Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : support vector machine

Pattern Recognition of Composite Motions based on EMG Signal via Machine Learning

Noof T. Mahmooda; Mahmuod H. Al-Muifraje; Sameer K. Salih; Thamir R. Saeed

Engineering and Technology Journal, 2021, Volume 39, Issue 2A, Pages 295-305
DOI: 10.30684/etj.v39i2A.1743

In the past few years, physical therapy plays a crucial role during rehabilitation. Numerous efforts are made to demonstrate the effectiveness of medical/ clinical and human-machine interface (HMI) applications. One of the most common control methods is using electromyography (EMG) signals generated by muscle contractions to implement the prosthetic human body parts. This paper presents an EMG signal classification system based on the EMG signal. The data is collected from biceps and triceps muscles for six different motions, i.e., bowing, clapping, handshaking, hugging, jumping, and running using a Myo armband with eight electromyography sensors. The Root Mean Square, Difference Absolute Standard Deviation Value, and Principle Component Analysis are used to extract the raw signal data and enhance classification accuracy. The machine learning method is applied, i.e., Support Vector Machine and K-Nearest Neighbors are used for classification; the results show that the K-Nearest Neighbors method achieves a higher accuracy percentage than the SVM. Making high training accuracy for different physical actions helps implement human prosthetic parts to help the people who suffer from an amputee.

Mapping LCLU Using Python Scripting

Oday Z. Jasim; Khalid I. Hasoon; Noor E. Sadiqe

Engineering and Technology Journal, 2019, Volume 37, Issue 4A, Pages 140-147
DOI: 10.30684/etj.37.4A.5

Land cover land use changes constantly with the time at local, regional, and global scales, therefore, remote sensing provides wide, and broad information for quantifying the location, extent, and variability of change; the reason and processes of change; and the responses to and consequences of change. And considering to the importance of mapping of (LCLU). For that reason this study will focus on the problems arising from the traditional classification (LCLU) that based on spatial resolution only which leads to prediction a thematic map with noisy classes, and using a new method that depend on spectral and spatial resolution to produce an acceptable classification and producing a thematic map with an acceptable database by using artificial neural network (ANN) and python in additional to other program. In this study the methods of classification were studied through using two images for the same study area , rapid eye image which has three spectral bands with high spatial resolution(5m) and Landsat 8 image (high spectral resolution with eight bands), also several programs like ENVI version 5.1, Arc GIS version 10.3, Python 3, and GPS. The result for this research was sensuousness as geometrics accuracy accepted in map production.

Classification of Gender Face Image Based on Slantlet Transform

Nidaa Flaih Hassan; Reem Majeed Ibrahim

Engineering and Technology Journal, 2016, Volume 34, Issue 4, Pages 566-577

Image Face classification has been an effective research area over last two or three decades and it is considered as a challenging research topic. In this paper a new classification algorithm is proposed for gender classification based on face image.The proposed algorithm consists of two phases: training and testing phases.In the training phase five steps are implemented to classify gender images; at first step the face in a digital image is segmented so as to eliminate the undesirablebackground, the redundancy and suppression of noise is reduced using Slantlet Transform in step two. From transformed face images,Eigen faces feature is extracted using Principle Component Analysis (PCA). In step three to reduce the number of dimensions without losing information (Eigen value is used as a vector of features), in the final step decision whether the face image is male or female is done by applying Support Vector Machine (SVM).
The experimental outcome indicate that the SVM classifier achieves precision of 89% whenthe classification process using Wavelet 'Transform, and 93 % with Slantlet' Transform for the same number of the test-set.

Improvement of Face Recognition System Based on Linear Discrimination Analysis and Support Vector Machine

Thair A. Salh; Mustafa Zuhaer Nayef

Engineering and Technology Journal, 2013, Volume 31, Issue 12, Pages 2261-2272

Face recognition is one of the most important research fields in many of applications
and it is used in various domains including human computer interaction, security
systems and personal identification. Many of face recognition systems have been
developed for decades. In general, the accuracy of the face recognition system is
determined by the accuracy of the method that is used to extract features and the
accurate of the classification method. This paper introduces an improvement of face
recognition system by using Linear Discrimination Analysis and Support Vector
Machine. Two types of experiments off-line and on-line are done. In off-line
experiment, the Olivetti Research Laboratory face database is used and in on-line
experiment, DVD Maker 2 adapter is used to capture live image from digital camera,
and digitalize it to be compared with training database. The Comparison with Linear
Discriminate Analysis and Artificial Neural Network is implemented .The results show
that the proposed method gives better results in off-line experiment than previous
methods in terms of recognition rate.

On the Use of Supervised Learning Method for Authorship Attribution

Walaa M. Khalaf

Engineering and Technology Journal, 2012, Volume 30, Issue 2, Pages 282-292

In this paper we investigate the use of a supervised learning method for the
authorship attribution that is for the identification of the author of a text. We
suggest a new, simple and efficient method, which is merely based on counting the
number of repetitions of each alphabetic letter in the text, instead of using the
traditional classification properties; such as the contents of the text and style of the
author; which falls into four feature categories: lexical, syntactic, structural, and
content-specific. Furthermore, we apply a spherical classification method.
We apply the proposed technique to the work of two Italian writers, Dante
Alighieri and Brunetto Latini. With almost high reliability, the spherical classifier
proved its ability to discriminate between the selected authors.
Finally the results are compared with those obtained by means of a standard
Support Vector Machine classifier.