Aboul Ella Hassanien (Abo)

Machine learning and computer vision techniques have applied for evaluating food quality as well as crops grading. In this paper, a new classification system has been proposed to classify infected/uninfected tomato fruits according to its external surface. The system is based on feature fusion method with color and texture features. Color moments, GLCM, and Wavelets energy and entropy have been used in the proposed system. Principle Component Analysis (PCA) technique has been used to reduce the feature vector obtained after fusion to avoid dimensionality problem and save time and cost. Support vector machine (SVM) was used to classify tomato images into 2 classes; infected/uninfected using Min-Max and Z-Score normalization methods. The dataset used in this research contains 177 tomato fruits each was captured from four faces (Top, Side1, Side2, and End). Using 70% of the total images for training phase and 30% for testing, our proposed system achieved accuracy 92%.

This paper proposes a new approach of 3D watermarking by ensuring the optimal preservation of mesh surfaces. The minimal surface distortion is enforced during watermark embedding stage using Genetic Algorithm (GA) optimization. The watermark embedding is performed only on set of selected vertices come out from k-means clustering technique. These vertices are used as candidates for watermark carriers that will hold watermark bits stream. A 3D surface preservation function is defined according to the distance of a vertex displaced by watermarking to the original surface. A study of the proposed methodology has high robustness against the common mesh attacks while preserving the original object surface during watermarking.

This paper presents an automated retinal blood vessels segmentation
approach based on flower pollination search algorithm (FPSA). The flower pollination
search is a new algorithm based on the flower pollination process of flowering
plants. The FPSA searches for the optimal clustering of the given retinal
image into compact clusters under some constrains. Shape features are used to
further enhance the clustering results using local search method. The proposed
retinal blood vessels approach is tested on a publicly available databases DRIVE
a of retinal images. The results demonstrate that the performance of the proposed
approach is comparable with state of the art techniques in terms of accuracy, sensitivity
and specificity.

In this paper, we investigate the eect of using an optimum
number of clusters with Fuzzy C-Means clustering, for Liver CT image
segmentation. The optimum number of clusters to be used was measured
using the average silhouette value. The evaluation was carried out using
the Jaccard index, in which we concluded that using the optimum number
of clusters may not necessarily lead to the best segmentation results.

The orphan drug certification process from the European committee is
depending on experts opinions that it is not similar to any other drug, this stage is
very complicated and those opinions differ based on the expertise. So, this paper
introduces computational model that gives one accurate probability of similarity,
using multiple fingerprints measurements to similarity, and fuse these measurements
by data fusion rules, that give one probability of similarity helping experts
to determine that drug is similar to existing anyone or not.

Major complication of heart valve diseases is congestive heart valve failure. The heart is of essential significance to human beings. Auscultation with a stethoscope is considered as one of the techniques used in the analysis of heart diseases. Heart auscultation is a difficult task to determine the heart condition and requires some superior training of medical doctors. Therefore, the use of computerized techniques in the diagnosis of heart sounds may help the doctors in a clinical environment. Hence, in this study computer-aided heart sound diagnosis is performed to give support to doctors in decision making. In this study, a novel hybrid Rough-Bijective soft set is developed for the classification of heart valve diseases. A rough set (Quick Reduct) based feature selection technique is applied before classification for increasing the classification accuracy. The experimental results demonstrate that the overall classification accuracy offered by the employed Improved Bijective soft set approach (IBISOCLASS) provides higher accuracy compared with other classification techniques including hybrid Rough-Bijective soft set (RBISOCLASS), Bijective soft set (BISOCLASS), Decision table (DT), Naïve Bayes (NB) and J48.

In this paper, a novel approach is introduced to compare web sites by analysing their web page content. Each web page can be expressed as a set of entities called MicroGenres, which in turn are abstractions about design patterns and genres for representing the page content. This description is useful for web page and web site classification and for a deeper insight into the web site׳s social context.

The web site comparison is useful for extracting patterns which can be used for improving Web search engine effectiveness, the identification of best practices in web site design and of course in the organization of web page content to personalize the web user experience on a web site.

The effectiveness of the proposed approach was tested in a real world case, with e-shop web sites showing that a web site can be represented in a high level of abstraction by using MicroGenres, the contents of which can then be compared and given a measure corresponding to web site similarity. This measure is very useful for detecting web communities on the Web, i.e., a group of web sites sharing similar contents, and the result is essential in performing a focused and effective information search as well as minimizing web page retrieval.