English العربية کوردی

ئەز   Mayyadah Ramiz Mahmood

Assistant Professor

Specialties

Artificial Intelligence Image Processing

mayyadah.mahmood@uoz.edu.krd
9647503253908
Iraq , Zakho
0
Awards
12
Published Journal Articles
0
Thesis
0
Training Course

0
Book Chapter
5
Conference
0
Presentation
0
Workshop

Academic Title

Assistant Professor

2023-06-04

Lecturer

2019-05-12

Assistant Lecturer

2010-03-25

Published Journal Articles

Science Journal of University of Zakho (Issue : 12) (Volume : 11)
CLASSIFIED COVID-19 BY DENSENET121-BASED DEEP TRANSFER LEARNING FROM CT-SCAN IMAGES

The COVID-19 disease, which has recently emerged and has been considered a worldwide pandemic, has... See more

The COVID-19 disease, which has recently emerged and has been considered a worldwide pandemic, has had a significant impact on the lives of millions of people and has forced a substantial load on healthcare organizations. Numerous deep-learning models have been utilized for diagnosing coronaviruses from chest computed tomography (CT) images. However, in light of the limited availability of datasets on COVID-19, the pre-trained deep learning networks were used. The main objective of this research is to construct and develop an automated approach for the early detection and diagnosis of COVID-19 in thoracic CT images. This paper proposes the DDTL-COV model, a deep transfer learning model based on DenseNet121, to classify patients on CT scans as either COVID or non-COVID, utilizing weights obtained from the ImageNet dataset. Two datasets were used to train the DDTL-COV model: the SARS-CoV-2 CT-scan dataset and the COVID19-CT dataset. In the SARS-CoV-2 CT dataset, the model achieved a good accuracy of 99.6%. However, on the second dataset (COVID19-CT dataset), its performance shows an accuracy rate of 89%. These results show that the model performed better than alternative methods.

 2024-03
Indonesian Journal of Electrical Engineering and Computer Science (Issue : 3) (Volume : 29)
COVID-19 detection based on convolution neural networks from CT-scan images: a review

The COVID-19 outbreak has been affecting the health of people all around the world. With... See more

The COVID-19 outbreak has been affecting the health of people all around the world. With the number of confirmed cases and deaths still rising daily, so the main aim is to detect positive cases as soon as and provide them with the necessary treatment. The utilization of imaging data including chest x-rays and computed tomography (CT) was proven that is would be beneficial for quickly diagnosing COVID-19. Since Computerized Tomography provides a huge number of images, recognizing these visual traits would be difficult and take enormous amounts of time for radiologists so automated diagnosis technologies including deep learning (DL) models are recently for COVID19 screening in CT scans. This review paper presents different researches which used deep learning approaches including various models of convolutional neural networks (CNN) used in image classification tasks well, and large training, like ResNet, VGG, AlexNet, LeNet, GoogleNet, and others for COVID-19 diagnosing and severity assessments using chest CT images. As a result, automated COVID-19 analysis on CT images is essential to save medical personnel and essential time for disease prevention

 2023-03
Academic Journal of Nawroz University (AJNU) (Volume : 11)
Comparison Between a Knowledge-Based System for COVID-19 using Compressed Internet of Things Data: A Review

The world is now experiencing a pneumonia outbreak caused by a novel coronavirus. The huge... See more

The world is now experiencing a pneumonia outbreak caused by a novel coronavirus. The huge volume of medical literature on coronavirus has useful information that may assist medical research communities in addressing specific challenges. Health care professionals may improve their policies by quickly reviewing and getting specific data regarding coronavirus from various published research and the larger struggle against infectious disease. It has developed a technique for extracting actionable knowledge that automatically gathers pertinent data from sections and paragraphs related to a particular topic. There are continuous efforts to construct intelligent systems capable of automatically extracting useful information from many unstructured texts. In this paper, the comparison between many papers based on IoT and the knowledge base for solving the problem of Covid-19 has been conducted

 2022-11
Indonesian Journal of Electrical Engineering and Computer Science (Issue : 3) (Volume : 27)
Performance evaluation of chi-square and relief-F feature selection for facial expression recognition

Pattern recognition is a crucial part of machine learning that has recently piqued scientists' interest.... See more

Pattern recognition is a crucial part of machine learning that has recently piqued scientists' interest. The feature selection method utilized has an impact on the dataset's correctness and learning and training duration. Learning speed, comprehension and execution ease, and properly chosen features influence all high-quality outcomes. The two feature selection methods, relief-F and chi-square, are compared in this research. Each technique assesses and ranks attributes based on distinct criteria. Six of the most important features with the highest ranking have been chosen. The six features are utilized to compare the performance accuracy ratios of the four classifiers: k-nearest neighbor (KNN), naive Bayes (NB), multilayer perceptron (MLP), and random forests (RF) in terms of expression recognition. The final goal of the proposed strategy is to employ the least number of features from both feature selection methods to distinguish the four classifiers' accuracy performance. The proposed approach was trained and tested using the CK+ facial expression recognition dataset. According to the findings of the experiment, RF is the best accurate classifier on chi-square feature selection, with an accuracy of 94.23%. According to a dataset utilized in this study, the relief-F feature selection approach had the best classifier, KNN, with an accuracy of 94.93%.

 2022-09
Journal of Soft Computing and Data Mining (Issue : 2716621) (Volume : 3)
Analyses the Performance of Data Warehouse Architecture Types

The concept of storing historical data for retrieving them when needed has been conceived, and... See more

The concept of storing historical data for retrieving them when needed has been conceived, and the idea was primitive to build repositories for historical data to store these data, despite the use of a specific technique for recovering these data from various storage modes. The data warehouse is the most reliable and widely used technology for scheduling, forecasting, and managing corporations. Also, it is concerned with the data storage facility that extensive collection of data. Data warehouses are called ancient modern techniques; since the early days of relational databases, the critical component of decision support, increasing focus for the database industry. Many commercial products and services are now available, and almost all of the primary database management system vendors provide them. When opposed to conventional online transaction processing applications, decision support puts slightly different demands on database technology. This paper analyzes the performance of the data warehouse architectures by studying and comparing many research works in this field. The study involves extracting, transforming, and loading the data from different recourses and the important characteristic of the architecture types. Furthermore, the tools and application service techniques used to build data warehouse architecture.

 2022-06
Qubahan Academic Journal (Issue : 2) (Volume : 1)
A Comprehensive Survey of Big Data Mining Approaches in Cloud Systems

Cloud computing, data mining, and big online data are discussed in this paper as hybridization... See more

Cloud computing, data mining, and big online data are discussed in this paper as hybridization possibilities. The method of analyzing and visualizing vast volumes of data is known as the visualization of data mining. The effect of computing conventions and algorithms on detailed storage and data communication requirements has been studied. When researching these approaches to data storage in big data, the data analytical viewpoint is often explored. These terminology and aspects have been used to address methodological development as well as problem statements. This will assist in the investigation of computational capacity as well as new knowledge in this area. The patterns of using big data were compared in many articles. In this paper, we research Big Data Mining Approaches in Cloud Systems and address cloud-compatible problems and computing techniques to promote Big Data Mining in Cloud Systems.

 2021-04
Indonesian Journal of Electronic Engineering and Computer Science (Volume : 21)
Classification techniques’ performance evaluation for facial expression recognition

Facial expression recognition could be implemented via analyzing facial changes in actions that form the... See more

Facial expression recognition could be implemented via analyzing facial changes in actions that form the facial expression. This paper provides a comparative approach for facial expression recognition, based on Chi-Square feature selection method. This method is used to determine the most distinguished features of face images (six features) using six classification algorithms which are (K- Nearest Neighbor, Decision tree (J48), Radial Base Function, Support Vector Machine, Multi-Layer Perceptron and Random Forest). These classifiers are used for the mission of expression recognition. The ultimate purpose of the provided method is to distinguish the most influential and accurate performance algorithm based on the smallest number of features. This is done via analyzing and appraising the classifiers’ performance. The provided approach has been applied on CK+ dataset. The experimental results show that Random Forest is proven to be the most accurate classifier with the ratio of 94.23%.

 2021-02
International Journal of Multidisciplinary Research and Publications (Issue : 2) (Volume : 3)
Vaccine Tracker/SMS Reminder System: Design and Implementation

Every era introduces different challenges to healthcare organizations, and the start of the twenty-first century... See more

Every era introduces different challenges to healthcare organizations, and the start of the twenty-first century has been no different. Today there is the unprecedented concentration on the quality of health services. A strong health-care system distributes quality services to all people, where and whenever they need it. Vaccination is one of the most important health-care services for the prevention and control of immuno-preventable diseases and, therefore it is essential to keep the vaccine card up-to-date and accessible to enable its benefits. This paper suggests a solution to remind the child’s parents to make an appointment for the next upcoming vaccination. The proposed system sends reminder short messages service (SMS) and an email message to remind the child's parents based on the vaccine schedule recommended in the Kurdistan region. The proposed system aimed at improving completion of the infant primary immunization by Automatic vaccine updates, reminders for free, and track vaccines for multiple babies from the health-care clinical center.

 2020-09
IOSR Journal of Computer Engineering (IOSR-JCE) (Volume : 22)
An Investigation for Mobile Malware Behavioral and Detection Techniques Based on Android Platform

Every era introduces different challenges to healthcare organizations, and the start of the twenty-first century... See more

Every era introduces different challenges to healthcare organizations, and the start of the twenty-first century has been no different. Today there is the unprecedented concentration on the quality of health services. A strong health-care system distributes quality services to all people, where and whenever they need it. Vaccination is one of the most important health-care services for the prevention and control of immuno-preventable diseases and, therefore it is essential to keep the vaccine card up-to-date and accessible to enable its benefits. This paper suggests a solution to remind the child’s parents to make an appointment for the next upcoming vaccination. The proposed system sends reminder short messages service (SMS) and an email message to remind the child's parents based on the vaccine schedule recommended in the Kurdistan region. The proposed system aimed at improving completion of the infant primary immunization by Automatic vaccine updates, reminders for free, and track vaccines for multiple babies from the health-care clinical center.

 2020-08
TEST (Issue : 2020) (Volume : 83)
A Tree Method for Managing Documents in Mongodb

A Tree Method for Managing Documents in Mongodb.

 2020-07
International Journal of Multidisciplinary Research and Publications (Issue : 2) (Volume : 3)
Client/Server Remote Control Administration System: Design and Implementation

Networks of computers are everywhere, and it implemented in all sectors, including an IT, industrial,... See more

Networks of computers are everywhere, and it implemented in all sectors, including an IT, industrial, managerial sector, and need to be accessed remotely. Remote administration offers access to a remote device. As more departments of Information Technology centralize and reorganize to keep costs down, many remote sites are left with no IT support on-site. Remote computer administration is increasingly common due to the major cost advantages; many activities can be completed, and not needs to be personally accessed by the administrator. In this paper, the remotecontrol administration system has designed and implemented based on client/ server architecture. The main goal of the proposed system allows clients to request help from remote servers over the Network.

 2020-07
IOSR Journal of Computer Engineering (Issue : 3) (Volume : 22)
Cloud Computing Resources Impacts on Heavy-Load Parallel Processing Approaches

One of the most important subject which many researchers depending on it by applying many... See more

One of the most important subject which many researchers depending on it by applying many algorithms and methods is Cloud Computing. Some of these methods were used to enhance performance, speed, and advantage of task level parallelism and some of these methods used to deal with big data and scheduling. Many others decrease the computation’s quantity in the process of implementation; specially decrease the space of the memory. Parallel data processing is one of the common applications of infrastructure, which is classified as a service in cloud computing. The purpose of this paper is to review parallel processing in cloud. However, the results and methods are inconsistent; therefore, the scheduling concepts give easy method to use the resources and process the data in parallel and decreasing the overall implementation time of processing algorithms. Overall, this review give us and open new doors for using the suitable technique in parallel data processing filed. As a result our work show according to many factors which strategies is better.

 2020-07

Conference

ICMAICS 2020
 2021-03
Two Feature Selection Methods Comparison Chi-square and Relief-F for Facial Expression Recognition

Feature selection method represents one of the main keys that has direct influence on classification accuracy. During the last two decades, researchers have given a lot of attention in feature selection approaches due to their... See more

Feature selection method represents one of the main keys that has direct influence on classification accuracy. During the last two decades, researchers have given a lot of attention in feature selection approaches due to their importance. This paper provides a comparative approach between the two feature selection methods: Chi-Square and Relief-F. The two methods rank the features according to their score. The first highest six emotion features from the both methods are selected. The six features are used to compare the accuracy ratio among the four classifiers: Support Vector Machine, K-Nearest, Decision Tree, and Radial Base Function. These classifiers are used for the mission of expression recognition and to compare their proportional performance. The ultimate aim of the provided approach is to use minimum number of features from the both methods in order to distinguish the performance accuracy of the four classifiers. The provided approach has been applied on CK+ facial expression recognition dataset. The result of the experiment illustrates that K-Nearest Neighbour is the most accurate classifier on the both feature selection methods according to the employed dataset. The K-Nearest Neighbour accuracy average rate for Chi-square is 94.18% and for Relief-F is 94.93%.

ICMAICS 2020
 2021-03
An Analytical Appraisal for Supervised Classifiers’ Performance on Facial Expression Recognition Based on Relief-F Feature Selection

Face expression recognition technology is one of the most recently developed fields in machine learning and has profoundly helped its users through forensic, security, and biometric applications. Many researchers and program developers have allocated their... See more

Face expression recognition technology is one of the most recently developed fields in machine learning and has profoundly helped its users through forensic, security, and biometric applications. Many researchers and program developers have allocated their time and energy to figure out various techniques which would add to the technology’s functionality and accuracy. Face expression recognition is a complicated computational process in which is implemented via analyzing changes in facial traits that follow different emotional reactions. This paper endeavors to inspect accuracy ratio of six classifiers based on Relief-F feature selection method, relying on the utilization of the minimum quantity of attributes. The classifiers in which the paper attempts to inspect are Multi-Layer Perceptron, Random Forest, Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Radial Basis Function. The experiment illustrates that K-Nearest Neighbor is the most accurate classifier with the total accuracy ratio of 94.93% amongst the rest when applied on CK+ Dataset.

ICOASE2019
 2019-05
Different Model for Hand Gesture Recognition with a Novel Line Feature Extraction

Abstract— Hand gestures are commonly used for communication between both impaired community and normal people. Sign languages stand for the human languages of deaf people. They form the most growing domain of research worldwide. A... See more

Abstract— Hand gestures are commonly used for communication between both impaired community and normal people. Sign languages stand for the human languages of deaf people. They form the most growing domain of research worldwide. A number of techniques were developed in this area lately. It is recognized by means of deducing the features involved in the use of hand gesture. As a matter of fact, various approaches, namely the vision-based, the data-glove-based, the colored-marker and the Electromyogram (EMG) approaches have been utilized by researchers to recognize the different hand gestures implemented in many different fields such as the whole approaches which can be divided into four main categories, viz. Data Collection, Image Processing, Feature Extraction, and Gesture Recognition. Only a few of those categories have been discussed in this paper to be compared between the accuracy rates by applying Artificial Neural Network (ANN) classification. This classification is based on different models and a novel method for Real-Time Hand Gesture Recognition System (RTHGRS). The latter has used one line (fifty features) extracted from black and white processed images to recognize the numbers from (1-10) in Kurdish Sign Language (KurdSL) using one hand only with accuracy 98%.

2018 International Conference on Advanced Science and Engineering (ICOASE)
 2018-10
Dynamic Hand Gesture Recognition System for Kurdish Sign Language Using Two Lines of Features

Hand gesture recognition forms a great difficulty for computer vision especially in dynamics. Sign language has been significant and an interesting application field of dynamic hand gesture recognition system. The recognition of human hands formed... See more

Hand gesture recognition forms a great difficulty for computer vision especially in dynamics. Sign language has been significant and an interesting application field of dynamic hand gesture recognition system. The recognition of human hands formed an- extremely complicated mission. The solution for such a difficulty requires a robust hand tracking method which depends on an effective feature and classifier. This paper presents a novel, fast and simple method for dynamic hand gesture recognition based on two lines (hundred) of features extracted from two rows of a Real-Time video. Feature selections have been used for hand shape representation to recognize the dynamic word for Kurdish Sign Language. The features extracted in real time from pre-processed hand object were represented through the optimization values of binary captured frame. Finally, an Artificial Neural Network classifier is used to …

International Conference of Reliable Information and Communication Technology
 2017-04
A Comparative Study of a New Hand Recognition Model Based on Line of Features and Other Techniques

Information technologies are developed and grown all over the world. They depend on the computer system. Some techniques such as hand recognition are used for performing accurate recognition. The main goal of this research is... See more

Information technologies are developed and grown all over the world. They depend on the computer system. Some techniques such as hand recognition are used for performing accurate recognition. The main goal of this research is to develop a system that analyzes specific human gestures then interpret this information by using computer system. This paper represents a comparative study between a new novel system called Real Time Hand Gesture Recognition System RTHGRS based on one line of features and other various techniques. The research has come out with 98% recognition compared to other researches in this filed.