, CHRIST (Deemed to be University), Bannerghatta Road Campus, Bangalore, India; Kandral A., School of Business and Management, CHRIST (Deemed to be University), Bannerghatta Road Campus, Bangalore, India; Shylaja H.N., Department of Commerce, Manipal Academy of Higher Education, Manipal, India; Raj V J.P., School of Business and Management, CHRIST (Deemed to be University), Bannerghatta Road Campus, Bangalore, India; Vellayan S., School of Business and Management, CHRIST (Deemed to be University), Bannerghatta Road Campus, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mary, Teena; Sreeja, C.S. Title A name given to the resource Attention-based CNN for Adversarial File Fragment Detection Against Padding and Bit-Flip Attacks 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 Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2025;pp.935-942 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECMSN68058.2025.11382778" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECMSN68058.2025.11382778</a> <a href="https://www.scopus.com/pages/publications/105035543391?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035543391?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 Mary T., Christ University, Department of Computer Science, Bengaluru, India; Sreeja C.S., Christ University, Department of Computer Science, Bengaluru, India Description An account of the resource File fragment classification represents a critical task within digital forensics and cybersecurity that aims to recover fragmented files when their metadata is not available. Even though cutting-edge deep learning models achieve 77-79% accuracy on clean fragments, none of the existing file fragment classification systems currently include detection mechanisms against adversarial attacks, thus remaining defenseless against attackers using byte-level perturbations. This paper addresses this gap by proposing the first adversarial detection framework for file fragment classification. This paper presents an attention-based CNN that combines byte embeddings with both spatial and channel attention mechanisms to detect byte-level perturbations before actual classification. Evaluated over 30.72 million fragments across 75 file types, the detector reaches an accuracy of 91.44% against five attack strategies: null-byte padding, random-byte padding, cross-file padding, random bit-flipping, and header-targeted bit-flipping, at 91.34% recall, 95.46% specificity, and 0.9819 AUC-ROC. With 1.31 M parameters and 1 ms inference time per fragment, the detector enables practical deployment as a preprocessing filter within two-stage forensic pipelines screening suspicious fragments before reaching standard classifiers. This foundational work sets up the first comprehensive benchmark for adversarial robustness evaluation specifically in file fragment classification. 2025 IEEE. Subject The topic of the resource adversarial detection; attention mechanism; cybersecurity; deep learning; digital forensics; file fragment classification Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158242-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Manju, V.N.; Dayana, D.S.; Patwari, Neha; Bindu Madavi, K.P.; Krishna Sowjanya, K. Title A name given to the resource Attention-Enhanced Vision Transformer Model for Precise 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 Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ETCC65847.2025.11108453" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ETCC65847.2025.11108453</a> <a href="https://www.scopus.com/pages/publications/105015616471?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015616471?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 Manju V.N., Dept. of Computer Science and Engineering, CMR Institute of Technology, Bengaluru, India; Dayana D.S., Dept. of Networking and Communications, SRM Institute of Science and Technology, Chengalpet District, Kattankulathur, India; Patwari N., Dept. of Information Technonology, Thakur College of Engineering & Tech., Mumbai, India; Bindu Madavi K.P., Dept. of Computer Science and Engineering, Christ University, Bengaluru, India; Krishna Sowjanya K., Dept. of Computer Science and Engineering, CMR Institute of Technology, Bengaluru, India Description An account of the resource Skin cancer is one of the most prevalent and potentially fatal diseases, requiring early and accurate detection for effective treatment. Recent advances in deep learning have significantly improved automated skin lesion classification, but traditional Convolutional Neural Networks (CNNs) struggle with capturing long-range dependencies in dermoscopic images. To address this limitation, we propose a Preprocessing-Optimized Vision Transformer (ViT) Model that enhances lesion detection using attention-based feature fusion. Our methodology includes contrast enhancement (CLAHE), hair removal (DullRazor), lesion segmentation (K-Means + Otsus Thresholding), and data augmentation, ensuring robust model training. The proposed Attention-Enhanced ViT Model effectively learns global contextual features from dermoscopic images through self-attention mechanisms. The proposed model is evaluated our model on the ISIC Skin Cancer Dataset, achieving an accuracy of 94.6%, precision of 92.8%, recall of 93.5%, and an AUC-ROC score of 0.97, outperforming traditional CNN-based models such as ResNet50 (92.1% accuracy) and EfficientNet-B0 (93.3% accuracy). Our results demonstrate that integrating preprocessing techniques with Vision Transformers significantly enhances classification performance, making this approach a viable solution for real-world computer-aided dermatology. 2025 IEEE. Subject The topic of the resource Attention Mechanism; Contrast Enhancement (CLAHE); Dermoscopic Image Analysis; Feature Fusion; Lesion Segmentation; Skin Cancer Detection; Vision Transformer (ViT) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152476-0; 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 Thiyagarajan, C.; Deepa, S.; Suguanthi, G. Meena; Sharmila, G. Title A name given to the resource Attention-Powered Deep Learning for Employee Analytics: A Multi-Model Approach 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;1771 LNNS;pp.132-144 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-14041-8_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-14041-8_11</a> <a href="https://www.scopus.com/pages/publications/105038754706?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038754706?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 Thiyagarajan C., Department of Computer Science, PSG College of Arts and Science Coimbatore, Coimbatore, India; Deepa S., Department of Computer Science, CHRIST University, Bangalore, India; Suguanthi G.M., School of Management Sri Krishna College of Engineering and Technology, Coimbatore, India; Sharmila G., Department of Computer Applications, MLA Academy of Higher Learning, Bangalore, India Description An account of the resource In the ever-evolving field of human resources analytics, there is the integration of the latest techniques of machine learning that can strongly enhance decision-making. This paper introduces a revolutionary architecture for multi-model neural networks that integrate disparate networks in analyzing the background, development, performance, and engagement of an employee for all key elements of this employee. Each of the processes with attention fine-tunes the importance of features and therefore largely improves the concentration and interpretability of results. These networks are thus ensured of thorough analysis in the form of in-depth evaluation, which enables classification to be discrete and into clear performance categories. Preparation of raw data was also done with much care; we used the Employee/HR Dataset from Kaggle in order to process this raw data before its use in deep learning application. Our proposed architecture outperformed by accurately classifying the employee performance categories, with result showing a high classification accuracy of 86.49% on the test set. This study, therefore, establishes that customized neural network architectures are applicable in supporting organizations in realizing their data driven culture and in making human resource operations more efficient. 2026, Springer Science and Business Media Deutschland GmbH. All rights reserved. Subject The topic of the resource Attention Mechanisms; Data Preprocessing; Deep Learning; Employee Performance Evaluation; Human Resources Analytics; Machine Learning; Multi-Model Architectures; Neural Networks Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303214040-1; 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 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 Hemanth, K.S.; Harisha Naik, T.; Kartik, N.; Kumar, N. Nanda; Senthilkumar, S.; Ramya, R. Title A name given to the resource Attentional Deep Learning with Inverse Transform Sampling for Robust Respiratory Sound 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 Journal of Information Technology Management;Volume;18;Issue;1;pp.123-140 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.22059/jitm.2026.1062557" target="_blank" rel="noreferrer noopener">https://doi.org/10.22059/jitm.2026.1062557</a> <a href="https://www.scopus.com/pages/publications/105038337718?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038337718?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 Hemanth K.S., Department of Computer Science, Christ University, Bengaluru, India; Harisha Naik T., Department of Computer Applications, Presidency College, Bangalore, India; Kartik N., Department of Commerce, Manipal Academy of Higher Education, Manipal, India; Kumar N.N., Department of Computer Science and Engineering, Excel Engineering College, Tamilnadu, Komarpalayampalyam, India; Senthilkumar S., Department of Computer Science and Engineering, Vinayaka Mission`s Kirupananda Variyar Engineering College, (Vinayaka Mission`s Research Foundation), Salem, India; Ramya R., Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Tamilnadu, Erode, India Description An account of the resource The necessity for efficient breathing sound classification systems originates from respiratory diseases, which impair oxygen-carbon dioxide exchange and impact lung function. Feature extraction and pattern categorization are general components of such systems. Because of their effectiveness with big datasets, deep neural networks have acquired popularity recently in the category of breathing sounds. Enhancing medical care requires cooperation amongst researchers, medical professionals, and patients. An attentional deep learning model with inverse transform sampling is presented in this study to classify respiratory diseases from audio data. Robust models were developed to classify and detect respiratory elements using the Respiratory Sound dataset. The primary objectives include effectively determining lung sounds and determining respiratory illnesses. The architectures of CNN, VGG16, and ResNet50 were developed to extract features and categorize data. Also, the pre-trained models ResNet50 and VGG16 identify critical characteristics in spectrum pictures more accurately. Inverse transfer sampling is used to rectify class imbalance in respiratory datasets. The models achieved 98% accuracy with the CNN model, 83% accuracy with VGG16, and 95% accuracy with ResNet50. Moreover, LSTM and CRNN models offer more information on how respiratory illnesses are classified. 2026, Hemanth K S, Harisha Naik T, N Kartik, N Nanda kumar, S Senthilkumar and Ramya R. Subject The topic of the resource CNN; Deep Neural Network; Inverse Transform Sampling; Pre-trained Models; Respiratory Diseases Publisher An entity responsible for making the resource available University of Tehran Relation A related resource ISSN: 20085893; 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 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 Attenuation parameters of polyvinyl alcohol-tungsten oxide composites at the photon energies 5.895, 6.490, 59.54 and 662 keV Subject The topic of the resource mass attenuation coefficient; radiation shielding; tungsten filled polyvinyl alcohol composite Description An account of the resource The growing demand for lightweight, non-toxic and effective X-A nd ?-ray shielding materials in various fields has led to the exploration of various polymer composites for shielding applications. In this study, tungsten filled polyvinyl alcohol (PVA) composites of varying WO3 concentrations (0-50 wt%) were prepared by solution cast technique. The structural, morphological, and thermal properties of the prepared composite films were studied using X-ray diffraction technique (XRD), Scanning electron microscopy (SEM) and Thermogravimetric analysis (TGA). The AC conductivity studies showed the low conductivity property of the composites. The X-ray (5.895 and 6.490 keV) and ?-ray (59.54 and 662 keV) attenuation studies performed using CdTe and NaI(Tl) detector spectrometers revealed a noticeable increase in shielding efficiency with increase in filler wt%. The effective atomic number (Zeff) calculated by the direct method agreed with the values obtained using Auto-Zeff software. The % heaviness showed that tungsten filled polyvinyl alcohol composites are lighter than traditional shielding materials. 2020 M V Muthamma et al., published by Sciendo 2020. Creator An entity primarily responsible for making the resource Muthamma M.V.; Gudennavar B.S.; Gudennavar S.B. Source A related resource from which the described resource is derived Polish Journal of Medical Physics and Engineering, Vol-26, No. 2, pp. 77-85. Publisher An entity responsible for making the resource available Sciendo Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.2478/pjmpe-2020-0009" target="_blank" rel="noreferrer noopener">https://doi.org/10.2478/pjmpe-2020-0009</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090297228&doi=10.2478%2Fpjmpe-2020-0009&partnerID=40&md5=c12f9e39d6b38e0bd3b8d25a498d5766" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090297228&doi=10.2478%2fpjmpe-2020-0009&partnerID=40&md5=c12f9e39d6b38e0bd3b8d25a498d5766</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 14254689 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 Muthamma M.V., Department of Physics and Electronics, Christ, Deemed to Be University, Bangalore Central Campus, Karnataka, Bengaluru, 560029, India; Gudennavar B.S., Department of Physics and Electronics, Christ, Deemed to Be University, Bangalore Central Campus, Karnataka, Bengaluru, 560029, India; Gudennavar S.B., Department of Physics and Electronics, Christ, Deemed to Be University, Bangalore Central Campus, Karnataka, Bengaluru, 560029, 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 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 Attenuation properties of epoxy-Ta2O5 and epoxy-Ta2O5-Bi2O3 composites at ?-ray energies 59.54 and 662 keV Subject The topic of the resource composites; films; mechanical properties; thermogravimetric analysis (TGA) Description An account of the resource Epoxy resin filled with suitable high Z elements can be a potential shield for X-rays and ?-rays. In this work, we present the ?-ray attenuation properties of epoxy composites filled with (030 wt%) Tantalum pentoxide (Ta2O5) and Ta2O5-Bi2O3, which were prepared by open mold cast technique. X-ray diffraction patterns showed crystalline peaks of Ta2O5 and bismuth oxide (Bi2O3) in the prepared epoxy-Ta2O5 and epoxy-Ta2O5-Bi2O3 composites. Homogeneity of the samples at higher filler wt% was revealed by SEM images. Mechanical characterization showed the enhanced mechanical strength of epoxy-Ta2O5-Bi2O3 composites compared to epoxy-Ta2O5. Higher storage modulus and glass transition temperature of the epoxy-Ta2O5-Bi2O3 composites showed enhanced stiffness and thermal stability when compared to neat and epoxy-Ta2O5. Decrease in the value of tan(?) at higher content of filler loadings indicated the good adhesion between filler and matrix. Mass attenuation coefficients of epoxy-Ta2O5 (30 wt%) composites at ?-ray energies 59.54 and 662 keV were found to be 0.876 cm2 g1 and 0.084 cm2 g1, while that of epoxy-Ta2O5-Bi2O3 (30 wt% Bi2O3) composite were 1.271 cm2 g1 and 0.088 cm2 g1, respectively. The epoxy-5% Ta2O5-30% Bi2O3 composites with higher ?/? value and tensile strength may be a potential ?-ray shield in various radiation environments. 2020 Wiley Periodicals, Inc. Creator An entity primarily responsible for making the resource Muthamma M.V.; Bubbly S.G.; Gudennavar S.B. Source A related resource from which the described resource is derived Journal of Applied Polymer Science, Vol-137, No. 44 Publisher An entity responsible for making the resource available John Wiley and Sons Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/app.49366" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/app.49366</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085062629&doi=10.1002%2Fapp.49366&partnerID=40&md5=29af63d5ebb9d79acc0525170576348a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085062629&doi=10.1002%2fapp.49366&partnerID=40&md5=29af63d5ebb9d79acc0525170576348a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 218995; CODEN: JAPNA 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 Muthamma M.V., Department of Physics and Electronics, CHRIST (Deemed to be University), Bengaluru, India; Bubbly S.G., Department of Physics and Electronics, CHRIST (Deemed to be University), Bengaluru, India; Gudennavar S.B., Department of Physics and Electronics, CHRIST (Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach Subject The topic of the resource Attention-based gated recurrent unit network; Improved K-means clustering; Recursive feature elimination; Synthetic minority oversampling technique Description An account of the resource Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and nae Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work. The Author(s) 2024. Creator An entity primarily responsible for making the resource Rao G.M.; Ramesh D.; Sharma V.; Sinha A.; Hassan M.M.; Gandomi A.H. Source A related resource from which the described resource is derived Scientific Reports, Vol-14, No. 1 Publisher An entity responsible for making the resource available Nature Research 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/10.1038/s41598-024-56931-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1038/s41598-024-56931-4</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189302362&doi=10.1038%2Fs41598-024-56931-4&partnerID=40&md5=2ed2b3b9b1b72920fc2132a33589f632" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189302362&doi=10.1038%2fs41598-024-56931-4&partnerID=40&md5=2ed2b3b9b1b72920fc2132a33589f632</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 20452322; PubMed ID: 38570560 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 Rao G.M., Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Jharkhand, Dhanbad, 826004, India, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, India; Ramesh D., Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Jharkhand, Dhanbad, 826004, India, Department of Computer Science, University of Economics and Human Sciences, Warsaw, Poland; Sharma V., Computer Science Department, Christ University, Delhi NCR Campus, Delhi NCR, Ghaziabad, India; Sinha A., Department of Computer Science, ICFAI Tech School, ICFAI University, Jharkhand, Ranchi, India; Hassan M.M., Computer Science and Engineering, Discipline Khulna University, Khulna, 9208, Bangladesh; Gandomi A.H., Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, 2007, NSW, Australia, University Research and Innovation Center (EKIK), uda University, Budapest, 1034, Hungary 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 Suresh, G.; Zimik, Michael Yuivamung; Kumara, R. Naveen; Prakash, M. Title A name given to the resource Attitude and intention to adopt FinTech services by Indian rural households 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 Business Innovation and Research;Volume;39;Issue;2;pp.161-179 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJBIR.2026.152078" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJBIR.2026.152078</a> <a href="https://www.scopus.com/pages/publications/105032249418?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032249418?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Suresh G., Symbiosis Institute of Business Management, Symbiosis International (Deemed to be University), Bengaluru, India; Zimik M.Y., Department of Commerce, CHRIST University, Karnataka, Bangalore, 560029, India; Kumara R.N., Department of Commerce, CHRIST University, Karnataka, Bangalore, 560029, India; Prakash M., Department of Commerce, CHRIST University, Karnataka, Bangalore, 560029, India Description An account of the resource FinTech has been a game changer for many business players. Due to financial technology, there is a paradigm shift in how finance-oriented companies operate today. The study aims to identify the factors driving FinTech adoption amongst rural households. A questionnaire with five points Likert scale has been used for data collection. The technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT) are used for this study. The study found that factors such as perceived trust, perceived usefulness and perceived risk have a major say in adopting FinTech services. The study is a breakthrough for FinTech companies in identifying factors that induce rural users to adopt FinTech. The study helps to improve the existing FinTech apps to attract and tap the rural segments by focusing on these aspects. Copyright 2026 Inderscience Enterprises Ltd. Subject The topic of the resource behavioural intention; BI; FinTech; PEOU; perceived ease of use; perceived trust; perceived usefulness; PT; PU; TAM; technology acceptance model Publisher An entity responsible for making the resource available Inderscience Publishers Relation A related resource ISSN: 17510252; 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 https://archives.christuniversity.in/files/original/37d9824e2b9ddeed7057b50324463990.pdf 5ae1359f66735d8fc2be17ec462f6c7d 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 MPHIL Mphil Mphil 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 Attitude and perception of tourists in Karnataka towards Climate Change Creator An entity primarily responsible for making the resource Bhaskar Sailesh Date A point or period of time associated with an event in the lifecycle of the resource 2019 Source A related resource from which the described resource is derived Tourism Description An account of the resource Climate change has a strong relationship with the tourism industry. According to the United Nation's World Tourism Organization???s Davos Declaration (2007) tourism industry contributes on an average about 5% of the global Carbon di Oxide emission in terms of radioactive forcing. Studies pertaining to the state of Karnataka, India indicate that most parts of the state could experience 1.5oC to 2oC warming relative to the level during the pre-industrial period of the 1880s by as early as 2030s under the likely high emission scenario (Kelkar et al, 2004; Dwarakish et al, 2009'; Kumar et al, 2015). Travel decisions to a large extent are influenced by the destination???s images of Sun, Sand, Sea, availability of snow and perceptions of other such climatic variables such as temperature, precipitation and humidity (De Freitas, 2001). Therefore, studying tourist???s perceptions of these environmental changes are crucial for the survival of the tourism industry especially in these climate-sensitive regions (Gossling & Hall, 2006a). Besides, understanding the attitudes and perceptions of different segments of the tourists towards their role in climate change adaptation and mitigation is a fundamental step in designing educational and communication campaigns which could be successfully implemented in this industry (Becken, 2010; Gossling & Hall, 2006; Saarinen et al, 2006; Scott et al, 2008; Martin & Lopez, 2014). Therefore, this study aims to boost our understanding of the relationship between tourism and climate change issues among tourists in Karnataka, India. The study consists of five chapters, namely Introduction, Review of Literature, Report on present investigation, Results and Discussion, Summary and Conclusion. The first chapter Introduces the relationship between tourism and climate change issues and brings out the significance of the role of travel decisions the environment. The second chapter analyses earlier studies conducted on the area of climate change, environment and tourism to gain a better understanding of the existing knowledge and to identify research gaps. The third chapter focuses on the research design of this study. It covers sampling plans, questionnaire design, constructs measurement, plan of analysis and Pilot study. The fourth chapter deals with the analysis of data which were collected for the study followed by the discussion of results. The fifth chapter summarizes the entire study and explains the findings and limitations of the study, Suggestions and conclusion. 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 Attitude of generations: Does it matter online? Subject The topic of the resource Attitude; Generation X; Generation Y; Generation Z; Web Atmospherics Description An account of the resource Generational examinations are turning out to be necessary with the characteristics they exhibit. This research work aimed at establishing the interceding relationship of disposition of three distinctive generations-Generation X, Generation Y, and Generation Z. In complete, 1200 responses were acquired from both male and female respondents of each generational class dependent on online purchase data collected by employing Google Forms. For the investigation, the model utilized the SOR framework. The results indicated that attitude does not play a vital role in the purchase intention of Generation X followed by the partial mediation of attitude for Generation Y and full mediation effect for Generation Z. This steady increment of attitudinal change underpins the examination by setting up proof that every age shifts in their mentality and purchasing conduct. Online retailers must concentrate on showcasing systems and create online visual merchandising cues which outwardly advance and make a feeling of stimulating attitude for generations. The current study also added value to the existing literature by classifying the customer base not merely on age, but also on their technological perspective of distinguishing web atmospheric cues and catering to their needs from a generational outlook. The study also took into account the importance of the organism's role played by attitude in the S-O-R framework. In this manner, the study helps marketers to design methodologies and plan online visual marketing space for better generational reaction and benefit. 2021, Associated Management Consultants Pvt. Ltd.. All rights reserved. Creator An entity primarily responsible for making the resource Thomas M.R.; Mathew J. Source A related resource from which the described resource is derived Indian Journal of Marketing, Vol-51, No. 4, pp. 44-57. Publisher An entity responsible for making the resource available Associated Management Consultants Pvt. Ltd. Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.17010/ijom/2021/v51/i4/158470" target="_blank" rel="noreferrer noopener">https://doi.org/10.17010/ijom/2021/v51/i4/158470</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108263143&doi=10.17010%2Fijom%2F2021%2Fv51%2Fi4%2F158470&partnerID=40&md5=df65306ab673502657fe9a60d3007fa7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108263143&doi=10.17010%2fijom%2f2021%2fv51%2fi4%2f158470&partnerID=40&md5=df65306ab673502657fe9a60d3007fa7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9738703 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 Thomas M.R., Department of Commerce, CHRIST (Deemed to be University), Bengaluru, Karnataka, 560 029, India; Mathew J., Department of Management Studies, CHRIST (Deemed to be University), Bengaluru, Karnataka, 560 029, 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 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 Kamate, Wasim; Kurup, Rahul R.; Roy, Amitabh; Pillai, Anuradha; Malhotra, Puja; Hajare, Ashok Title A name given to the resource Attitude of Parents Towards Various Behavior Management Techniques Utilized in Pediatric Dental Treatments 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 Research Journal of Pharmacy and Technology;Volume;18;Issue;3;pp.1380-1385 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.52711/0974-360X.2025.00199" target="_blank" rel="noreferrer noopener">https://doi.org/10.52711/0974-360X.2025.00199</a> <a href="https://www.scopus.com/pages/publications/105005217658?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005217658?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 Kamate W., Department of Oral Pathology and Microbiology, School of Dental Sciences, Krishna Vishwa-Vidyapeeth, MS, Karad, 415110, India; Kurup R.R., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India; Roy A., Institute of Management Sciences, University of Lucknow, India; Pillai A., Department of Computer Science and Engineering, Symbiosis Institute of Technology, Pune, Maharashtra, Lavale, India; Malhotra P., Department of Prosthodontics, Faculty of Dental Sciences, SGT University, Haryana, India; Hajare A., Bharati Vidyapeeth College of Pharmacy Palus, Maharashtra, Sangli, India Description An account of the resource Dental experts are trusted to apply the knowledge and abilities they have acquired during their dental education to the diagnosis and effective treatment of any dental illness. When it comes to pediatric patients, however, the dentist's responsibility is different. However, without the right behavior management method (BMT), therapy outcomes would not be effective. Sometimes young children behave disruptively during dental visits, which makes it easier or harder for the dentist to perform dental work. Nonetheless, before being applied to children, behavior management strategies need the parents' acceptance and consent. This review's objective is to evaluate the dentists' use of effective behavior modification techniques (BMT) as well as the parents' attitudes regarding these techniques. RJPT All right reserved. Subject The topic of the resource Behavior management techniques (BMT); Child behavior; Dental treatment; Parent attitudes; Pediatric dentistry Publisher An entity responsible for making the resource available Research Journal of Pharmacy and Technology Relation A related resource ISSN: 9743618; 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 https://archives.christuniversity.in/files/original/3063b2dce06aac3f63ee3d385e2e1b33.jpg eaf6dd898d08366e50d529f851b64ac0 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 Attitude of public towards higher education: Conceptual analysis / Subject The topic of the resource Higher Education in India Description An account of the resource Scholedge International Journal Of Multidisciplinary And Allied Studies, Vol.2, Issue 12, pp.19-28, ISSN No: 2394-336X. Creator An entity primarily responsible for making the resource Roy, Ms Anuja., Kareem, Jacqueline. Source A related resource from which the described resource is derived <a href="https://thescholedge.org/index.php/sijmas/article/view/263" target="_blank" title="Attitude of public towards higher education: Conceptual analysis" rel="noreferrer noopener">https://thescholedge.org/index.php/sijmas/article/view/263</a> Publisher An entity responsible for making the resource available Scholedge The scholarity Publishing Date A point or period of time associated with an event in the lifecycle of the resource 2015-02-10 Language A language of the resource English Type The nature or genre of the resource Journal Article 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 Cyriac, Jineesh; Mathew, P.M.; Thomas, Sudha Title A name given to the resource Attitude toward inter-religious marriage: interplay of generational shifts with religious affiliations and educational attainments 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 Journal of Family Social Work;Volume;29;Issue;3;pp.80-97 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/10522158.2026.2645193" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/10522158.2026.2645193</a> <a href="https://www.scopus.com/pages/publications/105035436068?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035436068?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 Cyriac J., Department of Social Work, CHRIST (Deemed to be University), Bangalore, India; Mathew P.M., Department of Social Work, CHRIST (Deemed to be University), Bangalore, India; Thomas S., Department of Social Work, CHRIST (Deemed to be University), Bangalore, India Description An account of the resource This quantitative research examined the interaction of generational shifts, religious affiliations, and educational attainments in shaping attitudes toward inter-religious marriage. Data were collected from 1231 Indian respondents from iGen/Gen Z, Xennials & Millennials, and Baby Boomers through a demographic response sheet and the Attitude Scale developed by Parker et al. where lower ratings signified positive attitudes and higher ratings indicated negative attitudes. The result revealed that generational shifts were significantly associated with religion (?2 = 96.6, p=<.001) and education (?2 = 279, p=<.001). Significant interaction effects were found between generational shifts and religious affiliations (F = 5.36, p <.001, ?2 p =.017) and generational shifts and educational attainments (F = 6.79, p <.001, ?2 p =.027) concerning attitudes toward inter-religious marriage. This study uncovered the interaction of the demographic variables in shaping the attitude toward inter-religious marriage. 2026 Taylor & Francis Group, LLC. Subject The topic of the resource Attitudes; education; generation; inter-religious marriage; religion Publisher An entity responsible for making the resource available Routledge Relation A related resource ISSN: 10522158; 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 https://archives.christuniversity.in/files/original/64cb42a49fc8ab099eec88ab0a21ca44.pdf 23a685f10f1b5353397f345d6b44948f 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 Attitude towards the medical profession among higher secondary students in relation to medical aptitude, parental influence, peer influence and perceived social expectations Subject The topic of the resource Education Description An account of the resource Attitude towards the Medical Profession includes concepts, motives or beliefs associated with the profession of medical science. Medical science is a profession that challenges the intellectual competency of the aspirant owing to the strenuous demands of the career. It is essential that possession of an appropriate attitude and a realistic understanding of the demands of medical science be a pre-requisite, especially if the higher secondary adolescent aspires this as his or her vocation for life. As theformation of attitude is based on several intrinsic and extrinsic factors, a profession like medical science faces stiff competition among candidates to qualify with high expectations from family and society. The current study throws light on the attitude of higher secondary students towards the profession of medical science in relation to medical aptitude, parental influence, peer influence and perceived social expectations, as the independent variables. It aimed to explore the role played by medical aptitude, parental influence, peer influence and perceived social expectations in the formation of attitude towards the medical profession and the differences in the formation of this attitude among higher secondary students based on their gender and type of pre-university college that they belonged to. The study adopted was a descriptive study-cum-survey that employed a quantitative approach using the paradigm of post-positivism. The participants for the study comprised of 396 second-year higher secondary students from pre-university colleges of Bangalore South, who were selected by convenience sampling method. Creator An entity primarily responsible for making the resource Mathew, Minu Mary - 1444603 Publisher An entity responsible for making the resource available CHRIST (Deemed to be University) 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 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 61000155 Title A name given to the resource Attitude towards the medical profession among higher secondary students in relation to medical aptitude, parental innfluene, peer influence and perceived social expectations Subject The topic of the resource Education Description An account of the resource Attitude towards the medical profession includes concepts, motives or beliefs associated with the profession of medical science. A profession that challenges the intellectual competency of the aspirant to qualify- with soaring expectations from family and society- the demands of the career entail the individual to possess an appropriate attitude and a realistic understanding of it. The current study explores the role of medical aptitude, parental influence, peer influence and perceived social expectations in the formation of attitude towards the medical profession and their differences among higher secondary students based on their gender and type of pre-university college. The study-cum-survey employed a quantitative approach using the paradigm of post-positivism. The participants comprised of 396 second-year pre-university students from colleges of Bangalore South, who were selected by convenience sampling, with Physics, Chemistry and Biology as their core subjects. The findings reveal peer influence to be essential in the development of professionalism and a sense of service mindedness. Parental influence and the higher secondary adolescent s perceived social expectations act as significant predictors in the formation of favourable attitude. However, gender and the type of pre-university colleges bear no significant difference with respect to their attitude towards the profession. The study has set a base on what needs to be focussed in our country on the next generation of medical professionals. It is hoped that educational and medical institutions, policy and curriculum drafters, parents and teachers realise that a combination of sound medical aptitude, parental influence, peer influence and perceived social expectations resonates in the formation of a favourable attitude towards medical science. Assessing those higher secondary students qualifying into medical science would enable to analyse differences that existed, if any, in their entry-level and post-qualifying attitude towards the medical profession. Creator An entity primarily responsible for making the resource Mathew, Minu Mary. 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 2020-01-01 Contributor An entity responsible for making contributions to the resource Thomas, Kennedy Andrew 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/10603/341678" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/341678</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 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 Srinivasaiah, Raghavendra; Jankatti, Santosh Kumar; Jinachandra, Niranjana Shravanabelagola; Lamani, Manjunath Ramanna; Lakshmi, Bellam Vijaya; Bhelwa, Rishita Title A name given to the resource Attribute optimization to improve breast cancer prediction using machine learning techniques 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 IAES International Journal of Artificial Intelligence;Volume;15;Issue;2;pp.1327-1338 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijai.v15.i2.pp1327-1338" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v15.i2.pp1327-1338</a> <a href="https://www.scopus.com/pages/publications/105036013166?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036013166?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 Srinivasaiah R., Department of Artificial Intelligence and Data Science Engineering, School of Engineering and Technology, CHRIST University, Bangalore, India; Jankatti S.K., Department of Computer Science and Technology, Dayananda Sagar University, Bangalore, India; Jinachandra N.S., Department of Mechanical Engineering, CHRIST University, Bangalore, India; Lamani M.R., Department of Computer Science and Engineering, Moodlakatte Institute of Technology, Kundapura, India; Lakshmi B.V., Department of Computer Science and Engineering, CHRIST University, Bangalore, India; Bhelwa R., Department of Computer Science and Engineering, CHRIST University, Bangalore, India Description An account of the resource Breast cancer (BC) arises when cells grow out of control. It affects women more than men. Seeking cancer treatment can be both costly and time-consuming, with test results spanning from a few hours to several weeks. The duration of these tests depends on the number of attributes within the dataset. This research paper endeavors to optimize the dataset attributes and find the accuracy of the optimized dataset. The primary goal is to reduce features using recursive feature elimination to minimize the time taken for the test result. This work discusses the machine learning technique and the random forest (RF) algorithm, which helps determine the parameter accuracy on the Wisconsin BC diagnostic dataset. The method achieves an accuracy of 96.49% with only eighteen attributes. It has aided the healthcare industry in finding BC in less time and improving the treatment. Copyright (c) 2026 Peddireddy Venkateswara Reddy, Alaguchamy Parivazhagan. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Subject The topic of the resource Attribute optimization; Breast cancer prediction; Machine learning; Random forest classifier; Wisconsin Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Relation A related resource ISSN: 20894872; 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 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 Lalrengpuii; Pullot, Lijeesh Title A name given to the resource Attributes of virtual influencers: Science mapping and future agenda 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 Multidisciplinary Reviews;Volume;9;Issue;7;Article No.;e2026370; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.31893/multirev.2026370" target="_blank" rel="noreferrer noopener">https://doi.org/10.31893/multirev.2026370</a> <a href="https://www.scopus.com/pages/publications/105027516289?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027516289?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 Lalrengpuii, Christ University, Karnataka, Bengaluru, India; Pullot L., Christ University, Karnataka, Bengaluru, India Description An account of the resource Virtual influencers, constructed using advanced computer-generated imagery and artificial intelligence, have emerged as disruptive forces in the realm of influencer marketing. In contrast to their human counterparts, virtual influencers provide brands with enhanced control, adaptability, and scalability, effectively engaging audiences on a global scale across various digital platforms. However, despite their increasing prevalence and commercial uptake, scholarly exploration into their attributes and impacts remains sporadic and underdeveloped. This study conducts a systematic literature review and bibliometric analysis of 172 peer-reviewed articles indexed in Scopus, employing the SPAR-4-SLR methodology to delineate the knowledge framework surrounding virtual influencers. By utilizing Biblioshiny from the bibliometrix package and VOSviewer for analytical and visualization purposes, this research reveals that attributes such as trustworthiness, expertise, attractiveness, similarity, and anthropomorphism dominate the current discourse. Conversely, attributes such as authenticity, source realness, emotional expression, novelty, autonomy, and interactivity have been identified as largely underexplored. The bibliometric analysis illustrates a marked increase in academic output since 2020, primarily concentrated in journals focused on marketing, social sciences, and human?computer interaction. Thematic mapping further highlights key areas of focus, with motor themes identified as anthropomorphism, virtual reality, and consumer engagement, indicating their foundational importance in the current landscape of virtual influencer research. Several research gaps persist, particularly in the areas of cross-cultural consumer responses to virtual influencers, ethical considerations, parasocial interactions, and comparative effectiveness analyses between virtual and human influencers. Overall, our study synthesizes the literature, systematically categorizes the key attributes of virtual influencers, and proposes a future research agenda aimed at advancing theoretical frameworks and managerial effectiveness. The insights gained here will guide researchers in identifying pertinent variables for empirical investigation and assisting practitioners in crafting strategies that enhance consumer trust, engagement, and purchase intention, ultimately navigating the evolving terrain of digital marketing more effectively. 2026, Malque Publishing. All rights reserved. Subject The topic of the resource anthropomorphism; bibliometric analysis; influencer marketing; SPAR-4-SLR Publisher An entity responsible for making the resource available Malque Publishing Relation A related resource ISSN: 25953982; 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 Format The file format, physical medium, or dimensions of the resource online