Aboul Ella Hassanien (Abo)

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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.

Fish detection and identication are important steps towards
monitoring sh behavior. The importance of such monitoring step comes
from the need for better understanding of the sh ecology and issuing
conservative actions for keeping the safety of this vital food resource.
The recent advances in machine learning approaches allow many appli-
cations to easily analyze and detect a number of sh species. The main
competence between these approaches is based on two main detection
parameters: the time and the accuracy measurements. Therefore, this
paper proposes a sh detection approach based on BAT optimization
algorithm (BA). This approach aims to reduce the classication time
within the sh detection process. The performance of this system was
evaluated by a number of well-known machine learning classiers, KNN,
ANN, and SVM. The approach was tested with 151 images to detect the
Nile Tilapia sh species and the results showed that k-NN can achieve
high accuracy 90%, with feature reduction ratio close to 61% along with
a noticeable decrease in the classication time.

One way hash functions play a fundamental role for data integrity, message authentication, and digital signature in modern information security. In this paper we proposed a fast one-way hash function to optimize the time delay with strong collision resistance, assures a good compression and one-way resistance. It is based on the standard secure hash function (SHA-1) algorithm. The analysis indicates that the proposed algorithm which we called (fSHA-1) is collision resistant and assures a good compression and pre-image resistance. In addition, the executing time compared with the standard secure hash function is much shorter.

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Fuzzy C-Means Clustering, FCM, is an iterative algorithm whose aim is to find the center or centroid of data clusters that minimize an assigned dissimilarity function. The degree of being in a certain cluster can be defined in terms of the distance to the cluster-centroid. The domain knowledge is used to formulate an appropriate measure. However the Euclidean distance is considered as a general measure for such value. The calculation of the Euclidean distance doesn't take into consideration the degree of relevance of each feature to the classification model. In this paper, scoring methods like ChiMerge and Mutual information are used in the FCM model to improve the calculation of the Euclidean distance. Experimental results demonstrate the better performances of the improved FCM on UCI benchmark data sets rather than the ordinary FCM, where the ordinary FCM uses in classification either all features or the most important features while the improved FCM uses all the features but the Euclidean Distance will be calculated according to the relevance degree of each feature.

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Epilepsy is one of the most common a chronic neurological disorders of the brain that affect millions of the world's populations. It is characterized by recurrent seizures, which are physical reactions to sudden, usually brief, excessive electrical discharges in a group of brain cells. Hence, seizure identification has great importance in clinical therapy of epileptic patients. Electroencephalogram (EEG) is most commonly used in epilepsy detection since it includes precious physiological information of the brain. However, it could be a challenge to detect the subtle but critical changes included in EEG signals. Feature extraction of EEG signals is core trouble on EEG-based brain mapping analysis. This paper will extract ten features from EEG signal based on discrete wavelet transform (DWT) for epilepsy detection. These numerous features will help the classifiers to achieve a good accuracy when utilize to classify EEG signal to detect epilepsy. Subsequently, the results have illustrated that DWT has been adopted to extract various features i.e., Entropy, Min, Max, Mean, Median, Standard deviation, Variance, Skewness, Energy and Relative Wave Energy (RWE).

Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of the optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this paper, a novel meta-heuristic optimizer, namely chaotic crow search algorithm (CCSA), is proposed to overcome these problems. The proposed CCSA is applied to optimize feature selection problem for 20 benchmark datasets. Ten chaotic maps are employed during the optimization process of CSA. The performance of CCSA is compared with other well-known and recent optimization algorithms. Experimental results reveal the capability of CCSA to find an optimal feature subset which maximizes the classification performance and minimizes the number of selected features. Moreover, the results show that CCSA is superior compared to CSA and the other algorithms. In addition, the experiments show that sine chaotic map is the appropriate map to significantly boost the performance of CSA.