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Bassam H. Abd

Bio: Bassam H. Abd is an academic researcher from University of Technology, Iraq. The author has contributed to research in topics: Facial expression & Statistical classification. The author has an hindex of 2, co-authored 6 publications receiving 10 citations.

Papers
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Proceedings ArticleDOI
05 Jun 2016
TL;DR: A simple and reliable Field Programmable Gate Array (FPGA) based ECG analysis system is discussed and an R peak detection system is modeled that identifies the time instances, at which the R peak occurred.
Abstract: The analysis of the electrocardiogram (ECG) signal is used extensively in the diagnosis of different heart diseases. One of the major tasks to be provided is the accurate determination of the QRS complex. Due to its characteristic shape QRS detection in an ECG signal is necessary for efficient extraction of beat-to-beat intervals (RR). In this paper, a simple and reliable Field Programmable Gate Array (FPGA) based ECG analysis system is discuss. An R peak detection system is modeled that identifies the time instances, at which the R peak occurred. The developed algorithm implements into a Field Programmable Gate Array for the ECG signals feature extraction. The proposed system is built using VHDL by Xilinx ISE 14.6 package. Simulation of the algorithm is performed using MATLAB Version 8.3.0.532 (R2014a) System Generator. Simulator was taking the MITBIH Arrhythmia ECG database as input.

6 citations

Journal ArticleDOI
TL;DR: This work introduces different classification techniques to detect confusion by collecting an actual database that can be used to evaluate the performance for every CDS employing facial expressions and selecting appropriate facial features.
Abstract: Confusion detection systems (CDSs) that need Noninvasive, mobile, and cost-effective methods use facial expressions as a technique to detect confusion. In previous works, the technology that the system used represents a major gap between this proposed CDS and other systems. This CDS depends on the Facial Action Coding System (FACS) that is used to extract facial features. The FACS shows the motion of the facial muscles represented by Action Units (AUs); the movement is represented with one facial muscle or more. Seven AUs are used as possible markers for detecting confusion that has been implemented in the form of a single vector of facial action; the AUs that have been used in this work are AUs 4, 5, 6, 7, 10, 12, and 23. The database used to calculate the performance of the proposed CDS is gathered from 120 participants (91males, 29 females), between the ages of 18-45. Four types of classification algorithms are used as individuals; these classifiers are (VG-RAM), (SVM), Logistic Regression and Quadratic Discriminant classifiers. The best success rate was found when using Logistic Regression and Quadratic Discriminant. This work introduces different classification techniques to detect confusion by collecting an actual database that can be used to evaluate the performance for every CDS employing facial expressions and selecting appropriate facial features.

4 citations

Proceedings ArticleDOI
01 Aug 2019
TL;DR: An overview of several methods used for face tracking based on tracking a set of landmark points projected on the face and also a review of classification techniques that used in order to recognize grammatical expressions are mentioned.
Abstract: In recent years, many studies have been conducted for analysis of facial expressions. The importance of facial expressions is particularly evident in sign languages because they help in social communication between deaf and dumb among themselves as well as their interaction with the world. These facial expressions are called Grammatical Facial Expressions (GFEs). In this paper, an overview of several methods used for face tracking based on tracking a set of landmark points projected on the face and also a review of classification techniques that used in order to recognize grammatical expressions. This paper also mentions available datasets that used to facilitate linguistic and computational research on signed languages and the gestural components of spoken languages.

3 citations

Journal ArticleDOI
TL;DR: In this work, three artificial neural networks methods, namely, Back Propagation neural network (BPNN), Radial Basis Function Network (RBFN) and, K-nearest neighbor (KNN), used to forecast the level of hepatitis intensity and the results show that the prediction result by the KNN network will be better than the two other methods in time record to reach an automatic diagnosis with an error rate of less than 1.
Abstract: This work aims to design an intelligent model capable of diagnosing and predicting the severity of the hepatitis of illness that assists physicians to make an accurate decision. The main contribution is achieved by adopting a new multiclass classifier approach based on a collected real database with new proposed features that reflect the precise situation of the disease. In this work, three artificial neural networks (ANNs) methods, namely, Back Propagation neural network (BPNN), Radial Basis Function Network (RBFN) and, K-nearest neighbor (KNN), used to forecast the level of hepatitis intensity. Real data Collected from the Gastroenterology and Liver Education Hospital of the City of Medicine in Baghdad used as modeling and forecasting samples, respectively, to compare the results of forecasting. The results show that the prediction result by the KNN network will be better than the two other methods in time record to reach an automatic diagnosis with an error rate of less than 1%. Diagnosis accuracy was 99.33% for 2-class and 88% for 5-class, which considered excellent accuracy.

2 citations

Proceedings ArticleDOI
01 Dec 2018
TL;DR: The accuracy of the KNN overcome the rest of the classifier with 100% accuracy for the diagnosis of hepatitis disease.
Abstract: This study aims to optimize the accuracy of diseases diagnosis, where many studies have been conducted to challenge the highest diagnostic accuracy of hepatitis disease because the early and correct diagnosis increases the chance of saving the patient's life from this deadly disease. Therefore, in this paper, we have done a good test for three classifications, namely: support vector machine (SVM), multilayer perceptron (MLP) and K-nearest neighbor (KNN). The accuracy of the KNN overcome the rest of the classifier with 100% accuracy for the diagnosis of hepatitis disease. We used the same division of data used in previous works for a fair comparison using the datasets gotten from the UCI machine learning database, with 19 features. This result is the best yet.

2 citations


Cited by
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Journal ArticleDOI
01 Jan 2021
TL;DR: An extensive review of the progress of applying Artificial Intelligence in forecasting and detecting liver diseases and then summarizes related limitations of the studies followed by future research is provided.
Abstract: There has been a rapid growth in the use of automatic decision-making systems and tools in the medical domain. By using the concepts of big data, deep learning, and machine learning, these systems extract useful information from large medical datasets and help physicians in making accurate and timely decisions regarding predictions and diagnosis of diseases. In this regard, this study provides an extensive review of the progress of applying Artificial Intelligence in forecasting and detecting liver diseases and then summarizes related limitations of the studies followed by future research.

15 citations

Journal ArticleDOI
TL;DR: In this paper, a deep convolutional neural network (CNN) was proposed for deep learning in the context of image recognition, where the network was trained on images of the human body.
Abstract: التجزئة التلقائية لالآفة الجلدية في الصور الجلدية هي عنصر أساسي في تشخيص سرطان الجلد. إزالة الضوضاء عن الصورة في آفة سرطان الجلد هي وصف لمعالجة الصور التي تشير إلى تقنيات استعادة الصورة لتطوير صورة في لمسة محددة مسبقا. تعد إزالة الضوضاء هي الخطوة المهمة في معالجة الصور لاستعادة صورة جيدة الجودة والتي يمكن استخدامها في العديد من العمليات مثل التجزئة ، والاكتشاف ، وما إلى ذلك. يقترح هذا العمل تقنية جديدة لتقليل وتفتيت ورم الآفة الجلدية. في البداية ، استخدم Deep Convolution Neural Network (CNN) لإزالة الضوضاء والهياكل غير المرغوب فيها للصور. ثم ، يتم اقتراح آلية جديدة لتقسيم آفة الجلد إلى صور الجلد على أساس محيط نشط مباشرة مع عمليات المورفولوجية. يتم تطبيق ومقارنة تقنيات مختلفة لإزالة الضوضاء والتجزئة على آفة الجلد. تظهر الخوارزمية المقترحة تحسنا في نتائج كل من خفض الضوضاء وتقسيمها.

15 citations

Journal ArticleDOI
TL;DR: Analysis and comparison of the efficiency of pattern recognition techniques using a low-computational power device for drift fault detection in sensors, such as the Raspberry Pi 4, shows that from the given classifiers the (ANN) and (KNN) outperform to the (SVM).

4 citations

Journal ArticleDOI
TL;DR: In this paper, the authors compared the diagnostic method of COVID-19 with other methods of deep learning created with the use of radiology images and found that RestNet50 pre-trained and DCNN model gives accuracy of 98%, which is the highest reported so far from among other proposed models were discussed in this paper.

3 citations

Journal ArticleDOI
TL;DR: In this work, three artificial neural networks methods, namely, Back Propagation neural network (BPNN), Radial Basis Function Network (RBFN) and, K-nearest neighbor (KNN), used to forecast the level of hepatitis intensity and the results show that the prediction result by the KNN network will be better than the two other methods in time record to reach an automatic diagnosis with an error rate of less than 1.
Abstract: This work aims to design an intelligent model capable of diagnosing and predicting the severity of the hepatitis of illness that assists physicians to make an accurate decision. The main contribution is achieved by adopting a new multiclass classifier approach based on a collected real database with new proposed features that reflect the precise situation of the disease. In this work, three artificial neural networks (ANNs) methods, namely, Back Propagation neural network (BPNN), Radial Basis Function Network (RBFN) and, K-nearest neighbor (KNN), used to forecast the level of hepatitis intensity. Real data Collected from the Gastroenterology and Liver Education Hospital of the City of Medicine in Baghdad used as modeling and forecasting samples, respectively, to compare the results of forecasting. The results show that the prediction result by the KNN network will be better than the two other methods in time record to reach an automatic diagnosis with an error rate of less than 1%. Diagnosis accuracy was 99.33% for 2-class and 88% for 5-class, which considered excellent accuracy.

2 citations