Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Pattern Recognition

Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation

Ahmed M. Abed; Sabah A. Gitaffa; Abbas H. Issa

Engineering and Technology Journal, 2021, Volume 39, Issue 5A, Pages 859-869
DOI: 10.30684/etj.v39i5A.2002

Fault detection plays a serious role in high-cost and safety-critical processes. There are two main drivers for continuous improvement in the area of early detection of process faults safety and reliability of technical plants. Detect fault in Geophone string sensors (SG-10) are very important in oil exploration to avoid loss economy. Methods are developed to enable earlier detection of process faults than the traditional limit and trend checking based on a single process variable and the development of these methods is a key matter. Classification methods will be used for pattern recognition and as such is appropriate for fault detection. In supervised training input-output pairs, both for normal and fault conditions, are presented to the network. The models were trained on the free fault and fault sensors. Then the Quadratic Support Vector Machine (QSVM) and k-Nearest Neighbor (KNN) as the classifiers are used. The test results for measuring the performance of 1232 sample classifiers from data show that the accuracy of fault-free sensor recognition is 97.4 % and 100% consecutively for these classifiers.

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.

Modified Multi-Category Digital Learning Networks for Red Blood Cell Inspection

Mahmuod Hamza AL-Muifraje; Suhad Q.G.H.Haddad

Engineering and Technology Journal, 2015, Volume 33, Issue 6, Pages 1284-1298

This paper reports research conducted into classification of red blood cells using multi-category digital learning networks. It is an effective solution for providing healthcare with reduced cost, especially for the rural and far away patients. Digital learning network offer an alternative approach to neural network design. It often referred as( RAM-Based Architectures) , or ( Weightless Neural Networks), since their neurons can be implemented by RAM node that usually input and output binary values with no weight between nodes. The system presented in this paper fulfills the requirements of simplicity and efficiency making it attractive to practical use in present day for industrial and medical environments. Many parameters have been investigated in detail which affects the recognition rate. These parameters are presented to allow the system to be optimized, giving an increase in the performance of the system. Modification method of Feedback Digital Learning Network, which is an improving process of Digital Learning Network, has been implemented. The obtained results showed that high performance can be achieved (96.6% as correct, 2.2% as reject, and 1.1% as error), providing evidence of the validity of the proposed technique.

GUI Simulation for Movement of Human Arm Driven by EMG Signal

Abbas Hussien Miry; Abduladhem A.Ali; Mohammed Zeki Al-Faiz

Engineering and Technology Journal, 2011, Volume 29, Issue 8, Pages 1597-1609

This work presents a simulation methodology applied to a human arm. It is
aimed to allow the human-assisting manipulators to perform complex movement
based on electromyography (EMG) signal for patient person in Virtual Reality
(VR). This work achieves better classification with multiple parameters based KNearest
Neighbor for different movements of a prosthetic arm. A K- Nearest
Neighbor (K-NN) rule is one of the simplest and the most important methods in
pattern recognition. The method implements in the 3D space and uses the
MATLAB Ver.2009a approach. This methodology can be used with different
robots to test the behavior of system and the different motion

One Parameter Composite Semigroups of Linear Bounded Operators in Strong Operator Topology of Schatten Class Cp

Samir Kasim Hassan; Al-Taie M; Al-Malki Anam; Al-Attar Abeer; Mustafa Khaleel Ismael; Fatema Ahmed Sadeq; Radhi A .Zboon; Jehad R.Kider; Samir K .Hassan; Hussain J. M. Alalkawi; Raad H. Majid; Rawaa A. Alomairy; Luma Abdul Ghani Zghair; Hadia Kadhim J.Al-Ogili; Assifa M. Mohamad; Abbas Sheyaa Alwan; Haider L. Aneed; Assim H Yousif; Salema Sultan Salman; Abbas Hussien Miry; Abduladhem A.Ali; Mohammed Zeki Al-Faiz; Sabah N. mahmood; Khansaa Dawood Selman; Shaymaa Tareq Kadhim

Engineering and Technology Journal, 2011, Volume 29, Issue 8, Pages 1463-1470

For semigroups of linear bounded operators on Hilbert spaces, the problem of
being in Cp , 0 Keywords