Omar S. Soliman

A bio inspired fuzzy k-modes clustring algorithm

This paper proposes a bio inspired fuzzy K-Modes clustering algorithm using fuzzy particle swarm optimization (FPSO) and fuzzy k-modes (FK-Modes) algorithm for clustering categorical data. It integrates concepts of FK-Modes algorithm to handle the uncertainty phenomena and FPSO to reach global optimal solution of clustering optimization problem. The proposed FPSO-FK-Modes algorithm was implemented and evaluated using slandered benchmark data sets and performance measures. Experimental results showed that the proposed FPSO-FK-Modes algorithm performed well compared with FK-modes and Genetic FK-modes (GA- FK-modes) algorithm using adjusted rand index.

Contrast Enhancement of Breast MRI Images Based on Fuzzy Type-II

The high incidence of breast cancer in women has increased significantly in the recent years. Breast MRI involves the use of magnetic resonance imaging to look specifically at the breast. Contrast-enhanced breast MRIs acquired by contrast injection have been shown to be very sensitive in the detection of breast cancer, but are also time-consuming and cause waste of medical resources. This paper utilizes the use of type-II fuzzy sets to enhance the contrast of the breast MRI image. To evaluate the performance of our approach, we run tests over different MRI breast images and show that the overall accuracy offered by the employed approach is high.

μTESLA-based secure routing protocol for wireless sensor networks

Wireless sensor networks are highly prone to security threats due to resource constraints and the broadcast nature of the transmission medium. Directed diffusion protocol is one of the routing protocols for wireless sensor networks that are not designed with security in mind and are particularly susceptible to different security attacks. In this paper, a secure routing protocol for wireless sensor networks, based on the directed diffusion routing algorithm, is presented. The proposed secure routing protocol uses the μTESLA (micro Timed, Efficient, Streaming, Loss-tolerant Authentication) broadcasting authentication algorithm in order to authenticate the acknowledgement messages sent from the sink to the source nodes for confirming the delivery of the data-event messages. A simulation based performance evaluation for the proposed protocol was conducted against black hole and acknowledgement-spoofing attacks. Simulations show that, compared to the original directed diffusion protocol, the proposed secure routing protocol achieved better event-delivery and event-dropping ratios. However, it resulted higher cost in the mean dissipated energy and average delay in some situations due to acknowledgement and authentication processes for delivered events and also due to the retransmissions of non-acknowledged events.

Interactive Fuzzy Goal Programming Model Based on Differential Evolution for Regional Sustainability Development under Climate

In this paper, we present an interactive fuzzy non-linear goal programming (FNLGP) model to evaluate Regional sustainability development (RSD under climate change in agriculture sector.. A solution methodology of the FNLGP model is presented. A Differential Evolution (DE) algorithm with variable step length is designed and implemented to optimize the resulting FNLGP. The proposed FNLGP model is more flexible than conventional goal programming and it is capable of evaluating RSD under different climate change scenarios. It provides decision support tool to test different alternative policies based on the degree of uncertainty. Introducing fuzzy terms in the model provides an assessment to uncertainty associated with various climate change predictions and information ambiguity.

A Bio-Inspired Memetic Particle Swarm Optimization Algorithm for Multi-objective Optimization Problems

This paper proposes a bio-inspired particle swarm optimization algorithm that incorporates random walk for local search techniques in the non-dominated sorting Particle Swarm Optimization (PSO) algorithm in addition to the mechanism of crowding distance, resulting in an efficient and effective optimization method. The proposed algorithm was implemented and evaluated using different benchmark test problems including unconstrained and constrained problems. The obtained results were compared with published ones. The results showed that the proposed bio-inspired algorithm generates a precise well distributed set of non-dominated solutions justifying the superiority of the random walk method.

Remote Sensing Satellite Images Classification Using Support Vector Machine and Particle Swarm Optimization

This paper introduces a classification system for remote sensing ASTER satellite imagery using SVM and particle swarm optimization (PSO) algorithm. The proposed system starts with the identification of selected area of study. This is followed by a pre-processing phase using mapping polynomial algorithm as geometric correction. Followed by, applying threshold algorithm for image segmentation. Then features are extracted using object based algorithm. Followed by, image classification using SVM and particle swarm optimization(PSO). The PSO is employed as a fast global optimization algorithm instead of using traditional algorithm such as Karush-Kuhn-Tucker conditions. It is implemented and evaluated on real two selected area of interest in the North-Eastern part of the Eastern Desert of Egypt (Halaib Triangle)and (Wadi Shait). The obtained results carried out that the usage of RBF kernel function has the highest classification accuracy ratio as well as Polynomial kernel function.

A Rough Clustering Algorithm Based on Entropy Information

This paper proposes a clustering algorithm based on concept of rough computing and Entropy information to cluster objects into manageable smaller groups with similar characteristics or equivalence classes. The concept of rough computing is utilized for handling uncertainty associated with information ambiguity in clustering process. The Entropy information algorithm is employed to transform continuous data into categorical data. The proposed algorithm is capable to cluster different data types; different sources for both numerical and categorical data. The proposed algorithm is implemented and tested for a pharmaceutical company data set as a real case study. The clusters purity is used as a performance measure to evaluate the performance of clusters quality of the proposed algorithm. The comparison study verified that the proposed rough clustering algorithm based on entropy information has the highest clustering quality according to the purity and overall purity evaluation criteria.

Correlation Based Feature Selection Using Quantum Bio Inspired Estimation of Distribution Algorithm

Correlation based feature Selection (CFS) evaluates different subsets based on the pairwise features correlations and the features-class correlations. Machine learning techniques are applied to CFS to help in discovering the most possible differnt combinations of features especillay in large feature spaces. This paper introduces a quantum bio inspired estimation of distribution algorithm (EDA) for CFS. The proposed algorithm integrates the quantum computing concepts, vaccination process with the immune clonal selection (QVICA) and EDA. It is employed as a search technique for CFS to find the optimal feature subset from the features space. It is implemented and evaluated using benchmark dataset KDD-cup99 and compared with the GA algorithm. The obtained results showed the ability of QVICA-with EDA to obtain better feature subsets with fewer length, higher fitness values and in a reduced computation time.

A model-based decision support tool using fuzzy optimization for climate change

This paper proposes a model-based decision support tool using fuzzy optimization for assessing regional sustainability development (RSD) under climate change. The proposed tool integrates fuzzy goal programming (FGP) model with a fuzzy analytic hierarchy process (FAHP) to determine the optimal policy for RSD under climate change in the agriculture sector. The FAHP is used to handle the non-cost criteria, aspiration levels and to find the relative weights of multiple conflicting objectives in FGP model by introducing a linguistic variable that model the decision makers preferences. The proposed tool is capable for providing different alternative policies based on the degree of uncertainty. The proposed FGP-FAHP is able to allow the decision makers to test different sustainability development policies at deferent levels such as regional, sub-regions, goals/indicators and sub-goals.

E-Contract Securing System Using Digital Signature Approach

This paper presents an e-contract securing system, using the digital signature approach, for various e-commerce applications. The proposed system is composed of three phases, namely, e-contract hashing and digital signing phases that are applied at sender’s side, with addition to digital signature verification phase that is applied at the corresponding receiver’s side. The implementation of the proposed system shows accurate and effective results in terms of signing and verification.