Asmaa Sabet Anwar

Bio: Asmaa Sabet Anwar is an academic researcher from Cairo University. The author has contributed to research in topics: Median filter & k-nearest neighbors algorithm. The author has an hindex of 3, co-authored 3 publications receiving 78 citations.

Human Ear Recognition Using Geometrical Features Extraction

Abstract: The biometrics recognition has been paid more attention by people with the advancement of technology nowadays. The human ear is a perfect source of data for passive person identification. Ear seems to be a good candidate solution since ear is visible, their images are easy to take and structure of ear does not change radically over time. Ear satisfies biometric characteristic (universality, distinctiveness, permanence and collectability). In this paper we presented a new algorithm for ear recognition based on geometrical features extraction like (shape, mean, centroid and Euclidean distance between pixels). Firstly, we made a pre-processing phase by making all images have the same size. Then we used the snake model to detect the ear, and we applied median filter to remove noise, also we converted the images to binary format. After that we used canny edge and made some enhancement on the image, largest boundary is calculated and distance matrix is created then we extracted the image features. Finally, the extracted features were classified by using nearest neighbor with absolute error distance. This method is invariant to scaling, translation and rotation. The experimental results showed that the proposed approach gives better results and obtained over all accuracy almost 98%.

Improving The Security of Images Transmission

Abstract: The security of images transferred via internet is very important issue. The proposed system tries to satisfy all requirements of security to ensure that any medical image related to any patient do not allow to be accessed via any unauthorized person. We can use an encryption scheme for encrypt medical image. Only the authorized person can decrypt this medical image and can obtain the original image. The ownership of these medical images is very important to improve. We can identify the ownership of any medical image by using watermark related to the owner of this medical image. We used name of the patient and serial number. Capture the ear image of the owner then extract features from ear after that encrypt those features then used it as watermark. The size of the medical image is very effective point in transmitting via internet. Because of this the proposed system used mix of compression techniques applied on medical image before sending via internet. Run time is very important in any system and complexity is very important aspect in computer science. To reduce run time and complexity of the proposed system, we can use DWT to separates an image into approximation image LL, HL LH and HH.

Human ear recognition using SIFT features

Abstract: Biometrics have lately been receiving attention in popular media. Biometrics deal with identification and verification of individuals based on their behavioral or physiological characteristics. Biometrics will become one of the most important ways of the identification technology. Ear recognition might be a good solution since ear is visible, ear images are easy to be taken, and the ear structure does not change radically over time. In this paper an algorithm based on SIFT features for ear recognition is proposed. SIFT key points are extracted from ear image and an augmented vector of extracted SIFT features are created for matching. Firstly, a pre-processing phase is done by converting image to gray level. Then a median filter is applied to smooth the image and to remove noise if found. Edge detection is used for cropping ear part from the image. Then the SIFT features were extracted from ear image. Finally, the extracted features were classified by using minimum distance classifier. This method is invariant to scaling, translation and rotation. The experimental results showed that the proposed approach gives better results compared with other researchers and obtained over all accuracy almost 95.2 % for IIT Delphi database and 100% for AMI database.

Secure Medical Data Transmission Model for IoT-Based Healthcare Systems

Abstract: Due to the significant advancement of the Internet of Things (IoT) in the healthcare sector, the security, and the integrity of the medical data became big challenges for healthcare services applications. This paper proposes a hybrid security model for securing the diagnostic text data in medical images. The proposed model is developed through integrating either 2-D discrete wavelet transform 1 level (2D-DWT-1L) or 2-D discrete wavelet transform 2 level (2D-DWT-2L) steganography technique with a proposed hybrid encryption scheme. The proposed hybrid encryption schema is built using a combination of Advanced Encryption Standard, and Rivest, Shamir, and Adleman algorithms. The proposed model starts by encrypting the secret data; then it hides the result in a cover image using 2D-DWT-1L or 2D-DWT-2L. Both color and gray-scale images are used as cover images to conceal different text sizes. The performance of the proposed system was evaluated based on six statistical parameters; the peak signal-to-noise ratio (PSNR), mean square error (MSE), bit error rate (BER), structural similarity (SSIM), structural content (SC), and correlation. The PSNR values were relatively varied from 50.59 to 57.44 in case of color images and from 50.52 to 56.09 with the gray scale images. The MSE values varied from 0.12 to 0.57 for the color images and from 0.14 to 0.57 for the gray scale images. The BER values were zero for both images, while SSIM, SC, and correlation values were ones for both images. Compared with the state-of-the-art methods, the proposed model proved its ability to hide the confidential patient’s data into a transmitted cover image with high imperceptibility, capacity, and minimal deterioration in the received stego-image.

Elements Of Style

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Sine cosine optimization algorithm for feature selection

Abstract: Nowadays, a dataset includes a huge number of features with irrelevant and redundant ones. Feature selection is required for a better machine-learning algorithms' performance. A system for feature selection is proposed in this work using a sine cosine algorithm (SCA). SCA is a new stochastic search algorithm for optimization problems. SCA optimization adaptively balances the exploration and exploitation to find the optimal solution quickly. The SCA can quickly explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporates both classification accuracy and feature size reduction. The proposed system was tested on 18 datasets and shows an advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

Feature selection approach based on moth-flame optimization algorithm

Abstract: In this work, a feature selection algorithm based on moth-flame optimization (MFO) is proposed. Moth-flame optimization (MFO) is a recently proposed swarm intelligent optimization algorithm that mimics the motion of moths. The proposed algorithm is applied in the domain of machine learning for feature selection to find the optimal feature combination using wrapper-based feature selection mode. In wrapper-based feature selection, a machine learning technique is used in the evaluation step. Despite it is very costly in time, this technique proved to have a good performance in classification accuracy. MFO is exploited in this study as a searching method to find optimal feature set, maximizing classification performance. The proposed algorithm is compared against particle swarm optimization (PSO) and genetic algorithms (GA). A set of UCI data sets is used for comparison using different assessment indicators. Results prove the efficiency of the proposed algorithm in comparison to other algorithms.

Feature selection based on antlion optimization algorithm

Abstract: In this work, a model for feature selection based on antlion optimization (ALO) is proposed. Feature sets always have redundant, dependant and correlated features that badly affect the classification performance and increases training time. Therefore, feature selection becomes a must to remove irrelevant features and enhances classification generalization. Wrapper-based feature selection is a method that selects a feature set maximizing a given classifier performance criteria and hence requires efficient searching method to find optimal feature combinations. Antlion optimization is a recently proposed swarm optimizer with good searching capability. ALO is exploited in this study as searching method to find optimal feature set maximizing classification performance. ALO algorithm mimics the hunting mechanism of antlions in nature. The proposed model is evaluated using different evaluation criteria on 18 different data sets and is compared to two common search methods namely particle swarm optimization (PSO) and genetic algorithm (GA) and proves an advance in classification performance and selected feature set.