AIMC Topic: Machine Learning

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An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms.

The International journal of eating disorders
OBJECTIVE: Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) te...

[Artificial intelligence-based ECG analysis: current status and future perspectives-Part 1 : Basic principles].

Herzschrittmachertherapie & Elektrophysiologie
Even though electrocardiography is a diagnostic procedure that is now more than 100 years old, medicine cannot do without it. On the contrary, interest in the procedure and its clinical significance is even increasing again. Reports on the evaluation...

Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis.

Sensors (Basel, Switzerland)
Sentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people's opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared a...

Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments.

Sensors (Basel, Switzerland)
In recent years, due to the advancements in machine learning, object detection has become a mainstream task in the computer vision domain. The first phase of object detection is to find the regions where objects can exist. With the improvements in de...

A temporal dependency feature in lower dimension for lung sound signal classification.

Scientific reports
Respiratory sounds are expressed as nonlinear and nonstationary signals, whose unpredictability makes it difficult to extract significant features for classification. Static cepstral coefficients such as Mel-frequency cepstral coefficients (MFCCs), h...

Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics.

Applied microbiology and biotechnology
Fungi produce several bioactive metabolites, pigments, dyes, antioxidants, polysaccharides, and industrial enzymes. Fungal products are also the primary sources of functional food and nutrition, and their pharmacological products are used for healthy...

Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score.

Computational intelligence and neuroscience
Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by p...

Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images.

Computational intelligence and neuroscience
Plaque deposits in the carotid artery are the major cause of stroke and atherosclerosis. Ultrasound imaging is used as an early indicator of disease progression. Classification of the images to identify plaque presence and intima-media thickness (IMT...

Prediction of chronic kidney disease and its progression by artificial intelligence algorithms.

Journal of nephrology
BACKGROUND AND OBJECTIVE: Aim of nephrologists is to delay the outcome and reduce the number of patients undergoing renal failure (RF) by applying prevention protocols and accurately monitoring chronic kidney disease (CKD) patients. General practitio...

High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites.

Journal of the American Society for Mass Spectrometry
Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a ra...