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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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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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Performance Optimization for USB/IP (Universal Serial Bus/Internet Protocol) Network 1

Performance Optimization for USB/IP (Universal Serial Bus/In...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Tiwari, Kaushal Nath National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350367720

There is a huge rise in the number of USB devices available these days, so is the need to access these devices remotely. This motivates the development of USB/IP (USB over Internet Protocol) solutions. However, the network speed of USB/IP remains a concern. This work addresses the important need for enhancing the performance of USB/IP networks. We explore two techniques to improve the network performance namely VPN Optimization and Port Forwarding. VPN Optimization achieves maximum variation of 20s. In Remote Port Forwarding it took lesser time by at most 8s and maximum difference 29s in Local Port Forwarding. © 2024 IEEE.

关键词： Network Port Speed USB/IP VPN

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A contemporary review of machine learning-based heart disease prediction 2

A contemporary review of machine learning-based heart diseas...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Gupta, Sukriti Tripathi, A. Srivastava, Chaynika Buddha Institute of Technology Department of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Heart disease remains a leading cause of global mortality, necessitating effective predictive tools. As per the World Heart Federation, it was found that over a billion people globally continue to be affected by heart disease and 20.5 million deaths in 2021 occurred and there is no one-size-fits-all approach which can improve cardiovascular health worldwide (***, 2023). Machine learning emerges as a valuable tool for predicting heart diseases, offering improved accuracy compared to conventional approaches. The study focuses on addressing the global challenge of heart disease by leveraging machine learning for predictive analysis. It focused on increasing early detection so that treatment can be made and save the lives of individuals. The investigation unfolds the way machine learning can be applied to the early identification of cardiac conditions in the modern era. While analyzing machine learning, it appeared as a powerful as well as efficient technology that operates on testing as well as training principles. The research aims to employ classification techniques so it would able to build predictive models that will further uncover hidden patterns and increase accuracy in diagnosis. © 2024 IEEE.

关键词： Adversarial machine learning

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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 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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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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Classification of Arrhythmias 12-Lead ECG Signals Based on 1 Dimensional Convolutional Neural Networks

Classification of Arrhythmias 12-Lead ECG Signals Based on 1...

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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Kurniawan, Arief Triwibowo, Bayu Aditya Fandiantoro, Dion Hayu Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Computer Engineering Indonesia

ISBN: (纸本)9798350364101

Heart disease is the highest cause of death in the world. Arrhythmia is an abnormality in the rhythm of the heartbeat. The heart beats too fast, too slow, or irregularly. Arrhythmias are not always dangerous, e.g., someone who does excessive activity has a faster heart rate. Then, a diagnosis is needed to classify arrhythmias. One method used is ECG (Electrocardiogram) signal analysis. The ECG signal consists of P, QRS Complex, and T waves. The morphology of the QRS is used for arrhythmia classification. Currently, cardiologists analyze ECG signals by observing directly. This method is depending on the level of expertise of the cardiologist. Previous research classified arrhythmias based on the QRS morphology from a single ECG lead. As 12-lead ECG devices have now become standard in ECG examinations because abnormalities can be observed from multiple angles. This study proposes the classification of arrhythmias in 12-lead ECG signals based on the morphology of QRS complex waves using a deep learning 1-dimensional Convolutional Neural Network. The output of deep learning is the classification of arrhythmias into four classes, namely: Normal, Right Bundle Branch Block, Premature Ventricular Contraction, and Atrial Premature Beat. The outcome of the proposed system is that each QRS segment is used as input for deep learning, which can improve classification performance compared to the classification carried out by each lead. The experimental results show the method can be done well, with an average Accuracy, Precision, Sensitivity, and F1-Score were 98.8%, 99.2%, 99.2%, and 99.2%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning based Smart Crop Recommendation System 4

Machine Learning based Smart Crop Recommendation System

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Shwetha, A.N. Prabodh, C.P. Siddaganga Institute of Technology Department of Computer Science and Engineering Tumakuru India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349900

Many challenges are faced by agricultural sector due to various dependent factors like changes in climatic conditions, variation in soil properties and farming practices. What to plant in their agricultural land is the common question for all farmers. Many farmers are failing to identify the crop that suits their agricultural land to get good yields. This results in following the same methods and crops what their ancestors were following. The crop yield always depends on multiple parameters like soil properties, climate conditions, water level etc. So if the farmers get suggestions regarding which crop to grow in their agricultural land would be of great help. The government is also helping farmers in many ways, one such scheme is soil testing. Soil testing is very important because through testing we can identify nutritional level, pH level and moisture level of soil which can optimize crop prediction. To address the above mentioned issues, this paper proposes a Crop Recommendation System(CRS) to help farmers based on machine learning algorithms to take wise decision to select a crop for their agricultural land. The CRS considers historical data, soil type, pH level, season, water level and amount of pesticides to provide crop recommendation for agricultural plots. The CRS system uses Naive Bayes algorithm to select the crop suitability. The CRS is a user friendly system that can be accessed through web or mobile. The user can give necessary input regarding their agricultural land and get crop recommendation which suits their farming land. The proposed system aims to improve crop yield, minimize resources and environmental impact. © 2024 IEEE.

关键词： Smart agriculture

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A Review on YOLO Algorithms for Social Distancing 5th

A Review on YOLO Algorithms for Social Distancing

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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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