Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Feature extraction


A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

Samaa S. Abdulwahab; Hussain Kareem Khleaf; Manal H. Jasim

Engineering and Technology Journal, 2021, Volume 39, Issue 7, Pages 1117-1132
DOI: 10.30684/etj.v39i7.1854

A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals. In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body. This is useful for individuals with mobility issues. Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands. These systems have developed very accurate translation systems. This paper is about the different methods for adapting the signals from the brain. It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research. This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification. Finally,  discussed is the application of EEG-based BCI.

A New Method Using Naive Bayes And RGBD Facial Identification Based on Extracted Features from Image Pixels

Wisam H. Ali

Engineering and Technology Journal, 2021, Volume 39, Issue 4A, Pages 632-641
DOI: 10.30684/etj.v39i4A.1936

Nowadays, life seems to have been resilient, particularly for those with physical disabilities. Recognition of AV letters is one of the critical and famously the difficult structures. This research has been developed based on the potential of the features in some applications than the statistical properties. While, these features have been resolved the lip movement for AV letters recognition, Naive Bayesian and Red green blue and depth RGBD have been adopted for visual letter identification. Naive Bayesian has 73.33% for usual recognition with three letters, each with ten frames, while RGBD classifier is 100%. Within that for this case, two scenarios were made with different forms of noise placed on the face of normal, normal + 10%, normal + 25% and normal + 75% noise. The first one trains and understands all classes, one after another. While the other is training 95 percent of RGBD and 83.3 percent for Naive Bayesian with recognition of one of the inflicted forms. RGBD identification is 100 percent for the second one, while 49.99 for the Naive Bayesian.

Eye Diseases Classification Using Back Propagation Artificial Neural Network

Hanaa M. Ahmed; Shrooq R. Hameed

Engineering and Technology Journal, 2021, Volume 39, Issue 1B, Pages 11-20
DOI: 10.30684/etj.v39i1B.1363

A human eye is a vital organ responsible for a person's vision. So, the early detection of eye diseases is essential. The objective of this paper deals with diagnosing of seven different external eye diseases that can be recognized by a human eye. These diseases cause problems either in eye pupil, in sclera of eye or in both or in eyelid. Color histogram and texture features extraction techniques with classification technique are used to achieve the goal of diagnosing external eye diseases. Hue Min Max Diff (HMMD) color space is used to extract color histogram and texture features which were fed to Back Propagation Artificial Neural Network (BPANN) for classification. The comparative study states that the features extracted from HMMD color space is better than other features like Histogram of Oriented Gradient (HOG) features and give the same accuracy as features extracted directly from medical expert recorded symptoms. The proposed method is applied on external eye diseases data set consisting of 416 images with an accuracy rate of 85.26315%, which is the major result that was achieved in this study.

Extract the Similar Images Using the Grey Level Co-Occurrence Matrix and the Hu Invariants Moments

Beshaier A. Abdulla; Yossra H. Ali; Nuha J. Ibrahim

Engineering and Technology Journal, 2020, Volume 38, Issue 5, Pages 719-727
DOI: 10.30684/etj.v38i5A.519

In the last years, many types of research have introduced different methods and techniques for a correct and reliable image retrieval system. The goal of this paper is a comparison study between two different methods which are the Grey level co-occurrence matrix and the Hu invariants moments, and this study is done by building up an image retrieval system employing each method separately and comparing between the results. The Euclidian distance measure is used to compute the similarity between the query image and database images. Both systems are evaluated according to the measures that are used in detection, description, and matching fields which are precision, recall, and accuracy, and addition to that mean square error (MSE) and structural similarity index (SSIM) is used. And as it shows from the results the Grey level co-occurrence matrix (GLCM) had outstanding and better results from the Hu invariants moment method.

Lung Cancer Detection from X-ray images by combined Backpropagation Neural Network and PCA

Israa S. Abed

Engineering and Technology Journal, 2019, Volume 37, Issue 5A, Pages 166-171
DOI: 10.30684/etj.37.5A.3

The lungs are portion of a complex unit, enlarging and relaxing numerus times every day to supply oxygen and exude CO2. Lung disease might occur from troubles in any part of it. Carcinoma often called Cancer is the generally rising and it is the most harmful disease happened in humankind. Carcinoma occurs because of uncontrolled growth of malignant cells inside the tissues of the lungs. Earlier diagnosis of cancer can help save large numbers of lives, while any delay or fail in detection may cause additional serious problems leading to sudden fatal death. The objective of this study is to design an automated system with an ability to improve the detection process in order to perform advanced recognition of the disease. The diagnosis techniques include: X-rays, MRI, CT images etc. X-ray is the common and low-cost technique that is widely used and it is relatively available for everyone. Rather than new techniques like CT and MRI, X-ray is human dependable, meaning it needs a Doctor and X-ray specialist in order to determine lung cases, so developing a system which can enhance and aid in diagnosis, can help specialist to determine cases in easily.

Proposed Image Similarity Metric with Multi Block Histogram used in Video Tracking

Alia K. Abdul Hassan; Hasanen S. Abdullah; Akbas E. Ali

Engineering and Technology Journal, 2016, Volume 34, Issue 4, Pages 578-584

One of the important requirements in the object detection and tracking is the extracting of efficient features to trackthe target in video sequence. The feature of colour in image is one of the most visual features widely used. The using ofcolour histogram is the most popular method for representing color feature. One of the problems of using colour histogram to represent feature is its lack of spatial information where it is used to represents statistical distribution of the coloursonly. In this paper a new similarity metric with multi block colour histogram of image is proposed. This metric will be used by an object tracking method where the similarity will be applied to get a decision of choosing the correct solution (location) of the object from many candidate locations

Texture Analysis of Brodatz Images Using Statistical Methods

Alyaa Hussain Ali; Alaa Noori Mazher

Engineering and Technology Journal, 2011, Volume 29, Issue 4, Pages 716-724

Textures are one of the important features in computer vision for many
applications. Most of attention has been focused on the texture features. An
important approach to region description is to quantify its texture content.
Although no formal definition of texture exists, intuitively this descriptor provides
measures of properties such as smoothing and regularity. The principal approaches
used in image processing to describe the texture of an image region are statistical,
structural, and spectral. In this paper the features were constructed using different
statistical methods. These are auto-correlation, edge frequency, primitive-length
and law’s method; all these methods were used for texture analysis of Brodatz
images. The result showed that the law’s autocorrelation method yields the best
result.