We propose a practical Convolution Neural Network (CNN) model termed the CNN for Semantic Segmentation for driver Assistance system (CSSA). It is a novel semantic segmentation model for probabilistic pixel-wise segmentation, which is able to predict pixel-wise class labels of a given input image. Recently, scene understanding has turned out to be one of the emerging areas of research, and pixel-wise semantic segmentation is a key tool for visual scene understanding. Among future intelligent systems, the Advanced Driver Assistance System (ADAS) is one of the most favorite research topic. The CSSA is a road scene understanding CNN that could be a useful constituent of the ADAS toolkit. The proposed CNN network is an encoder-decoder model, which is built on convolutional encoder layers adopted from the Visual Geometry Group’s VGG-16 net, whereas the decoder is inspired by segmentation network (SegNet). The proposed architecture mitigates the limitations of the existing methods based on state-of-the-art encoder-decoder design. The encoder performs convolution, while the decoder is responsible for deconvolution and un-pooling/up-sampling to predict pixel-wise class labels. The key idea is to apply the up-sampling decoder network, which maps the low-resolution encoder feature maps. This architecture substantially reduces the number of trainable parameters and reuses the encoder’s pooling indices to up-sample to map pixel-wise classification and segmentation. We have experimented with different activation functions, pooling methods, dropout units and architectures to design an efficient CNN architecture. The proposed network offers a significant improvement in performance in segmentation results while reducing the number of trainable parameters. Moreover, there is a considerable improvement in performance in comparison to the benchmark results over PASCAL VOC-12 and the CamVid.

This paper proposes a deep learning-based model to predict stock price movements. The proposed model is composed of a deep belief network (DBN) to learn the latent feature representation from stock prices, and a long short-term memory (LSTM) network to exploit long-range relations within the trading history. The prediction target of the model is the stock close price direction on the next day. To predict the trend of one stock, the feature of recent trading information is generated from the raw intra-day data through a pre-trained DBN. Then the extracted features are fed into an LSTM classifier to produce the prediction result for the next day. The proposed model was tested on 36 companies in the Shanghai Stock Exchange (SSE) and the Shenzhen Stock Exchange (SZSE), which were selected based on their weights in Chinese A-shares. The experiments cover a span of 12 years, from 2005 to 2016, and the results show that the proposed model offers notable improvements in predicting performance comparing with other learning models. It is also observed that some companies are more predictable than others, which implies that the proposed model can be used for financial portfolio construction.

In the field of preference-based evolutionary multiobjective optimization, optimization algorithms are required to search for the Pareto optimal solutions preferred by the decision maker (DM). The reference point is a type of techniques that effectively describe the preferences of DM. So far, the reference point is either static or interactive with the evolutionary process. However, the existing reference point techniques do not cover all application scenarios. A novel case, i.e., the reference point changes over time due to the environment change, has not been considered. This paper focuses on the multiobjective optimization problems with dynamic preferences of the DM. First, we propose a change model of the reference point to simulate the change of the preference by the DM over time. Then, a dynamic preference-based multiobjective evolutionary algorithm framework with a clonal selection algorithm (ĝa-NSCSA) and a genetic algorithm (ĝa-NSGA-II) is designed to solve such kind of optimization problems. In addition, in terms of practical applications, the experiments on the portfolio optimization problems with the dynamic reference point model are tested. Experimental results on the benchmark problems and the practical applications show that ĝa-NSCSA exhibits better performance among the compared optimization algorithms.