Special issue on soft computing and intelligent systems: Tools, techniques and applications

Sabu M. Thampi, El-Sayed M. El-Alfy, Sabu M. Thampi, El-Sayed M. El-Alfy
2017 Journal of Intelligent & Fuzzy Systems  
This special issue is a selected collection of papers submitted to the Second International Symposium on Intelligent Systems Technologies and Applications (ISTA'16), September 21-24, 2016, Jaipur, India. These papers have been reviewed and accepted for presentation at the symposium and for publication in the Journal of Intelligent & Fuzzy Systems (JIFS). In this special issue there are 44 papers covering a wide range of tools, techniques and applications of soft computing and intelligent
more » ... . The International Symposium on Intelligent Systems Technologies and Applications aims to bring together researchers in related fields to explore and discuss various aspects of intelligent systems technologies and their applications. It provides excellent opportunities for the presentation of interesting new research results and discussion about them, leading to knowledge transfer and generation of new ideas. There are seven papers addressing research problems in bioinfomratics and medical research. In [1], a classification system that combines feature selection techniques with semi-supervised fuzzy * Corresponding author. Sabu M. Thampi, Indian Institute of Information Technology and Management -Kerala (IIITM-K), Technopark Campus, Trivandrum 695581, -means (FCM) algorithm is proposed and evaluated on publicly available gene expression datasets. The results show that even with a few labeled samples, accurate prediction of cancer subtypes can be attained. In [2], a new texture-based feature extraction algorithm is proposed for extracting relevant and informative features from brain MR images having tumor. It is based on finding the texture description using nine different variants of texture objects and forming an intermediate texture index matrix using texture objects with high-pass and low-pass spiral filters. The resultant two-index matrix is used to generate the Texture Co-occurrence Matrix which helps to extract spatial and spectral domain features for brain MRI classification. Experiments on a dataset of 660 T1-weighted post contrast brain MR images with five different types of malignant tumors show that the proposed method can lead to significant results in abnormality classification when compared with the state-of-the-art GLCM and Run-length algorithms. In [3], a novel method is proposed for early detection and diagnosis of breast cancer in digital mammograms. The proposed technique uses Particle Swarm Optimization (PSO), Cellular Neural Network (CNN) and Probabilistic Neural Network (PNN). The breast mass texture feature extraction is carried out using Gray-Level Co-occurrence Matrix (GLCM)
doi:10.3233/jifs-169221 fatcat:f43kb5ygbfhlho5cfwogbcyegq