International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 3 Issue 2, February – 2019, Pages: 21-25 www.ijeais.org/ijaer 21 ANN for Predicting Movies Rates Category Ibrahim M. Nasser, Mohammed O. Al-Shawwa Department of Information Technology, Faculty of Engineering and Information Technology Al-Azhar UniversityGaza, Palestine Azhar.ibrahimn@gmail.com mohammed.o.alshawwa@gmail.com Abstract: We proposed an Artificial Neural Network (ANN) in this paper for predicting the rate category of movies. A dataset used obtained from UCI repository created for research purposes. Our ANN prediction model was developed and validated; validation results showed that the ANN model is able to 92.19% accurately predict the category of movies' rate. Keywords: Data Mining, Classification, Predictive Analysis, Artificial Neural Networks, movies classification 1. INTRODUCTION Artificial neural networks (ANNs) are, similar to our neural networks and offer a relatively good technique, which solves the problem of classification and prediction. ANN is a collection of mathematical models, which can simulate characteristics of biological neural systems and have likenesses with adaptive human learning. ANNs made of connecting processing elements called neurons, connected by links, which contain weight coefficients that are, playing the role of synapses in our neural system. The neurons often come in three layers: input layer, one or more hidden layers and output layer, (ANN Architecture is shown in figure (1)). ANNs handle data as biological neural networks, in addition, ANN has the possibility of recalling, learning and eliminating errors, and high speed of getting the solution, [1] because of that, the neural networks can be used for solving complex problems, like classification and prediction [2]. ANNs were effectively applied in variety of applications for solving difficult and real problems [3]. ANN were found to be more efficient and more accurate than other classification techniques [4]. Classification by a neural network is done in two separate phases. First, the network is trained on a dataset. Then the weights of the connections between neurons are fixed so the network is validated to determine the classifications of a new dataset [5] . In this paper, we used about 70% of the total sample data for network training, and 30% for network validation. While many models of ANNs have been proposed, the feed-forward neural networks (FNNs) are the most common and broadly used in many applications. Mathematically, the problem of training an FNN is the minimization of an error function E ; In another word, to find a minimizer w = (w1,w2,..., wn) such that w = min E(w), where E is the batch error computed by the sum of square differences over all examples of the training dataset. ∑ ∑( ) is the output of the j-th neuron that belongs to the L-th (output) layer, NL is the number of neurons of the output layer, tj,p is the anticipated response at the j-th neuron of the output layer at the input pattern p , and p represents the total number of patterns which used in the training dataset. A traditional technique to solve this problem is by an iterative gradient-based training process, which produces a series of weights { } starting from an initial point Using the iterative formula where k is the current iteration, is the learning rate and is the decent search direction [5]. Our study main purpose is to develop a neural network as classification technique to predict the category of movies rate. A dataset from UCI repository [6,7] was used for this purpose. International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 3 Issue 2, February – 2019, Pages: 21-25 www.ijeais.org/ijaer 22 Figure 1: ANN Architecture 1. Literature Review There are many studies involving Artificial Neural Network (ANN) for example : Artificial Neural Networks and expert systems were employed to obtain knowledge for the learner model in the Linear Programming Intelligent Tutoring System (LP-ITS) to be able to determine the academic performance level of the learners in order to offer him/her the proper difficulty level of linear programming problems to solve[8-12,15,18,21-23]; for predicting the performance of a sophomore student enrolled in engineering majors in the Faculty of Engineering and Information Technology in Al-Azhar University of Gaza was developed and tested [37,45]; ANN model was developed and tested to predict temperature in the surrounding environment [20]; for predicting critical cloud computing security issues by using Artificial Neural Network (ANNs) algorithms. However, they proposed the Levenberg–Marquardt based Back Propagation (LMBP) Algorithms to predict the performance for cloud security level [32]; for predicating the MPG rate for the forthcoming automobiles in the foremost relatively accurate evaluation for the approximated number which foresight the actual number to help through later design and manufacturing of later automobile [17,36]; to predict efficiency of antibiotics in treating various bacteria types [40]; to predict the rate of treatment expenditure on an individual or family in a country [46], for detecting early-stage non-small cell lung cancer (NSCLC) [38]; for the diagnosis of hepatitis virus [34,41]; for predicting the Letters from twenty dissimilar fonts for each letter [35], for Email Classification Using Artificial Neural Network [14]; Classification Prediction of SBRCTs Cancers Using Artificial Neural Network [16, 25]; for Diabetes Prediction Using Artificial Neural Network [29]; to predict Birth Weight [19]; 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 [13]; for Parkinson's Disease Prediction Using Artificial Neural Network[39,42,44]; for desktop PC Troubleshooting[ 27]; for Tomato Leaves Diseases Detection Using Deep Learning[26]; Plant Seedlings Classification Using Deep Learning [24,43]; for predicating software analysis and risk management [30,31]. 2. Methodology We got a movie ranking data set that created by Mehreen Ahmed. We used this dataset which to build and validate our ANN model. 2.1 Dataset Description Table 1: Original Dataset attributes description # Attribute Type 1. Movie Text 2. Year Integer 3. Genre Integer 4. Gross Integer 5. Budget Integer 6. Screens Integer 7. Sequel Integer 8. Sentiment Integer International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 3 Issue 2, February – 2019, Pages: 21-25 www.ijeais.org/ijaer 23 9. Views Integer 10. Likes Integer 11. Dislikes Integer 12. Comments Integer 13. Aggregate Followers Integer 14. Ratings Real 2.2 Dataset Preprocessing and Transformation We did some preprocessing and transformation so the data is fit for predictive analysis. We used the first 13 attributes as inputs to our model except movie, and year attributes. In addition, the Ratings attribute was the used as the class to be predicted based on the input attributes. We normalized the values of the attributes: gross, budget, screens, views, likes, dislikes, comments, and aggregate followers, so they became real because they were large integer numbers. Normalization formula was ⁄ We transform the class attribute (Ratings); we categorized rates based on the criteria showed below so it became nominal. Table 2: Ratings Transformation Interval Value Transformation 1 – 2.9 Bad 1 3 – 4.9 Good 2 5 – 7.9 Very good 3 8 10 Excellent 4 The resulted dataset description is shown in table (3). Table 3: Description after preprocessing # Attribute Type 1. Genre Integer 2. Gross Real 3. Budget Real 4. Screens Real 5. Sequel Integer 6. Sentiment Integer 7. Views Real 8. Likes Real 9. Dislikes Real 10. Comments Real 11. Aggregate Followers Real 12. Ratings Integer 2.3 The Neural Network The resulted ANN Model is shown in figure (2). International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 3 Issue 2, February – 2019, Pages: 21-25 www.ijeais.org/ijaer 24 Figure 2: Our ANN Model 2.4 Results Our ANN model was able to predict the rate class with 92.19% accuracy, after 11776860 learning cycles with about 1% training error rate as seen in figure (3). In addition, Our Model showed that the most attribute that has effect on the movie rate was "sequel". More details are shown in figure (4). Figure 3: Validation and errors rates Figure 4: Attributes Importance 3. Conclusion An artificial Neural Network for predicting the rate category of a movie was developed. The model was validated; it was 92.19 accurate in predict the rate category. This study showed that the neural network is able to predict movies rate category, so it can used for rating movies in the future. International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 3 Issue 2, February – 2019, Pages: 21-25 www.ijeais.org/ijaer 25 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). "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). "Developing an Expert System for Papaya Plant Disease Diagnosis." International Journal of Academic Engineering Research (IJAER) 3(4): 14-21. 5. Afana, M., et al. (2018). "Artificial Neural Network for Forecasting Car Mileage per Gallon in the City." International Journal of Advanced Science and Technology 124: 51-59. 6. Alajrami, E., et al. (2019). "Blood Donation Prediction using Artificial Neural Network." International Journal of Academic Engineering Research (IJAER) 3(10): 1-7. 7. Alajrami, E., et al. (2020). "Handwritten Signature Verification using Deep Learning." International Journal of Academic Mult idisciplinary Research (IJAMR) 3(12): 39-44. 8. Alajrami, M. A. and S. S. Abu-Naser (2020). "Type of Tomato Classification Using Deep Learning." International Journal of Academic Pedagogical Research (IJAPR) 3(12): 21-25. 9. Al-Daour, A. F., et al. (2020). "Banana Classification Using Deep Learning." International Journal of Academic Information Systems Research (IJAISR) 3(12): 6-11. 10. Alghoul, A., et al. (2018). "Email Classification Using Artificial Neural Network." International Journal of Academic Engineering Research (IJAER) 2(11): 8-14. 11. Alkronz, E. S., et al. (2019). "Prediction of Whether Mushroom is Edible or Poisonous Using Back-propagation Neural Network." International Journal of Academic and Applied Research (IJAAR) 3(2): 1-8. 12. Al-Massri, R., et al. (2018). "Classification Prediction of SBRCTs Cancers Using Artificial Neural Network." International Journal of Academic Engineering Research (IJAER) 2(11): 1-7. 13. Al-Mubayyed, O. M., et al. (2019). "Predicting Overall Car Performance Using Artificial Neural Network." International Journal of Academic and Applied Research (IJAAR) 3(1): 1-5. 14. Alqumboz, M. N. A. and S. S. Abu-Naser (2020). "Avocado Classification Using Deep Learning." International Journal of Academic Engineering Research (IJAER) 3(12): 30-34. 15. Al-Shawwa, M. and S. S. Abu-Naser (2019). "Predicting Birth Weight Using Artificial Neural Network." International Journal of Academic Health and Medical Research (IJAHMR) 3(1): 9-14. 16. Al-Shawwa, M. and S. S. Abu-Naser (2019). "Predicting Effect of Oxygen Consumption of Thylakoid Membranes (Chloroplasts) from Spinach after Inhibition Using Artificial Neural Network." International Journal of Academic Engineering Research (IJAER) 3(2): 15-20. 17. Al-Shawwa, M. O. and S. S. Abu-Naser (2020). "Classification of Apple Fruits by Deep Learning." International Journal of Academic Engineering Research (IJAER) 3(12): 1-7. 18. Alshawwa, I. A., et al. (2020). "Analyzing Types of Cherry Using Deep Learning. " International Journal of Academic Engineering Research (IJAER) 4 (1): 1-5. 19. Abu-Saqer, M. M., et al. (2020). "Type of Grapefruit Classification Using Deep Learning. " International Journal of Academic Information Systems Research (IJAISR) 4 (1): 1-5. 20. Al-Shawwa, M., et al. (2018). "Predicting Temperature and Humidity in the Surrounding Environment Using Artificial Neural Network." International Journal of Academic Pedagogical Research (IJAPR) 2(9): 1-6. 21. AlZamily, J. Y. and S. S. A. Naser (2020). "Lemon Classification Using Deep Learning." International Journal of Academic Pedagogical Research (IJAPR) 3(12): 16-20. 22. Ashqar, B. A. M. and S. S. Abu-Naser (2019). "Identifying Images of Invasive Hydrangea Using Pre-Trained Deep Convolutional Neural Networks." International Journal of Academic Engineering Research (IJAER) 3(3): 28-36. 23. Ashqar, B. A. M. and S. S. Abu-Naser (2019). "Image-Based Tomato Leaves Diseases Detection Using Deep Learning." International Journal of Academic Engineering Research (IJAER) 2(12): 10-16. 24. Ashqar, B. A., et al. (2019). "Plant Seedlings Classification Using Deep Learning." International Journal of Academic Information Systems Research (IJAISR) 3(1): 7-14. 25. Barhoom, A. M., et al. (2019). "Predicting Titanic Survivors using Artificial Neural Network." International Journal of Academic Engineering Research (IJAER) 3(9): 8-12. 26. Dalffa, M. A., et al. (2019). "Tic-Tac-Toe Learning Using Artificial Neural Networks." International Journal of Engineering and Information Systems (IJEAIS) 3(2): 9-19. 27. Dheir, I. M., et al. (2020). "Classifying Nuts Types Using Convolutional Neural Network." International Journal of Academic Information Systems Research (IJAISR) 3(12): 12-18. 28. El_Jerjawi, N. S. and S. S. Abu-Naser (2018). "Diabetes Prediction Using Artificial Neural Network." International Journal of Advanced Science and Technology 121: 55-64. 29. El-Kahlout, M. I. and S. S. Abu-Naser (2020). "Peach Type Classification Using Deep Learning." International Journal of Academic Engineering Research (IJAER) 3(12): 35-40. 30. El-Khatib, M. J., et al. (2019). "Glass Classification Using Artificial Neural Network." International Journal of Academic Pedagogical Research (IJAPR) 3(2): 25-31. 31. El-Mashharawi, H. Q., et al. (2020). "Grape Type Classification Using Deep Learning." International Journal of Academic Engineering Research (IJAER) 3(12): 41-45. 32. Elsharif, A. A., et al. (2020). "Potato Classification Using Deep Learning." International Journal of Academic Pedagogical Research (IJAPR) 3(12): 1-8. 33. Elzamly, A., et al. (2017). "Predicting Critical Cloud Computing Security Issues using Artificial Neural Network (ANNs) Algorithms in Banking Organizations." International Journal of Information Technology and Electrical Engineering 6(2): 40-45. 34. Heriz, H. H., et al. (2018). "English Alphabet Prediction Using Artificial Neural Networks." International Journal of Academic Pedagogical Research (IJAPR) 2(11): 8-14. 35. Jamala, M. N. and S. S. Abu-Naser (2018). "Predicting MPG for Automobile Using Artificial Neural Network Analysis." International Journal of Academic Information Systems Research (IJAISR) 2(10): 5-21. 36. Kashf, D. W. A., et al. (2018). "Predicting DNA Lung Cancer using Artificial Neural Network." International Journal of Academic Pedagogical Research (IJAPR) 2(10): 6-13. 37. Khalil, A. J., et al. (2019). "Energy Efficiency Predicting using Artificial Neural Network." International Journal of Academic Pedagogical Research (IJAPR) 3(9): 1-8. 38. Marouf, A. and S. S. Abu-Naser (2018). "Predicting Antibiotic Susceptibility Using Artificial Neural Network." International Journal of Academic Pedagogical Research (IJAPR) 2(10): 1-5. 39. Mettleq, A. S. A., et al. (2020). "Mango Classification Using Deep Learning." International Journal of Academic Engineering Research (IJAER) 3(12): 22-29. 40. Metwally, N. F., et al. (2018). "Diagnosis of Hepatitis Virus Using Artificial Neural Network." International Journal of Academic Pedagogical Research (IJAPR) 2(11): 1-7. 41. Musleh, M. M., et al. (2019). "Predicting Liver Patients using Artificial Neural Network." International Journal of Academic Information Systems Research (IJAISR) 3(10): 1-11. 42. Nabahin, A., et al. (2017). "Expert System for Hair Loss Diagnosis and Treatment." International Journal of Engineering and Information Systems (IJEAIS) 1(4): 160-169. 43. Nasser, I. M. and S. S. Abu-Naser (2019). "Artificial Neural Network for Predicting Animals Category." International Journal of Academic and Applied Research (IJAAR) 3(2): 18-24. 44. Nasser, I. M. and S. S. Abu-Naser (2019). "Lung Cancer Detection Using Artificial Neural Network." International Journal of Engineering and Information Systems (IJEAIS) 3(3): 17-23. 45. Nasser, I. M. and S. S. Abu-Naser (2019). "Predicting Books' Overall Rating Using Artificial Neural Network." International Journal of Academic Engineering Research (IJAER) 3(8): 11-17. 46. Nasser, I. M. and S. S. Abu-Naser (2019). "Predicting Tumor Category Using Artificial Neural Networks." International Journal of Academic Health and Medical Research (IJAHMR) 3(2): 1-7. 47. Nasser, I. M., et al. (2019). "A Proposed Artificial Neural Network for Predicting Movies Rates Category." International Journal of Academic Engineering Research (IJAER) 3(2): 21-25. 48. Nasser, I. M., et al. (2019). "Artificial Neural Network for Diagnose Autism Spectrum Disorder." International Journal of Academic Information Systems Research (IJAISR) 3(2): 27-32. 49. Nasser, I. M., et al. (2019). "Developing Artificial Neural Network for Predicting Mobile Phone Price Range." International Journal of Academic Information Systems Research (IJAISR) 3(2): 1-6. 50. Sadek, R. M., et al. (2019). "Parkinson's Disease Prediction Using Artificial Neural Network." International Journal of Academic Health and Medica l Research (IJAHMR) 3(1): 1-8. 51. Salah, M., et al. (2018). "Predicting Medical Expenses Using Artificial Neural Network." International Journal of Engineering and Information Systems (IJEAIS) 2(20): 11-17. 52. Samy, M. and A. Naser (2012). "Predicting learners performance using artificial neural networks in linear programming intelligent tutoring systems." International journal of artificial intelligence & aplpications 3. 53. Zaqout, I., et al. (2015). "Predicting Student Performance Using Artificial Neural Network: in the Faculty of Engineering and Information Technology." International Journal of Hybrid Information Technology 8(2): 221-228. 54. Abu Naser, S. S. (2018). "TOP 10 NEURAL NETWORK PAPERS: RECOMMENDED READING–ARTIFICIAL INTELLIGENCE RESEARCH." word press 1(1). 55. Almadhoun, H. R. and S. S. Abu Naser (2018). "Banana Knowledge Based System Diagnosis and Treatment." International Journal of Academic Pedagogical Research (IJAPR) 2(7): 1-11. 56. Salman, F. and S. S. Abu-Naser (2019). "Rule based System for Safflower Disease Diagnosis and Treatment." International Journal of Academic Engineering Research (IJAER) 3(8): 1-10. 57. Salman, F. M. and S. S. Abu-Naser (2019). "Expert System for Castor Diseases and Diagnosis." International Journal of Engineering and Information Systems (IJEAIS) 3(3): 1-10. 58. Nassr, M. S. and S. S. Abu Naser (2018). "Knowledge Based System for Diagnosing Pineapple Diseases." International Journal of Academic Pedagogical Research (IJAPR) 2(7): 12-19. 59. Mettleq, A. S. A. and S. S. Abu-Naser (2019). "A Rule Based System for the Diagnosis of Coffee Diseases." International Journal of Academic Information Systems Research (IJAISR) 3(3): 1-8. 60. Musleh, M. M. and S. S. Abu-Naser (2018). "Rule Based System for Diagnosing and Treating Potatoes Problems." International Journal of Academic Engineering Research (IJAER) 2(8): 1-9. 61. Khalil, A. J., et al. (2019). "Apple Trees Knowledge Based System." International Journal of Academic Engineering Research (IJAER) 3(9): 1-7. 62. Elzamly, A., et al. (2015). "Classification of Software Risks with Discriminant Analysis Techniques in Software planning Development Process." International Journal of Advanced Science and Technology 81: 35-48. 63. Elzamly, A., et al. (2015). "Predicting Software Analysis Process Risks Using Linear Stepwise Discriminant Analysis: Statistical Methods." Int. J. Adv. Inf. Sci. Technol 38(38): 108-115. 64. Elqassas, R. and S. S. Abu-Naser (2018). "Expert System for the Diagnosis of Mango Diseases." International Journal of Academic Engineering Research (IJAER) 2(8): 10-18. 65. Elsharif, A. A. and S. S. Abu-Naser (2019). "An Expert System for Diagnosing Sugarcane Diseases." International Journal of Academic Engineering Research (IJAER) 3(3): 19-27. 66. El-Mashharawi, H. Q. and S. S. Abu-Naser (2019). "An Expert System for Sesame Diseases Diagnosis Using CLIPS." International Journal of Academic Engineering Research (IJAER) 3(4): 22-29. 67. Dheir, I. and S. S. Abu-Naser (2019). "Knowledge Based System for Diagnosing Guava Problems." International Journal of Academic Information Systems Research (IJAISR) 3(3): 9-15. 68. El Kahlout, M. I. and S. S. Abu-Naser (2019). "An Expert System for Citrus Diseases Diagnosis." International Journal of Academic Engineering Research (IJAER) 3(4): 1-7. 69. Alshawwa, I. A., et al. (2019). "An Expert System for Coconut Diseases Diagnosis." International Journal of Academic Engineering Research (IJAER) 3(4): 8-13. 70. Al-Shawwa, M. and S. S. Abu-Naser (2019). "Knowledge Based System for Apple Problems Using CLIPS." International Journal of Academic Engineering Research (IJAER) 3(3): 1-11.