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Deciphering PCOS: Precision Diagnosis through Advanced Machine Learning Techniques 15

Deciphering PCOS: Precision Diagnosis through Advanced Machi...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Parihar, Varutri Gourisaria, Mahendra Kumar Singh, Jay Prakash Das, Himansu Panda, Amiya Ranjan Mishra, Soumya Ranjan Kiit Deemed to be University School of Computer Engineering Odisha Bhubaneshwar India Manipal University Jaipur Dept of Computer Science & Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350370249

PCOS, a complex hormonal disorder impacting women, necessitates precise detection for effective intervention, particularly concerning infertility. Male sex hormones called androgens, which are typically present in modest amounts in women, are produced abnormally by the ovaries in patients with polycystic ovarian syndrome (PCOS). Machine Learning (ML) emerges as a potent tool in this pursuit, outperforming traditional diagnostic methods. Our research delves into various ML techniques for PCOS detection, evaluating models based on f1beta score, accuracy, recall, and precision. Results unveil the Gradient Boosting Algorithm as the frontrunner, boasting a remarkable testing accuracy of 0.9016. By identifying PCOS at an early stage, our study not only aids in reducing miscarriage rates but also facilitates the rehabilitation of affected women. With its unparalleled reliability, the Gradient Boosting Algorithm emerges as a promising avenue for categorizing PCOS patients. This study emphasizes how machine learning (ML) can improve women's health by using precision diagnosis. © 2024 IEEE.

关键词： Classification Gradient Boosting Hormonal Disorder Machine Learning Polycystic Ovarian Syndrome

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Designing a Secure Audio / Text Based Captcha Using Neural Network 13

Designing a Secure Audio / Text Based Captcha Using Neural N...

引用

13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Pai, H. Aditya Anandkumar, M. Prasad, M.S. Guru Agarwal, Jyoti Christa, Sharon Dept. of Computer Science and Engineering Dehradun India Dept. of Computer Application Dehradun India

ISBN: (纸本)9781665462631

A CAPTCHA is used to reduce the misuse in the case of rising automated bots or software systems and also to avoid any corruption of the web services. The Captcha is used as solving a turing test problem by the users, before they get access to the software system. The paper discusses about the model that extracts the alphanumeric characters from either of audio or text-based captchas. Finally, the model's performance is found based on different parameters like accuracy level, specificity, precision etc. © 2023 IEEE.

关键词： Web services

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Leveraging Advanced Machine Learning Algorithms for Loan Repayment Prediction 15

Leveraging Advanced Machine Learning Algorithms for Loan Rep...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ranjan, Akshat Gourisaria, Mahendra Kumar Singh, Jay Prakash Panda, Amiya Ranjan Mishra, Soumya Ranjan Parihar, Varutri KIIT Deemed to be University School of Computer Engineering Odisha Bhubaneshwar India Manipal University Jaipur Dept of Computer Science & Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350370249

Accurately predicting loan repayment behavior is a critical challenge for financial institutions, which often face high default rates and financial instability due to inaccurate credit assessments. In order to address these problems, this study looks into the application of sophisticated machine learning methods such Random Forests, Decision Trees, Support Vector Machines, and Stacking Classifiers. The models, trained on diverse client variables, significantly outperform traditional credit scoring methods. The findings suggest that integrating these sophisticated models can enhance financial performance, reduce default risks, and enable more informed lending decisions. The study also examines potential avenues for future research, including the application of increasingly sophisticated machine learning techniques and the integration of new data sources to enhance models. This study advances the field of machine learning in finance by providing business and academics with insightful information that could result in stronger and more secure financial systems. © 2024 IEEE.

关键词： Adversarial machine learning

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The Role of Medical Expert Systems in Diagnosis of Chronic Kidney Diseases 2

The Role of Medical Expert Systems in Diagnosis of Chronic K...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Kumar, Kuchipudi Prasanth Bharati, K.F. Jawaharlal Nehru Technological University Dept. of Computer Science and Engineering Andhra Pradesh Ananthapuramu515002 India Jntua College of Engineering Dept. of Computer Science and Engineering Andhra Pradesh Ananthapuramu515002 India

ISBN: (纸本)9798350370188

The ability to forecast kidney diseases in real-time is critical for preserving patients' lives. Early detection of these diseases not only extends the patient's longevity but also reduces treatment costs. It is very advised to develop a reliable expertise system for detecting these problems before they worsen. These smart solutions have the potential to save many lives. This review study assesses the Dice score efficacy characteristics obtained from various methodologies utilized in previous studies for detecting renal abnormalities or chronic kidney diseases. It gives a complete overview of medical expert systems in proposed methodology and emphasizes the most effective methods for identifying chronic kidney disease (CKD). © 2024 IEEE.

关键词： Diagnosis

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Eye Tracking System Using Convolutional Neural Network 5

Eye Tracking System Using Convolutional Neural Network

引用

5th International Conference on computer and Applications, ICCA 2023

作者： Iskandar, Shatha Jaara, Zahraa Ali, Mohamad Abou Sbeity, Fatima Kasem, Abdallah Hamawy, Lara Lebanese International University Department of Biomedical Engineering Lebanon University of the Basque Country Dept.of Computer Science & Artificial Intelligence Spain Lebanese University Dept. of Physics & Electronics Lebanon Notre Dame University Dept. of Electrical & Computer Engineering Lebanon

ISBN: (纸本)9798350303254

Several diseases and disabilities, including cerebral palsy (CP), cause patients to lose their ability to communicate easily with the outside world. In this paper we aim to detect the movement of the iris that can be used to move a wheelchair for CP patients, write sentences by simply looking at an interactive screen, or even by moving an artificial arm. Thus, the two-dimensional CNN model and the coding method, we proposed, tracks the eye movement, specifically the movement of the iris, using VGG image annotator software and a dataset of several people's eyes images A dataset of 500 images from several patients, where only the eyes were cropped and studied, was taken to train the model about eye movement and detection. The proposed model achieved a mean squared error drop of about 49,720 from the original we started with, which was 50,000. © 2023 IEEE.

关键词： Neural network models

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ML Assisted Web Application Firewall 12

ML Assisted Web Application Firewall

引用

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kalariya, Pranav Jethva, Manthan Alginahi, Yasser University of Windsor Electrical and Computer Engineering Dept. Windsor Canada Computer Science Dept. Adrian College MI United States

ISBN: (纸本)9798350330366

This study presents the development and implementation of a sophisticated Web Application Firewall (WAF) empowered by machine learning techniques to bolster cybersecurity measures. Traditional WAFs primarily rely on rule-based systems, which may struggle to adapt to the evolving nature of web-based threats. In contrast, our proposed solution leverages machine learning algorithms to dynamically analyze and respond to emerging cyber threats, providing a more proactive and adaptive defense mechanism. The core functionality of the system involves the continuous monitoring of incoming web traffic, extracting relevant features, and utilizing a machine learning model to classify the traffic as either benign or malicious. The model is trained on historical data to recognize patterns and behaviors indicative of various cyber threats, including SQL injection, cross-site scripting, and other common attack vectors. Through this learning process, the system becomes adept at discerning malicious activities and adapting its defense strategies accordingly. The proposed model helps achieve higher precision in identifying the threat requests from normal requests. © 2024 IEEE.

关键词： Learning algorithms

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Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350378092

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Authentication and Authorization in Zero Trust IoT: A Survey 35

Authentication and Authorization in Zero Trust IoT: A Survey

引用

35th Irish Systems and Signals Conference, ISSC 2024

作者： James, Meha Newe, Thomas O'Shea, Donna O'Mahony, George D. Munster Technological University Dept. Computer Science Cork Ireland University of Limerick Dept. Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798350352986

A critical challenge in implementing Zero trust (ZT) in Internet of Things (IoT) environments is ensuring secure authentication and authorization mechanisms. The work in this article delves into the intricate realm of ZT by critically examining the state-of-the-art authentication and authorization processes within the ZT framework for IoT. Despite the wealth of work on ZT and Zero Trust Architecture (ZTA), notable research gaps persist, specifically in identity authentication and fine-grained access control. Given the vast and diverse ecosystem of IoT devices, device identification complexities arise from a lack of unique hardware identifiers, rendering traditional methods susceptible to cyber-attacks, such as, spoofing. For dynamic IoT environments, achieving the optimal implementation in access policies poses a significant challenge. Numerous ZTA methodologies, integrating access controls, blockchain frameworks, and advancements in Artificial Intelligence (AI) and Machine Learning (ML), have been implemented to tackle these challenges. However, research gaps remain, particularly in understanding the behavioral aspects within these frameworks. As a result, this article focuses on identifying how device behavioral aspects can improve authentication and authorization in ZT for IoT. Finally, the article concludes by highlighting how these challenges can serve as future research directions. © 2024 IEEE.

关键词： Authorization

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Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Rathod, Vijayalaxmi N. Goudar, R.H. Dhananjaya, G.M. Patil, Minal Hukkeri, Geetabai S. Kaliwal, Rohit B. Dept. of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi India Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru India

ISBN: (纸本)9798331505134

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in healthcare, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain. © 2024 IEEE.

关键词： Diseases

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Anomaly Detection from Web Log Data Using Machine Learning Model 7

Anomaly Detection from Web Log Data Using Machine Learning M...

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7th International Conference on computer Applications in Electrical engineering-Recent Advances, CERA 2023

作者： Mishra, Amit Kumar Bagla, Piyush Sharma, Ravi Pandey, Neeraj Kumar Tripathi, Neha Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India National Institute of Technology Department of Computer Science & Engineering Jalandhar India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305005

The information in the logs produced by the servers, devices, and applications can be utilized to assess the system's health. It's crucial to manually review logs, for instance, during upgrades, to verify whether the update and data movement went smoothly. Manual testing is insufficiently trustworthy, and manual log examination takes much time and effort. In this paper, we propose to search log files for anomalous sequences using the machine learning methods K-Means and DBSCAN. The two data representation approaches examined in this study were feature vector representation and IDF representation. The effectiveness of the deployed machine learning algorithms was examined using evaluation measures like F1 score, recall, and precision. The study found considerable differences in the algorithms' capacities to spot anomalies, with some algorithms being better at seeing various types of abnormal arrangements than their overall prevalence. By using the study's findings, the user might be able to spot strange arrangements after manually sifting through the log file. © 2023 IEEE.

关键词： Anomaly detection

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