462
20
11858
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Dublin Core
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Conference Papers
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Faculty Publications- Conference Papers
Dublin Core
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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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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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Faculty Publications- Conference Papers
Dublin Core
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Title
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Experimental Investigation of Nano Hexagonal Boron Nitride Reinforcement in Aluminum Alloys Through Casting Method
Subject
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AA 7050; Aluminum metal matrix composites; Nano hexagonal boron nitride; Nanocomposites; Sir casting
Description
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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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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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Impact of Artificial Intelligence on the Social Media Marketing Strategies
Subject
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AI; machine learning; smartphones; social media marketing (SMM)
Description
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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.
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/HISET61796.2024.00112" 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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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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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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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
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Title
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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
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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
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/ICEECT61758.2024.10739207" 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
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/.
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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
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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
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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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
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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
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.
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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/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
Type
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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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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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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.
Source
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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.
Date
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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
Language
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English
Type
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Conference paper
Coverage
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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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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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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.
Creator
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Tripathi S.; Tiwari A.; Singh S.; Roy A.; Singla J.; Chaudhry R.
Source
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Proceedings - 2024 International Conference on Healthcare Innovations, Software and Engineering Technologies, HISET 2024, pp. 377-379.
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/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
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
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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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Smart Certificates Using Blockchain: A Review
Subject
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Digital Certificates; Remix Ethereum IDE; Smart Contracts
Description
An account of the resource
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.
Source
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Lecture Notes in Networks and Systems, Vol-1207 LNNS, pp. 231-239.
Publisher
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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/10.1007/978-3-031-77229-0_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
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
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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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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A Heuristic Approach to Resolve Priority-Driven Unbalanced Transportation Problem (PUTP)
Subject
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Demand priority; MMPUTP; Unbalanced transportation problem; VAM
Description
An account of the resource
This research addresses the priority-driven unbalanced transportation Problem (PUTP), characterized by a situation where the overall demand surpasses the available supply. We propose the Max-flow Min-cost Priority-driven Unbalanced Transportation Problem (MMPUTP) as a heuristic approach to handle this issue effectively. The strategy of MMPUTP focuses on optimizing resource allocation and reducing costs, making it highly effective in fulfilling high priority needs in a cost-efficient manner. Through a comparison with Vogel's Approximation Method (VAM) over different sets of problems ranging in size from 5?5 to 50?50, the effectiveness of the MMPUTP algorithm is evident. The findings underscore the significance of choosing the right algorithm based on the size and complexity of the problem set in the context of the Priority-driven Unbalanced Transportation Problem, with MMPUTP proving to be a flexible and reliable option in various situations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Arif A.S.; Babu M.A.; Islam M.N.; Sharif Uddin M.; Poonia R.C.
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Smart Innovation, Systems and Technologies, Vol-409 SIST, pp. 337-349.
Publisher
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Springer Science and Business Media Deutschland GmbH
Date
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2024-01-01
Identifier
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<a href="https://doi.org/10.1007/978-981-97-7094-6_27" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7094-6_27</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210084748&doi=10.1007%2F978-981-97-7094-6_27&partnerID=40&md5=27cc06ab036cb06a4a7cc85eb280cff3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210084748&doi=10.1007%2f978-981-97-7094-6_27&partnerID=40&md5=27cc06ab036cb06a4a7cc85eb280cff3</a>
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ISSN: 21903018; ISBN: 978-981977093-9
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
Arif A.S., Department of Mathematics, Jahangirnagar University, Savar, Dhaka, Bangladesh; Babu M.A., Department of Physical Sciences, Independent University, Bangladesh, Dhaka, 1229, Bangladesh; Islam M.N., Department of Mathematics, Jahangirnagar University, Dhaka, 1342, Bangladesh; Sharif Uddin M., Department of Industrial Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj, 11942, Saudi Arabia; Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Delhi-NCR, New Delhi, 201003, 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
A name given to the resource
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Contextual Recommendation System: A Revolutionary Approach Using Hadoop, Spark, NLP and LLMs
Subject
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framework; hadoop; llms; nlp; recommendations; spark
Description
An account of the resource
This study presents a novel framework for contextual recommendations on platforms like Wikipedia, integrating Hadoop, Spark, NLP, and LLMs. Leveraging these technologies, the framework aims to enhance user experiences by delivering personalized article suggestions aligned with their current interests. Through scalable data processing, advanced NLP techniques, and LLM-powered semantic understanding, the framework offers a transformative approach to recommendation systems, promising to revolutionize knowledge exploration on digital platforms. 2024 IEEE.
Creator
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Reddy Kandula B.; Jacob 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/ICEECT61758.2024.10738951" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEECT61758.2024.10738951</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211113048&doi=10.1109%2FICEECT61758.2024.10738951&partnerID=40&md5=2eeb17e6208d3f5cf8d4254034e0dd84" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211113048&doi=10.1109%2fICEECT61758.2024.10738951&partnerID=40&md5=2eeb17e6208d3f5cf8d4254034e0dd84</a>
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ISBN: 979-835037809-2
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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
Reddy Kandula B., CHRIST(Deemed to be University), Department of Data Science, Lavasa, India; Jacob L., CHRIST(Deemed to be University), Department of Data 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
A name given to the resource
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Machine Learning-Based Imputation Techniques Analysis and Study
Subject
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imputation technique; KNN; Missing Data; Random Forest; SVM
Description
An account of the resource
Missing values are a significant problem in data analysis and machine learning applications. This study looks at the efficacy of machine learning (ML) - based imputation strategies for dealing with missing data. K-nearest Neighbours (KNN), Random Forest, Support Vector Machines (SVM), and Median/Mean Imputation were among the techniques explored. To address the issue of missing data, the study employs k-nearest neighbors, Random Forests, and SVM algorithms. The dataset's imbalance is considered, and the mean F1 score is employed as an evaluation criterion, using cross-validation to ensure consistent results. The study aims to identify the most effective imputation strategy within ML models, offering crucial insights about their adaptability across various scenarios. The study aims to determine the best plan for data preprocessing in machine learning by comparing approaches. Finally, the findings help to improve our knowledge and application of imputation techniques in real-world data analysis and machine learning. 2024 IEEE.
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Pindiyan A.V.; Pramila R.M.
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/ICEECT61758.2024.10739330" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEECT61758.2024.10739330</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211147858&doi=10.1109%2FICEECT61758.2024.10739330&partnerID=40&md5=99c1155a500a68eb3ad90091c3eee417" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211147858&doi=10.1109%2fICEECT61758.2024.10739330&partnerID=40&md5=99c1155a500a68eb3ad90091c3eee417</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
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Pindiyan A.V., CHRIST University, Department of Data Science, Bangalore, India; Pramila R.M., CHRIST University, Department of Data Science, Bangalore, 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
A name given to the resource
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Smart Vehicle Recognition System on Indian Roads Under Rainy Conditions
Subject
The topic of the resource
MAE (Mean absolute error); PixelLib; SD (standard deviation); Semantic segmentation
Description
An account of the resource
Recognition of vehicles under the different weather condition is very challenging. This work aims to recognize vehicles on Indian road in accordance with their visibility. It is important to recognize the surround roadside objects, particularly front and rare vehicles to avoid the accidents. Especially in raining conditions vehicle recognition is rate traffic surveillance cameras get decreases due to water droplets. Hence, we proposed a method for recognition of vehicles on road in rainy condition using image processing in computer vision techniques to improve the recognition rate. In the proposed method, an instance segmentation technique is used to segment the vehicles in Indian road scene and the visual noise and texture features are analysed and computed in the segmented images to recognize the vehicles more accurately in rainy conditions. By integrating the visual noise features with the texture feature and instance segmentation, the accuracy of vehicle recognition is improved. The experimental findings demonstrated that the suggested approach could more accurately predict the visibility of vehicles in rainy weather conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Arunkumar K.L.; Poornima K.M.; Danti A.; Manjunatha H.T.
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Lecture Notes in Electrical Engineering, Vol-1236 LNEE, pp. 421-430.
Publisher
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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_30" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-5866-1_30</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209950971&doi=10.1007%2F978-981-97-5866-1_30&partnerID=40&md5=24d0b1229eedfbabd3c3feb849841609" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209950971&doi=10.1007%2f978-981-97-5866-1_30&partnerID=40&md5=24d0b1229eedfbabd3c3feb849841609</a>
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ISSN: 18761100; ISBN: 978-981975865-4
Format
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Online
Language
A language of the resource
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
Arunkumar K.L., 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; Manjunatha H.T., Department of MCA, Jawaharlal Nehru New College of Engineering, Shimoga, India
-
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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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 Stroke Prediction: Leveraging Ensemble Learning for Improved Healthcare
Subject
The topic of the resource
Bagging; Ensemble Learning; Machine Learning; Stacking; Stroke Prediction
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Stroke, a potentially deadly medical disorder, requires excellent prediction and prevention measures to minimize its impact on individuals and healthcare systems. In this study, ensemble learning techniques are employed to enhance the accuracy of stroke prediction. The method combines four different machine learning algorithms, Adaboost, CatBoost, XGBoost, and LightGBM, to produce a strong predictive model. The data was composed of a rich set of demographic, medical, and lifestyle information. The data was preprocessed and features were engineered to maximize predictive performance. Results showed that the stacked ensemble model, which is composed of Adaboost, CatBoost, XGB, LightGBM, and Logistic Regression, meta-model, outperformed other models. The model has the potential to be used as a decision support tool in an early stroke risk assessment system, enhancing clinician decision-making and improving healthcare outcomes. 2024 IEEE.
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James E.; Jacob L.; Reddy K.B.
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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/ICCCNT61001.2024.10725209" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCNT61001.2024.10725209</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211187039&doi=10.1109%2FICCCNT61001.2024.10725209&partnerID=40&md5=9ad2b2a9cbfcab860755f54f62522c14" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211187039&doi=10.1109%2fICCCNT61001.2024.10725209&partnerID=40&md5=9ad2b2a9cbfcab860755f54f62522c14</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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James E., CHRIST(Deemed to be University), Data Science, Lavasa, India; Jacob L., CHRIST(Deemed to be University), Data Science, Lavasa, India; Reddy K.B., CHRIST(Deemed to be University), Data Science, Lavasa, India
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Conference Papers
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Investigating Key Contributors to Hospital Appointment No-Shows Using Explainable AI
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AI; Artificial Intelligence; Explainable; Health Analytics; Health Informatics; Healthcare; Hospital; Interpretable; LIME; Machine Learning; No-Shows; SHAP; XAI
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The healthcare sector has suffered from wastage of resources and poor service delivery due to the significant impact of appointment no-shows. To address this issue, this paper uses explainable artificial intelligence (XAI) to identify major predictors of no-show behaviours among patients. Six machine learning models were developed and evaluated on this task using Area Under the Precision-Recall Curve (AUC-PR) and F1-score as metrics. Our experiment demonstrates that Support Vector Classifier and Multilayer Perceptron perform the best, with both scoring the same AUC-PR of 0.56, but different F1-scores of 0.91 and 0.92, respectively. We analysed the interpretability of the models using Local Interpretable Model-agnostic Explanation (LIME) and SHapley Additive exPlanations (SHAP). The outcome of the analyses demonstrates that predictors such as the patients' history of missed appointments, the waiting time from scheduling time to the appointments, patients' age, and existing medical conditions such as diabetes and hypertension are essential flags for no-show behaviours. Following the insights gained from the analyses, this paper recommends interventions for addressing the issue of medical appointment no-shows. 2024 IEEE.
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Yiye V.; Ugbomeh O.; Ezenkwu C.P.; Ibeke E.; Sharma V.; Alkhayyat A.
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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/ICEECT61758.2024.10739123" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEECT61758.2024.10739123</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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Yiye V., Robert Gordon University, School of Creative and Cultural Business, Aberdeen, United Kingdom; Ugbomeh O., Robert Gordon University, School of Creative and Cultural Business, Aberdeen, United Kingdom; Ezenkwu C.P., Robert Gordon University, School of Computing, Engrg.,and Tech, Aberdeen, United Kingdom; Ibeke E., Robert Gordon University, School of Computing, Engrg.,and Tech, Aberdeen, United Kingdom; Sharma V., Christ University, Computer Science Department, India; Alkhayyat A., Islamic University, College of Technical Engineering, Najaf, Iraq
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Conference Papers
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Faculty Publications- Conference Papers
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Congestion Avoidance in Vehicular Ad Hoc Network MAC Layer Using Harmony SearchModified Laying Chicken Algorithm (HS-MLCA)
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Congestion avoidance; Harmony search; HS-MLCA; MAC; VANET
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To address congestion in the MAC layer and enhance overall performance, the HS-MLCA is proposed. This algorithm incorporates the principles of both Harmony Search and Laying Chicken Algorithm to optimize resource allocation and congestion control. At the MAC layer, HS-MLCA offers several advantages over traditional congestion control schemes. Firstly, it leverages the Harmony Search algorithm, which is known for its ability to exploit the best outcomes in search processes. By exploring the solution space and exploiting promising regions, HS-MLCA optimizes resource allocation in the MAC layer. The integration of the Laying Chicken Algorithm (LCA) further enhances performance by improving convergence speed and solution accuracy. This hybrid approach leverages the strengths of both Harmony Search (HS) and LCA, resulting in more efficient and effective resource management. The Laying Chicken Algorithm simulates the behavior of laying hens in terms of resource allocation and competition. This approach contributes to provide the solution in quality and convergence speed, as the algorithm adapts to the dynamic nature of the MAC layer and the varying traffic conditions in VANETs. By combining the strengths of Harmony Search and Laying Chicken Algorithm, HS-MLCA offers improved performance in terms of congestion control in the MAC layer. It optimizes resource allocation, minimizes collisions and packet loss, reduces delay, and enhances overall network efficiency. These improvements ultimately lead to better quality of service, increased network capacity, and enhanced user experience in VANETs. It is worth noting that the specific performance improvements and benefits of HS-MLCA may vary depending on the implementation details, network conditions, and the specific VANET scenario. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Manimekala B.; Prabha S.P.; Suresh S.
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Lecture Notes in Networks and Systems, Vol-1030 LNNS, pp. 161-174.
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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/10.1007/978-981-97-3745-1_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-3745-1_14</a>
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ISSN: 23673370; ISBN: 978-981973744-4
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Online
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English
Type
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Conference paper
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Manimekala B., Department of Computer Science, Christ University, Bengaluru, India; Prabha S.P., Department of Computer Applications, Dayananda Sagar College of Arts, Science and Commerce, Bengaluru, India; Suresh S., Department of Computer Applications, Dayananda Sagar College of Arts, Science and Commerce, Bengaluru, India
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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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Conference Papers
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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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Rating-Based Cyberbullying Detection with Text, Emojis on Social Media
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Cyberbullying Detection; Offensive Rating; Swear Words Dataset; TF-IDF with Random Forest
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In the dynamic landscape of online interactions, cyberbullying has become pervasive, profoundly impacting user's digital well-being. Public figures, especially celebrities and influencers, face heightened vulnerability to online harassment, exacerbated by the post-pandemic surge in social media usage. To address this challenge, our research adopts a holistic approach to detect cyberbullying in text, considering both textual content and the nuanced expressions conveyed through emojis on social media platforms. We employed a diverse set of machine learning and deep learning models, including Support Vector Classifier, Logistic Regression, Random Forest, XGBoost, LSTM, Bi-LSTM, GRU, and Bi-GRU, to accurately classify non cyberbullying or cyberbullying text. Beyond classification, our study introduces an offensive rating system, assigning severity ratings on a 1-5 scale to identify cyberbullying instances. A critical aspect is the establishment of a threshold value which depends on user security and safety ethics of different social media platforms; texts surpassing this trigger an automatic recommendation to block the user, ensuring a proactive response to minimize harm. This recent contribution not only comprehensively addresses cyberbullying but also empowers society. 2024 IEEE.
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Arjun Krishna K.; Jadhav D.; Vijayalakshmi S.; Palathara T.S.
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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/ICEECT61758.2024.10739238" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEECT61758.2024.10739238</a>
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ISBN: 979-835037809-2
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Online
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English
Type
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Conference paper
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Arjun Krishna K., Christ University, Department of Data Science, India; Jadhav D., Christ University, Department of Data Science, India; Vijayalakshmi S., Christ University, Department of Data Science, India; Palathara T.S., Christ University, Department of Data Science, India