Published in Artificial Intelligence Review
Artificial neural networks (ANNs) have achieved significant success in tackling classical and modern machine learning problems. As learning problems grow in scale and complexity, and expand into multi-disciplinary territory, a more modular approach for scaling ANNs will be needed. Modular neural networks (MNNs) are neural networks that embody the c...
Published in Artificial Intelligence Review
Gearbox is an important part of mechanical equipment. If a fault cannot be timely detected, it will cause significant economic losses. In order to solve the problem of early fault diagnosis quickly and accurately, this paper proposes a feature extraction method by the decomposition of feature value to the waveform of signal, and inputs the extracte...
Published in Artificial Intelligence Review
Deep convolutional network is commonly stacked by vast number of nonlinear convolutional layers. Deep fused network can improve the training process of deep convolutional network due to its capability of learning multi-scale representations and of optimizing information flow. However, the depth in a deep fused network does not contribute to the ove...
Published in Artificial Intelligence Review
Electroencephalogram (EEG) signals can be used to identify the human brain in different disease conditions. Nonetheless, it is difficult to detect the subtle and vital differences in EEG simply by visual inspection because of the non-stationary nature of EEG signals. Specifically, in order to find the epileptogenic focus for medical treatment in th...
Published in Artificial Intelligence Review
Hand gestures are spatio-temporal patterns which can be characterized by collections of spatio-temporal features. Recognition of hand gestures is to find the re-occurrences of such spatio-temporal patterns through pattern matching. However, dynamic hand gestures have many obstacles for accurate recognition, including poor lighting conditions, camer...
Published in Artificial Intelligence Review
Forecasting is a very important and difficult task for various economic activities. Despite the great evolution of time series modeling, forecasters are still in the hunt for better strategies to improve mathematical models and come up with more accurate predictions. In this respect, several new models, mixing autoregressive processes to artificial...
Published in Artificial Intelligence Review
Many feature weighting methods have been proposed to evaluate feature saliencies in recent years. Neural-network (NN) feature weighting, as a supervised method, is founded upon the mapping from input features to output decisions, and implemented by evaluating the sensitivity of network outputs to its inputs. Through training on sample data, NN impl...
Published in Artificial Intelligence Review
Named entity recognition (NER) is an important natural language processing (NLP) task with many applications. We tackle the problem of Arabic NER using deep learning based on Arabic word embeddings that capture syntactic and semantic relationships between words. Deep learning has been shown to perform significantly better than other approaches for ...
Published in Artificial Intelligence Review
Supervised machine learning studies are gaining more significant recently because of the availability of the increasing number of the electronic documents from different resources. Text classification can be defined that the task was automatically categorized a group documents into one or more predefined classes according to their subjects. Thereby...
Published in Artificial Intelligence Review
The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Technique...
