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TOP 05 ARTIFICIAL INTELLIGENCE & APPLICATIONS RESEARCH ARTICLES FROM 2016 ISSUE

PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL NEURAL NETWORKS

    Nick Z. Zacharis,Department of Computer Systems Engineering, Technological Educational Institute of Piraeus,Athens, Greece

    ABSTRACT

    Along with the spreading of online education, the importance of active support of students involved in online learning processes has grown. The application of artificial intelligence in education allows instructors to analyze data extracted from university servers, identify patterns of student behavior and develop interventions for struggling students. This study used student data stored in a Moodle server and predicted student success in course, based on four learning activities - communication via emails, collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to predict student performance on a blended learning course environment. The model predicted the performance of students with correct classification rate, CCR, of 98.3%.

    KEYWORDS

    Artificial Neural Networks, Blended Learning, Student Achievement, Learning Analytics, Moodle Data.


    For More Details :
    http://aircconline.com/ijaia/V7N5/7516ijaia02.pdf

    Volume Link :
    http://airccse.org/journal/ijaia/current2016.html



ARABIC ONLINE HANDWRITING RECOGNITION USING NEURAL NETWORK

    Abdelkarim Mars1 and Georges Antoniadis2 1Laboratory LIDILEM, Alpes University, Grenoble, French 2Laboratory LIDILEM, Alpes University, Grenoble, French

    ABSTRACT

    This article presents the development of an Arabic online handwriting recognition system. To develop our system, we have chosen the neural network approach. It offers solutions for most of the difficulties linked to Arabic script recognition. We test the approach with our collected databases. This system shows a good result and it has a high accuracy (98.50% for characters, 96.90% for words).

    KEYWORDS

    Neural Network, Handwriting recognition, Online, Arabic Script


    For More Details :
    http://aircconline.com/ijaia/V7N5/7516ijaia04.pdf

    Volume Link :
    http://airccse.org/journal/ijaia/current2016.html



SELF LEARNING COMPUTER TROUBLESHOOTING EXPERT SYSTEM

    Amanuel Ayde Ergado,Department of Information science, Jimma University, Jimma, Ethiopia

    ABSTRACT

    In computer domain the professionals were limited in number but the numbers of institutions looking for computer professionals were high. The aim of this study is developing self learning expert system which is providing troubleshooting information about problems occurred in the computer system for the information and communication technology technicians and computer users to solve problems effectively and efficiently to utilize computer and computer related resources. Domain knowledge was acquired using semi structured interview technique, observation and document analysis. Domain experts were purposively selected for the interview question. The conceptual model of the expert system was designed by using a decision tree structure which is easy to understand and interpret the causes involved in computer troubleshooting. Based on the conceptual model, the expert system was developed by using ‘if – then’ rules. The developed system used backward chaining to infer the rules and provide appropriate recommendations. According to the system evaluators 83.6% of the users were satisfied with the prototype

    KEYWORDS

    expert system, computer troubleshooting, self learning, knowledge based system


    For More Details :
    http://aircconline.com/ijaia/V7N1/7116ijaia05.pdf

    Volume Link :
    http://airccse.org/journal/ijaia/current2016.html



A MODIFIED VORTEX SEARCH ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATION

    Berat Doğan,Department of Biomedical Engineering, Inonu University, Malatya, Turkey

    ABSTRACT

    The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS algorithm, candidate solutions are generated around the current best solution by using a Gaussian distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some problems along. Especially, for the functions those have a number of local minimum points, to select a single point to generate candidate solutions leads the algorithm to being trapped into a local minimum point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions are generated around a number of points at each iteration pass. Computational results showed that with the help of this modification the global search ability of the existing VS algorithm is improved and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark numerical function set.

    KEYWORDS

    Metaheuristics, Numerical Function Optimization, Vortex Search Algorithm, Modified Vortex Search Algorithm


    For More Details :
    http://aircconline.com/ijaia/V7N3/7316ijaia04.pdf

    Volume Link :
    http://airccse.org/journal/ijaia/current2016.html



A REVIEW ON OPTIMIZATION OF LEAST SQUARES SUPPORT VECTOR MACHINE FOR TIME SERIES FORECASTING

    Yuhanis Yusof1 and Zuriani Mustaffa2 1School of Computing, Universiti Utara Malaysia, Malaysia 2 Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Malaysia

    ABSTRACT

    Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution strategy. In order to utilize the LSSVM capability in data mining task such as prediction, there is a need to optimize its hyper parameters. This paper presents a review on techniques used to optimize the parameters based on two main classes; Evolutionary Computation and Cross Validation

    KEYWORDS

    Least Squares Support Vector Machine, Evolutionary Computation, Cross Validation, Swarm Intelligence


    For More Details :
    http://aircconline.com/ijaia/V7N2/7216ijaia03.pdf

    Volume Link :
    http://airccse.org/journal/ijaia/current2016.html







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