Jun, 2015

Computer Science

University of Malaya

Jul, 2009

Computer Science

Nawroz University

Sep, 2021
Digital Image Processing:

Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Nowadays, image processing is among rapidly growing technologies. It forms core research area within engineering and computer science disciplines too.

MATLAB Programing:

MATLAB is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other language


Mathematics is an area of knowledge, which includes the study of such topics as numbers (arithmetic and number theory), formulas and related structures.

Microsoft Office:

Microsoft Office, or simply Office, is a family of client software, server software, and services developed by Microsoft.

Visual Studio:

Microsoft Visual Studio is an integrated development environment from Microsoft. It is used to develop computer programs, as well as websites, web apps, web services and mobile apps

C++ Language:

C++ is a powerful general-purpose programming language. It can be used to develop operating systems, browsers, games, and so on. C++ supports different ways of programming like procedural, object-oriented, functional, and so on. This makes C++ powerful as well as flexible.

JAVA Language:

Java is a high-level, class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible.

Image Classification:

Image classification is the process of categorizing and labeling groups of pixels or vectors within an image based on specific rules. The categorization law can be devised using one or more spectral or textural characteristics. Two general methods of classification are 'supervised' and 'unsupervised'.

Neural Network:

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.

Publication Journal
Oct, 2021
Real-Time Object Recognition Using Deep-Learning

Academic Journal of Nawroz University (Issue: 2) (Volume: 10)

The great interest at the moment is focused on the field of technology, especially artificial intelligence, also do not devoid of our daily life of the use of phone applications and computer programs, that increasing of phone and computer usage demands more programs and applications to satisfy the needs of users. However, this approach starts with acquirement an image of a particular medical or engineering tool, is displayed on the computer through a webcam, whether this image is a photo or digital through a display screen, or even the device itself, the computer will identify the tool and a simplified explanation of the way it works with a video demonstration throughout MATLAB IDE for implementing this project as well as easy to use by anyone even the user doesn’t have any experience software. Finally, this approach has been created this project to save time and effort for the users instead of searching on a specifictool that they need about its name, how to use so we tried to facility this matter. The proposed algorithm got accurate result, for the doctor’s tools the accuracy was 95.8 %, for the engineer’s tools was 98.3 % and for mix of them was approximately 94.1 %.

Nov, 2019
Combination of Local Binary Pattern and Face Geometric Features for Gender Classification from Face Images


In the recent time bioinformatics take wide field in image processing and computer vision. Gender classification is essentially the task of identifying the person gender based on the facial image. Currently the gender classification by facial images becomes very popular due to the current visual instruments. There are different algorithms of gender classification, and each algorithm has a different approach to extract the facial feature from the input image and perform the classification. However, the single type face feature cannot be enough to represent the detailed in facial images. In this paper, we propose a new approach which consists in combining the local binary patterns (LBP) and the face geometric features to classify gender from the face images. The Histogram equalization is used to adjust the contrast of the input image. For encoding the gray level pixel, the LBP is used as a binary quantization, then the face GLCMs are used to extract the geometric structure of the face image. For gender classification, the Support Vector Machine is used as the classifier. The face images from AT&T face dataset is used to perform the experiments. The experimental results show that the application of both LBP, and the GLCMs features improves the performance the classification of gender in face images.

Oct, 2019
Face Recognition based on Histogram Equalization and LBP algorithm

Academic Journal of Nawroz University (Issue: No 3 (2019)) (Volume: 8)

In the recent time bioinformatics take wide field in image processing. Face recognition which is basically the task of recognizing a person based on its facial image. It has become very popular in thelast two decades, mainly because of the new methods developed and the high quality of the current visual instruments. There are different types of face recognition algorithms, and each method has a different approach to extract the image features and perform the matching with the input image. In this paper the Local Binary Patterns (LBP) was used, which is a particular case of the Texture Spectrum model, and powerful feature for texture classification. The face recognition system consists of recognizing the faces acquisition from a given data base via two phases. The most useful and unique features of the face image are extracted in the feature extraction phase. In the classification the face image is compared with the images from the database.The proposed algorithm for face recognition in this paper adopt the LBP features encode local texture information with default values. Apply histogram equalization and Resize the image into 80x60, divide it to five blocks, then Save every LBP featureas a vector table.Matlab R2019a was used to build the face recognition system. The Results which obtained are accurate and they are 98.8% overall (500 face image).Keywords:local binary pattern (LBP), feature extraction, classification, pattern recognition, histogram, feature vector.

Sep, 2019
A comparative Study of Particle Swarm Optimization and Genetic Algorithm

Journal of Advanced Computer Science & Technology (Issue: 2 (2019)) (Volume: 8)

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms:Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.

Oct, 2017
Web Security: Detection Cross Scripting in PHP Web Application using Genetic Algorithm

International Journal of Advanced Computer Science and Application (Issue: No. 5, 2007) (Volume: 8)

Cross site scripting (XSS) is one of the major threats to the web application security, where the research is still underway for an effective and useful way to analyse the source code of web application and removes this threat. XSS occurs by injecting the malicious scripts into web applicationand it can lead to significant violations at the site or for the user. Several solutions have been recommended for their detection. However, their results do not appear to be effective enough to resolve the issue. This paper recommended a methodology for the detection of XSS from the PHP web applicationusing genetic algorithm (GA) and static analysis. The methodology enhances the earlier approaches of determining XSS vulnerability in the web application by eliminating the infeasible paths from the control flow graph (CFG). This aids in reducing the false positive rate in the outcomes. The results of the experiments indicated that our methodology is more effectual in detecting XSS vulnerability from the PHP web application compared to the earlier studies, in terms of the false positive rates and the concrete susceptible paths determined by GA Generator.

Aug, 2015
Human Computer Interface Using Hand Gesture Recognition Based On Neural Network


Gesture is one of the most vivid and dramatic way of communications between human and computer. Hence, there has been a growing interest to create easy-to-use interfaces by directly utilizing the natural communication and management skills of humans. This paper presents a hand gesture interface for controlling media player using neural network. The proposed algorithm recognizes a set of four specific hand gestures, namely: Play, Stop, Forward, and Reverse. Our algorithm is based on four phases, Image acquisition, Hand segmentation, Features extraction, and Classification. A frame from the webcam camera is captured, and then skin detection is used to segment skin regions from background pixels. A new image is created containing hand boundary. Hand shape features extraction, are used to describe the hand gesture. An artificial neural network has been utilized as a gesture classifier, as well. 120 gesture images have been used for training. The obtained average classification rate is 95%. The proposed algorithm develops an alternative input device to control the media player, and also offers different gesture commands and can be useful in real-time applications. Comparisons with other hand gesture recognition systems have revealed that our system shows better performance in terms accuracy.