Development of Human Computer Interface

growth of Human calculator InterfaceDevelopment of Human Computer Interface Based on Cognitive put Integrated With entropy exploit TechniquesM. Mayilvaganan, D. KalpanadeviAbstractFirst review line of work abtaboo the relevant literature survey in enact to pronounce the surgical operation of learning ability and knowledge, way, circumspection, by the category of cognitive scientific disciplines which is analyze by various datarmation archeo analytic site techniques. In this report card way the concepts of cognitive c atomic number 18 for and information digging techniques which be dod to respect the usability of system found on metrics for analyzing occupation closure resources. The act of instrument of cognitive process in the Human computer Interface(HCI) system contributes to harbor better effect of the serviceman behavior which forget be analyses by data excavation technique of variety and thud process send word be proposed to evaluate the know ledge of person in efficient manner. This implies that the clevernesss will be stimulated over time through with(predicate) intentional underpin and also helps for various resources establish on different categorize.Keywords GOM Model, entropy archeological site techniques, Human Computer Interface system, Observational regularity, C4.5, Nave mouth, K-means, Weka Tool. fundamentData mining also called companionship Discovery in Databases (KDD) in the field of fall ining novel and potentially holdful information from large tot up of data. In recent years, there has been increasing interest on the use of data mining to investigate scientific questions for problem solving give awayline, an flying field of charitable thinking, behavior, analyse the executing from the knowledge criteria are gather by the techniques of data mining 1. An ability of cognitive mathematical process is essential in various environment, which is influenced by many qualitative attributes are included for forming the data sight. Data mining techniques such(prenominal) as K-nearest neighbor, finale tree, Nave Bayes, queasy network, Fuzzy, Genetic and other techniques are applied in various environments 3. This paper describes about literature survey on to analyse the cognitive performance integrates with data mining techniques.2. Cognitive process in Human Computer Interaction (HCI)Human computer interaction is concerned with how people use computer system to perform tasks, usually in a actual life work restrainting. To evaluate the competing task by use usability criteria ground on cognitive examples. Cognitive processes is the process that involve knowledge, attention, memory, producing and understanding the language, problem solving and decision making. All these are very important for military man behavior. The working process of each task muckle be analyze by data mining techniques for finding the human behavior, attitude and attention performance in resp ect way.2.1 Collecting Qualitative Data through Cognitive processScope of knowledge is accumulated information, problem solving schemas, performance readinesss, expertise, memory capacity, problem representation ability, abstraction and categorization abilities, deductive cogitate sciences, long-term concentration ability, motivation, efficiency and accuracy.Data which is collected by using kind of techniques like Video and audio recording, software logging, S advise converters, think- clamorously protocols or pencil and paper field notes. These techniques can be followed by some(prenominal) cognitive determines such as GOM model, KLM model, Cognitive complexness which has to be evaluating by language base model such as didactics Language grammar, confinement Action Language, problem Action Grammar, and Knowledge compendium of tasks.In cognitive complexity, the tasks can be assessed by analysing the number of entities that call for to be related in a single representat ion. For eg The hypothesis ideas such as collecting the personal data, family background, academic details, extracurricular activities, activities while during studies and so on, are the basic attributes for analysing the performance skill for necessitate person.The techniques are outlined for abridgment of cognitive complexity in general cognition, cognitive development, mathematics education, reasoning tasks, psychometric test items, and industrial decision making, problem solving etc. The role of questionary format on the basis of problem solving, reasoning task, deportment methods to analyse in effective way.2.2 GOMS ModelGOMS model stands for Goals, Operators, Methods, and Selection blueprints necessary to perform a task. Tasks are broken down into their components to predict performance times.Figure.1 represents the process of GOM model describe asGoals are purposeivesOperators are the actions that transfigure the system state or the cognitive state.Methods are desc ription of procedures for achieving goals stored in the substance abusers knowledgestructure of the task built-up for the problem solving.Fig.1 GOM modelSelection Rules are If Then statements to enable the user to choose amidst the methodsunder the time complexity.2.3 Keystroke Level Model (KLM)KLM is derived from GOMS and describes the time taken to lam sub-task using the system facilities. Total time taken for an action is arrived at by simply adding together the times for each component task. To recover the predicted time for a task and add the times for individual operators establish on Fitt law, Steering Law.2.4 Cognitive Complexity TheoryCognitive complexity theory is an extension of GOMS. It attempts to predict how difficult learn and use a system will be base on a GOMS model of the task and its required knowledge, a model of the user current knowledge and a list of the items of knowledge to be learned in order for the user to be able to make error- free use of the system .2.5 Knowledge Analysis of Tasks (KAT)KAT is an evaluate model to identify the task gather from variety of techniques including interviews and questionnaires, observation, rating scales, repertory grids and conduct online test for problem solving. The end tasks will analysis for the performer by producing the result. KAT involves several bes such as discern the person goals, sub goal and subtasksWork out order in which sub goal are to be carried out.Identify task strategies.Identify procedures.Identify Task Objects and Actions.3. Data tap Concepts Integrates To HCIThe variety of domain values which are related with performance base on their required result carried out by cognitive process model. From fig.1 shows such factor may be founded by means of analysis based on data mining techniques. Usability criteria can be measured by noteting performance targets in the system design at the stage of effectiveness, learning ability, and flexibility, attitude which is evaluated by eit her survey or investigateal method. An analytical evaluation method is followed by the GOMS model, KLM model and Cognitive complexity theory for end-user testing through knowledge task analysis. After observational evaluation happened, the collection of data will be stored in the database. exploitation data mining techniques, preprocessing, data cleaning and transformation are carried out for avoid the redundancy and clear the noisy data from the database. After preprocessing, several algorithmic happens are applied to discover the knowledge and performance factor are analysed to identify the human ability.4. Data mining techniquesAn application of Data mining is a rich focus of Classification algorithm, Association algorithm, chunk algorithm which can be applied to the field of some resources it concerns with developing methods that discover the knowledge from data originating from any other resource environment.Fig.1. Methodology swear out of analyse skill by Data Mining Te chniques4.1 Classification TechniquesIn Classification process, the derive model is to predict the class of objects whose class strike out is unknown. The derived model is based on the analysis of asset of knowledge data.In educational data mining, the work of data was predicted by logical rule of the Classification algorithms with the represent of common domain values for analyzing the qualitative performance of required details.In this case study, it can be predicting the human behavior through HCI by given the problem solving question, observational process and other resources. In this technique, it can be classified the cognitive operation of cognitive demeanor such as logical reasoning, analytical ability, Numerical ability, match profile for skill learning, personality analysis and other styles for analyzing the skill for the human user from the collected dataset systematically.In C4.5 algorithm construct in which intensify by ID3 algorithm and it works in divide and con quer method. At the beginning stage the root is present to associate with training data set. The rule set is formed from the initial state of decision tree. for each one path from the initial state, the condition will be evaluate and change by the effect of rule and an outcomes will put on the required leaf, the step will continuous when it comes discarding the condition. Let freq (Ci, S) stand for the number of samples in S that belong to class Ci (out of k possible classes), and S denotes the number of samples in the set S. Then the entropy of the set S equation (1)After set T has been partitioned in accordance with n outcomes of one attribute test X equation (2)gain (x) = info (T) infox(T)In Nave Bayes algorithm, to reduce computation in evaluating P (XCi), the naive assumption of class conditional is made. This presumes that the values of the attributes are conditionally independent of one another, given the class label of the tuple. The data set predicts that tuple X belongs to the class Ci. equation (3)By Bayes theorem, the classic for which P (Ci X) is maximized is called the maximum posteriori hypothesis.P (Ci X) = P(XCi)P(Ci) / P(X) equation (4)The classic for which P (Ci X) is maximized is called the maximum posteriori hypothesis. It can easily estimate the probabilities P(x1Ci)P(x2Ci)P(xnCi) from the training tuples by the following relationship. equation (5)4.3 clump TechniquesCluster analysis is used to segment a large set of data into subsets called clusters. It is the process of meetinging or organizing a set of objects into explicit group based on some similarity or difference measure among the individual objects, such that the objects in the same group are more similar to each other than those in other groups 2. by means of this technique, it can be cluster the skill level in style wise or any other pattern and analyse in each cognitive style in grouped manner.In this paper, K-means clustering can be used to analyse the smorgasbord of training tuple from the rule base relation, then it can be grouped the performance of skill in pattern wise. K-means algorithm takes the input parameter and partitions a set of n objects into k clusters. Cluster similarity is measured in interpret to the mean value of the objects in a cluster based on center of gravity. For each of the remaining object is assigned to the cluster based on the distance. Iteration can be repeated until the function can satisfied. equation (6)where E is the sum of the square error for all objects in the data set p is the point in space representing a given object and mi is the mean of cluster Ci, the distance from the object to its cluster center is squared, and the distances are summed. The resulting of k clusters as compact and group can be formed for the required pattern.Experimentation of Training Data set and Result Analysis Using Data mining TechniquesFrom this look survey, it can be analysed and produced an idea to propose the human performance based on cognitive process through Human Computer porthole by interacting from computer system. The training data set can be experimented in data mining techniques to analysis the behavior of the human user via computer system.In this experiment, Classification technique approach was obtained accuracy to classification for forum data. Using Weka tool the classification algorithm was provided to experiment with sample data set by the given attributes like logical reasoning, numerical ability and personality for analyse the skill level of human user. Through clustering technique it can be analysed the performance of skill level from the classified training data set.Weka provides the range of the functioning in style wise and estimates the accuracy of resulting predicting model in classification algorithms are C4.5 and Nave Bayes techniques used in the analyzing process. These techniques are decision making rule process which can be worked in prospect evaluation model on the analysi s of a set of training data.If logical_reasoning = good and Numerial_ability = good and personality=good thenPerformance= Good_skill_userIf logical_reasoning = little and Numerial_ability = scummy and personality=good thenPerformance= Average_skill_userIf logical_reasoning = poor and Numerial_ability = poor and personality=poor thenPerformance= below _ medium _skill_userTABLE.1 PERFORMANCE whole tone FOR TRAINING DATA OF 200 SAMPLE INSTANCEFrom table.1, shows the measuring the performance of execution time and correctly classified instance based on the proposed algorithm for predicting in rule. In second experiment, the data clustering method can be used for checking the similarity based on the criteria of performance like Good skill user, Average learning user and below average skill user using K-means algorithm technique.Fig. 2 Clustering Performance in pattern wise analysis6. ConclusionIn this studied, it can be concluded that an idea of Human computer user interface which integrate with respect to cognitive models for analyzing human behavior of skill gathered by using problem solving using data mining techniques. By using 200 instance of sample training data set, which can be predicted by the rule of classification techniques of C4.5 and Nave Bayes algorithm which can be produced their efficiency are C4.5 classified by execution time of accuracy is 0.25 second and 170 instance are correctly classified. Nave Bayes algorithm classified by execution time of accuracy is 0.1 second and 142 instances are correctly classified. From the above analysis more instance of classifier is C4.5 algorithm was well suited for classification to skill analysis. Finally, it can be analysis by category wise based on pattern then produce 80% of Good skill user, 40% of Average Skill user and 5% below average skill user using K-means clustering algorithm.References7.1 Book1 Jiawei Han and Micheline Kamber, Data Mining Concepts and Techniques, 2nd ed., Morgan Kaufmann Publis hers, 2006.Arun K Pujari, Data mining techniques, University Press (India) Private Limited.David Hand, Heikki Mannila adhraic Smyth, Principles of Data Mining, MIT Press, 2001.Anderson, J.R, The Architecture of Cognition, Harvard University Press, Cambridge (1983).7.2 Journal Article5 Richard E.Clark, Cognitive Task Analysis, October 14, 2006.6 Chipman, S. F., Schraagen, J. M., Shalin, V. L., Introduction to Cognitive task analysis7 David H. Jonassen, Analysis of Task Procedures, Copyright emailprotected 1986.7.3 Conference Proceedings8 Bainbridge, L. The change in concepts needed to line for human behaviour in complexdynamic tasks, IEEE Transactions on Systems, Man and Cybernetics, Part A Systems andHumans, 27, 351359.9 Arbi Ghazarian, Pauses in man-machine interactions a clue to users Skill levels and their userinterface requirements, Int. J. Cognitive Performance Support, Vol. 1, No. 1, 2013.10 Sheikh,L Tanveer B. and Hamdani,S., Interesting Measures for Mining Association Ru les.IEEE-INMIC Conference December. 2004.11 M. O. Mansur, M.Sap and M. Noor, Outlier Detection Technique in Data mining A queryPerceptive, In Postgraduate Annual Research Seminar, 2005.1