Deep learning approaches' use in financial market forecasting has recently drawn a lot of attention from both investors and scholars. The Transformer framework, initially created for natural language processing, i...
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According to the World Health Organization (WHO), approximately 537 million individuals are currently living with diabetes, with projections suggesting an annual increase of 3%. Efficient techniques for determining th...
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The growing importance of data analytics in biomedicine is increasingly becoming recognized in biomedical engineering curricula through the introduction of machine learning classes that generally run in parallel to, b...
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ISBN:
(纸本)9798350324471
The growing importance of data analytics in biomedicine is increasingly becoming recognized in biomedical engineering curricula through the introduction of machine learning classes that generally run in parallel to, but separately from, more traditional engineering courses, such as signal and systems analysis. We propose a new approach that systematically integrates signal processing and systems analysis with key techniques in machine learning. In the proposed course, the student obtains hands-on experience in applying algorithms that can be applied to practical problems of physiological signal conditioning, analysis and interpretation. This is achieved by exposing the student to a sequence of 4 applications-based modules that represent different biomedical engineering problems: human activity recognition from wearable devices, epileptic seizure detection, quantification of dynamic respiratory-cardiac coupling in humans under different conditions, and detection of sleep apnea episodes from heart rate variability data. Within each module, the student gains the experience of working with the data in question "from the ground up". We also introduce a general plan for assessment of student learning, and discuss the expected outcomes and limitations of this integrative approach to teaching.
The utility of ubiquitous computing systems drives large-scale software development with millions of lines of code (LOC). As there are vast code sets, it also increases the possibility of coding errors since it is dif...
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ISBN:
(纸本)9798350326970
The utility of ubiquitous computing systems drives large-scale software development with millions of lines of code (LOC). As there are vast code sets, it also increases the possibility of coding errors since it is difficult for even highly trained software engineers to write flawless code. Such flawed software can lead to severe issues once deployed. McCabe and Halstead proposed feature extractors for source code to define software quality. Based on the static features proposed by McCabe and Halstead, we run a series of feature engineering techniques and different machine learning models to detect code defects and use explainable algorithms to assess the prediction quality. We report different processing pipeline combinations to detect defects and compare the approaches. We conclude the paper with comments on the nature of the dataset and establish a baseline for further research.
With the explosion of generative AI, rapid innovation in educational technology can lead to extraordinary advances for teaching and learning-as well AI tools that are ineffective or even harmful to learning. AI should...
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With the wide application of embedded system in today's society, many students and technicians have joined the learning army of embedded system, and domestic universities have also carried out corresponding embedd...
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Aiming at the low accuracy of straw burning recognition and the inability to identify tiny straw burning flames, an improved Graph-YOLOV3 algorithm is proposed to identify straw burning images, making it more accurate...
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A particular kind of neural network called an autoencoder (AE) is trained to discover a reduced and effective representation (encoding) of input data. In most of the applications of AE, it is found that this model has...
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In the contemporary digital era, spreading false information via social media and Internet platforms poses a significant challenge. Not only the data has become increasingly multimodal, lack of sufficient labeled data...
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Lately, creative displaying methods have been utilized to foster dependable expectation models for microgrids. Computational Insight (CI) innovations are perceived as a vital methodology for improving and planning mic...
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