Ibrahim El-Henawy

Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.

2-Levels of clustering strategy to detect and locate copy-move forgery in digital images

Understanding is considered a key purpose of image forensic science in order to find out if a digital image is authenticated or not. It can be a sensitive task in case images are used as necessary proof as an impact judgment. it’s known that There are several different manipulating attacks but, this copy move is considered as one of the most common and immediate one, in which a region is copied twice in order to give different information about the same scene, which can be considered as an issue of information integrity. The detection of this kind of manipulating has been recently handled using methods based on SIFT. SIFT characteristics are represented in the detection of image features and determining matched points. A clustering is a key step which always following SIFT matching in-order to classify similar matched points to clusters. The ability of the image forensic tool is represented in the assessment of the conversion that is applied between the two duplicated images of one region and located them correctly. Detecting copy-move forgery is not a new approach but using a new clustering approach which has been purposed by using the 2-level clustering strategy based on spatial and transformation domains and any previous information about the investigated image or the number of clusters need to be created is not necessary. Results from different data have been set, proving that the proposed method is able to individuate the altered areas, with high reliability and dealing with multiple cloning.

A Novel Hybrid Flower Pollination Algorithm with Chaotic Harmony Search for Solving Sudoku Puzzles

Flower Pollination algorithm (FPA) is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, a new hybrid optimization method called improved Flower Pollination Algorithm with Chaotic Harmony Search (FPCHS) is proposed. The method combines the standard Flower Pollination algorithm (FPA) with the chaotic Harmony Search (HS) algorithm to improve the searching accuracy. The FPCHS algorithm is used to solve Sudoku puzzles. Numerical results show that the FPCHS is accurate and efficient in comparison with standard Harmony Search, (HS) algorithm.

Chaotic Harmony Search Algorithm with Different Chaotic Maps for Solving Assignment Problems

This paper presents an improved version of a harmony metaheuristic algorithm with different chaotic maps, (IHSCH), for solving the linear assignment problem. The proposed algorithm uses chaotic behavior to generation a candidate solution in a behavior similar to acoustic monophony. Numerical results show that the IHSCH is accurate and efficient in comparison with harmony search (HS), improved harmony search (IHS) algorithm and traditional methods (Hungarian method).

Map-guided trajectory-based position verification for vehicular networks

Vehicular networks are expected to enable vehicles on the road to exchange safety information; enhancing traffic flow and minimizing accidents. With vehicle positions being the most frequently exchanged information in vehicular networks, it becomes imperative to establish a strong level of trust in the announced positions before a vehicle initiates a response. This paper proposes a position verification scheme as part of a misbehavior detection framework that encompasses analysis techniques needed for the verification of exchanged vehicular messages. The scheme involves the estimation of a vehicles trajectory via the integration of road and map information, as opposed to only depending on the vehicles kinematics for future trajectory prediction. The trajectory estimation procedure uses the vehicles previously received position updates to find a bestfit area within the road topology in which the vehicle is expected to be. The vehicles current position announcement is compared against this plausible area to decide whether the position is consistent with the expected location. Our proposed scheme is verified via extensive simulations, developed on ns2, demonstrating the importance of integrating road and map information into position verification.

A modified flower pollination algorithm for the multidimensional knapsack problem: human-centric decision making

In this paper, a new modified version of the flower pollination algorithm based on the crossover for solving the multidimensional knapsack problems called (MFPA) is proposed. MFPA uses the sigmoid function as a discretization method to deal with the discrete search space. The penalty function is added to the evaluation function to recognize the infeasible solutions and assess them. A two-stage procedure is called FRIO is used to treat the infeasible solutions. MFPA uses an elimination procedure to decrease any duplication in the population in order to increase the diversity. The proposed algorithm is verified on a set of benchmark instances, and a comparison with other algorithms available in literature is shown. Several statistical and descriptive analysis was done such as recoding the results of the best, mean, worst, standard deviation, success rate, and time to prove the effectiveness and robustness of MFPA. The empirical results show that the proposed algorithm can be an effective algorithm as human-centric decision-making model for solving the multidimensional knapsack problems.

A Hybrid Swarm Intelligence Technique for Solving Integer Multi-objective Problems

The multi-objective integer programming problems are considered time consuming. In the past, mathematical structures were used that can get benefits of high processing powers and parallel processing. A general approach to generate all non-dominated solutions of the multiobjective integer programming (MOIP) Problem is developed. In this paper, a hybridization of two different swarm intelligent approaches, stochastic diffusion search, and particle swarm optimization techniques is presented for solving integer multi-objective problems. The hybrid implementation allows us to avoid certain drawbacks and weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. Our hybrid implementation allows the MOIP algorithm to reach the optimal solution in a considerably shorter time than is needed to solve the model using the entire dataset directly within the model. Our hybrid approach outperforms the results obtained by each technique separately. It is able to find the optimal solution in a shorter time than each technique on its own, and the results are highly competitive with the state-of-the-art in large-scale optimization. Furthermore, according to our results, combining the PSO with SDS approach for solving IP problems appears to be an interesting research area in combinatorial optimization.

Recognition of phonetic Arabic figures via wavelet based Mel Frequency Cepstrum using HMMs

Abstract This work describes the recognition of phonetic Arabic figures. Speech recognition technology has made steady progress in its 50 years history and has succeeded in creating several substantial applications. The goal of speech recognition research is to produce a machine which will recognize accurately the normal human speech from any speaker. To improve the performance of recognition system, an effective and robust technique is proposed to extract speech feature. The input speech signal is decomposed into various frequency channels based on time-frequency multi-resolution property of wavelet analysis. For capturing the characteristics of the signal, the Mel-Frequency Cepstrum Coefficients “MFCCs” of the wavelet channels are calculated. Hidden Markov Models “HMMs” were used for the recognition stage. Different forms of wavelet functions were used to evaluate the best wavelet signal to extract the best features of the signals. It is found that the wavelet signal “db8” gives the highest values of recognition accuracy rate. A recognition rate of 98% was obtained using the proposed feature extraction technique. A comparison between different features of speech is given. The features based on the Cepstrum give accuracy of 94% for speech recognition while the features based on the short time energy in time domain give accuracy of 92%. The features based on formant frequencies give accuracy of 95.5%. It is clear that the features based on MFCCs with accuracy of 98% give the best accuracy rate. So the features depend on MFCCs with HMMs may be recommended for recognition of the spoken Arabic digits.

On-line signature verification based on PCA feature reduction and statistical analysis

This paper presents a novel online signature verification method that uses PCA for dimensional-reduction of signature snapshot. The resulting vectors from PCA are submitted to a multilayer perceptron (MLP) neural network with EBP and sigmoid activation function. In the other hand, Dynamic features such as x, y coordinates, pressure, velocity, acceleration, pen down time, distance, altitude, azimuth and inclination angles, etc. are processed statistically. During enrollment, five reference signatures are captured from each user. One-way ANOVA is used to analyze relative X-Coordinates in 6 groups (5 reference group, 1 testing group). ANOVA test will be repeated for relative Y-Coordinates, pressure value, azimuth and inclination angles. Thus, the algorithm will fill up a vector of five distances (F-scores) between all the possible pairs of testing and reference vectors. The resulting vector is compared to a threshold vector. Our database includes 130 genuine signatures and 170 forgery signatures. Our verification system has achieved a false acceptance rate (FAR) of 2% and a false rejection rate (FRR) of 5%

A Novel Whale Optimization Algorithm for Cryptanalysis in Merkle-Hellman Cryptosystem

With the advance of the communication technology and the massive flow of information across the internet, it is becoming urgent to keep the confidentiality of the transmitted information. Using the internet has been extended to several fields such as e-mail, e-commerce, e-learning, health and medicine, shopping, and so on. Cryptography is the study of different techniques for securing the communication between the sender and the receiver. One of the most known cryptosystems is Merkle–Hellman Knapsack Cryptosystem (MHKC). It is one of the earliest Public Key Cryptosystem (PKC) that is used to secure the messages between the sender and the receiver. Developing a powerful cryptosystem comes after studying the fragility points of the current cryptosystems. The Whale Optimization Algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social behavior of humpback whales. WOA has validated excellent performance in solving the continuous problems and the engineering optimization problems. This paper introduces a novel Modified version of WOA (MWOA) for cryptanalysis of MHKC. The sigmoid function is used to map the continuous values into discrete one. A penalty function is added to the evaluation function to deal with the infeasible solutions. The mutation operation is employed for improving the solutions. The results show that MWOA is more effective and robust than other algorithms in the literature.