145
20
16481
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Conference Papers
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Faculty Publications- Conference Papers
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Automated Leukaemia Prediction and Classification Using Deep Learning Techniques
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accuracy; activation function; and associative memory; bone marrow; classification; DL; Leukaemia; neural network; neurons
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Leukemia is typically diagnosed based on an abnormal blood count, frequently an elevated White Blood Cell (WBC) count. The diagnosis is established through bone marrow, replaced by neoplastic cells. Acute Lymphoblastic Leukemia (ALL) is a type of leukaemia that affects the blood and bone marrow. Leukaemia primarily affects children and adults around the world. Early leukaemia detection is critical for appropriately treating patients, especially children. This research aims to present a diagnostic method that uses computational intelligence and image processing algorithms to identify blast cells from ALL images. The medical image is prepared initially using the preprocessing and segmentation technique for efficient classification. In this research, the type is accomplished using Bidirectional Associative Memory Neural Networks (BAMNN), where the accuracy is 96.87%, the highest classification rate and outperforms the existing technique. 2023 IEEE.
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Narendran M.; Amarendra K.; Revathy D.; Divyapushpalakshmi M.; Sharma D.K.; Upreti K.
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2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 420-425.
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Institute of Electrical and Electronics Engineers Inc.
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2023-01-01
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<a href="https://doi.org/10.1109/ICCSAI59793.2023.10421708" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCSAI59793.2023.10421708</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186641652&doi=10.1109%2FICCSAI59793.2023.10421708&partnerID=40&md5=76197f35db710995a317bfa5462e7c87" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186641652&doi=10.1109%2fICCSAI59793.2023.10421708&partnerID=40&md5=76197f35db710995a317bfa5462e7c87</a>
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ISBN: 979-835036996-0
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Online
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English
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Conference paper
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Narendran M., Kcg College of Technology, Department of Information Technology, Tamil Nadu, Chennai, India; Amarendra K., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Andhra Pradesh, Vaddeswaram, 522502, India; Revathy D., Karpaga Vinayaga College of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nadu, Chengalpet, India; Divyapushpalakshmi M., Gitam University, Department of Computer Science and Engineering, Bengaluru, 561203, India; Sharma D.K., Jaypee University of Engineering and Technology, Department of Mathematics, Madhya Pradesh, Guna, 473226, India; Upreti K., Christ (Deemed to Be University), Department of Computer Science, Delhi -NCR, Ghaziabad, India
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Articles
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Automated lung cancer T-Stage detection and classification using improved U-Net model
Subject
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Computed tomography; Deep learning; Lung nodule; Segmentation; TNM staging; U-Net
Description
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Lung cancer results from the uncontrolled growth of abnormal cells. This research proposes an automated, improved U-Net model for lung cancer detection and tumor staging using the TNM system. A novel mask-generation process using thresholding and morphological operations is developed for the U-Net segmentation process. In the pre-processing stage, an advanced augmentation technique and contrast limited adaptive histogram equalization (CLAHE) are implemented for image enhancement. The improved U-Net model, enhanced with an advanced residual network (ARESNET) and batch normalization, is trained to accurately segment the tumor region from lung computed tomography (CT) images. Geometrical parameters, including perimeter, area, convex area, solidity, roundness, and eccentricity, are used to find precise T-stage of lung cancer. Validation using performance metrics such as accuracy, specificity, sensitivity, precision, and recall shows the proposed hybrid method is more accurate than existing approaches, achieving a staging accuracy of 94%. This model addresses the need for a highly accurate automated technique for lung cancer staging, essential for effective detection and treatment. 2024 Institute of Advanced Engineering and Science. All rights reserved.
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Sathiyamurthy B.K.; Madhaiyan V.K.
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International Journal of Electrical and Computer Engineering, Vol-14, No. 6, pp. 6817-6826.
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Institute of Advanced Engineering and Science
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2024-01-01
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<a href="https://doi.org/10.11591/ijece.v14i6.pp6817-6826" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijece.v14i6.pp6817-6826</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206235686&doi=10.11591%2Fijece.v14i6.pp6817-6826&partnerID=40&md5=c5cf2a65179676f42ce821f77351dee3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206235686&doi=10.11591%2fijece.v14i6.pp6817-6826&partnerID=40&md5=c5cf2a65179676f42ce821f77351dee3</a>
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ISSN: 20888708
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Online
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English
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Article
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Sathiyamurthy B.K., Department of Information Science and Engineering, RV Institute of Technology and Management, Bangalore, Affiliate to Visvesvaraya Technological University, Belagavi, India, Department of Computer Science and Engineering, CHRIST University, Bangalore, India; Madhaiyan V.K., Department of Information Science and Engineering, RV Institute of Technology and Management, Bangalore, Affiliate to Visvesvaraya Technological University, Belagavi, India
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Title
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Book Chapter
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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
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Automated neurological brain disease detection in magnetic resonance imaging using deep learning approaches
Description
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A neurological type of brain disease called multiple sclerosis (MS) impairs how well the nervous system is able to function efficiently and causes people to experience visual, sensory, and problems with movement. Multiple methods of detection have been proposed so far for diagnosing MS; among them, magnetic resonance imaging (MRI) has drawn a lot of interest from healthcare providers. The ability to quickly diagnose lesions related to MS depends on a fundamental understanding of the anatomy and workings of the brain that MRI technology provides doctors. Using an MRI for diagnosing MS is tedious, time-consuming, and prone to human error. In the present investigation, lesion activity involves preprocessing and segmentation of the MS images from two time points using deep learning approaches. 2024 by IGI Global. All rights reserved.
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Thilagavathi S.; Sridhar D.; Jawahar S.
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Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases, pp. 150-178.
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IGI Global
Date
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2024-01-01
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<a href="https://doi.org/10.4018/979-8-3693-1281-0.ch008" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-1281-0.ch008</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190858680&doi=10.4018%2F979-8-3693-1281-0.ch008&partnerID=40&md5=c3a1adc6976ad800bc6428fc3b776eb0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190858680&doi=10.4018%2f979-8-3693-1281-0.ch008&partnerID=40&md5=c3a1adc6976ad800bc6428fc3b776eb0</a>
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ISBN: 979-836931282-7; 979-836931281-0
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Online
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English
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Book chapter
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Thilagavathi S., Department of Computer Science, Sri Krishna Adithya College of Arts and Science, Coimbatore, India; Sridhar D., School of Computer Science, Dr. Vishwanath Karad MIT World Peace University, Pune, India; Jawahar S., School of Sciences, CHRIST Deemed to be University, Ghaziabad, India
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Title
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Conference Papers
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Title
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Automated Organic Web Harvesting on Web Data for Analytics
Subject
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Automated scraping; Data scraping; Indexing; Organic web harvesting; SERP pages
Description
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Automated Web search and web data extraction has become an inevitable part of research in the area of web mining. The web scraping has immense influence on ecommerce, market research, web indexing and much more. Most of the web information is presented in an unstructured or free format. Web scraping helps every user to retrieve, analyze and use the data suitably according to their requirement. There exist different methodologies for web scraping. Major web scraping tools are rule based systems. In the proposed work, an automated method for web information extraction using Computer Vision is proposed and developed. The proposed automated web scraping method comprises of automated URL extraction virtual extraction of required data and storing the data in a structured format which is useful in market research. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Jacob L.; Thomas K.T.
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Lecture Notes in Networks and Systems, Vol-290, pp. 131-141.
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Springer Science and Business Media Deutschland GmbH
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2021-01-01
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<a href="https://doi.org/10.1007/978-981-16-4486-3_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-4486-3_14</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115095553&doi=10.1007%2F978-981-16-4486-3_14&partnerID=40&md5=3cf24f57d1e1d4d94666d9e3f7c7b09c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115095553&doi=10.1007%2f978-981-16-4486-3_14&partnerID=40&md5=3cf24f57d1e1d4d94666d9e3f7c7b09c</a>
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ISSN: 23673370; ISBN: 978-981164485-6
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Online
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English
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Conference paper
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Jacob L., Christ University, Bangalore, India; Thomas K.T., Christ University, Bangalore, India
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Articles
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Automated Risk Management Based Software Security Vulnerabilities Management
Subject
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integrated security management system; machine learning; quantitative risk assessment; Quantitative threat modeling; software security
Description
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An automated risk assessment approach is explored in this work. The focus is to optimize the conventional threat modeling approach to explore software system vulnerabilities. Data produced in the software development processes are better leveraged using Machine Learning approaches. A large amount of industry knowledge around security vulnerabilities can be leveraged to enhance current threat modeling approaches. Work done here is in the ecosystem of software development processes that use Agile methodology. Insurance business domain data are explored as a target for this study. The focus is to enhance the traditional threat modeling approach with a better quantitative approach and reduce the biases introduced by the people who are part of software development processes. This effort will help bridge multiple data sources prevalent across the software development ecosystem. Bringing these various data sources together will assist in understanding patterns associated with security aspects of the software systems. This perspective further helps to understand and devise better controls. Approaches explored so far have considered individual areas of software development and their influence on improving security. There is a need to build an integrated approach for a total security solution for the software systems. A wide variety of machine learning approaches and ensemble approaches will be explored. The insurance business domain is considered for the research here. CWE (Common Weaknesses Enumeration) mapping from industry knowledge are leveraged to validate the security needs from the industry perspective. This combination of industry and company data will help get a holistic picture of the software system's security. Combining the industry and company data helps lay down the path for an integrated security management system in software development. The risk management framework with the quantitative threat modeling process is the work's uniqueness. This work contributes toward making the software systems secure and robust with time. 2013 IEEE.
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Althar R.R.; Samanta D.; Kaur M.; Singh D.; Lee H.-N.
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IEEE Access, Vol-10, pp. 90597-90608.
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Institute of Electrical and Electronics Engineers Inc.
Date
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2022-01-01
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<a href="https://doi.org/10.1109/ACCESS.2022.3185069" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ACCESS.2022.3185069</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133705570&doi=10.1109%2FACCESS.2022.3185069&partnerID=40&md5=34390481d0a1b2825811ff60629c9d14" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133705570&doi=10.1109%2fACCESS.2022.3185069&partnerID=40&md5=34390481d0a1b2825811ff60629c9d14</a>
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All Open Access; Gold Open Access
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ISSN: 21693536
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Online
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English
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Article
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Althar R.R., Christ University, Data Science Department, Karnataka, Bangalore, 560029, India, First American India Private Ltd., Qms, Karnataka, Bangalore, 560038, India; Samanta D., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Kaur M., First American India Private Ltd., Qms, Karnataka, Bangalore, 560038, India; Singh D., First American India Private Ltd., Qms, Karnataka, Bangalore, 560038, India; Lee H.-N., First American India Private Ltd., Qms, Karnataka, Bangalore, 560038, India
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Articles
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Automated segmentation and classification of nuclei in histopathological images
Subject
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annotated; classifier; computer-assisted diagnosis; deep learning; digital image analysis; histopathological images; machine learning; nuclear; nuclei; segmentation; whole slide images
Description
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Various kinds of cancer are detected and diagnosed using histopathological analysis. Recent advances in whole slide scanner technology and the shift towards digitisation of whole slides have inspired the application of computational methods on histological data. Digital analysis of histopathological images has the potential to tackle issues accompanying conventional histological techniques, like the lack of objectivity and high variability. In this paper, we present a framework for the automated segmentation of nuclei from human histopathological whole slide images, and their classification using morphological and colour characteristics of the nuclei. The segmentation stage consists of two methods, thresholding and the watershed transform. The features of the segmented regions are recorded for the classification stage. Experimental results show that the knowledge from the selected features is capable of classifying a segmented object as a candidate nucleus and filtering out the incorrectly identified segments. Copyright 2022 Inderscience Enterprises Ltd.
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Vincent S.; Chandra J.
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International Journal of Biomedical Engineering and Technology, Vol-38, No. 3, pp. 249-266.
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Inderscience Publishers
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2022-01-01
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<a href="https://doi.org/10.1504/IJBET.2022.121739" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJBET.2022.121739</a>
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ISSN: 17526418
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Online
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English
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Article
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Vincent S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Chandra J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
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Conference Papers
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Automated Single Responsibility Principle Enforcement: A Step Toward Reusable and Maintainable Code
Subject
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Code smells; Cyclometric complexity; Halstead volume; Interface analysis; Single responsibility principle
Description
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In this study, we delve into the sphere of automated code scrutiny, specifically concentrating on compliance with the single responsibility principle (SRP), a key principle in software architecture. The SRP proposes that a class should have a singular reason for modification, thereby enhancing code cohesion and facilitating its maintenance and reusability. The study presents a pioneering system that utilizes a holistic strategy to ascertain SRP compliance within code. This system rigorously inspects code interfaces, the interaction points among various software components. Through this process, we extract critical insights into the codes maintainability and reusability. An optimally designed interface can significantly improve code management and foster its reuse, leading to superior software design efficiency. Beyond interface inspection, our system also explores complexity metrics such as cyclomatic complexity and hassel volume. Cyclomatic complexity offers a numerical indicator of the count of linearly independent paths traversing a programs source code, serving as a measure of code complexity. Hassel volume is an additional metric that can quantify code complexity. Moreover, our system employs code smell detection methodologies to identify instances of high interdependence between classes, often a sign of SRP breaches. High interdependence, or tight coupling, complicates code modification and maintenance. The system integrates the conclusions from these varied analyses to determine SRP compliance. The outcomes of this investigation highlight a hopeful trajectory toward automated SRP detection. This could provide developers with tools that proactively foster the development of well-organized and maintainable code, thereby enhancing software design quality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Jacob B.A.; Manohar M.; Roy G.
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Lecture Notes in Networks and Systems, Vol-1112 LNNS, pp. 343-353.
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Springer Science and Business Media Deutschland GmbH
Date
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-97-6684-0_28" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-6684-0_28</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215655517&doi=10.1007%2F978-981-97-6684-0_28&partnerID=40&md5=7ac1173f69be0421e06c009b900c3c26" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215655517&doi=10.1007%2f978-981-97-6684-0_28&partnerID=40&md5=7ac1173f69be0421e06c009b900c3c26</a>
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ISSN: 23673370; ISBN: 978-981976683-3
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Online
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English
Type
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Conference paper
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Jacob B.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Manohar M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Roy G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India
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Articles
Article
Faculty Publications -Articles
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Automated testbed and real-time port analysis for reconfigurable inputoutput boards
Subject
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analog read; analog write; Automation; digital read; digital write; RIO boards
Description
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In the computational world, automation plays a vital role in every aspect. The idea of developing and testing Reconfigurable InputOutput (RIO) Boards automatically without manual interaction is a challenging task. Initially, testing was done manually which takes a lot of time and also requires human interaction. With the proposed idea one can reduce human interaction and testing time with smart automated design setup. This testing includes testing of various functionalities of the RIO boards. This can be done by testing the features of each port with its pin configuration. To be more precise, in this process an automated testbed is designed and algorithms are proposed to verify the features of each pin such as Digital Read, Digital Write, Analog Read and Analog Write of the RIO boards along with the motor pins. Thus, the proposed method makes the setup simple, without any complications by giving the instructions to perform the testing process for each board without human interaction. Results show that the proposed method can reduce time consumption 95%, human interaction by 95% and increase testing accuracy to 87%. 2020 Informa UK Limited, trading as Taylor & Francis Group.
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P K.; O S G.
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International Journal of Computer Integrated Manufacturing, Vol-33, No. 45971, pp. 1156-1166.
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Taylor and Francis Ltd.
Date
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2020-01-01
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<a href="https://doi.org/10.1080/0951192X.2020.1803141" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/0951192X.2020.1803141</a>
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ISSN: 0951192X; CODEN: ICIME
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Online
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English
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Article
Coverage
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P K., Department of Computer Science and Engineering, Christ (Deemed to Be University), Bangalore, India; O S G., Department of Computer Science and Engineering, Christ (Deemed to Be University), Bangalore, India
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Dublin Core
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Title
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Automated Verification of Open/Closed Principle: A Code Analysis Approach
Subject
The topic of the resource
Open Closed Principle; SOLID Principles
Description
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The SOLID principles are foundational to software engineering, focusing on the maintainability, scalability, and extensibility of software systems. The Open/Closed Principle (OCP), a pivotal element among these principles, underscores the need to design software modules that are open for extension yet closed for modification. This research explores automated verification techniques for OCP, addressing the validation of software modules through extensibility and adaptability assessments. The principal objectives involve the development of a code analysis approach and a methodology capable of automating the verification of adherence to OCP in developed codes, providing actionable insights to software developers. The system focuses on specific aspects of OCP, including inheritance, abstraction, and polymorphism, and aims to provide clear indications of where violations occur within a codebase. The implementation uses the Abstract Syntax Tree (AST) analysis to examine class definitions. The automated analysis of Python code using the defined rules offers a clear understanding of OCP adherence. Results are presented in Pandas DataFrames, indicating potential violations and providing developers with actionable insights to enhance code quality and maintainability. Overall, the automated code verification system aims to enhance code quality and adherence to fundamental design principles, paving the way for advancements in automated code analysis and software engineering practices. 2024 IEEE.
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Roy G.; Manohar M.; Jacob B.A.
Source
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2024 5th International Conference for Emerging Technology, INCET 2024
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Institute of Electrical and Electronics Engineers Inc.
Date
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2024-01-01
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<a href="https://doi.org/10.1109/INCET61516.2024.10593301" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/INCET61516.2024.10593301</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200990985&doi=10.1109%2FINCET61516.2024.10593301&partnerID=40&md5=da21e9096b41925629c4ddc4a7b3016c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200990985&doi=10.1109%2fINCET61516.2024.10593301&partnerID=40&md5=da21e9096b41925629c4ddc4a7b3016c</a>
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ISBN: 979-835036115-5
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Online
Language
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English
Type
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Conference paper
Coverage
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Roy G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Manohar M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Jacob B.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Khan, Fazal; Priya, Sharon Roji C.; Burhanuddin, Ibrahim; Kurian, Joshua P.K.
Title
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Automated Waste Segregation using Raspberry Pi and Deep Learning
Date
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01-01-2025
Source
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16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;2;pp.8620-8625
Identifier
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<a href="https://www.scopus.com/pages/publications/105034833401?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034833401?origin=resultslist</a>
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Khan F., Department of Computer Science and Engineering, Christ University, Bangalore, India; Priya S.R.C., Department of Computer Science and Engineering, Christ University, Bangalore, India; Burhanuddin I., Department of Computer Science and Engineering, Christ University, Bangalore, India; Kurian J.P.K., Department of Computer Science and Engineering, Christ University, Bangalore, India
Description
An account of the resource
With rapid urbanization and increasing waste generation, efficient waste segregation has become a critical challenge for sustainable waste management. Traditional waste disposal methods rely heavily on manual sorting, which is inefficient, labor-intensive, and prone to errors, leading to improper recycling and environmental hazards. To address these problems, a clever waste segregation method is presented in this research. It automatically sorts waste into four categoriesglass, metal, plastic, and paper/cardboardusing computer vision and machine learning. A 720p webcam is used to collect images in real time, and the system is powered by a Raspberry Pi 4B with 4GB of RAM. A Convolutional Neural Network (CNN) model that was developed using the TrashNet dataset forms its basis. The model can correctly identify the waste in the photos due to an optimized training method that incorporates data augmentation, regularization strategies, and early stopping to prevent overfitting. An SG90 servo motor controls the lid, ensuring the garbage is placed in the appropriate compartment, while an MG996R servo motor swings the bin into place after the waste has been classified. The bin and lid go back to their initial places once the garbage has been dumped, preparing the system for usage again. Here, we are able to combine automated mobility, automated categorization, and real-time waste detection with embedded technologies, machine learning, and automation to separate waste with the least amount of human intervention. Furthermore, the system's scalability and adaptability make it appropriate for smart city initiatives, urban trash management, and wider industrial application. Consequently, this technology helps to tackle intelligent waste management problems, which facilitates the emergence of a sustainable and eco-friendly future. The system achieved a testing accuracy of 88.1%, showcasing its effectiveness and reliability. Grenze Scientific Society, 2025.
Subject
The topic of the resource
automation; computer vision; Convolutional Neural Network (CNN); environmental sustainability; machine learning; Real-time waste detection; smart waste management; TrashNet dataset; Waste segregation
Publisher
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Grenze Scientific Society
Language
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English
Type
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Conference paper
Rights
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Restricted Access; Hardcopy may be available in the library
-
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ae8597b1fb62da5294ca14f6fc0ece85
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Title
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Patents
Subject
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Patent
Description
An account of the resource
Patent
Patent
Patents
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Title
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Automatic blood group identification device /
Subject
The topic of the resource
Bio-Chemistry
Description
An account of the resource
Patent Number: 202141026963, Applicant: Ajit Danti.
Our invention Automatic Blood Group Identification Device is an event of a coffee price image process system that enables the determination of human blood sorts. to see the blood sorts, it had been used the plate check that accommodates the mixture antigens specifics of blood sorts determination and also the sample blood of the donor.
Creator
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Danti, Ajith.
Source
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<a href="https://drive.google.com/file/d/1XddX-9KSD6tY9Atb5HHGieOShF9fMFjw/view" target="_blank" title="Automatic blood group identification device /" rel="noreferrer noopener">https://drive.google.com/file/d/1XddX-9KSD6tY9Atb5HHGieOShF9fMFjw/view</a>
Publisher
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Intellectual Property India
Date
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2021-03-29
Language
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English
Type
The nature or genre of the resource
Patent
-
Dublin Core
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Title
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Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine Vision
Subject
The topic of the resource
Classifier; Color features; Texture features; Vegetable disease
Description
An account of the resource
This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article. 2019, Springer Nature Singapore Pte Ltd.
Creator
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Madgi M.; Danti A.
Source
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Communications in Computer and Information Science, Vol-1037, pp. 529-537.
Publisher
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Springer Verlag
Date
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2019-01-01
Identifier
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<a href="https://doi.org/10.1007/978-981-13-9187-3_47" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-13-9187-3_47</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070213879&doi=10.1007%2F978-981-13-9187-3_47&partnerID=40&md5=4c49a9444db620b141b87208955a7355" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070213879&doi=10.1007%2f978-981-13-9187-3_47&partnerID=40&md5=4c49a9444db620b141b87208955a7355</a>
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Restricted Access
Relation
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ISSN: 18650929; ISBN: 978-981139186-6
Format
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Online
Language
A language of the resource
English
Type
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Conference paper
Coverage
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Madgi M., K. L. E. Institute of Technology, Hubballi, 580030, India; Danti A., Christ (Deemed to be University), Bengaluru, 560074, India
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https://archives.christuniversity.in/files/original/cd74d5a8c9d50a7ffe8f234ebd557443.png
ae8597b1fb62da5294ca14f6fc0ece85
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Title
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Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
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Title
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Automatic detection of violence activity-A tool for women's safety /
Subject
The topic of the resource
Computer Science
Description
An account of the resource
Patent Number: 202041006858, Applicant: Debanjan Konar.
Human activity detection has gained popularity owing to wide range of applications from! game development to surveillance. Recent development of Deep Pose (Human pose detection using deep neural network) allows extrapolating the skeletal structures and joint sequences of the actors in a video sequence. This work aims to promote the violence-activities of human-being using Deep Pose on multi-person interaction in real-time.
Creator
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Samanta, Debabrata.
Source
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<a href="https://drive.google.com/file/d/17aVFsluz0AUCNBsKIDKIGhlv0Iw5T4K4/view" target="_blank" title="Automatic detection of violence activity-A tool for women's safety /" rel="noreferrer noopener">https://drive.google.com/file/d/17aVFsluz0AUCNBsKIDKIGhlv0Iw5T4K4/view</a>
Publisher
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Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2020-03-17
Language
A language of the resource
English
Type
The nature or genre of the resource
Patent
-
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ae8597b1fb62da5294ca14f6fc0ece85
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Title
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Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
Dublin Core
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Title
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Automatic detection of violence activity-A tool for women's safety /
Subject
The topic of the resource
Computer Science
Description
An account of the resource
Patent Number: 202041006858, Applicant: Debanjan Konar.
Human activity detection has gained popularity owing to wide range of applications from game development to surveillance. Recent development of Deep Pose (Human pose detection using deep neural network.
Creator
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Bhattacharyya, Siddhartha.
Source
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<a href="https://drive.google.com/file/d/17aVFsluz0AUCNBsKIDKIGhlv0Iw5T4K4/view" target="_blank" title="Automatic detection of violence activity-A tool for women's safety /" rel="noreferrer noopener">https://drive.google.com/file/d/17aVFsluz0AUCNBsKIDKIGhlv0Iw5T4K4/view</a>
Publisher
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Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2020-03-21
Language
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English
Type
The nature or genre of the resource
Patent
-
Dublin Core
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Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
A name given to the resource
Automatic Diagnosis of Autism Spectrum Disorder Detection Using a Hybrid Feature Selection Model with Graph Convolution Network
Subject
The topic of the resource
ABFO; Autism spectrum disorder; Graph convolutional networks; mRMR; SVM-RFE
Description
An account of the resource
A neurodevelopmental disorder is called an autism spectrum disorder (ASD) that influences a persons assertion, interaction, and learning abilities. The consequences and severity of symptoms of ASD will vary from person to person; the disorder is mainly diagnosed in children aged 15years and older, and its symptoms may include unusual behaviors, interests, and social challenges. If it is not resolved at this stage, it will become severe in the coming days. So, in this manuscript, we propose a way to automatically tell if someone has ASD that works well by using a combination of feature selection and deep learning. Four phases comprise the proposed model: preprocessing, feature extraction, feature selection, and prediction. At first, the collected images are given to the preprocessing stage to remove the noise. Then, for each image, three types of features are extracted: the shape feature, texture feature, and histogram feature. Then, optimal features are selected to minimize computational complexity and time consumption using a new technique based on a combination of adaptive bacterial foraging optimization (ABFO), support vector machines-recursive feature elimination (SVM-RFE), minimum redundancy and maximum relevance (mRMR). Then, the graph convolutional network (GCN) classifier uses the selected features to identify an image as normal or autistic. According to the research observations, our models accuracy is enhanced to 97.512%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Creator
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Lamani M.R.; Benadit P.J.
Source
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SN Computer Science, Vol-5, No. 1
Publisher
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Springer
Date
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2024-01-01
Identifier
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<a href="https://doi.org/10.1007/s42979-023-02439-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-023-02439-z</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180210216&doi=10.1007%2Fs42979-023-02439-z&partnerID=40&md5=b01c9cae8e1de2938bc2945e56cff620" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180210216&doi=10.1007%2fs42979-023-02439-z&partnerID=40&md5=b01c9cae8e1de2938bc2945e56cff620</a>
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Restricted Access
Relation
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ISSN: 2662995X
Format
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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
Lamani M.R., CHRIST (Deemed to be University), Computer Science and Engineering, Karnataka, Bangalore, 560074, India; Benadit P.J., CHRIST (Deemed to be University), Computer Science and Engineering, Karnataka, Bangalore, 560074, India
-
https://archives.christuniversity.in/files/original/2bb3b0c18f61f60fa38185d1fbf43fb9.pdf
eacc2bd6f774d3803762afcba5216bc1
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Title
A name given to the resource
MPHIL
Mphil
Mphil Thesis
Dublin Core
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Title
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Automatic emotional stress recognition in children with stuttering
Creator
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M Yashaswi Alva
Date
A point or period of time associated with an event in the lifecycle of the resource
2015
Source
A related resource from which the described resource is derived
Computer Science
-
https://archives.christuniversity.in/files/original/992cdc34bf14a81c3dfb29e8dbc05a52.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
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Title
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Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Automatic fertilizer dispenser robot /
Subject
The topic of the resource
Mechanical Design
Description
An account of the resource
Patent Number: 354319-001, Applicant: Ravikumar R.
Creator
An entity primarily responsible for making the resource
K, Kiran.
Source
A related resource from which the described resource is derived
<a href="https://drive.google.com/file/d/1sU5l1D5wBRVIfnnc6Z3fJJLPHQfSoW_E/view" target="_blank" title="Automatic fertilizer dispenser robot /" rel="noreferrer noopener">https://drive.google.com/file/d/1sU5l1D5wBRVIfnnc6Z3fJJLPHQfSoW_E/view</a>
Publisher
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Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2022-05-19
Language
A language of the resource
English
Type
The nature or genre of the resource
Patent
-
https://archives.christuniversity.in/files/original/d17465b03b70b53ac6e51ea3a1f7354b.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
Dublin Core
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Title
A name given to the resource
Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Automatic fertilizer dispenser robot /
Subject
The topic of the resource
Mechanical Design
Description
An account of the resource
Patent Number: 354319-001, Applicant: Ravikumar R.
Creator
An entity primarily responsible for making the resource
Y B, Ravi Kumar.
Source
A related resource from which the described resource is derived
<a href="https://drive.google.com/file/d/1sU5l1D5wBRVIfnnc6Z3fJJLPHQfSoW_E/view" target="_blank" title="Automatic fertilizer dispenser robot /" rel="noreferrer noopener">https://drive.google.com/file/d/1sU5l1D5wBRVIfnnc6Z3fJJLPHQfSoW_E/view</a>
Publisher
An entity responsible for making the resource available
Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2022-05-19
Language
A language of the resource
English
Type
The nature or genre of the resource
Patent
-
https://archives.christuniversity.in/files/original/98bc7171edd3197f857131f07247116a.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
Dublin Core
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Title
A name given to the resource
Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Automatic fertilizer dispenser robot /
Subject
The topic of the resource
Mechanical Design
Description
An account of the resource
Patent Number: 354319-001, Applicant: Ravikumar R.
Creator
An entity primarily responsible for making the resource
Sasidhar, Jangam.
Source
A related resource from which the described resource is derived
<a href="https://drive.google.com/file/d/1sU5l1D5wBRVIfnnc6Z3fJJLPHQfSoW_E/view" target="_blank" title="Automatic fertilizer dispenser robot /" rel="noreferrer noopener">https://drive.google.com/file/d/1sU5l1D5wBRVIfnnc6Z3fJJLPHQfSoW_E/view</a>
Publisher
An entity responsible for making the resource available
Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2022-05-19
Language
A language of the resource
English
Type
The nature or genre of the resource
Patent
-
https://archives.christuniversity.in/files/original/30f6d8f9f7ea297e468861fc7fe05c9c.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
Dublin Core
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Title
A name given to the resource
Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Automatic fertilizer dispenser robot /
Subject
The topic of the resource
Mechanical Design
Description
An account of the resource
Patent Number: 354319-001, Applicant: Ravikumar R.
Creator
An entity primarily responsible for making the resource
K, Ramesha.
Source
A related resource from which the described resource is derived
<a href="https://drive.google.com/file/d/1sU5l1D5wBRVIfnnc6Z3fJJLPHQfSoW_E/view" target="_blank" title="Automatic fertilizer dispenser robot /" rel="noreferrer noopener">https://drive.google.com/file/d/1sU5l1D5wBRVIfnnc6Z3fJJLPHQfSoW_E/view</a>
Publisher
An entity responsible for making the resource available
Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2022-05-19
Language
A language of the resource
English
Type
The nature or genre of the resource
Patent