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5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Kumar, Vijay Murmu, Mahendra Kumar Computer Engineering Department National Institute of Technology Kurukshetra136119 India

ISBN: (纸本)9783031609343

Object detection is one of the appealing branch of the study in the fields of computer science. Object detection defines some popular object detection models in deep learning such as YOLO (You Only Look Once), Single Sort Detector, R-CNN etc. It can be applied to solve many important challenges in computer vision, human-computer interaction such as health-care monitoring, autonomous driving, anomaly detection etc. Social distancing terminology has been coined repeatedly in-light of COVID19. Social distancing is an inherent part of health-care systems. In general, nowadays, social distancing applications have become more important due to the transmission of various diseases like smallpox, Scabies, COVID19, etc. It happens due to not maintaining of specified distance between infected peoples. The paper proposes a review of real-time applications of YOLO based object detection in social distancing. The present work is useful to understand the overview of YOLO models that can be applied for social distancing. Additionally, comparisons between the various YOLO versions with respect to different performance metrics concerning speed, accuracy, and computational complexity. These results are vital for the selection of an optimal YOLO version for real-time social distancing application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Image Segmentation Techniques for Bone Cancer Identification in X-ray and MRI Imagery 2

Image Segmentation Techniques for Bone Cancer Identification...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Likitha, M. Shidaganti, Ganeshayya I Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350372892

Integrating computer science with biology is crucial for advancing bone cancer diagnostics, particularly in distinguishing between benign and malignant tumors in X-ray and MRI images. The rapid evolution of medical imaging technology necessitates updated diagnostic methods to meet modern healthcare demands. Advanced image processing techniques, such as Watershed segmentation, are pivotal in precisely defining tumor boundaries. This research focuses on enhancing bone cancer detection by evaluating various machine learning methods and optimizing image clarity. Techniques like KAZE feature extraction, combined with XGBoost algorithms, demonstrate superior accuracy in identifying malignant regions compared to traditional classifiers such as CNN, ADABoost, and Random Forest. By integrating these advanced techniques, the ability to differentiate between benign and malignant tumors is significantly improved, leading to enhanced diagnostic accuracy and early detection rates. This approach holds promise for revolutionizing clinical practices and improving patient outcomes through more effective and automated diagnostics. © 2024 IEEE.

关键词： Magnetic resonance imaging

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A study of learning models for COVID-19 disease prediction

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2581-2600页

作者： Jain, Sakshi Roy, Pradeep Kumar School of Information Technology Vellore Institute of Technology TN Vellore632014 India Department of Computer Science and Engineering Indian Institute of Information Technology Surat Gujarat Surat394190 India

Coronavirus belongs to the family of Coronaviridae. It is responsible for COVID-19 communicable disease, which has affected 213 countries and territories worldwide. Researchers in computational fields have been active in proposing techniques to filter the information and recommendations about this disease and provide surveillance in controlling this outbreak. Researchers used Chest X-ray images, abdominal Computed Tomography scans, and Tweet datasets for building machine learning and deep learning-based models for COVID-19 predictions and forecasting purposes. Accuracy, sensitivity, specificity, precision, and F1-measure are the five primary evaluation criteria researchers employ to evaluate the quality of their study. This article summarises research works on COVID-19 based on machine learning and deep learning models. The analysis of these research works, along with their limitations and source of datasets, will give a quick start for future research to arrive at a defined direction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Coronavirus

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Bridging the Knowledge Gap: Improving BERT models for answering MCQs by using Ontology-generated synthetic MCQA Dataset 37

Bridging the Knowledge Gap: Improving BERT models for answer...

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37th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2024

作者： Sahil, Sahil Kumar, P. Sreenivasa Department of Computer Science and Engineering Indian Institute of Technology Madras India

BERT-based models possess impressive language understanding capabilities but often lack domain-specific knowledge, limiting their performance on specialised tasks such as medical multiple-choice question answering (MCQA). In this paper, we study how biomedical ontologies, rich repositories of medical knowledge, can be harnessed to enhance BERT-based models for medical MCQA task. Our contributions include OntoMCQA-Gen, a system which leverages different biomedical ontologies to construct BioOntoMCQA, a large synthetic MCQA dataset. OntoMCQA-Gen exploits the subclass-class relationships, definitions of concepts, and also synonym relationships from the ontologies to create this dataset of MCQs automatically. We then use this synthetic dataset to fine-tune various BERT-based models to answer medical MCQs. We evaluated these fine-tuned BERT models on the challenging MedMCQA and MedQA datasets of questions from admission examinations for medical degrees in India and USA, respectively. Our evaluation study on these datasets shows that fine-tuning the BERT-based models on BioOntoMCQA results in significantly improved accuracy scores. BioBERT and PubMedBERT, pretrained on the large medical corpus, have also shown significant improvements with our technique of fine-tuning ontology-generated synthetic data. This finding highlights the effectiveness of incorporating biomedical ontologies to enhance the BERT-based model in the medical domain. Moreover, our results underscore the importance of using ontology-generated data along with model adaptation for specialised domains, contributing to a novel advancement in natural language processing. Copyright © 2024 by the authors.

关键词： Ontology

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Quantification of Reliability in Object-Oriented Software: Functionality Perspective 1

Quantification of Reliability in Object-Oriented Software: F...

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1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Yadav, Anuj Kumar Gupta, C.L.P. Bansal Institute of Engineering and Technology Department of Computer Science Lucknow India

ISBN: (纸本)9798350373042

Object-oriented design and development are now commonly used in software development environments. Important design concepts including inheritance, coupling, cohesion, and encapsulation are supported by this method. The work in progress aims at offering a functionally-based, robust estimating technique for object-oriented design. Functionality plays a critical part in producing high-quality products within predetermined time and cost limits. This paper introduces an approach for quantifying functionality and reliability, acknowledging the paramount importance of functionality during the design phase. Statistical analyses affirm functionality as a significant factor influencing software reliability. Building on the proposed mapping of object-oriented design features to functionality, a multiple regression equation has been constructed to calculate the functionality of design hierarchies. It is clear that usefulness has a favourable effect on object-oriented systems' dependability. In order to determine dependability based on functionality, an additional multiple regression equation is created. Lastly, experimental trials are used to validate the suggested model. © 2024 IEEE.

关键词： Software reliability

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Ovulytics: A Machine Learning Approach for Precision Diagnosis of PCOD, PCOD and Infertility 1

Ovulytics: A Machine Learning Approach for Precision Diagnos...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Jha, Tanushree Sirisha, M. Bhargavi, M.S. Bangalore Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518943

Polycystic Ovary Syndrome (PCDS) and Polycystic Ovarian Disease (PCOD) are common endocrine disorders that affect a significant percentage of women of reproductive age, often leading to complications such as infertility. Early detection and differentiation of these conditions are important for timely intervention and effective management. This research proposes a machine learning-based solution to classify and predict the occurrence of PCDS, PCOD, and PCDS with infertility using Random Forest, Logistic Regression, and XGBoost. Each algorithm is trained and tested to predict the level of presence of PCDS, PCOD, and infertility-related PCDS. Among the models, Random Forest demonstrates superior performance. However, the importance of our work extends beyond performance metrics. This study illustrates how machine learning can be applied to complex, real-world healthcare challenges, such as early diagnosis and differentiation of overlapping conditions like PCDS and PCOD. The ability to leverage machine learning in this context aids earlier detection of these conditions, allowing for timely interventions that can have the potential to prevent further complications, such as infertility, cardiovascular diseases, and metabolic disorders. The potential impact of this research is substantial, offering scalable, non-invasive, and cost-effective diagnostic solutions, thus improving outcomes for millions of women worldwide and transforming reproductive healthcare practices. © 2024 IEEE.

关键词： Logistic regression

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Security and Service Vulnerabilities with HTTP/3 16

Security and Service Vulnerabilities with HTTP/3

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Hari Hara Sudhan, S. Kulkarni, Sameer G. Indian Institute of Technology Gandhinagar Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The adoption of Hypertext Transfer Protocol v3 (HTTP/3 or H3) is on the rise. In this context, we analyze the security vulnerabilities of H3, specifically with the QUIC protocol, and the associated challenges they pose to the commonly used network middleboxes. First, we demonstrate how the connection migration feature of QUIC can be used by malicious clients to launch denial of service (DoS) attacks through resource exhaustion of the connection state tables in the simple network address and port translation (NAPT) devices. Further, we show that the connection migration feature disrupts the services of various critical network middleboxes like Layer3/4 load-balancers, rate-limiters, and intrusion detection/prevention systems that rely on the connection state table for their faithful operation. We also present a feasible solution to mitigate the DoS attacks in connection tracking middleboxes. Second, we show how the spin bit in QUIC short header packets can act as a highly reliable covert channel to exchange information stealthily across two end-points. Nonetheless, this spin-bit can also be used to shield from spoofing attacks. Although H3, by virtue of QUIC, aims to be a secure and privacy-preserving protocol, the existence of such vulnerabilities calls for the community to explore and adopt additional measures to make H3 a truly secure protocol. © 2024 IEEE.

关键词： Denial of Service HTTP/3 Middlebox Network functions QUIC Security

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A Multi-Functional Web Tool for Comprehensive Threat Detection Through IP Address Analysis

A Multi-Functional Web Tool for Comprehensive Threat Detecti...

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2024 IEEE Conference on engineering informatics, ICEI 2024

作者： Tanan, Cebajel Kulkarni, Sameer G. Das, Tamal Hanawal, Manjesh K. Indian Institute of Technology Computer Science and Engineering Department Dharwad India

ISBN: (纸本)9798331505776

In recent years, the advances in digitalisation have also adversely contributed to the significant rise in cybercrimes. Hence, building the threat intelligence to shield against rising cybercrimes has become a fundamental requisite. Internet Protocol (IP) addresses play a crucial role in the threat intelligence and prevention of cyber crimes. However, we have noticed the lack of one-stop, free, and open-source tools that can analyse IP addresses. Hence, this work introduces a comprehensive web tool for advanced IP address characterisation. Our tool offers a wide range of features, including geolocation, blocklist check, VPN detection, proxy detection, bot detection, Tor detection, port scan, and accurate domain statistics that include the details about the name servers and registrar information. In addition, our tool calculates a confidence score based on a weighted sum of publicly accessible online results from different reliable sources to give users a dependable measure of accuracy. Further, to improve performance, our tool also incorporates a local database for caching the results, to enable fast content retrieval with minimal external Web API calls. Our tool supports domain names and IPv4 addresses, making it a multi-functional and powerful IP analyser tool for threat intelligence. © 2024 IEEE.

关键词： Internet protocols

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Study Exploring The Use Of Iot And ML In Pregnant Women Healthcare Monitoring System 2

Study Exploring The Use Of Iot And ML In Pregnant Women Heal...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Hemanth, N.G. Aishwarya, S.S. Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

In developing countries, rural areas often lack integrated medical systems, leading to pregnant women not receiving regular checkups, resulting in higher death rates for newborns and parents. This presents significant medical issues for women. A healthcare kit for pregnant women is designed to help them monitor their health and well-being. The kit, equipped with sensors that track heart rate, temperature, and other vital metrics, provides a convenient way for pregnant women to track ztheir health and identify potential problems early. By providing real-time data, the kit enables women to seek medical attention if needed, reducing the need for costly and time-consuming medicalinterventions. © 2024 IEEE.

关键词： Medical problems

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Property Price Prediction 3

Property Price Prediction

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Robin, Aleena Anna Prasad, Gouri Jiji, Jyothsna Raajesh, Sanjay Sheena, K.V. Muthoot Institute of Technology and Science Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

This data analytics project is designed for the real estate industry, with the primary aim of predicting property prices, including lands, buildings, and houses linear regression model and also by carefully analyzing and interpreting current market trends. Although numerous existing systems share the common objective of property price prediction, our proposed system brings distinct advantages. One of them is its ability to explore rental options for rooms and buildings. What truly sets our system apart is its comprehensive approach to property valuation. It achieves this by incorporating a user-specific data analysis component, which considers critical factors like user loan availability. Essentially, it bridges the gap between the realm of property investment and the realm of financial planning, equipping users with essential insights to make sound financial decisions in the context of real estate. This project has been meticulously designed with the unique needs of the real estate industry in mind, offering a transformative tool that assists individuals in making smarter choices concerning property investments, rentals, and loans. By providing a comprehensive, user-focused approach, it paves the way for more informed decision-making in the ever-evolving landscape of the real estate market, ultimately promoting financial prudence and responsible financial planning. © 2024 IEEE.

关键词： Data analytics project Linear regression Market trends analysis Predicting property prices Real estate industry Rental options exploration

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