7th International Conference on Soft Computing, Artificial Intelligence and Applications (SCAI 2018)

7th International Conference on Soft Computing, Artificial Intelligence and Applications (SCAI 2018)

December 22-23, 2018, Sydney, Australia

http://iccsea2018.org/scai/index.html

TO SUBMIT YOUR PAPER, PLEASE CLICK THE FOLLOWING LINK:

http://acity2018.org/submission2/index.php

ADAPTIVE LEARNING MANAGEMENT SYSTEM USING SEMANTIC WEB TECHNOLOGIES

ADAPTIVE LEARNING MANAGEMENT SYSTEM USING SEMANTIC WEB TECHNOLOGIES 

M.Farida Begam1 and Gopinath Ganapathy2 1Department of Information Technology, Manipal University Dubai, U.A.E 2 School of Computer Science and Engineering, Bharathidasan University, India

ABSTRACT 

Ontologies and semantic web services are the basics of next generation semantic web. This upcoming technologies are useful in many fields such as bioinformatics, business collaboration, Data integration and etc. E-learning is also the field in which semantic web technologies can be used to provide dynamism in learning methodologies. E-learning includes set of tasks which may be instructional design, content development, authoring, delivery, assessment, feedback and etc. that can be sequenced and composed as workflow. Web based Learning Management Systems should concentrate on how to satisfy the e-learners requirements. In this paper we have suggested the theoretical framework ALMS-Adaptive Learning management System which focuses on three aspects 1) Extracting the knowledge from the use's interaction, behaviour and actions and translate them into semantics which are represented as Ontologies 2) Find the Learner style from the knowledge base and 3)deriving and composing the workflow depending upon the learner style. The intelligent agents are used in each module of the framework to perform reasoning and finally the personalized workflow for the e-learner has been recommended. 

KEYWORDS 

Learning Style, Learning Objects, workflow, web services, Semantics, Ontology, LMS, ALMS, and OWL-S

Original Source URL : http://airccse.org/journal/ijsc/papers/4113ijsc01.pdf

http://airccse.org/journal/ijsc/current2013.html

A METAHEURISTIC OPTIMIZATION ALGORITHM FOR THE SYNCHRONIZATION OF CHAOTIC MOBILE ROBOTS

A METAHEURISTIC OPTIMIZATION ALGORITHM FOR THE SYNCHRONIZATION OF CHAOTIC MOBILE ROBOTS 

Jiann-Horng Lin 

Department of Information Management, I-Shou University, Taiwan 

jhlin@isu.edu.tw 

ABSTRACT 

We provide a scheme for the synchronization of two chaotic mobile robots when a mismatch between the parameter values of the systems to be synchronized is present. We have shown how meta-heuristic optimization can be used to adapt the parameters in two coupled systems such that the two systems are synchronized, although their behavior is chaotic and they have started with different initial conditions and parameter settings. The controlled system synchronizes its dynamics with the control signal in the periodic as well as chaotic regimes. The method can be seen also as another way of controlling the chaotic behavior of a coupled system. In the case of coupled chaotic systems, under the interaction between them, their chaotic dynamics can be cooperatively self-organized. A synergistic approach to meta-heuristic optimization search algorithm is developed. To avoid being trapped into local optimum and to enrich the searching behavior, chaotic dynamics is incorporated into the proposed search algorithm. A chaotic Levy flight is firstly incorporated in the proposed search algorithm for efficiently generating new solutions. And secondly, chaotic sequence and a psychology factor of emotion are introduced for move acceptance in the search algorithm. We illustrate the application of the algorithm by estimating the complete parameter vector of a chaotic mobile robot. 

KEYWORDS 

optimization search algorithm, chaotic dynamics, psychology factor of emotion, chaotic sequence 

Original Source URL : http://airccse.org/journal/ijsc/papers/4213ijsc04.pdf

http://airccse.org/journal/ijsc/current2013.html 

EXPLORING SOUND SIGNATURE FOR VEHICLE DETECTION AND CLASSIFICATION USING ANN

EXPLORING SOUND SIGNATURE FOR VEHICLE DETECTION AND CLASSIFICATION USING ANN 

Jobin George1 , Anila Cyril2 , Bino I. Koshy3 and Leena Mary4 1Department of EC Engineering, Rajiv Gandhi Institute of Technology, Kottayam, India jobing4@gmail.com 2,3 Department of Civil Engineering, Rajiv Gandhi Institute of Technology, Kottayam, India anilacyril110@gmail.com, bino@rit.ac.in 4Department of CS and Engineering, Rajiv Gandhi Institute of Technology, Kottayam, India leena.mary@rit.ac.in 

ABSTRACT 

This paper attempts to explore the possibility of using sound signatures for vehicle detection and classification purposes. Sound emitted by vehicles are captured for a two lane undivided road carrying moderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound of horns, random but identifiable back ground noises, continuous high energy noises on the back ground are the different challenges encountered in the data collection. Different features were explored out of which smoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Melfrequency ceptral coefficients extracted from fixed regions around the detected peaks along with the manual vehicle labels are utilised to train an Artificial Neural Network (ANN). The classifier for four broad classes heavy, medium, light and horns was trained. The ANN classifier developed was able to predict categories well. 

KEYWORDS 

Traffic characterises, vehicle detection, multilayer feedforward neural network, vehicle classification.  

Original Source URL : http://airccse.org/journal/ijsc/papers/4213ijsc03.pdf

http://airccse.org/journal/ijsc/current2013.html

HYBRID PARTICLE SWARM OPTIMIZATION FOR SOLVING MULTI-AREA ECONOMIC DISPATCH PROBLEM

HYBRID PARTICLE SWARM OPTIMIZATION FOR SOLVING MULTI-AREA ECONOMIC DISPATCH PROBLEM 

Huynh Thi Thanh Binh School of Information and Communication Technology, HaNoi University of Science and Technology, Ha Noi, Viet Nam 

binhht@soict.hut.edu.vn 

ABSTRACT 

We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PSO-TVAC) and RCGA. 

KEYWORDS 

Multi-Area Economic Dispatch, Particle Swarm Optimization.  

Original Source URL : http://airccse.org/journal/ijsc/papers/4213ijsc02.pdf

http://airccse.org/journal/ijsc/current2013.html

International Journal on Soft Computing ( IJSC )

International Journal on Soft Computing ( IJSC )

ISSN: 2229 - 6735 [Online] ; 2229 - 7103 [Print]

http://airccse.org/journal/ijsc/ijsc.html

Contact Us

Here's where you can reach us : ijscjournal@yahoo.com or ijscjournal@airccse.org

ANALYTICAL FORMULATIONS FOR THE LEVEL BASED WEIGHTED AVERAGE VALUE OF DISCRETE TRAPEZOIDAL FUZZY NUMBERS

ANALYTICAL FORMULATIONS FOR THE LEVEL BASED WEIGHTED AVERAGE VALUE OF DISCRETE TRAPEZOIDAL FUZZY NUMBERS 

Resmiye Nasiboglu1* , Rahila Abdullayeva2 

1Department of Computer Science, Dokuz Eylul University, Izmir, Turkey 2Department of Informatics, Sumgait State University, Sumgait, Azerbaijan 

ABSTRACT 

In fuzzy decision-making processes based on linguistic information, operations on discrete fuzzy numbers are commonly performed. Aggregation and defuzzification operations are some of these often used operations. Many aggregation and defuzzification operators produce results independent to the decisionmaker’s strategy. On the other hand, the Weighted Average Based on Levels (WABL) approach can take into account the level weights and the decision maker's "optimism" strategy. This gives flexibility to the WABL operator and, through machine learning, can be trained in the direction of the decision maker's strategy, producing more satisfactory results for the decision maker. However, in order to determine the WABL value, it is necessary to calculate some integrals. In this study, the concept of WABL for discrete trapezoidal fuzzy numbers is investigated, and analytical formulas have been proven to facilitate the calculation of WABL value for these fuzzy numbers. Trapezoidal and their special form, triangular fuzzy numbers, are the most commonly used fuzzy number types in fuzzy modeling, so in this study, such numbers have been studied. Computational examples explaining the theoretical results have been performed. 

KEYWORDS 

Fuzzy number;Trapezoidal; Weighted level-based averaging; Defuzzification. 

Original Source URL : http://aircconline.com/ijsc/V9N3/9318ijsc01.pdf

http://airccse.org/journal/ijsc/current.html