This research aims to explore the use of modern complex defensive machinelearning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image inf...
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machinelearning has become a disruptive force that is advancing technology and changing industries. With an emphasis on algorithmic techniques, real-world applications, and important future research avenues, this stu...
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machinelearning (ML) has revolutionized numerous fields. This progress has largely been credited to the development of ML algorithms and models, but this focus overshadows the engineering required to effectively depl...
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This study investigates enhancing intrusion detection systems (IDS) through machinelearning and advanced feature engineering. I evaluate classification algorithms, including k-nearest neighbors (KNN) and support vect...
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The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks;therefore, the number of cyber attacks in the information sec...
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The detection and localization of water pipe leaks are essential for maintaining the efficiency and sustainability of water distribution systems. Traditional methods, such as visual inspection, acoustic detection, and...
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The detection and localization of water pipe leaks are essential for maintaining the efficiency and sustainability of water distribution systems. Traditional methods, such as visual inspection, acoustic detection, and pressure testing, are often labour-intensive, time-consuming, and may not provide real-time monitoring, leading to significant water loss, infrastructure damage, and increased operational costs. advances in machinelearning and deep learning technologies offer a promising alternative, enabling the development of automated, accurate, and timely leak detection systems. This study presents a simulation-based approach to generate datasets for leak detection and localization within pipe systems. We implemented and compared five models: Ridge Regression, Lasso Regression, Decision Tree Regression, Support Vector Regression, and Artificial Neural Network (ANN). Among these, Decision Tree Regression and ANN demonstrated superior performance in accurately detecting and localizing leaks. Our findings suggest that ANN is particularly effective for leak localization, providing a robust solution to minimize water loss, infrastructure damage, and environmental impact while ensuring the reliability of water distribution systems.
The goal of this project is to improve interior navigation by creating an augmented reality (AR) indoor navigation system. The system combines real-time data from cameras and inertial sensors with computer vision and ...
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Cloud computing is a cost-effective way to host and deliver services over the internet, but the large volume of data transmitted makes the cloud network a target for malicious attacks. To protect against these threats...
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Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machinelearning and signal processing techniques for effective identification. The process begins with pre-...
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Diabetic retinopathy emerges as a consequence of untreated chronic diabetes, posing a risk of total blindness if not promptly addressed. Early diagnosis and treatment are pivotal in averting the severe consequences as...
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