As age progresses, people are exposed to more diseases, one of the most important diseases is dementia. Dementia's is a difficult disease to diagnose. Most medical diagnoses are based on the pen- paper cognitive t...
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A common retinal condition called diabetic retinopathy can cause blindness. Diabetes mellitus is a primary cause of diabetic retinopathy. To prevent vision loss, initial identification and intervention are essential. ...
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The drastic technological advancements in the field of autonomous vehicles and connected cars lead to substantial progression in the commercial values of automobile industries. However, these advancements force the Or...
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With the increasing demand for security in the day-To-day lives of people, especially when in public spaces, surveillance system helps to monitor human behavior, prevent illegal activities and detect anomalous events....
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This paper argues that natural interaction with a machine can be realized and improved by using learning algorithms. Through the use of supervised and reinforcement learning algorithms, a robot was created that can be...
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Deep learning (DL) has gained vast popularity in the research community in the recent years. The implementation of various DL algorithms in Radio-frequency energy harvesting (RFEH), Wireless power transmission (WPT), ...
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Several studies have evaluated leaf image data and protected plants from diseases using machine learning classifiers. To classify the leaves in an image, the majority of the suggested classifiers take hand-crafted fea...
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In the current energy environment, new power systems have become the development direction of future power systems due to their high efficiency, reliability, and intelligence. As an important component of the new powe...
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In this paper, we investigate one-class and clustering problems by using statistical learning theory. To establish a universal framework, a unsupervised learning problem with predefined threshold η is formally descri...
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The success of machine learning on a given task depends on, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate learning algorithm and setting its hype...
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The success of machine learning on a given task depends on, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate learning algorithm and setting its hyperparameters for a given data set can be a challenging task, especially for users who are not experts in machine learning. Previous work has examined using meta-features to predict which learning algorithm and hyperparameters should be used. However, choosing a set of meta-features that are predictive of algorithm performance is difficult. Here, we propose to apply collaborative filtering techniques to learning algorithm and hyperparameter selection, and find that doing so avoids determining which meta-features to use and outperforms traditional meta-learning approaches in many cases.
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