168 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 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 A Case Study on Zonal Analysis of Cybercrimes Over a Decade in India Description An account of the resource Human intelligence has transformed the world through various innovative technologies. One such transformative technology is the internet. The world of the internet, known as cyberspace, though powerful, is also where most crimes occur. Cybercrime is one of the significant factors in cybersecurity, which plays a vital role in information technology and needs to be addressed with high priority. This chapter is a case study where we analyze cybercrimes in India. The data collected from NCRB for 2010 to 2020 are a primary source for the analysis. A detailed analysis of cybercrime across India is done by dividing locations into seven zones: central, east, west, north, south, northeast, and union territories. Cybercrimes reported in each zone are examined to identify which zone requires immediate measures to be taken to provide security. The work also identifies the top ten states which rank high in cybercrime. The main aim of this chapter is to provide a detailed analysis of crimes that occurred and the measures taken to curb them. Along with the primary data, secondary data from CERT-In are also used to provide an analysis of measures taken for handling cybercrime over a decade. The outcome facilitates various stakeholders to better bridge the gap in handling cybercrime incidences, thus helping in incidence prevention and response services as well as security quality management services. 2023 selection and editorial matter, Narasimha Rao Vajjhala and Kenneth David Strang; individual chapters, the contributors. Creator An entity primarily responsible for making the resource Diya C.R.; Umme Salma M.; Beerannavar C.R. Source A related resource from which the described resource is derived Cybersecurity for Decision Makers, pp. 127-145. 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/10.1201/9781003319887_9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003319887_9</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162645390&doi=10.1201%2F9781003319887_9&partnerID=40&md5=9e6ce6c43522beeb06fe9977ddb6e251" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162645390&doi=10.1201%2f9781003319887_9&partnerID=40&md5=9e6ce6c43522beeb06fe9977ddb6e251</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-100331988-7; 978-103233496-7 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 Diya C.R., School of Law, Christ University, Bangalore, India; Umme Salma M., Kuvempu University, Mangalore University, India; Beerannavar C.R., Faculty of Law, Symbiosis International University, Pune, India, University of Cambridge, United Kingdom 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 Dkhar, Josaiah Murfeal; John, Tegil; Rai, Pranab Title A name given to the resource Harnessing Machine Learning toOptimize Social Media Marketing 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;1497 LNNS;pp.581-598 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-9184-5_43" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-9184-5_43</a> <a href="https://www.scopus.com/pages/publications/105028353338?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028353338?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 Dkhar J.M., Christ University, Bengaluru, India; John T., Christ University, Bengaluru, India; Rai P., Christ University, Bengaluru, India Description An account of the resource Machine learning (ML) has transformed the way digital advertising is done and analyzed by social media data. This paper explores ML in targeted advertising, including techniques such as supervised learning, neural networks, and NLP. While ML improves campaign precision and consumer engagement, it also presents challenges such as algorithmic bias, data privacy concerns, and computational scalability. This study is a synthesis of existing research and explores real-world applications, providing a critical analysis of MLs capacity to optimize social media advertising. It argues that while ML may provide exceptional possibilities for customization and engagement, its success can only be ensured through appropriate ethical practices, transparency, and innovation in technology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Digital advertisement; Machine learning; NLP; Social media marketing Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981969183-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Child mental health: The role of different attributional styles Subject The topic of the resource Anxiety; Attributional style; Depression; Mental health; Resilience; Satisfaction with life Description An account of the resource Background: High prevalence of mental health issues in the twenty-first century accounts for a lion share in the worldwide burden of disease. There is an alarming decrease in the onset of half of the mental health problems. Hence, it is necessary to explore the current situation and figure out the causes and preventive measures as well as the appropriate mental health enhancement measures. Individual characteristics, such as thinking patterns and perception, have an impact on the mental health. Attributional style is one source of cognitive vulnerability which influences mental health disorders. Therefore, the present study examines whether there are any variations in the mental health of children with different attributional styles. Methods: The current research adopted a cross-sectional research design and selected 150 school going students [74 males and 76 females] between 10-13 years of age as participants. The Child Attributional Style Questionnaire [CASQ], Satisfaction with Life Scale-Children [SWLS-C], Brief Resilience Scale, and Revised Child Anxiety and Depression Scale [RCADS] are used to gather information. Results: The results indicated that children with a pessimistic attributional style experienced more depression and generalized anxiety than children with other two attributional styles. In terms of gender differences in mental health, female students with pessimistic attributional style significantly differed from their counterparts on depression [?2 [2] = 10.131, p = 0.006] and separation anxiety [?2 [2] = 6.456, p = 0.040]. Conclusion: Attributional style seems to have a significant role in depression and anxiety in female children. Although male children did not show any statistically significant results, they were more likely to be pessimistic in terms of their attributional style, which makes them vulnerable to mental health issues. 2020, Indian Association for Child and Adolescent Mental Health. All rights reserved. Creator An entity primarily responsible for making the resource Dmello Z.; Thomas S. Source A related resource from which the described resource is derived Journal of Indian Association for Child and Adolescent Mental Health, Vol-16, No. 1, pp. 33-48. Publisher An entity responsible for making the resource available Indian Association for Child and Adolescent Mental Health 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/10.1177/0973134220200103" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/0973134220200103</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090251079&doi=10.1177%2F0973134220200103&partnerID=40&md5=3e2c168b79bc782a31d82867f8af724f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090251079&doi=10.1177%2f0973134220200103&partnerID=40&md5=3e2c168b79bc782a31d82867f8af724f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9731342 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 Dmello Z., Department of Psychology, CHRIST [Deemed to be] University, Bengaluru, Karnataka, India; Thomas S., Department of Psychology, CHRIST [Deemed to be] University, Bengaluru, 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 Doctor, Daizy; Varghese, Arun; Sunajadevi, Kalathiparambil Rajendra Pai; Pinheiro, Dephan; Krishna Mohan, Mothi Title A name given to the resource Anticorrosive studies of Chitosan/TiO2/g-C3N4 composite on mild steel in saline and acidic conditions 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 Surfaces and Interfaces;Volume;64;Issue;;Article No.;106464; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.surfin.2025.106464" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.surfin.2025.106464</a> <a href="https://www.scopus.com/pages/publications/105002577152?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002577152?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 Doctor D., Department of Chemistry, Christ University, Karnataka, Bangalore, 560029, India; Varghese A., Department of Chemistry, Christ University, Karnataka, Bangalore, 560029, India; Sunajadevi K.R.P., Department of Chemistry, Christ University, Karnataka, Bangalore, 560029, India; Pinheiro D., Department of Chemistry, Christ University, Karnataka, Bangalore, 560029, India; Krishna Mohan M., Department of Sciences and Humanities, Christ University, Karnataka, Bangalore, 560074, India Description An account of the resource This work focuses on the synthesis of a nanocomposite coating that enhances the anticorrosive property of the metal. The nanocomposite under study is a synergistic blend of chitosan, titanium dioxide (TiO2), and graphitic carbon nitride (g-C3N4), effectively challenging the corrosion problem faced by various industries. The environment-friendly and natural properties of chitosan, the photocatalytic activity of TiO2 nanoparticles, and the efficient electrical conductivity of g-C3N4 make the composite an ideal material for studying anticorrosion activity. Experimental techniques like XPS, XRD, HR-TEM, FE-SEM, TGA, BET surface area, and FTIR analysis have been employed to characterize the nanocomposite. Weight loss studies indicate the efficacy of the nanocomposite on mild steel in 3.5 % NaCl and 1 M HCl. The corrosion behavior of the nanocomposite is examined by Tafel curves and electrochemical impedance analysis. The results indicate that the inhibition efficiency of chitosan/TiO2/g-C3N4 nanocomposite is 99 % with a charge transfer resistance value of 152.43 ?, which is more effective in the corrosion inhibition of mild steel than chitosan, TiO2, and g-C3N4 when taken separately. The anticorrosive coating prepared using this composite can be applied on different surfaces under various environmental conditions to reduce corrosion. 2025 Elsevier B.V. Subject The topic of the resource Anticorrosion; Anticorrosive coatings; Chitosan composites; Chitosan/TiO2/g-C3N4 Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 24680230; 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 Grey Wolf Optimization Guided Non-Local Means Denoising for Localizing and Extracting Bone Regions from X-Ray Images Subject The topic of the resource Convolutional neural network; Grey wolf optimization; Multi-Res U-Net; Non-local means denoising; Semantic segmentation Description An account of the resource The key focus of the current study is implementation of an automated semantic segmentation model to localize and extract bone regions from digital X-ray images. Methods: The proposed segmentation framework uses a pre-processing stage which follows convolutional neural network (CNN) obtained segmentation stage to extract the bone region from X-ray images, mainly for diagnosing critical conditions such as osteoporosis. Since the presence of noise is critical in image analysis, the X-ray images are initially processed with a grey wolf optimization (GWO) guided non-local means (NLM) denoising. The segmentation stage uses a Multi-Res U-Net architecture with attention modules. Findings: The proposed methodology shows superior results while segmenting bone regions from real X-ray images. The experiments include an ablation study that substantiates the need for the proposed denoising approach. Several standard segmentation benchmarks such as precision, recall, Dice-score, specificity, Intersection over Union (IOU), and total accuracy have been used for a comprehensive study. The proposed architectural has good impact compared to the state-of-the-art bone segmentation models and is compared both quantitatively and qualitatively. Novelty: The denoising using GWO-NLM adaptively chose the denoising parameters based on the required conditions and can be reused in other medical image analysis domains with minimal finetuning. The design of the proposed CNN model also aims at better performance on the target datasets. 2023 Biomedical & Pharmacology Journal. Creator An entity primarily responsible for making the resource Dodamani P.S.; Danti A. Source A related resource from which the described resource is derived Biomedical and Pharmacology Journal, Vol-16, No. 2, pp. 935-946. Publisher An entity responsible for making the resource available Oriental Scientific Publishing Company 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/10.13005/bpj/2676" target="_blank" rel="noreferrer noopener">https://doi.org/10.13005/bpj/2676</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165669738&doi=10.13005%2Fbpj%2F2676&partnerID=40&md5=03e010fb60b5a09bc1e013628f96263e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165669738&doi=10.13005%2fbpj%2f2676&partnerID=40&md5=03e010fb60b5a09bc1e013628f96263e</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 9746242 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 Dodamani P.S., CSE, Christ University, Bangalore, 560057, India; Danti A., CSE, Christ University, Bangalore, 560057, 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 Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs Subject The topic of the resource BMD; CNN models; DXA; X-rays Description An account of the resource Osteoporosis is a condition that affects the entire skeletal system, resulting in a decreased density of bone mass and the weakening of bone tissues micro-architecture. This leads to weaker bones that are more susceptible to fractures. Detecting and measuring bone mineral density has always been a critical area of focus for researchers in the diagnosis of bone diseases such as osteoporosis. However, existing algorithms used for osteoporosis diagnosis encounter challenges in obtaining accurate results due to X-ray image noise and variations in bone shapes, especially in low-contrast conditions. Therefore, the development of efficient algorithms that can mitigate these challenges and improve the accuracy of osteoporosis diagnosis is essential. In this research paper, a comparative analysis was conducted Assessing the accuracy and efficiency of the latest deep learning CNN model, such as VGG16, VGG19, DenseNet121, Resnet50, and InceptionV3 in detecting to Classify Normal and Osteoporosis cases. The study employed 830 X-ray images of the Spine, Hand, Leg, Knee, and Hip, comprising Normal (420) and Osteoporosis (410) cases. Various performance metrics were utilized to evaluate each model. The findings indicate that DenseNet121 exhibited superior performance with an accuracy rate of 93.4% Achieving an error rate of 0.07 and a validation loss of only 0.57 compared with other models considered in this study. 2023, International journal of online and biomedical engineering. All Rights Reserved. Creator An entity primarily responsible for making the resource Dodamani P.S.; Danti A. Source A related resource from which the described resource is derived International journal of online and biomedical engineering, Vol-19, No. 8, pp. 66-87. Publisher An entity responsible for making the resource available International Association of Online Engineering 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/10.3991/ijoe.v19i08.39235" target="_blank" rel="noreferrer noopener">https://doi.org/10.3991/ijoe.v19i08.39235</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164622680&doi=10.3991%2Fijoe.v19i08.39235&partnerID=40&md5=b3a7889c49ececa313337be7417578f5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164622680&doi=10.3991%2fijoe.v19i08.39235&partnerID=40&md5=b3a7889c49ececa313337be7417578f5</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 26268493 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 Dodamani P.S., Christ University, Karnataka, Bangalore, India; Danti A., Christ University, Karnataka, 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 Diagnosis of Osteoporosis from X-ray Images using Automated Techniques Subject The topic of the resource Bone mineral density; Osteoporosis; Residual Block; Skip connections.; U-net Description An account of the resource Osteoporosis is Bone Disease most commonly seen in aged people due to various food habits and life style habits. The bone becomes so brittle and weak which may break just from a fall. So, it is required to attend this Issue as there are various challenges faced by medical domain to identify and treat Osteoporosis. In this paper we focus on identifying and detecting osteoporosis using X-ray images using modified U-net Architecture using Residual Block and skip connections and done comparison study with existing models, as per state-of-art our model outcomes issues in existing model and obtain better accuracy. 2022 IEEE. Creator An entity primarily responsible for making the resource Dodamani P.S.; Danti A. Source A related resource from which the described resource is derived 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022, pp. 497-500. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/COM-IT-CON54601.2022.9850579" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/COM-IT-CON54601.2022.9850579</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137523180&doi=10.1109%2FCOM-IT-CON54601.2022.9850579&partnerID=40&md5=b37f1038e7f91e30eb44e9582864a0dd" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137523180&doi=10.1109%2fCOM-IT-CON54601.2022.9850579&partnerID=40&md5=b37f1038e7f91e30eb44e9582864a0dd</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166549602-5 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 Dodamani P.S., Christ University, Computer Science and Engineering, Bangalore, India; Danti A., Christ University, Computer Science and Engineering, 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 Assesment of bone mineral density in X-ray images using image processing Subject The topic of the resource Bone densitometry; Bone mineral Density; DEXA; Osteoporosis Description An account of the resource X-ray application in medical fields has given rise to various research challenges related to bone, due to its wide usage in finding out the disease related to human anatomy. It has lot of research challenges to solve using available wide application of medical imaging techniques and inspired by this, a novel X-ray images based survey was conducted to understand the role of Xray images in medical field. Bone mass density identification is the standard procedure to monitor the risk of fracture in bone using DEXA. Lot of research has been carried out to calculate BMD using X-ray images and it provided prominent results. Since Xray is economically affordable and very economical compared to DEXA, we have decided to work on X-ray images. This paper explains us about various current advancements and disadvantages with respect to X-ray image in medical sector and various techniques related to BMD calculation. X-ray images characteristics and its fundamentals in the medical field for identifying bone related diseases are also discussed. 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth. Creator An entity primarily responsible for making the resource Dodamani P.S.; Danti A. Source A related resource from which the described resource is derived Proceedings of the 2021 8th International Conference on Computing for Sustainable Global Development, INDIACom 2021, pp. 905-908. 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 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/INDIACom51348.2021.00162" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/INDIACom51348.2021.00162</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107969876&doi=10.1109%2FINDIACom51348.2021.00162&partnerID=40&md5=271eb2791f264a25924edd63147c8232" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107969876&doi=10.1109%2fINDIACom51348.2021.00162&partnerID=40&md5=271eb2791f264a25924edd63147c8232</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-938054443-4 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 Dodamani P.S., Christ University, Department of Computer Science and Engineering, Bangalore, India; Danti A., Christ University, Department of Computer Science and Engineering, 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 Dodamani, Pooja Shivanand; Palanisamy, Kanmani; Danti, Ajit Title A name given to the resource Novel Approach for Osteoporosis Classification Using X-ray Images Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Biomedical and Pharmacology Journal;Volume;18;pp.203-216 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.13005/bpj/3082" target="_blank" rel="noreferrer noopener">https://doi.org/10.13005/bpj/3082</a> <a href="https://www.scopus.com/pages/publications/105001193909?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001193909?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 Dodamani P.S., Computer Science and Engineering, Christ University, Bangalore, India; Palanisamy K., Christ University, Computer Science and Engineering, CSE, Bangalore, India; Danti A., Computer Science and Engineering, Christ University, Bangalore, India Description An account of the resource This research delves into the technical advancements of image segmentation and classification models, specifically the refined Pix2Pix and Vision Transformer (ViT) architectures, for the crucial task of osteoporosis detection using X-ray images. The improved Pix2Pix model demonstrates noteworthy strides in image segmentation, achieving a specificity of 97.24% and excelling in the reduction of false positives. Simultaneously, the modified ViT models, especially the MViT-B/16 variant, exhibit superior accuracy at 96.01% in classifying osteoporosis cases, showcasing their proficiency in identifying critical medical conditions. These models are poised to revolutionize osteoporosis diagnosis, providing clinicians with accurate tools for early detection and intervention. The synergies between the Pix2Pix and ViT models open avenues for nuanced approaches in automated diagnostic systems, with the potential to significantly improve clinical results and contribute to the broader landscape of medical image analysis. As osteoporosis remains a prevalent and often undiagnosed condition, the technical insights from this study hold substantial importance in advancing the field, emphasizing the critical role of accurate diagnostic tools in improving patient care and health outcomes. 2025 Oriental Scientific Publishing Company. All rights reserved. Subject The topic of the resource BMD; Osteoporosis; Pix2Pix Segmentation; ViT Classification; X-ray Images Publisher An entity responsible for making the resource available Oriental Scientific Publishing Company Relation A related resource ISSN: 9746242; 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 Doddamani, Tejshwini; Balasubramanian, Balamuralikrishnan; Kakkalameli, Siddappa; Sebastian, Joseph Kadanthottu; Pappuswamy, Manikantan; Almutairi, Bader O.; Almutairi, Mikhlid H.; Daphedar, Azharuddin Title A name given to the resource Phytofabricated Silver Nanoparticles Derived from Leea crispa Leaf Extract: Antituberculosis and Anticancer Activities 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 BioResources ;Volume;20;Issue;1;pp.452-464 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.15376/biores.20.1.452-464" target="_blank" rel="noreferrer noopener">https://doi.org/10.15376/biores.20.1.452-464</a> <a href="https://www.scopus.com/pages/publications/85217718818?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85217718818?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 Doddamani T., Department of Botany, Environmental Biology Laboratory, Karnatak University, Karnataka, Dharwad, 580 003, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, 05006, South Korea; Kakkalameli S., Department of Botany, Environmental Biology Laboratory, Davangere University, Karnataka, Davangere, 577002, India; Sebastian J.K., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India; Pappuswamy M., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India; Almutairi B.O., Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia; Almutairi M.H., Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia; Daphedar A., Department of Botany, Environmental Biology Laboratory, Karnatak University, Karnataka, Dharwad, 580 003, India, Department of Botany, Anjuman Arts, Science and Commerce College, Karnataka, Vijayapur, 586 101, India Description An account of the resource Aqueous extracts of Leea crispa used for producing silver nanoparticles (AgNPs) with the supplementation of the external capping substance, were determined by UV-Visible spectroscopy. The synthesized nanoparticles were examined for their antituberculosis and anticancer activities. The presence of phytoconstituents available for reducing silver ions and to form the AgNPs was confirmed using FTIR analysis. The XRD and TEM examination validated the AgNPs spherical particle shape and size of 15 to 85 nm and their face-centered cubic crystal form. Additionally, the FTIR spectrum revealed variation in the band values in the range 1384.0 to 3419.4 cm-1, respectively, and the EDX noted a strong band at 3 keV induced the presence of metallic silver. The AgNPs exhibited comparatively potential anti-tuberculosis activity (0.2 to 100 g/mL) respectively. Alternatively, various doses of AgNPs, 12.5 to 400 g/mL documented considerable activity towards the human breast cancer cell lines. The percentage of cell viability increased at 12.5g/mL and declined at 400 ng/mL concentrations of AgNPs solution. The AgNPs synthesized from L. crispa exhibited potential activity against life-threatening tuberculosis and cancer cells. 2025, North Carolina State University. All rights reserved. Subject The topic of the resource AgNO3; Anti-cancer; Anti-tuberculosis; Leea crispa; MCF-7 Cell line; MTT Assay; TEM Publisher An entity responsible for making the resource available North Carolina State University Relation A related resource ISSN: 19302126; 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 Effectiveness of Learning Management System (LMS) in Sustainable Learning and Development among Bank Employees Subject The topic of the resource Course Effectiveness; Learner Satisfaction; Learning and Development Practices; Learning Management System Description An account of the resource Learning Management Systems in the form of E-Learning platform is currently an evolving scenario for the primary means of delivering various courses across educational, business, industries and vocational learning environments in the form of Learning and Development activities in all the sectors. LMS is a challenging and resource-intensive task requires demanding substantial knowledge, time, and effort. Consequently, there emerged a necessity in both research and practical applications to establish the personalized usage process of an LMS. Despite its significant impact on the outcomes of such an Information System (IS), the usage process has to be analysed. The researcher developed a conceptual model to delineate with set of factors to influence LMS course in Learning and Development Practices in industry context. Researcher revealed specific set of factors such as interface design, content presentation format, transfer of learning, and feedback mechanisms significantly impact learner satisfaction among Bank employees in their Learning and Development activities. Moreover, learner satisfaction depends on the application platform and content. The findings offer a valuable insight to design a corporate education system, with the quality content delivery and practical delivery. By considering these results, designers can develop more integrated and effective LMS to cater the needs and satisfaction of Learning and Development activities among Bank employees. 2024, Creative Publishing House. All rights reserved. Creator An entity primarily responsible for making the resource Doe M.B.; Dhanabagiyam S.; Boopathy S.; Arthi T.S.; Naidu C.B.A.; Thalari S.K. Source A related resource from which the described resource is derived Journal of Ecohumanism, Vol-3, No. 8, pp. 2694-2703. Publisher An entity responsible for making the resource available Creative Publishing House 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/10.62754/joe.v3i8.4918" target="_blank" rel="noreferrer noopener">https://doi.org/10.62754/joe.v3i8.4918</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210426057&doi=10.62754%2Fjoe.v3i8.4918&partnerID=40&md5=451fdbaf595e15a48b176f9d37b58e5d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210426057&doi=10.62754%2fjoe.v3i8.4918&partnerID=40&md5=451fdbaf595e15a48b176f9d37b58e5d</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 27526798 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 Doe M.B., Sri Ramakrishna Engineering College, Tamil Nadu, Coimbatore, India; Dhanabagiyam S., Christ (Deemed to be University), Bengaluru, India; Boopathy S., Christ (Deemed to be University), Bengaluru, India; Arthi T.S., Sri Ramakrishna Engineering College, Tamil Nadu, Coimbatore, India; Naidu C.B.A., Christ (Deemed to be University), Bengaluru, India; Thalari S.K., CMR Institute of Technology, 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 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 Analysis of Online In-Destination Booking Service Processes in the Travel Industry: A Case Study Subject The topic of the resource Customer satisfaction; Information quality; Key players; Online in-destination booking service; Purchase intention; Service quality; System quality; Travel industry Description An account of the resource This article presents a comprehensive analysis of the online in-destination booking service processes within the dynamic landscape of the travel industry. Utilizing a case study approach, the research investigates the various stages involved in providing travel-related services, focusing on the key players. The study employs a quantitative method to assess the information quality, system quality, service quality, customer satisfaction, and purchase intention of online in-destination booking. The research highlights the investigation of the usability of online travel booking systems and identifies the purchase intention of customers towards online travel booking websites. To address the research objectives, the participants are selected using a nonprobability sampling method. The sample size of the study is 225 from in and around Coimbatore. The sampling procedure used is convenience sampling. The sampling is selected based on convenience and accessibility to the residents. The findings reveal that there exists a significant difference in respondents opinions on quality criteria: system quality and service quality. Additionally, the study finds that the loading time of online travel booking websites is positively correlated with quality criteria and features of travel apps. By examining a specific case within the travel sector, this study contributes valuable insights that can inform strategic decision-making for businesses operating in the online in-destination booking space. The results aim to guide industry players in enhancing their operational efficiency, leveraging technology advancements, and aligning their services with evolving customer expectations, ultimately fostering sustainable growth in the competitive travel market. 2024, Bentham Books imprint. Creator An entity primarily responsible for making the resource Doe M.B.; Dhanabagiyam S.; Irfan M. Source A related resource from which the described resource is derived Trends in Business Process Modeling and Digital Marketing: Case Studies and Emerging Technologies, pp. 28-41. Publisher An entity responsible for making the resource available Bentham Science Publishers 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/10.2174/9789815274707124010007" target="_blank" rel="noreferrer noopener">https://doi.org/10.2174/9789815274707124010007</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216216635&doi=10.2174%2F9789815274707124010007&partnerID=40&md5=9cfd5bc75b9f9a9e8e78696accc63938" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216216635&doi=10.2174%2f9789815274707124010007&partnerID=40&md5=9cfd5bc75b9f9a9e8e78696accc63938</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-981527470-7; 978-981527471-4 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 Doe M.B., Department of Management Studies, Sri Ramakrishna Engineering College, Coimbatore, India; Dhanabagiyam S., School of Business and Management, Christ University, Bengaluru, India; Irfan M., School of Business and Management, Christ 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 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 Doli, Diella; Canhasi, Dalina; Vinay, M.; Deshishku, Lea; Etemi, Blerta Prevalla; Samanta, Debabrata Title A name given to the resource Smart Finances: A Web-Based System for Personalized Financial Management 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;1603 LNNS;pp.1-10 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-03769-5_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-03769-5_1</a> <a href="https://www.scopus.com/pages/publications/105020665097?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020665097?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 Doli D., Department of Computing and Information Technologies, Rochester Institute of Technology, Prishtina, Kosovo; Canhasi D., Department of Computing and Information Technologies, Rochester Institute of Technology, Prishtina, Kosovo; Vinay M., CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Deshishku L., Department of Computing and Information Technologies, Rochester Institute of Technology, Prishtina, Kosovo; Etemi B.P., Department of Computing and Information Technologies, Rochester Institute of Technology, Prishtina, Kosovo; Samanta D., Department of Computing and Information Technologies, Rochester Institute of Technology, Prishtina, Kosovo Description An account of the resource Smart Finances is a dynamic and user-friendly web application designed to assist users in managing and understanding their personal finances with ease. Built using HTML, CSS, JavaScript, and PHP with MySQL for backend functionality and data persistence, the platform offers a suite of tools, including an interest calculator, investment calculator, budget manager, and interactive spending analysis. The intuitive interface enhances usability and promotes financial literacy, especially among students and individuals new to personal finance. The application supports both guest and registered user modes, offering tiered access to features based on authentication. Smart Finances aims to make money management more approachable and insightful through accessible design and practical functionality. Initial user testing with 20 participants indicated a 30% improvement in budgeting accuracy and a high satisfaction rate (88%) with the usability of the interface. These results validate the systems impact on financial literacy and user engagement. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Budgeting Tools; Chart.js; Expense Tracking; MySQL; Personal Finance Management; PHP; Student Finance; Web Application Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303203768-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Dominic Andrew, P.; Jose, Anna Mariya; Jayapandian, N.; Angel, Chinnapalli Neha; Brar, Krishjeet Title A name given to the resource Advanced Malware Analysis and Detection Using Deep 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 Conference Proceedings - 2025 IEEE 4th International Conference on Data, Decision and Systems, ICDDS 2025;pp.7-12 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDDS67737.2025.11344689" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDDS67737.2025.11344689</a> <a href="https://www.scopus.com/pages/publications/105033355386?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033355386?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 Dominic Andrew P., Christ University, Department of Cse, Bangalore, India; Jose A.M., Christ University, Department of Cse, Bangalore, India; Jayapandian N., Christ University, Department of Cse, Bangalore, India; Angel C.N., Christ University, Department of Cse, Bangalore, India; Brar K., Christ University, Department of Cse, Bangalore, India Description An account of the resource Malware is malicious software that is used to cause harm to the computer systems, networks or users across several operating systems, such as Windows, macOS, iOS, Android and Linux. The identification and categorisation of malware is a difficult subject with no one-size-fits-all solution due to the constant evolution of the malware and lack of standardised detection frameworks. The use of deep learning models in cybersecurity encourages the growth of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) techniques. The goal of this research is to automate the way of analysing malware using a simple framework without the need of complex software. This article focuses on the application of Neural Networks, a deep learning model in detecting and analysing the behavior of malware. The model performed better when compared to other techniques by achieving an accuracy of 99.43%, precision of 99.05% and a F1 score of 0.99, which was trained on a large dataset containing 1,38,048 samples. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Cyber-attack; Deep Learning; Machine Learning; Malware Detection; Neural Network; Random Forest Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155479-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Dominic Andrew, P.; Jose, Anna Mariya; Jayapandian, N.; Angel, Chinnapalli Neha; Brar, Krishjeet Title A name given to the resource Advanced Malware Analysis and Detection Using Deep 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 Conference Proceedings - 2025 IEEE 4th International Conference on Data, Decision and Systems, ICDDS 2025;pp.7-12 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDDS67737.2025.11344689" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDDS67737.2025.11344689</a> <a href="https://www.scopus.com/pages/publications/105033355386?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033355386?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 Dominic Andrew P., Christ University, Department of Cse, Bangalore, India; Jose A.M., Christ University, Department of Cse, Bangalore, India; Jayapandian N., Christ University, Department of Cse, Bangalore, India; Angel C.N., Christ University, Department of Cse, Bangalore, India; Brar K., Christ University, Department of Cse, Bangalore, India Description An account of the resource Malware is malicious software that is used to cause harm to the computer systems, networks or users across several operating systems, such as Windows, macOS, iOS, Android and Linux. The identification and categorisation of malware is a difficult subject with no one-size-fits-all solution due to the constant evolution of the malware and lack of standardised detection frameworks. The use of deep learning models in cybersecurity encourages the growth of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) techniques. The goal of this research is to automate the way of analysing malware using a simple framework without the need of complex software. This article focuses on the application of Neural Networks, a deep learning model in detecting and analysing the behavior of malware. The model performed better when compared to other techniques by achieving an accuracy of 99.43%, precision of 99.05% and a F1 score of 0.99, which was trained on a large dataset containing 1,38,048 samples. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Cyber-attack; Deep Learning; Machine Learning; Malware Detection; Neural Network; Random Forest Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155479-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Zero forcing number of degree splitting graphs and complete degree splitting graphs Subject The topic of the resource splitting graph; zero forcing number Description An account of the resource A subset Z V(G) of initially colored black vertices of a graph G is known as a zero forcing set if we can alter the color of all ver- tices in G as black by iteratively applying the subsequent color change condition. At each step, any black colored vertex has exactly one white neighbor, then change the color of this white vertex as black. The zero forcing number Z(G), is the minimum number of vertices in a zero forcing set Z of G (see [11]). In this paper, we compute the zero forcing num- ber of the degree splitting graph (DS-Graph) and the complete degree splitting graph (CDS-Graph) of a graph. We prove that for any simple graph, Z[DS(G)] k + t, where Z(G) = k and t is the number of newly introduced vertices in DS(G) to construct it. 2019 Sciendo. All rights reserved. Creator An entity primarily responsible for making the resource Dominic C. Source A related resource from which the described resource is derived Acta Universitatis Sapientiae, Mathematica, Vol-11, No. 1, pp. 40-53. 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 2019-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.2478/ausm-2019-0004" target="_blank" rel="noreferrer noopener">https://doi.org/10.2478/ausm-2019-0004</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071463657&doi=10.2478%2Fausm-2019-0004&partnerID=40&md5=9e7b72c264c89061be9298d6d2e9dd6b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071463657&doi=10.2478%2fausm-2019-0004&partnerID=40&md5=9e7b72c264c89061be9298d6d2e9dd6b</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 18446094 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 Dominic C., Department of Mathematics, CHRIST (Deemed to Be 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 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 3-Sequent achromatic sum of graphs Subject The topic of the resource 3-Sequent vertex coloring; 3-sequent vertex coloring number; 3s achromatic number; 3s achromatic sum Description An account of the resource Three vertices x,y,z in a graph G are said to be 3-sequent if xy and yz are adjacent edges in G. A 3-sequent coloring (3s coloring) is a function ?: V (G) ?{1, 2,...,k} such that if x,y and z are 3-sequent vertices, then either ?(x) = ?(y) or ?(y) = ?(z) (or both). The 3-sequent achromatic number of a graph G, denoted ?3s(G), equals the maximum number of colors that can be used in a coloring of the vertices' of G such that if xy and yz are any two sequent edges in G, then either x or z is colored the same as y. The 3-sequent achromatic sum of a graph G, denoted a'3s(G), is the greatest sum of colors among all proper 3s-coloring that requires ?3s(G) colors. This research initiates the study of 3-sequent achromatic sum and finds the exact values of this parameter for some known graphs. Furthermore, we calculate the a'3s(G) of corona product, Cartesian product of the graphs and some important results have been proved and a comparative study is carried out. 2021 World Scientific Publishing Company. Creator An entity primarily responsible for making the resource Dominic C.; Devassia J.V. Source A related resource from which the described resource is derived Discrete Mathematics, Algorithms and Applications, Vol-13, No. 4 Publisher An entity responsible for making the resource available World Scientific Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S1793830921500397" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S1793830921500397</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097382406&doi=10.1142%2FS1793830921500397&partnerID=40&md5=4e61a2febc0c2d2d0348e833bc2d2ca0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097382406&doi=10.1142%2fS1793830921500397&partnerID=40&md5=4e61a2febc0c2d2d0348e833bc2d2ca0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17938309 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dominic C., Department of Mathematics, Christ (Deemed to Be University), Bangalore, Karnataka, 560029, India; Devassia J.V., Tecnologico de Monterrey, School of Engineering and Sciences, Mexico, Mexico 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 Cop-edge critical generalized Petersen and Paley graphs Subject The topic of the resource Cops and Robbers; Paley graphs; Petersen graphs; vertex-pursuit games Description An account of the resource Cop Robber game is a two player game played on an undirected graph. In this game, the cops try to capture a robber moving on the vertices of the graph. The cop number of a graph is the least number of cops needed to guarantee that the robber will be caught. We study cop-edge critical graphs, i.e. graphs G such that for any edge e in E(G) either c(G?e) < c(G) or c(G?e) > c(G). In this article, we study the edge criticality of generalized Petersen graphs and Paley graphs. 2023 Azarbaijan Shahid Madani University. Creator An entity primarily responsible for making the resource Dominic C.; Witkowski L.; Witkowski M. Source A related resource from which the described resource is derived Communications in Combinatorics and Optimization, Vol-8, No. 2, pp. 359-378. Publisher An entity responsible for making the resource available Azarbaijan Shahid Madani University 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/10.22049/cco.2022.27308.1229" target="_blank" rel="noreferrer noopener">https://doi.org/10.22049/cco.2022.27308.1229</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148897010&doi=10.22049%2Fcco.2022.27308.1229&partnerID=40&md5=d15b050ad5b68f6119a4515f128e212a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148897010&doi=10.22049%2fcco.2022.27308.1229&partnerID=40&md5=d15b050ad5b68f6119a4515f128e212a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 25382128 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 Dominic C., Department of Mathematics, CHRIST (Deemed to be university), Karnataka, Bengaluru, 560029, India; Witkowski L., Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Pozna?, Poland; Witkowski M., Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Pozna?, Poland 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 Early prediction of lungs cancer by deep learning algorithms from the CT images with LBP features Subject The topic of the resource CT images; Deep Learning methods; LBP Features; Smoothening Description An account of the resource The early prediction of the any type of cancer can save the lives of many especially if it is lung cancer which is one of the deadly diseases in the world. Thus the early prediction is implemented we can increase life expectancy and bring the mortality level low. Although there are various methods to detect the lung cancer cells by X-ray and CT scans, however the CT images are more preferred. The 2D images like CT scans are used to get medical results more accurate. The proposed method here will discuss how the LBP features are used to analyze the CT images with the support of Deep Learning methods. In this research work we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. LBP features helps in estimating the distribution of local binary pattern of an image. A final result with 93% is achieved after the training of the processed images by LBP features. 2020 SERSC. Creator An entity primarily responsible for making the resource Dominic D.; Balachandran K. Source A related resource from which the described resource is derived International Journal of Advanced Science and Technology, Vol-29, No. 5, pp. 3863-3867. Publisher An entity responsible for making the resource available Science and Engineering Research Support Society 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="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084219908&partnerID=40&md5=8cb15094fd714beb8b2b5875d74ef74a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084219908&partnerID=40&md5=8cb15094fd714beb8b2b5875d74ef74a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 20054238 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 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 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 Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix Subject The topic of the resource Computer tomography (CT); LBP features; preprocessing; processing; smoothening Description An account of the resource Early detection of any sort of cancer, particularly lung cancer, which is one of the worlds most lethal illnesses, can save many lives. Life expectancy can be improved and the degree of mortality reduced by adopting the early forecast. While there are different methods like X-ray and CT scans to detect lung cancer cells, CT images resulted as more favored. The 2D images are used for more accurate medical results, such as CT scans. The proposed approach here will address how to interpret the CT images for the Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix. This research will explore how the image conversion can be achieved through different methods of image processing to obtain better results from CT images. The Confusion Matrix helps to estimate inequality in a picture pattern. After the evaluation of the processed images by Confusion Matrix, a final accuracy with a result of 93% is obtained. 2023 Scrivener Publishing LLC. Creator An entity primarily responsible for making the resource Dominic D.; Balachandran K. Source A related resource from which the described resource is derived Data Engineering and Data Science: Concepts and Applications, pp. 317-333. Publisher An entity responsible for making the resource available wiley 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/10.1002/9781119841999.ch13" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781119841999.ch13</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174117232&doi=10.1002%2F9781119841999.ch13&partnerID=40&md5=2eb43c0745c7d7a34892f39529afac72" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174117232&doi=10.1002%2f9781119841999.ch13&partnerID=40&md5=2eb43c0745c7d7a34892f39529afac72</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-111984199-9; 978-111984187-6 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 Dominic D., School of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India; Balachandran K., School of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India