Search results for: Data mining andInformation Extraction
6512 Indigenous Engagement: Towards a Culturally Sensitive Approach for Inclusive Economic Development
Authors: K. N. Penna, E. J. Hoffman, T. R. Carter
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This paper suggests that cultural landscape management plans in an Indigenous context are more effective if designed by taking into consideration context-related social and cultural aspects, adopting people-centred and cultural-based approaches for instance. In relation to working in Indigenous and mining contexts, we draw upon and contribute to international policies on human rights that promote the development of management plans that are co-designed through genuine engagement processes. We suggest that the production of management plans that are built upon culturally relevant frameworks leads to more inclusive economic development, a greater sense of trust, and shared managerial responsibilities. In this paper, three issues related to Indigenous engagement and cultural landscape management plans will be addressed: (1) the need for effective communication channels between proponents and Traditional Owners (Australian original Aboriginal peoples who inhabited specific regions), (2) the use of a culturally sensitive approach to engage local representatives in the decision-making processes, and (3) how design of new management plans can help in establishing shared management.
Keywords: Culture-Centred Approach, Holons’ Hierarchy, Inclusive Economic Development, Indigenous Engagement.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4786511 Effective Digital Music Retrieval System through Content-based Features
Authors: Bokyung Sung, Kwanghyo Koo, Jungsoo Kim, Myung-Bum Jung, Jinman Kwon, Ilju Ko
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In this paper, we propose effective system for digital music retrieval. We divided proposed system into Client and Server. Client part consists of pre-processing and Content-based feature extraction stages. In pre-processing stage, we minimized Time code Gap that is occurred among same music contents. As content-based feature, first-order differentiated MFCC were used. These presented approximately envelop of music feature sequences. Server part included Music Server and Music Matching stage. Extracted features from 1,000 digital music files were stored in Music Server. In Music Matching stage, we found retrieval result through similarity measure by DTW. In experiment, we used 450 queries. These were made by mixing different compression standards and sound qualities from 50 digital music files. Retrieval accurate indicated 97% and retrieval time was average 15ms in every single query. Out experiment proved that proposed system is effective in retrieve digital music and robust at various user environments of web.
Keywords: Music Retrieval, Content-based, Music Feature and Digital Music.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15196510 Predicting Protein-Protein Interactions from Protein Sequences Using Phylogenetic Profiles
Authors: Omer Nebil Yaveroglu, Tolga Can
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In this study, a high accuracy protein-protein interaction prediction method is developed. The importance of the proposed method is that it only uses sequence information of proteins while predicting interaction. The method extracts phylogenetic profiles of proteins by using their sequence information. Combining the phylogenetic profiles of two proteins by checking existence of homologs in different species and fitting this combined profile into a statistical model, it is possible to make predictions about the interaction status of two proteins. For this purpose, we apply a collection of pattern recognition techniques on the dataset of combined phylogenetic profiles of protein pairs. Support Vector Machines, Feature Extraction using ReliefF, Naive Bayes Classification, K-Nearest Neighborhood Classification, Decision Trees, and Random Forest Classification are the methods we applied for finding the classification method that best predicts the interaction status of protein pairs. Random Forest Classification outperformed all other methods with a prediction accuracy of 76.93%Keywords: Protein Interaction Prediction, Phylogenetic Profile, SVM , ReliefF, Decision Trees, Random Forest Classification
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16136509 Classifying Biomedical Text Abstracts based on Hierarchical 'Concept' Structure
Authors: Rozilawati Binti Dollah, Masaki Aono
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Classifying biomedical literature is a difficult and challenging task, especially when a large number of biomedical articles should be organized into a hierarchical structure. In this paper, we present an approach for classifying a collection of biomedical text abstracts downloaded from Medline database with the help of ontology alignment. To accomplish our goal, we construct two types of hierarchies, the OHSUMED disease hierarchy and the Medline abstract disease hierarchies from the OHSUMED dataset and the Medline abstracts, respectively. Then, we enrich the OHSUMED disease hierarchy before adapting it to ontology alignment process for finding probable concepts or categories. Subsequently, we compute the cosine similarity between the vector in probable concepts (in the “enriched" OHSUMED disease hierarchy) and the vector in Medline abstract disease hierarchies. Finally, we assign category to the new Medline abstracts based on the similarity score. The results obtained from the experiments show the performance of our proposed approach for hierarchical classification is slightly better than the performance of the multi-class flat classification.Keywords: Biomedical literature, hierarchical text classification, ontology alignment, text mining.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20116508 Algorithm for Determining the Parameters of a Two-Layer Soil Model
Authors: Adekitan I. Aderibigbe, Fakolujo A. Olaosebikan
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The parameters of a two-layer soil can be determined by processing resistivity data obtained from resistivity measurements carried out on the soil of interest. The processing usually entails applying the resistivity data as inputs to an optimisation function. This paper proposes an algorithm which utilises the square error as an optimisation function. Resistivity data from previous works were applied to test the accuracy of the new algorithm developed and the result obtained conforms significantly to results from previous works.
Keywords: Algorithm, earthing, resistivity, two-layer soil-model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 33326507 MRI Reconstruction Using Discrete Fourier Transform: A tutorial
Authors: Abiodun M. Aibinu, Momoh J. E. Salami, Amir A. Shafie, Athaur Rahman Najeeb
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The use of Inverse Discrete Fourier Transform (IDFT) implemented in the form of Inverse Fourier Transform (IFFT) is one of the standard method of reconstructing Magnetic Resonance Imaging (MRI) from uniformly sampled K-space data. In this tutorial, three of the major problems associated with the use of IFFT in MRI reconstruction are highlighted. The tutorial also gives brief introduction to MRI physics; MRI system from instrumentation point of view; K-space signal and the process of IDFT and IFFT for One and two dimensional (1D and 2D) data.
Keywords: Discrete Fourier Transform (DFT), K-space Data, Magnetic Resonance (MR), Spin, Windows.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 51116506 Assessing and Visualizing the Stability of Feature Selectors: A Case Study with Spectral Data
Authors: R.Guzman-Martinez, Oscar Garcia-Olalla, R.Alaiz-Rodriguez
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Feature selection plays an important role in applications with high dimensional data. The assessment of the stability of feature selection/ranking algorithms becomes an important issue when the dataset is small and the aim is to gain insight into the underlying process by analyzing the most relevant features. In this work, we propose a graphical approach that enables to analyze the similarity between feature ranking techniques as well as their individual stability. Moreover, it works with whatever stability metric (Canberra distance, Spearman's rank correlation coefficient, Kuncheva's stability index,...). We illustrate this visualization technique evaluating the stability of several feature selection techniques on a spectral binary dataset. Experimental results with a neural-based classifier show that stability and ranking quality may not be linked together and both issues have to be studied jointly in order to offer answers to the domain experts.
Keywords: Feature Selection Stability, Spectral data, Data visualization
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15266505 A Video-based Algorithm for Moving Objects Detection at Signalized Intersection
Authors: Juan Li, Chunfu Shao, Chunjiao Dong, Dan Zhao, Yinhong Liu
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Mixed-traffic (e.g., pedestrians, bicycles, and vehicles) data at an intersection is one of the essential factors for intersection design and traffic control. However, some data such as pedestrian volume cannot be directly collected by common detectors (e.g. inductive loop, sonar and microwave sensors). In this paper, a video based detection algorithm is proposed for mixed-traffic data collection at intersections using surveillance cameras. The algorithm is derived from Gaussian Mixture Model (GMM), and uses a mergence time adjustment scheme to improve the traditional algorithm. Real-world video data were selected to test the algorithm. The results show that the proposed algorithm has the faster processing speed and more accuracy than the traditional algorithm. This indicates that the improved algorithm can be applied to detect mixed-traffic at signalized intersection, even when conflicts occur.Keywords: detection, intersection, mixed traffic, moving objects.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20336504 Governance, Risk Management, and Compliance Factors Influencing the Adoption of Cloud Computing in Australia
Authors: Tim Nedyalkov
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A business decision to move to the cloud brings fundamental changes in how an organization develops and delivers its Information Technology solutions. The accelerated pace of digital transformation across businesses and government agencies increases the reliance on cloud-based services. Collecting, managing, and retaining large amounts of data in cloud environments make information security and data privacy protection essential. It becomes even more important to understand what key factors drive successful cloud adoption following the commencement of the Privacy Amendment Notifiable Data Breaches (NDB) Act 2017 in Australia as the regulatory changes impact many organizations and industries. This quantitative correlational research investigated the governance, risk management, and compliance factors contributing to cloud security success. The factors influence the adoption of cloud computing within an organizational context after the commencement of the NDB scheme. The results and findings demonstrated that corporate information security policies, data storage location, management understanding of data governance responsibilities, and regular compliance assessments are the factors influencing cloud computing adoption. The research has implications for organizations, future researchers, practitioners, policymakers, and cloud computing providers to meet the rapidly changing regulatory and compliance requirements.
Keywords: Cloud compliance, cloud security, cloud security governance, data governance, privacy protection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9126503 Preconcentration and Determination of Cyproheptadine in Biological Samples by Hollow Fiber Liquid Phase Microextraction Coupled with High Performance Liquid Chromatography
Authors: Najari Moghadam Sh., Qomi M., Raofie F., Khadiv J.
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In this study, a liquid phase microextraction by hollow fiber (HF-LPME) combined with high performance liquid chromatography-UV detector was applied to preconcentrate and determine trace levels of Cyproheptadine in human urine and plasma samples. Cyproheptadine was extracted from 10 mL alkaline aqueous solution (pH: 9.81) into an organic solvent (n-octnol) which was immobilized in the wall pores of a hollow fiber. Then was back-extracted into an acidified aqueous solution (pH: 2.59) located inside the lumen of the hollow fiber. This method is simple, efficient and cost-effective. It is based on pH gradient and differences between two aqueous phases. In order to optimize the HF-LPME some affecting parameters including the pH of donor and acceptor phases, the type of organic solvent, ionic strength, stirring rate, extraction time and temperature were studied and optimized. Under optimal conditions enrichment factor, limit of detection (LOD) and relative standard deviation (RSD(%), n=3) were up to 112, 15 μg.L−1 and 2.7, respectively.
Keywords: Biological samples, Cyproheptadine, hollow fiber, liquid phase microextraction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22336502 Control Chart Pattern Recognition Using Wavelet Based Neural Networks
Authors: Jun Seok Kim, Cheong-Sool Park, Jun-Geol Baek, Sung-Shick Kim
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Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.
Keywords: Control chart pattern recognition, Multi-resolution wavelet analysis, Bi-directional Kohonen network, Back-propagation network, Feature extraction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24806501 New Findings on the User’s Preferences about Data Visualization of Online Reviews
Authors: Elizabeth Simão Carvalho, Marcirio Silveira Chaves
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The information visualization is still a knowledge field that lacks from a solid theory to support it and there is a myriad of existing methodologies and taxonomies that can be combined and adopted as guidelines. In this context, it is necessary to pre-evaluate as much as possible all the assumptions that are considered for its design and development. We present an exploratory study (n = 123) to detect the graphical preferences of travelers using accommodation portals of Web 2.0 (e.g. tripadvisor.com). We took into account some of the most relevant ground rules applied in the field to map visually data and design end-user interaction. Moreover, the evaluation process was completely data visualization oriented. We found out that people tend to refuse more advanced types of visualization and that a hybrid combination between radial graphs and stacked bars should be explored. In sum, this paper introduces new findings about the visual model and the cognitive response of users of accommodation booking websites.
Keywords: Information visualization, Data visualization, Visualization evaluation, Online reviews, Booking portal, Hotel booking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17206500 Rapid Monitoring of Earthquake Damages Using Optical and SAR Data
Authors: Saeid Gharechelou, Ryutaro Tateishi
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Earthquake is an inevitable catastrophic natural disaster. The damages of buildings and man-made structures, where most of the human activities occur are the major cause of casualties from earthquakes. A comparison of optical and SAR data is presented in the case of Kathmandu valley which was hardly shaken by 2015-Nepal Earthquake. Though many existing researchers have conducted optical data based estimated or suggested combined use of optical and SAR data for improved accuracy, however finding cloud-free optical images when urgently needed are not assured. Therefore, this research is specializd in developing SAR based technique with the target of rapid and accurate geospatial reporting. Should considers that limited time available in post-disaster situation offering quick computation exclusively based on two pairs of pre-seismic and co-seismic single look complex (SLC) images. The InSAR coherence pre-seismic, co-seismic and post-seismic was used to detect the change in damaged area. In addition, the ground truth data from field applied to optical data by random forest classification for detection of damaged area. The ground truth data collected in the field were used to assess the accuracy of supervised classification approach. Though a higher accuracy obtained from the optical data then integration by optical-SAR data. Limitation of cloud-free images when urgently needed for earthquak evevent are and is not assured, thus further research on improving the SAR based damage detection is suggested. Availability of very accurate damage information is expected for channelling the rescue and emergency operations. It is expected that the quick reporting of the post-disaster damage situation quantified by the rapid earthquake assessment should assist in channeling the rescue and emergency operations, and in informing the public about the scale of damage.
Keywords: Sentinel-1A data, Landsat-8, earthquake damage, InSAR, rapid monitoring, 2015-Nepal earthquake.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10566499 Structural Damage Detection via Incomplete Modal Data Using Output Data Only
Authors: Ahmed Noor Al-Qayyim, Barlas Ozden Caglayan
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Structural failure is caused mainly by damage that often occurs on structures. Many researchers focus on to obtain very efficient tools to detect the damage in structures in the early state. In the past decades, a subject that has received considerable attention in literature is the damage detection as determined by variations in the dynamic characteristics or response of structures. The study presents a new damage identification technique. The technique detects the damage location for the incomplete structure system using output data only. The method indicates the damage based on the free vibration test data by using ‘Two Points Condensation (TPC) technique’. This method creates a set of matrices by reducing the structural system to two degrees of freedom systems. The current stiffness matrices obtain from optimization the equation of motion using the measured test data. The current stiffness matrices compare with original (undamaged) stiffness matrices. The large percentage changes in matrices’ coefficients lead to the location of the damage. TPC technique is applied to the experimental data of a simply supported steel beam model structure after inducing thickness change in one element, where two cases consider. The method detects the damage and determines its location accurately in both cases. In addition, the results illustrate these changes in stiffness matrix can be a useful tool for continuous monitoring of structural safety using ambient vibration data. Furthermore, its efficiency proves that this technique can be used also for big structures.Keywords: Damage detection, two points–condensation, structural health monitoring, signals processing, optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26996498 Broadband PowerLine Communications: Performance Analysis
Authors: Justinian Anatory, Nelson Theethayi, M. M. Kissaka, N. H. Mvungi
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Power line channel is proposed as an alternative for broadband data transmission especially in developing countries like Tanzania [1]. However the channel is affected by stochastic attenuation and deep notches which can lead to the limitation of channel capacity and achievable data rate. Various studies have characterized the channel without giving exactly the maximum performance and limitation in data transfer rate may be this is due to complexity of channel modeling being used. In this paper the channel performance of medium voltage, low voltage and indoor power line channel is presented. In the investigations orthogonal frequency division multiplexing (OFDM) with phase shift keying (PSK) as carrier modulation schemes is considered, for indoor, medium and low voltage channels with typical ten branches and also Golay coding is applied for medium voltage channel. From channels, frequency response deep notches are observed in various frequencies which can lead to reduce the achievable data rate. However, is observed that data rate up to 240Mbps is realized for a signal to noise ratio of about 50dB for indoor and low voltage channels, however for medium voltage a typical link with ten branches is affected by strong multipath and coding is required for feasible broadband data transfer.
Keywords: Powerline Communications, branched network, channel model, modulation, channel performance, OFDM.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18336497 An Empirical Investigation of Big Data Analytics: The Financial Performance of Users versus Vendors
Authors: Evisa Mitrou, Nicholas Tsitsianis, Supriya Shinde
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In the age of digitisation and globalisation, businesses have shifted online and are investing in big data analytics (BDA) to respond to changing market conditions and sustain their performance. Our study shifts the focus from the adoption of BDA to the impact of BDA on financial performance. We explore the financial performance of both BDA-vendors (business-to-business) and BDA-clients (business-to-customer). We distinguish between the five BDA-technologies (big-data-as-a-service (BDaaS), descriptive, diagnostic, predictive, and prescriptive analytics) and discuss them individually. Further, we use four perspectives (internal business process, learning and growth, customer, and finance) and discuss the significance of how each of the five BDA-technologies affect the performance measures of these four perspectives. We also present the analysis of employee engagement, average turnover, average net income, and average net assets for BDA-clients and BDA-vendors. Our study also explores the effect of the COVID-19 pandemic on business continuity for both BDA-vendors and BDA-clients.
Keywords: BDA-clients, BDA-vendors, big data analytics, financial performance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6186496 Spatial Services in Cloud Environment
Authors: Sašo Pečnik, Borut Žalik
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Cloud Computing is an approach that provides computation and storage services on-demand to clients over the network, independent of device and location. In the last few years, cloud computing became a trend in information technology with many companies that transfer their business processes and applications in the cloud. Cloud computing with service oriented architecture has contributed to rapid development of Geographic Information Systems. Open Geospatial Consortium with its standards provides the interfaces for hosted spatial data and GIS functionality to integrated GIS applications. Furthermore, with the enormous processing power, clouds provide efficient environment for data intensive applications that can be performed efficiently, with higher precision, and greater reliability. This paper presents our work on the geospatial data services within the cloud computing environment and its technology. A cloud computing environment with the strengths and weaknesses of the geographic information system will be introduced. The OGC standards that solve our application interoperability are highlighted. Finally, we outline our system architecture with utilities for requesting and invoking our developed data intensive applications as a web service.
Keywords: Cloud Computing, Geographic Information System, Open Geospatial Consortium, Interoperability, Spatial data, Web- Services.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17126495 Selection of an Optimum Configuration of Solar PV Array under Partial Shaded Condition Using Particle Swarm Optimization
Authors: R. Ramaprabha
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This paper presents an extraction of maximum energy from Solar Photovoltaic Array (SPVA) under partial shaded conditions by optimum selection of array size using Particle Swarm Optimization (PSO) technique. In this paper a detailed study on the output reduction of different SPVA configurations under partial shaded conditions have been carried out. A generalized MATLAB M-code based software model has been used for any required array size, configuration, shading patterns and number of bypass diodes. Comparative study has been carried out on different configurations by testing several shading scenarios. While the number of shading patterns and the rate of change are very low for stationary SPVA but these may be quite large for SPVA mounted on a mobile platforms. This paper presents the suitability of PSO technique to select optimum configuration for mobile arrays by calculating the global peak (GP) of different configurations and to transfer maximum power to the load.
Keywords: Global peak, Mobile PV arrays, Partial shading, optimization, PSO.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 42356494 The Trend of Injuries in Building Fire in Tehran from 2002 to 2012
Authors: Mohammadreza Ashouri, Majid Bayatian
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Analysis of fire data is a way for the implementation of any plan to improve the level of safety in cities. Such an analysis is able to reveal signs of changes in a given period and can be used as a measure of safety. The information of about 66,341 fires (from 2002 to 2012) released by Tehran Safety Services and Fire-Fighting Organization and data on the population and the number of households provided by Tehran Municipality and the Statistical Yearbook of Iran were extracted. Using the data, the fire changes, the rate of injuries, and mortality rate were determined and analyzed. The rate of injuries and mortality rate of fires per one million population of Tehran were 59.58% and 86.12%, respectively. During the study period, the number of fires and fire stations increased by 104.38% and 102.63%, respectively. Most fires (9.21%) happened in the 4th District of Tehran. The results showed that the recorded fire data have not been systematically planned for fire prevention since one of the ways to reduce injuries caused by fires is to develop a systematic plan for necessary actions in emergency situations. To determine a reliable source for fire prevention, the stages, definitions of working processes and the cause and effect chains should be considered. Therefore, a comprehensive statistical system should be developed for reported and recorded fire data.
Keywords: Fire statistics, fire analysis, accident prevention, Tehran.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7696493 Automated Heart Sound Classification from Unsegmented Phonocardiogram Signals Using Time Frequency Features
Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan
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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.Keywords: Pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12836492 Choosing R-tree or Quadtree Spatial DataIndexing in One Oracle Spatial Database System to Make Faster Showing Geographical Map in Mobile Geographical Information System Technology
Authors: Maruto Masserie Sardadi, Mohd Shafry bin Mohd Rahim, Zahabidin Jupri, Daut bin Daman
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The latest Geographic Information System (GIS) technology makes it possible to administer the spatial components of daily “business object," in the corporate database, and apply suitable geographic analysis efficiently in a desktop-focused application. We can use wireless internet technology for transfer process in spatial data from server to client or vice versa. However, the problem in wireless Internet is system bottlenecks that can make the process of transferring data not efficient. The reason is large amount of spatial data. Optimization in the process of transferring and retrieving data, however, is an essential issue that must be considered. Appropriate decision to choose between R-tree and Quadtree spatial data indexing method can optimize the process. With the rapid proliferation of these databases in the past decade, extensive research has been conducted on the design of efficient data structures to enable fast spatial searching. Commercial database vendors like Oracle have also started implementing these spatial indexing to cater to the large and diverse GIS. This paper focuses on the decisions to choose R-tree and quadtree spatial indexing using Oracle spatial database in mobile GIS application. From our research condition, the result of using Quadtree and R-tree spatial data indexing method in one single spatial database can save the time until 42.5%.Keywords: Indexing, Mobile GIS, MapViewer, Oracle SpatialDatabase.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 40366491 OCR for Script Identification of Hindi (Devnagari) Numerals using Error Diffusion Halftoning Algorithm with Neural Classifier
Authors: Banashree N. P., Andhe Dharani, R. Vasanta, P. S. Satyanarayana
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The applications on numbers are across-the-board that there is much scope for study. The chic of writing numbers is diverse and comes in a variety of form, size and fonts. Identification of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], machine printed or handwritten characters/numerals are recognized. There are plentiful approaches that deal with problem of detection of numerals/character depending on the sort of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent our work focused on a technique in feature extraction i.e. Local-based approach, a method using 16-segment display concept, which is extracted from halftoned images & Binary images of isolated numerals. These feature vectors are fed to neural classifier model that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. Experimentation result shows that recognition rate of halftoned images is 98 % compared to binary images (95%).
Keywords: OCR, Halftoning, Neural classifier, 16-segmentdisplay concept.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17166490 On Developing an Automatic Speech Recognition System for Standard Arabic Language
Authors: R. Walha, F. Drira, H. El-Abed, A. M. Alimi
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The Automatic Speech Recognition (ASR) applied to Arabic language is a challenging task. This is mainly related to the language specificities which make the researchers facing multiple difficulties such as the insufficient linguistic resources and the very limited number of available transcribed Arabic speech corpora. In this paper, we are interested in the development of a HMM-based ASR system for Standard Arabic (SA) language. Our fundamental research goal is to select the most appropriate acoustic parameters describing each audio frame, acoustic models and speech recognition unit. To achieve this purpose, we analyze the effect of varying frame windowing (size and period), acoustic parameter number resulting from features extraction methods traditionally used in ASR, speech recognition unit, Gaussian number per HMM state and number of embedded re-estimations of the Baum-Welch Algorithm. To evaluate the proposed ASR system, a multi-speaker SA connected-digits corpus is collected, transcribed and used throughout all experiments. A further evaluation is conducted on a speaker-independent continue SA speech corpus. The phonemes recognition rate is 94.02% which is relatively high when comparing it with another ASR system evaluated on the same corpus.Keywords: ASR, HMM, acoustical analysis, acoustic modeling, Standard Arabic language
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17806489 Design and Implementation a Virtualization Platform for Providing Smart Tourism Services
Authors: Nam Don Kim, Jungho Moon, Tae Yun Chung
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This paper proposes an Internet of Things (IoT) based virtualization platform for providing smart tourism services. The virtualization platform provides a consistent access interface to various types of data by naming IoT devices and legacy information systems as pathnames in a virtual file system. In the other words, the IoT virtualization platform functions as a middleware which uses the metadata for underlying collected data. The proposed platform makes it easy to provide customized tourism information by using tourist locations collected by IoT devices and additionally enables to create new interactive smart tourism services focused on the tourist locations. The proposed platform is very efficient so that the provided tourism services are isolated from changes in raw data and the services can be modified or expanded without changing the underlying data structure.Keywords: Internet of Things, IoT platform, service platform, virtual file system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10946488 A ZVS Flyback DC-DC Converter using Multilayered Coreless Printed-Circuit Board(PCB) Step-down Power Transformer
Authors: Hari Babu Kotte, Radhika Ambatipudi, Dr. Kent Bertilsson
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The experimental and theoretical results of a ZVS (Zero Voltage Switching) isolated flyback DC-DC converter using multilayered coreless PCB step down 2:1 transformer are presented. The performance characteristics of the transformer are shown which are useful for the parameters extraction. The measured energy efficiency of the transformer is found to be more than 94% with the sinusoidal input voltage excitation. The designed flyback converter has been tested successfully upto the output power level of 10W, with a switching frequency in the range of 2.7MHz-4.3MHz. The input voltage of the converter is varied from 25V-40V DC. Frequency modulation technique is employed by maintaining constant off time to regulate the output voltage of the converter. The energy efficiency of the isolated flyback converter circuit under ZVS condition in the MHz frequency region is found to be approximately in the range of 72-84%. This paper gives the comparative results in terms of the energy efficiency of the hard switched and soft switched flyback converter in the MHz frequency region.Keywords: Coreless PCB step down transformer, DC-DCconverter, Flyback, Hard Switched Converter, MHz frequencyregion, Multilayered PCB transformer, Zero Voltage Switching
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 42446487 Service-Oriented Architecture for Object- Centric Information Fusion
Authors: Jeffrey A. Dunne, Kevin Ligozio
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In many applications there is a broad variety of information relevant to a focal “object" of interest, and the fusion of such heterogeneous data types is desirable for classification and categorization. While these various data types can sometimes be treated as orthogonal (such as the hull number, superstructure color, and speed of an oil tanker), there are instances where the inference and the correlation between quantities can provide improved fusion capabilities (such as the height, weight, and gender of a person). A service-oriented architecture has been designed and prototyped to support the fusion of information for such “object-centric" situations. It is modular, scalable, and flexible, and designed to support new data sources, fusion algorithms, and computational resources without affecting existing services. The architecture is designed to simplify the incorporation of legacy systems, support exact and probabilistic entity disambiguation, recognize and utilize multiple types of uncertainties, and minimize network bandwidth requirements.Keywords: Data fusion, distributed computing, service-oriented architecture, SOA
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14696486 Automated Process Quality Monitoring with Prediction of Fault Condition Using Measurement Data
Authors: Hyun-Woo Cho
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Detection of incipient abnormal events is important to improve safety and reliability of machine operations and reduce losses caused by failures. Improper set-ups or aligning of parts often leads to severe problems in many machines. The construction of prediction models for predicting faulty conditions is quite essential in making decisions on when to perform machine maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of machine measurement data. The calibration model is used to predict two faulty conditions from historical reference data. This approach utilizes genetic algorithms (GA) based variable selection, and we evaluate the predictive performance of several prediction methods using real data. The results shows that the calibration model based on supervised probabilistic principal component analysis (SPPCA) yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.Keywords: Prediction, operation monitoring, on-line data, nonlinear statistical methods, empirical model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16586485 Soccer Video Edition Using a Multimodal Annotation
Authors: Fendri Emna, Ben-Abdallah Hanêne, Ben-Hamadou Abdelmajid
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In this paper, we present an approach for soccer video edition using a multimodal annotation. We propose to associate with each video sequence of a soccer match a textual document to be used for further exploitation like search, browsing and abstract edition. The textual document contains video meta data, match meta data, and match data. This document, generated automatically while the video is analyzed, segmented and classified, can be enriched semi automatically according to the user type and/or a specialized recommendation system.Keywords: XML, Multimodal Annotation, recommendation system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14416484 Using Probe Person Data for Travel Mode Detection
Authors: Muhammad Awais Shafique, Eiji Hato, Hideki Yaginuma
Abstract:
Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, Support Vector Machine (SVM) and Adaptive boosting (AdaBoost) are employed. Moreover a unique method to improve the prediction results from these algorithms is also proposed. Results suggest that the prediction accuracy of AdaBoost after improvement is relatively better than the rest.
Keywords: Accelerometer, AdaBoost, GPS, Mode Prediction, Support vector Machine.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24506483 Effects of Signaling on the Performance of Directed Diffusion Routing Protocol
Authors: Apidet Booranawong
Abstract:
In an original directed diffusion routing protocol, a sink requests sensing data from a source node by flooding interest messages to the network. Then, the source finds the sink by sending exploratory data messages to all nodes that generate incoming interest messages. This protocol signaling can cause heavy traffic in the network, an interference of the radio signal, collisions, great energy consumption of sensor nodes, etc. According to this research problem, this paper investigates the effect of sending interest and exploratory data messages on the performance of directed diffusion routing protocol. We demonstrate the research problem occurred from employing directed diffusion protocol in mobile wireless environments. For this purpose, we perform a set of experiments by using NS2 (network simulator 2). The radio propagation models; Two-ray ground reflection with and without shadow fading are included to investigate the effect of signaling. The simulation results show that the number of times of sent and received protocol signaling in the case of sending interest and exploratory data messages are larger than the case of sending other protocol signals, especially in the case of shadowing model. Additionally, the number of exploratory data message is largest in one round of the protocol procedure.
Keywords: Directed diffusion, Flooding, Interest message, Exploratory data message, Radio propagation model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1784