In this study, machinelearning calculations such as Support Vector machine (SVM), K-Nearest Neighbours (KNN), Logistic Regression, and Random Forest are explored to classify cardiac arrhythmias utilizing Electrocardi...
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This study employs advanced machinelearning (ML) algorithms to predict and analyze farmer suicides in India using a comprehensive Kaggle dataset spanning 2001 to 2012. When focusing on high accuracy, algorithms like ...
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machinelearning (ML), particularly deep learning, has seen vast advancements, leading to the rise of machinelearning-Enabled systems (MLS). However, numerous software engineering challenges persist in propelling the...
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ISBN:
(纸本)9798350329964
machinelearning (ML), particularly deep learning, has seen vast advancements, leading to the rise of machinelearning-Enabled systems (MLS). However, numerous software engineering challenges persist in propelling these MLS into production, largely due to various run-time uncertainties that impact the overall Quality of Service (QoS). These uncertainties emanate from ML models, software components, and environmental factors. Self-adaptation techniques present potential in managing run-time uncertainties, but their application in MLS remains largely unexplored. As a solution, we propose the concept of a machinelearning Model Balancer, focusing on managing uncertainties related to ML models by using multiple models. Subsequently, we introduce AdaMLS, a novel self-adaptation approach that leverages this concept and extends the traditional MAPE-K loop for continuous MLS adaptation. AdaMLS employs lightweight unsupervised learning for dynamic model switching, thereby ensuring consistent QoS. Through a self-adaptive object detection system prototype, we demonstrate AdaMLS's effectiveness in balancing system and model performance. Preliminary results suggest AdaMLS surpasses naive and single state-of-the-art models in QoS guarantees, heralding the advancement towards self-adaptive MLS with optimal QoS in dynamic environments.
The heart is a critical organ at the heart of the circulatory system, acting as a powerful muscle pump that keeps life going by constantly pumping blood throughout the body. Heart illnesses, often known as cardiovascu...
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ADHD is a neurodevelopmental disorder that has been found to affect children's concentration and behavior. Early and accurate diagnosis are critical for timely intervention. This paper suggests a machinelearning ...
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As the transportation and information industries continue to advance, the increasing variety of application scenarios, devices with computing capabilities, and a growing number of open ports have heightened security r...
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ISBN:
(纸本)9798350375084;9798350375077
As the transportation and information industries continue to advance, the increasing variety of application scenarios, devices with computing capabilities, and a growing number of open ports have heightened security risks for vehicle networks. To improve the accuracy of detecting abnormal traffic in vehicle networks, we propose a model based on ensemble learning with a Stacking model integration approach. This method includes a meta-classifier composed of decision trees, extremely randomized trees, and extreme gradient boosting. The final classification prediction results are obtained by linearly stacking input features and weights into a SoftMax meta-learner. Additionally, the research enhances the classification accuracy of network flow data through parameter optimization. Testing results on the real automotive hacker attack dataset, Car-Hacking, show that this method achieves an accuracy rate of up to 99.2% in detecting denial of service, gear spoofing, and RPM spoofing attack types, and up to 97.5% accuracy in Fuzzy attack types. The study indicates that this model has a low false positive rate, high detection accuracy, and high detection rate, significantly outperforming traditional detection methods based on other machinelearning technologies.
Sleep apnea is a prevalent and serious sleep disorder caused by breathing interruptions during sleep, which is lead to critical health issues like cardiovascular diseases. Traditional diagnostic methods, such as polys...
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Due to the current development of networked systems and the emergence of global cyber threats, efficient and fast NIDS are required. The suggested article aims to analyse new and improved AI and machinelearning (ML) ...
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IoT device applications are rapidly expanding in the current environment. Today, we can find IoT devices in a variety of industries, including agriculture, home security, entertainment, health, transportation, and edu...
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Technology now a days has become the most important requirement in *** the use of technology increases the threats also enhanced as most of the applications always require internet to access data through network. Due ...
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