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With the rapid rise in the number of people buying houses, the demand for interior space design has also increased accordingly. The diversification of existing room types and the diversity of the public's perception of fashion make interior designers in short supply. The future of computer science and technology in the field of automatic design of indoor areas will be immeasurable. This paper proposes an automatic layout method for spatial area design based on convolutional neural networks (CNN). CNN methods are a fast and efficient method. By mimicking the designer's design process, it proposes a two-stage algorithm that defines the room first and the wall later, and the algorithm also provides a large-scale dataset called RPLAN that contains more than 80,000 interior layout plans from real residential buildings. Starting from the prediction living room, the automatic layout of the indoor areas is completed by iteration. A large number of empirical results show that the interior area design effect of this method is comparable to the interior design floor plan of professional designers. [ABSTRACT FROM AUTHOR]

With the rapid development of computer science and Internet technology, face recognition technology is widely used in such as public security, judicial and criminal investigation, public security, information security and access control system, such as public security system need to find out the criminals in the system library, or from the entrance control system quickly identify and match the identity of relevant personnel information. As a stable, intuitive and highly recognizable biometric feature, human face is being paid more and more attention by researchers. Compared with other bioinformation recognition methods, face recognition is characterized by direct, friendliness and convenience. Users are not easy to resist, and compared with other recognition technologies, they are easy to be accepted by users, so it has received attention and research. This paper designs a face recognition system based on a convolutional neural network. Compared with the traditional face recognition method, the convolutional neural network model does not need manual complex and time-consuming feature extraction algorithm design, only need to design an effective neural network model, and then end-to-end training on a large number of training samples for simple and efficient training, can get a good identification effect. The design uses the target detection algorithm to conduct accurate, real-time, efficient face recognition, and can accurately identify the people in the camera. [ABSTRACT FROM AUTHOR]

Facial expression is one of the most significant elements which can tell us about the mental state of any person. A human can convey approximately 55% of information nonverbally and the remaining almost 45% through verbal communication. Automatic facial expression recognition is presently one of the most difficult tasks in the computer science field. Applications of facial expression recognition (FER) are not just limited to understanding human behavior and monitoring person's mood and the mental state of humans. It is also penetrating into other fields such as criminology, holographic, smart healthcare systems, security systems, education, robotics, entertainment, and stress detection. Currently, facial expressions are playing an important role in medical sciences, particularly helping the patients with bipolar disease, whose mood changes very frequently. In this study, an algorithm, automated framework for facial detection using a convolutional neural network (FD-CNN) is proposed with four convolution layers and two hidden layers to improve accuracy. An extended Cohn-Kanade (CK+) dataset is used that includes facial images of different males and females with expressions such as anger, fear, disgust, contempt, neutral, happy, sad, and surprise. In this study, FD-CNN is performed in three major steps that include preprocessing, feature extraction, and classification. By using this proposed method, an accuracy of 94% is obtained in FER. In order to validate the proposed algorithm, K-fold cross-validation is performed. After validation, sensitivity and specificity are calculated which are 94.02% and 99.14%, respectively. Furthermore, the f1 score, recall, and precision are calculated to validate the quality of the model which is 84.07%, 78.22%, and 94.09%, respectively. [ABSTRACT FROM AUTHOR]

Automated seizure detection system based on electroencephalograms (EEG) is an interdisciplinary research problem between computer science and neuroscience. Epileptic seizure affects 1% of the worldwide population and can lead to severe long-term harm to safety and life quality. The automation of seizure detection can greatly improve the treatment of patients. In this work, we propose a neural network model to extract features from EEG signals with a method of arranging the dimension of feature extraction inspired by the traditional method of neurologists. A postprocessor is used to improve the output of the classifier. The result of our seizure detection system on the TUSZ dataset reaches a false alarm rate of 12 per 24 hours with a sensitivity of 59%, which approaches the performance of average human detector based on qEEG tools. [ABSTRACT FROM AUTHOR]