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There have been insurmountable obstacles for sharing instructions with disabled students but now they can be easily overcome by using the free programs TeamViewer in combination with Skype, Krut Computer Recorder and Zoomtext because they allow to transmit all instructional material in accessible format in real time from the lecture computer directly to the screen of the disabled student. Moreover, they allow for efficient one on one tutoring by allowing the visually impaired student to follow with Zoomtext without limiting the visual field of the online instructor. These programs can be used to extend computer lab hours and thus give more time to disabled students to complete their lab assignments. Logging remotely into the instructor's computer allows beaming lectures directly into the screen of visually impaired students. These programs can enhance online collaborations even for non disabled people and help to better integrate disabled people into an increasingly specialized and competitive workforce

Land usage analysis takes advantage of the multi-band imagery for classification and recognition. Multi-bands data contains reliable information compared to the raw image formats e.g. RGB, HIS, HSV and other color spaces. In this paper, we advocate the usage of non-parametric machine learning algorithms for land usage analysis. From the non-parametric algorithms, we propose a random forest approach for land use analysis. Our analysis is concerned with the classification of land into seven classes. We have shown that non-parametric classifier the “Random Forest” is well suited to the task of multi-band land usage analysis. In the experimentation setup, we have compared the random forest with the state-of-the-art classifiers. Based on the SPOT-5 imagery, we have shown that the random forest outperforms the state-of-the-art classifiers including Naive Bayesian, Mutli-Layer Perceptron, Bayesian Network, SVM, Radial Basis Function Network (RBF) and Ada-boost. We further show that for the land use analysis, increasing the number of trees has no effect on the performance of the random forest and therefore the runtime of the random forest can be reduced compare to all the other classifiers. The best F-score is achieved using 4 trees and 10 Fold Cross Validation.

We investigate the performance evaluation of merging (fusing) the classification capabilities of classifiers for the land use analysis. For the fusion approach, we select the parametric and non-parametric classifiers. The set used includes: Bayesian Network, Multi-layer Perceptron (MLP), Support Vector Machines (SVM) and Random Forest. These classifiers are selected based on their good over-all performance for the land use analysis and in general for other classification tasks. We evaluate the concept on both the high and low resolution multispectral satellite imagery. The performance of the approach is evaluated using F-score, computation time and accuracy. Based on the experimental evaluation, we advocate the use of classifier fusion for the low resolution satellite imagery. While for high resolution satellite imagery, the fusion shows slight improvement in performance.