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Articles by Sulieman Bani-Ahmad
Total Records ( 2 ) for Sulieman Bani-Ahmad
  Sulieman Bani-Ahmad and Gultekin Ozsoyoglu
  We validate the research pyramid model of research evolution. Moreover, we propose and evaluate two algorithms to identify research pyramids. Finally, we improve publication scores in terms of accuracy and separability via publications’ research pyramids. Accurately ranking publications enables users to aggregate pertinent results quickly and easily. Studies show that citation-based publication-importance functions, e.g., PageRank and Citation Count, are extremely skewed and have accuracy problems. Based on the notion of research pyramids we propose a priori technique to assign more effective and accurate publication importance scores. We showed that the proposed technique provides more accurate and significantly less skewed publication scores than citation-based techniques. Our experiments showed 16-25% improvement in search outputs accuracy measured for the top-k search results.
  Dheeb Al Bashish , Malik Braik and Sulieman Bani-Ahmad
  The aim of this study is to design, implement and evaluate an image-processing-based software solution for automatic detection and classification of plant leaf diseases. Studies show that relying on pure naked-eye observation of experts to detect and classify such diseases can be prohibitively expensive, especially in developing countries. Providing fast, automatic, cheap and accurate image-processing-based solutions for that task can be of great realistic significance. The methodology of the proposed solution is image-processing-based and is composed of four main phases; in the first phase we create a color transformation structure for the RGB leaf image and then, we apply device-independent color space transformation for the color transformation structure. Next, in the second phase, the images at hand are segmented using the K-means clustering technique. In the third phase, we calculate the texture features for the segmented infected objects. Finally, in the fourth phase the extracted features are passed through a pre-trained neural network. As a testing step we use a set of leaf images taken from Al-Ghor area in Jordan. Present experimental results indicate that the proposed approach can significantly support an accurate and automatic detection and recognition of leaf diseases. The developed Neural Network classifier that is based on statistical classification perform well in all sampled types of leaf diseases and can successfully detect and classify the examined diseases with a precision of around 93%. In conclusion, the proposed detection models based neural networks are very effective in recognizing leaf diseases, whilst K-means clustering technique provides efficient results in segmentation RGB images.
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