Aboul Ella Hassanien (Abo)

Support Vector Machine (SVM) is one of the widely used algorithms for classification and regression problems. In SVM, penalty parameter C and kernel parameters can have a significant impact on the complexity and performance of SVM. In this paper, an Optimal Foraging Algorithm (OFA) is proposed to optimize the main parameters of SVM and reduce the classification error. Six public benchmark datasets were employed for evaluating the proposed (OFA-SVM). Also, five well-known and recently optimization algorithms are used for evaluation. These algorithms are Artificial Bee Colony (ABC), Genetic Algorithm (GA), Chicken Swarm Optimization (CSO), Particle Swarm Optimization (PSO) and Bat Algorithm (BA). The experimental results show that the proposed OFA-SVM obtained superior results. Also, the results demonstrate the capability of the proposed OFA-SVM to find optimal values of SVM parameters.

In this paper, a modified cuckoo search algorithm with rough sets is presented to deal with high dimensionality data through feature selection. The modified cuckoo search algorithm imitates the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds. The modified cuckoo search uses the rough sets theory to build the fitness function that takes the number of features in reduct set and the classification quality into account. The proposed algorithm is tested and validated benchmark on several benchmark datasets drawn from the UCI repository and using different evaluation criteria as well as a further analysis is carried out by means of the Analysis of Variance test. In addition, the proposed algorithm is experimentally compared with the existing algorithms on discrete datasets. Finally, two learning algorithms, namely K-nearest neighbors and support vector machines are used to evaluate the performance of the proposed approach. The results show that the proposed algorithm can significantly improve the classification performance.

In this paper, a new compromise algorithm for multi-objective transportation problem (MO-TP) is developed, which is inspired by Zimmermann's fuzzy programming and the neutrosophic set terminology. The proposed NCPA is characterized by assigning three membership functions for each objective namely, truth membership, indeterminacy membership and falsity membership. With the membership functions for all objectives, a neutrosophic compromise programming model is constructed with the aim to find best compromise solution (BCS). This model can cover a wide spectrum of BCSs by controlling the membership functions interactively. The performance of the NCPA is validated by measuring the ranking degree using TOPSIS approach. Illustrative examples are reported and compared with exists models in the literature. Based on the provided comparisons, NCPA is superior to fuzzy and different approaches.

In this paper, a new compromise algorithm for multi-objective transportation problem (MO-TP) is developed, which is inspired by Zimmermann's fuzzy programming and the neutrosophic set terminology. The proposed NCPA is characterized by assigning three membership functions for each objective namely, truth membership, indeterminacy membership and falsity membership. With the membership functions for all objectives, a neutrosophic compromise programming model is constructed with the aim to find best compromise solution (BCS). This model can cover a wide spectrum of BCSs by controlling the membership functions interactively. The performance of the NCPA is validated by measuring the ranking degree using TOPSIS approach. Illustrative examples are reported and compared with exists models in the literature. Based on the provided comparisons, NCPA is superior to fuzzy and different approaches.

In the recent years, there are massive digital images collections in many fields of our life, which led the technology to find methods to search and retrieve these images efficiently. The content-based is one of the popular methods used to retrieve images, which depends on the color, texture and shape descriptors to extract features from images. However, the performance of the content-based image retrieval methods depends on the size of features that are extracted from images and the classification accuracy. Therefore, this problem is considered as a multi-objective and there are several methods that used to manipulate it such as NSGA-II and NSMOPSO. However, these methods have drawbacks such as their time and space complexity are large since they used traditional non-dominated sorting methods. In this paper, a new non-dominated sorting based on multi-objective whale optimization algorithm is proposed for content-based image retrieval (NSMOWOA). The proposed method avoids the drawbacks in other non-dominated sorting multi-objective methods that have been used for content-based image retrieval through reducing the space and time complexity. The results of the NSMOWOA showed a good performance in content-based image retrieval problem in terms of recall and precision.

In this paper, a modified cuckoo search algorithm with rough sets is presented to deal with high dimensionality data through feature selection. The modified cuckoo search algorithm imitates the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds. The modified cuckoo search uses the rough sets theory to build the fitness function that takes the number of features in reduct set and the classification quality into account. The proposed algorithm is tested and validated benchmark on several benchmark datasets drawn from the UCI repository and using different evaluation criteria as well as a further analysis is carried out by means of the Analysis of Variance test. In addition, the proposed algorithm is experimentally compared with the existing algorithms on discrete datasets. Finally, two learning algorithms, namely K-nearest neighbors and support vector machines are used to evaluate the performance of the proposed approach. The results show that the proposed algorithm can significantly improve the classification performance.

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Multidimensionality in social networks is a great issue that
came out into view as a result of that most social media sites such as
Facebook, Twitter, and YouTube allow people to interact with each other
through dierent social activities. The community detection in such mul-
tidimensional social networks has attracted a lot of attention in the recent
years. When dealing with these networks the concept of community de-
tection changes to be, the discovery of the shared group structure across
all network dimensions such that members in the same group interact
with each other more frequently than those outside the group. Most of
the studies presented on the topic of community detection assume that
there is only one kind of relation in the network. In this paper, we propose
a multi-objective approach, named MOGA-MDNet, to discover commu-
nities in multidimensional networks, by applying genetic algorithms. The
method aims to nd community structure that simultaneously maximizes
modularity, as an objective function, in all network dimensions. This
method does not need any prior knowledge about number of communi-
ties. Experiments on synthetic and real life networks show the capability
of the proposed algorithm to successfully detect the structure hidden
within these networks.

Disabilities, specially Spinal Cord Injuries (SCI), affect people behaviors, their response, and the participation in daily activities. People with SCI need long care, cost, and time to improve their heath status. So, the rehabilitation of people with SCI on different period of times is required. In this paper, we proposed an automated system to estimate the rehabilitation length of stay of patients with SCI. The proposed system is divided into three phases; (1) pre-processing phase, (2) classification phase, and (3) rehabilitation length of stay measurement phase. The proposed system is automating International Classification of Functioning, Disability and Health classification (ICF) coding process, monitoring progress in patient status, and measuring the rehabilitation time based on support vector machines algorithm. The proposed system used linear and radial basis (RBF) kernel functions of support vector machines (SVMs) classification algorithm to classify data. The accuracy obtained was full match on training and testing data for linear kernel function and 93.3 % match for RBF kernel function.

Starting from the last century, animals identification
became important for several purposes, e.g. tracking,
controlling livestock transaction, and illness control. Invasive and
traditional ways used to achieve such animal identification in
farms or laboratories. To avoid such invasiveness and to get more
accurate identification results, biometric identification methods
have appeared. This paper presents an invariant biometric-based
identification system to identify cattle based on their muzzle
print images. This system makes use of Speeded Up Robust
Feature (SURF) features extraction technique along with with
minimum distance and Support Vector Machine (SVM) classifiers.
The proposed system targets to get best accuracy using minimum
number of SURF interest points, which minimizes the time
needed for the system to complete an accurate identification.
It also compares between the accuracy gained from SURF
features through different classifiers. The experiments run 217
muzzle print images and the experimental results showed that
our proposed approach achieved an excellent identification rate
compared with other previous works.

Feature sets are always dependent, redundant and noisy in almost all application domains. These problems in The data always declined the performance of any given classifier as it make it difficult for the training phase to converge effectively and it affect also the running time for classification at operation and training time. In this work a system for feature selection based on multi-objective gray wolf optimization is proposed. The existing methods for feature selection either depend on the data description; filter-based methods, or depend on the classifier used; wrapper approaches. These two main approaches lakes of good performance and data description in the same system. In this work gray wolf optimization; a swarm-based optimization method, was employed to search the space of features to find optimal feature subset that both achieve data description with minor redundancy and keeps classification performance. At the early stages of optimization gray wolf uses filter-based principles to find a set of solutions with minor redundancy described by mutual information. At later stages of optimization wrapper approach is employed guided by classifier performance to further enhance the obtained solutions towards better classification performance. The proposed method is assessed against different common searching methods such as particle swarm optimization and genetic algorithm and also was assessed against different single objective systems. The proposed system achieves an advance over other searching methods and over the other single objective methods by testing over different UCI data sets and achieve much robustness and stability.