International Conference on Intelligent Computing 

About: International Conference on Intelligent Computing is an academic conference. The conference publishes majorly in the area(s): Artificial neural network & Computer science. Over the lifetime, 10433 publications have been published by the conference receiving 45755 citations.

Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning

Abstract: In recent years, mining with imbalanced data sets receives more and more attentions in both theoretical and practical aspects. This paper introduces the importance of imbalanced data sets and their broad application domains in data mining, and then summarizes the evaluation metrics and the existing methods to evaluate and solve the imbalance problem. Synthetic minority over-sampling technique (SMOTE) is one of the over-sampling methods addressing this problem. Based on SMOTE method, this paper presents two new minority over-sampling methods, borderline-SMOTE1 and borderline-SMOTE2, in which only the minority examples near the borderline are over-sampled. For the minority class, experiments show that our approaches achieve better TP rate and F-value than SMOTE and random over-sampling methods.

Cloud Storage as the Infrastructure of Cloud Computing

Abstract: As an emerging technology and business paradigm, Cloud Computing has taken commercial computing by storm. Cloud computing platforms provide easy access to a company’s high-performance computing and storage infrastructure through web services. With cloud computing, the aim is to hide the complexity of IT infrastructure management from its users. At the same time, cloud computing platforms provide massive scalability, 99.999% reliability, high performance, and specifiable configurability. These capabilities are provided at relatively low costs compared to dedicated infrastructures. This article gives a quick introduction to cloud storage. It covers the key technologies in Cloud Computing and Cloud Storage, several different types of clouds services, and describes the advantages and challenges of Cloud Storage after the introduction of the Cloud Storage reference model.

MCPSO: A multi-swarm cooperative particle swarm optimizer

Abstract: This paper presents a new optimization algorithm - MCPSO, multi-swarm cooperative particle swarm optimizer, inspired by the phenomenon of symbiosis in natural ecosystems. MCPSO is based on a master-slave model, in which a population consists of one master swarm and several slave swarms. The slave swarms execute a single PSO or its variants independently to maintain the diversity of particles, while the master swarm evolves based on its own knowledge and also the knowledge of the slave swarms. According to the co-evolutionary relationship between master swarm and slave swarms, two versions of MCPSO are proposed, namely the competitive version of MCPSO (COM-MCPSO) and the collaborative version of MCPSO (COL-MCPSO), where the master swarm enhances its particles based on an antagonistic scenario or a synergistic scenario, respectively. In the simulation studies, several benchmark functions are performed, and the performances of the proposed algorithms are compared with the standard PSO (SPSO) and its variants to demonstrate the superiority of MCPSO.

Kernel-Based reinforcement learning

Abstract: We consider the problem of approximating the cost-to-go functions in reinforcement learning By mapping the state implicitly into a feature space, we perform a simple algorithm in the feature space, which corresponds to a complex algorithm in the original state space Two kernel-based reinforcement learning algorithms, the e -insensitive kernel based reinforcement learning (e – KRL) and the least squares kernel based reinforcement learning (LS-KRL) are proposed An example shows that the proposed methods can deal effectively with the reinforcement learning problem without having to explore many states

Fall Detection by Wearable Sensor and One-Class SVM Algorithm

Abstract: The fall is a crucial problem in the elderly people’s daily life, and the early detection of fall is very important to rescue the subjects and avoid the badly prognosis. In this paper, we use a wearable tri-axial accelerometer to capture the movement data of human body, and propose a novel fall detection method based on one-class support vector machine (SVM). The one-class SVM model is trained by the positive samples from the falls of younger volunteers and a dummy, and the outliers from the non-fall daily activities of younger and the elderly volunteers. The preliminary results show that this method can detect the falls effectively, and reduce the probability of being damaged in the experiments for the elderly people.