101
20
2150
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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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Smart Certificates Using Blockchain: A Review
Subject
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Digital Certificates; Remix Ethereum IDE; Smart Contracts
Description
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When making job offers, it is usual practice for businesses to check applicants academic credentials. The organization that issued the certificate must authenticate it for the employer to ensure that it is genuine. Because of the lengthy process involved in certificate verification, the selection process takes longer overall while establishing the legitimacy of a certificate. To address this issue, blockchain provides a verified distributed ledger along with a cryptography mechanism to thwart academic credential counterfeiting. The Blockchain also provides a standardized platform for document access, storage, and verification that takes the least amount of time. The review of the methodologies and performance of the same has been covered in the paper. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Sabu I.R.; Saju S.; Mary Anita E.A.; Lapin V.
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Lecture Notes in Networks and Systems, Vol-1207 LNNS, pp. 231-239.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-3-031-77229-0_23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-77229-0_23</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210180748&doi=10.1007%2F978-3-031-77229-0_23&partnerID=40&md5=18d78f394cf96f3e4e19be81b6c18cda" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210180748&doi=10.1007%2f978-3-031-77229-0_23&partnerID=40&md5=18d78f394cf96f3e4e19be81b6c18cda</a>
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ISSN: 23673370; ISBN: 978-303177228-3
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Online
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English
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Sabu I.R., Christ University, Bangalore, India; Saju S., Christ University, Bangalore, India; Mary Anita E.A., Christ University, Bangalore, India; Lapin V., North-Caucasus Federal University, Stavropol, Russian Federation
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Title
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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
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Analyzing the Consumer Buying Behavior by Adapting Artificial Intelligence (AI)
Subject
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artificial intelligence (AI); business; consumer; industry 4.0; technology
Description
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In any business consumers or customers are important part of the market, so it is necessary to attract more customers for increasing the profits. The current research in this area has demonstrated that artificial intelligence (AI) has a substantial impact on the end customer, contrary to the widespread notion that it has more of an impact on industry than other manufacturers. There are many studies on the various applications of AI in analyzing and visualizing the consumer behavior. Thus, it is been observed the behavior of consumer is not same for same businesses, it varies from consumer to consumer. In other respects, AI is changing how consumers act right now. In coming year's use of AI will become common where the human dependable businesses also get automated with time. 2024 IEEE.
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Tripathi S.; Tiwari A.; Singh S.; Roy A.; Singla J.; Chaudhry R.
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Proceedings - 2024 International Conference on Healthcare Innovations, Software and Engineering Technologies, HISET 2024, pp. 377-379.
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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/HISET61796.2024.00110" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/HISET61796.2024.00110</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211327100&doi=10.1109%2FHISET61796.2024.00110&partnerID=40&md5=bf48747ae59aba47f6b3a8e080d86005" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211327100&doi=10.1109%2fHISET61796.2024.00110&partnerID=40&md5=bf48747ae59aba47f6b3a8e080d86005</a>
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ISBN: 979-835036070-7
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Online
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English
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Conference paper
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Tripathi S., Christ Deemed to Be University, Delhi, India; Tiwari A., Christ Deemed to Be University, Delhi, India; Singh S., Sharda University, Sharda School of Business Studies, Greater Noida, India; Roy A., University of Lucknow, Institute of Management Sciences, Lucknow, India; Singla J., Himachal Pradesh Kendriya Vishwavidyalya Business School, Himachal Pradesh, India; Chaudhry R., Institute of Management Studies and Research, Rohtak, India
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Title
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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
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Application of AI in Determining the Strategies for the Startups
Subject
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Artificial Intelligence (AI); Business; Language; Robots; Startups
Description
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Startups face unique challenges in developing viable strategies to create a competitive advantage and achieve long-term success in today's rapid and global business climate. Using tools powered by AI and algorithms, startups may harness massive amounts of data, generate relevant insights, and make intelligent choices across a variety of business processes. The research begins with an examination of the fundamental concepts beneath AI and how they are used in the business sector. It illustrates how organizations may simplify and improve important activities such as market research, customer segmentation, and trend analysis using artificial intelligence (AI), natural language understanding (NLU), as well as predictive analytics. The report also delves into extensive case studies and real-world examples of businesses that have effectively integrated AI into their business decision-making processes. These examples highlight the practical benefits of AI-driven insights that include enhanced resource allocation, customer targeting, as well as operational performance. It highlights the importance of ethical AI methodologies, transparency, and safeguards to ensure unbiased and fair decision-making. Finally, this study demonstrates how AI has the potential to profoundly transform how entrepreneurs design and implement their strategies. By leveraging AI-driven perspectives, startups may handle complex market dynamics with more precision and agility, increasing their chances of enduring and succeeding in a competitive business climate. The study's findings provide a road map for organizations wishing to apply AI in strategic decision-making processes. 2024 IEEE.
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Rajendran V.; Tiwari A.; Tripathi V.
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Proceedings - 2024 International Conference on Healthcare Innovations, Software and Engineering Technologies, HISET 2024, pp. 374-376.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/HISET61796.2024.00109" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/HISET61796.2024.00109</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211331830&doi=10.1109%2FHISET61796.2024.00109&partnerID=40&md5=f7d0263dd8b572ff404f8838db133d7a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211331830&doi=10.1109%2fHISET61796.2024.00109&partnerID=40&md5=f7d0263dd8b572ff404f8838db133d7a</a>
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ISBN: 979-835036070-7
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Online
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English
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Conference paper
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Rajendran V., Christ (Deemed to Be) University, Delhi, India; Tiwari A., Christ (Deemed to Be) University, Delhi, India; Tripathi V., Xavier Institute of Management & Entrepreneurship, Bangalore, India
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Title
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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
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TumorInsight: GAN-Augmented Deep Learning for Precise Brain Tumor Detection
Subject
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CLAHE; DCGAN; Deep Learning; Gamma Correction; MRI
Description
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In addition to the shortage in data as well as the low quality of MRI images, one of the most difficult tasks in contemporary medical imaging is the diagnosis of tumors in brain. This work presents a new approach to enhance diagnostic accuracy using sophisticated preprocessing techniques. Combining BRATS 2023 and Cheng et al. datasets to apply cutting-edge deep learning preprocessing methods with Generative Adversarial Networks (GANs), specifically DCGAN, Contrast Limited Adaptive Histogram Equalization (CLAHE), and gamma correction, it aims to significantly improve the quality of MRI images. As a result, updated data should be generated with greater precision and detail, making it possible to identify tumor-affected areas with greater accuracy. Thorough assessment, demonstrated by metrics such as Accuracy (0.98), Specificity (0.99), Sensitivity (0.99), AUC (0.65), Dice Coefficient (0.67), and Precision (0.71), highlights possible advancements in brain tumor identification and treatment, thereby highlighting the effectiveness of the suggested approach. 2024 IEEE.
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Shekhar R.; Rana M.
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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024, pp. 100-105.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/I4C62240.2024.10748429" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/I4C62240.2024.10748429</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211910236&doi=10.1109%2FI4C62240.2024.10748429&partnerID=40&md5=fbf5f91324151da3a25cb0d187c4e238" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211910236&doi=10.1109%2fI4C62240.2024.10748429&partnerID=40&md5=fbf5f91324151da3a25cb0d187c4e238</a>
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ISBN: 979-833152853-9
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Online
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English
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Conference paper
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Shekhar R., School of Computing, Dit University, Dehradun, India; Rana M., School of Sciences, Christ University, Delhi NCR, India
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Title
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Conference Papers
Conference Paper
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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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The Design of Driver Fatigue Detection Based on Eye Blinking and Mouth Yawing
Subject
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Correlation coefficient; Intelligent transportation system (ITS); Template matching; Viola-Jones
Description
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In modern era, the Intelligent Transportation System (ITS) is very essential for the betterment of transport management, autonomous vehicles and especially for safe driving. The statistics suggest that the major severe accidents occur because of drivers drowsiness. The main objective of this work is to give the alert alarm when the driver is falling asleep. In the proposed study, the driver's face is detected using the Viola Jones algorithm, and a novel approach to detecting eye blinks using template matching and a similarity measure. For effective eye tracking, the normalized correlation coefficient is calculated. The correlation score is used to identify eye blinks since a blink causes a significant change in the correlation score. In tracking of mouth yawing finding the darkest region between the lips. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Manjunatha H.T.; Poornima K.M.; Danti A.; Arunkumar K.L.
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Lecture Notes in Electrical Engineering, Vol-1236 LNEE, pp. 431-446.
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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/10.1007/978-981-97-5866-1_31" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-5866-1_31</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209934580&doi=10.1007%2F978-981-97-5866-1_31&partnerID=40&md5=6636cbb697f952e76a1626831281197d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209934580&doi=10.1007%2f978-981-97-5866-1_31&partnerID=40&md5=6636cbb697f952e76a1626831281197d</a>
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ISSN: 18761100; ISBN: 978-981975865-4
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Online
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English
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Conference paper
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Manjunatha H.T., Department of MCA, Jawaharlal Nehru New College of Engineering, Shimoga, India; Poornima K.M., Department of CSE, Jawaharlal Nehru New College of Engineering, Shimoga, India; Danti A., Department of AIML, Christ University, Bangalore, India; Arunkumar K.L., Department of MCA, Jawaharlal Nehru New College of Engineering, Shimoga, India
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Title
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Conference Papers
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Dublin Core
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Title
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Privacy-preserving federated learning in healthcare: Fundamentals, state of the art and prospective research directions
Subject
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Architecture; Collaborative learning; Federated Learning; Health care; Privacy Preservation
Description
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Recent collaborations in medical diagnostic systems are based on data private collaborative learning using Federated Learning (FL). In this approach, multiple organizations train a machine-learning model at the same time eventually leading to global model generation. This paper reviews the fundamentals of FL and its evolution path in Healthcare. The objective of this review is to scope a wide variety of healthcare applications in FL. Exactly what research direction is moving in interesting for research communities to guide their future course. This review uniquely focuses on examining numerous FL-based healthcare implementations, detailing their core methodologies and performance metrics, which, to our knowledge, have not been previously available. Privacy-preserving collaborative distributed learning through federated learning in healthcare enhances research collaborations, thereby resulting in better-performing models. This comprehensive review will act as a valuable reference for researchers exploring new FL applications in the healthcare domain. 2024 IEEE.
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Namitha T.N.; Raghavendra S.; Vinith R.
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2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024, pp. 1438-1443.
Publisher
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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/AIC61668.2024.10730929" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIC61668.2024.10730929</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210231969&doi=10.1109%2FAIC61668.2024.10730929&partnerID=40&md5=98e86c7df937a22aea610003596951bb" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210231969&doi=10.1109%2fAIC61668.2024.10730929&partnerID=40&md5=98e86c7df937a22aea610003596951bb</a>
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ISBN: 979-835038459-8
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Online
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English
Type
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Conference paper
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Namitha T.N., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Raghavendra S., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Vinith R., Amrita Vishwa Vidyapeetham, School of Artificial Intelligence, Coimbatore, India
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Conference Papers
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Faculty Publications- Conference Papers
Dublin Core
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Enhancing Cybersecurity: Machine Learning Techniques for Phishing URL Detection
Subject
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gradient boosting; gridsearchcv
Description
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Phishing attacks exploit user vulnerabilities in cybersecurity awareness by tricking them to fake websites designed to steal confidential data. This study proposes a method for detecting phishing URLs using machine learning. The proposed method analyzes various URL characteristics, such as length, subdomain levels, and the presence of suspicious patterns, which are key indicators of phishing attempts. Gradient Boosting was selected due to its robustness in handling complex, non-linear relationships between features, making it particularly effective in distinguishing between legitimate and phishing URLs, by evaluating the Gradient Boosting classifier on a dataset with 10,000 entries and 50 features, the method achieves an accuracy of 99%.This approach has the potential to enhance web browsers with add-ons or middleware that alert users from potential phishing sites which will be based solely on URL. 2024 IEEE.
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Garnayak S.; Kumar A.; Sahoo S.; Gouda B.; Sharma V.
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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/ICEECT61758.2024.10739207" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEECT61758.2024.10739207</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211189476&doi=10.1109%2FICEECT61758.2024.10739207&partnerID=40&md5=289f0aa1a7fade2a0e86ea8c76dcb18a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211189476&doi=10.1109%2fICEECT61758.2024.10739207&partnerID=40&md5=289f0aa1a7fade2a0e86ea8c76dcb18a</a>
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ISBN: 979-835037809-2
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Online
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English
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Conference paper
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Garnayak S., C.V Raman Global University, Computer Science And Engineering, Bhubaneswar, India; Kumar A., C.V Raman Global University, Computer Science And Engineering, Bhubaneswar, India; Sahoo S., C.V Raman Global University, Computer Science And Engineering, Bhubaneswar, India; Gouda B., C.V Raman Global University, Computer Science And Engineering, Bhubaneswar, India; Sharma V., CHRIST University, Computer Science Department, Delhi NCR, India
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Conference Papers
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Dublin Core
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Insights of Evolving Methods Towards Screening of AI-Enhanced Malware in IoT Environment
Subject
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Artificial Intelligence; detection; Internet of Things; Malware; prevention; Security; Threats
Description
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Internet-of-Things (IoT) has been encountering a series of potential form of threats since past half decades. Artificial Intelligence (AI), which is frequently seen to be adopted to solve various challenges in IoT operation, has now been adopted even by attackers for their malicious purposes. Of all forms of threats, AI-enhanced malwares are one of the most potential forms of threats which has its extensive effectiveness towards the complete operation of the entire IoT environment. Hence, this manuscript discusses existing detection and prevention approaches evolved in current literatures to understand various taxonomies of solution-based methodologies for circumventing such threats. The paper also contributes towards highlighting the potential open-ended issues that are yet to be addressed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Shahabadkar R.; Nandeeswar S.B.; Soman R.; Sangeetha G.
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Lecture Notes in Networks and Systems, Vol-1120 LNNS, pp. 694-704.
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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/10.1007/978-3-031-70518-2_57" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-70518-2_57</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210812213&doi=10.1007%2F978-3-031-70518-2_57&partnerID=40&md5=9ad21107c217298867738a5644654d34" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210812213&doi=10.1007%2f978-3-031-70518-2_57&partnerID=40&md5=9ad21107c217298867738a5644654d34</a>
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ISSN: 23673370; ISBN: 978-303170517-5
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Online
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English
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Conference paper
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Shahabadkar R., Department of CSE, AMC Engineering College, Karnataka, Bangaluru, 560083, India; Nandeeswar S.B., Department of CSE (AIML), AMC Engineering College, Karnataka, Bengaluru, India; Soman R., Department of CSE, BNM Institute of Technology, Banashankari Stage II, Karnataka, Bangaluru, 560070, India; Sangeetha G., Department of Computer Science, Christ University, Karnataka, Bengaluru, India
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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
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Enhancing Early Detection of Cardiovascular Disease through Feature Optimization Methods
Subject
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Cardiovascular detection (CVD); Heart disease; Machine Learning; Optimization
Description
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cardiovascular diseases are the most common reason for mortality around the world. Early detection of the ailment can help to reduce the mortality rate considerably. The ever-growing technologies like machine learning algorithms and deep learning models can be used for this purpose. The AI models thus developed can be used for health sector for assisting doctors in assessing the stage of the disease and detection and tracking of the clots in the cardio blood vessels. The proposed work uses two benchmark datasets for analysing the performance of various machine learning algorithms including KNN, Nae Bayes, Decision Tree and Random Forest. The performance was compares based on the AUC %. The method feature reduction were used here to reduce the computational complexity of the model. The results show that Random Forest Algorithm gave the best result when compared to other algorithms in case of UCI dataset and MLP classifier gave best results for Kaggle dataset. 2024 IEEE.
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Didwania S.; Amrutha K.; Resmi K.R.; Vineetha K.R.
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2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024, pp. 119-122.
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/AIC61668.2024.10731049" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIC61668.2024.10731049</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210244884&doi=10.1109%2FAIC61668.2024.10731049&partnerID=40&md5=705fceb939cbca153284d24d92c99c5a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210244884&doi=10.1109%2fAIC61668.2024.10731049&partnerID=40&md5=705fceb939cbca153284d24d92c99c5a</a>
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ISBN: 979-835038459-8
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Online
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English
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Conference paper
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Didwania S., Christ University, Department of Computer Science, Bengaluru, India; Amrutha K., Christ University, Department of Computer Science, Bengaluru, India; Resmi K.R., Christ University, Department of Computer Science, Bengaluru, India; Vineetha K.R., Christ University, Department of Computer Science, Bengaluru, India
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Conference Papers
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Faculty Publications- Conference Papers
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Impact of Artificial Intelligence on the Social Media Marketing Strategies
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AI; machine learning; smartphones; social media marketing (SMM)
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The use of social media is increasing as the use of smartphones is increasing, various applications on smartphones are now becoming good platform for market. As the use of smartphones is increasing the use of different artificial intelligence (AI) technologies making the phones smarter. The social media is now one of the most globally crowded platforms with millions of users. Most of the businesses are now turning their marketing strategies by highlighting the digital marketing from various platforms. Thus, the focus of study is to find the increasing impact of AI on the social media related marketing strategies. Different studies highlight the different impacts of social media and marketing using different AI tools and platforms which makes customer to find the best product as per their choice. So, social media marketing has become simpler and more adaptable thanks to the development of artificial intelligence. 2024 IEEE.
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Tripathi S.; Tiwari A.; Singh S.; Sani A.; Aswale N.; Patil A.
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Proceedings - 2024 International Conference on Healthcare Innovations, Software and Engineering Technologies, HISET 2024, pp. 383-385.
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/HISET61796.2024.00112" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/HISET61796.2024.00112</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211380105&doi=10.1109%2FHISET61796.2024.00112&partnerID=40&md5=9067e4ddd429b96a0ec395f0c18d3144" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211380105&doi=10.1109%2fHISET61796.2024.00112&partnerID=40&md5=9067e4ddd429b96a0ec395f0c18d3144</a>
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ISBN: 979-835036070-7
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Online
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English
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Conference paper
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Tripathi S., Christ (Deemed to Be) University, Delhi, India; Tiwari A., Christ (Deemed to Be) University, Delhi, India; Singh S., Sharda University, Sharda School of Business Studies, Uttar Pradesh, India; Sani A., Brainware University, Management School of Management and Commerce, Kolkata, India; Aswale N., Vishwakarma University, Faculty of Commerce and Management, Pune, India; Patil A., Krishna Vishwa Vidyapeeth (Deemed to Be University), Department of Radio-Diagnosis, Karad, India
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Conference Papers
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Experimental Investigation of Nano Hexagonal Boron Nitride Reinforcement in Aluminum Alloys Through Casting Method
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AA 7050; Aluminum metal matrix composites; Nano hexagonal boron nitride; Nanocomposites; Sir casting
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Aluminum metal matrix composites (AlMMCs) have a significant impact on a variety of industries that seek for innovation, efficiency, and sustainability. AlMMCs are substantial because of the special combination of properties that make them an essential part of contemporary production and design. Custom made properties of the AlMMCs can be obtained by the reinforcing different ceramic particles. Among the reinforcements, nano hexagonal boron nitride were rarely used. Hexagonal boron nitride particles have self-lubrication properties and it is one of the promising substitutes of graphite. The incorporation of hexagonal boron nitride (hBN) as a reinforcement material in aluminum alloys has garnered significant attention in recent years. This paper provides an overview of the reinforcement of nano hBN in aluminum alloys through casting method and highlights the mechanical and thermal properties of these alloys. The results show that the wear rate of the composite at 2wt.% is 9.91% lower for a load of 40 N when compared to unreinforced composite. Furthermore, the impact of hBN content, dispersion, and processing parameters on the properties of the composites is analyzed. The unique structural and thermal properties of hBN, along with excellent lubricating abilities, make it a promising candidate for reinforcing aluminum composites. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Menachery N.; Thomas S.; Deepanraj B.
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Springer Proceedings in Physics, Vol-415 SPP, pp. 311-318.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-3-031-69966-5_30" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-69966-5_30</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211245282&doi=10.1007%2F978-3-031-69966-5_30&partnerID=40&md5=2d8c5095be7c7a869dde89403bdc0c1a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211245282&doi=10.1007%2f978-3-031-69966-5_30&partnerID=40&md5=2d8c5095be7c7a869dde89403bdc0c1a</a>
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ISSN: 9308989; ISBN: 978-303169965-8
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Online
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English
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Conference paper
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Menachery N., Department of Mechanical Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India, Department of Mechanical Engineering, Jyothi Engineering College, Kerala, Thrissur, India; Thomas S., Department of Mechanical Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Deepanraj B., Department of Mechanical Engineering, College of Engineering, Prince Mo-hammad Bin Fahd University, Al Khobar, Saudi Arabia
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Conference Papers
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Digital Forensics Chain of Custody Using Blockchain
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Blockchain; Chain of Custody; Digital Evidence; Digital Forensics; Smart Contracts
Description
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In todays world, Digital Forensics is a crucial subject with much scope as data storage becomes more decentralised. The collection and preservation of digital media is a topic of concern across the Cyber Security and Digital Forensics field. With Cloud Infrastructure and other technologies, data is not permanently stored in one place and gathering and analysing it can become a headache for Forensic Investigators. Blockchain, however, works as a decentralised, distributed peer-to-peer network and thus can be considered a suitable solution for the mentioned problems. With the help of a blockchain network and Smart Contracts, Digital Forensics can be significantly improved to adapt to modern digital architecture. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Fernando C.A.; Thomas R.; Mary Anita E.A.; Lapin V.
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Lecture Notes in Networks and Systems, Vol-1207 LNNS, pp. 63-73.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-3-031-77229-0_7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-77229-0_7</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210170538&doi=10.1007%2F978-3-031-77229-0_7&partnerID=40&md5=9bcfc47e80925f7ac814878bb92fd3e2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210170538&doi=10.1007%2f978-3-031-77229-0_7&partnerID=40&md5=9bcfc47e80925f7ac814878bb92fd3e2</a>
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ISSN: 23673370; ISBN: 978-303177228-3
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Online
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English
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Conference paper
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Fernando C.A., Christ University, Bangalore, India, North-Caucasus Federal University, Stavropol, Russian Federation; Thomas R., Christ University, Bangalore, India, North-Caucasus Federal University, Stavropol, Russian Federation; Mary Anita E.A., Christ University, Bangalore, India, North-Caucasus Federal University, Stavropol, Russian Federation; Lapin V., Christ University, Bangalore, India, North-Caucasus Federal University, Stavropol, Russian Federation
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Conference Papers
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Design and Stress Analysis of the Frame for an Electric Bike
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ANSYS Mechanical APDL; Bicycle frame; Eco-friendly
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Global emissions have been on the rise since the industrial era because of the increased energy-intensive human activities, which is a direct cause of global warming and climate change. Of the total emissions, around 17% is from the transportation sector, which significantly contributes to the emissions. One of the easiest ways to be more sustainable is to choose electric vehicles instead of Internal combustion engines. Almost 75% of the vehicles registered in India are two-wheelers, but there are no affordable and reliable electric two-wheelers. This research works to optimize and analyze the design of a step-through frame design for an electric bicycle. The frame design is analyzed by providing boundary and loading conditions with two different materials (Steel-AISI4130 and Aluminum AL6061). The numerical analysis is carried out using ANSYS APDL. The result of von Mises stress is 166MPa and 160.4MPa for steel and aluminum, respectively. The result of stress and displacement is within the acceptable limit. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Niranjana S.J.; Shivakumar S.; Raghavendra S.; Ravikumar H.C.
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Smart Innovation, Systems and Technologies, Vol-409 SIST, pp. 207-215.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-97-7094-6_16" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7094-6_16</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210083331&doi=10.1007%2F978-981-97-7094-6_16&partnerID=40&md5=e44d02263befee1882177ac479f32106" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210083331&doi=10.1007%2f978-981-97-7094-6_16&partnerID=40&md5=e44d02263befee1882177ac479f32106</a>
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ISSN: 21903018; ISBN: 978-981977093-9
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Online
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English
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Conference paper
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Niranjana S.J., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to Be University), Bangalore, 560074, India; Shivakumar S., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to Be University), Bangalore, 560074, India; Raghavendra S., Department of Computer Science Engineering, CHRIST (Deemed to Be University), Bangalore, 560074, India; Ravikumar H.C., Department of Computer Science Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, 560082, India
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Conference Papers
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An Alternative Deep Learning Approach for Early Diagnosis of Malaria
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ConvNeXt; Deep Learning; Malaria detection; ResNet; Transfer Learning
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Considering the malaria disease-related moralities prevailing mainly in underdeveloped countries, early detection and treatment of malaria must be an essential strategy for lowering morbidity and fatality rates. Detection of Malaria using traditional investigation methods through blood samples and expert judgments was found to be time-consuming. In this paper, the authors introduced a Machine Learning automated system to eliminate the need for human intervention, which in turn enables early detection of malaria. The study has used various Deep Learning techniques such as traditional Convolutional Neural Network (CNN), VGG19, ConvNeXtXLarge, ConvNeXtBase, ConvNeXtSmall, ConvNeXtTiny, InceptionResnetv2, Xception, DenseNet169, EfficientNetB7, MobileNet, ResNet50, and NasNetLarge as base models. These models have been trained and tested with microscopic blood smear images dataset and observed that ConvNeXtXLarge detects malarial parasites with an accuracy of 96%. The proposed method outperforms the existing approaches in terms of both accuracy and speed. The findings of this work can contribute to the development of more accurate and efficient automated systems for early detection of Malaria. 2024 IEEE.
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Vrindavanam J.; Mary Jasmine E.; Kamath G.; Narayanan A.
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2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/ICCCNT61001.2024.10724604" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCNT61001.2024.10724604</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211165967&doi=10.1109%2FICCCNT61001.2024.10724604&partnerID=40&md5=3b68d8d62ca43feed6aeb2ac341752c6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211165967&doi=10.1109%2fICCCNT61001.2024.10724604&partnerID=40&md5=3b68d8d62ca43feed6aeb2ac341752c6</a>
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ISBN: 979-835037024-9
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Online
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English
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Conference paper
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Vrindavanam J., Dayananda Sagar University, Department of Computer Science and Engineering(AI & ML), Bengaluru, India; Mary Jasmine E., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Kamath G., Dayananda Sagar University, Department of Computer Science and Engineering(AI & ML), Bengaluru, India; Narayanan A., Dayananda Sagar University, Department of Computer Science and Engineering(AI & ML), Bengaluru, India
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Conference Papers
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Exploring Advances in Machine Learning and Deep Learning for Anticipating Air Quality Index and Forecasting Ambient Air Pollutants: A Comprehensive Review with Trend Analysis
Subject
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air pollutants; AQI; Artificial Neural Network; Linear Regression; Prediction
Description
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India and the rest of the world are growing more and more worried about polluted atmosphere on a daily basis. A comprehensive prevision and prognostication of air quality parameters is vital due to the major harm that air pollution causes to both the environment and public health, causing concern on a global scale. In-depth analyses of the methods for predicting ambient air pollutants, like carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter with diameters less than 10? (PM10) and less than 2.5? (PM2.5), and ozone (O3), are provided in this work in tandem with the modeling of the Air Quality Index (AQI).To further enhance the anticipated precision and applicability of these models, the assessment additionally employs trend analysis to determine precedents and new trends in air quality. This paper offers insights into recent advances in algorithms using deep learning and machine learning for anticipating AQI and forecasting pollutant concentrations by combining current research in this topic. In order to inform policy decisions and measures aimed at reducing air pollution and its adverse effects on public health, trend analysis integration affords a more thorough comprehension of the dynamics of air quality. 2024 IEEE.
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Rajan D.; Rohini V.
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2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/ICCCNT61001.2024.10725099" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCNT61001.2024.10725099</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211171976&doi=10.1109%2FICCCNT61001.2024.10725099&partnerID=40&md5=81e37958607ee4de99469b0352c11b14" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211171976&doi=10.1109%2fICCCNT61001.2024.10725099&partnerID=40&md5=81e37958607ee4de99469b0352c11b14</a>
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ISBN: 979-835037024-9
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Online
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English
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Conference paper
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Rajan D., Christ (Deemed to be University), Department of computer science, Bangalore, India, Cmr Institute of Technology, Department of Mca, Bangalore, India; Rohini V., Christ (Deemed to be University), Department of computer science, Bangalore, India
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Conference Papers
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Examining the Benefits of AI in Wearable Sensor-based Healthcare Solutions
Subject
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Adjustments; Emergence; Generation; Healthcare; Personalized
Description
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The emergence of the AI generation has introduced adjustments to the manner healthcare solutions are advanced and applied. Wearable sensor-based total healthcare answers were revolutionized by leveraging AI in diverse packages. AI may be used to enhance the accuracy and precision of facts analysis, simplify information collection procedures, and pick out affected person-unique styles from the accrued data. Furthermore, AI can provide both actual-time and predictive analytics abilities, which are particularly useful for devising personalized healthcare offerings. Its provision of scalable systems hurries up the traits of various programs and has enabled personalized healthcare answers to be deployed in a shorter length. In spite of a number of the associated challenges, including data privacy troubles, AI-based wearable sensor-based healthcare answers can revolutionize patient tracking and timely detection of capability health conditions, improve preventive fitness care, and decrease healthcare fees. 2024 IEEE.
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Takkalapally P.; Kasula B.Y.; Patil S.C.; Logeshwaran J.
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2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/ICCCNT61001.2024.10725054" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCNT61001.2024.10725054</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211152416&doi=10.1109%2FICCCNT61001.2024.10725054&partnerID=40&md5=611c77337e5c5d4eb77e7b4f32ca658c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211152416&doi=10.1109%2fICCCNT61001.2024.10725054&partnerID=40&md5=611c77337e5c5d4eb77e7b4f32ca658c</a>
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ISBN: 979-835037024-9
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Online
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English
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Conference paper
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Takkalapally P., Lewis University, Computing and Mathematical Sciences, Department of Engineering, United States; Kasula B.Y., University Of The Cumberlands, Dept of Information Technology, Williamsburg, KY, United States; Patil S.C., United States; Logeshwaran J., Christ (Deemed to be University), Department of Computer Science, Karnataka, Bengaluru, 560029, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Title
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Segment Anything Model (SAM) to Segment lymphocyte from Blood Smear Images
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Deep learning; Lymphocyte; Segment Anything Model; Segmentation; white blood cells
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Automated lymphocyte segmentation from smear images plays an important role in disease diagnosis and monitoring, aiding in the assessment of immune system function and pathology detection. This study proposes an approach for lymphocyte segmentation utilizing Segment Anything Model (SAM) which is a deep learning model. Our method leverages a pre trained SAM architecture and fine-tunes it on a custom dataset comprising smear images containing lymphocytes. The pretrained model's ability of versatile segmentation combined with fine-tuning on the specific dataset enhances its performance in accurately identifying lymphocyte boundaries. We evaluate the proposed approach on a diverse set of smear images, demonstrating its effectiveness in segmenting lymphocytes with impressive IOU score and Dice Score. SAM deep learning model, fine-tuned on custom datasets, holds promise for robust and efficient lymphocyte segmentation from blood smear images. 2024 IEEE.
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Patil A.P.; Hiremath M.
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2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024, pp. 1080-1084.
Publisher
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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/AIC61668.2024.10730867" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIC61668.2024.10730867</a>
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ISBN: 979-835038459-8
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Online
Language
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English
Type
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Conference paper
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Patil A.P., Christ (Deemed to Be University), Department of Computer Science, India, Cmr Institute of Technology, Department of Computer Application, Bengaluru, India; Hiremath M., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, India
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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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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
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Mental Health Stigma: Strategies for Destigmatization in Healthcare Settings
Subject
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Destigmatization; ENACT program; IBH therapies; Mental Health Advocacy; RESHAPE program
Description
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Mental illness is one of the most common disabilities in the world. The term "mental illness stigma"describes harmful practices and misconceptions that lead to a detrimental effect on the mental health, motivation, and self-worth of those who suffer from mental illnesses. Health care services are important for treating and reducing the negative stigma of mental health, as they are areas where patients seek relief and support. The study aims to investigate the causes and how to reduce them. Explores ways to disrupt the health care environment, specifically the RESHAPE program, which focuses on the concept of "critical". This review paper looks at 8-10 papers on mental health and stigma and how stigma will be reduced. The results show that a large number of doctors and students are stigmatized, negatively affecting the lives of people affected by mental illness. RESHAPE, KAP, and IBH therapies are also effective ways to minimize mental health stigma. This intervention aims to educate public health workers, promote social cohesion, and integrate treatment into primary health care, improving treatment into primary health care, improving treatment quality and patient outcomes. The study draws attention to the importance of stigma reduction efforts in the long term in health education and practice emphasis. 2024 IEEE.
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Verma A.; Panda K.T.; Sharma V.; Tripathy H.K.; Al-Khasawneh M.A.; Alkhayyat A.
Source
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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024
Publisher
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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/ICEECT61758.2024.10738916" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEECT61758.2024.10738916</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211165635&doi=10.1109%2FICEECT61758.2024.10738916&partnerID=40&md5=a8658e120081e05ce3e05b2502c72620" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211165635&doi=10.1109%2fICEECT61758.2024.10738916&partnerID=40&md5=a8658e120081e05ce3e05b2502c72620</a>
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Restricted Access
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ISBN: 979-835037809-2
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Verma A., Kalinga Institute of Industrial Technology, India; Panda K.T., Kalinga Institute of Industrial Technology, India; Sharma V., CHRIST University, Computer Science Department, India; Tripathy H.K., Kalinga Institute of Industrial Technology, India; Al-Khasawneh M.A., Skyline University College, University City Sharjah, School of Computing, Sharjah, 1797, United Arab Emirates, Applied Science Private University, Applied Science Research Center, Amman, Jordan, Jadara University Research Center, Jadara University, Jordan; Alkhayyat A., The Islamic University, College of Technical Engineering, Najaf, Iraq
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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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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
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A Comparative Study of ML and DL Approaches for Twitter Sentiment Classification
Subject
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comparative analysis; deep learning models; machine learning models; sentiment classification; text preprocessing; word embedding
Description
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This research made use of various machine learning (ML) and deep learning (DL) methods - such as support vector machines, random forests, logistic regression, naive Bayes, and XGBoost, convolutional neural networks (CNNs), and feedforward neural networks (FNNs) - for tweet analysis to investigate public sentiment towards Ola and Uber. The objective is to determine the most effective method for distinguishing between good and negative tweets. Feature engineering techniques improve the algorithms interpretation of tweet content. To balance out the disparity between positive and negative tweets. The project aims to uncover customer wants and concerns on Twitter to help Ola and Uber, in addition to improving Algorithms Accuracy. The study intends to help these ride-hailing businesses make educated modifications to boost customer happiness by closely examining tweets. Essentially, the study assesses how well various ML and DL algorithms comprehend user feedback on Uber and Ola. The overarching goal is to not only enhance computational methods but also contribute to the improvement of these ride-hailing services, ultimately fostering a more positive online environment for Ola and Uber enthusiasts. In summary, the study investigates sentiment analysis techniques on Twitter to optimize understanding of Ola and Uber-related tweets, aiming to facilitate positive changes for the ride-hailing services and their customers, promoting a friendlier Twitter community. 2024 IEEE.
Creator
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Aamir M.; Lekha J.; Sweety
Source
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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2024-01-01
Identifier
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<a href="https://doi.org/10.1109/ICEECT61758.2024.10738938" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEECT61758.2024.10738938</a>
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ISBN: 979-835037809-2
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Aamir M., Christ (Deemed to be University), School of Science, Lavasa, India; Lekha J., Christ (Deemed to be University), School of Science, Lavasa, India; Sweety, Christ (Deemed to be University), School of Science, Lavasa, India
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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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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
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Parkinsons Disease Progression Prediction using Advanced Machine Learning Techniques
Subject
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CatBoost regressor; extraTrees regressor; machine learning; parkinsons' disease; proteomic data; unified parkinson's disease rating scale (UPDRS)
Description
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Parkinson's disease (PD) is a neurodegenerative condition that affects people over time and significantly lowers their quality of life. Patients with PD experience both motor and non-motor symptoms. Through clinical evaluation, the Unified Parkinson's Disease Rating Scale (UPDRS) is used to quantify the severity of Parkinson's disease. No definitive diagnostic tests for PD currently exist. Emerging machine learning techniques show potential to forecast future UPDRS scores for making informed medical decisions and enable better disease management. This paper studies research leveraging proteomic data to forecast PD prognosis, focusing on advanced machine learning techniques like CatBoost Regressor, ElasticNet, XGBoost Regressor, RandomForest Regressor, ExtraTrees Regressor and DecisionTree Regressor. 2024 IEEE.
Creator
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Bhattacharya T.; Thomas K.T.; Mathew L.
Source
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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2024-01-01
Identifier
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<a href="https://doi.org/10.1109/ICEECT61758.2024.10739044" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEECT61758.2024.10739044</a>
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Restricted Access
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A related resource
ISBN: 979-835037809-2
Format
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Online
Language
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English
Type
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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
Bhattacharya T., Christ (Deemed To Be) University, Department of Data Science, Pune, India; Thomas K.T., Christ (Deemed To Be) University, Department of Data Science, Pune, India; Mathew L., Christ (Deemed To Be) University, Department of Data Science, Pune, India