Search

Focusing on non-iterative approaches in training feed-forward neural networks, this special issue includes 12 papers to share the latest progress, current challenges, and potential applications of this topic. This editorial presents a background of the special issue and a brief introduction to the 12 contributions. [ABSTRACT FROM AUTHOR]

In the process of facial expression recognition, face detection is the prerequisite, image preprocessing is the foundation, facial expression feature extraction is the key, and facial expression classification is the target. Effective feature extraction in this process can improve the accuracy of facial expression classifications. On the other hand, traditional facial expression recognition methods are not only complicated in the feature extraction process, but also unable to obtain more in-depth high-semantic features and deep features from the original image. To solve the above problems, this paper proposes a facial expression recognition method based on multi-channel fusion and lightweight neural network. First, a cascade classifier based on Haar features is used to detect the face region of the facial expression image. Second, the local binary pattern (LBP) is used to extract the local texture features from the face region. Third, face edge features are simultaneously obtained by performing edge detection in the face region based on the Canny edge detection algorithm. Fourth, the obtained face image, LBP texture feature image, and edge detection Canny image are fused, and the fused image is input into the constructed lightweight neural network for training and recognition. Experiments are carried out on the public image databases Facial Expression Recognition 2013 (Fer2013) and extended Cohn–Kanade (CK +) using the hold-out cross-validation method. The experimental results show that the proposed method effectively extracts more complete image features by combining traditional feature extraction algorithms with deep learning feature extraction algorithms, improves the accuracy and robustness of facial expression recognition, and has better recognition rate and generalization ability compared to other mainstream methods. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is a branch of machine learning that uses deep neural networks (DNNs) to extract knowledge from raw data. DL techniques have been used successfully in many intelligence domains, such as general approximation, computer vision, pattern recognition, and many more. Planning problems with small search space can be solved by exhaustive exploration of the search space, whereas the big search space of some problems exposes the search space explosion due to computational limitations. This subject motivates us to propose an approach using DL techniques for solving such planning problems. The proposed approach tries to learn the knowledge about the application order of actions, before solving the given (main) planning problem. Actually, it reduces the size of the given planning problem such that it can be solved by exhaustive exploration of the search space. After solving the reduced problem successfully, a DNN is learned from the explored search space. The proposed approach then employs the learned DNN to solve the given planning problem. The proposed approach deals with the planning problems specified through graph transformations language because of its superiorities compared to planning domain definition languages. The main contribution of the proposed approach is that it uses DL techniques, for the first time, to solve planning problems specified through graph transformations. Based on experimental results, the proposed approach outperforms state-of-the-art techniques in terms of execution speed, accuracy, and generating short-length plans with the exploration of lower states. [ABSTRACT FROM AUTHOR]