International Journal of Academic and Applied Research (IJAAR) ISSN: 2000-005X Vol. 3 Issue 1, January – 2019, Pages: 1-4 http://www.ijeais.org/ijaar 1 ANN for Predicting Overall Car Performance Osama M. Al-Mubayyed Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University Gaza, Palestine Abstract: In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.62 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas. Keywords: Artificial neural networks, educational data mining, performance. 1. INTRODUCTION This study evaluates the utilization of neural networks for predicting suitability of a car. It justifies the use of neural networks in this industry for the prediction process. Generally, car manufacturing industries include design, development, manufacturing, marketing and sale of different equipment for motor vehicles. The set of companies and factories involved in design, manufacturing, marketing, and sale of motor vehicles are a part of this industry. In 2009, more than 80 million motor vehicles including regular cars and commercial cars were manufactured around the world. In 2008, a total number of 81.9 million cars were sold in the world, with 24.9 million sold in Europe, 23.4 million sold in Asia and Pacific Region, 21.4 million sold in the US and Canada, 5.4 million sold in Latin America, 3.4 million sold in the Middle East, and 2.4 million sold in Africa[1]. When the market was suffering a recession in the US and Japan, Asia and South America expressively grew and got sturdier. Furthermore, it appears that big markets in Russia, Brazil, India and China have practiced a rapid growth. The car industry, as one of the biggest industries in the world holding a great amount of persons, financial and time resources, is in grim need of precise predictions of its future and its contestants in order to reach great and sensitive decisions [2]. Maybe one of the major concerns of the managers and manufacturers in the car industry and the investors in this field is the prediction of cars sales and arrangement for the future manufacturing volume. If a manager can have a more precise prediction regarding the future sales volume and car demand, they can unconditionally enhance the investment volume, employee workforce and optimally use time to reach optimal decisions and convey out instruction plans [3]. 2. ARTIFICIAL NEURAL NETWORKS Artificial intelligence has started from the very beginning, how can we simulate that mind, how can devices have the ability to discriminate, how can we make that device that distinguishes between water and fire? These devices, despite their high speed and ability to deal with millions of data in parts of the second could not do a lot of things done by the human, for example I bring a small child in the tenth, and offer him a set of images, all this simple child will know the image of the cat, tree and other images. This process is simple may be very complex too for the device and even Super-Computer too![4-10] From here scientists began to question and research, how this person can do these things, and here the mind is the answer, i.e. trying to imitate the mind and simulating it. But this word of mind despite its simplicity that the human mind is very complex, so scientists had to search more and try to find the things that make up the mind, and from here was the beginning to study the nerve cell, which is the simplest model exists in mind, and the collection of these cells to make a network of cells, and from here we began studying neural networks (note that we are now talking about neural networks within the brain [11-16]. Artificial neural networks (ANNs) are parallel computational models comprised of densely interconnected, adaptive processing units, characterized by an inherent propensity for learning from experience and also discovering new knowledge [17-25]. Due to their excellent capability of self-learning and self-adapting, they have been extensively studied and have been successfully utilized to tackle difficult real-world problems and are often found to be more efficient and more accurate than other classification techniques [26-35]. Classification with a neural network takes place in two distinct phases. First, the network is trained on a set of International Journal of Academic and Applied Research (IJAAR) ISSN: 2000-005X Vol. 3 Issue 1, January – 2019, Pages: 1-4 http://www.ijeais.org/ijaar 2 paired data to determine the input-output mapping. The weights of the connections between neurons are then fixed and the network is used to determine the classifications of a new set of data. Although many different models of ANNs have been proposed [36-45], the Feedforward Neural Networks (FNNs) are the most common and widely used in a variety of applications [46-57]. 3. METHODOLOGY By looking deeply through the cars types and soliciting the experience of human experts on cars performance, a number of factors that are considered to have an effect on customer response were outlined. These factors were cautiously studied and synchronized into a convenient number appropriate for computer coding within the Just Neural Network (JNN) environment. These factors were classified as input variables as shown in table 1. The output variables embody some likely levels of performance of cars in terms of Excel file with a study summary performance of cars as shown in table 2. There are 1728 instances in the car data set. The data set then was divided into training set and validation set. The ANN model was trained with data set and validated with the validation data set. Furthermore, the Importance of the factors was determined and the most influential factor was known. Table 1: Input Data Transformation Attributes: Usage: Type: Values: Buying Input Categorical v-high, high, med, low Maint Input Categorical v-high, high, med, low Doors Input Categorical 2, 3, 4, 5-more Persons Input Categorical 2, 4, more Lug_boot Input Categorical small, med, big Safety Input Categorical low, med, high The output Variable: The attribute which represent the output variable is called overall which can be Un-acceptable, acceptable, good, or very good. Table2: shows the output variable Attributes Usage Type Values Overall Output Categorical unacc, acc, good, vgood Figure 1: A snap shot of the data in JNN environment Figure 2: Most influencing factories on ANN model International Journal of Academic and Applied Research (IJAAR) ISSN: 2000-005X Vol. 3 Issue 1, January – 2019, Pages: 1-4 http://www.ijeais.org/ijaar 3 Figure 3: Final ANN model Architecture Figure 4: Training and validation Summary 4. CONCLUSIONS An artificial Neural Network model for predicating overall performance of the car was presented. The model used feed forward backpropagation algorithm for training. The factors for the model were obtained from expert in the field. This study showed the potential of the artificial neural network for predicating overall performance of the car. International Journal of Academic and Applied Research (IJAAR) ISSN: 2000-005X Vol. 3 Issue 1, January – 2019, Pages: 1-4 http://www.ijeais.org/ijaar 4 References 1. Abu-Naser, S. S. (2012). "Predicting learners performance using artificial neural networks in linear programming intelligent tutoring system." International Journal of Artif icial 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). 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