Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. The (...) evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (shrink)

Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition. A number of factors were identified that may affect of oxygen consumption of thylakoid membranes from spinach. Factors such as curve, herbicide, dose, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some inhibition of photosynthesis in farms. (...) The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition with 100% accuracy. (shrink)

Objective: Patients' satisfaction is one of the most important goals in complete denture therapy, and there are many factors influencing this parameter. This study aimed to determine patient satisfaction with conventional removable complete denture made by clinical year students at the Faculty of Dentistry, Al Azhar University - Gaza. Methods: A sample of 85 patient who had conventional removable complete denture made by clinical year students at the Faculty of Dentistry Al Azhar University - Gaza filled a questionnaire two months (...) after denture insertion. The questionnaire involving four sections including personal information, history, denture evaluation in aspects of mastication, speech, aesthetics and retention, and patient comfort. Results: Of the 85 patient studied, 81% were males, and the mean age was 60.3 years SD+-9.64. The majority (n= 67, 79 %) lost their teeth because of bad oral hygiene. The overall satisfaction level was (92.1%), and patients were more satisfied with their upper denture. A significant association was found between the patient satisfaction and prior denture use experience (p=0.013). No significant correlation was established between the patient satisfaction and their gender (p=0.188), age group (p=0.640) and employment status (p=0.667).Conclusion: The patients have shown a high level of overall satisfaction. A significant association was found between the overall satisfaction level of patients and prior denture use experience. No statistically significant association was observed between the overall satisfaction level of patients and with each of age group, employment status and gender. (shrink)

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying (...) PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%. (shrink)

In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. The evaluation (...) of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (shrink)