Hybrid Mammogram Classification Using Rough Set and Fuzzy Classifier

We propose a computer aided detection (CAD) system for the detection and classification of suspicious regions in mammographic images. This system combines a dimensionality reduction module (using principal component analysis), a feature extraction module (using independent component analysis), and a feature subset selection module (using rough set model). Rough set model is used to reduce the effect of data inconsistency while a fuzzy classifier is integrated into the system to label subimages into normal or abnormal regions. The experimental results show that this system has an accuracy of 84.03% and a recall percentage of 87.28%

Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA

Breast cancer is a major cause of death and morbidity among women all over the world, and it is a fact that early detection is a key in improving outcomes. Therefore development of algorithms that aids radiologists in identifying changes in breast tissue early on is essential. In this work an algorithm that investigates the use of principal components analysis (PCA) is developed to identify suspicious regions on mammograms. The algorithm employs linear structure and curvelinear modeling prior to PCA implementations. Evaluation of the algorithm is based on the percentage of correct classification, false positive (FP) and false negative (FN) in all experimental work using real data. Over 90% accuracy in block classification is achieved using mammograms from MIAS database

Prediction of Critical Flashover Voltage of High Voltage Insulators Leveraging Bootstrap Neural Network

Understanding the flashover performance of the outdoor high voltage insulator has been in the interest of many researchers recently. Various studies have been performed to investigate the critical flashover voltage of outdoor high voltage insulators analytically and in the laboratory. However, laboratory experiments are expensive and time-consuming. On the other hand, mathematical models are based on certain assumptions which compromise on the accuracy of results. This paper presents an intelligent system based on Artificial Neural Networks (ANN) to predict the critical flashover voltage of High-Temperature Vulcanized (HTV) silicone rubber in polluted and humid conditions. Various types of learning algorithms are used, such as Gradient Descent (GD), Levenberg-Marquardt (LM), Conjugate Gradient (CG), Quasi-Newton (QN), Resilient Backpropagation (RBP), and Bayesian Regularization Backpropagation (BRBP) to train the ANN. The number of neurons in the hidden layers along with the learning rate was varied to understand the effect of these parameters on the performance of ANN. The proposed ANN was trained using experimental data obtained from extensive experimentation in the laboratory under controlled environmental conditions. The proposed model demonstrates promising results and can be used to monitor outdoor high voltage insulators. It was observed from obtained results that changing of the number of neurons, learning rates, and learning algorithms of ANN significantly change the performance of the proposed algorithm

A Proposal of a New Chaotic Map for Application in the Image Encryption Domain

Several chaos-based image encryption schemes have been proposed in the last decade. Each encryption scheme has pros and cons regarding its speed, complexity, and security. This paper proposes a new chaotic map called Power-Chaotic Map (PCM). Characteristics of the proposed PCM, such as chaotic behaviour, randomness, sensitivity, and s-unimodality, are investigated. As an application of the proposed chaotic map, an image encryption scheme is proposed to encrypt greyscale and text images. The proposed three-phase image encryption scheme performs a series of substitution and permutation operations. The Pixel-Level phase utilises the PCM’s generated keystreams to perform the substitution operation of image pixels. The Row-Level phase permutates, via a proposed pseudorandom number generator, pixel locations of each row and then shuffles row locations. Finally, the Column-Level phase performs a substitution operation on pixels of each column. Performance of the proposed PCM-based image encryption scheme is investigated through histogram analysis, statistical correlation analysis, key sensitivity, encryption performance of text images, and permutation and substitution properties. Experimental results indicate that the PCM has a wider range of chaotic behaviour than well-known one-dimensional maps, meets the s-unimodality property, has high sensitivity, and generates keystreams with random-like behaviour. Furthermore, results indicate that the PCM-based image encryption scheme provides high encryption security for text images, high key sensitivity, immunity against brute-force attacks, strong statistical correlation results, strong encryption performance, and low computational complexity

An Efficient Hybrid Classifier for Cancer Detection

The early detection of cancer in both healthy and high-risk populations offers increased opportunity for treatment and curative intent. In this paper, we propose a hybrid classifier that produces an efficient classification system for cancer detection in cell datasets. The first part of this work investigates the performance of artificial neural networks (ANN) such as Self-Organizing Feature Map (SOM) and Learning Vector Quantization (LVQ), while in the second part, we present our investigation on the performances of Decision Tree (DT) and its pruning model. We also, in the third part, present our proposal for a new hybrid classifier that is based on the Random Forest (RF) and the combination of the LVQ and DT. Experimental results of the proposed hybrid classifier indicate that the hybrid classifier effectively avoids the drawbacks of individual classifiers and has high anti-noise performance

A Mobile Social and Communication Tool for Autism

Autism is a complex neurobiological disorder that is prevalence worldwide. Most autistic children have weak communication and social skills. This re-searches aims to develop and test a mobile application, named MyVoice, which supports local autistic children. The proposed design and features are discussed, and a prototype is evaluated and tested by two therapists and an autistic child. Experimental results indicate positive feedback in terms of ease of use, aesthetic, and simplicity. Parents of the autistic child are satisfied with different features such as the alert notification. Results also indicate that autistic children need about one week to easily interact with MyVoice

An Efficient Hybrid Classifier for Cancer Detection

The early detection of cancer in both healthy and high-risk populations offers increased opportunity for treatment and curative intent. In this paper, we propose a hybrid classifier that produces an efficient classification system for cancer detection in cell datasets. The first part of this work investigates the performance of artificial neural networks (ANN) such as Self-Organizing Feature Map (SOM) and Learning Vector Quantization (LVQ), while in the second part, we present our investigation on the performances of Decision Tree (DT) and its pruning model. We also, in the third part, present our proposal for a new hybrid classifier that is based on the Random Forest (RF) and the combination of the LVQ and DT. Experimental results of the proposed hybrid classifier indicate that the hybrid classifier effectively avoids the drawbacks of individual classifiers and has high anti-noise performance.</span

A Computer-Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

Screening mammograms is a repetitive task that causes fatigue and eye strain since for every thousand cases analyzed by a radiologist, only 3–4 are cancerous and thus an abnormality may be overlooked. Computer-aided detection (CAD) algorithms were developed to assist radiologists in detecting mammographic lesions. In this paper, a computer-aided detection and diagnosis (CADD) system for breast cancer is developed. The framework is based on combining principal component analysis (PCA), independent component analysis (ICA), and a fuzzy classifier to identify and label suspicious regions. This is a novel approach since it uses a fuzzy classifier integrated into the ICA model. Implemented and tested using MIAS database. This algorithm results in the classification of a mammogram as either normal or abnormal. Furthermore, if abnormal, it differentiates it into a benign or a malignant tissue. Results show that this system has 84.03% accuracy in detecting all kinds of abnormalities and 78% diagnosis accuracy

An Educational Arabic Sign Language Mobile Application for Children with Hearing Impairment

This research addresses the need for a mobile application to teach Arabic sign language to deaf children in the Arab world. Therefore, the team develops an educational android application to teach deaf children the Arabic sign language using gamification. Also, the research aims to investigate the impact of using a mobile application for Arabic sign language on children’s learning performance. To achieve its goals, the research entails two main stages. The first stage focuses on building the mobile application based on the literature review and searching the Google and Apple stores for similar applications. In the second stage, an experiment is conducted on 10 deaf children—the experimental group adopted the mobile application for the learning process, whereas the control group followed the traditional learning approach. A pre-test and post-test were conducted, and the results show that children who used the game-based mobile application performed better in their quiz