Kareem Kamal A. Ghany

Firefly Optimization Algorithm for Feature Selection

In this paper, a system for feature selection based on firefly algorithm (FFA) optimization is proposed. Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might reduce the classification accuracy because of the large search space. The main goal of attribute reduction is to choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy from using all attributes. A system for feature selection is proposed in this paper using a modified version of the firefly algorithm (FFA) optimization. The modified FFA algorithm adaptively balance the exploration and exploitation to quickly find the optimal solution. FFA is a new evolutionary computation technique, inspired by the flash lighting process of fireflies. The FFA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system was tested on eighteen data sets and proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

Biometrics template security on cloud computing

Cloud computing is the concept of using maximum remote services through a network using various minimum resources, it provides these resources to users via internet. There are many critical problems appeared with cloud computing such as data privacy, security, and reliability etc. But we find that security is the most important between these problems. In this research paper, the proposed approach is to eliminate the concerns regarding data security using bio hash function for biometrics template security to enhance the security performance in cloud as per different perspective of cloud customers. Experiments using well know benchmark CASIA fingerprint-V5 data sets show that the obtained results proved that using Bio-hash function approach is more efficient in protecting the biometric template compared to Crypto-Biometric Authentication approach and the error rate is minimized by 25%.

Kekre's transform for protecting fingerprint template

In this paper, Kekres transform is proposed for protecting fingerprint template and a symmetric bio-hash function is used to improve the protection of the fingerprint biometric template. In the proposed approach; firstly the principal curve approach is used to extract the features from the fingerprint template. Then, we applied a transformation phase using Kekres transform. It starts by calculating the row and the column mean vectors of fingerprint image to get Kekre transform feature vectors. Finally, we adapt the bio-hash function to calculate the translation, rotation, and the error rate to increase the security performance. Experiments using well know benchmark CASIA fingerprint-V5 data sets show that the obtained results proved that the kekre transform approach is more efficient compared to class distribution preserving (CDP) approach and the error rate is minimized by %20.

A hybrid biometric approach embedding DNA data in fingerprint images

In this paper, we present a robust image watermarking approach to hide DNA sequence data into fingerprint templates for copyright protection based on the discrete wavelet transform. A multi-bit watermark, based on DNA data, is embedded into the low frequency sub-band of the fingerprint image. During the inverse process, we employ DNA decoding to extract the image signature from the watermarked image. We evaluate our proposed method using statistical measures including mean squared error, peak signal-to-noise-ratio, and structural similarity index, and the experimental results confirm that the watermarks generated using our approach are invisible.

A hybrid dragonfly algorithm with extreme learning machine for prediction

In this work, a proposed hybrid dragonfly algorithm (DA) with extreme learning machine (ELM) system for prediction problem is presented. ELM model is considered a promising method for data regression and classification problems. It has fast training advantage, but it always requires a huge number of nodes in the hidden layer. The usage of a large number of nodes in the hidden layer increases the test/evaluation time of ELM. Also, there is no guarantee of optimality of weights and biases settings on the hidden layer. DA is a recently promising optimization algorithm that mimics the moving behavior of moths. DA is exploited here to select less number of nodes in the hidden layer to speed up the performance of the ELM. It also is used to choose the optimal hidden layer weights and biases. A set of assessment indicators is used to evaluate the proposed and compared methods over ten regression data sets from the UCI repository. Results prove the capability of the proposed DA-ELM model in searching for optimal feature combinations in feature space to enhance ELM generalization ability and prediction accuracy. The proposed model was compared against the set of commonly used optimizers and regression systems. These optimizers are namely, particle swarm optimization (PSO) and genetic algorithm (GA). The proposed DA-ELM model proved an advance overall compared methods in both accuracy and generalization ability.

An Orphan Drug Legislation System

Orphan drugs are a treatment for rare diseases. From that, comes the importance of orphan drug development and discovery. For an orphan drug to be approved by the FDA, it does not have to be similar to any approved orphan drug. So chemists opinions are important to determine the probability of similarity. It is too hard to check all orphan drugs for any rare disease. It takes a long time and big effort, so we introduce in this study a system that classifies the orphan drugs according to their probability of structural similarity. It also compares between them and the unauthorized orphan drug to determine the closest orphan drug to it. That system helps chemists to study a certain orphan database using the five features. That system provides better results. It provides chemists with the clusters of orphan drugs after adding the drug that needs to be authorized to its cluster.