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In this article, we developed an approach for detecting brain regions that contribute to Alzheimer's disease (AD) using support vector machine (SVM) classifiers and the recently developed self regulating particle swarm optimization (SRPSO) algorithm. SRPSO employs strategies inspired by the principles of learning in humans to achieve faster and better optimization results. The classifiers for distinguishing...
The death of the patients is an important event in the intensive care unit (ICU), mortality risk prediction thus offers much information for clinical decision making. However, Patient ICU mortality prediction faces challenges in many aspects, such as high dimensionality, imbalance distribution. In this paper, we modified the cost-sensitive principal component analysis (CSPCA), which is denoted by...
In view of the support vector machine (SVM) model applied in vibrant fault diagnosis for hydro-turbine generating unit, it exists problems of parameter settings and classification-plane incline due to unequal sample, which leads to lower diagnosis accuracy. As a new bionic intelligent optimization algorithm for glowworm swarm optimization(GSO), it has the characteristics of strong versatility and...
A hybrid least square support vector machine (LSSVM) is proposed to predict the boiler combustion efficiency. In this approach, a principal component analysis (PCA) is employed to reconstruct new variables as the input of the predictive model. Then, a particle swarm optimization (PSO) algorithm optimized LSSVM is proposed. The parameters of LSSVM are optimized dynamically by PSO and the output value...
Finding efficient solutions for search and optimisation problems has inspired many researchers to utilise nature informed algorithms, where the interactions in swarm could lead to promising solutions for challenging problems. One problem in machine learning is class imbalance, which occurs in real-world applications such as medical diagnosis. This problem can bias the classification or make it entirely...
For better resolving the safety risk early warning of the apron effectively, the attribute reduction algorithm based on Rough Set is used to simplify the set as the warning index set of the apron is too large. The improved Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of Support Vector Machine. Combined with the Rough Set and SVM which is optimized by the improved...
Nowadays, Peer-to-Peer computing technology (P2P) is widely used on Internet, which has brought great challenges to effective management of the network. As a result, it is very important to recognize P2P applications as to maintain network. In essence, to identify traffic of P2P is a problem belongs to pattern recognition. As one of the optimal classifiers, support vector machine (SVM) has special...
Opinion mining is an automation technique of textual data from opinion sentence that produce sentiment information. It is also called sentiment analysis that involves the construction of a system for collecting and classifying opinions about a product review done by understanding, extracting and processing the text in an opinion sentence become positive, negative, and neutral. One of the techniques...
Due to the particularity of the aero-engine bearing and the limitation of the test conditions, it is difficult to get enough fault class sample data and the misclassification cost that misjudge fault sample to normal sample is higher than the opposite misjudgment, therefore the diagnosis of aero-engine bearing belongs to the typical small sample problem which is also unbalance. In order to solve this...
The medical datasets have many features if the features have a tendency of mutation then the risk of disease increases which makes difficult to provide a diagnosis of disease. In the dataset, every feature is a contributor for prediction accuracy, the selection of significant features from the dataset is a challenging task. The feature selection technique based on metaheuristic algorithms is used...
The accurate prediction of crude oil output plays an important role in the development of oilfield planning. This paper proposes a least squares support vector machine model based on the optimization of particle swarm algorithm (PSO-LSSVM) to predict the crude oil output. Each pair of penalty factor and kernel function parameter was taken as a particle, which follows the optimal particle in the current...
Intelligent fault diagnosis has became a focus of fault diagnosis, which can quickly and efficiently process collected signals and obtain high accurate diagnosis results. Traditionally, intelligent fault diagnosis subjects to a lot of prior knowledge. In this work, a novel method based on the sparse features is proposed. The sparse features directly are extracted from raw mechanical vibration signals...
This paper proposes a method for the problem of processing high-dimensional data. When one has thousands of features (attributes) in a dataset, it is hard to achieve an efficient feature selection. To cope with this problem, we propose the use of a binary particle swarm optimization algorithm combined with the C4.5 as a classifier in the fitness function for the selection of informative attributes...
The ability to closely track the traffic load of base stations is very important for resource management and energy saving in green communications. Thus how to predict the future traffic accurately is critical and some recent studies show that correlation of traffic load exists among neighboring base stations. Inspired by these conclusions, this paper proposes a novel base station traffic prediction...
Fruit fly Optimization Algorithm (FOA) is a kind of relatively new swarm intelligence optimization algorithm with strong performance. In this paper a kind of FOA based on dynamic population and direction correct (DPDC-FOA) is proposed on the question of premature convergence. By changing the fruit fly group's search range, number of individuals and group position selection strategy during the movement...
Classification is part of various applications and it is an important problem that represents active research topic. Support vector machine is one of the widely used and very powerful classifier. The accuracy of support vector machine highly depends on learning parameters. Optimal parameters can be efficiently determined by using swarm intelligence algorithms. In this paper, we proposed recent elephant...
This paper proposed an intelligent approach to predict the biochar yield. The biochar is an important renewable energy that produced from biomass thermochemical processes with yields that depend on different operating conditions. There are some approaches that are used to predict the production of biochar such as least square support vector machine. However, this approach suffers from some drawbacks...
Accurate prediction of the traffic state can help to solve the problem of urban traffic congestion, providing guiding advices for people's travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction algorithm, which is based on Multi-kernel Support Vector Machine (MSVM) and Adaptive Particle Swarm Optimization (APSO). Firstly, we explore both the nonlinear and...
This study aimed to discover the locations of the unbalance faults on rotating machine discs. In this regard, fair comparisons between the performances of empirical mode decomposition, ensemble empirical mode decomposition and discrete wavelet transform were performed to determine unbalance fault location in a rotating machinery. In order to obtain the required data, a rotating machinery fault simulator...
The kernel function's selection has a great impact on the performance of support vector regression (SVR). A new method of nonlinear model predictive control (NMPC) based on polynomial kernel SVR is put forward, and multi-agent particle swarm optimization algorithm is introduced to obtain the optimal control law of rolling optimization in NMPC. Compares with the NMPC based on quadratic polynomial kernel...
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