Amr Badr - Academia.edu (original) (raw)

Papers by Amr Badr

$Research paper thumbnail of A CSP\AM-BA-SVM Approach for Motor Imagery BCI System$

IEEE Access, 2018

Brain-computer interface (BCI) has become extremely popular in recent decades. It gained its sign... more Brain-computer interface (BCI) has become extremely popular in recent decades. It gained its significance from the intention of helping paralyzed people communicate with the external environment. One of the major challenges facing BCI systems is obtaining reliable classification accuracy of motor imagery (MI) mental tasks. In this paper, a novel CSP\AM-BA-SVM approach is proposed using bio-inspired algorithms for feature selection and classifier optimization to improve classification accuracy of the MI-BCI systems. The proposed approach applies optimum selection of time interval for each subject. The features are extracted from EEG signal using the common spatial pattern (CSP). Binary CSP is extended to multi-class problems by utilizing one-vs-one strategy. This paper introduces applying a hybrid attractor metagene (AM) algorithm along with the Bat optimization algorithm (BA) to select the most discriminant CSP features and optimize SVM parameters. The efficacy of the proposed approach was examined using three data sets. The proposed approach has achieved 78.55% accuracy and 0.71 mean kappa for BCI Competition IV data set 2a, 86.6% accuracy and 0.82 mean kappa for BCI Competition III data set IIIa, and 85% for the binary class BCI Competition III data set IVa. For multi-class data sets, the proposed approach outperforms winners of BCIC IV, 2a and BCIC III, IIIa with kappa 0.14 and 0.17, respectively. For binary class BCIC III, IVa, it performed slightly better than existing studies in the literature by ≈ 0.5%. The proposed CSP\AM-BA-SVM transcends the traditional CSP\SVM approach and other existing studies.

International Journal of Intelligent Engineering and Systems, 2019

The challenge of processing the Microarray datasets with its high dimensionality opened multiple ... more The challenge of processing the Microarray datasets with its high dimensionality opened multiple research directions. Different feature selection techniques have been employed to reduce the dimensionality of such Microarray datasets before being attempted by classification algorithms. This study presents an ensemble feature selection approach based on t-test and Genetic Algorithm with five different classification algorithms as its fitness function: Support Vector Machine, Random Forest, Nearest Centroid, K Nearest Neighbour, and Maximum Likelihood with 5fold cross validation. The proposed approach has been applied on two different datasets for Lung cancer; Microarray Gene Expression and DNA methylation datasets aiming to find the Lung cancer biomarker genes. The experimental results showed that the three genes (DLX5, KRT5, and SELENBP1) resulted from processing both datasets have higher classification accuracy (92.31%) compared to separately processing the Gene Expression and the DNA methylation datasets with accuracies 90.38% and 86.54% respectively. Moreover, the classification accuracy achieved using the three aforementioned genes could not be achieved by other research studies unless by using more genes.

International Journal of Advanced Computer Science and Applications, 2017

The paper presents approaches for nodule detection and extraction in axial lung computed tomograp... more The paper presents approaches for nodule detection and extraction in axial lung computed tomography. The goal is to detect correctly pulmonary nodule to recognize and screen lung cancer patients. The pulmonary nodule detection is very challenging problem. The proposed model developed a hybrid efficient model based on affine-invariant representation and shape of segmented nodule. Due to large number of extracted features for all slices on patient, feature selection is an important step to select the most important feature for classification. We apply forward stepwise least squares regression that maximizes the Rsquared value, this criterion provides a fast preprocessing feature selection assessment for systems with huge volumes of features based on a linear models framework. Moreover, gradient boosting have been suggested to select the relevant features based on boosting approach. Classification of patients has been done by support vector machine. Kaggle DSB dataset is used to test the accuracy of our model. The results show major improvement in accuracy and the features are reduced.

Membrane Computing is an emergent and promising branch of Natural Computing. Designing P systems ... more Membrane Computing is an emergent and promising branch of Natural Computing. Designing P systems is heavy constitutes a difficult problem. The candidate has often had an idea about the problem solution form. On the other hand, finding the exact and precise configurations and rules is a hard task, especially if there is no tool used to help in the designing process. The clonal selection algorithm, which is inspired from the vertebrate immune system, is introduced here to help in designing a P system that performs a specific task. This paper illustrates the use of the clonal selection algorithm with adaptive mutation in P systems design and compares it with genetic algorithms previously used to achieve the same purpose. Experimental results show that clonal selection algorithm surpasses genetic algorithms with a great difference.

The automatic diagnosis of breast cancer is an important medical problem. This paper hybridizes m... more The automatic diagnosis of breast cancer is an important medical problem. This paper hybridizes metaphors from cells membranes and intercommunication between compartments with clonal selection principle together with fuzzy logic to produce a fuzzy rule system in order to be used in diagnosis. The fuzzy-membrane-immune algorithm suggested were implemented and tested on the Wisconsin breast cancer diagnosis (WBCD) problem. The developed solution scheme is compared with five previous works based on neural networks and genetic algorithms. The algorithm surpasses all of them. There are two motivations for using fuzzy rules with the membrane-immune algorithm in the underline problem. The first is attaining high classification performance. The second is the possibility of attributing a confidence measure (degree of benignity or malignancy) to the output diagnosis, beside the simplicity of the diagnosis system, which means that the system is human interpretable.

International Journal of Computers Communications & Control, 2009

The construction of artificial systems by drawing inspiration from natural systems is not a new i... more The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates ...

Communications in Computer and Information Science, 2011

Feature selection is a typical search problem where each state in the search space represents a s... more Feature selection is a typical search problem where each state in the search space represents a subset of features candidate for selection. Out of n features, 2n subsets can be constructed, hence, an exhaustive search of all subsets becomes infeasible when n is relatively large. Therefore, Feature selection is done by employing a heuristic search algorithm that tries to reach the optimal feature subset. Here, we propose a new wrapper feature selection and weighting algorithm called Artificial Immune Feature Selection Algorithm (AIFSA); the algorithm is based on the metaphors of the Clonal Selection Algorithm (CSA). AIFSA, by itself, is not a classification algorithm, rather it utilizes well-known classifiers to evaluate and promote candidate feature subset. Experiments were performed on textual datasets like WebKB and Syskill&Webert web page ratings. Experimental results showed AIFSA competitive performance over traditional well-known filter feature selection approaches as well as some wrapper approaches existing in literature.

International Journal of Computer Applications, 2013

Clustering is a primary method for DB mining. The clustering process becomes very challenge when ... more Clustering is a primary method for DB mining. The clustering process becomes very challenge when the data is different densities, different sizes, different shapes, or has noise and outlier. Many existing algorithms are designed to find clusters. But, these algorithms lack to discover clusters of different shapes, densities and sizes. This paper presents a new algorithm called DBCLUM which is an extension of DBSCAN to discover clusters based on density. DBSCAN can discover clusters with arbitrary shapes. But, fail to discover different-density clusters or adjacent clusters. DBCLUM is developed to overcome these problems. DBCLUM discovers clusters individually then merges them if they are density similar and joined. By this concept, DBCLUM can discover different-densities clusters and adjacent clusters. Experiments revealed that DBCLUM is able to discover adjacent clusters and different-densities clusters and DBCLUM is faster than DBSCAN with speed up ranges from 11% to 52%.

International Journal of Computer Applications, 2012

This paper presents an application of supervised machine learning approaches to the classificatio... more This paper presents an application of supervised machine learning approaches to the classification of the colon cancer gene expression data. Established feature selection techniques based on principal component analysis (PCA), independent component analysis (ICA), genetic algorithm (GA) and support vector machine (SVM) are, for the first time, applied to this data set to support learning and classification. Different classifiers are implemented to investigate the impact of combining feature selection and classification methods. Learning classifiers implemented include K-Nearest Neighbors (KNN) and support vector machine. Results of comparative studies are provided, demonstrating that effective feature selection is essential to the development of classifiers intended for use in high dimension domains. This research also shows that feature selection helps increase computational efficiency while improving classification accuracy..

Annals of Surgical Oncology

المجلة الدولیة للفقه والقضاء والتشریع, 2021

Turkish Journal of Mathematics and Computer Science, 2021

COVID-19 has become the most important and crucial agenda in the world in the last year. COVID-19... more COVID-19 has become the most important and crucial agenda in the world in the last year. COVID-19 has taken many lives around the world and millions of people have been infected. To get rid of this depression caused by COVID-19, many countries have started big campaigns for vaccine production. In this study, data on infection cases and vaccinations conducted in England, Germany, Israel, Russia, and the USA were analyzed from January 3, 2020, to March 3, 2021. We used univariate time series models, where the results are very accurate, rather than epmdicolgical models. In this article we used BATS, TBATS, Holt's linear trend, and ARIMA models to recognize the pattern of spread of covid 19 infection cases. The best models are specified for all countries that have the least error according to MAPE. Findings obtained in this study have been reported extensively in England, Germany, Israel, Russia, and the USA with tables and figures. Using the results and forecasts obtained in this study, England, Germany, Israel, Russia, and the USA can take COVID-19 measures for the future.

In this research a collection of artificial intelligence techniques are combined together to opti... more In this research a collection of artificial intelligence techniques are combined together to optimize the process of clustering textual transcripts obtained from audio sources. Since clustering techniques have drawbacks that if not taken care of will produce sub optimal clustering solutions, it’s essential to attempt to optimize the clustering algorithms to avoid sub optimal solutions. As an attempt to overcome this problem, different artificial intelligence techniques are applied to avoid clustering problems. The main objectives of this research is to optimize automatic topic clustering of transcribed speech documents, and investigate the impact of applying genetic algorithm optimization and initial centroid selection optimization (ICSO) in combination with K-means clustering algorithm using Chi-Square similarity measure on the accuracy and the sum of square distances (SSD) of the selected clustering algorithm. The evaluation showed that using ICSO with genetic algorithm and K-mean...

Egypt. Comput. Sci. J., 2009

Hepatitis C is a predominant genotype found throughout the Middle East and parts of Africa, with ... more Hepatitis C is a predominant genotype found throughout the Middle East and parts of Africa, with high population prevalence in Egypt. Due to the world’s constant effort to find treatment for this fatal disease; many researches and trials have been made. It has become evident that virus C itself envelopes a self destructive gene [1], which if activated by a specific order to the mRNA aboard the virus, forms interferon. Interferon is an anti-viral if produced from virus C itself becomes specific only to it. Through a variety of bioinformatics tools we architect algorithms to enhance the chance of finding this gene which order the mRNA to produce the virus C specific interferon .Tools such as RNA to protein synthesis, gene prediction, protein classification and gene classification have been constructed and tried to reach this goal .As a result of these trials, several matches were made with alternating percentages but at least acknowledging the possibility this interferon/RNA analysis.

Genetic algorithms are considered. Three algorithms are designed and executed to obtain purely em... more Genetic algorithms are considered. Three algorithms are designed and executed to obtain purely empirical analysis conclusions in order to turn to purely theoretical analysis results about the behavior of genetic algorithms as a finite dimensional Markov and lumped Markov chains, which confirm the conjectures from these experiments and in order to introduce a complete framework toward a new philosophy of machine intelligence. First, we model genetic algorithms using a finite dimensional Markov and lumped Markov chains. Second, we carry on a particle analysis (the basic component) and analyze the convergence properties of these algorithms. Third, we produce two unified Markov and lumped Markov approaches for analysis for a complete framework and propose unique chromosomes for a purely successful optimization of these algorithms. Furthermore, for the Markov approach, we obtain purely theoretical analysis for a classification and Stationary distributions of chains. For the lumped Markov...

IEEE Access, 2020

Intrusion Detection System (IDS) plays a very important role in security systems. Among its diffe... more Intrusion Detection System (IDS) plays a very important role in security systems. Among its different types, Network Intrusion Detection System (NIDS) has an effective role in monitoring computer networks systems for malicious and illegal activities. In the literature, the detection of DoS and Probe attacks were with reasonable accuracy in most of the NIDS researches. However, the detection accuracy of other categories of attacks is still low, such as the R2L and U2R in KDDCUP99 dataset along with the Backdoors and Worms in UNSW-NB15 dataset. Computational Intelligence (CI) techniques have the characteristics to address such imprecision problem. In this research, a Hybrid Nested Genetic-Fuzzy Algorithm (HNGFA) framework has been developed to produce highly optimized outputs for security experts in classifying both major and minor categories of attacks. The adaptive model is evolved using two-nested Genetic-Fuzzy Algorithms (GFA). Each GFA consists of two-nested Genetic Algorithms (GA). The outer is to evolve fuzzy sets and the inner is to evolve fuzzy rules. The outer GFA assists the inner GFA in training phase, where the best individual in outer GFA interacts with the weak individual in inner GFA to generate new solutions that enhance the prediction of mutated attacks. Both GFA interact together to evolve the best rules for normal, major and minor categories of attacks through the optimization process. Several experiments have been conducted with different settings over different datasets. The obtained results show that the developed model has good accuracy and is more efficient compared with several state-of-the-art techniques.

International Dental & Medical Journal of Advanced Research - VOLUME 2015, 2017

Recent advances in implant designs, manufacturing, and techniques have led to high success rate i... more Recent advances in implant designs, manufacturing, and techniques have led to high success rate in solving the problems of retention and stability of conventional dentures, especially the mandibular ones. [1] Overdentures supported by two implants have become increasingly popular within the past 20 years and considered the standard of care for edentulous mandible and the least costly implant prosthetic option. [2-5] However, there are concerns that overdentures supported by only a few anterior implants might lead to progressive resorption of the posterior alveolar ridges. [6] Many retentive means between the implants and the denture can be incorporated using different forms of attachment designs, such as the ball attachment, the magnetic attachment, the telescopic attachment, and the bar and clip ones. The

The International Arab Journal of Information Technology, 2019

A compilation of artificial intelligence techniques are employed in this research to enhance the ... more A compilation of artificial intelligence techniques are employed in this research to enhance the process of clustering transcribed text documents obtained from audio sources. Many clustering techniques suffer from drawbacks that may cause the algorithm to tend to sub optimal solutions, handling these drawbacks is essential to get better clustering results and avoid sub optimal solutions. The main target of our research is to enhance automatic topic clustering of transcribed speech documents, and examine the difference between implementing the K-means algorithm using our Initial Centroid Selection Optimization (ICSO) [16] with genetic algorithm optimization with Chi-square similarity measure to cluster a data set then use a self-organizing map to enhance the clustering process of the same data set, both techniques will be compared in terms of accuracy. The evaluation showed that using K-means with ICSO and genetic algorithm achieved the highest average accuracy.

Egyptian Dental Journal, 2017

The purpose of the present study was to evaluate radiographically (the crestal bone level change)... more The purpose of the present study was to evaluate radiographically (the crestal bone level change) and clinically (gingival crevicular fluid and implant stability) in cases with four implants supporting mandibular fixed detachable prostheses versus telescopic overdenture. Methodology: Twenty completely edentulous patients were treated with four interforaminal implants to support either fixed detachable prostheses with distal cantilevers or telescopic overdenture. The peri-implant marginal bone loss was assessed radiographically at distal surface of posterior implant bilaterally was calculated in two different intervals. The first interval (0-6months) and the second interval (0-12months) using a digital panoramic imaging system. Also clinical evaluation for checking the implant stability using the perio-test and measuring the gingival crevicular fluid. Results: Regarding the gingival crevicular fluid, the comparison between both groups at the time of delivery and after 6 months, there was no significant difference between the two groups. But there was a significant difference after 12 months. From the obtained data of periotest, it was recognized that there was no significant difference between the two groups regarding the implant stability. But there was a significant difference within each group at the different follow-up periods. The data of marginal bone loss at the distal surface of posterior implant on both sides revealed that there was a significant difference between the two groups at the 1 st and 2 nd intervals. Conclusion: Both telescopic overdenture and fixed detachable prostheses considered a viable successful treatment option for rehabilitating completely edentulous cases. Telescopic overdentures showed less crestal bone loss at the distal implants than that with the fixed detachable design. The gingival crevicular fluid decreases gradually throughout the study period in both designs denoting successful oral hygiene measurements.