Mahmoud Shoman

Adaptive k-means clustering algorithm for MR breast image segmentation

Image segmentation is vital for meaningful analysis and interpretation of the medical images. The most popular method for clustering is k-means clustering. This article presents a new approach intended to provide more reliable magnetic resonance (MR) breast image segmentation that is based on adaptation to identify target objects through an optimization methodology that maintains the optimum result during iterations. The proposed approach improves and enhances the effectiveness and efficiency of the traditional k-means clustering algorithm. The performance of the presented approach was evaluated using various tests and different MR breast images. The experimental results demonstrate that the overall accuracy provided by the proposed adaptive k-means approach is superior to the standard k-means clustering technique.

MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier

This article introduces a hybrid approach that combines the advantages of fuzzy sets, ant-based clustering and multilayer perceptron neural networks (MLPNN) classifier, in conjunction with statistical-based feature extraction technique. An application of breast cancer MRI imaging has been chosen and hybridization system has been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: Benign or Malignant. The introduced hybrid system starts with an algorithm based on type-II fuzzy sets to enhance the contrast of the input images. This is followed by an improved version of the classical ant-based clustering algorithm, called adaptive ant-based clustering to identify target objects through an optimization methodology that maintains the optimum result during iterations. Then, more than twenty statistical-based features are extracted and normalized. Finally, a MLPNN classifier was employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether the cancer is Benign or Malignant. To evaluate the performance of presented approach, we present tests on different breast MRI images. The experimental results obtained, show that the adaptive ant-based segmentation is superior to the classical ant-based clustering technique and the overall accuracy offered by the employed hybrid technique confirm that the effectiveness and performance of the proposed hybrid system is high.

Audio-Visual Speech Recognition for People with Speech Disorders

recognition of disorder people is a difficult task due to the lack of motor-control of the speech articulators. Multimodal speech recognition can be used to enhance the robustness of disordered speech. This paper introduces an automatic speech recognition system for people with dysarthria speech disorder based on both speech and visual components. The Mel-Frequency Cepestral Coefficients (MFCC) is used as features representing the acoustic speech signal. For the visual counterpart, the Discrete Cosine Transform (DCT) Coefficients are extracted from the speakers mouth region. Face and mouth regions are detected using the Viola-Jones algorithm. The acoustic and visual input features are then concatenated on one feature vector. Then, the Hidden Markov Model (HMM) classifier is applied on the combined feature vector of acoustic and visual components. The system is tested on isolated English words spoken by disorder speakers from UA-Speech data. Results of the proposed system indicate that visual features are highly effective and can improve the accuracy to reach 7.91% for speaker dependent experiments and 3% for speaker independent experiments.

Arabic Unit Selection Emotional Speech Synthesis using Blending Data Approach

This paper introduce the work done to build an Arabic unit selection voice that could carry emotional information. Three emotional sates were covered; normal, sad and questions. An emotional speech classifier was used to enhance the intelligibility of the used recorded speech database. The classification information was employed in the proposed target cost to produce more natural and emotive synthetic speech. The system is evaluated according to the naturalness and emotiveness of the produced speech. The system evaluations show significant increase in the naturalness and emotiveness scores.

An integrated classification method for brain computer interface system

A channel of communication for both human brain and computer system is provided via a system called Brain Computer Interface (BCI). The vital aim of BCI research is to develop a system that helps the disabled people to interact with other persons and allows their interaction with the external environments or as an additional man-machine interaction channel for healthy users. Different techniques have been developed in the literature for the classification of brain signals. The purpose of this work is to deveolp a novel method of analyzing the EEG signals. We have used high pass filter to remove artifacts, DWT algorithms for feature extraction and features like Mean Absolute Value, Root Mean Square, and Simple Square Integral are used. The neural network algorithm is used to find the correct class label for EEG signal after clustering the feature vectors using K-Nearest Neighbor algorithm. It has been depicted from results that the proposed integrated technique outperforms a better performance than methods mentioned in literature.

Cancer classification ensemble system based on gene expression profiles

Cancer classification based on molecular level investigation has gained the interest of researches as it provides a systematic, accurate and objective diagnosis for different cancer types. It has also been applied in a wide range of applications such as drug discovery, cancer prediction and diagnosis which is a very important issue for cancer treatment. Besides, it helps in understanding the function of genes and the interaction between genes in normal and abnormal conditions. In this paper, an effective cancer classification ensemble system is proposed. Ensemble classifiers increase not only the performance of the classification, but also the confidence of the results.

Trajectory Learning Using Principal Component Analysis

Robots are increasingly used in numerous life applications. Therefore, humans are looking forward to create productive robots. Robot learning is the process of obtaining additional information to accomplish an objective configuration. Moreover, robot learning from demonstration is to guide the robot the way to perform a particular task derived from human directions. Traditionally, modeling the demonstrated data was applied on discrete data which would result in learning outcome distortions. So as to overcome such distortion, preprocessing of the raw data is necessary. In this paper, trajectory learning from demonstration scheme is proposed. In our proposed scheme, the raw data are initially preprocessed by employing the principal component analysis algorithm. We experimentally compare our proposed scheme with the most recent proposed schemes. It is found that the proposed scheme is capable of increasing the efficiency by minimizing the error in comparison to the other recent work with significant reduced computational cost.

Long-Distance Continuous Space Language Modeling for Speech Recognition

The n-gram language models has been the most frequently used language model for a long time as they are easy to build models and require the minimum effort for integration in different NLP applications. Although of its popularity, n-gram models suffer from several drawbacks such as its ability to generalize for the unseen words in the training data, the adaptability to new domains, and the focus only on short distance word relations. To overcome the problems of the n-gram models the continuous parameter space LMs were introduced. In these models the words are treated as vectors of real numbers rather than of discrete entities. As a result, semantic relationships between the words could be quantified and can be integrated into the model. The infrequent words are modeled using the more frequent ones that are semantically similar. In this paper we present a long distance continuous language model based on a latent semantic analysis (LSA). In the LSA framework, the word-document co-occurrence matrix is commonly used to tell how many times a word occurs in a certain document. Also, the word-word co-occurrence matrix is used in many previous studies. In this research, we introduce a different representation for the text corpus, this by proposing long-distance word co-occurrence matrices. These matrices to represent the long range co-occurrences between different words on different distances in the corpus. By applying LSA to these matrices, words in the vocabulary are moved to the continuous vector space. We represent each word with a continuous vector that keeps the word order and position in the sentences. We use tied-mixture HMM modeling (TM-HMM) to robustly estimate the LM parameters and word probabilities. Experiments on the Arabic Gigaword corpus show improvements in the perplexity and the speech recognition results compared to the conventional n-gram.