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Evaluation of Student's Performance and Learning Efficiency Based on ANFIS

Evaluation of Student's Performance and Learning Efficiency ...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Norazah Yusof Nur Ariffin Mohd Zin Noraniah Mohd Yassin Paridah Samsuri Faculty of Computer Science and Information System Universiti Teknologi Malaysia Skudai Johor Malaysia

This work focuses on a systematic approach in assessing and reasoning the student's performance and efficiency level in programming technique course. There are four criteria required to indicate the student's performance and efficiency level which are scores earned, time spent, number of attempts and help needed. A fuzzy rule base model that has been proposed in previous work is found to be insufficient in deciding all possible conditions. To deal with this problem, this work focuses on the adaptive neuro-fuzzy inference system (ANFIS) approach in determining the possible conditions in order to form a fuzzy rule based system of a student model. The back- propagation is utilized as the learning mechanism for the neural network to solve the incompleteness in the decision made by human experts. By training the neural network with 18 human decisions that are certain, the neural network has successfully derived other decisions to form a complete fuzzy rule base and able to adjust its parameter by learning mechanism. However, some of the decisions are found illogically classified.

关键词： Fuzzy systems Biological neural networks Humans Knowledge based systems Adaptive systems Learning systems Fuzzy neural networks Fuzzy sets pattern recognition Computer science

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A New Approach to Solve Traveling Salesman Problem Using Genetic Algorithm Based on Heuristic Crossover and Mutation Operator

A New Approach to Solve Traveling Salesman Problem Using Gen...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Gohar Vahdati Mehdi Yaghoubi Mahdieh Poostchi M. B. Naghibi S. Computer Department Islamic Azad University Iran Computer Department Iran University of Science and Technology (IUST) Tehran Iran Electr. Dept. Ferdowsi Univ. of Mashhad Mashhad Iran

This paper proposes a new solution for Traveling Salesman Problem (TSP), using genetic algorithm. A heuristic crossover and mutation operation have been proposed to prevent premature convergence. Presented operations try not only to solve this challenge by means of a heuristic function but also considerably accelerate the speed of convergence by reducing excessively the number of generations. By considering TSP's evaluation function, as a traveled route among all n cities, the probability of crossover and mutation have been adaptively and nonlinearly tuned. Experimental results demonstrate that proposed algorithm due to the heuristic performance is not easily getting stuck in local optima and has a reasonable convergent speed to reach the global optimal solution. Besides, implementation of the algorithm does not have any complexities.

关键词： Traveling salesman problems Genetic algorithms Genetic mutations Cities and towns Biological cells pattern recognition Acceleration Cost function Ant colony optimization Heuristic algorithms

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3D Volumetric CT Liver Segmentation Using Hybrid Segmentation Techniques

3D Volumetric CT Liver Segmentation Using Hybrid Segmentatio...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Wan Nural Jawahir Wan Yussof Hans Burkhardt Pattern Recognition and Image Processing Albert Ludwigs University of Freiburg Freiburg im Breisgau Germany

The first step for computer-aided diagnosis for liver of CT scans is the identification of liver region. To deal with multislice CT scans, automatic liver segmentation is required. In this paper, we propose a liver segmentation algorithm using hybrid techniques by combining morphological-based, region-based and histogram-based techniques to segment volumetric CT data. A morphological-based technique is used to find the initial liver tissue from the first slice which is set as a starting slice and region-based is used for further processing for the rest slices, which incorporates seed point generation from Euclidean distance transform (EDT) image on the previous slice for region growing on the current slice. In order to remove neighboring abdominal organs of the liver which connect to the liver organ, the histogram-based technique is used by finding the left and right histogram tail threshold (HTT) and we repeat the use of morphology filtering and large contour detecting for liver smoothing.

关键词： Liver Computed tomography Computer aided diagnosis Image segmentation Euclidean distance Abdomen Histograms Tail Morphology Filtering

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A Function Principle Approach to Jaccard Ranking Fuzzy Numbers

A Function Principle Approach to Jaccard Ranking Fuzzy Numbe...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Nazirah Ramli Daud Mohamad Department of Mathematics and Statistics Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Pahang Bandar Pusat Jengka Pahang Malaysia

Ranking of fuzzy numbers plays an important role in practical use and has become a prerequisite procedure for decision-making problem in fuzzy environment. Various techniques of ranking fuzzy numbers have been developed and one of them is based on the similarity measure technique. Jaccard index similarity measure has been introduced in ranking the fuzzy numbers where the fuzzy maximum and fuzzy minimum are obtained by using the extension principle. However, this approach is only applicable to normal fuzzy numbers and therefore, fails to rank the non-normal fuzzy numbers. Besides that the extension principle does not preserve the type of membership function of the fuzzy numbers and also involves laborious mathematical operations. In this paper, a simple vertex fuzzy arithmetic operation namely function principle is applied in the Jaccard ranking index. This method is capable to rank both normal and non-normal fuzzy numbers in a simpler manner. It has also improved the ranking results by the original Jaccard ranking method and some of the existing ranking methods.

关键词： Arithmetic Decision making Mathematics pattern recognition Statistics Standards development Fuzzy sets

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Arrhythmia Beat Classification Using Pruned Fuzzy K-Nearest Neighbor Classifier

Arrhythmia Beat Classification Using Pruned Fuzzy K-Nearest ...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： M. Arif M. U. Akram F. A. Afsar Department of Electrical Engineering Air University Islamabad Pakistan Elixir technologies Pakistan Private Limited Islamabad Pakistan Department of Computer & Information Sciences PIEAS Islamabad Pakistan

In this paper, pruned fuzzy k-nearest neighbor (PFKNN) classifier is proposed to classify different types of arrhythmia beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification which can be very time consuming and requires large storage space. Hence, we have proposed a time efficient pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using the pruning algorithm with Fuzzy KNN, we have achieved beat classification accuracy of 97% and geometric mean of sensitivity is 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used.

关键词： Electrocardiography Electronic mail Databases Training data Decision support systems Feature extraction Wavelet analysis Neural networks Fuzzy logic pattern recognition

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United Zernike invariants for character images

United Zernike invariants for character images

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1st international Visual Informatics conference, IVIC 2009

作者： Bakar, Norsharina Abu Mariyam Shamsuddin, Siti Soft Computing Research Group K-Economy Research Alliance Universiti Teknologi Malaysia Skudai Johor Malaysia

ISBN: (纸本)3642050352

Feature extraction is one of the major components in traditional pattern recognition. There are many methods for extracting the features, either structural approach or global approach. In this paper, we present integrated formulation of Zernike Moments and United Moment Invariant for extracting the character images accordingly. The extraction values are validated by measuring the Inter-class and intra-class analysis to illustrate the effectiveness of the proposed solution. The results yield that the proposed method are feasible and better for extracting the images for both inter-class and intra-class analysis. © 2009 Springer-Verlag.

关键词： Feature extraction

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Optimization of LRT Route for Mobile Web Application Engine

Optimization of LRT Route for Mobile Web Application Engine

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international conference of soft computing and pattern recognition (SOCPAR)

作者： A. M. Haziq Lim N. S. Wan Sazli B. Hussin A. A. Azlianor S. M. Suhaizan K. Massila Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Malacca Malaysia

Integrated light rail transit (LRT) system in the capital of Malaysia connects some key districts to historical places, interesting places, business areas and shopping malls are concentrated. The train services are running independently but have interchanges to integrate from one different LRT lines. This may leads the traveler facing difficulty when they are choosing incorrect destination station especially on different LRT lines which contribute to time consuming and high costing. In previous research we already implement the mobile Web application architecture where the destination-oriented routes need to be dynamically generated by determining the nearest station according to the specific places. In this paper, we proposed the used of Dijkstra's algorithms to provide more effective and intelligent shortest path system to provide the solution for traveler to reach the desired destination.

关键词： Light rail systems Engines Shape Curve fitting Phase detection Image segmentation Iterative algorithms pattern recognition Information science Data mining

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Surface Defect Characterization in Polishing Process Using Contour Dispersion

Surface Defect Characterization in Polishing Process Using C...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Adnan Rachmat Anom Besari Ruzaidi Zamri Khairul Anuar A. Rahman Md. Dan Md. Palil Anton Satria Prabuwono Faculty of Manufacturing Engineering Universiti Teknikal Malaysia Melaka Malaysia Faculty of Manufacturing Engineering Universiti Kebangsaan Malaysia Malaysia

Automatic surface defect detection with vision systems can bring manufacturers a number of significant benefits, especially when used on-line. This non-contact method may present an alternative to allow the surface defect to be measured rapidly and with an acceptable accuracy. One of the most promising of the non-contact methods in terms of speed and accuracy is the computer vision technique. This paper basically defines a surface defect characterization using contour dispersion. The basic idea of this research is to find an optimal gray-level threshold value for separating objects of interest in an image from the background based on their gray-level distribution using contour dispersion level to find the characteristic of surface defect. Next, the research direction has been suggested to develop an automatic polishing robot system using vision sensor based on surface defect characterization.

关键词： Shape Curve fitting Phase detection Image segmentation Iterative algorithms pattern recognition Information science Data mining Computer graphics Shearing

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A New Solution Approach for Grouping Problems Based on Evolution Strategies

A New Solution Approach for Grouping Problems Based on Evolu...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Ali Husseinzadeh Kashan Masoud Jenabi Mina Husseinzadeh Kashan Department of Industrial Engineering Amirkabir University of Technology슠 Tehran Iran Department of Industrial Engineering Tarbiat Moalem University Tehran Iran

Since its foundation in 1994, the grouping genetic algorithm (GGA) is the only evolutionary algorithm heavily modified to suit the structure of grouping problems. In this paper we design the grouping version of evolution strategies (ES). It is well-known that ES maintains a Gaussian mutation, recombination and a selection operator for optimizing non-linear continuous functions. Therefore, the development of grouping evolution strategies (GES) for solving grouping problems that are discrete in nature, calls for developing operators having the major characteristics of the original ones and being respondent to the structure of grouping problems. We propose a mutation operator analogous to the original one that works with groups instead of scalars and use it in a two phase procedure to generate the new solution. We implement (1+Lambda)-GES and evaluate its performance versus GGA on some of hard benchmarked instances of the bin packing problem. Computational results testify that our approach is efficient and can be regarded as a promising solver for the wide class of grouping problems.

关键词： Encoding Genetic algorithms Industrial engineering Evolutionary computation Genetic mutations Job shop scheduling pattern recognition Benchmark testing Partitioning algorithms Cost function

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Rough set based ensemble prediction for topic specific web crawling

Rough set based ensemble prediction for topic specific web c...

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7th international conference on Advances in pattern recognition

作者： Saha, Suman Murthy, C. A. Pal, Sankar K. Indian Stat Inst Ctr Soft Comp Res Kolkata W Bengal India

ISBN: (纸本)9780769535203

The rapid growth of the world wide web had made the problem of useful resource discovery an important one in recent years. Several techniques such as focused crawling and intelligent crawling have recently been proposed for topic specific resource discovery. All these crawlers use the hypertext features behavior in order to perform topic specific resource discovery. A focused crawler uses the relevance score of the crawled page to score the unvisited URLs extracted from it. The scored URLs are then added to the frontier. Then it picks up the best URL to crawl next. Focused crawlers rely on different types of features of the crawled pages to keep the crawling scope within the desired domain and they are obtained from URL, anchor text, link structure and text contents of the parent and ancestor pages. Different focused crawling algorithms use these different set of features to predict the relevance and quality of the unvisited Web pages. In this article a combined method based on Rough Set Theory has been proposed. It combines the available predictions using decision rules and can build much larger domain-specific collections with less noise. Our experiment in this regard has provided better Harvest rate and better Target recall for focused crawling.

关键词： Web resource discovery Classification

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