AIMC Topic: Neural Networks, Computer

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A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence.

Chemosphere
Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major t...

Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater.

Bioresource technology
In this a, three-layered feedforward-backpropagation artificial neural network (BPANN) model was developed and employed to evaluate COD removal an upflow anaerobic sludge blanket (UASB) reactor treating industrial starch processing wastewater. At the...

Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining.

Journal of neuroscience methods
BACKGROUND: Clustering approaches used in functional magnetic resonance imaging (fMRI) research use brain activity to divide the brain into various parcels with some degree of homogeneous characteristics, but choosing the appropriate clustering algor...

Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier.

Journal of biomedical informatics
Electronic health records (EHRs) contain critical information useful for clinical studies. Early assessment of patients' mortality in intensive care units is of great importance. In this paper, a Deep Rule-Based Fuzzy System (DRBFS) was proposed to d...

Multiple Kernel Learning Model for Relating Structural and Functional Connectivity in the Brain.

Scientific reports
A challenging problem in cognitive neuroscience is to relate the structural connectivity (SC) to the functional connectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fixed SC ar...

Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white matter hyperintensities (WMH) characteristic of brains with none or mild vascular pathology ...

Enhanced prediction of recombination hotspots using input features extracted by class specific autoencoders.

Journal of theoretical biology
In yeast and in some mammals the frequencies of recombination are high in some genomic locations which are known as recombination hotspots and in the locations where the recombination is below average are consequently known as coldspots. Knowledge of...

Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

Computers in biology and medicine
Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and...

DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis.

IEEE journal of biomedical and health informatics
Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers working on sets of hand-crafted features, some of which modeled lesion aspects of special importance for dermatologists. Recently, the broad adoption of convolutio...