In this report we consider the semi-supervised learning problem for multi-label image classification, aiming at effectively taking advantage of both labeled and unlabeled training data in the training process. In particular, we implement and analyze various semi-supervised learning approaches including a support vector machine (SVM) method facilitated by principal component analysis (PCA), and ...

A new method is proposed to establish the statistical fractal model for liver diseases classification. Firstly, the fractal theory is used to construct the high-order tensor, and then Generalized N-dimensional Principal Component Analysis (GND-PCA) is used to establish the statistical fractal model and select the feature from the region of liver; at the same time different features have differe...

This paper presents a new kernel framework for hyperspectral images classification. In this paper, a new feature extraction algorithm based on wavelet kernel non-negative matrix factorization (WKNMF) for hyperspectral remote sensing images is proposed. By using the feature of multi-resolution analysis, the new method can improve the nonlinear mapping capability of kernel non-negative matrix fac...

Soft Computing methods provide solutions to biologically inspired problem of medical domain like breast cancer. Neural Networks, Fuzzy Logic and Genetic Algorithms contribute novel algorithms to deal with breast cancer. Breast cancer can be diagnosed using soft computing methods. In this paper, we try to produce effective diagnosis of breast cancer by using feature reduction and classification ...

Web page classification provides an efficient information search to internet users. However, presently most of the web directories are still being classified manually or semiautomatically. This paper analyses the concept of the statistical analysis methods known as Principal Component Analysis (PCA) and Independent Component Analysis (ICA). The main purpose for using integration of PCA and ICA ...

Heart disease is a term that assigns to a large number of healthcare conditions related to heart. These medical conditions describe the unexpected health conditions that directly control the heart and all its parts. The main objective of this research is to develop an efficient heart disease prediction system using feature extraction and SVM classifier that can be used to predict the occurrence...

Phase contrast microscopy is a widely-used technique to observe multi-cellular processes. Two template matching based approaches are proposed to automatically segment cells migrating in 3D Extracellular Matrix (ECM) from phase contrast images. The first approach is based on Partial Least Square Regression (PLSR), while the second applies Principal Component Analysis (PCA) and Support Vector Mac...

In this project, we applied supervised learning algorithms to detect drivers based on their driving behavior. Using labeled driving data (time, speed, acceleration, heading and gas usage), we created a set of features such as maximum of speed, standard deviation of acceleration as well as additional complex features like variation of heading and variation of MPG over time. In addition, we appli...

Intrusion detection system (IDS) is a system that gathers and analyzes information from various areas within a computer or a network to identify attacks made against these components. This research proposed an Intrusion Detection Model (IDM) for detection intrusion attempts, the proposal is a hybrid IDM because it considers both features of network packets and host features that are sensitive t...