Hala H. Zayed

ARSC: Augmented Reality Student Card

Augmented Reality (AR) is the technology of adding virtual objects to the real scenes through enabling the addition of missing information at real life. As the lack of resources is a problem that can be solved through AR, this paper represents and explains the usage of AR technology in what can be named Augmented Reality Student Card (ARSC) for serving the education field. ARSC uses single static markers combined in one card for assigning different objects, leaving the choice to the computer application for minimizing the tracking process. ARSC is designed to be a useful low cost solution for serving the education field. ARSC represents any lesson in a 3D format that helps students to visualize the facts, interact with theories and deal with the information in a totally new effective and interactive way. ARSC can be used in offline, online and game applications with seven markers, four of them are used as a joystick game controller. One of the novelties in this paper is that full experimental tests had been made for the ARTag marker set for sorting them according to their efficiency. The results of the tests are used in this research to choose the most efficient markers for ARSC, and can be used for further researches. The experimental work that had been made in this paper also shows the constraints for marker creation for an AR application. Due to the need to work for online and offline application, merging of toolkits and libraries has been made, as presented in this paper. ARSC was examined by a number of students of both genders with average age between 10–17 years and it was found to have a great acceptance among them.

Remote Computer-Aided Breast Cancer Detection and Diagnosis System Based on Cytological Images

The purpose of this study is to develop an intelligent remote detection and diagnosis system for breast cancer based on cytological images. First, this paper presents a fully automated method for cell nuclei detection and segmentation in breast cytological images. The locations of the cell nuclei in the image were detected with circular Hough transform. The elimination of false-positive (FP) findings (noisy circles and blood cells) was achieved using Otsus thresholding method and fuzzy c-means clustering technique. The segmentation of the nuclei boundaries was accomplished with the application of the marker-controlled watershed transform. Next, an intelligent breast cancer classification system was developed. Twelve features were presented to several neural network architectures to investigate the most suitable network model for classifying the tumor effectively. Four classification models were used, namely, multilayer perceptron using back-propagation algorithm, probabilistic neural network (PNN), learning vector quantization, and support vector machine (SVM). The classification results were obtained using tenfold cross validation. The performance of the networks was compared based on resulted error rate, correct rate, sensitivity, and specificity. Finally, we have merged the proposed computer-aided detection and diagnosis system with the telemedicine platform. This is to provide an intelligent, remote detection, and diagnosis system for breast cancer patients based on the Web service. The proposed system was evaluated using 92 breast cytological images containing 11502 cell nuclei. Experimental evidence shows that the proposed method has very effective results even in the case of images with high degree of blood cells and noisy circles. In addition, two benchmark data sets were evaluated for comparison. The results showed that the predictive ability of PNN and SVM is stronger than the others in all evaluated data sets.

Automated cell nuclei segmentation for breast fine needle aspiration cytology

Abstract Breast cancer detection and segmentation of cytological images is the standard clinical practice for the diagnosis and prognosis of breast cancer. This paper presents a fully automated method for cell nuclei detection and segmentation in breast cytological images. The images are enhanced with histogram stretching and contrast-limited adaptive histogram equalization (CLAHE). The locations of the cell nuclei in the image are detected with circular Hough transform (CHT) and local maximum filtering. The elimination of false positive findings (noisy circles and blood cells) is achieved using Otsu’s thresholding method and fuzzy C-means clustering technique. The segmentation of the nuclei boundaries is accomplished with the application of the marker controlled watershed transform in the gradient image, using the nuclei markers extracted in the detection step. The proposed method is evaluated using 92 breast cytological images containing 11,502 cell nuclei. Experimental evidence shows that the proposed method has very effective results even in the case of images with high degree of blood cells, noisy circles.

Intrusion Detection: Supervised Machine Learning

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS).The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate. Categroy: Ubiquitous computing

An Image Retrieval Approach Based on Composite Features and Graph Matching

Content-Based Image Retrieval (CBIR) considers the characteristics of the image itself, for example its shapes, colors and textures. The Current approaches to CBIR differ in terms of which image features are extracted. Recent work deals with combination of distances or scores from different and independent representations. This work attempts to induce high level semantics from the low level descriptors of the images. In this paper, we propose a new approach that integrates techniques of salient, color and texture features. Our approach extracts interest salient regions that work as local descriptors. A greedy graph matching algorithm with a proposed modified scoring function is applied to determine the final image rank. The proposed approach is appropriate for accurately retrieving images even in distortion cases such as geometric deformations and noise. This approach was tested on proprietary image databases. Also an offline case study is developed where our approach is tested on images retrieved from Google keyword based image search engine. The results show that a combination of our approach as a local image descriptor with another global descriptor outperforms other approaches.

Blind Signature Schemes based on ElGamal Signature for Electronic Voting: A Survey

The Electronic Voting (E-Voting) became a truly crucial part in the democracy of our life in which the election data is recorded, stored and prepared fundamentally as computerized data. Important basic properties of E-Voting are eligibility, privacy, fairness, uniqueness, receipt-freeness and verifiability. In addition to properties of blind signature such as correctness, blindness, anonymity and unforgeability. Blind signature allows to obtain a signature from the signer who signs a message without reading the content of the message. This paper presented a survey on blind signature schemes based on ElGamal Signature. The aim of this paper is to compare the existing blind signature schemes based on modifications of their parameters such as blinding factor, blinded message, blind signature, and Signature pair that satisfy these basic properties.

Face Age Estimation Approach based on Deep Learning and Principle Component Analysis

This paper presents an approach for age estimation based on faces through classifying facial images into predefined age-groups. However, a task such as the one at hand faces several difficulties because of the different aspects of every single person. Factors like exposure, weather, gender and lifestyle all come into play. While some trends are similar for faces from a similar age group, it is problematic to distinguish the aging aspects for every age group. This paper’s concentration is in four chosen age groups where the estimation takes place. We employed a fast and effective machine learning method: deep learning so that it could solve the age categorization issue. Principal component analysis (PCA) was used for extracting features and reducing face image. Age estimation was applied to three different aging datasets from Morph and experimental results are reported to validate its efficiency and robustness. Eventually, it is evident from the results that the current approach has achieved high classification results compared with support vector machine (SVM) and k-nearest neighbors (K-NN).

An Efficient Semantic Image Retrieval based on Color and Texture Features and Data Mining Techniques

Content-based Image Retrieval (CBIR) is retrieving the desired images from huge collections. The user queries are becoming very specific and traditional text-based methods cannot efficiently handle them. CBIR system retrieves the image via low-level features such as color, texture and shape. In this work, we propose CBIR system that retrieves images from a database based on the semantic features of them. Our methodology divide the query image into 100 regions. And then, extracts Features Vector from each region and label each one with the suitable concept like (Sky, Sand, Water, trunks, foliage, rocks,..., and Grass). The labeling process in performed semi-automatically using k-means clustering and KNN classification algorithms. The system has been evaluated by recall and precision measures and compared to other recent works. The results of the paper reflects the efficiency of the system for retrieving images with up to 98% recognition ratio. General Terms Image processing, Image representations, Visual contentbased indexing and retrieval.

Understanding Medical Text Related to Breast Cancer: A Review

Breast Cancer is a harmful disease that has caused millions of women deaths. There are a huge number of publications on breast cancer research which offers a good source of information. Identifying breast cancer biomarkers is not a trivial task. There are many approaches used to identify and extract the needed information more efficiently from structured/unstructured text, uncover relationships and hidden rules from the huge amount of information such as text mining, machine learning and data mining. This paper reviews some of research literature on breast cancer using these approaches.

Semi-Automatic Semantic Based Natural Images Retrieval System

Content-based Image Retrieval (CBIR) is a term referring to looking for digital images by analyzing content of images rather than its metadata. CBIR system retrieves the image via low-level features such as color, texture and shape. In this work, we propose an improved CBIR system that retrieves images from a database based on the semantic features of them. The methodology that divide image and extracts low-level features from each region and label each one with the suitable concept (Sky, Sand, Water, trunks, foliage, rocks,..., and Grass). The results of the paper reflects the efficiency of the system for retrieving images with up to 98% recognition ratio.