AIMC Topic: Machine Learning

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An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decoding.

NeuroImage. Clinical
Automated clinical EEG analysis using machine learning (ML) methods is a growing EEG research area. Previous studies on binary EEG pathology decoding have mainly used the Temple University Hospital (TUH) Abnormal EEG Corpus (TUAB) which contains appr...

Machine learning applications to improve flavor and nutritional content of horticultural crops through breeding and genetics.

Current opinion in biotechnology
Over the last decades, significant strides were made in understanding the biochemical factors influencing the nutritional content and flavor profile of fruits and vegetables. Product differentiation in the produce aisle is the natural consequence of ...

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms.

Journal of chemical information and modeling
Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. With the increasing population of diabetes, there is an urgent need to develop drugs to treat diabetes...

AndroPred: an artificial intelligence-based model for predicting androgen receptor inhibitors.

Journal of biomolecular structure & dynamics
Androgen receptor (AR), a steroid receptor, plays a pivotal role in the pathogenesis of prostate cancer (PCa). AR controls the transcription of genes that help cells avoid apoptosis and proliferate, thereby contributing to the development of PCa. Und...

Artificial intelligence for dementia-Applied models and digital health.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available.

Data-driven crash prediction by injury severity using a recurrent neural network model based on Keras framework.

International journal of injury control and safety promotion
With the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method, with the help of clustering proc...

Novel Machine Learning Algorithms for Prediction of Treatment Decisions in Adult Patients With Class III Malocclusion.

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons
BACKGROUND: Management of Class III (Cl III) dentoskeletal phenotype is often expert-driven.

Hardware deployment of deep learning model for classification of breast carcinoma from digital mammogram images.

Medical & biological engineering & computing
Cancer is an illness that instils fear in many individuals throughout the world due to its lethal nature. However, in most situations, cancer may be cured if detected early and treated properly. Computer-aided diagnosis is gaining traction because it...

ChatGPT Helped Me Write This Talk Title, but Can It Read an Echocardiogram?

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
While multidisciplinary collaboration in echocardiography is not new, machine learning has the potential to further improve it. In this transcript of the ASE 2023 Annual Feigenbaum lecture, advancements in foundation models are discussed, including t...

A freshwater algae classification system based on machine learning with StyleGAN2-ADA augmentation for limited and imbalanced datasets.

Water research
Automated algae classification using machine learning is a more efficient and effective solution compared to manual classification, which can be tedious and time-consuming. However, the practical application of such a classification approach is restr...