Philosophy

ANN for Tic-Tac-Toe Learning

International Journal of Engineering and Information Systems (IJEAIS) ISSN: 2000-000X Vol. 3 Issue 2, February - 2019, Pages: 9-17  9 ANN for Tic-Tac-Toe Learning Mohaned Abu Dalffa Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University Gaza, Palestine Abstract: Throughout this research, imposing the training of an Artificial Neural Network (ANN) to play tic-tac-toe bored game, by training the ANN to play the tic-tac-toe logic using the set of mathematical combination of the sequences that could be played by the system and using both the Gradient Descent Algorithm explicitly and the Elimination theory rules implicitly. And so on the system should be able to produce imunate amalgamations to solve every state within the game course to make better of results of winnings or getting draw. Keywords: Tic-Tac-Toe, neural network, ANN, prediction. 1. INTRODUCTION Warren McCulloch and Walter Pitts [1] created a computational model for neural networks based on mathematics and algorithms called threshold logic. This model paved the way for neural network research to split into two approaches. One approach focused on biological processes in the brain while the other focused on the application of neural networks to artificial intelligence. This work led to work on nerve networks and their link to finite automata [2]. Artificial Neural Networks are computing algorithms that can solve complex problems imitating animal brain processes in a simplified manner [3]. Perceptron-type neural networks consist of artificial neurons or nodes, which are information processing units arranged in layers and interconnected by synaptic weights (connections). Neurons can filter and transmit information in a supervised fashion in order to build a predictive model that classifies data stored in memory. A typical ANN model is a three-layered network of interconnected nodes: the input layer, the hidden layer, and the output layer. The nodes between input and output layers can form one or more hidden layers. Every neuron in one layer has a link to every other neuron in the next layer, but neurons belonging to the same layer have no connections between them (Figure 1). The input layer receives information from the outside world, the hidden layer performs the information processing and the output layer produces the class label or predicts continuous values. The values from the input layer entering a hidden node are multiplied by weights, a set of predetermined numbers, and the products are then added to produce a single number. This number is passed as an argument to a nonlinear mathematical function, the activation function, which returns a number between 0 and 1[4]. Figure 1. Neural network architecture. Figure 2. Neural network active node. In Fig.2, the net sum of the weighted inputs entering a node j and the output activation function that converts a neuron's weighted input to its output activation (the most commonly used is the sigmoid function), are given by the equations respectively. International Journal of Engineering and Information Systems (IJEAIS) ISSN: 2000-000X Vol. 3 Issue 2, February - 2019, Pages: 9-17  10 The neuron, and therefore the ANN, has two modes of operation, the training mode and the using mode. During the training phase, a data set with actual inputs and outputs will be used as examples to teach the system how to predict outputs. This supervised learning begins with random weights and, by using gradient descent search algorithms like Backpropagation, adjusts the weights to be applied to the task at hand. The difference between target output values and obtained values is used in the error function to drive learning [12]. The error function depends on the weights, which need to be modified in order to minimize the error. For a given training set {(x 1 ,t 1 ), (x 2 ,t 2 ),, (x k ,t k )} consisting of k ordered pairs of n inputs and m dimensional vectors (n-inputs, moutputs), which are called the input and output patterns, the error for the output of each neuron can be defined by the equation: while the error function of the network that must be minimized is given by: Where Oj is the output produced when the input pattern x j from the training set enters the network, and t j is the target value [13]. During the training mode, each weight is changed adding to its previous value the quantity Where γ is a constant that gives the learning rate. The higher the learning rate, the faster the convergent will be, but the searching path may trapped around the optimal solution and convergence become impossible. Once a set of good weights have been found, the neural network model can take another dataset with unknown output values and predict automatic the corresponding outputs. 2. TIC-TAC-TOE THEORY Tic-tac-toe is played on a three-by-three grid (see figure 3). Each player takes turn to place a symbol on an open square. One player's symbol is "X" and the other's is "O". The game is over once a player has three signs in a row: horizontally, ver tically, or diagonally (as shown in figure 4). The game can end with a draw result (as shown in figure 5), if there is no possibility of winning for both players. Figure 3: Empty board Figure 4: Palyer X win Figure 5: Draw game The Tic-Tac-Toe game can be generalized to an (m, n, k) game in which two players take turns to put a symbol of their own color on an m × n board, with the goal of getting k of their own color in a row. Tic-tac-toe is specifically (3, 3, 3) game, where m =3, n=3 and k= 3 in this game [6]. If played correctly, the game will end in a draw, making tic-tac-toe a pointless game [8]. 3. FUN HISTORICAL FACTS Games played on three-in-a-row boards can be traced back to ancient Egypt [9], where such game boards have been found on roofing tiles dating from around 1300 BCE[10]. An early variation of tic-tac-toe was played in the Roman Empire, around the first century BC. It was called terni lapilli (three pebbles at a time) and instead of having any number of pieces, each player only had three, thus they had to move them around to empty spaces to keep playing [11]. The game's grid markings have been found chalked all over Rome. Another closely related International Journal of Engineering and Information Systems (IJEAIS) ISSN: 2000-000X Vol. 3 Issue 2, February - 2019, Pages: 9-17  11 ancient game is Three Men's Morris which is also played on a simple grid and requires three pieces in a row to finish,[12] and Picaria, a game of the Puebloans. The different names of the game are more recent. The first print reference to "noughts and crosses", the British name, appeared in 1858, in an issue of Notes and Queries[13]. The first print reference to a game called "tick-tack-toe" occurred in 1884, but referred to "a children's game played on a slate, consisting in trying with the eyes shut to bring the pencil down on one of the numbers of a set, the number hit being scored". "Tic-tac-toe" may also derive from "tick-tack", the name of an old version of backgammon first described in 1558. The US renaming of "noughts and crosses" as "tic-tac-toe" occurred in the 20th century[14]. In 1952, OXO (or Noughts and Crosses), developed by British computer scientist Alexander S. Douglas for the EDSAC computer at the University of Cambridge, became one of the first known video games[15,16]. The computer player could play perfect games of tic-tac-toe against a human opponent.11 In 1975, tic-tac-toe was also used by MIT students to demonstrate the computational power of Tinkertoy elements. The Tinkertoy computer, made out of (almost) only Tinkertoys, is able to play tic-tac-toe perfectly [17]. It is currently on display at the Museum of Science, Boston. 4. COMBINATORY When considering only the state of the board, and after taking into account board symmetries (i.e. rotations and reflections), there are only 138 terminal board positions. A combinatorics study of the game shows that when "X" makes the first move every time, the game is won as follows[18]:  91 distinct positions are won by (X)  44 distinct positions are won by (O)  3 distinct positions are drawn (often called a "cat's game[19]") 5. STRATEGY A player can play a perfect game of tic-tac-toe (to win or at least, draw) if each time it is his turn to play, he chooses the first available move from the following list, as used in Newell and Simon's 1972 tic-tac-toe program[20]. 1Win: If the player has two in a row, they can place a third to get three in a row. 2Block: If the opponent has two in a row, the player must play the third themselves to block the opponent. 3Fork: Create an opportunity where the player has two threats to win (two non-blocked lines of 2). 4Blocking an opponent's fork: If there is only one possible fork for the opponent, the player should block it. Otherwise, the player should block any forks in any way that simultaneously allows them to create two in a row. Otherwise, the player should create a two in a row to force the opponent into defending, as long as it doesn't result in them creating a fork. For example, if "X" has two opposite corners and "O" has the center, "O" must not play a corner in order to win. (Playing a corner in this scenario creates a fork for "X" to win.) 5Center: A player marks the center. (If it is the first move of the game, playing on a corner gives the second player more opportunities to make a mistake and may therefore be the better choice; however, it makes no difference between perfect players.) 6Opposite corner: If the opponent is in the corner, the player plays the opposite corner. 7Empty corner: The player plays in a corner square. 8Empty side: The player plays in a middle square on any of the 4 sides. International Journal of Engineering and Information Systems (IJEAIS) ISSN: 2000-000X Vol. 3 Issue 2, February - 2019, Pages: 9-17  12 Optimal strategy for player X if starting in a corner. In each grid, the shaded red X denotes the optimal move, and the location of O's next move gives the next subgrid to examine. Note that only two sequences of moves by O (both starting with center, top-right, left-mid) lead to a draw, with the remaining sequences leading to wins from X. The first player, who shall be designated "X", has 3 possible positions to mark during the first turn. Superficially, it might seem that there are 9 possible positions, corresponding to the 9 squares in the grid. However, by rotating the board, we will find that in the first turn, every corner mark is strategically equivalent to every other corner mark. The same is true of every edge (side middle) mark. For strategy purposes, there are therefore only three possible first marks: corner, edge, or center. Player X can win or force a draw from any of these starting marks; however, playing the corner gives the opponent the smallest choice of squares which must be played to avoid losing[21]. This might suggest that the corner is the best opening move for X, however another study [22] shows that if the players are not perfect, an opening move in the center is best for X. The second player, who shall be designated "O", must respond to X's opening mark in such a way as to avoid the forced win. Player O must always respond to a corner opening with a center mark, and to a center opening with a corner mark. An edge opening must be answered either with a center mark, a corner mark next to the X, or an edge mark opposite the X. Any other responses will allow X to force the win. Once the opening is completed, O's task is to follow the above list of priorities in order to force the draw, or else to gain a win if X makes a weak play. More detailedly, to guarantee a draw, O should adopt the following strategies: Optimal strategy for player O. Player O can always force a win or draw by taking center. If it is taken by X, then O must take a corner International Journal of Engineering and Information Systems (IJEAIS) ISSN: 2000-000X Vol. 3 Issue 2, February - 2019, Pages: 9-17  13 If X plays corner opening move, O should take center, and then an edge, forcing X to block in the next move. This will stop any forks from happening. When both X and O are perfect players and X chooses to start by marking a corner, O takes the center, and X takes the corner opposite the original. In that case, O is free to choose any edge as its second move. However, if X is not a perfect player and has played a corner and then an edge, O should not play the opposite edge as its second move, because then X is not forced to block in the next move and can fork. If X plays edge opening move, O should take center or one of the corners adjacent to X, and then follow the above list of priorities, mainly paying attention to block forks. If X plays center opening move, O should take corner, and then follow the above list of priorities, mainly paying attention to block forks. When X plays corner first, and O is not a perfect player, the following may happen: If O responds with a center mark (best move for them), a perfect X player will take the corner opposite the original. Then O should play an edge. However, if O plays a corner as its second move, a perfect X player will mark the remaining corner, blocking O's 3-in-a-row and making their own fork. If O responds with a corner mark, X is guaranteed to win, by simply taking any of the other two corners and then the last, a fork. (since when X takes the third corner, O can only take the position between the two X's. Then X can take the only remaining corner to win). Further details Consider a board with the nine positions numbered as follows: When X plays 1 as their opening move, then O should take 5. Then X takes 9 (in this situation, O should not take 3 or 7, O should take 2, 4, 6 or 8): X1 → O5 → X9 → O2 → X8 → O7 → X3 → O6 → X4, this game will be a draw. or 6 (in this situation, O should not take 4 or 7, O should take 2, 3, 8 or 9. In fact, taking 9 is the best move, since a non-perfect player X may take 4, then O can take 7 to win).  X1 → O5 → X6 → O2 → X8, then O should not take 3, or X can take 7 to win, and O should not take 4, or X can take 9 to win, O should take 7 or 9.  X1 → O5 → X6 → O2 → X8 → O7 → X3 → O9 → X4, this game will be a draw.  X1 → O5 → X6 → O2 → X8 → O9 → X4 (7) → O7 (4) → X3, this game will be a draw.  X1 → O5 → X6 → O3 → X7 → O4 → X8 (9) → O9 (8) → X2, this game will be a draw.  X1 → O5 → X6 → O8 → X2 → O3 → X7 → O4 → X9, this game will be a draw.  X1 → O5 → X6 → O9, then X should not take 4, or O can take 7 to win, X should take 2, 3, 7 or 8.  X1 → O5 → X6 → O9 → X2 → O3 → X7 → O4 → X8, this game will be a draw.  X1 → O5 → X6 → O9 → X3 → O2 → X8 → O4 (7) → X7 (4), this game will be a draw.  X1 → O5 → X6 → O9 → X7 → O4 → X2 (3) → O3 (2) → X8, this game will be a draw.  X1 → O5 → X6 → O9 → X8 → O2 (3, 4, 7) → X4/7 (4/7, 2/3, 2/3) → O7/4 (7/4, 3/2, 3/2) → X3 (2, 7, 4), this game will be a draw. If X is not a perfect player, X may take 2 or 3 as second move. Then this game will be a draw, X cannot win.  X1 → O5 → X2 → O3 → X7 → O4 → X6 → O8 (9) → X9 (8), this game will be a draw.  X1 → O5 → X3 → O2 → X8 → O4 (6) → X6 (4) → O9 (7) → X7 (9), this game will be a draw. If X plays 1 opening move, and O is not a perfect player, the following may happen: Although O takes the only good position (5) as first move, but O takes a bad position as second move:  X1 → O5 → X9 → O3 → X7, then X can take 4 or 8 to win.  X1 → O5 → X6 → O4 → X3, then X can take 2 or 9 to win.  X1 → O5 → X6 → O7 → X3, then X can take 2 or 9 to win. Although O takes good positions as the first two moves, but O takes a bad position as third move: 1 2 3 4 5 6 7 8 9 International Journal of Engineering and Information Systems (IJEAIS) ISSN: 2000-000X Vol. 3 Issue 2, February - 2019, Pages: 9-17  14  X1 → O5 → X6 → O2 → X8 → O3 → X7, then X can take 4 or 9 to win.  X1 → O5 → X6 → O2 → X8 → O4 → X9, then X can take 3 or 7 to win. O takes a bad position as first move (except of 5, all other positions are bad):  X1 → O3 → X7 → O4 → X9, then X can take 5 or 8 to win.  X1 → O9 → X3 → O2 → X7, then X can take 4 or 5 to win.  X1 → O2 → X5 → O9 → X7, then X can take 3 or 4 to win.  X1 → O6 → X5 → O9 → X3, then X can take 2 or 7 to win 6. THE METHODOLOGY The EasyNN-Plus program was used to develop the ANN model. The structure of the neural network was set as feedforward, in which the output layer connects only to the previous layer. ANN training used 83% of the 958 cases (798) and 17% of cases (160) were selected for the validation set. All cases were randomly selected by the EasyNN-Plus program. The parameters considered the input layer for the neural network training can be seen in the following adjacent figures, combined with other parameters describing the combinations of the tic-tac-toe game regression. A total of nine input parameters were used in the development of the ANN model (see Appendix). Several configurations of ANN were tested in other to find the best performing combination of number of hidden layers and nodes per layer. In all configurations, Eq. was used as activation function to smooth the output signal of each node: Where x is the sum of the weighted input of each previous node plus the bias of the node itself. ANN results were evaluated based on the coefficient of determination of all strategies possible to the game mind, the mean bias error. The accuracy of these ANNs was then compared to the accuracy of the original ANN (which includes all eight input parameters). Technique and Description Gradient Descent Formula was used in terms of measuring and evaluating each suitable move would and could be used: Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point. If instead one takes steps proportional to the positive of the gradient, one approaches a local maximum of that function; the procedure is then known as gradient ascent. Data Used in tic-tac-toe International Journal of Engineering and Information Systems (IJEAIS) ISSN: 2000-000X Vol. 3 Issue 2, February - 2019, Pages: 9-17  15 Table 1: Input and output attributes SN. Attribute Name Type of Attribute 1 top-left-square: {x,o,b} Input 2 top-middle-square: {x,o,b} Input 3 top-right-square: {x,o,b} Input 4 middle-left-square: {x,o,b} Input 5 middle-middle-square: {x,o,b} Input 6 middle-right-square: {x,o,b} Input 7 bottom-left-square: {x,o,b} Input 8 bottom-middle-square: {x,o,b} Input 9 bottom-right-square: {x,o,b} Input 10 Class: {positive, negative} Output The original dataset was normalized to be ready for Just NN environment. Dataset consists of 958 samples. It was divided into 83% of the total sample for training and 17% for the total samples into validation. That means the size of the training sample is equal 498 and the size of the validation samples is 160. The ANN model consist of three layers: one layer for input (9 neuron), one Hidden Layer (3 neuron) and one output layer (one neuron) (as shown in Figure 6). The ANN model was trained and validated. The number of cycles was 3580 and the error rate was equal 0.018237 and the accuracy was 99.38% (as shown Figure 7). The relative importance of the attribute was found as in Figure 8. Figure 6: Neural Network Design International Journal of Engineering and Information Systems (IJEAIS) ISSN: 2000-000X Vol. 3 Issue 2, February - 2019, Pages: 9-17  16 Figure 7: Neural Network model training and validation Figure 8: Neural Network relative importance of the attributes 7. CONCLUSION In this research, we presented the training of an Artificial Neural Network (ANN) to play tic-tac-toe bored game, by training the ANN to play the tic-tac-toe logic using the set of mathematical combination of the sequences that could be played by the system and using both the Gradient Descent Algorithm explicitly and the Elimination theory rules implicitly. The accuracy of the ANN model we got was 99.38%. 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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.


Philosophy

An Intelligent Tutoring System for Cloud Computing

International Journal of Academic Research and Development 76 International Journal of Academic Research and Development ISSN: 2455-4197, Impact Factor: RJIF 5.22  Volume 2; Issue 1; January 2017; Page No. 76-80 An intelligent tutoring system for cloud computing 1 Hasan Abdulla Abu Hasanein, 2 Samy S Abu Naser 1 Department of Information Technology, Israa University, Gaza, Palestine 2 Department of Information Technology, Al-Azhar University, Gaza, Palestine Abstract Intelligent tutoring system (ITS) is a computer system which aims to provide immediate and customized or reactions to learners, usually without the intervention of human teacher's instructions. Secretariats professional to have the common goal of learning a meaningful and effective manner through the use of a variety of computing technologies enabled. There are many examples of professional Secretariats used in both formal education and in professional settings that have proven their capabilities. There is a close relationship between private lessons intelligent, cognitive learning and design theories; and there are ongoing to improve the effectiveness of ITS research. And it aims to find a solution to the problem of over-reliance on students' teachers for quality education. The program aims to provide access to high-quality education to every student, and therefore the reform of the education system as a whole. In this paper, we will use Intelligent Tutoring System Builder (ITSB) to build an education system on cloud computing in terms of the concept of cloud computing and components and how to take advantage of cloud computing in the field. Keywords: intelligent tutoring system, intelligent tutoring system builder, cloud computing 1. Introduction It is no secret one of the great technological development in which we live in the present, bringing to the use of computers and the Internet of the elements of daily life such as water and electricity, and in the light of this great development was necessary to take advantage of it in the field of education. Intelligent tutoring system (ITS) is one of the areas which have emerged recently and aimed to take advantage as much as possible of evolution in which we live. Intelligent tutoring system (ITS) is software which aims to provide immediate instructions and customized or reactions to learners usually without the intervention of human teacher. The goal of building intelligent Privacy lessons cloud computing system is to teach students the general concepts and components of mathematical computing and its importance in our daily lives and how to use them in the field of education [5, 6]. 2. Literature Review There are many intelligent tutoring systems in various fields [4-34]. Since the launch of these systems, programmers racing to develop intelligent tutoring systems to help learners overcome the difficulties they face. A lot of previous research papers were read about the subject of intelligent tutoring but no research paper was found related to cloud computing. 3. ITS Architecture Fig 1: ITS architecture International Journal of Academic Research and Development 77 You may need the design and selection of stages of intelligence information. Learner information collection agent enables a system to collect personal information, previous educational experiences and results of pre-agent learner. Also record activities such as mouse movements and learning time period and exam scores. Learner profiling agent and agent activity monitoring are two types of profiling the factors that apply to the making and improving the situation. The profiling agent learner resides partial information from different learner and determines the level of intelligence, and learning and former preference learning of learners. Agent activity monitoring assessment of learning time of each session, the results of the exam and the exam has spent time and in this way determine the path of learning and knowledge level of the learner [4]. In the next step, when the agent modeling personal learner receives information of learners in the design stage, and begins to learn or improve modeling. After this step model automatically sent to the learning and planning agent in the selection phase. Undersecretary of analyzes based tutorial on model and then update it. And finally sent a personal learning plan learner agent. The Under educational materials such as personal contents, exams, feedback is vital for leaners. 3.1 Domain Model Architecture This domain of a set of chapters consists all of them many lessons, information becomes more difficult when you move from each chapter to another, came the chapters and lessons are organized within them as follows [1, 2, 3]:  Introduction to Cloud Computing  What cloud computing  The history of the emergence of cloud computing  What is the basic idea of cloud computing?  Cloud Computing Component  Software as a Service  Platform as a service  Infrastructure as a service.  The benefits and obstacles to cloud computing  The benefits of cloud computing  Obstacles and threats to use cloud computing  Cloud Computing Service Providers  What is the role of cloud computing service provider?  What are the main leading companies in the cloud computing service?  Security in cloud computing  Use of cloud computing  Cloud computing in trade  Cloud Computing in Education  Cloud computing in companies 3.2 Student Model Architecture Each student when using the system for the first time the rules of the art is by default set up a special file the student and give him a user name and password, then the student Login to your account through a dedicated student user interface and then provide the student to choose the part you want presided, and the system in turn offer lessons within this segment so that the student can study them. There in the system a set of questions for each lesson and each of these lessons have a certain level of difficulty through the student answers to these questions, the system determining the level of the student. If it exceeds the student questions the degree of success, the collection system is considered to be a student of this part has been successfully completed, and thus the system moves the student to the next part and so on. 3.3 Pedagogical Module Architecture The teacher enters the main parts of the material and then enters each part of the lessons. The teacher then enter the questions and you're adding each question according to the levels they want, and can also determine the degree of success for each lesson and that if a student got on top of them, the sign system is transferred to the next level. This section also contains an interface through which the teacher to follow up the students who use the system, so that it can see the levels at which they arrived, and it can monitor the performance of students and the extent of their understanding of the lessons. 3.4 User Interface Model This ITSB tool used to build our current ITS supports two classes of users: students and teachers. When the teacher logs in the system, the teacher can add/modify/delete lessons, exercises, answers of exercises, initial ITS basic information, initial information about students, configure/adjust the color, font name, and size of all combo boxes, buttons, and menus.. A screenshot of the teacher interface is shown in Fig 2, Fig 3, Fig 4, and Fig 5. However, when the student logs into the system, he/she can study the lessons, examples and questions. When the student clicks on Exercises button then "new problem" button is enabled and the student can click on it to be given a new problem to solve. The student should click on the "check" button to check his/her answer is correct or not. When the student wants to see his/her performance he/she should click on "stats" button. See Fig 6 and Fig 7. Fig 2: Login Screen International Journal of Academic Research and Development 78 Fig 3: Add basic ITS data Fig 4: Add Student initial data Fig 5: choose the font, color, sizes for other screens Fig 6: Lessons and examples area Fig 7: Exercises area 4. Evaluation We have evaluated the intelligent tutoring system teaching cloud computing by presenting it to a group of teachers who specializes in cloud computing and another group of students. Both groups were asked to evaluate system. Then we asked them to fill questionnaire about it. The questionnaire consisted of questions regarding advantages of ITS, completeness of the material, quality of the ITS design. The results of the evaluation by both groups were very acceptable. 5. Conclusion In this paper, we have designed an Intelligent Tutoring System teaching cloud computing by using ITSB authoring tool. The ITS system was designed to ease the study of cloud computing lessons by the students and overcome the difficulties they face. The ITS for cloud computing architecture and requirements of each part of International Journal of Academic Research and Development 79 the ITS system have been explained. We conducted an evaluation of the ITS two groups of teachers and students and the results were very acceptable. References 1. Rafaels RJ. Cloud Computing: From Beginning to End, 2015. 2. Arshdeep B, Vijay M. Cloud Computing: A HandsOn Approach, 2013. 3. Thomas E. Cloud Computing: Concepts, Technology & Architecture and Cloud Computing Design Patterns, 2015. 4. Abu Naser S. JEE-Tutor: An Intelligent Tutoring System for Java Expression Evaluation. Information Technology Journal, Scialert. 2008; 7(3):528-532. 5. Abu Eloun NN, Abu Naser SS. Mathematics Intelligent Tutoring System. International Journal of Advanced Scientific Research, 2017; 2(1). 6. Abu-Naser S, Ahmed A, Al-Masri N, Deeb A, Moshtaha E, Abu Lamdy M. An Intelligent Tutoring System for Learning Java Objects. International Journal of Artificial Intelligence and Applications (IJAIA). 2011; 2(2). 7. Abu-Naser S, Al-Masri A, Sultan YA, Zaqout I. A prototype decision support system for optimizing the effectiveness of elearning in educational institutions. International Journal of Data Mining & Knowledge Management Process (IJDKP), 2011; 1:113. 8. Akkila AN, Abu Naser SS. Teaching the Right Letter Pronunciation in Reciting the Holy Quran Using Intelligent Tutoring System. International Journal of Advanced Research and Development. 2017; 2(1). 9. Al-Bastami BH, Abu Naser SS. Design and Development of an Intelligent Tutoring System for C# Language, European Academic Research. 2017; 4(10). 10. Al-Bayed1 MH, Abu Naser SS. An Intelligent Tutoring System for Health Problems Related To Addiction of Video Game Playing. International Journal of Advanced Scientific Research. 2017; 2(1). 11. Aldahdooh R, Abu Naser SS. Development and Evaluation of the Oracle Intelligent Tutoring System (OITS). European Academic Research. 2017; 4(10). 12. Alhabbash MI, Mahdi AO, Abu Naser SS. An Intelligent Tutoring System for Teaching Grammar English Tenses. European Advanced Research, 2016; 4(9):7743-7757. 13. Al-Hanjori MM, Shaath MZ, Abu Naser SS. Learning Computer Networks Using Intelligent Tutoring System. International Journal of Advanced Research and Development. 2017; 2(1). 14. Almurshidi SH, Abu Naser SS. Stomach Disease Intelligent Tutoring System. International Journal of Advanced Research and Development, 2017; 2(1). 15. Almurshidi SH, Naser SSA. Design and Development of Diabetes Intelligent Tutoring System. European Academic Research. 2017; 4(9):8117-8128. 16. Al-Nakhal MA, Abu Naser SS. An Intelligent Tutoring System for learning Computer Theory. European Academic Research. 2017; 4(10). 17. El Haddad IA, Abu Naser SS. ADO-Tutor: Intelligent Tutoring System for leaning ADO.NET. European Academic Research. 2017; 4(10). 18. Elnajjar AA, Abu Naser SS. DSE-Tutor: An Intelligent Tutoring System for Teaching DES Information Security Algorithm. International Journal of Advanced Research and Development. 2017; 2(1). 19. Hilles MM, Abu Naser SS. Knowledge-based Intelligent Tutoring System for Teaching Mongo Database. European Academic Research, 2017; 4(10). 20. Mahdi AO, Alhabbash MI, Abu Naser SS. An intelligent tutoring system for teaching advanced topics in information security. World Wide Journal of Multidisciplinary Research and Development, 2016; 2(12):1-9. 21. Mahmoud AY, Barakat MS, Ajjour MJ. Design and Development of ELearning University System (Journal of Multidisciplinary Engineering Science Studies (JMESS), 2016; 2(5):498-504. 22. Naser S, Evaluating the effectiveness of the CPPTutor an intelligent tutoring system for students learning to program in C++. Journal of Applied Sciences Research; , 2009; 5(1):109-114. 23. Naser SA. A comparative study between Animated Intelligent Tutoring Systems (AITS) and Video-based Intelligent Tutoring Systems (VITS). Al-Aqsa University Journal, 2001; 5(1):1. 24. Naser SA. An Agent Based Intelligent Tutoring System For Parameter Passing In Java Programming. Journal of Theoretical & Applied Information Technology, 2008; 4(7). 25. Naser SA, Ahmed A, Al-Masri N, Sultan YA. Human Computer Interaction Design of the LP-ITS: Linear Programming Intelligent Tutoring Systems. International Journal of Artificial Intelligence & Applications (IJAIA), 2011; 2(3):6070. 26. Naser SSA. Intelligent tutoring system for teaching database to sophomore students in Gaza and its effect on their performance. Information Technology Journal; Scialert. 2006; 5(5):916-922. 27. Naser SSA. Developing an intelligent tutoring system for students learning to program in C++. Information Technology Journal; Scialert, 2008; 7(7):1055-1060. 28. Naser SSA. A Qualitative Study of LP-ITS: Linear Programming Intelligent Tutoring System. International Journal of Computer Science & Information Technology, 2012; 4(1):209-220. 29. Naser SSA. Predicting learners performance using artificial neural networks in linear programming intelligent tutoring system. International Journal of Artificial Intelligence & Applications, 2012; 3(2):6573. 30. Naser SSA. ITSB: An Intelligent Tutoring System Authoring Tool. Journal of Scientific and Engineering Research, 2016; 3(5):63-71. 31. Naser SSA, Sulisel O. The effect of using computer aided instruction on performance of 10th grade International Journal of Academic Research and Development 80 biology in Gaza. Journal of the College of Education. 2000; 4:9-37. 32. Naser SSA. Developing visualization tool for teaching AI searching algorithms, Information Technology Journal, Scialert, 2008; 7(2):350-355. 33. Shaath MZ, Al-hanjouri M, Abu Naser SS, ALdahdooh R. Photoshop (CS6) Intelligent Tutoring System. International Journal of Academic Research and Development 2017; 2(1). 34. Alawar MW, Abu Naser SS. CSS-Tutor: An Intelligent Tutoring System for CSS and HTML. International Journal of Academic Research and Development 2017; 2(1).


Philosophy

International Journal of Academic Health and Medical Research (IJAHMR) ISSN: 2000-007X Vol. 3 Issue 5, May - 2019, Pages: 7-10 7 Assessment of Serum Zinc and Albumin Levels among Newly Diagnosed Patients with Pulmonary Tuberculosis in Khartoum State Nuha Eljaili Abubaker, Hassan Siddig AbdElgader Omar, Hind Haidar Ahmed 1*Nuha Eljaili Abubaker Head Department of Clinical Chemistry, College of Medical Laboratory Science, Sudan University of Science and Technology, Khartoum, Sudan  1*Hind Haidar Ahmed Department of Microbiology, College of Medical Laboratory Science, Sudan University of Science and Technology, Khartoum, Sudan  2* Hassan Siddig AbdElgader Omar 2 College of Medical Laboratory Science, AL-Neelin University of Science and Technology, Khartoum, Sudan Abstract: Background: Tuberculosis is a major public health problem world-wide, it is contagious disease caused by organism mycobacterium tuberculosis. The aim of this study was to assess the level of zinc and albumin in newly diagnosed patients with pulmonary tuberculosis in Khartoum state. Methods: Fifty blood samples were collected from newly diagnosed pulmonary tuberculosis patients during the period between April to May 2017, chosen randomly from Abu Anga Teaching Hospital and fifty blood samples from apparently healthy individuals serve as control group. Estimation of serum albumin level was performed using spectrophotometer and estimation of serum zinc by using flame atomic absorption spectrophotometer. The results were analyzed by using (SPSS) computer program. Results: The study showed that, serum level of zinc and albumin were significantly decrease in patients compared to control group (mean± SD for cases versus control). For zinc :( 0.16±0.05 versus 0.58± 0.13 mg/l, P. value= 0.000). For albumin: (2.5±0.58 versus 4.1±0.31g/dl, P. value= 0.000). The result of this study showed that, there was statically significant decrease in the mean of BMI in patients compared to control group. Mean ±SD for case versus control (18.7±0.17 versus 21.5±1.7). The result of this study showed that, pulmonary tuberculosis most common among age between (20-39) years (52%) and (48%) of patients between age (40-58) years and this disease most abundant in males (78%) than females (22%). Persons correlation showed that there was no correlation between serum zinc level and age (r =-0.128, p. value=0.374) also there was no correlation between the level of serum albumin and age (r=0.137, p. value =0.344). Conclusion: The serum levels of zinc and albumin were significantly decreased in newly diagnosed patients with pulmonary tuberculosis in Khartoum state. Keywords: Pulmonary tuberculosis patients, Zink level, Albumin level 1. INTRODUCTION Tuberculosis is a major public health problem worldwide. It is responsible for more than 2 million deaths each year's, kills more adults than any other disease; especially developing countries is the second biggest killer. Globally it is contagious disease caused by organism mycobacterium tuberculosis, aerobic non-motile bacillus, it is a bacterium can attack any part of the body, most commonly the lung. It is spreads through the air, when infectious people cough, sneeze, talk or spit. The propel TB germs known as bacilli into air, inhalation of very small numbers of bacilli will lead Mycobacterium tuberculosis infection (1). Mycobacterium tuberculosis infect about one-third of world's population, of whom more than 80% live in developing countries like Iran and India (2). Annually, more than 8 million people have developed TB and approximately 1.8 million cases result in death (3). More than 80% of TB patients live in Asia and subSaharan Africa, considered by WHO to be "high burden countries." Sub-Saharan Africa has the highest incidence of the disease. (4,5,6) India, China, Indonesia, Bangladesh, and Pakistan together account for more than half of the global estimate of active TB. (7) Population increases in South Asia and central Africa alone will account for ~75% of new cases of active TB in the next ten years (8).For the most part, active TB cases have steadily declined in western and central Europe, North and South America, and in the Middle East, but are increasing in sub-Saharan Africa and the former Soviet Union (4,6) Malnutrition may predispose individuals who have latent infection to active disease as malnutrition affects cellmediated immunity, which decreases the body defense to TB and increases the risk of infection (9). For this reason, malnutrition and tuberculosis have a long history of being associated with each other (10). Micronutrient like zinc play important role in many biological system such as, biochemical reactions in metabolism, maintenance of nutritional health, growth of the human tissues and organs, zinc is used by the cells of the immune system to destroy bacteria such as, tubercle or Escherichia coli (4). Deficiency of zinc impairs over all immune function and resistance to infection (11). The reason for low serum zinc International Journal of Academic Health and Medical Research (IJAHMR) ISSN: 2000-007X Vol. 3 Issue 5, May - 2019, Pages: 7-10 8 level in TB patient could be multi-factorial. Firstly, a change in distribution of zinc in the body tissues is known to occur in chronic infections, with a net flow of zinc to the liver for synthesis of acute phase reactants including metalo enzymes. Secondly, zinc might be utilized by tuberculosis for growth and multiplication (12). Albumin is a globular protein; serum albumin is the protein of human blood plasma. There is relationship between TB and nutrition. Serum albumin is macronutrient, most common essential parameter used for evaluation of nutritional status patient with TB (13). 2. MATERIALS AND METHODS This study was conducted in Abu Anga Teaching Hospital, Khartoum -Sudan from April to May 2017. A total of 120 individual were enrolled in this study; divided into two groups, 60 healthy individual (Control group) and 60 patients newly diagnosed with pulmonary tuberculosis (cases group). The study was approved by hospital's ethics committee. Informed consent was obtained from patients before blood sampling. Inclusion criteria Patients newly diagnosed with pulmonary tuberculosis infection and healthy individual serve as control were included in this study. Exclusion criteria Patients with other diseases that affect the levels of serum zinc and albumin such as liver diseases, Chronic renal failure, nephritic syndrome, previous anti-TB treatment, using supplementation containing zinc and Patients that refuse to participate in this study were excluded. Blood sample and Analysis About 2ml of venous blood was collected from the antecubital vein by taking aseptic precautions. Care was taken to prevent venous stasis during the sample collection. The blood was allowed to clot and the serum was separated by centrifugation. The estimation of the parameters was carried out within 4-6 hrs. The samples were analyzed for Serum zinc by using atomic absorption method and Serum albumin was measured by using spectrophotometer method at 630nm.(14) The internal control sera of two different levels were used to calibrate the instruments. Data was analyzed using SPSS computer program version (21), the mean and standard deviation were obtained and the independent T test used for comparison (p value of ≤ 0.05) was considered significant and person correlation was used for correlation. 3. RESULTS In comparison with the controls, tuberculosis patients had statistical significantly lower means of serum Zinc and albumin in case group compared to control group, zinc (mean ±SD), for cases versus control,(0.16±0.04 versus 0.58±0.13mg/l, P. value = 0.000), albumin (mean ±SD, for cases versus control, (2.45±0.57 versus 4.07±0.30g/dl, P. value = 0.000). The mean of BMI in patients with pulmonary tuberculosis, was significantly decreased compared to control group (mean± SD for cases versus control, 18.7±0.17 versus 21.5±1.7) as in table (1). Table (1): Mean concentration of serum zinc and albumin in case and control groups: Variables Case (n=60) Mean ± SD Control (n=60) Mean ± SD P. value Zinc mg/l 0.16 ± 0.04 0.58 ± 0.13 0.000 Albumin g/dl 2.45 ± 0.57 4.07 ± 0.30 0.000 BMI kg/ cm2 18.7±0.17 21.5±1.7 0.000 *Result given in mean ±SD. *P. value < 0.05 consider significant. Independent sample T test was used for comparison. The present study observed that, no statistical significant difference between means concentration of serum zinc and albumin among age in case group. The mean of serum zinc and albumin in patients between (20-39) years versus the mean in patients between (40-58) years, (mean ± SD; zinc,( 0.17 ± 0.05 versus 0.16 ± 0.05 mg/dl, p. value =0.268) albumin (2.4 ± 0.54 versus 2.5 ± 0.62 g/dl, p. value= 0.592) as in table (2). Table (2): Mean concentration of serum zinc and albumin among age in case group. Variables (20-39 years) n=52% Mean ± SD (40-58 years) n=48% Mean ± SD P .value Zinc mg/l 0.17 ± 0.05 0.16 ± 0.05 0.268 Albumin g/dl 2.4 ± 0.54 2.5 ± 0.62 0.592 *Result given in mean± SD. *P .value < 0.05 consider significant. Independent sample T test was used for comparison. In this study, 78% of patients were males, while 22% were females as shown in figure (1) International Journal of Academic Health and Medical Research (IJAHMR) ISSN: 2000-007X Vol. 3 Issue 5, May - 2019, Pages: 7-10 9 Figure (1): Gender distribution in case group. The result showed that, 52% of patients at age range between (20-39) years and 48% of patients at age range between (4058) years, as shown in figure (2). Figure (2): Age distribution among case group. Correlation between the level of serum zinc and age showed, there was no statistical significant difference (r= 0.128, P. value= 0.374) as in figure (3) Zinc mg/l .4.3.2.10.0 AG E 60 50 40 30 20 10 Figure (3): Correlation between serum zinc and age (r= 0 .128, P. value= 0.374). Correlation between serum albumin level and age showed, there was no statistical significant difference (r=0.137, P .value = 0.344) as in figure (4). A lbumin g/dl 3.53.02.52.01.51.0 AG E 60 50 40 30 20 10 Figure (4): correlation between serum albumin and age (r=0.137, P .value =0.344). 4. DISCUSSION Pulmonary tuberculosis is a global disease affecting about third of the world's population with its attendant mortality and morbidity (15). Pulmonary tuberculosis can affect many substances in the body by decreasing them. This study was carried out to assess serum zinc and albumin levels in newly diagnosed patients with pulmonary tuberculosis in Khartoum state. From the finding of this study the mean serum levels of zinc and albumin were significantly decreased in case group in comparison to control group (P.value=0.000). This result agreed with another study, which showed that; serum zinc and albumin concentration frequently decreased in case group compared to control group (16). The reduction in zinc and albumin concentration in TB patients were considered to be nutritional factors, enteropathy and acute phase reactant proteins, redistribution of zinc from plasma to other tissues, or a reduction of the hepatic production of metabollothionin, a protein that transport zinc to the liver, albumin level in the serum decreases with increase in severity. This is because of hemodynamic changes that occur in response to T cell reaction and decreased synthesis as a result of direct inhibition by cytokines (17). Parallel finding to this results observed by other studies carried by many authors (16, 17, 18), their findings confirmed that patients with pulmonary tuberculosis at greater risk of development of low serum zinc and albumin and place them at risk of developing life threatening situation. Result of this study showed that, there was significant decreased in BMI in patients with pulmonary tuberculosis compared to control group (P.value=0.000), this result agreed with another studies done in Indonesia and Malawi (19, 20) . The low BMI in patients with pulmonary tuberculosis may be due to poor dietary intake, anorexia and impaired absorption of nutrients or increased catabolism (13). Low BMI is a known risk factor for mortality (21). The result of current study showed that pulmonary tuberculosis most common among age (20-58) years. These results in agreement with previous study, which showed that pulmonary tuberculosis infection is common among, age (18-54) years. (15).The finding of this study revealed that, the majority of patients with pulmonary tuberculosis participate in this study were male (78%) while female was (22%). These results in agreement with previous study that showed pulmonary tuberculosis infection is more common in male than female (18). It is of interest to observe that there was no correlation between zinc level and age of patients. This result agreed with another result, which showed that, there was no correlation between zinc levels and age (21). According to Males 78% Femal es 22% 0% 10% 20% 30% 40% 50% 60% 70% 20-39 40-58 62% 38% Age group/years International Journal of Academic Health and Medical Research (IJAHMR) ISSN: 2000-007X Vol. 3 Issue 5, May - 2019, Pages: 7-10 10 finding of this present study, there was no correlation between serum albumin level in PTB patients and age. These results agreed with another study, which showed no correlation was found between age and serum albumin level (16) . 5. CONCLUSION According to the results of this study, it concluded that serum zinc and albumin levels are decreased in patients with pulmonary tuberculosis, BMI is decreased in patients with pulmonary tuberculosis and there are no correlation between zinc, albumin level and age. 6. ACKNOWLEDGMENT We would like to express our appreciation to all participants in the study. We thank the staff of Abu Anga Teaching Hospital in Khartoum State for their co-operations. Finally, we appreciate everyone who supports us. REFERENCES 1. Bhandari, S., Badade, Z.G., Neupane, Y. Role of Micronutrient Zinc in Pulmonary Tuberculosis. Journal of Universal College of Medical Sciences. 2013; 15: 1-3. Organization WHO. Tuberculosis. Saudi Med Journal.2013; 34:1205-1207. 3. Organization WHO. The Global Tuberculosis Control .WHO Journal.2016; 4:437-439. 4. 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