Search results for: Learning algorithm
4399 Tipover Stability Enhancement of Wheeled Mobile Manipulators Using an Adaptive Neuro- Fuzzy Inference Controller System
Authors: A. Ghaffari, A. Meghdari, D. Naderi, S. Eslami
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In this paper an algorithm based on the adaptive neuro-fuzzy controller is provided to enhance the tipover stability of mobile manipulators when they are subjected to predefined trajectories for the end-effector and the vehicle. The controller creates proper configurations for the manipulator to prevent the robot from being overturned. The optimal configuration and thus the most favorable control are obtained through soft computing approaches including a combination of genetic algorithm, neural networks, and fuzzy logic. The proposed algorithm, in this paper, is that a look-up table is designed by employing the obtained values from the genetic algorithm in order to minimize the performance index and by using this data base, rule bases are designed for the ANFIS controller and will be exerted on the actuators to enhance the tipover stability of the mobile manipulator. A numerical example is presented to demonstrate the effectiveness of the proposed algorithm.Keywords: Mobile Manipulator, Tipover Stability Enhancement, Adaptive Neuro-Fuzzy Inference Controller System, Soft Computing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19534398 Method to Improve Channel Coding Using Cryptography
Authors: Ayyaz Mahmood
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A new approach for the improvement of coding gain in channel coding using Advanced Encryption Standard (AES) and Maximum A Posteriori (MAP) algorithm is proposed. This new approach uses the avalanche effect of block cipher algorithm AES and soft output values of MAP decoding algorithm. The performance of proposed approach is evaluated in the presence of Additive White Gaussian Noise (AWGN). For the verification of proposed approach, computer simulation results are included.Keywords: Advanced Encryption Standard (AES), Avalanche Effect, Maximum A Posteriori (MAP), Soft Input Decryption (SID).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19344397 Cost and Productivity Experiences of Pakistan with Aggregate Learning Curve
Authors: Jamshaid ur Rehman, Shahida Wizarat
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The principal focus of this study is on the measurement and analysis of labor learnings in Pakistan. The study at the aggregate economy level focus on the labor productivity movements and at large-scale manufacturing level focus on the cost structure, with isolating the contribution of the learning curve. The analysis of S-shaped curve suggests that learnings are only below one half of aggregate learning curve and other half shows the retardation in learning, hence retardation in productivity movements. The study implies the existence of learning economies in term of cost reduction that is input cost per unit produced decreases by 0.51 percent every time the cumulative production output doubles.Keywords: Cost, Inflection Point, Learning Curve, Minima, Maxima, and Productivity
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17694396 Design A Situated Learning Environment Using Mixed Reality Technology - A Case Study
Authors: Rasimah Che Mohd Yusoff, Halimah Badioze Zaman, Azlina Ahmad
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Mixed Reality (MR) is one of the newest technologies explored in education. It promises the potential to promote teaching and learning and making learners- experience more “engaging". However, there still lack of research on designing a virtual learning environment using MR technology. In this paper, we describe the Mixed Reality technology, the characteristics of situated learning as instructional design for virtual environment using mixed reality technology. We also explain a case study that implemented those design and also the system overview.Keywords: authentic activity. authentic context, mixed reality, situated learning
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23444395 Learning User Keystroke Patterns for Authentication
Authors: Ying Zhao
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Keystroke authentication is a new access control system to identify legitimate users via their typing behavior. In this paper, machine learning techniques are adapted for keystroke authentication. Seven learning methods are used to build models to differentiate user keystroke patterns. The selected classification methods are Decision Tree, Naive Bayesian, Instance Based Learning, Decision Table, One Rule, Random Tree and K-star. Among these methods, three of them are studied in more details. The results show that machine learning is a feasible alternative for keystroke authentication. Compared to the conventional Nearest Neighbour method in the recent research, learning methods especially Decision Tree can be more accurate. In addition, the experiment results reveal that 3-Grams is more accurate than 2-Grams and 4-Grams for feature extraction. Also, combination of attributes tend to result higher accuracy.Keywords: Keystroke Authentication, Pattern recognition, MachineLearning, Instance-based Learning, Bayesian, Decision Tree.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28044394 Implemented 5-bit 125-MS/s Successive Approximation Register ADC on FPGA
Authors: S. Heydarzadeh, A. Kadivarian, P. Torkzadeh
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Implemented 5-bit 125-MS/s successive approximation register (SAR) analog to digital converter (ADC) on FPGA is presented in this paper.The design and modeling of a high performance SAR analog to digital converter are based on monotonic capacitor switching procedure algorithm .Spartan 3 FPGA is chosen for implementing SAR analog to digital converter algorithm. SAR VHDL program writes in Xilinx and modelsim uses for showing results.Keywords: Analog to digital converter, Successive approximation, Capacitor switching algorithm, FPGA
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 43484393 Flipped Classroom in Bioethics Education: A Blended and Interactive Online Learning Courseware that Enhances Active Learning and Student Engagement
Authors: Molly P. M. Wong
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In this study, a blended and interactive e-learning Courseware that our team developed will be introduced, and our team’s experiences on how the e-learning Courseware and the flipped classroom benefit student learning in bioethics in the medical program will be shared. This study is a continuation of the previously established study, which provides a summary of the well-developed e-learning Courseware in a blended learning approach and an update on its efficiency and efficacy. First, a collection of animated videos capturing selected topics of bioethics and related ethical issues and dilemma will be introduced. Next, a selection of problem-based learning videos (“simulated doctor-patient role play”) with pop-up questions and discussions will be further discussed. Our findings demonstrated that these activities launched by the Courseware strongly engaged students in bioethics education and enhanced students’ critical thinking and creativity. Moreover, the educational benefits of the online art exhibition, art jamming and competition will be discussed, through which students could express bioethics through arts and enrich their learning in medical research in an interactive, fun and entertaining way, strengthening their interests in bioethics. Furthermore, online survey questionnaires and focus group interviews were conducted. Our results indicated that implementing the e-learning Courseware with a flipped classroom in bioethics education enhanced both active learning and student engagement. In conclusion, our Courseware not only reinforces education in art, bioethics and medicine, but also benefits students in understanding and critical thinking in socio-ethical issues, and serves as a valuable learning tool in bioethics teaching and learning.
Keywords: Bioethics, courseware, e-learning, flipped classroom.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4754392 Mobile Learning Adoption in Saudi Arabia
Authors: Mohamed E. Seliaman, M. S. Al-Turki
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This paper investigates the use of mobile phones and tablets for learning purposes among university students in Saudi Arabia. For this purpose, an extended Technology Acceptance Model (TAM) is proposed to analyze the adoption of mobile devices and smart phones by Saudi university students for accessing course materials, searching the web for information related to their discipline, sharing knowledge, conducting assignments etc.Keywords: Saudi Arabia, TAM, Mobile learning, e-learning, smart phones.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 36704391 A Deep-Learning Based Prediction of Pancreatic Adenocarcinoma with Electronic Health Records from the State of Maine
Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen
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Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.
Keywords: Cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7974390 Ranking and Unranking Algorithms for k-ary Trees in Gray Code Order
Authors: Fateme Ashari-Ghomi, Najme Khorasani, Abbas Nowzari-Dalini
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In this paper, we present two new ranking and unranking algorithms for k-ary trees represented by x-sequences in Gray code order. These algorithms are based on a gray code generation algorithm developed by Ahrabian et al.. In mentioned paper, a recursive backtracking generation algorithm for x-sequences corresponding to k-ary trees in Gray code was presented. This generation algorithm is based on Vajnovszki-s algorithm for generating binary trees in Gray code ordering. Up to our knowledge no ranking and unranking algorithms were given for x-sequences in this ordering. we present ranking and unranking algorithms with O(kn2) time complexity for x-sequences in this Gray code orderingKeywords: k-ary Tree Generation, Ranking, Unranking, Gray Code.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20894389 Use of Technology to Improve Students’ Attitude in Learning Mathematics of Non-Mathematics Undergraduate Students
Authors: Asia Majeed
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This paper will investigate a form of learning mathematics by integrating technology in mathematics specifically for the university first-year calculus class to support students’ engagement in learning which influences students' conceptual and procedural understanding of the calculus content in a better way. The students with good grades in high school calculus generally struggle in first-year university calculus classes in learning mathematical analysis concepts. This problem has to be addressed. If this problem is not resolved, then most likely students with less ability to do mathematics might not able to complete their degrees. In this work, MATLAB is used to help students in learning and in improving calculus concepts.
Keywords: Calculus, first-year university students, teaching strategies, MATLAB.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3654388 A Study of Learning to Enhance Career Skills Consistent with Disruptive Innovation in the Creative Strategies for Advertising Course
Authors: Kornchanok Chidchaisuwan
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This project is a study of learning activities of creating experience from actual work performance to enhance career skills and technological usage abilities for uses in advertising career work performance for undergraduate students who enroll in the Creative Strategies for Advertising Course. The instructional model consisted of two learning approaches: (1) simulation-based learning, which is the learning with the use of simulations of working in various sections of creative advertisement work with their own work process and steps as well as the virtual technology learning in advertising companies; and (2) project-based learning, which is the learning that the learners engage in actual work performance based on the process of creating and producing creative advertisement works to be present on new media channels. The results of learning management showed that the effects on the students in various aspects were as follows: (1) the students had experience in the advertising process at the higher level; and (2) the students had work performance skills from the actual work performance that enabled them to possess the abilities to create and present their own work; also, they had created more efficient work outcomes and disseminated them on new media channels at a better level.
Keywords: Technical literacy, career skill, experience, simulation-based learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3754387 Neural Network Based Approach for Face Detection cum Face Recognition
Authors: Kesari Verma, Aniruddha S. Thoke, Pritam Singh
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Automatic face detection is a complex problem in image processing. Many methods exist to solve this problem such as template matching, Fisher Linear Discriminate, Neural Networks, SVM, and MRC. Success has been achieved with each method to varying degrees and complexities. In proposed algorithm we used upright, frontal faces for single gray scale images with decent resolution and under good lighting condition. In the field of face recognition technique the single face is matched with single face from the training dataset. The author proposed a neural network based face detection algorithm from the photographs as well as if any test data appears it check from the online scanned training dataset. Experimental result shows that the algorithm detected up to 95% accuracy for any image.Keywords: Face Detection, Face Recognition, NN Approach, PCA Algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22824386 Investigation of Water Vapour Transport Properties of Gypsum Using Genetic Algorithm
Authors: Z. Pavlík, J. Žumár, M. Pavlíková, J. Kočí, R. Černý
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Water vapour transport properties of gypsum block are studied in dependence on relative humidity using inverse analysis based on genetic algorithm. The computational inverse analysis is performed for the relative humidity profiles measured along the longitudinal axis of a rod sample. Within the performed transient experiment, the studied sample is exposed to two environments with different relative humidity, whereas the temperature is kept constant. For the basic gypsum characterisation and for the assessment of input material parameters necessary for computational application of genetic algorithm, the basic material properties of gypsum are measured as well as its thermal and water vapour storage parameters. On the basis of application of genetic algorithm, the relative humidity dependent water vapour diffusion coefficient and water vapour diffusion resistance factor are calculated.Keywords: Water vapour transport, gypsum block, transient experiment, genetic algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16464385 Peer Assessment in the Context of Project-Based Learning Online
Authors: Y. Benjelloun Touimi, N. Faddouli, S. Bennani, M. Khalidi Idrissi
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The pedagogy project has been proven as an active learning method, which is used to develop learner-s skills and knowledge.The use of technology in the learning world, has filed several gaps in the implementation of teaching methods, and online evaluation of learners. However, the project methodology presents challenges in the assessment of learners online. Indeed, interoperability between E-learning platforms (LMS) is one of the major challenges of project-based learning assessment. Firstly, we have reviewed the characteristics of online assessment in the context of project-based teaching. We addressed the constraints encountered during the peer evaluation process. Our approach is to propose a meta-model, which will describe a language dedicated to the conception of peer assessment scenario in project-based learning. Then we illustrate our proposal by an instantiation of the meta-model through a business process in a scenario of collaborative assessment on line.Keywords: Online project based learning, meta-model, peer assessment process.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23494384 An Iterative Method for the Least-squares Symmetric Solution of AXB+CYD=F and its Application
Authors: Minghui Wang
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Based on the classical algorithm LSQR for solving (unconstrained) LS problem, an iterative method is proposed for the least-squares like-minimum-norm symmetric solution of AXB+CYD=E. As the application of this algorithm, an iterative method for the least-squares like-minimum-norm biymmetric solution of AXB=E is also obtained. Numerical results are reported that show the efficiency of the proposed methods.
Keywords: Matrix equation, bisymmetric matrix, least squares problem, like-minimum norm, iterative algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14754383 The Determinants of Senior Students' Behavioral Intention on the Blended E-Learning for the Ceramics Teaching Course at the Active Aging University
Authors: Horng-Jyh Chen, Yi-Fang Chen, Chien-Liang Lin
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In this paper, the authors try to investigate the determinants of behavioral intention of the blended E-learning course for senior students at the Active Ageing University in Taiwan. Due to lower proficiency in the use of computers and less experience on learning styles of the blended E-learning course for senior students will be expected quite different from those for most young students. After more than five weeks course for two years the questionnaire survey is executed to collect data for statistical analysis in order to understand the determinants of the behavioral intention for senior students. The object of this study is at one of the Active Ageing University in Taiwan total of 84 senior students in the blended E-learning for the ceramics teaching course. The research results show that only the perceived usefulness of the blended E-learning course has significant positive relationship with the behavioral intention.
Keywords: Active Aging University, blended E-learning, ceramics teaching course, behavioral intention
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17444382 Machine Learning Development Audit Framework: Assessment and Inspection of Risk and Quality of Data, Model and Development Process
Authors: Jan Stodt, Christoph Reich
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The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.Keywords: Audit, machine learning, assessment, metrics.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9624381 Improvement of Learning Motivation and Negotiation of Learning Disorders of Students Using Integrative Teaching Methodology
Authors: Juris Porozovs, Daina Voita, Anda Kauliņa, Toms Voits, Evita Vaļēviča
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Integrative teaching methodology is based on connecting and summarizing knowledge from different subjects in order to create better understanding of different disciplines and improvement of competences in general. Integrative teaching methodology was implemented and realised during one academic year in 17 Latvian schools according with specially worked out programme by specialists of different fields for adaptation in social environment of children and young people with learning, cognitive functions and motor disorders. Implemented integrative teaching methodology consisted from three subsections which were specialised for adaptation in social environment, improvement of cognitive functions and improvement and harmonization of personality. The results of investigation showed that the use of integrative teaching methodology is an effective way for improvement of learning motivation and negotiation of learning disorders of different age schoolchildren.Keywords: Adaptation in social environment, integrative teaching methodology, learning disorders, learning motivation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19404380 A Genetic Algorithm for Optimum Design of PID Controller in Load Frequency Control
Authors: T. Hussein
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In this paper, determining the optimal proportionalintegral- derivative (PID) controller gains of an single-area load frequency control (LFC) system using genetic algorithm (GA) is presented. The LFC is notoriously difficult to control optimally using conventionally tuning a PID controller because the system parameters are constantly changing. It is for this reason the GA as tuning strategy was applied. The simulation has been conducted in MATLAB Simulink package for single area power system. the simulation results shows the effectiveness performance of under various disturbance.Keywords: Load Frequency Control (LFC), PID controller and Genetic Algorithm (GA).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 37304379 SMCC: Self-Managing Congestion Control Algorithm
Authors: Sh. Jamali, A. Eftekhari
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Transmission control protocol (TCP) Vegas detects network congestion in the early stage and successfully prevents periodic packet loss that usually occurs in TCP Reno. It has been demonstrated that TCP Vegas outperforms TCP Reno in many aspects. However, TCP Vegas suffers several problems that affect its congestion avoidance mechanism. One of the most important weaknesses in TCP Vegas is that alpha and beta depend on a good expected throughput estimate, which as we have seen, depends on a good minimum RTT estimate. In order to make the system more robust alpha and beta must be made responsive to network conditions (they are currently chosen statically). This paper proposes a modified Vegas algorithm, which can be adjusted to present good performance compared to other transmission control protocols (TCPs). In order to do this, we use PSO algorithm to tune alpha and beta. The simulation results validate the advantages of the proposed algorithm in term of performance.Keywords: Self-managing, Congestion control, TCP.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14514378 Self Organizing Mixture Network in Mixture Discriminant Analysis: An Experimental Study
Authors: Nazif Çalış, Murat Erişoğlu, Hamza Erol, Tayfun Servi
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In the recent works related with mixture discriminant analysis (MDA), expectation and maximization (EM) algorithm is used to estimate parameters of Gaussian mixtures. But, initial values of EM algorithm affect the final parameters- estimates. Also, when EM algorithm is applied two times, for the same data set, it can be give different results for the estimate of parameters and this affect the classification accuracy of MDA. Forthcoming this problem, we use Self Organizing Mixture Network (SOMN) algorithm to estimate parameters of Gaussians mixtures in MDA that SOMN is more robust when random the initial values of the parameters are used [5]. We show effectiveness of this method on popular simulated waveform datasets and real glass data set.Keywords: Self Organizing Mixture Network, MixtureDiscriminant Analysis, Waveform Datasets, Glass Identification, Mixture of Multivariate Normal Distributions
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15044377 Scheduling a Flexible Flow Shops Problem using DEA
Authors: Fatemeh Dadkhah, Hossein Ali Akbarpour
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This paper considers a scheduling problem in flexible flow shops environment with the aim of minimizing two important criteria including makespan and cumulative tardiness of jobs. Since the proposed problem is known as an Np-hard problem in literature, we have to develop a meta-heuristic to solve it. We considered general structure of Genetic Algorithm (GA) and developed a new version of that based on Data Envelopment Analysis (DEA). Two objective functions assumed as two different inputs for each Decision Making Unit (DMU). In this paper we focused on efficiency score of DMUs and efficient frontier concept in DEA technique. After introducing the method we defined two different scenarios with considering two types of mutation operator. Also we provided an experimental design with some computational results to show the performance of algorithm. The results show that the algorithm implements in a reasonable time.Keywords: Data envelopment analysis, Efficiency, Flexible flow shops, Genetic algorithm
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18054376 Application of Neural Networks to Predict Changing the Diameters of Bubbles in Pool Boiling Distilled Water
Authors: V. Nikkhah Rashidabad, M. Manteghian, M. Masoumi, S. Mousavian, D. Ashouri
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In this research, the capability of neural networks in modeling and learning complicated and nonlinear relations has been used to develop a model for the prediction of changes in the diameter of bubbles in pool boiling distilled water. The input parameters used in the development of this network include element temperature, heat flux, and retention time of bubbles. The test data obtained from the experiment of the pool boiling of distilled water, and the measurement of the bubbles form on the cylindrical element. The model was developed based on training algorithm, which is typologically of back-propagation type. Considering the correlation coefficient obtained from this model is 0.9633. This shows that this model can be trusted for the simulation and modeling of the size of bubble and thermal transfer of boiling.
Keywords: Bubble Diameter, Heat Flux, Neural Network, Training Algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14454375 The Motivating and Demotivating Factors at the Learning of English Center in Thailand
Authors: Bella Llego
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This study aims to investigate the motivating and de-motivating factors that affect the learning ability of students attending the English Learning Center in Thailand. The subjects of this research were 20 students from the Hana Semiconductor Co., Limited. The data were collected by using questionnaire and analyzed using the SPSS program for the percentage, mean and standard deviation. The research results show that the main motivating factor in learning English at Hana Semiconductor Co., Ltd. is that it would help the employees to communicate with foreign customers and managers. Other reasons include the need to read and write e-mails, and reports in English, as well as to increase overall general knowledge. The main de-motivating factor is that there is a lot of vocabulary to remember when learning English. Another de-motivating factor is that when homework is given, the students have no time to complete the tasks required of them at the end of the working day.
Keywords: Motivating, demotivating, English learning center, student communicate.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12064374 Designing a Framework for Network Security Protection
Authors: Eric P. Jiang
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As the Internet continues to grow at a rapid pace as the primary medium for communications and commerce and as telecommunication networks and systems continue to expand their global reach, digital information has become the most popular and important information resource and our dependence upon the underlying cyber infrastructure has been increasing significantly. Unfortunately, as our dependency has grown, so has the threat to the cyber infrastructure from spammers, attackers and criminal enterprises. In this paper, we propose a new machine learning based network intrusion detection framework for cyber security. The detection process of the framework consists of two stages: model construction and intrusion detection. In the model construction stage, a semi-supervised machine learning algorithm is applied to a collected set of network audit data to generate a profile of normal network behavior and in the intrusion detection stage, input network events are analyzed and compared with the patterns gathered in the profile, and some of them are then flagged as anomalies should these events are sufficiently far from the expected normal behavior. The proposed framework is particularly applicable to the situations where there is only a small amount of labeled network training data available, which is very typical in real world network environments.Keywords: classification, data analysis and mining, network intrusion detection, semi-supervised learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17844373 A Text Clustering System based on k-means Type Subspace Clustering and Ontology
Authors: Liping Jing, Michael K. Ng, Xinhua Yang, Joshua Zhexue Huang
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This paper presents a text clustering system developed based on a k-means type subspace clustering algorithm to cluster large, high dimensional and sparse text data. In this algorithm, a new step is added in the k-means clustering process to automatically calculate the weights of keywords in each cluster so that the important words of a cluster can be identified by the weight values. For understanding and interpretation of clustering results, a few keywords that can best represent the semantic topic are extracted from each cluster. Two methods are used to extract the representative words. The candidate words are first selected according to their weights calculated by our new algorithm. Then, the candidates are fed to the WordNet to identify the set of noun words and consolidate the synonymy and hyponymy words. Experimental results have shown that the clustering algorithm is superior to the other subspace clustering algorithms, such as PROCLUS and HARP and kmeans type algorithm, e.g., Bisecting-KMeans. Furthermore, the word extraction method is effective in selection of the words to represent the topics of the clusters.
Keywords: Subspace Clustering, Text Mining, Feature Weighting, Cluster Interpretation, Ontology
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24444372 Real Time Video Based Smoke Detection Using Double Optical Flow Estimation
Authors: Anton Stadler, Thorsten Ike
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In this paper, we present a video based smoke detection algorithm based on TVL1 optical flow estimation. The main part of the algorithm is an accumulating system for motion angles and upward motion speed of the flow field. We optimized the usage of TVL1 flow estimation for the detection of smoke with very low smoke density. Therefore, we use adapted flow parameters and estimate the flow field on difference images. We show in theory and in evaluation that this improves the performance of smoke detection significantly. We evaluate the smoke algorithm using videos with different smoke densities and different backgrounds. We show that smoke detection is very reliable in varying scenarios. Further we verify that our algorithm is very robust towards crowded scenes disturbance videos.Keywords: Low density, optical flow, upward smoke motion, video based smoke detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14024371 OCR For Printed Urdu Script Using Feed Forward Neural Network
Authors: Inam Shamsher, Zaheer Ahmad, Jehanzeb Khan Orakzai, Awais Adnan
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This paper deals with an Optical Character Recognition system for printed Urdu, a popular Pakistani/Indian script and is the third largest understandable language in the world, especially in the subcontinent but fewer efforts are made to make it understandable to computers. Lot of work has been done in the field of literature and Islamic studies in Urdu, which has to be computerized. In the proposed system individual characters are recognized using our own proposed method/ algorithms. The feature detection methods are simple and robust. Supervised learning is used to train the feed forward neural network. A prototype of the system has been tested on printed Urdu characters and currently achieves 98.3% character level accuracy on average .Although the system is script/ language independent but we have designed it for Urdu characters only.Keywords: Algorithm, Feed Forward Neural Networks, Supervised learning, Pattern Matching.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 30134370 OPTIMAL Placement of FACTS Devices by Genetic Algorithm for the Increased Load Ability of a Power System
Authors: A. B.Bhattacharyya, B. S.K.Goswami
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This paper presents Genetic Algorithm (GA) based approach for the allocation of FACTS (Flexible AC Transmission System) devices for the improvement of Power transfer capacity in an interconnected Power System. The GA based approach is applied on IEEE 30 BUS System. The system is reactively loaded starting from base to 200% of base load. FACTS devices are installed in the different locations of the power system and system performance is noticed with and without FACTS devices. First, the locations, where the FACTS devices to be placed is determined by calculating active and reactive power flows in the lines. Genetic Algorithm is then applied to find the amount of magnitudes of the FACTS devices. This approach of GA based placement of FACTS devices is tremendous beneficial both in terms of performance and economy is clearly observed from the result obtained.Keywords: FACTS Devices, Line Power Flow, OptimalLocation of FACTS Devices, Genetic Algorithm.
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