Aboul Ella Hassanien (Abo)

The global prevalence of hepatitis C Virus (HCV) is approximately 3% and one-fifth of all HCV carriers live in the Middle East, where Egypt has the highest global incidence of HCV infection. Quantitative structure-activity relationship (QSAR) models were used in many applications for predicting the potential effects of chemicals on human health and environment. The adaptive neuro-fuzzy inference system (ANFIS) is one of the most popular regression methods for building a nonlinear QSAR model. However, the quality of ANFIS is influenced by the size of the descriptors, so descriptor selection methods have been proposed, although these methods are affected by slow convergence and high time complexity. To avoid these limitations, the antlion optimizer was used to select relevant descriptors, before constructing a nonlinear QSAR model based on the PIC50 and these descriptors using ANFIS. In our experiments, 1029 compounds were used, which comprised 579 HCVNS5B inhibitors (PIC50 < ~14) and 450 non-HCVNS5B inhibitors (PIC50 > ~14). The experimental results showed that the proposed QSAR model obtained acceptable accuracy according to different measures, where R2 was 0.952 and 0.923 for the training and testing sets, respectively, using cross-validation, while R2 LOO
was 0.8822 using leave-one-out (LOO).

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 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.

The accumulating data are easy to store but the ability of understanding and using it does not keep track with its growth. So researches focus on the nature of knowledge processing in the mind. This paper proposes a semantic model (CKRMCC) based on cognitive aspects that enables cognitive computer to process the knowledge as the human mind and find a suitable representation of that knowledge. In cognitive computer, knowledge processing passes through three major stages: knowledge acquisition and encoding, knowledge representation, and knowledge inference and validation. The core of CKRMCC is knowledge representation, which in turn proceeds through four phases: prototype formation phase, discrimination phase, generalization phase, and algorithm development phase. Each of those phases is mathematically formulated using the notions of real-time process algebra. The performance efficiency of CKRMCC is evaluated using some datasets from the well-known UCI repository of machine learning datasets. The acquired datasets are divided into training and testing data that are encoded using concept matrix. Consequently, in the knowledge representation stage, a set of symbolic rule is derived to establish a suitable representation for the training datasets. This representation will be available in a usable form when it is needed in the future. The inference stage uses the rule set to obtain the classes of the encoded testing datasets. Finally, knowledge validation phase is validating and verifying the results of applying the rule set on testing datasets. The performances are compared with classification and regression tree and support vector machine and prove that CKRMCC has an efficient performance in representing the knowledge using symbolic rules.

This article introduces a computer-aided diagnosis
system of the heart valve disease using binary particle swarm
optimization and support vector machine, in conjunction with
K-nearest neighbor and with leave-one-out cross-validation. The
system was applied in a representative heart dataset of 198
heart sound signals, which come both from healthy medical cases
and from cases suffering from the four most usual heart valve
diseases: aortic stenosis (AS), aortic regurgitation (AR), mitral
stenosis (MS) and mitral regurgitation (MR). The introduced
approach starts with an algorithm based on binary particle
swarm optimization to select the most weighted features. This
is followed by performing support vector machine to classify
the heart signals into two outcome: healthy or having a heart
valve disease, then its classified the having a heart valve disease
into four outcomes: aortic stenosis (AS), aortic regurgitation
(AR), mitral stenosis (MS) and mitral regurgitation (MR). The
experimental results obtained, show that the overall accuracy
offered by the employed approach is high compared with other
techniques.

Due to the gradual expansion in data volume used in social networks and cloud computing, the term “Big data” has appeared with its challenges to store the immense datasets. Many tools and algorithms appeared to handle the challenges of storing big data. NoSQL databases, such as Cassandra and MongoDB, are designed with a novel data management system that can handle and process huge volumes of data. Partitioning data in NoSQL databases is considered one of the critical challenges in database design. In this paper, a MapReduce Rendezvous Hashing-Based Virtual Hierarchies (MR-RHVH) framework is proposed for scalable partitioning of Cassandra NoSQL database. The MapReduce framework is used to implement MR-RHVH on Cassandra to enhance its performance in highly distributed environments. MR-RHVH distributes the nodes to rendezvous regions based on a proposed Adopted Virtual Hierarchies strategy. Each region is responsible for a set of nodes. In addition, a proposed bloom filter evaluator is used to ensure the accurate allocation of keys to nodes in each region. Moreover, a number of experiments were performed to evaluate the performance of MR-RHVH framework, using YCSB for database benchmarking. The results show high scalability rate and less time consuming for MR-RHVH framework over different recent systems.

In this paper, two novel bio-inspired optimization algorithms; namely Dragonfly Algorithm (DA) and Grey Wolf Optimizer (GWO), have been applied for fulfilling the goal of feature set dimensional reduction. The proposed classification system has been tested via solving the problem of Electromyography (EMG) signal classification with optimal features subset selection. The obtained experimental results showed that the GWO based Support Vector Machines (SVM) classification algorithm has achieved an accuracy of 93.22% using 31% of the total extracted features. It also outperformed both the typical SVM algorithm, with no feature set optimization, and the DA based optimized feature set SVM classification, for the tested EMG dataset.

WSN as a new category of computer-based computing platforms and network structures is showing new applications in different areas such as environmental monitoring, health care and military applications. Although there are a lot of secure image processing schemas designed for image transmission over a network, the limited resources and the dynamic environment make it invisible to be used with Wireless Sensor Networks (WSNs). In addition, the current secure data transmission schemas in WSN are concentrated on the text data and are not applicable for image transmission's applications. Furthermore, secure image transmission is a big challenging issue in WSNs especially for the application that uses image as its main data such as military applications. The reason why is because the limited resources of the sensor nodes which are usually deployed in unattended environments. This chapter introduces a secure image processing and transmission schema in WSN using Elliptic Curve Cryptography (ECC) and Homomorphic Encryption (HE).

Despite the great efforts to assure quality in higher education institutions, the ambiguity of its related concepts and requirements constitute a big challenge when trying to implement it as an automated information system. The present work introduces a framework for an automated information system that manages the quality assurance in higher educations institutions. The aim of designing such a system is to provide an automation tool that avoids unnecessary and redundant tasks associated to quality in higher education institutions. In addition, the proposed system helps all higher education stockholders to handle and monitor their tasks. Moreover, it aims to help the quality assurance center in a higher education institution to apply its qualitys standards, and to make sure that they are being maintained and enhanced. This information system contains a core module and 17 sub-modules, which are described in this paper.

Recently, Wireless Sensor Networks (WSNs) are widely used for monitoring and tracking applications. Sensor mobility adds extra flexibility and greatly expands the application space. Due to the limited energy and battery lifetime for each sensor, it can remain active only for a limited amount of time. To avoid the drawbacks of the classical coverage model, especially if a sensor died, K-coverage model requires at least k sensor nodes monitor any target to consider it covered. This paper proposed a new model that uses the Genetic Algorithm (GA) to optimize the coverage requirements in WSNs to provide continuous monitoring of specified targets for longest possible time with limited energy resources. Moreover, we allow sensor nodes to move to appropriate positions to collect environmental information. Our model is based on the continuous and variable speed movement of mobile sensors to keep all targets under their cover all times. To further prove that our proposed model is better than other related work, a set of experiments in different working environments and a comparison with the most related work are conducted. The improvement that our proposed method achieved regarding the network lifetime was in a range of 26%–41.3% using stationary nodes while it was in a range of 29.3%–45.7% using mobile nodes. In addition, the network throughput is improved in a range of 13%–17.6%. Moreover, the running time to form the network structure and switch between nodes’ modes is reduced by 12%.

Despite the great efforts to assure quality in higher education institutions, the ambiguity of its related concepts and requirements constitute a big challenge when trying to implement it as an automated information system. The present work introduces a framework for an automated information system that manages the quality assurance in higher educations institutions. The aim of designing such a system is to provide an automation tool that avoids unnecessary and redundant tasks associated to quality in higher education institutions. In addition, the proposed system helps all higher education stockholders to handle and monitor their tasks. Moreover, it aims to help the quality assurance center in a higher education institution to apply its qualitys standards, and to make sure that they are being maintained and enhanced. This information system contains a core module and 17 sub-modules, which are described in this paper.

Manual segmentation of liver computerized tomography (CT) images is very time consuming, so it is desired to develop a computer-based approach for the analysis of liver
CT images that can precisely segment the liver without any human intervention. This paper presents normalized cuts graph partitioning approach for liver segmentation from CT images. To evaluate the performance of the presented approach, we present tests on different liver CT images. Experimental results obtained show that the overall accuracy offered by the employed normalized cuts technique is high compared to the well known K-means segmentation approach.