AIMC Topic: Machine Learning

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Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions.

Neural networks : the official journal of the International Neural Network Society
We implemented a functional neuronal network that was able to learn and discriminate haptic features from biomimetic tactile sensor inputs using a two-layer spiking neuron model and homeostatic synaptic learning mechanism. The first order neuron mode...

Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques.

Journal of clinical hypertension (Greenwich, Conn.)
Hypertension is a significant public health issue. The ability to predict the risk of developing hypertension could contribute to disease prevention strategies. This study used machine learning techniques to develop and validate a new risk prediction...

Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping.

The Plant journal : for cell and molecular biology
The rapid selection of salinity-tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high-throughput plant phenotyping technologies have been adopted that use plant morphological and physio...

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Psychiatry research
Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is th...

Identifying bladder rupture following traumatic pelvic fracture: A machine learning approach.

Injury
INTRODUCTION: Bladder rupture following blunt pelvic trauma is rare though can have significant sequelae. We sought to determine whether machine learning could help predict the presence of bladder injury using certain factors at the time of presentat...

Using a Variable-Friction Robot Hand to Determine Proprioceptive Features for Object Classification During Within-Hand-Manipulation.

IEEE transactions on haptics
Interactions with an object during within-hand manipulation (WIHM) constitutes an assortment of gripping, sliding, and pivoting actions. In addition to manipulation benefits, the re-orientation and motion of the objects within-the-hand also provides ...

Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations.

Journal of medical systems
Few studies have addressed the predictive value of arterial stiffness determined by pulse wave velocity (PWV) in a high-risk population with no prevalent cardiovascular disease and with obesity, hypertension, hyperglycemia, and preserved kidney funct...

Representation learning in intraoperative vital signs for heart failure risk prediction.

BMC medical informatics and decision making
BACKGROUND: The probability of heart failure during the perioperative period is 2% on average and it is as high as 17% when accompanied by cardiovascular diseases in China. It has been the most significant cause of postoperative death of patients. Ho...

Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach.

Genomics
Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miR...

Deep learning: definition and perspectives for thoracic imaging.

European radiology
Relevance and penetration of machine learning in clinical practice is a recent phenomenon with multiple applications being currently under development. Deep learning-and especially convolutional neural networks (CNNs)-is a subset of machine learning,...