BACKGROUND Facial expressions are important in facilitating human communication and interactions. Also, they are used as an important tool in behavioural studies and in medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for non-invasive mood detection. The purpose of the present study was to develop an intelligent system for facial ima...

The static and analytic scheduling approach is very difficult to follow and is not always applicable in real-time. Most of the scheduling algorithms are designed to be established in offline environment. However, we are challenged with three characteristics in real cases: First, problem data of jobs are not known in advance. Second, most of the shop’s parameters tend to be stochastic. Third, th...

Diagnostic errors are the most frequent non-operative medical errors. Diagnosis should be more data-driven than trial-anderror. Machine Learning provides techniques for classification and regression purposes which can be used for solving diagnostic problems in different medical domains. Predictive analysis of fatal ailments like cancer using existing data can serve as a diagnosis tool for docto...

The current study proposes a two-step approach for pore facies characterization in the carbonate reservoirs with an example from the Kangan and Dalanformations in the South Pars gas field. In the first step, pore facies were determined based on Mercury Injection Capillary Pressure (MICP) data incorporation with the Hierarchical Clustering Analysis (HCA) method. In the next step, polynomial meta...

Frequency modulation (FM) is an important building block of complex sounds that include speech signals. Exploring the neural mechanisms of FM coding with computer modeling could help understand how speech sounds are processed in the brain. Here, we modeled the single unit responses of auditory neurons recorded from the midbrain of anesthetized rats. These neurons displayed spectral temporal rec...

Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great success in supervised learning problems, and multiple instance learning as a typical weakly-supervised learning method is effective for many applications in computer vision, biometrics, nature language processing, etc. In...

A functional relationship between two variables, applied mass to a weighing platform and estimated mass using Multi-Layer Perceptron Artificial Neural Networks is approximated by a linear function. Linear relationships and correlation rates are obtained which quantitatively verify that the Artificial Neural Network model is functioning satisfactorily. Estimated mass is achieved through recallin...