Israa N. Mahmood; Hasanen Abdullah
Abstract
Lung cancer is one of the most fatal cancers in the world for both genders. It has a high mortality rate compared to other types of cancer. Early detection can save lives and enhance the treatment process. As a result, the demand for approaches to detect cancer at an early stage is growing. In this paper, ...
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Lung cancer is one of the most fatal cancers in the world for both genders. It has a high mortality rate compared to other types of cancer. Early detection can save lives and enhance the treatment process. As a result, the demand for approaches to detect cancer at an early stage is growing. In this paper, an Artificial Neural Network (ANN) model is developed to identify the level of having lung cancer based on environmental, diagnostic, and statistical factors. The features that highly affect the risk level of lung cancer were identified. The model's performance was assessed using a variety of criteria, including accuracy, precision, recall, and f-measure. Experimental results show that the model attains a high accuracy rate of 91.79% and risk factors like obesity, alcohol use, genetic risk, and coughing of blood can lead to lung cancer.
Yasmin Abdul Ghani Abdul Kareem; Dr. Ahmed Khalaf Hamoudi; Ahmad Saeed Mohammad
Volume 13, Issue 2 , August 2013, , Page 19-25
Abstract
Abstract –A fast, simple and effective method to recognize different hand writing
numbers is presented. Hand writing recognition took high attention in the recent years
by researcher of the intelligent systems, since it can be used in many applications such
as car plate recognition and bank account ...
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Abstract –A fast, simple and effective method to recognize different hand writing
numbers is presented. Hand writing recognition took high attention in the recent years
by researcher of the intelligent systems, since it can be used in many applications such
as car plate recognition and bank account checking. The purpose of this paper is to
develop a method for hand writing numbers detection by using artificial neural network.
The suggested work is divided mainly into four stages and is proposed to resolve the
digits number (i.e., hand writing numbers). Image sample of hand writing numbers is
acquired by a digital camera or scanner, and then it is converted by using the suggested
work which is consisted of four stages to resolve digits. Artificial neural network
(ANN) was applied to recognize the hand writing numbers. Learning method of the
ANN is back propagation and all process handled by MATLAB language.
Dr. Yousif I. Al-Mashhadany
Volume 11, Issue 2 , December 2011, , Page 44-55
Abstract
Abstract:
Surface electromyography (SEMG) measurement technique for the signal was produced through the contraction of muscles in a human body. The surface electrode is connected on the skin of the muscle. This paper presents an off-line design for the estimation of the actual joint angle of a human ...
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Abstract:
Surface electromyography (SEMG) measurement technique for the signal was produced through the contraction of muscles in a human body. The surface electrode is connected on the skin of the muscle. This paper presents an off-line design for the estimation of the actual joint angle of a human leg obtained in performing flexion-extension of the leg at slow and high speeds movement. The design is composed of two phases. The first is measurement of real EMG signal of human leg performance by using SEMG technique and processing this signal with filtering, amplification and then normalized with maximum amplitude. The second phase is to design an artificial neural network (ANN) and train it to predict the joint angle from the parameters extracted from the SEMG signal. Three main parameters of EMG signal are used in the prediction process: Number of turns in a specific time period, duration of signal repetition and amplitude of signal. The design of ANN includes the identification of a performing human leg EMG signal with two speed levels (slow-fast) and estimation of knee joint angle by recognition process depending on the parameters of real measured EMG signal. The real EMG signal is measured from full leg-extension to full leg-flexion by (3 sec) with slow motion and (1 sec) at fast motion.
Root mean square (RMS) errors were calculated between the actual angle (measured by the trigonometric formula was applied with any human leg gives real EMG signal measurement) and the angle predicted by the neural network design. This design is simulated by using MATLAB Ver. R2010a, and satisfying results are obtained. That explains the ability of estimation of joint angle for human leg, where the RMS errors are obtained from (0.065) to (0.015) at fast speed leg flexion -extension and from (0.018) to (0.0026) at slow speed leg flexion-extension.
