AIMC Topic: Humans

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A Turing Test for Molecular Generators.

Journal of medicinal chemistry
Machine learning approaches promise to accelerate and improve success rates in medicinal chemistry programs by more effectively leveraging available data to guide a molecular design. A key step of an automated computational design algorithm is molecu...

A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition.

Sensors (Basel, Switzerland)
Human activity recognition has become an important research topic within the field of pervasive computing, ambient assistive living (AAL), robotics, health-care monitoring, and many more. Techniques for recognizing simple and single activities are ty...

Machine learning identifies abnormal Ca transients in human induced pluripotent stem cell-derived cardiomyocytes.

Scientific reports
Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca transients is crucial for evaluating cardiomyocyte function that requires l...

Unsupervised learning for large-scale corneal topography clustering.

Scientific reports
Machine learning algorithms have recently shown their precision and potential in many different use cases and fields of medicine. Most of the algorithms used are supervised and need a large quantity of labeled data to achieve high accuracy. Also, mos...

Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis.

Scientific reports
Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous workload for nuclear medicine physicians. So, we aimed to architecture an automatic image interpretin...

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer.

Scientific reports
Patients with pancreatic cancer have a poor prognosis, therefore identifying particular tumor characteristics associated with prognosis is important. This study aims to investigate the utility of radiomics with machine learning using F-fluorodeoxyglu...

Using machine learning to understand the implications of meteorological conditions for fish kills.

Scientific reports
Fish kills, often caused by low levels of dissolved oxygen (DO), involve with complex interactions and dynamics in the environment. In many places the precise cause of massive fish kills remains uncertain due to a lack of continuous water quality mon...

Deep transfer learning for reducing health care disparities arising from biomedical data inequality.

Nature communications
As artificial intelligence (AI) is increasingly applied to biomedical research and clinical decisions, developing unbiased AI models that work equally well for all ethnic groups is of crucial importance to health disparity prevention and reduction. H...

Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats.

Environmental monitoring and assessment
Environmental monitoring guides conservation and is particularly important for aquatic habitats which are heavily impacted by human activities. Underwater cameras and uncrewed devices monitor aquatic wildlife, but manual processing of footage is a si...