Search results for: Learning Algorithms

Authors: Faisal Aburub, Wael Hadi

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Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: Classification, data mining, evaluation measures, groundwater.

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Authors: Khalid Albishi

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This paper participates in giving new vision and explains the learning and acquisition processes of English language by analyzing a certain context. Five important factors in English language acquisition and learning are discussed and suitable solutions are provided. The factors are compared with the learners' linguistic background at Bisha College of Technology BCT attempting to link the issues faced by students and the research done on similar situations. These factors are phonology, age of acquisition, motivation, psychology and courses of English. These factors are very important; because they interfere and affect specific learning processes at BCT context and general English learning situations.

Keywords: Acquisition, age, factors, language, learning.

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Authors: Şahin Emrah Amrahov, Fatih Aybar, Serhat Doğan

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In this paper we consider the rule reduct generation problem. Rule Reduct Generation (RG) and Modified Rule Generation (MRG) algorithms, that are used to solve this problem, are well-known. Alternative to these algorithms, we develop Pruning Rule Generation (PRG) algorithm. We compare the PRG algorithm with RG and MRG.

Keywords: Rough sets, Decision rules, Rule induction, Classification.

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Authors: Seyed Habib A. Rahmati

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During last decades, developing multi-objective evolutionary algorithms for optimization problems has found considerable attention. Flexible job shop scheduling problem, as an important scheduling optimization problem, has found this attention too. However, most of the multi-objective algorithms that are developed for this problem use nonprofessional approaches. In another words, most of them combine their objectives and then solve multi-objective problem through single objective approaches. Of course, except some scarce researches that uses Pareto-based algorithms. Therefore, in this paper, a new Pareto-based algorithm called controlled elitism non-dominated sorting genetic algorithm (CENSGA) is proposed for the multi-objective FJSP (MOFJSP). Our considered objectives are makespan, critical machine work load, and total work load of machines. The proposed algorithm is also compared with one the best Pareto-based algorithms of the literature on some multi-objective criteria, statistically.

Keywords: Scheduling, Flexible job shop scheduling problem, controlled elitism non-dominated sorting genetic algorithm

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Authors: Dorine M. Mattar, Rim M. El Khoury

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This paper examined the influence of matching students- learning preferences with the teaching methodology adopted, on their academic performance in an accounting course in two types of learning environment in one university in Lebanon: classes with PowerPoint (PPT) vs. conventional classes. Learning preferences were either for PPT or for Conventional methodology. A statistically significant increase in academic achievement is found in the conventionally instructed group as compared to the group taught with PPT. This low effectiveness of PPT might be attributed to the learning preferences of Lebanese students. In the PPT group, better academic performance was found among students with learning/teaching match as compared with students with learning/teaching mismatch. Since the majority of students display a preference for the conventional methodology, the result might suggest that Lebanese students- performance is not optimized by PPT in the accounting classrooms, not because of PPT itself, but because it is not matching the Lebanese students- learning preferences in such a quantitative course.

Keywords: Accounting education, learning preferences, learning/teaching match, Lebanon, Student performance.

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Authors: Lun-Ping Hung, Nan-Chen Hsieh, Chia-Ling Ho, Chien-Liang Chen

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Currently, most of distance learning courses can only deliver standard material to students. Students receive course content passively which leads to the neglect of the goal of education – “to suit the teaching to the ability of students". Providing appropriate course content according to students- ability is the main goal of this paper. Except offering a series of conventional learning services, abundant information available, and instant message delivery, a complete online learning environment should be able to distinguish between students- ability and provide learning courses that best suit their ability. However, if a distance learning site contains well-designed course content and design but fails to provide adaptive courses, students will gradually loss their interests and confidence in learning and result in ineffective learning or discontinued learning. In this paper, an intelligent tutoring system is proposed and it consists of several modules working cooperatively in order to build an adaptive learning environment for distance education. The operation of the system is based on the result of Self-Organizing Map (SOM) to divide students into different groups according to their learning ability and learning interests and then provide them with suitable course content. Accordingly, the problem of information overload and internet traffic problem can be solved because the amount of traffic accessing the same content is reduced.

Keywords: Distance Learning, Intelligent Tutoring System(ITS), Self-Organizing Map (SOM)

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Authors: Sujit K. Basak, Marguerite Wotto, Paul Bélanger

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This paper presents a conceptual framework on the critical success factors of e-learning implementation in higher education, derived from an in-depth survey of literature review. The aim of this study was achieved by identifying critical success factors that affect for the successful implementation of e-learning. The findings help to articulate issues that are related to e-learning implementation in both formal and non-formal higher education and in this way contribute to the development of programs designed to address the relevant issues.

Keywords: Critical success factors, e-learning, higher education, life-long learning.

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Authors: Nor Maniha Abdul Ghani, Faradila Naim, Tan Piow Yon

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This project focuses on the development of a line follower algorithm for a Two Wheels Balancing Robot. In this project, ATMEGA32 is chosen as the brain board controller to react towards the data received from Balance Processor Chip on the balance board to monitor the changes of the environment through two infra-red distance sensor to solve the inclination angle problem. Hence, the system will immediately restore to the set point (balance position) through the implementation of internal PID algorithms at the balance board. Application of infra-red light sensors with the PID control is vital, in order to develop a smooth line follower robot. As a result of combination between line follower program and internal self balancing algorithms, we are able to develop a dynamically stabilized balancing robot with line follower function.

Keywords: infra-red sensor, PID algorithms, line followerBalancing robot

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Authors: E. Dado, R. Beheshti

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Recent developments in information and communication technologies (ICT) have created excellent conditions for profoundly enhancing the traditional learning and teaching practices. New modes of teaching in higher education subjects can profoundly enhance ones ability to proactively constructing his or her personal learning universe. These developments have contributed to digital learning environments becoming widely available and accessible. In addition, there is a trend towards enlargement and specialization in higher education in Europe. With as a result that existing Master of Science (MSc) programmes are merged or new programmes have been established that are offered as joint MSc programmes to students. In these joint MSc programmes, the need for (common) digital learning environments capable of surmounting the barriers of time and location has become evident. This paper discusses the past and ongoing efforts to establish such common digital learning environments in two joint MSc programmes in Europe and discusses the way technology-based learning environments affect the traditional way of learning.

Keywords: education, engineering, learning environments, ICT.

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Authors: R. Gomathi, D. Sharmila

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The emergence of the Semantic Web technology increases day by day due to the rapid growth of multiple web pages. Many standard formats are available to store the semantic web data. The most popular format is the Resource Description Framework (RDF). Querying large RDF graphs becomes a tedious procedure with a vast increase in the amount of data. The problem of query optimization becomes an issue in querying large RDF graphs. Choosing the best query plan reduces the amount of query execution time. To address this problem, nature inspired algorithms can be used as an alternative to the traditional query optimization techniques. In this research, the optimal query plan is generated by the proposed SAPSO algorithm which is a hybrid of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms. The proposed SAPSO algorithm has the ability to find the local optimistic result and it avoids the problem of local minimum. Experiments were performed on different datasets by changing the number of predicates and the amount of data. The proposed algorithm gives improved results compared to existing algorithms in terms of query execution time.

Keywords: Semantic web, RDF, Query optimization, Nature inspired algorithms, PSO, SA.

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Authors: Stephen Akuma, Timothy Ndera

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Information seekers get “lost in hyperspace” due to the voluminous documents updated daily on the internet. Adaptive Hypermedia Systems (AHS) are used to direct learners to their target goals. One of the most common AHS designed to help information seekers to overcome the problem of information overload is the Adaptive Education Hypermedia System (AEHS). However, this paper focuses on AEHS that adopts the learning preference of high school students and deliver learning content according to this preference throughout their learning experience. The research developed a prototype system for predicting students’ learning preference from the Visual, Aural, Read-Write and Kinesthetic (VARK) learning style model and adopting the learning content suitable to their preference. The predicting strength of several classifiers was compared and we found Support Vector Machine (SVM) to be more accurate in predicting learning style based on users’ preferences.

Keywords: Hypermedia, adaptive education, learning style, lesson content, user profile, prediction, feedback, adaptive hypermedia, learning style.

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Authors: Sayyed R Mousavi

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The Far From Most Strings Problem (FFMSP) is to obtain a string which is far from as many as possible of a given set of strings. All the input and the output strings are of the same length, and two strings are said to be far if their hamming distance is greater than or equal to a given positive integer. FFMSP belongs to the class of sequences consensus problems which have applications in molecular biology. The problem is NP-hard; it does not admit a constant-ratio approximation either, unless P = NP. Therefore, in addition to exact and approximate algorithms, (meta)heuristic algorithms have been proposed for the problem in recent years. On the other hand, in the recent years, hybrid algorithms have been proposed and successfully used for many hard problems in a variety of domains. In this paper, a new metaheuristic algorithm, called Constructive Beam and Local Search (CBLS), is investigated for the problem, which is a hybridization of constructive beam search and local search algorithms. More specifically, the proposed algorithm consists of two phases, the first phase is to obtain several candidate solutions via the constructive beam search and the second phase is to apply local search to the candidate solutions obtained by the first phase. The best solution found is returned as the final solution to the problem. The proposed algorithm is also similar to memetic algorithms in the sense that both use local search to further improve individual solutions. The CBLS algorithm is compared with the most recent published algorithm for the problem, GRASP, with significantly positive results; the improvement is by order of magnitudes in most cases.

Keywords: Bioinformatics, Far From Most Strings Problem, Hybrid metaheuristics, Matheuristics, Sequences consensus problems.

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Authors: A. Zimnas, D. Kleftouris, N. Valkanos

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Today-s Information and Knowledge Society has placed new demands on education and a new paradigm of education is required. Learning, facilitated by educational systems and the pedagogic process, is globally undergoing dramatic changes. The aim of this paper is the development of a simple Instructional Design tool for E-Learning, named IDEL (Instructional Design for Electronic Learning), that provides the educators with facilities to create their own courses with the essential educational material and manage communication with students. It offers flexibility in the way of learning and provides ease in employment and reusability of resources. IDEL is a web-based Instructional System and is designed to facilitate course design process in accordance with the ADDIE model and the instructional design principles with emphasis placed on the use of technology enhanced learning. An example case of using the ADDIE model to systematically develop a course and its implementation with the aid of IDEL is given and some results from student evaluation of the tool and the course are reported.

Keywords: Education, E-learning, Instructional Design.

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Authors: F. Fissou Amigue, S. Ndjakomo Essiane, S. Pérabi Ngoffé, G. Abessolo Ondoa, G. Mengata Mengounou, T. P. Nna Nna

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This document proposes a method for determining the optimal point of integration of distributed generation (DG) in distribution grid. Slime mould optimization is applied to determine best node in case of one and two injection point. Problem has been modeled as an optimization problem where the objective is to minimize joule loses and main constraint is to regulate voltage in each point. The proposed method has been implemented in MATLAB and applied in IEEE network 33 and 69 nodes. Comparing results obtained with other algorithms showed that slime mould optimization algorithms (SMOA) have the best reduction of power losses and good amelioration of voltage profile.

Keywords: Optimization, distributed generation, integration, slime mould algorithm.

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Authors: T. Aydin, M. F. Alaeddinoglu

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People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatiotemporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newlyformed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.

Keywords: Apriori algorithm, association rules, data mining, spatio-temporal data.

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Authors: Srinivasan Latha, M. R. Christhu Raj, Rajeev Sukumaran

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Active learning strategies have completely rewritten the concept of teaching and learning. Academicians have clocked back to Socratic approaches of questioning. Educators have started implementing active learning strategies for effective learning with the help of tools and technology. As Generation-Y learners are mostly visual, engaging them using visualization techniques play a vital role in their learning process. The facilitator has an important role in intrinsically motivating the learners using different approaches to create self-learning interests. Different visualization techniques were used along with lectures to help students understand and appreciate the concepts. Anonymous feedback was collected from learners. The consolidated report shows that majority of learners accepted the usage of visualization techniques was helpful in understanding concepts as well as create interest in learning the course. This study helps to understand, how the use of visualization techniques help the facilitator to engage learners effectively as well create and intrinsic motivation for their learning.

Keywords: Visualization techniques, concept maps, mind maps, argument maps, flowchart, tree diagram, problem solving.

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Authors: Bala Dhandayuthapani Veerasamy

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Rapid steps made in the field of Information and Communication Technology (ICT) has facilitated the development of teaching and learning methods and prepared them to serve the needs of an assorted educational institution. In other words, the information age has redefined the fundamentals and transformed the institutions and method of services delivery forever. The vision is the articulation of a desire to transform the method of teaching and learning could proceed through e-learning. E-learning is commonly deliberated to use of networked information and communications technology in teaching and learning practice. This paper deals the general aspects of the e-leaning with its issues, developments, opportunities and challenges, which can the higher institutions own.

Keywords: 2D & 3D Animations, challenges, E-learning, Flash, HTML, issues, Multimedia, opportunities, VRML

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Authors: Aleksandar Tsenov

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The main goal of this work is to propose a way for combined use of two nontraditional algorithms by solving topological problems on telecommunications concentrator networks. The algorithms suggested are the Simulated Annealing algorithm and the Genetic Algorithm. The Algorithm of Simulated Annealing unifies the well known local search algorithms. In addition - Simulated Annealing allows acceptation of moves in the search space witch lead to decisions with higher cost in order to attempt to overcome any local minima obtained. The Genetic Algorithm is a heuristic approach witch is being used in wide areas of optimization works. In the last years this approach is also widely implemented in Telecommunications Networks Planning. In order to solve less or more complex planning problem it is important to find the most appropriate parameters for initializing the function of the algorithm.

Keywords: Concentrator network, genetic algorithm, simulated annealing, UCPL.

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Authors: Kafuko M. Maria, Namisango Fatuma, Byomire Gorretti

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Mobile learning (m-learning) is steadily growing and has undoubtedly derived benefits to learners and tutors in different learning environments. This paper investigates the variation in benefits derived from enhanced classroom learning through use of m-learning platforms in the context of a developing country owing to the fact that it is still in its initial stages. The study focused on how basic technology-enhanced pedagogic innovation like cell phone-based learning is enhancing classroom learning from the learners’ perspective. The paper explicitly indicates the opportunities presented by enhanced learning to a conventional learning environment like a physical classroom. The findings were obtained through a survey of two universities in Uganda in which data was quantitatively collected, analyzed and presented in a three banded diagram depicting the variation in the obtainable benefits. Learners indicated that a smartphone is the most commonly used device. Learners also indicate that straight lectures, student to student plus student to lecturer communication, accessing learning material and assignments are core activities. In a TEL environment support by smartphones, learners indicated that they conveniently achieve the prior activities plus discussions and group work. Learners seemed not attracted to the possibility of using TEL environment to take lectures, as well as make class presentations. The less attractiveness of these two factors may be due to the teacher centered approach commonly applied in the country’s education system.

Keywords: Technology enhanced learning, mobile learning classroom learning, perceived benefits.

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Authors: Amir Gholami Pastaki, Ali Reza Sahab, Seyed Mehdi Sadeghi

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LSP routing is among the prominent issues in MPLS networks traffic engineering. The objective of this routing is to increase number of the accepted requests while guaranteeing the quality of service (QoS). Requested bandwidth is the most important QoS criterion that is considered in literatures, and a various number of heuristic algorithms have been presented with that regards. Many of these algorithms prevent flows through bottlenecks of the network in order to perform load balancing, which impedes optimum operation of the network. Here, a modern routing algorithm is proposed as MIRAD: having a little information of the network topology, links residual bandwidth, and any knowledge of the prospective requests it provides every request with a maximum bandwidth as well as minimum end-to-end delay via uniform load distribution across the network. Simulation results of the proposed algorithm show a better efficiency in comparison with similar algorithms.

Keywords: new generation networks, QoS, traffic engineering, MPLS, QoS based routing, LSP

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Authors: C. Sanchis-Borras, J. M. Molina-Garcia-Pardo, P. Degauque, M. Lienard

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The objective of this contribution is to study the performances in terms of bit error rate, of space-time code algorithms applied to MIMO communication in tunnels. Indeed, the channel characteristics in a tunnel are quite different than those of urban or indoor environment, due to the guiding effect of the tunnel. Therefore, MIMO channel matrices have been measured in a straight tunnel, in a frequency band around 3GHz. Correlation between array elements and properties of the MIMO matrices are first studied as a function of the distance between the transmitter and the receiver. Then, owing to a software tool simulating the link, predicted values of bit error rate are given for VLAST, OSTBC and QSTBC algorithms applied to a MIMO configuration with 2 or 4 array elements. Results are interpreted from the analysis of the channel properties.

Keywords: MIMO, propagation channel, space-time algorithms, tunnel.

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life due to the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or COVID-19 induced pneumonia. The early prediction and classification of such lung diseases help reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans are pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publicly available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scans, COVID-19, deep learning, image processing, pneumonia, lung disease.

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Authors: Li Shoutao, Gordon Lee

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Evolutionary robotics is concerned with the design of intelligent systems with life-like properties by means of simulated evolution. Approaches in evolutionary robotics can be categorized according to the control structures that represent the behavior and the parameters of the controller that undergo adaptation. The basic idea is to automatically synthesize behaviors that enable the robot to perform useful tasks in complex environments. The evolutionary algorithm searches through the space of parameterized controllers that map sensory perceptions to control actions, thus realizing a specific robotic behavior. Further, the evolutionary algorithm maintains and improves a population of candidate behaviors by means of selection, recombination and mutation. A fitness function evaluates the performance of the resulting behavior according to the robot-s task or mission. In this paper, the focus is in the use of genetic algorithms to solve a multi-objective optimization problem representing robot behaviors; in particular, the A-Compander Law is employed in selecting the weight of each objective during the optimization process. Results using an adaptive fitness function show that this approach can efficiently react to complex tasks under variable environments.

Keywords: adaptive fuzzy neural inference, evolutionary tuning

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Authors: M. Kalamani, S. Valarmathy, M. Krishnamoorthi

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In this paper, Fuzzy C-Means clustering with Expectation Maximization-Gaussian Mixture Model based hybrid modeling algorithm is proposed for Continuous Tamil Speech Recognition. The speech sentences from various speakers are used for training and testing phase and objective measures are between the proposed and existing Continuous Speech Recognition algorithms. From the simulated results, it is observed that the proposed algorithm improves the recognition accuracy and F-measure up to 3% as compared to that of the existing algorithms for the speech signal from various speakers. In addition, it reduces the Word Error Rate, Error Rate and Error up to 4% as compared to that of the existing algorithms. In all aspects, the proposed hybrid modeling for Tamil speech recognition provides the significant improvements for speechto- text conversion in various applications.

Keywords: Speech Segmentation, Feature Extraction, Clustering, HMM, EM-GMM, CSR.

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Authors: F. Sarabchi, M. E. Kalantari

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In this paper, Optimum adaptive loading algorithms are applied to multicarrier system with Space-Time Block Coding (STBC) scheme associated with space-time processing based on singular-value decomposition (SVD) of the channel matrix over Rayleigh fading channels. SVD method has been employed in MIMO-OFDM system in order to overcome subchannel interference. Chaw-s and Compello-s algorithms have been implemented to obtain a bit and power allocation for each subcarrier assuming instantaneous channel knowledge. The adaptive loaded SVD-STBC scheme is capable of providing both full-rate and full-diversity for any number of transmit antennas. The effectiveness of these techniques has demonstrated through the simulation of an Adaptive loaded SVDSTBC system, and the comparison shown that the proposed algorithms ensure better performance in the case of MIMO.

Keywords: OFDM, MIMO, SVD, STBC, Adaptive Loading.

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Authors: Min Kyeong Cha, Ju Yeon Mun, Seong Baeg Kim

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Recently, ‘play of learning’ becomes important and is  emphasized as a useful learning tool. Therefore, interest in  edutainment contents is growing. Storytelling is considered first as a  method that improves the transmission of information and learner's  interest when planning edutainment contents. In this study, we  designed edutainment contents in the form of an adventure game that  applies the storytelling method. This content provides questions and  items constituted dynamically and reorganized learning contents  through analysis of test results. It allows learners to solve various  questions through effective iterative learning. As a result, the learners  can reach mastery learning.

 

Keywords: Storytelling, edutainment, mastery learning, computer operating principle.

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Authors: A. Meydanlioglu, F. Arikan

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In recent years, thanks to the development of information and communication technologies, the computer and internet have been used widely in higher education. Internet-based education is impacting traditional higher education as online components increasingly become integrated into face- to- face (FTF) courses. The goal of combined internet-based and traditional education is to take full advantage of the benefits of each platform in order to provide an educational opportunity that can promote student learning better than can either platform alone. Research results show that the use of hybrid learning is more effective than online or FTF models in higher education. Due to the potential benefits, an increasing number of institutions are interested in developing hybrid courses, programs, and degrees. Future research should evaluate the effectiveness of hybrid learning. This paper is designed to determine the impact of hybrid learning on higher education.

Keywords: E-learning, higher education, hybrid learning, online education.

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Authors: Said Elkassimi, Said Safi, B. Manaut

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In recent years, multi-antenna techniques are being considered as a potential solution to increase the flow of future wireless communication systems. The objective of this article is to study the emission and reception system MIMO (Multiple Input Multiple Output), and present the different reception decoding techniques. First we will present the least complex technical, linear receivers such as the zero forcing equalizer (ZF) and minimum mean squared error (MMSE). Then a nonlinear technique called ordered successive cancellation of interferences (OSIC) and the optimal detector based on the maximum likelihood criterion (ML), finally, we simulate the associated decoding algorithms for MIMO system such as ZF, MMSE, OSIC and ML, thus a comparison of performance of these algorithms in MIMO context.

Keywords: Multiple Input Multiple Outputs (MIMO), ZF, MMSE, Ordered Interference Successive Cancellation (OSIC), ML, Interference Successive Cancellation (SIC).

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Authors: C.Kia, M.R.Arshad, A.H.Adom, P.A.Wilson

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This paper presents recent work on the improvement of the robotics vision based control strategy for underwater pipeline tracking system. The study focuses on developing image processing algorithms and a fuzzy inference system for the analysis of the terrain. The main goal is to implement the supervisory fuzzy learning control technique to reduce the errors on navigation decision due to the pipeline occlusion problem. The system developed is capable of interpreting underwater images containing occluded pipeline, seabed and other unwanted noise. The algorithm proposed in previous work does not explore the cooperation between fuzzy controllers, knowledge and learnt data to improve the outputs for underwater pipeline tracking. Computer simulations and prototype simulations demonstrate the effectiveness of this approach. The system accuracy level has also been discussed.

Keywords: Fuzzy logic, Underwater target tracking, Autonomous underwater vehicles, Artificial intelligence, Simulations, Robot navigation, Vision system.

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: Classification algorithms; data mining; tourism; knowledge discovery.

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