Search results for: teacher training
474 Modeling and Simulation of Position Estimation of Switched Reluctance Motor with Artificial Neural Networks
Authors: Oguz Ustun, Erdal Bekiroglu
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In the present study, position estimation of switched reluctance motor (SRM) has been achieved on the basis of the artificial neural networks (ANNs). The ANNs can estimate the rotor position without using an extra rotor position sensor by measuring the phase flux linkages and phase currents. Flux linkage-phase current-rotor position data set and supervised backpropagation learning algorithm are used in training of the ANN based position estimator. A 4-phase SRM have been used to verify the accuracy and feasibility of the proposed position estimator. Simulation results show that the proposed position estimator gives precise and accurate position estimations for both under the low and high level reference speeds of the SRM
Keywords: Artificial neural networks, modeling andsimulation, position observer, switched reluctance motor.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2044473 ANFIS Approach for Locating Faults in Underground Cables
Authors: Magdy B. Eteiba, Wael Ismael Wahba, Shimaa Barakat
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This paper presents a fault identification, classification and fault location estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in the distribution system.
Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a training process on the ANFIS. Then an accurate fault classifier and locator algorithm was designed, trained and tested using current samples only. The results obtained from ANFIS output were compared with the real output. From the results, it was found that the percentage error between ANFIS output and real output is less than three percent. Hence, it can be concluded that the proposed technique is able to offer high accuracy in both of the fault classification and fault location.
Keywords: ANFIS, Fault location, Underground Cable, Wavelet Transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2729472 HERMES System: a Virtual Reality Simulator for the Angioplasty Intervention Training
Authors: Giovanni Aloisio, Lucio T. De Paolis, Luciana Provenzano, Lucio Colizzi, Gianluca Pantile
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One of the essential requirements in order to have a realistic surgical simulator is real-time interaction by means of a haptic interface is. In fact, reproducing haptic sensations increases the realism of the simulation. However, the interaction need to be performed in real-time, since a delay between the user action and the system reaction reduces the user immersion. In this paper, we present a prototype of the coronary stent implant simulator developed in the HERMES Project; this system allows real-time interactions with a artery by means of a specific haptic device; thus the user can interactively navigate in a reconstructed artery and force feedback is produced when contact occurs between the artery walls and the medical instrumentsKeywords: Collision Detection, Haptic Interface, Real-Time Interaction, Surgical Simulator.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2053471 Voice Command Recognition System Based on MFCC and VQ Algorithms
Authors: Mahdi Shaneh, Azizollah Taheri
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The goal of this project is to design a system to recognition voice commands. Most of voice recognition systems contain two main modules as follow “feature extraction" and “feature matching". In this project, MFCC algorithm is used to simulate feature extraction module. Using this algorithm, the cepstral coefficients are calculated on mel frequency scale. VQ (vector quantization) method will be used for reduction of amount of data to decrease computation time. In the feature matching stage Euclidean distance is applied as similarity criterion. Because of high accuracy of used algorithms, the accuracy of this voice command system is high. Using these algorithms, by at least 5 times repetition for each command, in a single training session, and then twice in each testing session zero error rate in recognition of commands is achieved.Keywords: MFCC, Vector quantization, Vocal tract, Voicecommand.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3137470 Lifelong Education for Teachers: A Tool for Achieving Effective Teaching and Learning in Secondary Schools in Benue State, Nigeria
Authors: P. I. Adzongo, O. A. Aloga
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The purpose of the study was to examine lifelong education for teachers as a tool for achieving effective teaching and learning. Lifelong education enhances social inclusion, personal development, citizenship, employability, teaching and learning, community and the nation. It is imperative that the teacher needs to update his knowledge regularly to be able to perform optimally, since he has a major position in the inculcation of desirable elements in students, and the challenges of lifelong education were also discussed. Descriptive survey design was adopted for the study. A simple random sampling technique was used to select 80 teachers as sample from a population of 105 senior secondary school teachers in Makurdi Local Government Area of Benue State. A 20-item self designed questionnaire subjected to expert validation and reliability was used to collect data. The reliability Alpha coefficient of 0.87 was established using Cronbach’s Alpha technique, mean scores and standard deviation were used to answer the 2 research questions while chi-square was used to analyse data for the 2 null hypotheses, which states that lifelong education for teachers is not a significant tool for achieving effective teaching and lifelong education for teachers does not significantly impact on effective learning. The findings of the study revealed that, lifelong education for teachers can be used as a tool for achieving effective teaching and learning, and the study recommended among others that government, organizations and individuals should in collaboration put lifelong education programmes for teachers on the priority list. The paper concluded that the strategic position of lifelong education for teachers towards enhanced teaching, learning and the production of quality manpower in the society makes it imperative for all hands to be on “deck” to support the programme financially and otherwise.Keywords: Lifelong Education, Tool, Effective Teaching and Learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1458469 Causal Relation Identification Using Convolutional Neural Networks and Knowledge Based Features
Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi
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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.
Keywords: Causal relation identification, convolutional neural networks, natural Language Processing, Machine Learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2241468 Performance Evaluation for Weightlifting Lifter by Barbell Trajectory
Authors: Ying-Chen Lin, Ching-Ting Hsu, Wei-Hua Ho
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The purpose of this study is to investigate the kinematic characteristics and differences of the snatch barbell trajectory of 53 kg class female weight lifters. We take the 2014 Taiwan College Cup players as examples, and tend to make kinematic applications through the proven weightlifting barbell track system. The competition videos are taken by consumer camcorder with a tripod which set up at the side of the lifter. The results will be discussed in three parts, the first part is various lifting phase, the second part is the compare lifting between success and unsuccessful, and the third part is to compare the outstanding player with the general. Conclusion through the barbell can be used to observe the trajectories of our players lifting the usual process cannot be observed in the presence of malfunction or habits, so that the coach can find the problem and guide the players more accurately. Our system can be applied in practice and competition to increase the resilience of the lifter on the field.
Keywords: Computer aided sport training, Kinematic, Trajectory, Weightlifting.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3219467 Problems of Lifelong Education Course in Information and Communication Technology
Authors: Hisham Md Suhadi, Faaizah Shahbodin, Jamaluddin Hashim
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The study is the way to identify the problems that occur in organizing short course’s lifelong learning in the information and communication technology (ICT) education which are faced by the lecturer and staff at the Mara Skill Institute and Industrial Training Institute in Pahang Malaysia. The important aspects of these issues are classified to five which are selecting the courses administrative. Fifty lecturers and staff were selected as a respondent. The sample is selected by using the non-random sampling method purpose sampling. The questionnaire is used as a research instrument and divided into five main parts. All the data that gain from the questionnaire are analyzed by using the SPSS in term of mean, standard deviation and percentage. The findings showed, there are the problems occur in organizing the short course for lifelong learning in ICT education.Keywords: Lifelong education, information and communication technology (ICT), short course, ICT education, courses administrative.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1794466 Real-Time Hand Tracking and Gesture Recognition System Using Neural Networks
Authors: Tin Hninn Hninn Maung
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This paper introduces a hand gesture recognition system to recognize real time gesture in unstrained environments. Efforts should be made to adapt computers to our natural means of communication: Speech and body language. A simple and fast algorithm using orientation histograms will be developed. It will recognize a subset of MAL static hand gestures. A pattern recognition system will be using a transforrn that converts an image into a feature vector, which will be compared with the feature vectors of a training set of gestures. The final system will be Perceptron implementation in MATLAB. This paper includes experiments of 33 hand postures and discusses the results. Experiments shows that the system can achieve a 90% recognition average rate and is suitable for real time applications.
Keywords: Hand gesture recognition, Orientation Histogram, Myanmar Alphabet Language, Perceptronnetwork, MATLAB.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4675465 Changes in Postural Stability after Coordination Exercise
Authors: Ivan Struhár, Martin Sebera, Lenka Dovrtělová
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The aim of this study was to find out if the special type of exercise with elastic cord can improve the level of postural stability. The exercise programme was conducted twice a week for 3 months. The participants were randomly divided into an experimental group and a control group. The electronic balance board was used for testing of postural stability. All participants trained for 18 hours at the time of experiment without any special form of coordination programme. The experimental group performed 90 minutes plus of coordination exercise. The result showed that differences between pre-test and post-test occurred in the experimental group. It was used the nonparametric Wilcoxon t-test for paired samples (p=0.012; the significance level 95%). We calculated effect size by Cohen´s d. In the experimental group d is 1.96 which indicates a large effect. In the control group d is 0.04 which confirms no significant improvement.
Keywords: Balance board, balance training, coordination, stability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2250464 Binary Mixture of Copper-Cobalt Ions Uptake by Zeolite using Neural Network
Authors: John Kabuba, Antoine Mulaba-Bafubiandi, Kim Battle
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In this study a neural network (NN) was proposed to predict the sorption of binary mixture of copper-cobalt ions into clinoptilolite as ion-exchanger. The configuration of the backpropagation neural network giving the smallest mean square error was three-layer NN with tangent sigmoid transfer function at hidden layer with 10 neurons, linear transfer function at output layer and Levenberg-Marquardt backpropagation training algorithm. Experiments have been carried out in the batch reactor to obtain equilibrium data of the individual sorption and the mixture of coppercobalt ions. The obtained modeling results have shown that the used of neural network has better adjusted the equilibrium data of the binary system when compared with the conventional sorption isotherm models.Keywords: Adsorption isotherm, binary system, neural network; sorption
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2026463 Novel Approach for Promoting the Generalization Ability of Neural Networks
Authors: Naiqin Feng, Fang Wang, Yuhui Qiu
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A new approach to promote the generalization ability of neural networks is presented. It is based on the point of view of fuzzy theory. This approach is implemented through shrinking or magnifying the input vector, thereby reducing the difference between training set and testing set. It is called “shrinking-magnifying approach" (SMA). At the same time, a new algorithm; α-algorithm is presented to find out the appropriate shrinking-magnifying-factor (SMF) α and obtain better generalization ability of neural networks. Quite a few simulation experiments serve to study the effect of SMA and α-algorithm. The experiment results are discussed in detail, and the function principle of SMA is analyzed in theory. The results of experiments and analyses show that the new approach is not only simpler and easier, but also is very effective to many neural networks and many classification problems. In our experiments, the proportions promoting the generalization ability of neural networks have even reached 90%.Keywords: Fuzzy theory, generalization, misclassification rate, neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1526462 Awareness of Value Addition of Sweet Potato (Ipomoea batatas (L.) Lam) In Osun State, Nigeria
Authors: A. M. Omoare, E. O. Fakoya, O. E. Fapojuwo, W. O. Oyediran
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Awareness of value addition of sweet potato has received comparatively little attention in Nigeria despite its potential to reduce perishability and enhanced utilization of the crop in diverse products forms. This study assessed the awareness of value addition of sweet potato in Osun State, Nigeria. Multi-stage random sampling technique was used to select 120 respondents for the study. Data obtained were analyzed using descriptive statistics and multiple regression analysis. Findings showed that most (75.00%) of the respondents were male with mean age of 42.10 years and 96.70% of the respondents had formal education. The mean farm size was 2.30 hectares. Majority (75.00%) of the respondents had more than 10 years farming experience. Awareness of value addition of sweet potato was very low among the respondents. It was recommended that sweet potato farmers should be empowered through effective and efficient extension training on the use of modern processing techniques in order to enhance value addition of sweet potato.
Keywords: Awareness, value addition, sweet potato, perishability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2915461 The Cloud Systems Used in Education: Properties and Overview
Authors: Agah Tuğrul Korucu, Handan Atun
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Diversity and usefulness of information that used in education are have increased due to development of technology. Web technologies have made enormous contributions to the distance learning system especially. Mobile systems, one of the most widely used technology in distance education, made much easier to access web technologies. Not bounding by space and time, individuals have had the opportunity to access the information on web. In addition to this, the storage of educational information and resources and accessing these information and resources is crucial for both students and teachers. Because of this importance, development and dissemination of web technologies supply ease of access to information and resources are provided by web technologies. Dynamic web technologies introduced as new technologies that enable sharing and reuse of information, resource or applications via the Internet and bring websites into expandable platforms are commonly known as Web 2.0 technologies. Cloud systems are one of the dynamic web technologies that defined as a model provides approaching the demanded information independent from time and space in appropriate circumstances and developed by NIST. One of the most important advantages of cloud systems is meeting the requirements of users directly on the web regardless of hardware, software, and dealing with install. Hence, this study aims at using cloud services in education and investigating the services provided by the cloud computing. Survey method has been used as research method. In the findings of this research the fact that cloud systems are used such studies as resource sharing, collaborative work, assignment submission and feedback, developing project in the field of education, and also, it is revealed that cloud systems have plenty of significant advantages in terms of facilitating teaching activities and the interaction between teacher, student and environment.
Keywords: Cloud systems, cloud systems in education, distance learning, e-learning, integration of information technologies, online learning environment.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1007460 Using Self Organizing Feature Maps for Classification in RGB Images
Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami
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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feedforward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on selforganizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.Keywords: Classification, SOFM, neural network, RGB images.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2305459 The Carbon Trading Price and Trading Volume Forecast in Shanghai City by BP Neural Network
Authors: Liu Zhiyuan, Sun Zongdi
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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.
Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1378458 Talent Selection for Present Conception of Women Sports Gymnastics and Practical Verification of the Test Battery
Authors: G. Bago, P. Hedbávný, M. Kalichová
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The aim of the contribution is to project and consequently verify a testing battery which in practice would facilitate the selection of talented gymnasts for current concept of men´ s gymnastics. Based on study of professional literature a test array consisting of three parts projected – power testing, speed testing and flexibility testing– was projected. The evaluating scales used in the tests are standardized. This test array was applied to girls aged 6 - 7 during recruitment for Sokol Brno I. and SG Pelhrimov Gymnastic Club. After 6 months of training activity the projected set of tests was applied again. The results were evaluated through observation and questionnaire and they were consequently transformed into charts. Recommendation for practice was proposed based on these results.
Keywords: Talent selection, sports gymnastics, power testing, speed testing, flexibility testing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2120457 Consumer Product Demand Forecasting based on Artificial Neural Network and Support Vector Machine
Authors: Karin Kandananond
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The nature of consumer products causes the difficulty in forecasting the future demands and the accuracy of the forecasts significantly affects the overall performance of the supply chain system. In this study, two data mining methods, artificial neural network (ANN) and support vector machine (SVM), were utilized to predict the demand of consumer products. The training data used was the actual demand of six different products from a consumer product company in Thailand. The results indicated that SVM had a better forecast quality (in term of MAPE) than ANN in every category of products. Moreover, another important finding was the margin difference of MAPE from these two methods was significantly high when the data was highly correlated.Keywords: Artificial neural network (ANN), Bullwhip effect, Consumer products, Demand forecasting, Supply chain, Support vector machine (SVM).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2995456 A Review on Soft Computing Technique in Intrusion Detection System
Authors: Noor Suhana Sulaiman, Rohani Abu Bakar, Norrozila Sulaiman
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Intrusion Detection System is significant in network security. It detects and identifies intrusion behavior or intrusion attempts in a computer system by monitoring and analyzing the network packets in real time. In the recent year, intelligent algorithms applied in the intrusion detection system (IDS) have been an increasing concern with the rapid growth of the network security. IDS data deals with a huge amount of data which contains irrelevant and redundant features causing slow training and testing process, higher resource consumption as well as poor detection rate. Since the amount of audit data that an IDS needs to examine is very large even for a small network, classification by hand is impossible. Hence, the primary objective of this review is to review the techniques prior to classification process suit to IDS data.Keywords: Intrusion Detection System, security, soft computing, classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1854455 Learning Process Enhancement for Robot Behaviors
Authors: Saeed Mohammed Baneamoon, Rosalina Abdul Salam, Abdullah Zawawi Hj. Talib
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Designing a simulated system and training it to optimize its tasks in simulated environment helps the designers to avoid problems that may appear when designing the system directly in real world. These problems are: time consuming, high cost, high errors percentage and low efficiency and accuracy of the system. The proposed system will investigate and improve the efficiency and accuracy of a simulated robot to choose correct behavior to perform its task. In this paper, machine learning, which uses genetic algorithm, is adopted. This type of machine learning is called genetic-based machine learning in which a distributed classifier system is used to improve the efficiency and accuracy of the robot. Consequently, it helps the robot to achieve optimal action.Keywords: Machine Learning, Genetic-Based MachineLearning, Learning Classifier System, Behaviors.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1518454 Age-Based Interface Design for Children’s CAPT Systems
Authors: Saratu Yusuf Ilu, Mumtaz B. Mustafa, Siti Salwah Salim, Mehdi Malekzadeh
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Children today use computer based application in various activities especially for learning and education. Many of these tools and application such as the Computer Aided Pronunciation Training (CAPT) systems enable children to explore and experience them with little supervision from the adults. In order for these tools and application to have maximum effect on the children’s learning and education, it must be attractive to the children to use them. This could be achieved with the proper user interface (UI) design. As children grow, so do their ability, taste and preferences. They interact differently with these applications as they grow older. This study reviews several articles on how age factors influence the UI design. The review focuses on age related abilities such as cognitive, literacy, concentration and feedback requirement. We have also evaluated few of existing CAPT systems and determine the influence of age-based factors on the interface design.
Keywords: Children, age-based interaction, learning application, age-based UI.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1977453 Emotion Recognition Using Neural Network: A Comparative Study
Authors: Nermine Ahmed Hendy, Hania Farag
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Emotion recognition is an important research field that finds lots of applications nowadays. This work emphasizes on recognizing different emotions from speech signal. The extracted features are related to statistics of pitch, formants, and energy contours, as well as spectral, perceptual and temporal features, jitter, and shimmer. The Artificial Neural Networks (ANN) was chosen as the classifier. Working on finding a robust and fast ANN classifier suitable for different real life application is our concern. Several experiments were carried out on different ANN to investigate the different factors that impact the classification success rate. Using a database containing 7 different emotions, it will be shown that with a proper and careful adjustment of features format, training data sorting, number of features selected and even the ANN type and architecture used, a success rate of 85% or even more can be achieved without increasing the system complicity and the computation time
Keywords: Classification, emotion recognition, features extraction, feature selection, neural network
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4678452 The Modified Eigenface Method using Two Thresholds
Authors: Yan Ma, ShunBao Li
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A new approach is adopted in this paper based on Turk and Pentland-s eigenface method. It was found that the probability density function of the distance between the projection vector of the input face image and the average projection vector of the subject in the face database, follows Rayleigh distribution. In order to decrease the false acceptance rate and increase the recognition rate, the input face image has been recognized using two thresholds including the acceptance threshold and the rejection threshold. We also find out that the value of two thresholds will be close to each other as number of trials increases. During the training, in order to reduce the number of trials, the projection vectors for each subject has been averaged. The recognition experiments using the proposed algorithm show that the recognition rate achieves to 92.875% whilst the average number of judgment is only 2.56 times.Keywords: Eigenface, Face Recognition, Threshold, Rayleigh Distribution, Feature Extraction
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1479451 Design and Implementation of a Neural Network for Real-Time Object Tracking
Authors: Javed Ahmed, M. N. Jafri, J. Ahmad, Muhammad I. Khan
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Real-time object tracking is a problem which involves extraction of critical information from complex and uncertain imagedata. In this paper, we present a comprehensive methodology to design an artificial neural network (ANN) for a real-time object tracking application. The object, which is tracked for the purpose of demonstration, is a specific airplane. However, the proposed ANN can be trained to track any other object of interest. The ANN has been simulated and tested on the training and testing datasets, as well as on a real-time streaming video. The tracking error is analyzed with post-regression analysis tool, which finds the correlation among the calculated coordinates and the correct coordinates of the object in the image. The encouraging results from the computer simulation and analysis show that the proposed ANN architecture is a good candidate solution to a real-time object tracking problem.
Keywords: Image processing, machine vision, neural networks, real-time object tracking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3496450 MIM: A Species Independent Approach for Classifying Coding and Non-Coding DNA Sequences in Bacterial and Archaeal Genomes
Authors: Achraf El Allali, John R. Rose
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A number of competing methodologies have been developed to identify genes and classify DNA sequences into coding and non-coding sequences. This classification process is fundamental in gene finding and gene annotation tools and is one of the most challenging tasks in bioinformatics and computational biology. An information theory measure based on mutual information has shown good accuracy in classifying DNA sequences into coding and noncoding. In this paper we describe a species independent iterative approach that distinguishes coding from non-coding sequences using the mutual information measure (MIM). A set of sixty prokaryotes is used to extract universal training data. To facilitate comparisons with the published results of other researchers, a test set of 51 bacterial and archaeal genomes was used to evaluate MIM. These results demonstrate that MIM produces superior results while remaining species independent.Keywords: Coding Non-coding Classification, Entropy, GeneRecognition, Mutual Information.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1712449 Features for Measuring Credibility on Facebook Information
Authors: Kanda Runapongsa Saikaew, Chaluemwut Noyunsan
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Nowadays social media information, such as news, links, images, or VDOs, is shared extensively. However, the effectiveness of disseminating information through social media lacks in quality: less fact checking, more biases, and several rumors. Many researchers have investigated about credibility on Twitter, but there is no the research report about credibility information on Facebook. This paper proposes features for measuring credibility on Facebook information. We developed the system for credibility on Facebook. First, we have developed FB credibility evaluator for measuring credibility of each post by manual human’s labelling. We then collected the training data for creating a model using Support Vector Machine (SVM). Secondly, we developed a chrome extension of FB credibility for Facebook users to evaluate the credibility of each post. Based on the usage analysis of our FB credibility chrome extension, about 81% of users’ responses agree with suggested credibility automatically computed by the proposed system.
Keywords: Facebook, social media, credibility measurement.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3655448 ML Detection with Symbol Estimation for Nonlinear Distortion of OFDM Signal
Authors: Somkiat Lerkvaranyu, Yoshikazu Miyanaga
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In this paper, a new technique of signal detection has been proposed for detecting the orthogonal frequency-division multiplexing (OFDM) signal in the presence of nonlinear distortion.There are several advantages of OFDM communications system.However, one of the existing problems is remain considered as the nonlinear distortion generated by high-power-amplifier at the transmitter end due to the large dynamic range of an OFDM signal. The proposed method is the maximum likelihood detection with the symbol estimation. When the training data are available, the neural network has been used to learn the characteristic of received signal and to estimate the new positions of the transmitted symbol which are provided to the maximum likelihood detector. Resulting in the system performance, the nonlinear distortions of a traveling wave tube amplifier with OFDM signal are considered in this paper.Simulation results of the bit-error-rate performance are obtained with 16-QAM OFDM systems.
Keywords: OFDM, TWTA, nonlinear distortion, detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1662447 An Empirical Analysis of HRM in Different Pharmaceutical Departments of Different Pharmaceutical Industries in Pakistan
Authors: Faisal Ali, Mansoor Shuakat, Lirong Cui, Helena Uhde, Rabia Riasat, Janeth J. Marwa
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HR is a department that enhances the power of employee performance in regard with their services, and to make the organization strategic objectives. The main concern of HR department is to organize people, focus on policies and their system. The empirical study shows the relationship between HRM (Human Resource Management practices) and their Job Satisfaction. The Hypothesis is testing on a sample of overall 320 employees of 5 different Pharmaceutical departments of different organizations in Pakistan. The important thing as Relationship of Job satisfaction with HR Practices, Impact on Job Satisfaction with HR Practices, Participation of Staff of Different Departments, HR Practices effects the Job satisfaction, Recruitment or Hiring and Selection effects the Job satisfaction, Training and Development, Performance and Appraisals, Compensation affects the Job satisfaction , and Industrial Relationships affects the Job satisfaction. After finishing all data analysis, the conclusion is that lots of Job related activities raise the confidence of Job satisfaction of employees with their salary and other benefits.Keywords: HRM, HR practices, job satisfaction, TQM.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1828446 Learning Monte Carlo Data for Circuit Path Length
Authors: Namal A. Senanayake, A. Beg, Withana C. Prasad
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This paper analyzes the patterns of the Monte Carlo data for a large number of variables and minterms, in order to characterize the circuit path length behavior. We propose models that are determined by training process of shortest path length derived from a wide range of binary decision diagram (BDD) simulations. The creation of the model was done use of feed forward neural network (NN) modeling methodology. Experimental results for ISCAS benchmark circuits show an RMS error of 0.102 for the shortest path length complexity estimation predicted by the NN model (NNM). Use of such a model can help reduce the time complexity of very large scale integrated (VLSI) circuitries and related computer-aided design (CAD) tools that use BDDs.Keywords: Monte Carlo data, Binary decision diagrams, Neural network modeling, Shortest path length estimation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1580445 Machine Learning for Aiding Meningitis Diagnosis in Pediatric Patients
Authors: Karina Zaccari, Ernesto Cordeiro Marujo
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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.
Keywords: Machine learning, medical diagnosis, meningitis detection, gradient boosting.
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