International Conference on Machine Learning and Cybernetics 

About: International Conference on Machine Learning and Cybernetics is an academic conference. The conference publishes majorly in the area(s): Fuzzy logic & Artificial neural network. Over the lifetime, 8656 publications have been published by the conference receiving 50274 citations.

Particle swarm optimization for traveling salesman problem

Abstract: This paper proposes a new application of particle swarm optimization for traveling salesman problem. We have developed some special methods for solving TSP using PSO. We have also proposed the concept of swap operator and swap sequence, and redefined some operators on the basis of them, in this way the paper has designed a special PSO. The experiments show that it can achieve good results.

Smart home research

Abstract: This paper is a survey for smart home research, from definition to current research status. First we give a definition to smart home, and then describe the smart home elements, typical research projects, smart home networks research status, smart home appliances and challenges at last.

On the convergence analysis and parameter selection in particle swarm optimization

Abstract: A PSO with increasing inertia weight, distinct from a widely used PSO with decreasing inertia weight, is proposed in this paper. Far from drawing conclusions from sole empirical study or rule of thumb, this algorithm is derived from particle trajectory study and convergence analysis. Four standard test functions are used to confirm its validity finally. From the experiments, it is clear that a PSO with increasing inertia weight outperforms the one with decreasing inertia weight, both in convergent speed and solution precision, with no additional computing load.

Speech emotion recognition based on HMM and SVM

Abstract: Automatic emotion recognition in speech is a current research area with a wide range of applications in human-machine interactions. This paper uses two classification methods, the hidden Markov model (HMM) and the support vector machine (SVM), to classify five emotional states: anger, happiness, sadness, surprise and a neutral state. In the HMM method, 39 candidate instantaneous features were extracted, and the sequential forward selection (SFS) method was used to find the best feature subset. The classification performance of the selected feature subset was then compared with that of the Mel frequency cepstrum coefficients (MFCC). Within the method based on SVM, a new vector measuring the difference between Mel frequency scale sub-bands energies is proposed. The performance of the K-nearest neighbors (KNN) classifier using the proposed vector was also investigated. Both gender dependent and gender independent experiments were conducted on the Danish emotional speech (DES) database. The recognition rates by the HMM classifier were 98.9% for female subjects, 100% for male subjects, and 99.5% for gender independent cases. When the SVM classifier and the proposed feature vector were employed, correct classification rates of 89.4%, 93.6% and 88.9% were obtained for male, female and gender independent cases respectively.

An efficient iris recognition system

Abstract: Iris recognition, a relatively new biometric technology, has great advantages, such as variability, stability and security, and is most promising for high security environments. A new iris recognition algorithm is proposed in this paper, which adopts Independent Component Analysis (ICA) to extract iris texture feature and a competitive learning mechanism to recognize iris patterns. Experimental results show that the algorithm is efficient and adaptive to the environment, e.g. it works well even for blurred iris images, variable illumination, and interference of eyelids and eyelashes.