500 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 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 Lung cancer detection using image processing techniques Subject The topic of the resource Binarization Approach (BA); Feature Extraction; Gabor Filter (GF); MRI Images; Thresholding Approach (TA) Description An account of the resource Lung cancer is one of the hazardous disease which leads to high death rates in the world. A cancer is an irregular growth of cells that can be characteristically derived from a single irregular cell and that may spread to whole part of the lung. So, it is necessary to find it at the earlier stages and take basic steps to cure.CT scan is one of the sensitive method used in the medical field for treating the patients. The quality of the image is very important for detection of lung cancer. Pre-processing of an image is a necessary process, as there is a difficulty in detecting cancer cells in an image due to the presence of noise and low-quality of images. To reduce the volume of these problems, diagnosis of lung cancer steps like image enhancement, image segmentation, feature extraction methods can be used. For processing and implementation of these methods Matlab tool has been used. This paper focuses on improving the quality of image and to optimise the work. Implementation is done using image processing toolbox that is available in Matlab tool.The whole idea of this research is to show the improved work in the existing system and to get more agreeable results. RJPT All right reserved. Creator An entity primarily responsible for making the resource Sindhu Priya S.; Ramamurthy B. Source A related resource from which the described resource is derived Research Journal of Pharmacy and Technology, Vol-11, No. 5, pp. 2045-2049. Publisher An entity responsible for making the resource available Research Journal of Pharmacy and Technology Date A point or period of time associated with an event in the lifecycle of the resource 2018-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.5958/0974-360X.2018.00379.7" target="_blank" rel="noreferrer noopener">https://doi.org/10.5958/0974-360X.2018.00379.7</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050911902&doi=10.5958%2F0974-360X.2018.00379.7&partnerID=40&md5=0249167eed8e3dbf193eb6f99015b84e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050911902&doi=10.5958%2f0974-360X.2018.00379.7&partnerID=40&md5=0249167eed8e3dbf193eb6f99015b84e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9743618 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 Sindhu Priya S., Department of Computer Science, Christ (Deemed to be university), Hosur Road, Bengaluru, India; Ramamurthy B., Department of Computer Science, Christ (Deemed to be university), Hosur Road, 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 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 Lung Cancer Diagnosis from CT Images Based on Local Energy Based Shape Histogram (LESH) Feature Extration and Pre-processing Subject The topic of the resource Feature extraction; Histogram; LESH techniques; Pre-processing; Segmentation Description An account of the resource Lung cancer as of now is one of the dreaded diseases and it is destroying humanity never before. The mechanism of detecting the lung cancer will bring the level down of mortality and increase the life expectancy accuracy 13% from the detected cancer diagnosis from 24% of all cancer deaths. Although various methods are adopted to find the cancer, still there is a scope for improvement and the CT images are still preferred to find if there is any cancer in the body. The medical images are always a better one to find with the cancer in the human body. The proposed idea is, how we can improve the quality of the diagnosis form using pre-processing methods and Local energy shape histogram to improve the quality of the images. The deep learning methods are imported to find the varied results from the training process and finally to analyse the result. Medical examination is always part of our research and this result is always verified by the technicians. Major pre-processing techniques are used in this research work and they are discussed in this paper. The LESH technique is used to get better result in this research work and we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. The construction of the proposed method will include smoothing of the images with median filters, enhancement of the image and finally segmentation of the images with LESH techniques. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Dominic D.; Balachandran K. Source A related resource from which the described resource is derived Lecture Notes in Electrical Engineering, Vol-698, pp. 713-720. 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 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-7961-5_68" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-7961-5_68</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093818026&doi=10.1007%2F978-981-15-7961-5_68&partnerID=40&md5=6b41de3a0bca19a6728da08f9122d7dc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093818026&doi=10.1007%2f978-981-15-7961-5_68&partnerID=40&md5=6b41de3a0bca19a6728da08f9122d7dc</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18761100; ISBN: 978-981157960-8 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 Dominic D., Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India; Balachandran K., Department of Computer Science and Engineering, 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 Lung cancer prediction with advanced graph neural networks Subject The topic of the resource Graph attention network; Graph neural network; Lung cancer; Machine learning; Prediction Description An account of the resource This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions. 2025 Institute of Advanced Engineering and Science. All rights reserved. Creator An entity primarily responsible for making the resource Moozhippurath B.; Natarajan J. Source A related resource from which the described resource is derived Indonesian Journal of Electrical Engineering and Computer Science, Vol-37, No. 2, pp. 1077-1084. Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Date A point or period of time associated with an event in the lifecycle of the resource 2025-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijeecs.v37.i2.pp1077-1084" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijeecs.v37.i2.pp1077-1084</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210744116&doi=10.11591%2Fijeecs.v37.i2.pp1077-1084&partnerID=40&md5=93cece260be04be8e7b060a60039c23b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210744116&doi=10.11591%2fijeecs.v37.i2.pp1077-1084&partnerID=40&md5=93cece260be04be8e7b060a60039c23b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 25024752 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 Moozhippurath B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Natarajan J., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri 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 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 Moozhippurath, Bineesh; Natarajan, Jayapandian Title A name given to the resource Lung cancer prediction with advanced graph neural networks 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 Indonesian Journal of Electrical Engineering and Computer Science;Volume;37;Issue;2;pp.1077-1084 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijeecs.v37.i2.pp1077-1084" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijeecs.v37.i2.pp1077-1084</a> <a href="https://www.scopus.com/pages/publications/85210744116?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85210744116?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 Moozhippurath B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Natarajan J., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India Description An account of the resource This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions. 2025 Institute of Advanced Engineering and Science. All rights reserved. Subject The topic of the resource Graph attention network; Graph neural network; Lung cancer; Machine learning; Prediction Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Relation A related resource ISSN: 25024752; 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 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 Lung tuberculosis detection using x-ray images Subject The topic of the resource Canny edge detection; Graylevel threshold; Median filtering; Tuberculosis; Watershed segmentation; X-Ray Description An account of the resource This research work is based on the various experiments performed for the detection of lung tuberculosis using various methods like filtering, segmentation, feature extraction and classification. The results obtained from these experiments are discussed in this paper. Lung tuberculosis is a bacterial infection that causes more deaths in the world than any other infectious disease. Two billion people are infected with tuberculosis all around the world. Lung tuberculosis is a disease caused by a bacteria known as Mycobacterium tuberculosis or Tubercle bacillus. This research work strives to identify methods by which patients, who require second opinion for an already identified result, can save a lot of money. Once we receive X-ray image an input, pre-processing methods like Gaussian filter, median filter is applied. These filters help to remove unwanted noise and aid to get fine textural features. The output obtained from this is taken as an input and applied to water shed segmentation and gray level segmentation which helps to focus on the lung area of the obtained results. Output from these segmentation methods is fused to get a Region of Interest (ROI). From the ROI, the statistical features like area, major axis, minor axis, eccentricity, mean, kurtosis, skewness and entropy are extracted. Finally, we use KNN, Sequential minimal optimization (SMO), simple linear regression classification methods to detect lung tuberculosis. The results obtained in this paper suggests KNN classifier performs well than the other two classifiers. Research India Publications. Creator An entity primarily responsible for making the resource Antony B.; Nizar Banu P.K. Source A related resource from which the described resource is derived International Journal of Applied Engineering Research, Vol-12, No. 24, pp. 15196-15201. Publisher An entity responsible for making the resource available Research India Publications Date A point or period of time associated with an event in the lifecycle of the resource 2017-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057640992&partnerID=40&md5=409910db5874ad45fde5fea8099644d6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057640992&partnerID=40&md5=409910db5874ad45fde5fea8099644d6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9734562 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 Antony B., Department of Computer Science, Christ deemed to be University, Hosur Road, Bhavani Nagar, Bengaluru, 560029, Karnataka, India; Nizar Banu P.K., Department of Computer Science, Christ deemed to be University, Hosur Road, Bhavani Nagar, Bengaluru, 560029, Karnataka, 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 Sahu, Premananda; Kumar, Ashwani; Singh, Mahesh; Jain, Rituraj; Upreti, Kamal; Parashar, Jyoti Title A name given to the resource LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung 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 Sakarya University Journal of Computer and Information Sciences;Volume;8;Issue;2;pp.184-197 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.35377/saucis...1665478" target="_blank" rel="noreferrer noopener">https://doi.org/10.35377/saucis...1665478</a> <a href="https://www.scopus.com/pages/publications/105010079253?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010079253?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 Sahu P., Lovely Professional University, School of CSE, Punjab, Jalandhar, India; Kumar A., Bennett University, School of Computer Science Engineering & Technology, UP, Greater Noida, India; Singh M., Aditya University, Department of ECE, Surampalem, India; Jain R., Marwadi University, Department of Information Technology, Gujarat, Rajkot, India; Upreti K., Christ University, Department of Computer Science, Delhi NCR Campus, Ghaziabad, India; Parashar J., Bharati Vidyapeeth's Institute of Computer Applications & Management, Department of Computer Application, Delhi, India Description An account of the resource Early and accurate diagnosis, however, is still lacking for the most common form of lung cancer, and this remains one of the leading cancers leading to mortality. CT scans are widely used for lung cancer screening; however, their manual interpretation is time-consuming and prone to variability. This study introduces LungDxNet, a deep learning-based framework that integrates transfer learning to enhance diagnostic accuracy and efficiency. Using a large dataset of Low Dose CT (LDCT) scans, the system is built with fine-tuned pre-trained Convolutional Neural Networks (CNNs) such that feature extraction is reliable though minimal reducing radiation exposure. Consequently, LungDxNet involves the integration of component segmentation techniques that have been used to isolate the lung regions and discriminate the cancerous nodules from the malignant and benign cases. Very rigorous evaluations were performed on the model against both conventional machine learning and state of the art deep learning architectures. Results show that there is a substantial reduction of false positive and false negative resulting in a superior accuracy (98.88), sensitivity, and specificity. This design is to be scaled, robust and clinically applicable, making it a potential real world lung cancer diagnosis tool. Deep learning and transfer learning has excellent power to transform lung cancer detection, and this research brings awareness of how far we can optimise and integrate into clinical workflow. The model is enhanced for future work and adapted for real time diagnostic applications. 2025, Sakarya University. All rights reserved. Subject The topic of the resource Artificial Intelligence; Deep Learning; Low Dose CT; Lung Cancer; Machine Learning Publisher An entity responsible for making the resource available Sakarya University Relation A related resource ISSN: 26368129; 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 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 Singha, Ranjit; Singha, Surjit; Haokip, Alphonsa Diana; Sreethi, Rebeka R.; Thomas, Rejoice Title A name given to the resource Luxury consumption: The decision process 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 Effective Marketing and Consumer Behavior Tactics for High-End Products;pp.167-200 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-8739-9.ch007" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-8739-9.ch007</a> <a href="https://www.scopus.com/pages/publications/105008486631?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008486631?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 Singha R., Christ University, India; Singha S., Kristu Jayanti College, India; Haokip A.D., Don Bosco College, Maram, India; Sreethi R.R., Christ University, India; Thomas R., Christ University, India Description An account of the resource This chapter explores the luxury consumer decision process, highlighting the psychological, social, and cultural factors that influence luxury purchases. Consumers are driven by emotional and social motivations, such as self-expression and status, which shape their buying behaviors. Social and cultural differences require marketers to adapt strategies to diverse regional preferences. As digital technologies transform the luxury market, brands face the challenge of maintaining exclusivity while embracing e-commerce, social media, and influencer marketing. To remain competitive, luxury brands must leverage psychological insights and digital tools to create personalized, engaging experiences. The future of luxury consumption will depend on brands' ability to build meaningful connections with consumers, fostering loyalty and ensuring long-term success in an evolving market landscape. 2025, IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836938741-2; 979-836938739-9; 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 Lyrics of longing: Exploring the role of music in the lived experience of homesickness among college students Subject The topic of the resource coping; emotion; functions of music; nostalgia; perception Description An account of the resource The study investigates the multifaceted role of music during homesickness among first-year college students in India. As compared to other mental health outcomes, homesickness is a relatively understudied phenomenon, yet noteworthy due to its direct association with depression and anxiety. Although empirical evidence about music highlights its therapeutic potential for managing stress and anxiety, few studies have explored its role in connection with homesickness. The data for this study were collected through semi-structured interviews with 10 students about their perception of using music during homesickness. Through interpretative phenomenological analysis, the emerging themes pointed to a mixed influence, highlighting the bittersweet nature of music during homesickness. While music validates feelings and boosts confidence and motivation, it also triggers restorative nostalgia and serves as an escape from confronting homesickness. Moreover, native songs fostered an appreciation for ones culture and helped students connect with their roots. The study contributes to understanding how music is a versatile tool for students dealing with homesickness, offering solace and potential challenges. It serves as a guide to future intervention studies that could explore musics long-term influences. Recognising the diverse ways students perceive and respond to music provides valuable insights for developing tailored interventions and support systems. The Author(s) 2024. Creator An entity primarily responsible for making the resource Gidwani S.; Anchan V.; James N. Source A related resource from which the described resource is derived Psychology of Music Publisher An entity responsible for making the resource available SAGE Publications Ltd 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/10.1177/03057356241271032" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/03057356241271032</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205307017&doi=10.1177%2F03057356241271032&partnerID=40&md5=ff0a7a66272ff2350c0217cad301dff8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205307017&doi=10.1177%2f03057356241271032&partnerID=40&md5=ff0a7a66272ff2350c0217cad301dff8</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 3057356 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 Gidwani S., Department of Psychology, CHRIST (Deemed to be University), Bengaluru, India; Anchan V., Department of Psychology, CHRIST (Deemed to be University), Bengaluru, India; James N., Department of Psychology, 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 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 Gidwani, Simran; Anchan, Veenashree; James, Nisha Title A name given to the resource Lyrics of longing: Exploring the role of music in the lived experience of homesickness among college students 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 Psychology of Music;Volume;53;Issue;5;pp.762-778 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1177/03057356241271032" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/03057356241271032</a> <a href="https://www.scopus.com/pages/publications/85205307017?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85205307017?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 Gidwani S., Department of Psychology, CHRIST (Deemed to be University), Bengaluru, India; Anchan V., Department of Psychology, CHRIST (Deemed to be University), Bengaluru, India; James N., Department of Psychology, CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource The study investigates the multifaceted role of music during homesickness among first-year college students in India. As compared to other mental health outcomes, homesickness is a relatively understudied phenomenon, yet noteworthy due to its direct association with depression and anxiety. Although empirical evidence about music highlights its therapeutic potential for managing stress and anxiety, few studies have explored its role in connection with homesickness. The data for this study were collected through semi-structured interviews with 10 students about their perception of using music during homesickness. Through interpretative phenomenological analysis, the emerging themes pointed to a mixed influence, highlighting the bittersweet nature of music during homesickness. While music validates feelings and boosts confidence and motivation, it also triggers restorative nostalgia and serves as an escape from confronting homesickness. Moreover, native songs fostered an appreciation for ones culture and helped students connect with their roots. The study contributes to understanding how music is a versatile tool for students dealing with homesickness, offering solace and potential challenges. It serves as a guide to future intervention studies that could explore musics long-term influences. Recognising the diverse ways students perceive and respond to music provides valuable insights for developing tailored interventions and support systems. The Author(s) 2024 Subject The topic of the resource coping; emotion; functions of music; nostalgia; perception Publisher An entity responsible for making the resource available SAGE Publications Ltd Relation A related resource ISSN: 3057356; 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 m-quasi-?-Einstein contact metric manifolds Subject The topic of the resource (?,)-contact manifold; m-quasi-?-Einstein metric; Sasakian manifold; ?-Ricci soliton Description An account of the resource The goal of this article is to introduce and study the characterstics of m-quasi-?-Einstein metric on contact Riemannian manifolds. First, we prove that if a Sasakian manifold admits a gradient m-quasi-?-Einstein metric, then M is ?-Einstein and f is constant. Next, we show that in a Sasakian manifold if g represents an m-quasi-?-Einstein metric with a conformal vector field V, then V is Killing and M is ?-Einstein. Finally, we prove that if a non-Sasakian (?, )-contact manifold admits a gradient m-quasi-?-Einstein metric, then it is N(?)-contact metric manifold or a ?-Einstein. Kumara H.A., Venkatesha V., Naik D.M., 2022. Creator An entity primarily responsible for making the resource Kumara H.A.; Venkatesha V.; Naik D.M. Source A related resource from which the described resource is derived Carpathian Mathematical Publications, Vol-14, No. 1, pp. 61-71. Publisher An entity responsible for making the resource available Precarpathian National University 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/10.15330/cmp.14.1.61-71" target="_blank" rel="noreferrer noopener">https://doi.org/10.15330/cmp.14.1.61-71</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131010480&doi=10.15330%2Fcmp.14.1.61-71&partnerID=40&md5=c0077017f82b8c67499488be22b4ac81" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131010480&doi=10.15330%2fcmp.14.1.61-71&partnerID=40&md5=c0077017f82b8c67499488be22b4ac81</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 20759827 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 Kumara H.A., Kuvempu University, Karnataka, Shankaraghatta, 577451, India; Venkatesha V., Kuvempu University, Karnataka, Shankaraghatta, 577451, India; Naik D.M., CHRIST (Deemed to be University), Karnataka, Bangalore, 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 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 Balu, Vinoth; Rajagopal, Muruganandham; Joy, Rosewine; Sivasamy, Alagarsamy Title A name given to the resource Machinability and surface integrity for Mg AZ61A alloy composite by employing Taguchi integrated grey relational analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Next Materials;Volume;8;Issue;;Article No.;100670; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.nxmate.2025.100670" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.nxmate.2025.100670</a> <a href="https://www.scopus.com/pages/publications/105003249753?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003249753?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 Balu V., Department of Mechanical Engineering, Mangayarkarasi College of Engineering, Tamil Nadu, Madurai, 625402, India; Rajagopal M., Department of Operations and Analytics, Indus Business Academy, Karnataka, Bengaluru, 560062, India; Joy R., School of Business and Management, Christ University, Karnataka, Bengaluru, 560029, India; Sivasamy A., Department of Mechanical Engineering, Mahath Amma Institute of Engineering and Technology, Tamil Nadu, Pudukkottai, 622101, India Description An account of the resource The present experimental study seeking to identify the optimal processing parameters in WEDM of Mg AZ61A-ZrB2 composite using Taguchi integrated grey relational analysis (GRA). Wire-cut electric discharge machining (WEDM) is the most effective method of metal removing process which is utilized in a variety of industries, like defence, biomedical, automotive, and aerospace. It is widely used in the machining of conductive and hard materials like composites and super alloys. In this experiment, the Mg AZ61A alloy composite reinforced with 12 wt% ZrB2 particles was fabricated through stir casting method. The scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) mapping ensured the presence of matrix elements and reinforcement in the developed composite. The material removal rate (MRR) and surface roughness (Ra) were examined with relation to the processing factors such as pulse current (Ip), pulse on-time (Ton), and pulse off-time (Toff). The machining was conducted in compliance with Taguchi's L9 array. According to the GRA results, the optimal ranges of factors for achieving the better MRR and Ra were found at 4 amps of Ip, 15 ?s of Ton, and 45 ?s of Toff. The ANOVA results confirm that Ip was the most dominating factor that contributing to 37.58 %, next by Ton (30.96 %) and Toff (12.85 %), respectively. The confirmation test was demonstrated that the actual and predicted GRG values are fairly close to one another with 13.36 % improvement. The morphology of the machined surface was examined and it was shows the formation of a recast layer and the existence of flaws. 2025 The Authors Subject The topic of the resource ANOVA; AZ61A Mg alloy; Grey relational analysis; Stir casting; Wire EDM; ZrB2 Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 29498228; 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 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 Machine intelligence security : A methodological blend of fuzzy logic in industry 4.0 algorithms Subject The topic of the resource Algorithms; Decision-making; Fuzzy logic; Intelligent security; Machine intelligence Description An account of the resource The way things are made has changed a lot because of Industry 4.0. It has also led to a time with great technology and relationships. The paper discusses way to improve security in Machine Intelligence in the setting of Industry 4.0. The study uses a mix of methods to combine Fuzzy Logic with cutting-edge Industry 4.0 algorithms in order to deal with new hacking problems. Because fuzzy logic can deal with doubt and imprecision, it can be used to make current methods more reliable. This creates a complex and flexible security structure. The merger was carefully planned to make the methods for finding anomalies, reducing threats, and responding to incidents work better. The suggested method aims to make machine intelligence systems more resistant to complex cyber dangers by combining the best parts of Fuzzy Logic with Industry 4.0 algorithms. This study adds to the growing conversation about how to keep smart factory settings safe by focusing on a proactive and dynamic security model. The effects of this mix of methods could be felt in many different industries, making it possible to use advanced technologies in a safer and more reliable way in the age of Industry 4.0. 2024, Taru Publications. All rights reserved. Creator An entity primarily responsible for making the resource Srilatha P.; Bendale S.P.; Singh M.; Chakole N.; Dhote G.M.; Shelke N. Source A related resource from which the described resource is derived Journal of Discrete Mathematical Sciences and Cryptography, Vol-27, No. 2, pp. 689-701. Publisher An entity responsible for making the resource available Taru Publications 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/10.47974/JDMSC-1920" target="_blank" rel="noreferrer noopener">https://doi.org/10.47974/JDMSC-1920</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193953154&doi=10.47974%2FJDMSC-1920&partnerID=40&md5=61f5498837e36a29a4911c96b91b4553" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193953154&doi=10.47974%2fJDMSC-1920&partnerID=40&md5=61f5498837e36a29a4911c96b91b4553</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9720529 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 Srilatha P., Department of Artificial Intelligence and Data Science, Chaitanya Bharathi Institute of Technology, Telangana, Hyderabad, India; Bendale S.P., Department of Computer Engineering, NBN Sinhgad School of Engineering, Maharashtra, Pune, India; Singh M., School of Sciences, Christ (Deemed to be University), Karnataka, Bengaluru, India; Chakole N., Department of Electronics and Communication Engineering, Shri Ramdeobaba College of Engineering and Management, Maharashtra, Nagpur, India; Dhote G.M., Department of Mechanical Engineering, Yeshwantrao Chavan of Engineering, Maharashtra, Nagpur, India; Shelke N., Department of Computer Science & Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Nagpur Campus, Maharashtra, Pune, 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 Book Book Faculty Publications- Book 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 Machine Intelligence: Computer Vision and Natural Language Processing Description An account of the resource Machines are being systematically empowered to be interactive and intelligent in their operations, offerings. and outputs. There are pioneering Artificial Intelligence (AI) technologies and tools. Machine and Deep Learning (ML/DL) algorithms, along with their enabling frameworks, libraries, and specialized accelerators, find particularly useful applications in computer and machine vision, human machine interfaces (HMIs), and intelligent machines. Machines that can see and perceive can bring forth deeper and decisive acceleration, automation, and augmentation capabilities to businesses as well as people in their everyday assignments. Machine vision is becoming a reality because of advancements in the computer vision and device instrumentation spaces. Machines are increasingly software-defined. That is, vision-enabling software and hardware modules are being embedded in new-generation machines to be self-, surroundings, and situation-aware. Machine Intelligence emphasizes computer vision and natural language processing as drivers of advances in machine intelligence. The book examines these technologies from the algorithmic level to the applications level. It also examines the integrative technologies enabling intelligent applications in business and industry. Features: Motion images object detection over voice using deep learning algorithms Ubiquitous computing and augmented reality in HCI Learning and reasoning in Artificial Intelligence Economic sustainability, mindfulness, and diversity in the age of artificial intelligence and machine learning Streaming analytics for healthcare and retail domains Covering established and emerging technologies in machine vision, the book focuses on recent and novel applications and discusses state-of-the-art technologies and tools. 2024 Taylor & Francis Group, LLC. Creator An entity primarily responsible for making the resource Raj P.; Soundarabai P.B.; Augustine D.P. Source A related resource from which the described resource is derived Machine Intelligence: Computer Vision and Natural Language Processing, pp. 1-318. Publisher An entity responsible for making the resource available CRC Press 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/10.1201/9781003424550" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003424550</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173370779&doi=10.1201%2F9781003424550&partnerID=40&md5=293b0f0b1c504e1ecfa2c7d011a01a0c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173370779&doi=10.1201%2f9781003424550&partnerID=40&md5=293b0f0b1c504e1ecfa2c7d011a01a0c</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-100096031-0; 978-103220199-3 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 Book Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raj P., Site Reliability Engineering (SRE), Reliance Jio Platforms Ltd., Bangalore, India; Soundarabai P.B., Department of Computer Science, Christ University, Bangalore, India; Augustine D.P., Department of Computer Science, Christ University, 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 Chandra, Akkaraju Sailesh; Iyer, Lakshmi Shankar; Helen Josephine, V.L.; Shankar, Nisha Title A name given to the resource Machine Learning Algorithms for Optimizing Blockchain-Based Decentralized Autonomous Organizations Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTBIG68706.2025.11323619" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323619</a> <a href="https://www.scopus.com/pages/publications/105032887390?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032887390?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandra A.S., School of Business and Management, CHRIST (Deemed to Be University), India; Iyer L.S., School of Business and Management, CHRIST (Deemed to Be University), India; Helen Josephine V.L., School of Business and Management, CHRIST (Deemed to Be University), India; Shankar N., School of Business and Management, CHRIST (Deemed to Be University), India Description An account of the resource This research investigates the integration of machine learning algorithms within blockchain-based Decentralized Autonomous Organizations (DAOs) to enhance operational efficiency, resource allocation, decision-making, and governance. While DAOs provide a transparent and trustless mechanism for digital collaboration, they face challenges related to scalability, bias, data privacy, and coordination. We propose a novel framework that leverages supervises learning models for predictive analytics, reinforcement learning for autonomous decision-making, and unsupervised learning for anomaly detection in DAO voting and resource usage patterns. The study also addresses security and privacy risks by incorporating federated learning and homomorphic encryption. Our proposed model demonstrates improved throughput, decision accuracy, and fairness, as evidenced by performance benchmarks against traditional DAO implementations. The findings suggest that machine learning can significantly optimize DAO architecture and contribute to a more scalable, democratic, and intelligent decentralized ecosystem. 2025 IEEE. Subject The topic of the resource Anomaly Detection; Blockchain Technology; DAO Governance; Federated Learning; Homomorphic Encryption; Machine Learning; Natural Language Processing; Privacy Preservation; Reinforcement Learning; Resource Allocation Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157981-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Machine Learning Algorithms for Prediction of Mobile Phone Prices Subject The topic of the resource K-nearest neighbor; Machine learning; Mobile phone; Phone price prediction; Price range; Support vector machine Description An account of the resource The drastic growth of technology helps us to reduce the man work in our day-to-day life. Especially mobile technology has a vital role in all areas of our lives today. This work focused on a data-driven method to estimate the price of a new smartphone by utilizing historical data on smartphone pricing, and key feature sets to build a model. Our goal was to forecast the cost of the phone by using a dataset with 21 characteristics related to price prediction. Logistic regression (LR), decision tree (DT), support vector machine (SVM), Naive Bayes algorithm (NB), K-nearest neighbor (KNN) algorithm, XGBoost, and AdaBoost are only a few of the popular machine learning techniques used for the prediction. The support vector machine achieved the highest accuracy (97%) compared to the other four classifiers we tested. K-nearest neighbors 94% accuracy was close to that of the support vector machine. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Jose J.; Raj V.; Seaban S.V.; Jose D.V. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-537 LNNS, pp. 81-89. 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-99-3010-4_7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-3010-4_7</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174728762&doi=10.1007%2F978-981-99-3010-4_7&partnerID=40&md5=92c871fadd4287c346b74af097ec0584" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174728762&doi=10.1007%2f978-981-99-3010-4_7&partnerID=40&md5=92c871fadd4287c346b74af097ec0584</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981993009-8 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 Jose J., Department of Computer Science, Rajagiri College of Social Sciences, Kalamassery, India; Raj V., Department of Computer Science, Rajagiri College of Social Sciences, Kalamassery, India; Seaban S.V., Department of Computer Science, Rajagiri College of Social Sciences, Kalamassery, India; Jose D.V., Department of Computer Science, Christ University, 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 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 Machine Learning Algorithms for Predictive Maintenance in Hybrid Renewable Energy Microgrid Systems Subject The topic of the resource Energy Management; Hybrid Renewable Energy Systems; Machine Learning; Microgrid; Predictive Maintenance; Remaining Useful Life (RUL) Description An account of the resource The rapid expansion of hybrid renewable energy microgrid systems presents new challenges in maintaining system reliability and performance. This paper explores the application of machine learning algorithms for predictive maintenance in such systems, focusing on the early detection of potential failures to optimize operational efficiency and reduce downtime. By integrating real-time data from solar, wind, and storage components, the proposed models predict the remaining useful life (RUL) of critical components. The results demonstrate significant improvements in predictive accuracy, offering a robust solution for enhancing the reliability and longevity of renewable energy microgrids. The Authors, published by EDP Sciences. Creator An entity primarily responsible for making the resource Edwin Prabhakar P.B.; Rajarajeswari S.; Antad S.; Jeshurun S.B.; Badhoutiya A.; Chandrika S.; Suresh Babu D. Source A related resource from which the described resource is derived E3S Web of Conferences, Vol-591 Publisher An entity responsible for making the resource available EDP Sciences 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/10.1051/e3sconf/202459105002" target="_blank" rel="noreferrer noopener">https://doi.org/10.1051/e3sconf/202459105002</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211764825&doi=10.1051%2Fe3sconf%2F202459105002&partnerID=40&md5=f9f970983ec03a1283b410acacecd651" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211764825&doi=10.1051%2fe3sconf%2f202459105002&partnerID=40&md5=f9f970983ec03a1283b410acacecd651</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 25550403 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 Edwin Prabhakar P.B., Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Tamil nadu, Chennai, 600073, India; Rajarajeswari S., Department of S&H, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, 127, India; Antad S., Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India; Jeshurun S.B., School of Business and Management, CHRIST University, Bangaluru, India; Badhoutiya A., Department of Electrical Engineering, GLA University, Mathura, India; Chandrika S., Department of Mechanical Engineering, Dr.D.Y.Patil Institute of Technology, Pimrpi, Pune, India; Suresh Babu D., Department of EEE, School of Engineering, Mohan Babu University, Andhra Pradesh, Tirupati, 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 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 Machine Learning Algorithms for Stroke Risk Prediction Leveraging on Explainable Artificial Intelligence Techniques (XAI) Subject The topic of the resource Explain Like I'm Five (ELI5); Explainable Artificial Intelligence (XAI); Local Interpretable Model Agnostic Explanation (LIME); Machine Learning Description An account of the resource Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This research addresses this imperative by employing Explainable Artificial Intelligence (XAI) techniques to pinpoint stroke risk predictors. To bridge existing gaps, six machine learning models were assessed using key performance metrics. Utilising the Synthetic Minority Over-sampling Technique (SMOTE) to minimize the impact of the imbalanced nature of the dataset used in this research, the Random Forest algorithm emerged as the most effective among the algorithms with an accuracy of 94.5%, AUC-ROC of 0.95, recall of 0.96, precision of 0.93, and an F1 score of 0.95. This study explored the interpretation of these algorithms and results using Local Interpretable Model-agnostic Explanations (LIME) and Explain Like I'm Five (ELI5). With the interpretations, healthcare providers can gain insight into patients' stroke risk predictions. Key stroke risk factors highlighted by the study include Age, Marital Status, Glucose Level, Body Mass Index, Work Type, Heart Disease, and Gender. This research significantly contributes to healthcare and healthcare informatics by providing insights that can enhance strategies for stroke prevention and management, ultimately leading to improved patient care. The identified predictors offer valuable information for healthcare professionals to develop targeted interventions, fostering a proactive approach to mitigating the impact of strokes on individuals and the healthcare system. 2024 IEEE. Creator An entity primarily responsible for making the resource Ugbomeh O.; Yiye V.; Ibeke E.; Ezenkwu C.P.; Sharma V.; Alkhayyat A. Source A related resource from which the described resource is derived 2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024 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/ICEECT61758.2024.10739320" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEECT61758.2024.10739320</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211172203&doi=10.1109%2FICEECT61758.2024.10739320&partnerID=40&md5=2e43a5fb9306b35e3f01fa55e18d0efa" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211172203&doi=10.1109%2fICEECT61758.2024.10739320&partnerID=40&md5=2e43a5fb9306b35e3f01fa55e18d0efa</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835037809-2 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 Ugbomeh O., Robert Gordon University, School of Creative and Cultural Business, Aberdeen, United Kingdom; Yiye V., Robert Gordon University, School of Creative and Cultural Business, Aberdeen, United Kingdom; Ibeke E., Robert Gordon University, School of Computing, Engrg.,and Tech, Aberdeen, United Kingdom; Ezenkwu C.P., Robert Gordon University, School of Computing, Engrg.,and Tech, Aberdeen, United Kingdom; Sharma V., Christ University, Computer Science Department, India; Alkhayyat A., Islamic University, College of Technical Engineering, Najaf, Iraq 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 Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness Subject The topic of the resource AI Visuals; Artificial Intelligence; Convolutional Neural Networks; Drowsiness Detection; Image Processing; Machine Learning Description An account of the resource The number of automobiles on the road grows in lockstep with the advancement of vehicle manufacturing. Road accidents appear to be on the rise, owing to this growing proliferation of vehicles. Accidents frequently occur in our daily lives, and are the top ten causes of mortality from injuries globally. It is now an important component of the worldwide public health burden. Every year, an estimated 1.2 million people are killed in car accidents. Driver drowsiness and weariness are major contributors to traffic accidents this study relies on computer software and photographs, as well as a Convolutional Neural Network (CNN), to assess whether a motorist is tired. The Driver Drowsiness System is built on the Multi-Layer Feed-Forward Network concept CNN was created using around 7,000 photos of eyes in both sleepiness and non-drowsiness phases with various face layouts. These photos were divided into two datasets: training (80% of the images) and testing (20% of the images). For training purposes, the pictures in the training dataset are fed into the network. To decrease information loss as much as feasible, backpropagation techniques and optimizers are applied. We developed an algorithm to calculate ROI as well as track and evaluate motor and visual impacts. 2022 Boppuru Rudra Prathap et al., published by Sciendo. Creator An entity primarily responsible for making the resource Prathap B.R.; Kumar K.P.; Hussain J.; Chowdary C.R. Source A related resource from which the described resource is derived Journal of Automation, Mobile Robotics and Intelligent Systems, Vol-16, No. 2, pp. 64-73. 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14313/jamris-2-2022-17" target="_blank" rel="noreferrer noopener">https://doi.org/10.14313/jamris-2-2022-17</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163979397&doi=10.14313%2Fjamris-2-2022-17&partnerID=40&md5=d90101ed559869805e95d354932d772a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163979397&doi=10.14313%2fjamris-2-2022-17&partnerID=40&md5=d90101ed559869805e95d354932d772a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18978649 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 Prathap B.R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Hussain J., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Chowdary C.R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 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 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 Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness Subject The topic of the resource AI Visuals; Artificial Intelligence; Convolutional Neural Networks; Drowsiness Detection; Image Processing; Machine Learning Description An account of the resource The number of automobiles on the road grows in lock-step with the advancement of vehicle manufacturing. Road accidents appear to be on the rise, owing to this growing proliferation of vehicles. Accidents frequently occur in our daily lives, and are the top ten causes of mortality from injuries globally. It is now an important component of the worldwide public health burden. Every year, an estimated 1.2 million people are killed in car ac-cidents. Driver drowsiness and weariness are major con-tributors to traffic accidents this study relies on computer software and photographs, as well as a Convolutional Neural Network (CNN), to assess whether a motorist is tired. The Driver Drowsiness System is built on the Multi-Layer Feed-Forward Network concept CNN was created using around 7,000 photos of eyes in both sleepiness and non-drowsiness phases with various face layouts. These photos were divided into two datasets: training (80% of the images) and testing (20% of the images). For training purposes, the pictures in the training dataset are fed into the network. To decrease information loss as much as feasible, backpropagation techniques and optimizers are applied. We developed an algorithm to calculate ROI as well as track and evaluate motor and visual impacts. 2022, Industrial Research Institute for Automation and Measurements. All rights reserved. Creator An entity primarily responsible for making the resource Prathap B.R.; Kumar K.P.; Hussain J.; Chowdary C.R. Source A related resource from which the described resource is derived Journal of Automation, Mobile Robotics and Intelligent Systems, Vol-2022, No. 2, pp. 64-73. Publisher An entity responsible for making the resource available Industrial Research Institute for Automation and Measurements 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/10.14313/JAMRIS/2-2022/17" target="_blank" rel="noreferrer noopener">https://doi.org/10.14313/JAMRIS/2-2022/17</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160608854&doi=10.14313%2FJAMRIS%2F2-2022%2F17&partnerID=40&md5=514f8fe16529cfe502edfca5615bacd7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160608854&doi=10.14313%2fJAMRIS%2f2-2022%2f17&partnerID=40&md5=514f8fe16529cfe502edfca5615bacd7</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 18978649 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 Prathap B.R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Hussain J., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Chowdary C.R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 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 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 Machine Learning and Deep Learning Analysis of Vehicle Carbon Footprint Subject The topic of the resource climate change; fuel efficiency; greenhouse gas emissions; machine learning Description An account of the resource Clearly climate change is one of the most significant hazards to mankind nowadays. And daily the situation has become worse. No other way characterises climate change except through changes in the patterns of temperature and weather. Human activity generates the primary greenhouse gas emissions. Among these activities are burning coal, oil, natural gas, as well as other fuels; agricultural techniques, industrial operations, deforestation, burning coal, oil. Mostly resulting from human activities, the average temperature of the planet has significantly increased by almost 1.1 degrees Celsius since the late 1800s. One theory holds that internal combustion engines affect roughly thirteen percent. The objective of this work is to do an analysis of a complicated dataset involving fuel consumption in urban and highway environments as well as mixed combinations since the relevance of these variables in modelling attempts dictates. Reduced CO2 emissions emissions and environmental impact follow from reduced fuel use. The project used numerous machine learning and deep learning approaches to comprehend data analysis. Moreover, this work investigates the dataset to acquire knowledge and concurrently solves problems such overfitting and outliers. Control of complexity is achieved using several methods like VIF, PCA, and Cross-Validation. Models combining CNN and RNN performed really well with an accuracy of 0.99. The R-squared metrics are utilized in order to do the evaluation of the model. Apart from linear regression, support vector machines, Elastic Net with a rewardable accuracy, random forest was applied. It has rather good 0.98 accuracy. We can therefore state that our model analyzed the data properly and generated accurate output since the results we obtained during the assessment phase exactly the same ones we obtained during the training stage. Mass data cleansing is required as well as further study to increase machine learning model accuracy and performance. 2024 The authors. Creator An entity primarily responsible for making the resource Dhyan R.; Joy H.K.; Sridevi R.; Electa Alice J.A.; Vanusha D. Source A related resource from which the described resource is derived International Journal of Environmental Impacts, Vol-7, No. 2, pp. 287-292. Publisher An entity responsible for making the resource available International Information and Engineering Technology Association 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/10.18280/ijei.070213" target="_blank" rel="noreferrer noopener">https://doi.org/10.18280/ijei.070213</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201631162&doi=10.18280%2Fijei.070213&partnerID=40&md5=205c7868572c386ae4be4b41d8dbd976" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201631162&doi=10.18280%2fijei.070213&partnerID=40&md5=205c7868572c386ae4be4b41d8dbd976</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 23982640 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 Dhyan R., Department of Computer Science, Christ University, Bangalore, 560029, India; Joy H.K., Department of Computer Science, Christ University, Bangalore, 560029, India; Sridevi R., Department of Computer Science, Christ University, Bangalore, 560029, India; Electa Alice J.A., Department of Electronics and Communication Engineering, KS Institute of Technology, Bangalore, 560019, India; Vanusha D., Department of Computer Science and Engineering, SRM Institute of Technology, Chennai, 603203, India