AIMC Topic: Machine Learning

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Using machine learning to investigate the public's emotional responses to work from home during the COVID-19 pandemic.

The Journal of applied psychology
According to event system theory (EST; Morgeson et al., Academy of Management Review, 40, 2015, 515-537), the coronavirus disease 2019 (COVID-19) pandemic and resultant stay-at-home orders are novel, critical, and disruptive events at the environment...

A machine learning system with reinforcement capacity for predicting the fate of an ART embryo.

Systems biology in reproductive medicine
The aim of this work was o construct a score issued from a machine learning system with self-improvement capacity able to predict the fate of an ART embryo incubated in a time lapse monitoring (TLM) system. A retrospective study was performed. For th...

Speech emotion recognition based on transfer learning from the FaceNet framework.

The Journal of the Acoustical Society of America
Speech plays an important role in human-computer emotional interaction. FaceNet used in face recognition achieves great success due to its excellent feature extraction. In this study, we adopt the FaceNet model and improve it for speech emotion recog...

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images.

Journal of animal science
Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reac...

Advancing care for acute gastrointestinal bleeding using artificial intelligence.

Journal of gastroenterology and hepatology
The future of gastrointestinal bleeding will include the integration of machine learning algorithms to enhance clinician risk assessment and decision making. Machine learning algorithms have shown promise in outperforming existing clinical risk score...

Applications of artificial intelligence in pancreatic and biliary diseases.

Journal of gastroenterology and hepatology
The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI h...

Challenges of developing artificial intelligence-assisted tools for clinical medicine.

Journal of gastroenterology and hepatology
Machine learning, a subset of artificial intelligence (AI), is a set of computational tools that can be used to enhance provision of clinical care in all areas of medicine. Gastroenterology and hepatology utilize multiple sources of information, incl...

Clinical applications of artificial intelligence and machine learning-based methods in inflammatory bowel disease.

Journal of gastroenterology and hepatology
Our objective was to review and exemplify how selected applications of artificial intelligence (AI) might facilitate and improve inflammatory bowel disease (IBD) care and to identify gaps for future work in this field. IBD is highly complex and assoc...

[Artificial intelligence: the inscrutability of algorithms.].

Recenti progressi in medicina
The use of artificial intelligence radically changes the role of the doctor and his/her relationship with the patient, which becomes in fact a three-way relationship: artificial intelligence-doctor-patient, in which the first component is able to hea...