Aboul Ella Hassanien (Abo)

Apoptosis is an imperative constituent of various processes including proper progression and functioning of the immune system, embryonic development as well as chemical-induced cell death. Improper apoptosis is a reason in numerous human/animal’s conditions involving ischemic damage, neurodegenerative diseases, autoimmune disorders and various types of cancer. An outstanding feature of neurodegenerative diseases is the loss of specific neuronal populations. Thus, the detection of the dead cells is a necessity. This paper proposes a novel algorithm to achieve the dead cells detection based on color intensity and contrast changes and aims for fully automatic apoptosis detection based on image analysis method. A stained cultures images using Caspase stain of albino rats hippocampus specimens using light microscope (total 21 images) were used to evaluate the system performance. The results proved that the proposed system is efficient as it achieved high accuracy (98.89 ± 0.76 %) and specificity (99.36 ± 0.63 %) and good mean sensitivity level of (72.34 ± 19.85 %).

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Face sketch recognition is one of the recent biometrics,
which is used to identify criminals. In this paper, a
proposed model is used to identify face sketch images based
on local invariant features. In this model, two local invariant
feature extraction methods, namely, Scale Invariant Feature
Transform (SIFT) and Local Binary Patterns (LBP) are used
to extract local features from photos and sketches. Minimum
distance and Support Vector Machine (SVM) classifiers are used
to match the features of an unknown sketch with photos. Due to
high dimensional features, Direct Linear Discriminant Analysis
(Direct-LDA) is used. CHUK face sketch database images is used
in our experiments. The experimental results show that SIFT
method is robust and it extracts discriminative features than LBP.
Moreover, different parameters of SIFT and LBP are discussed
and tuned to extract robust and discriminative features.

Grey Wolf Optimization (GWO) algorithm is a
new meta-heuristic method, which is inspired by grey wolves,
to mimic the hierarchy of leadership and grey wolves hunting
mechanism in nature. This paper presents a hybrid model that
employs grey wolf optimizer (GWO) along with support vector
machines (SVMs) classification algorithm to improve the classification
accuracy via selecting the optimal settings of SVMs
parameters. The proposed approach consists of three phases;
namely pre-processing, feature extraction, and GWO-SVMs
classification phases. The proposed classification approach was
implemented by applying resizing, remove background, and
extracting color components for each image. Then, feature
vector generation has been implemented via applying PCA
feature extraction. Finally, GWO-SVMs model is developed
for selecting the optimal SVMs parameters. The proposed
approach has been implemented via applying One-againstOne
multi-class SVMs system using 3-fold cross-validation. The
datasets used for experiments were constructed based on real
sample images of bell pepper at different stages, which were
collected from farms in Minya city, Upper Egypt. Datasets
of total 175 images were used for both training and testing
datasets. Experimental results indicated that the proposed
GWO-SVMs approach achieved better classification accuracy
compared to the typical SVMs classification algorithm.

Abstract—This paper presents a bio-inspired optimized classification approach for assessing water quality. As fish liver histopathology is a good biomarker for detecting water pollution, the proposed classification approach uses fish liver microscopic images in order to detect water pollution and determine water
quality. The proposed approach includes three phases; preprocessing, feature extraction, and classification phases. Color histogram and Gabor wavelet transform have been utilized for feature extraction phase. The Machine Learning (ML) Support Vector Machines (SVMs) classification algorithm has been employed,
along with the bio-inspired Gray Wolf Optimization (GWO) algorithm for optimizing SVMs parameters, in order to classify water pollution degree. Experimental results showed that the average accuracy achieved by the proposed GWO-SVMs classification approach exceeded 95% considering a variety of
water pollutants.