International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 3 Issue 2, February – 2019, Pages: 15-19 www.ijeais.org/ijaer 15 ANN for Predicting the Effect of Oxygen Consumption of Thylakoid Membranes (Chloroplasts) from Spinach after Inhibition Mohammed Al-Shawwa Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University Gaza, Palestine 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 birth weight with 100% accuracy. Keywords: Artificial Neural Networks, oxygen consumption, ANN, Predictive Model. 1. INTRODUCTION The main objective predicts the effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition, to know the amount of oxygen consumption to protect the spinach plant from some diseases. This study seeks to explore the possibility of using the artificial neural network model to predict the effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition, at the lowest possible time and high accuracy in the results. Of course one would expect the effect of oxygen consumption after inhibition to be associated with several influential factors as mentioned earlier. On the other hand it is clear that it will be very difficult to find a mathematical model that may be an appropriate model for this relationship between performance/factors. However, one realistic method to prediction of the oxygen consumption after inhibition may be to study data on the background of the some factors. The practical approach to this type of problem is to apply a regression analysis in which data is better integrated into some functions. The result is an equation in which both input xj is multiplied by wj; the sum of all these products is constant,, and then an output of y = Σ wj xj +, is given, where j = 0.n. The problem here is that it is difficult to choose a suitable function to capture all data collection as well as automatically adjust the output in the case of more information, because prediction is controlled by a number of factors, and this control will not be any clear and known regression model. The artificial neural network, which simulates the human brain in solving a problem, is a more common approach that can address this type of problem. Thus, attempting to develop an adaptive system such as artificial neural network to predict the temperature based on the results of these factors [1]. The objectives of this study are:  To identify some appropriate factors that affects the oxygen consumption.  To convert these factors into appropriate models for adaptive system coding.  Designing an artificial neural network that can be used to predict effect oxygen consumption based on some predefined data. 2. THE ARTIFICIAL NEURAL NETWORKS An Artificial Neural Network (ANN) is an application of Artificial Intelligence [4-35]. ANN is an arithmetical model that is motivated by the organization and/or functional feature of biological neural networks. A neural network contains an interrelated set of artificial neurons, and it processes information using a connectionist form to computation. As a general rule an ANN is an International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 3 Issue 2, February – 2019, Pages: 15-19 www.ijeais.org/ijaer 16 adaptive system that adjusts its structure based on external or internal information that runs through the network during the learning process. Recent neural networks are non-linear numerical data modeling tools. They are usually used to model intricate relationships among inputs and outputs or to uncover patterns in data. ANN has been applied in numerous applications with considerable attainment [4-5]. For example, ANN has been effectively applied in the area of prediction, handwritten character recognition, evaluating prices of lodging [6-7]. Neurons are often grouped into layers. Layers are groups of neurons that perform similar functions. There are three types of layers. The input layer is the layer of neurons that receive input from the user program. The layer of neurons that send data to the user program is the output layer. Between the input layer and output layer are hidden layers. Hidden layer neurons are only connected only to other neurons and never directly interact with the user program. The input and output layers are not just there as interface points. Every neuron in a neural network has the opportunity to affect processing. Processing can occur at any layer in the neural network. Not every neural network has this many layers. The hidden layer is optional. The input and output layers are required, but it is possible to have on layer act as both an input and output layer [7]. ANN learning can be either supervised or unsupervised. Supervised training is accomplished by giving the neural network a set of sample data along with the anticipated outputs from each of these samples. Supervised training is the most common form of neural network training. As supervised training proceeds the neural network is taken through several iterations, or epochs, until the actual output of the neural network matches the anticipated output, with a reasonably small error. Each epoch is one pass through the training samples. Unsupervised training is similar to supervised training except that no anticipated outputs are provided. Unsupervised training usually occurs when the neural network is to classify the inputs into several groups. The training progresses through many epochs, just as in supervised training. As training progresses the classification groups are "discovered" by the neural network [6]. Training is the process by which these connection weights are assigned. Most training algorithms begin by assigning random numbers to the weight matrix. Then the validity of the neural network is examined. Next the weights are adjusted based on how valid the neural network performed. This process is repeated until the validation error is within an acceptable limit [5]. Validation of the system is done once a neural network has been trained and it must be evaluated to see if it is ready for actual use. This final step is important so that it can be determined if additional training is required. To correctly validate a neural network validation data must be set aside that is completely separate from the training data [7]. About 60% of the total sample data was used for network training in this paper. About 30% of the total sample data served as test and the remaining 10% used for validation of the system. 3. METHODOLOGY By looking deeply through literature and soliciting the experience of human experts in agriculture a number of factors have been identified that have an impact on of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition. These factors were carefully studied and synchronized in an appropriate number to encode the computer in the ANN environment. These factors were classified as input variables. Configuration variables reflect some possible levels of know effect of oxygen consumption from chloroplasts from spinach by values and factors. 3.1 The Input Variable The input variables specified are those that can be obtained simply from the farms. Input variables are: Table 1: Attributes of the Data set No. Attributes 1. curve 2. herbicide 3. dose International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 3 Issue 2, February – 2019, Pages: 15-19 www.ijeais.org/ijaer 17 3.2 The Output Variable The output variable represents the performance of the farms. The output variable depends on the input. Table 2: Output variables S/N Attributes 1. slope 3.3 Design of the Neural Networks Figure 1: Shows the Design of the Neural Networks International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 3 Issue 2, February – 2019, Pages: 15-19 www.ijeais.org/ijaer 18 Figure 2: Shows the Training, error, and validation of the data set. 3.4 The Back-propagation Training Algorithm o Initialize each wi to some small random value o Until the termination condition is met, Do o For each training example <(x1,...xn),t> Do o Input the instance (x1,...,xn) to the network and compute the network outputs ok o For each output unit k: k=ok(1-ok)(tk-ok) o For each hidden unit h: h=oh(1-oh) k wh,k k o For each network weight wj Do wi,j=wi,j+wi,j,where wi,j= j xi,j and is the learning rate. 4. EVALUATION OF THE NEURAL NETWORK As mentioned previously, the purpose of this experiment was to predict the effect of oxygen consumption of chloroplasts from spinach after inhibition. Where we used data, which provides the possibility to implement and test the neural network and its learning algorithm. Our neural network is a sensor expression designed to detect the presence of one of two sets of materials. Alternatively, human reading may be wrong. After training and validation, the network was tested using the test data set and the following results were obtained. This involves inputting variable input data into the grid without output variable results. The output from the grid is then compared with the actual variable data. The neural network was able to accurately forecast 100% of the excellent data (representing 4 inputs and based on the inputs.) We have outputs 100% is correct. 5. CONCLUSION The artificial neural network model was presented to predict the effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition based on specific inputs. The model was tested and the total score was 100%. Thus, the potential of the artificial neural network to predict the effect of oxygen consumption of chloroplasts from spinach after inhibition was reviewed. International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 3 Issue 2, February – 2019, Pages: 15-19 www.ijeais.org/ijaer 19 References 1. Abu-Naser, S. S. (2012). "Predicting learners performance using artificial neural networks in linear programming intelligent tutoring system." International Journal of Artificial Intelligence & Applications 3(2): 65. 2. Abu-Nasser, B. S. and S. S. Abu Naser (2018). "Rule-Based System for Watermelon Diseases and Treatment." International Journal of Academic Information Systems Research (IJAISR) 2(7): 1-7. 3. Abu-Nasser, B. S. and S. S. Abu-Naser (2018). "Cognitive System for Helping Farmers in Diagnosing Watermelon Diseases." International Journal of Academic Information Systems Research (IJAISR) 2(7): 17. 4. Abu-Saqer, M. M. and S. S. Abu-Naser (2019). 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