Papers by Hesham A. Hefny
Improving the performance of TAntNet-2 by using multi forward scouts
Dynamic Traffic Routing System is an important intelligent transport system that is used to direc... more Dynamic Traffic Routing System is an important intelligent transport system that is used to direct vehicles to good routes and consequently reduce congestion on the road network. The performance of dynamic routing system depends on a dynamic routing algorithm. AntNet algorithm is a routing algorithm inspired from the foraging behavior of ants. TAntNet is a family of dynamic routing algorithms that uses a threshold travel time to enhance the performance of AntNet algorithm when applied to traffic road networks. This paper presents new improvements on TAntNet algorithms. The new improving TAntNet algorithm uses Multi forward agents instead of one compared with AntNet and TAntNet-2. The new technique saves the discovered routes of each of the forward agents and the corresponding backward ant uses the best of them. Experiments showed better performance for the proposed new mechanism of launching multi forward agents for each single agent compared with the old mechanisms of launching only one forward agent for each backward agent.
Antlion Optimization Based Segmentation for MRI Liver Images
Advances in intelligent systems and computing, Oct 21, 2016
This paper proposes an approach for liver segmentation, depending on Antlion optimization algorit... more This paper proposes an approach for liver segmentation, depending on Antlion optimization algorithm. It is used as a clustering technique to accomplish the segmentation process in MRI images. Antlion optimization algorithm is combined with a statistical image of liver to segment the whole liver. The segmented region of liver is improved using some morphological operations. Then, mean shift clustering technique divides the segmented liver into a number of regions of interest (ROIs). Starting with Antlion algorithm, it calculates the values of different clusters in the image. A statistical image of liver is used to get the potential region that liver might exist in. Some pixels representing the required clusters are picked up to get the initial segmented liver. Then the segmented liver is enhanced using morphological operations. Finally, mean shift clustering technique divides the liver into different regions of interest. A set of 70 MRI images, was used to segment the liver and test the proposed approach. Structural Similarity index (SSIM) validates the success of the approach. The experimental results showed that the overall accuracy of the proposed approach, results in 94.49 % accuracy.
TAntNet-4
Advances in computational intelligence and robotics book series, 2017
Advances in computational intelligence and robotics book series, 2017
In the recent days, a great deal of researches is interested in segmentation of different organs ... more In the recent days, a great deal of researches is interested in segmentation of different organs in medical images. Segmentation of liver is as an initial phase in liver diagnosis, it is also a challenging task due to its similarity with other organs intensity values. This paper aims to propose a grey wolf optimization based approach for segmenting liver from the abdomen CT images. The proposed approach combines three parts to achieve this goal. It combines the usage of grey wolf optimization, statistical image of liver, simple region growing and Mean shift clustering technique. The initial cleaned image is passed to Grey Wolf (GW) optimization technique. It calculated the centroids of a predefined number of clusters. According to each pixel intensity value in the image, the pixel is labeled by the number of the nearest cluster. A binary statistical image of liver is used to extract the potential area that liver might exist in. It is multiplied by the clustered image to get an initial segmented liver. Then region growing (RG) is used to enhance the segmented liver. Finally, mean shift clustering technique is applied to extract the regions of interest in the segmented liver. A set of 38 images, taken in pre-contrast phase, was used for liver segmentation and testing the proposed approach. For evaluation, similarity index measure is used to validate the success of the proposed approach. The experimental results of the proposed approach showed that the overall accuracy offered by the proposed approach, results in 94.08% accuracy.
Moth-flame Optimization Based Segmentation for MRI Liver Images
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017, 2017
One of the most important aims in computerized medical image processing is to find out the anatom... more One of the most important aims in computerized medical image processing is to find out the anatomical structure of the required organ. The hepatic segmentation is very important for surgery planning and diagnosis. The difficulty of segmentation rises from the different volumes, the different lobes and the vascular arteries of liver. This paper proposes a successful approach for liver segmentation. The proposed approach depends on Moth-flame optimization (MFO) algorithm for clustering the abdominal image. The user picks up the required clusters that represent the liver to get the initial segmented image. Then the morphological operations produce the final segmented liver. A set of 70 MRI images, was used to segment the liver and test the proposed approach. Structural Similarity index (SSI) validates the success of the approach. The experimental results showed that the overall accuracy of the proposed approach, results in 95.66% accuracy.
Computers and Electronics in Agriculture, 2017
Plant diseases is one of the major bottlenecks in agricultural production that have bad eects on ... more Plant diseases is one of the major bottlenecks in agricultural production that have bad eects on the economic of any country. Automatic detection of such disease could minimize these eects. Features selection is a usual pre-processing step used for automatic disease detection systems. It is an important process for detecting and eliminating noisy, irrelevant, and redundant data. Thus, it could lead to improve the detection performance. In this paper, an improved moth-ame approach to automatically detect tomato diseases was proposed. The moth-ame tness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. The proposed algorithm used both of the power of exploration of the moth ame and the high performance of rough sets for the feature selection task to nd the set of features maximizing the classication accuracy which was evaluated using the support vector machine (SVM). The performance of the MFORSFS algorithm was evaluated using many benchmark datasets taken from UCI machine learning data repository and then compared with feature selection approaches based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) with rough sets. The proposed algorithm was then used in a real-life problem, detecting tomato diseases (Powdery mildew and early blight) where a real dataset of tomato disease were manually built and a tomato disease detection approach was proposed and evaluated using this dataset. The experimental results showed
A Hybrid Grey Wolf Based Segmentation with Statistical Image for CT Liver Images
Advances in Intelligent Systems and Computing, 2016
Liver segmentation is a main step in all automated liver diagnosis systems. This paper aims to pr... more Liver segmentation is a main step in all automated liver diagnosis systems. This paper aims to propose an approach for liver segmentation. It combines the usage of grey wolf optimization, statistical image of liver and simple region growing to segment the whole liver. Starting with Grey Wolf optimization algorithm, it calculates the centroid values of different clusters in CT images. A statistical image of liver is used to extract the potential area that liver might exist in. Then the segmented liver is enhanced using simple region growing technique (RG). A set of 38 images, taken in pre-contrast phase, was used to segment the liver and test the proposed approach. Similarity index is used to validate the success of the approach. The experimental results showed that the overall accuracy offered by the proposed approach, results in 94.08 % accuracy.
Artificial Bee Colony Based Segmentation for CT Liver Images
Medical Imaging in Clinical Applications, 2016
The objective of this paper is to evaluate an approach for CT liver image segmentation, to separa... more The objective of this paper is to evaluate an approach for CT liver image segmentation, to separate the liver, and segment it into a set of regions of interest (ROIs). The automated segmentation of liver is an essential phase in all liver diagnosis systems for different types of medical images. In this paper, the artificial bee colony optimization algorithm (ABC) aides to segment the whole liver. It is implemented as a clustering technique to achieve this mission. ABC calculates the centroid values of image clusters in CT images. Using the least distance between every pixel value and different centroids will result in a binary image for each cluster. Applying some morphological operations on every binary clustered image can help to remove small and thin objects. These objects represent parts of flesh tissues adjacent to the liver, sharp edges of other organs and tiny lesions spots inside the liver. This is followed by filling the large regions in each cluster binary image. Summation of the clusters’ binary images results in a reasonable image of segmented liver. Then, the segmented image of liver is enhanced using simple region growing technique (RG). Finally, one of ABC algorithm or watershed is applied once to extract the lesioned regions in the liver, which can be used by any classifier to determine the type of lesion. A set of 38 images, taken in pre-contrast phase, was used to segment the liver and test the proposed approach. Testing the results is handled using similarity index to validate the success of the approach. The experimental results showed that the overall accuracy offered by the proposed approach, results in 93.73 % accuracy.
Improving the performance of TAntNet-2 by using multi forward scouts
2015 Tenth International Conference on Computer Engineering & Systems (ICCES), 2015
Dynamic Traffic Routing System is an important intelligent transport system that is used to direc... more Dynamic Traffic Routing System is an important intelligent transport system that is used to direct vehicles to good routes and consequently reduce congestion on the road network. The performance of dynamic routing system depends on a dynamic routing algorithm. AntNet algorithm is a routing algorithm inspired from the foraging behavior of ants. TAntNet is a family of dynamic routing algorithms that uses a threshold travel time to enhance the performance of AntNet algorithm when applied to traffic road networks. This paper presents new improvements on TAntNet algorithms. The new improving TAntNet algorithm uses Multi forward agents instead of one compared with AntNet and TAntNet-2. The new technique saves the discovered routes of each of the forward agents and the corresponding backward ant uses the best of them. Experiments showed better performance for the proposed new mechanism of launching multi forward agents for each single agent compared with the old mechanisms of launching only one forward agent for each backward agent.
A proposed shadowed intuitionistic fuzzy numbers
2015 Tenth International Conference on Computer Engineering & Systems (ICCES), 2015
The methods of constructing shadowed sets have been developed to conserve the amount of uncertain... more The methods of constructing shadowed sets have been developed to conserve the amount of uncertainty for fuzzy sets. The shadowed sets designed by such methods usually maintain one type of uncertainty for fuzzy sets. Moreover, such techniques are considered inappropriate for higher types of fuzzy sets. This paper proposes a new approach for inducing shadowed sets from intuitionistic fuzzy numbers. The proposed approach devises improved shadowed sets by conserving different uncertainty measures of intuitionistic fuzzy numbers. The new shadowed sets are more accurate in terms of preserving more aspects of uncertainty embedded in the intuitionistic fuzzy numbers and providing favorable approach for inducing shadowed sets for higher types of fuzzy sets.
Hybrid multi-attribute decision making based on shadowed fuzzy numbers
2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS), 2015
Decision-making in an environment that includes different types of uncertainties represent real c... more Decision-making in an environment that includes different types of uncertainties represent real challenges for decision makers. Fuzzy sets and their extensions are famous forms of representing vague information. However, other different forms of uncertain data are commonly found in multiple attribute decision making (MADM) problems. This makes a serious difficulty for the decision maker to take a proper decision based on such hybrid types of uncertainties. There is a quite need for introducing an effective methodology to transform different types of uncertainties into a standard form. This paper, introduces an improved version of shadowed fuzzy numbers (SFNs) as useful transformation for different types of uncertainties. The new shadow number preserves the main characteristics of uncertainty for different types of fuzzy sets used in the problem. The new ranking method is proposed to manipulate shadowed fuzzy numbers. The features of new method are significant for decision applications.
Enhanced Region Growing Segmentation for CT Liver Images
Advances in Intelligent Systems and Computing, 2015
This paper intends to enhance the image for the next usage of region growing technique for segmen... more This paper intends to enhance the image for the next usage of region growing technique for segmenting the region of liver away from other organs. The approach depends on a preprocessing phase to enhance the appearance of the boundaries of the liver. This is performed using contrast stretching and some morphological operations to prepare the image for next segmentation phase. The approach starts with combining Otsu’s global thresholding with dilation and erosion to remove image annotation and machine’s bed. The second step of image preparation is to connect ribs, and apply filters to enhance image and deepen liver boundaries. The combined filters are contrast stretching and texture filters. The last step is to use a simple region growing technique, which has low computational cost, but ignored for its low accuracy. The proposed approach is appropriate for many images, where liver could not be separated before, because of the similarity of the intensity with other close organs. A set of 44 images taken in pre-contrast phase, were used to test the approach. Validating the approach has been done using similarity index. The experimental results, show that the overall accuracy offered by the proposed approach results in 91.3 % accuracy.
International Journal of Computer Applications, 2015
The shadowed sets are proposed by Pedrycz as a granule manner to approximate the fuzzy sets with ... more The shadowed sets are proposed by Pedrycz as a granule manner to approximate the fuzzy sets with preserving the uncertainty features. Many methods have been made in this context to maintain various characteristics of uncertainty for fuzzy sets. In this paper, a new method is proposed which it is preserve more than one kind of uncertainty in fuzzy sets. The new technique is based on the use of measures of uncertainty directly to induce ideal values of shadowed set. It's more simply and accurate for describing uncertainty. The features of new method are important for decision applications.
Using Textual Case-based Reasoning in Intelligent Fatawa QA System
Textual Case#Based Reasoning (TCBR) is an artificia l intelligence approach to problem solving an... more Textual Case#Based Reasoning (TCBR) is an artificia l intelligence approach to problem solving and learning in which textual expertise is collected in a library of past cases. One of the critical application domains is the Islamic Fatawa (religious verdict) domain, which refers to seeking a legal ruling for religious issues that Muslims all over the globe pose on a daily basis. Official religious organizations like Egypt's Dar al#Ifta 1 is responsible for receiving and answering people's religious inquiries daily. Due to the enormous number of inquiries Dar al#Ifta receives every day, it cannot be handled at the same time. This task actually requires a certain smart system that can help in fulfilling people's needs for answers. However, applying TCBR in the domain of issuing Fatawa faces several challenges related to the language syntax and semantics. The contribution of this paper is to propose an intelligent fatwa Questions Answering (QA) system that can overcome the challenges and respond to a user's inquiry through providing semantically closest inquiries that previously answered. Moreover, the paper shows how the proposed system can learn when a new inquiry arrives. Finally, results will be discussed.
Multimedia Tools and Applications, 2017
This paper proposes an approach for liver segmentation in MRI images based on Whale optimization ... more This paper proposes an approach for liver segmentation in MRI images based on Whale optimization algorithm (WOA). It is used to extract the different clusters in the abdominal image to support the segmentation process. A statistical image is prepared to define the potential liver position in the abdominal image. Then, WOA divides the image into a predefined number of clusters. The prepared statistical image is converted into a binary image and multiplied by the image clustered by WOA. This multiplication process removes a great part of other organs from the image. It is followed by some points, picked up by user interaction, representing the required clusters which reside in the area of liver. The morphological operations enhance the initial segmented liver and produces the final image. The proposed approach is tested using a set of 70 MRI images, annotated and approved by radiology specialists. The resulting image is validated using structural similarity index measure (SSIM), similarity index (SI) and other five measures. The overall accuracy of the experimental result showed accuracy of 96.75% using SSIM and 97.5 using SI%.
Improving the Performance of TAntNet-2 Using Scout Behavior
Communications in Computer and Information Science, 2014
ABSTRACT Dynamic routing algorithms play an important role in road traffic routing to avoid conge... more ABSTRACT Dynamic routing algorithms play an important role in road traffic routing to avoid congestion and to direct vehicles to better routes. TAntNet-2 algorithm presented a modified version of AntNet algorithm to dynamic traffic routing of road network. TAntNet-2 uses the pre-known information about the expected good travel time between sources and destinations for road traffic networks. Good travel time is used as a threshold value to fast direct the algorithm to good route, conserve on the discovered good route and remove unneeded computations. This paper presents a modified version of the TAntNet-2 routing algorithm that employs a behavior inspired from bee behavior when foraging for nectar. The new algorithm tries to avoid the effects of ants that take long route during searching for a good route. The modified algorithm introduces a new technique for launching ants according the quality of the discovered solution. The presented algorithm uses forward scout instead of forward ant and uses two forward scouts for each backward ant, in case of failing the first scout in finding accepted good route. The experimental results show high performance for the modified TAntNet-2 compared with TAntNet and TAntNet-2.
EXPMUL: an expert system for the design of multivariable control systems
International Journal of Systems Science, 1993
The development of an expert system environment for the design of M1MO control systems using the ... more The development of an expert system environment for the design of M1MO control systems using the frequency response approach is discussed. The aim is to provide assistance for non-experienced designers so that they can learn and gain experience of a specific area of control system theory. As a preliminary, we discuss the different configurations of expert systems used in control system design. The design heuristic is then declared, and followed by a comprehensive example. The implementation of our expert system is then illustrated, and a detailed application is also given.
An Enhanced Opinion Retrieval Approach on Arabic Text for Customer Requirements Expansion
Journal of King Saud University - Computer and Information Sciences
Term Weighting: A Multi-View Fuzzy Ontology Based Approach
Applied Mechanics and Materials, May 12, 2014
The paper proposes a term weighting algorithm for research papers. It weighs a research papers an... more The paper proposes a term weighting algorithm for research papers. It weighs a research papers annotated keywords according to a certain view. It uses a predefined multi-view fuzzy ontology and a stemmer NLP tool. The proposed algorithm is tested and results are compared with another ontology-based term weighting algorithm. The tests show that it enhances the resulted weights accuracy and decreases the execution time.
EXPMUL: an expert system for the design of multivariable control systems
Http Dx Doi Org 10 1080 00207729308949633, May 16, 2007
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Papers by Hesham A. Hefny