Aboul Ella Hassanien (Abo)

Fish image segmentation can be considered an essential process in developing a system for fish recognition. This task is challenging as different specimens, rotations, positions, illuminations, and backgrounds exist in fish images. In this research, a segmentation model is proposed for fish images using Salp Swarm Algorithm (SSA). The segmentation is formulated using Simple Linear Iterative Clustering (SLIC) method with initial parameters optimized by the SSA. The SLIC method is used to cluster image pixels to generate compact and nearly uniform superpixels. Finally, a thresholding using Otsu’s method helped to produce satisfactory results of extracted fishes from the original images under different conditions. A fish dataset consisting of real-world images was tested. In experiments, the proposed model shows robustness for different cases compared to conventional work.

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.

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).

Abstract: This paper presents a Fuzzy C-Means based image segmentation approach that benefits from the Minkowski distance as the dissimilarity measure, denoted as FCM-M, instead of the traditional Euclidean distance, herein identified as FCM-E. The proposed approach was applied on Liver CT images, and a thorough comparison between both methods was carried out. FCM-M provided better accuracy when compared to the traditional FCM-E, with an area under the ROC curve of 85.44% and 47.96%, respectively. In terms of statistical significant analysis, a twofold benefit was obtained from using the proposed approach: the performance of the image segmentation procedure was maintained, or even slightly increased in some situations, while the CPU processing time was significantly decreased. The advantages inherent to the proposed FCM-M pave the way to a whole new chain of fully automatic segmentation methods.

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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.

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

In this paper, a system for feature selection based on firefly algorithm (FFA) optimization is proposed. Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might reduce the classification accuracy because of the large search space. The main goal of attribute reduction is to choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy from using all attributes. A system for feature selection is proposed in this paper using a modified version of the firefly algorithm (FFA) optimization. The modified FFA algorithm adaptively balance the exploration and exploitation to quickly find the optimal solution. FFA is a new evolutionary computation technique, inspired by the flash lighting process of fireflies. The FFA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system was tested on eighteen data sets and proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators

Accurate forecasting for future events constitutes a fascinating challenge for theoretical and for applied researches. Foreign Exchange market (FOREX) is selected in this research to represent an example of financial systems with a complex behavior. Forecasting a financial time series can be a very hard task due to the inherent uncertainty nature of these systems. It seems very difficult to tell whether a series is stochastic or deterministic chaotic or some combination of these states. More generally, the extent to which a non-linear deterministic process retains its properties when corrupted by noise is also unclear. The noise can affect a system in different ways even though the equations of the system remain deterministic. Since a single reliable statistical test for chaoticity is not available, combining multiple tests is a crucial aspect, especially when one is dealing with limited and noisy data sets like in economic and financial time series. In this research, the authors propose an improved model for forecasting exchange rates based on chaos theory that involves phase space reconstruction from the observed time series and the use of support vector regression (SVR) for forecasting.Given the exchange rates of a currency pair as scalar observations, observed time series is first analyzed to verify the existence of underlying nonlinear dynamics governing its evolution over time. Then, the time series is embedded into a higher dimensional phase space using embedding parameters.In the selection process to find the optimal embedding parameters,a novel method based on the Differential Evolution (DE) geneticalgorithm(as a global optimization technique) was applied. The authors have compared forecasting accuracy of the proposed model against the ordinary use of support vector regression. The experimental results demonstrate that the proposed method, which is based on chaos theory and genetic algorithm,is comparable with the existing approaches.

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%.