Search results for: academic learning
198 Analysis on Iranian Wind Catcher and Its Effect on Natural Ventilation as a Solution towards Sustainable Architecture(Case Study: Yazd)
Authors: Mahnaz Mahmoudi Zarandi (Qazvin Islamic Azad University)
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wind catchers have been served as a cooling system, used to provide acceptable ventilation by means of renewable energy of wind. In the present study, the city of Yazd in arid climate is selected as case study. From the architecture point of view, learning about wind catchers in this study is done by means of field surveys. Research method for selection of the case is based on random form, and analytical method. Wind catcher typology and knowledge of relationship governing the wind catcher's architecture were those measures that are taken for the first time. 53 wind catchers were analyzed. The typology of the wind-catchers is done by the physical analyzing, patterns and common concepts as incorporated in them. How the architecture of wind catcher can influence their operations by analyzing thermal behavior are the archetypes of selected wind catchers. Calculating fluids dynamics science, fluent software and numerical analysis are used in this study as the most accurate analytical approach. The results obtained from these analyses show the formal specifications of wind catchers with optimum operation in Yazd. The knowledge obtained from the optimum model could be used for design and construction of wind catchers with more improved operation
Keywords: Fluent Software, Iranian architecture, wind catcher
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4477197 The Design of English Materials to Communicate the Identity of Mueang District, Samut Songkram for Ecotourism
Authors: Kitda Praraththajariya
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The main purpose of this research was to study how to communicate the identity of the Mueang district, SamutSongkram province for ecotourism. The qualitative data was collected through studying related materials, exploring the area, in-depth interviews with three groups of people: three directly responsible officers who were key informants of the district, twenty foreign tourists and five Thai tourist guides. A content analysis was used to analyze the qualitative data. The two main findings of the study were as follows: 1. The identity of Amphur (District) Mueang, SamutSongkram province. This establishment was near the Mouth of Maekong River for normal people and tourists, consisting of rest accommodations. There are restaurants where food and drinks are served, rich mangrove forests, Hoy Lod (Razor Clam) and mangrove trees. Don Hoy Lod, is characterized by muddy beaches, is a coastal wetland for Ramsar Site. It is at 1099th ranging where the greatest number of Hoy Lod (Razor Clam) can be seen from March to May each year. 2. The communication of the identity of AmphurMueang, SamutSongkram province which the researcher could find and design to present in English materials can be summed up in 4 items: 1) The history of AmphurMueang, SamutSongkram province 2) WatPhetSamutWorrawihan 3) The Learning source of Ecotourism: Don Hoy Lod and Mangrove forest 4) How to keep AmphurMueang, SamutSongkram province for ecotourism.
Keywords: Foreigner tourists, signified, semiotics, ecotourism.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1841196 Human Resources Recruitment Defining Peculiarities of Students as Job Seekers
Authors: O. Starineca
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Some organizations as employers have difficulties to attract job seekers and retain their employees. Strategic planning of Human Resources (HR) presumes broad analysis of perspectives including analysis of potential job seekers in the field. Human Resources Recruitment (HRR) influences employer brand of an organization and peculiarities of both external organizational factors and stakeholders. Defining peculiarities of the future job seekers, who could potentially become the employees of the organization, could help to adjust HRR tools and methods adapt to the youngest generation employees’ preferences and be more successful in selecting the best candidates, who are likely to be loyal to the employer. The aim of the empirical study is definition of some students’ as job seekers peculiarities and their requirements to their potential employer. The survey in Latvia, Lithuania and Spain. Respondents were students from these countries’ tertiary education institutions Public Administration (PA) or relevant study programs. All three countries students’ peculiarities have just a slight difference. Overall, they all wish to work for a socially responsible employer that is able to provide positive working environment and possibilities for professional development and learning. However, respondents from each country have own peculiarities. The study might have a practical application. PA of the examined countries might use the results developing employer brand and creating job advertisements focusing on recent graduates’ recruitment.
Keywords: Generation Y, human resources recruitment, public administration.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1190195 Intelligent Temperature Controller for Water-Bath System
Authors: Om Prakash Verma, Rajesh Singla, Rajesh Kumar
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Conventional controller’s usually required a prior knowledge of mathematical modelling of the process. The inaccuracy of mathematical modelling degrades the performance of the process, especially for non-linear and complex control problem. The process used is Water-Bath system, which is most widely used and nonlinear to some extent. For Water-Bath system, it is necessary to attain desired temperature within a specified period of time to avoid the overshoot and absolute error, with better temperature tracking capability, else the process is disturbed.
To overcome above difficulties intelligent controllers, Fuzzy Logic (FL) and Adaptive Neuro-Fuzzy Inference System (ANFIS), are proposed in this paper. The Fuzzy controller is designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. To design ANFIS, Fuzzy-Inference-System is combined with learning capability of Neural-Network.
It is analyzed that ANFIS is best suitable for adaptive temperature control of above system. As compared to PID and FLC, ANFIS produces a stable control signal. It has much better temperature tracking capability with almost zero overshoot and minimum absolute error.
Keywords: PID Controller, FLC, ANFIS, Non-Linear Control System, Water-Bath System, MATLAB-7.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5532194 Classifier Based Text Mining for Neural Network
Authors: M. Govindarajan, R. M. Chandrasekaran
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Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.Keywords: Back propagation, classification accuracy, textmining, time complexity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4200193 Perceptions of Educators on the Learners’ Youngest Age for the Introduction of ICTs in Schools: A Personality Theory Approach
Authors: K. E. Oyetade, S. D. Eyono Obono
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Age ratings are very helpful in providing parents with relevant information for the purchase and use of digital technologies by the children; this is why the non-definition of age ratings for the use of ICTs by children in schools is a major concern; and this problem serves as a motivation for this study whose aim is to examine the factors affecting the perceptions of educators on the learners’ youngest age for the introduction of ICTs in schools. This aim is achieved through two types of research objectives: the identification and design of theories and models on age ratings, and the empirical testing of such theories and models in a survey of educators from the Camperdown district of the South African KwaZulu-Natal province. A questionnaire is used for the collection of the data of this survey whose validity and reliability is checked in SPSS prior to its descriptive and correlative quantitative analysis. The main hypothesis supporting this research is the association between the demographics of educators, their personality, and their perceptions on the learners’ youngest age for the introduction of ICTs in schools; as claimed by existing research; except that the present study looks at personality from three dimensions: self-actualized personalities, fully functioning personalities, and healthy personalities. This hypothesis was fully confirmed by the empirical study conducted by this research except for the demographic factor where only the educators’ grade or class was found to be associated with the personality of educators.
Keywords: Age ratings, Educators, E-learning, Personality Theories.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1830192 Service Quality vs. Customer Satisfaction: Perspectives of Visitors to a Public University Library
Authors: Norazah Mohd Suki, Norbayah Mohd Suki
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This study proposes a conceptual model and empirically tests the relationships between customers and librarians (i.e. tangibles, responsiveness, assurance, reliability and empathy) with a dependent variable (customer satisfaction) regarding library services. The SERVQUAL instrument was administered to 100 respondents which comprises of staff and students at a public higher learning institution in the Federal Territory of Labuan, Malaysia. They were public university library users. Results revealed that all service quality dimensions tested were significant and influenced customer satisfaction of visitors to a public university library. Assurance is the most important factor that influences customer satisfaction with the services rendered by the librarian. It is imperative for the library management to take note that the top five service attributes that gained greatest attention from library visitors- perspective includes employee willingness to help customers, availability of customer representatives online for response to queries, library staff actively and promptly provide services, signs in the building are clear and library staff are friendly and courteous. This study provides valuable results concerning the determinants of the service quality and customer satisfaction of public university library services from the users' perspective.Keywords: Service Quality, Customer Satisfaction, SERVQUAL Model, Multiple Regression Analysis
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4883191 JaCoText: A Pretrained Model for Java Code-Text Generation
Authors: Jessica Lòpez Espejel, Mahaman Sanoussi Yahaya Alassan, Walid Dahhane, El Hassane Ettifouri
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Pretrained transformer-based models have shown high performance in natural language generation task. However, a new wave of interest has surged: automatic programming language generation. This task consists of translating natural language instructions to a programming code. Despite the fact that well-known pretrained models on language generation have achieved good performance in learning programming languages, effort is still needed in automatic code generation. In this paper, we introduce JaCoText, a model based on Transformers neural network. It aims to generate java source code from natural language text. JaCoText leverages advantages of both natural language and code generation models. More specifically, we study some findings from the state of the art and use them to (1) initialize our model from powerful pretrained models, (2) explore additional pretraining on our java dataset, (3) carry out experiments combining the unimodal and bimodal data in the training, and (4) scale the input and output length during the fine-tuning of the model. Conducted experiments on CONCODE dataset show that JaCoText achieves new state-of-the-art results.
Keywords: Java code generation, Natural Language Processing, Sequence-to-sequence Models, Transformers Neural Networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 796190 TOSOM: A Topic-Oriented Self-Organizing Map for Text Organization
Authors: Hsin-Chang Yang, Chung-Hong Lee, Kuo-Lung Ke
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The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.
Keywords: Self-organizing map, topic identification, learning algorithm, text clustering.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2014189 Use of Gaussian-Euclidean Hybrid Function Based Artificial Immune System for Breast Cancer Diagnosis
Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel
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Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.
Keywords: Artificial Immune System, Breast Cancer Diagnosis, Euclidean Function, Gaussian Function.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2102188 The Conceptual and Procedural Knowledge of Rational Numbers in Primary School Teachers
Authors: R. M. Kashim
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The study investigates the conceptual and procedural knowledge of rational number in primary school teachers, specifically, the primary school teachers level of conceptual knowledge about rational number and the primary school teachers level of procedural knowledge about rational numbers. The study was carried out in Bauchi metropolis in Bauchi state of Nigeria. A Conceptual and Procedural Knowledge Test was used as the instrument for data collection, 54 mathematics teachers in Bauchi primary schools were involved in the study. The collections were analyzed using mean and standard deviation. The findings revealed that the primary school mathematics teachers in Bauchi metropolis posses a low level of conceptual knowledge of rational number and also possess a high level of Procedural knowledge of rational number. It is therefore recommended that to be effective, teachers teaching mathematics most posses a deep understanding of both conceptual and procedural knowledge. That way the most knowledgeable teachers in mathematics deliver highly effective rational number instructions. Teachers should not ignore the mathematical concept aspect of rational number teaching. This is because only the procedural aspect of Rational number is highlighted during instructions; this often leads to rote - learning of procedures without understanding the meanings. It is necessary for teachers to learn rational numbers teaching method that focus on both conceptual knowledge and procedural knowledge teaching.
Keywords: Conceptual knowledge, primary school teachers, procedural knowledge, rational numbers.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1659187 Mining Network Data for Intrusion Detection through Naïve Bayesian with Clustering
Authors: Dewan Md. Farid, Nouria Harbi, Suman Ahmmed, Md. Zahidur Rahman, Chowdhury Mofizur Rahman
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Network security attacks are the violation of information security policy that received much attention to the computational intelligence society in the last decades. Data mining has become a very useful technique for detecting network intrusions by extracting useful knowledge from large number of network data or logs. Naïve Bayesian classifier is one of the most popular data mining algorithm for classification, which provides an optimal way to predict the class of an unknown example. It has been tested that one set of probability derived from data is not good enough to have good classification rate. In this paper, we proposed a new learning algorithm for mining network logs to detect network intrusions through naïve Bayesian classifier, which first clusters the network logs into several groups based on similarity of logs, and then calculates the prior and conditional probabilities for each group of logs. For classifying a new log, the algorithm checks in which cluster the log belongs and then use that cluster-s probability set to classify the new log. We tested the performance of our proposed algorithm by employing KDD99 benchmark network intrusion detection dataset, and the experimental results proved that it improves detection rates as well as reduces false positives for different types of network intrusions.Keywords: Clustering, detection rate, false positive, naïveBayesian classifier, network intrusion detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5525186 Integrating Artificial Neural Network and Taguchi Method on Constructing the Real Estate Appraisal Model
Authors: Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong
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In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices.
Keywords: Artificial Neural Network, Taguchi Method, Real Estate Valuation Model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3053185 Temporal Signal Processing by Inference Bayesian Approach for Detection of Abrupt Variation of Statistical Characteristics of Noisy Signals
Authors: Farhad Asadi, Hossein Sadati
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In fields such as neuroscience and especially in cognition modeling of mental processes, uncertainty processing in temporal zone of signal is vital. In this paper, Bayesian online inferences in estimation of change-points location in signal are constructed. This method separated the observed signal into independent series and studies the change and variation of the regime of data locally with related statistical characteristics. We give conditions on simulations of the method when the data characteristics of signals vary, and provide empirical evidence to show the performance of method. It is verified that correlation between series around the change point location and its characteristics such as Signal to Noise Ratios and mean value of signal has important factor on fluctuating in finding proper location of change point. And one of the main contributions of this study is related to representing of these influences of signal statistical characteristics for finding abrupt variation in signal. There are two different structures for simulations which in first case one abrupt change in temporal section of signal is considered with variable position and secondly multiple variations are considered. Finally, influence of statistical characteristic for changing the location of change point is explained in details in simulation results with different artificial signals.
Keywords: Time series, fluctuation in statistical characteristics, optimal learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 546184 Artificial Intelligence-Based Chest X-Ray Test of COVID-19 Patients
Authors: Dhurgham Al-Karawi, Nisreen Polus, Shakir Al-Zaidi, Sabah Jassim
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The management of COVID-19 patients based on chest imaging is emerging as an essential tool for evaluating the spread of the pandemic which has gripped the global community. It has already been used to monitor the situation of COVID-19 patients who have issues in respiratory status. There has been increase to use chest imaging for medical triage of patients who are showing moderate-severe clinical COVID-19 features, this is due to the fast dispersal of the pandemic to all continents and communities. This article demonstrates the development of machine learning techniques for the test of COVID-19 patients using Chest X-Ray (CXR) images in nearly real-time, to distinguish the COVID-19 infection with a significantly high level of accuracy. The testing performance has covered a combination of different datasets of CXR images of positive COVID-19 patients, patients with viral and bacterial infections, also, people with a clear chest. The proposed AI scheme successfully distinguishes CXR scans of COVID-19 infected patients from CXR scans of viral and bacterial based pneumonia as well as normal cases with an average accuracy of 94.43%, sensitivity 95%, and specificity 93.86%. Predicted decisions would be supported by visual evidence to help clinicians speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.
Keywords: COVID-19, chest x-ray scan, artificial intelligence, texture analysis, local binary pattern transform, Gabor filter.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 654183 A Methodology for Automatic Diversification of Document Categories
Authors: Dasom Kim, Chen Liu, Myungsu Lim, Soo-Hyeon Jeon, Byeoung Kug Jeon, Kee-Young Kwahk, Namgyu Kim
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Recently, numerous documents including large volumes of unstructured data and text have been created because of the rapid increase in the use of social media and the Internet. Usually, these documents are categorized for the convenience of users. Because the accuracy of manual categorization is not guaranteed, and such categorization requires a large amount of time and incurs huge costs. Many studies on automatic categorization have been conducted to help mitigate the limitations of manual categorization. Unfortunately, most of these methods cannot be applied to categorize complex documents with multiple topics because they work on the assumption that individual documents can be categorized into single categories only. Therefore, to overcome this limitation, some studies have attempted to categorize each document into multiple categories. However, the learning process employed in these studies involves training using a multi-categorized document set. These methods therefore cannot be applied to the multi-categorization of most documents unless multi-categorized training sets using traditional multi-categorization algorithms are provided. To overcome this limitation, in this study, we review our novel methodology for extending the category of a single-categorized document to multiple categorizes, and then introduce a survey-based verification scenario for estimating the accuracy of our automatic categorization methodology.Keywords: Big Data Analysis, Document Classification, Text Mining, Topic Analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1739182 A Weighted-Profiling Using an Ontology Basefor Semantic-Based Search
Authors: Hikmat A. M. Abd-El-Jaber, Tengku M. T. Sembok
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The information on the Web increases tremendously. A number of search engines have been developed for searching Web information and retrieving relevant documents that satisfy the inquirers needs. Search engines provide inquirers irrelevant documents among search results, since the search is text-based rather than semantic-based. Information retrieval research area has presented a number of approaches and methodologies such as profiling, feedback, query modification, human-computer interaction, etc for improving search results. Moreover, information retrieval has employed artificial intelligence techniques and strategies such as machine learning heuristics, tuning mechanisms, user and system vocabularies, logical theory, etc for capturing user's preferences and using them for guiding the search based on the semantic analysis rather than syntactic analysis. Although a valuable improvement has been recorded on search results, the survey has shown that still search engines users are not really satisfied with their search results. Using ontologies for semantic-based searching is likely the key solution. Adopting profiling approach and using ontology base characteristics, this work proposes a strategy for finding the exact meaning of the query terms in order to retrieve relevant information according to user needs. The evaluation of conducted experiments has shown the effectiveness of the suggested methodology and conclusion is presented.Keywords: information retrieval, user profiles, semantic Web, ontology, search engine.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3202181 Synchronous Courses Attendance in Distance Higher Education: Case Study of a Computer Science Department
Authors: Thierry Eude
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The use of videoconferencing platforms adapted to teaching offers students the opportunity to take distance education courses in much the same way as traditional in-class training. The sessions can be recorded and they allow students the option of following the courses synchronously or asynchronously. Three typical profiles can then be distinguished: students who choose to follow the courses synchronously, students who could attend the course in synchronous mode but choose to follow the session off-line, and students who follow the course asynchronously as they cannot attend the course when it is offered because of professional or personal constraints. Our study consists of observing attendance at all distance education courses offered in the synchronous mode by the Computer Science and Software Engineering Department at Laval University during 10 consecutive semesters. The aim is to identify factors that influence students in their choice of attending the distance courses in synchronous mode. It was found that participation tends to be relatively stable over the years for any one semester (fall, winter summer) and is similar from one course to another, although students may be increasingly familiar with the synchronous distance education courses. Average participation is around 28%. There may be deviations, but they concern only a few courses during certain semesters, suggesting that these deviations would only have occurred because of the composition of particular promotions during specific semesters. Furthermore, course schedules have a great influence on the attendance rate. The highest rates are all for courses which are scheduled outside office hours.
Keywords: Attendance, distance undergraduate education in computer science, student behavior, synchronous e-learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1422180 Performance Optimization of Data Mining Application Using Radial Basis Function Classifier
Authors: M. Govindarajan, R. M.Chandrasekaran
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Text data mining is a process of exploratory data analysis. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. This paper describes proposed radial basis function Classifier that performs comparative crossvalidation for existing radial basis function Classifier. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: direct Marketing. Direct marketing has become an important application field of data mining. Comparative Cross-validation involves estimation of accuracy by either stratified k-fold cross-validation or equivalent repeated random subsampling. While the proposed method may have high bias; its performance (accuracy estimation in our case) may be poor due to high variance. Thus the accuracy with proposed radial basis function Classifier was less than with the existing radial basis function Classifier. However there is smaller the improvement in runtime and larger improvement in precision and recall. In the proposed method Classification accuracy and prediction accuracy are determined where the prediction accuracy is comparatively high.Keywords: Text Data Mining, Comparative Cross-validation, Radial Basis Function, runtime, accuracy.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1538179 A Simulated Environment Approach to Investigate the Effect of Adversarial Perturbations on Traffic Sign for Automotive Software-in-Loop Testing
Authors: Sunil Patel, Pallab Maji
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To study the effect of adversarial attack environment must be controlled. Autonomous driving includes mainly 5 phases sense, perceive, map, plan, and drive. Autonomous vehicles sense their surrounding with the help of different sensors like cameras, radars, and lidars. Deep learning techniques are considered Blackbox and found to be vulnerable to adversarial attacks. In this research, we study the effect of the various known adversarial attacks with the help of the Unreal Engine-based, high-fidelity, real-time raytraced simulated environment. The goal of this experiment is to find out if adversarial attacks work in moving vehicles and if an unknown network may be targeted. We discovered that the existing Blackbox and Whitebox attacks have varying effects on different traffic signs. We observed that attacks that impair detection in static scenarios do not have the same effect on moving vehicles. It was found that some adversarial attacks with hardly noticeable perturbations entirely blocked the recognition of certain traffic signs. We observed that the daylight condition has a substantial impact on the model's performance by simulating the interplay of light on traffic signs. Our findings have been found to closely resemble outcomes encountered in the real world.
Keywords: Adversarial attack simulation, computer simulation, ray-traced environment, realistic simulation, unreal engine.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 403178 Vision-Based Collision Avoidance for Unmanned Aerial Vehicles by Recurrent Neural Networks
Authors: Yao-Hong Tsai
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Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.Keywords: Unmanned aerial vehicle, object tracking, deep learning, collision avoidance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 938177 Through Biometric Card in Romania: Person Identification by Face, Fingerprint and Voice Recognition
Authors: Hariton N. Costin, Iulian Ciocoiu, Tudor Barbu, Cristian Rotariu
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In this paper three different approaches for person verification and identification, i.e. by means of fingerprints, face and voice recognition, are studied. Face recognition uses parts-based representation methods and a manifold learning approach. The assessment criterion is recognition accuracy. The techniques under investigation are: a) Local Non-negative Matrix Factorization (LNMF); b) Independent Components Analysis (ICA); c) NMF with sparse constraints (NMFsc); d) Locality Preserving Projections (Laplacianfaces). Fingerprint detection was approached by classical minutiae (small graphical patterns) matching through image segmentation by using a structural approach and a neural network as decision block. As to voice / speaker recognition, melodic cepstral and delta delta mel cepstral analysis were used as main methods, in order to construct a supervised speaker-dependent voice recognition system. The final decision (e.g. “accept-reject" for a verification task) is taken by using a majority voting technique applied to the three biometrics. The preliminary results, obtained for medium databases of fingerprints, faces and voice recordings, indicate the feasibility of our study and an overall recognition precision (about 92%) permitting the utilization of our system for a future complex biometric card.Keywords: Biometry, image processing, pattern recognition, speech analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1930176 Using Satellite Images Datasets for Road Intersection Detection in Route Planning
Authors: Fatma El-zahraa El-taher, Ayman Taha, Jane Courtney, Susan Mckeever
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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions is critical to decisions such as crossing roads or selecting safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset are examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of detection of intersections in satellite images is evaluated.
Keywords: Satellite images, remote sensing images, data acquisition, autonomous vehicles, robot navigation, route planning, road intersections.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 703175 Computer-Aided Classification of Liver Lesions Using Contrasting Features Difference
Authors: Hussein Alahmer, Amr Ahmed
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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.
Keywords: CAD system, difference of feature, Fuzzy c means, Liver segmentation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1404174 MPPT Operation for PV Grid-connected System using RBFNN and Fuzzy Classification
Authors: A. Chaouachi, R. M. Kamel, K. Nagasaka
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This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW Photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and non-linear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network.
Keywords: MPPT, neuro-fuzzy, RBFN, grid-connected, photovoltaic.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3169173 Development of a Neural Network based Algorithm for Multi-Scale Roughness Parameters and Soil Moisture Retrieval
Authors: L. Bennaceur Farah, I. R. Farah, R. Bennaceur, Z. Belhadj, M. R. Boussema
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The overall objective of this paper is to retrieve soil surfaces parameters namely, roughness and soil moisture related to the dielectric constant by inverting the radar backscattered signal from natural soil surfaces. Because the classical description of roughness using statistical parameters like the correlation length doesn't lead to satisfactory results to predict radar backscattering, we used a multi-scale roughness description using the wavelet transform and the Mallat algorithm. In this description, the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each having a spatial scale. A second step in this study consisted in adapting a direct model simulating radar backscattering namely the small perturbation model to this multi-scale surface description. We investigated the impact of this description on radar backscattering through a sensitivity analysis of backscattering coefficient to the multi-scale roughness parameters. To perform the inversion of the small perturbation multi-scale scattering model (MLS SPM) we used a multi-layer neural network architecture trained by backpropagation learning rule. The inversion leads to satisfactory results with a relative uncertainty of 8%.Keywords: Remote sensing, rough surfaces, inverse problems, SAR, radar scattering, Neural networks and Fractals.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1581172 In Search of a Suitable Neural Network Capable of Fast Monitoring of Congestion Level in Electric Power Systems
Authors: Pradyumna Kumar Sahoo, Prasanta Kumar Satpathy
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This paper aims at finding a suitable neural network for monitoring congestion level in electrical power systems. In this paper, the input data has been framed properly to meet the target objective through supervised learning mechanism by defining normal and abnormal operating conditions for the system under study. The congestion level, expressed as line congestion index (LCI), is evaluated for each operating condition and is presented to the NN along with the bus voltages to represent the input and target data. Once, the training goes successful, the NN learns how to deal with a set of newly presented data through validation and testing mechanism. The crux of the results presented in this paper rests on performance comparison of a multi-layered feed forward neural network with eleven types of back propagation techniques so as to evolve the best training criteria. The proposed methodology has been tested on the standard IEEE-14 bus test system with the support of MATLAB based NN toolbox. The results presented in this paper signify that the Levenberg-Marquardt backpropagation algorithm gives best training performance of all the eleven cases considered in this paper, thus validating the proposed methodology.
Keywords: Line congestion index, critical bus, contingency, neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1774171 A Multi-layer Artificial Neural Network Architecture Design for Load Forecasting in Power Systems
Authors: Axay J Mehta, Hema A Mehta, T.C.Manjunath, C. Ardil
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In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.
Keywords: Power system, Load forecasting, Neural Network, Neuron, Stabilization, Network structure, Load.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3395170 Study Habits and Level of Difficulty Encountered by Maltese Students Studying Biology Advanced Level Topics
Authors: Marthese Azzopardi, Liberato Camilleri
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This research was performed to investigate the study habits and level of difficulty perceived by post-secondary students in Biology at Advanced-level topics after completing their first year of study. At the end of a two-year ‘sixth form’ course, Maltese students sit for the Matriculation and Secondary Education Certificate (MATSEC) Advanced-level biology exam as a requirement to pursue science-related studies at the University of Malta. The sample was composed of 23 students (16 taking Chemistry and seven taking some ‘Other’ subject at the Advanced Level). The cohort comprised seven males and 16 females. A questionnaire constructed by the authors, was answered anonymously during the last lecture at the end of the first year of study, in May 2016. The Chi square test revealed that gender plays no effect on the various study habits (c2 (6) = 5.873, p = 0.438). ‘Reading both notes and textbooks’ was the most common method adopted by males (71.4%), whereas ‘Writing notes on each topic’ was that mostly used by females (81.3%). The Mann-Whitney U test showed no significant difference in the study habits of students and the mean assessment mark obtained at the end of the first year course (p = 0.231). Statistical difference was found with the One-ANOVA test when comparing the mean assessment mark obtained at the end of the first year course when students are clustered by their Secondary Education Certificate (SEC) grade (p < 0.001). Those obtaining a SEC grade of 2 and 3 got the highest mean assessment of 68.33% and 66.9%, respectively [SEC grading is 1-7, where 1 is the highest]. The Friedman test was used to compare the mean difficulty rating scores provided for the difficulty of each topic. The mean difficulty rating score ranges from 1 to 4, where the larger the mean rating score, the higher the difficulty. When considering the whole group of students, nine topics out of 21 were perceived as significantly more difficult than the other topics. Protein synthesis, DNA Replication and Biomolecules were the most difficult, in that order. The Mann-Whitney U test revealed that the perceived level of difficulty in comprehending Biomolecules is significantly lower for students taking Chemistry compared to those not choosing the subject (p = 0.018). Protein Synthesis was claimed as the most difficult by Chemistry students and Biomolecules by those not studying Chemistry. DNA Replication was the second most difficult topic perceived by both groups. The Mann-Whitney U test was used to examine the effect of gender on the perceived level of difficulty in comprehending various topics. It was found that females have significantly more difficulty in comprehending Biomolecules than males (p=0.039). Protein synthesis was perceived as the most difficult topic by males (mean difficulty rating score = 3.14), while Biomolecules, DNA Replication and Protein synthesis were of equal difficulty for females (mean difficulty rating score = 3.00). Males and females perceived DNA Replication as equally difficult (mean difficulty rating score = 3.00). Discovering the students’ study habits and perceived level of difficulty of specific topics is vital for the lecturer to offer guidance that leads to higher academic achievement.
Keywords: Biology, Perceived difficulty, Post-secondary, Study habits.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1342169 Virtual Reality for Mutual Understanding in Landscape Planning
Authors: Ball J., Capanni N., Watt S.
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This paper argues that fostering mutual understanding in landscape planning is as much about the planners educating stakeholder groups as the stakeholders educating the planners. In other words it is an epistemological agreement as to the meaning and nature of place, especially where an effort is made to go beyond the quantitative aspects, which can be achieved by the phenomenological experience of the Virtual Reality (VR) environment. This education needs to be a bi-directional process in which distance can be both temporal as well as spatial separation of participants, that there needs to be a common framework of understanding in which neither 'side' is disadvantaged during the process of information exchange and it follows that a medium such as VR offers an effective way of overcoming some of the shortcomings of traditional media by taking advantage of continuing technological advances in Information, Technology and Communications (ITC). In this paper we make particular reference to this as an extension to Geographical Information Systems (GIS). VR as a two-way communication tool offers considerable potential particularly in the area of Public Participation GIS (PPGIS). Information rich virtual environments that can operate over broadband networks are now possible and thus allow for the representation of large amounts of qualitative and quantitative information 'side-by-side'. Therefore, with broadband access becoming standard for households and enterprises alike, distributed virtual reality environments have great potential to contribute to enabling stakeholder participation and mutual learning within the planning context.
Keywords: 3D, communication, geographical information systems, planning, public participation, virtual reality, visualisation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2021