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From a perspective of feature extraction, we present a histogram-based sparsity descriptor (HSD) which is derived from the robust principal component analysis (RPCA) and histogram technique. Given a test image, sparse error images with respect to each class can be obtained by using RPCA decomposition. In order to extract the facial features in terms of intensity distribution, a sparseness measure...
Extreme Learning Machine (ELM) is an algorithm for training single hidden layer feed-forward neural networks (SLFNs). Because ELM does not need the process of iterative learning, it is extremely faster than traditional learning algorithms such as back propagation algorithm and support vector machine. In ELM, the optimal solution with least squares norm is found by calculating the generalized inverse...
Man-machine game is an important component in the field of artificial intelligence. Game tree search algorithms and chess situation evaluation functions are mostly applied in the traditional chess game system. When the game tree method is used, the response time will be extended as the depth of tree. This paper proposes to use the stochastic weight assignment neural network (SWAN), trained by Extreme...
It is well known that feed-forward neural networks can be learnt from symbolic data although the learnt networks usually have poor performance. This paper explores the ability of a recently popular feed-forward neural network, i.e., Extreme Learning Machine (ELM) for modeling symbolic data. An experimental study is conducted to compare C4.5 (a very popular algorithm of learning from symbolic data)...
Daily healthcare is very important for our quality of life. Especially, in the aging countries like Japan, medical costs will increase in the near future. Almost all Japanese people have health examination in every year. By the health examination, Japanese people understand own health condition. If the results of health examination get worse, then we will have precise examinations to find a cause...
Inspired by Gaussian barebones differential evolution (GBDE), this study attempts to propose a new Gaussian mutation strategy, termed by GBDE/best-rand, to improve the solution accuracy. This study also proposes a hybrid crossover strategy, the hybridization of the binomial and arithmetic crossover strategies, for differential evolution (DE) to further balance the global search ability and convergence...
Recommender systems represent user preferences for the purpose of suggesting items to select or examine. In petroleum drilling safety check, there are many items (e.g. tool misused and warning signs ignored) to be checked during one day. However, existing recommender systems seldom apply time series and interaction methods for the issue. In this paper, we propose a recommender system with two techniques...
In recent years, medical institutions have very big data including medical images. The big image data analysis using the collected medical images is effective to increase the accuracy and the reproducibility of the surgery. Anterior cruciate ligament (ACL) injury causes knee joint instability, and affects on sports performance. Therefore, ACL reconstruction surgery is essential to keep their performance...
Using cloud storage, users can gain a reliable, enormous storage capacity with lower costs. However, it will cause enormous loss to clients if cloud storage service is vulnerable to attacks. In this paper, we have a deep research on the integrity of data storage in cloud and we propose a public privacy-preserving audit scheme, based on BLS signature and random sampling, to verifying the integrity...
In this paper, a new multi-objective cat swarm optimization algorithm has been proposed. The algorithm applies the part of individuals into the seeking mode and the other part of individuals into the tracing mode non-randomly. Cat map is used to initialize individuals of population. In this way, individuals can avoid trapping into local optimal in the final iteration process and the search ability...
The investors in financial market have shown great concerns in the events that may cause fluctuations in the capital market. Traditional event detection and type recognition methods were majorly based on text processing techniques while few research considers the financial time-series features. As we know, there are large amount of financial time-series data available such as stock transaction data...
Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical rough sets. As a result, it is necessary to analyze the randomness of statistical rough sets. In this paper,...
Facial expressions are considered to be an effective and non-verbal means of expressing the emotional states of humans in more natural and non-intrusive way. Automatically recognizing the emotions consequently contributes towards the advances in the areas such as human computer interaction, clinical psychology and data-driven animations. Deriving a relevant and reduced set of features is a vital step...
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