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Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis Using Machine Learning Approaches 5

Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis ...

引用

5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Sultan Bin Habib, Al Zadid Bin Syed, Md Asif Islam, Md. Ekramul Tasnim, Tanpia West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States West Virginia University Department of Industrial and Management Systems Engineering MorgantownWV26506 United States Stamford University Bangladesh Department of Computer Science & Engineering Dhaka1217 Bangladesh Green University of Bangladesh Department of Computer Science and Engineering Narayanganj Dhaka1461 Bangladesh

ISBN: (纸本)9798350394290

Polycystic Ovarian Syndrome (PCOS) is a widespread hormone problem for women of childbearing age. Women with PCOS may not ovulate;they might have high levels of androgens and have many small cysts on the ovaries. It can cause missed or irregular menstrual periods, excess hair growth, acne, infertility, and weight gain. Machine Learning (ML) can effectively diagnose this disease at an earlier stage as tons of medical data are available now. Traditional approaches to detect PCOS encompass a combination of clinical evaluation, medical history assessment, physical examination, and laboratory tests. These approaches aim to identify the characteristic symptoms and hormonal imbalances associated with PCOS. Physical examination requires good resources and costs time and money. In recent times, data-driven techniques have substantially advanced disease prediction within the medical field. We aim to utilize ML approaches, incorporating unique feature selection algorithms, to predict PCOS. This paper introduces a data-driven approach to PCOS diagnosis, combining Feature engineering and ML. Several feature selection approaches have been considered to select sets of features for training the ML model, including CatBoost, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), AdaBoost, Random Forest (RF). Results demonstrate that AdaBoost, with ten features selected by RF Feature Importance and Highest Correlation (HC), provides the highest test accuracy. © 2023 IEEE.

关键词： Adaptive boosting

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Effective Resource Utilization in Hadoop Using Ganglia 3

Effective Resource Utilization in Hadoop Using Ganglia

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Srungarapati, Beaula Pamarthi, Mounika Vakada, Vaishnavi Hegde, Anusha Bhowmik, Biswajit National Institute of Technology Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Department of Computer Science and Engineering Karnataka Mangalore Surathkal575025 India

ISBN: (纸本)9798331528201

The exponential growth of big data has led to the widespread adoption of Hadoop clusters for storing and processing large volumes of data. Efficient management of resources within these clusters is crucial for achieving optimal performance and cost efficiency. This research paper explores the use of Hadoop and Ganglia for monitoring and optimizing resource utilization in Hadoop clusters. The study demonstrates that leveraging Hadoop and Ganglia is an effective strategy for improving cluster performance and resource efficiency. Results show significant enhancements in cluster performance and resource utilization, highlighting the importance of proactive resource management in Hadoop environments. © 2024 IEEE.

关键词： Efficiency

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Intelligent Monitoring and Management of Smart Buildings Using Machine Learning: Optimizing User Behavior and Energy Efficiency

Intelligent Monitoring and Management of Smart Buildings Usi...

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Nijim, Mais Kanumuri, Viswas Albetaineh, Hisham Goyal, Ayosh Texas A&m University-Kingsville Department of Electrical Engineering and Computer Science KingsvilleTX78363 United States

ISBN: (纸本)9798350327595

Smart buildings are a crucial component of smart cities and smart grids, contributing to a more intelligent urban environment. The efficient utilization of energy in buildings is now a significant concern for sustainable societies. Whether they are residential or industrial. Smart buildings consume the majority of the energy produced, but in the context of smart grids, they are also designed to generate energy and help stabilize demand. Even small fluctuations in peak demand can result in substantial budget saving for customers and utilities. In this research, we explore the applicability of machine learning (ML) methods for load forecasting in smart buildings using smart sensor data that infers user behavior. Here, we utilized a smart building dataset encompassing four floors, 51 rooms, and 255 integrated sensors. Each smart building room comprises five types of calculations, including a P IR Sensor that helps to capture motion, a Carbon dioxide concentration sensor to check the purity of air, a Sensor to calculate temperature, a humidity sensor to maintain good conditioning, and a luminosity sensor. By using this data from these sensors, we can optimize and manage the energy saving. To evaluate the prediction performance of the input variables, we used to compare them we use mean squares error, mean absolute error and root mean square error. © 2023 IEEE.

关键词： carbon dioxide sensor Humidity sensor luminosity sensor machine learning PIR sensor

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Quantum Annealing-Based Machine Learning for Battery Health Monitoring Robust to Adversarial Attacks

Quantum Annealing-Based Machine Learning for Battery Health ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Akash, Alve Rahman Khot, Amey Kim, Taesic Texas A&m University-Kingsville Department of Electrical Engineering and Computer Science KingsvilleTX78363 United States

ISBN: (纸本)9798350316445

Battery health monitoring methods including machine learning (ML) models rely on trustworthiness of battery sensor data and features. As more battery systems require network connectivity for intelligent health monitoring, cyberattacks become a serious threat. A notable attack is an adversarial attack that generates stealthier input data misleading the ML results. This paper introduces an adversarial attack targeting battery health monitoring system and explores a quantum annealing (QA)-based machine learning method to mitigate the adversarial attacks. In this paper, a Quantum Boost (QBoost)-based state of health (SOH) estimation algorithm for electric vehicles is developed and trained by D-Wave QA processor and the performance is compared with a state-of the art random forest (RF)-based SOH estimation method. The experimental results show that the proposed QBoost method is more robust to the adversarial attack than the RF method, while the RF method provides better accuracy during normal operation. © 2023 IEEE.

关键词： Adversarial attack battery health monitoring quantum annealing processor quantum boost quantum machine learning

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Assessing the Impact Of Time Intervals On PM2.5 Prediction Using LSTM Neural Networks 3

Assessing the Impact Of Time Intervals On PM2.5 Prediction U...

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3rd International Conference on Advanced electrical engineering, ICAEE 2024

作者： Boulkamh, Selma Zeghib, Nadia Yaddaden, Yacine University of Constantine 2-Abdelhamid Mehri Lire Laboratory Constantine Algeria University of Quebec at Rimouski Department of Mathematics Computer Science and Engineering Canada

ISBN: (纸本)9798350349351

Air pollution is a global issue with profound implications to human health and environmental sustainability. Especially, PM2.5, which refers to particulate matter with a size of 2.5 microns or smaller, poses significant health risks. Therefore, accurately predicting PM2.5 levels is crucial. In this aim, various machine-learning methods are employed for PM2.5 prediction. The problem arises from the use of collected datasets at varied time intervals (5 minutes, 15 minutes, 1 hour, etc.), resulting in inconsistency across predictive approaches. While shorter time intervals seems to offer heightened sensitivity to changes, implying simpler prediction, this presumption may not be universally applicable. This paper investigates the impact of time intervals on PM2.5 prediction through a comparative analysis of datasets collected at different intervals (15 minutes, 1 hour, and 8 hours) using Long Short Term Memory Neural Networks (LSTM). By leveraging statistical analysis and predictive modeling techniques, we assess the dataset's distributions and evaluate prediction model performance. This study underscores the importance of interval selection in PM2.5 prediction and informs future efforts to enhance air quality surveillance and public health protection measures. © 2024 IEEE.

关键词： Prediction models

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Empirical Hypervolume Optimal μ-Distributions on Complex Pareto Fronts

Empirical Hypervolume Optimal μ-Distributions on Complex Pa...

引用

2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Shang, Ke Shu, Tianye Wu, Guotong Nan, Yang Pang, Lie Meng Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9781665430654

Hypervolume optimal μ-distribution is the distribution of μ solutions maximizing the hypervolume indicator of μ solutions on a specific Pareto front. Most studies have focused on simple Pareto fronts such as triangular and inverted triangular Pareto fronts. There is almost no study which focuses on complex Pareto fronts such as disconnected and partially degenerate Pareto fronts. However, most real-world multi-objective optimization problems have such a complex Pareto front. Thus, it is of great practical significance to study the hypervolume optimal μ-distribution on the complex Pareto fronts. In this paper, we study this issue by empirically showing the hypervolume optimal μ-distributions on the Pareto fronts of some representative artificial and real-world test problems. Our results show that, in general, maximizing the hypervolume indicator does not lead to uniformly distributed solution sets on the complex Pareto fronts. We also give some suggestions related to the use of the hypervolume indicator for performance evaluation of evolutionary multi-objective optimization algorithms. © 2023 IEEE.

关键词： complex Pareto front Hypervolume multi-objective optimization optimal μ-distribution

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PneuTeleCNN: Deep Learning-assisted Framework for Pneumonia Identification in Web-enabled Telemedicine Systems 10

PneuTeleCNN: Deep Learning-assisted Framework for Pneumonia ...

引用

10th International Conference on Web Research, ICWR 2024

作者： Mahida, Nikunjkumar Parikh, Keertan Amit Darji, Krisha Gupta, Rajesh Tanwar, Sudeep Shahinzadeh, Hossein Mohammad-Hosseini, Melika Nirma University Institute of Technology Department of Computer Science and Engineering Ahmedabad India Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tehran Iran Najafabad Branch Islamic Azad University Department of Electrical Engineering Najafabad Iran

ISBN: (纸本)9798350394986

An important development can be observed in the current environment of rapid technological progress in the healthcare industry, which can be demonstrated by the emergence of telemedicine as a key paradigm. Extensive use of telemedical techniques has happened with a particular focus on Tele-Pneumonia examinations, which provide doctors with crucial resources in remote diagnostic situations. In response to the necessity of accurate pneumonia prediction, we present a novel architecture PneuTeleCNN that makes utilization of pretrained models such as DenseNet-121, Visual Geometry Group (VGG-16), MobileNet, and InceptionV3. This thorough approach of PneuTeleCNN meticulously evaluates how effectively the system detects pneumonia in web-based telemedicine environments, establishing the stage for improved remote healthcare solutions. These model's performance is rigorously evaluated using key performance indicators, including accuracy, F1-score, recall, and precision. In addition, the evaluation includes sophisticated measures like the confusion matrix and receiver operating characteristic (ROC) curve, which offer an intricate perspective on the model's performance. To help in the identification of the model that performs better in this scenario, a comparative study is made more more straightforward by presenting of each model's validation accuracy and validation loss graphs. This PneuTeleCNN framework underscores the commitment to ensuring the robustness and reliability of the proposed telemedical approach for pneumonia detection in remote healthcare scenarios. © 2024 IEEE.

关键词： Telemedicine

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Understanding the Physical Design of Multi-Domain UAV Systems

Understanding the Physical Design of Multi-Domain UAV System...

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2025 International Conference on Unmanned Aircraft Systems, ICUAS 2025

作者： Ramos, Christian Rutherford, Matthew J. Valavanis, Kimon University of Denver Department of Electrical and Computer Engineering Denver 80210 CO United States University of Denver Department of Computer Science Denver 80210 CO United States

ISBN: (纸本)9798331513283

Unmanned multi-domain robotic systems are rapidly advancing, with many innovative platforms designed to operate across multiple environmental domains, typically involving the combination of air and either land, watersurface, or underwater capabilities. These systems are designed to seamlessly transition between and operate effectively in these diverse environments, opening up new possibilities for numerous fields - including military, research, search and rescue, and commercial applications. Categorization of the various forms of technology utilized in each innovative design is challenging due to the varying descriptions and definitions within each source publication. This paper is written to describe the current state of physical design for unmanned multidomain robotic platforms, as well as standardize the definitions and comparatively categorize the design features, propulsion methods, and domain-transition capabilities of many hybrid systems. Descriptions of the terminology used are provided throughout this article as each bi-domain system category is introduced. A complete comparative table of all findings is provided near the end of this manuscript, complete with respective categories, design features, and domain-transition details. © 2025 IEEE.

关键词： Drones

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Edge AI-Driven Video Analytics: A Mobile Deep Learning Framework for Victim Detection in SAR Robotics 3

Edge AI-Driven Video Analytics: A Mobile Deep Learning Frame...

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3rd FORTEI-International Conference on electrical engineering, FORTEI-ICEE 2024

作者： Ihsan, Muhammad Putra, Karisma Trinanda Mustar, Muhamad Yusvin Setyawan, Aufa Faiz Chu, Hsueh-Ting Chen, Hsing-Chung Department of Electrical Engineering Universitas Muhammadiyah Yogyakarta Bantul Indonesia Department of Electrical Engineering Center of Artificial Intelligence and Robotics Studies Universitas Muhammadiyah Yogyakarta Bantul Indonesia Department of Computer Science and Information Engineering Asia University Taichung Taiwan

ISBN: (纸本)9798331542207

In post-disaster search and rescue (SAR) missions, it is crucial for robots to distinguish between actual victims and dummy objects, despite their similar characteristics. Edge video analytics have demonstrated exceptional performance in such missions, however, the trade-off between high-performing deep learning models and computational complexity often results in inadequate computational efficiency for SAR robotics. This study tackles this challenge by deploying a deep learning (DL) algorithm, i.e., MobileNet Single Shot Detector (SSD), specifically tailored for embedded systems, to perform object detection and classification tasks on lab-scale SAR robots. The proposed algorithm is developed in Python and implemented on a low-powered edge computing device. The effectiveness of the proposed algorithm in object localization is highlighted by its mAP score of 0.6247 and an average IoU of 0.9036. Experimental tests using a confusion matrix showed that the model accurately predicted 42 dummy objects and 43 victim dolls. The model achieved an overall accuracy of 89.47%, with a precision of 1.0, a recall of 0.8947, and an F1 Score of 0.9444. Real-time testing confirmed the model’s robust performance, maintaining high accuracy and a stable frame rate of 47 FPS. These findings corroborate the efficacy of the MobileNet SSD algorithm in reliably detecting and differentiating objects within embedded SAR robotic operations. © 2024 IEEE.

关键词： Mobile edge computing

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Enhancing Financial Accessibility: A Tailored UPI Payment Application for Divyangjan 10

Enhancing Financial Accessibility: A Tailored UPI Payment Ap...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Bhowmik, Biswajit Sudhama, Kruthika K. Dongala, Joshitha R. Antony, Reshma T. Girish, K.K. National Institute of Technology Karnataka Maharshi Sushrut Cas Lab Brics Laboratory Department of Computer Science and Engineering Mangalore Surathkal575025 India

ISBN: (纸本)9798350384369

The emergence of financial technology (FinTech) has transformed the financial sector, introducing a new era characterized by state-of-the-art technologies that enhance speed, affordability, and accessibility. The proliferation of the internet and smartphones has further accelerated this transformation, fostering greater connectivity and global interaction. Subsequently, these advancements have significantly expanded financial inclusion, ensuring access to financial services for previously under-served populations. While the rise of FinTech has propelled financial inclusion for many, individuals with disabilities have not experienced commensurate improvements in their financial accessibility. As the banking sector increasingly migrates to online platforms, people with disabilities encounter barriers stemming from inaccessible websites, mobile applications, and online banking services. This paper introduces a specialized UPI payment application designed explicitly for individuals with disabilities. The objective is to integrate this underserved demographic into the digital financial landscape, fostering financial inclusion and enhancing access to essential financial services. © 2024 IEEE.

关键词： Decentralized finance

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