Search results for: features correspondence
1583 Identification of Arousal and Relaxation by using SVM-Based Fusion of PPG Features
Authors: Chi Jung Kim, Mincheol Whang, Eui Chul Lee
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In this paper, we propose a new method to distinguish between arousal and relaxation states by using multiple features acquired from a photoplethysmogram (PPG) and support vector machine (SVM). To induce arousal and relaxation states in subjects, 2 kinds of sound stimuli are used, and their corresponding biosignals are obtained using the PPG sensor. Two features–pulse to pulse interval (PPI) and pulse amplitude (PA)–are extracted from acquired PPG data, and a nonlinear classification between arousal and relaxation is performed using SVM. This methodology has several advantages when compared with previous similar studies. Firstly, we extracted 2 separate features from PPG, i.e., PPI and PA. Secondly, in order to improve the classification accuracy, SVM-based nonlinear classification was performed. Thirdly, to solve classification problems caused by generalized features of whole subjects, we defined each threshold according to individual features. Experimental results showed that the average classification accuracy was 74.67%. Also, the proposed method showed the better identification performance than the single feature based methods. From this result, we confirmed that arousal and relaxation can be classified using SVM and PPG features.Keywords: Support Vector Machine, PPG, Emotion Recognition, Arousal, Relaxation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24701582 Comparison between XGBoost, LightGBM and CatBoost Using a Home Credit Dataset
Authors: Essam Al Daoud
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Gradient boosting methods have been proven to be a very important strategy. Many successful machine learning solutions were developed using the XGBoost and its derivatives. The aim of this study is to investigate and compare the efficiency of three gradient methods. Home credit dataset is used in this work which contains 219 features and 356251 records. However, new features are generated and several techniques are used to rank and select the best features. The implementation indicates that the LightGBM is faster and more accurate than CatBoost and XGBoost using variant number of features and records.
Keywords: Gradient boosting, XGBoost, LightGBM, CatBoost, home credit.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 93701581 Automatic Extraction of Features and Opinion-Oriented Sentences from Customer Reviews
Authors: Khairullah Khan, Baharum B. Baharudin, Aurangzeb Khan, Fazal_e_Malik
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Opinion extraction about products from customer reviews is becoming an interesting area of research. Customer reviews about products are nowadays available from blogs and review sites. Also tools are being developed for extraction of opinion from these reviews to help the user as well merchants to track the most suitable choice of product. Therefore efficient method and techniques are needed to extract opinions from review and blogs. As reviews of products mostly contains discussion about the features, functions and services, therefore, efficient techniques are required to extract user comments about the desired features, functions and services. In this paper we have proposed a novel idea to find features of product from user review in an efficient way. Our focus in this paper is to get the features and opinion-oriented words about products from text through auxiliary verbs (AV) {is, was, are, were, has, have, had}. From the results of our experiments we found that 82% of features and 85% of opinion-oriented sentences include AVs. Thus these AVs are good indicators of features and opinion orientation in customer reviews.Keywords: Classification, Customer Reviews, Helping Verbs, Opinion Mining.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20831580 Deployment of Service Quality Characteristics
Authors: Shuki Dror
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This work discusses an innovative methodology for deployment of service quality characteristics. Four groups of organizational features that may influence the quality of services are identified: human resource, technology, planning, and organizational relationships. A House of Service Quality (HOSQ) matrix is built to extract the desired improvement in the service quality characteristics and to translate them into a hierarchy of important organizational features. The Mean Square Error (MSE) criterion enables the pinpointing of the few essential service quality characteristics to be improved as well as selection of the vital organizational features. The method was implemented in an engineering supply enterprise and provides useful information on its vital service dimensions.Keywords: HOQ, organizational features, service quality.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18521579 Genetic Algorithm for Feature Subset Selection with Exploitation of Feature Correlations from Continuous Wavelet Transform: a real-case Application
Authors: G. Van Dijck, M. M. Van Hulle, M. Wevers
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A genetic algorithm (GA) based feature subset selection algorithm is proposed in which the correlation structure of the features is exploited. The subset of features is validated according to the classification performance. Features derived from the continuous wavelet transform are potentially strongly correlated. GA-s that do not take the correlation structure of features into account are inefficient. The proposed algorithm forms clusters of correlated features and searches for a good candidate set of clusters. Secondly a search within the clusters is performed. Different simulations of the algorithm on a real-case data set with strong correlations between features show the increased classification performance. Comparison is performed with a standard GA without use of the correlation structure.Keywords: Classification, genetic algorithm, hierarchicalagglomerative clustering, wavelet transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12141578 Native Language Identification with Cross-Corpus Evaluation Using Social Media Data: 'Reddit'
Authors: Yasmeen Bassas, Sandra Kuebler, Allen Riddell
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Native Language Identification is one of the growing subfields in Natural Language Processing (NLP). The task of Native Language Identification (NLI) is mainly concerned with predicting the native language of an author’s writing in a second language. In this paper, we investigate the performance of two types of features; content-based features vs. content independent features when they are evaluated on a different corpus (using social media data “Reddit”). In this NLI task, the predefined models are trained on one corpus (TOEFL) and then the trained models are evaluated on a different data using an external corpus (Reddit). Three classifiers are used in this task; the baseline, linear SVM, and Logistic Regression. Results show that content-based features are more accurate and robust than content independent ones when tested within corpus and across corpus.
Keywords: NLI, NLP, content-based features, content independent features, social media corpus, ML.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3801577 Image Retrieval: Techniques, Challenge, and Trend
Authors: Hui Hui Wang, Dzulkifli Mohamad, N.A Ismail
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This paper attempts to discuss the evolution of the retrieval techniques focusing on development, challenges and trends of the image retrieval. It highlights both the already addressed and outstanding issues. The explosive growth of image data leads to the need of research and development of Image Retrieval. However, Image retrieval researches are moving from keyword, to low level features and to semantic features. Drive towards semantic features is due to the problem of the keywords which can be very subjective and time consuming while low level features cannot always describe high level concepts in the users- mind.Keywords: content based image retrieval, keyword based imageretrieval, semantic gap, semantic image retrieval.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25121576 Hybrid Anomaly Detection Using Decision Tree and Support Vector Machine
Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour
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Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.
Keywords: Intrusion detection system, decision tree, support vector machine, feature selection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12261575 Improving Classification in Bayesian Networks using Structural Learning
Authors: Hong Choon Ong
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Naïve Bayes classifiers are simple probabilistic classifiers. Classification extracts patterns by using data file with a set of labeled training examples and is currently one of the most significant areas in data mining. However, Naïve Bayes assumes the independence among the features. Structural learning among the features thus helps in the classification problem. In this study, the use of structural learning in Bayesian Network is proposed to be applied where there are relationships between the features when using the Naïve Bayes. The improvement in the classification using structural learning is shown if there exist relationship between the features or when they are not independent.Keywords: Bayesian Network, Classification, Naïve Bayes, Structural Learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25881574 Using the Keystrokes Dynamic for Systems of Personal Security
Authors: Gláucya C. Boechat, Jeneffer C. Ferreira, Edson C. B. Carvalho
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This paper presents a boarding on biometric authentication through the Keystrokes Dynamics that it intends to identify a person from its habitual rhythm to type in conventional keyboard. Seven done experiments: verifying amount of prototypes, threshold, features and the variation of the choice of the times of the features vector. The results show that the use of the Keystroke Dynamics is simple and efficient for personal authentication, getting optimum resulted using 90% of the features with 4.44% FRR and 0% FAR.Keywords: Biometrics techniques, Keystroke Dynamics, patternrecognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17291573 Investigation on Feature Extraction and Classification of Medical Images
Authors: P. Gnanasekar, A. Nagappan, S. Sharavanan, O. Saravanan, D. Vinodkumar, T. Elayabharathi, G. Karthik
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In this paper we present the deep study about the Bio- Medical Images and tag it with some basic extracting features (e.g. color, pixel value etc). The classification is done by using a nearest neighbor classifier with various distance measures as well as the automatic combination of classifier results. This process selects a subset of relevant features from a group of features of the image. It also helps to acquire better understanding about the image by describing which the important features are. The accuracy can be improved by increasing the number of features selected. Various types of classifications were evolved for the medical images like Support Vector Machine (SVM) which is used for classifying the Bacterial types. Ant Colony Optimization method is used for optimal results. It has high approximation capability and much faster convergence, Texture feature extraction method based on Gabor wavelets etc..Keywords: ACO Ant Colony Optimization, Correlogram, CCM Co-Occurrence Matrix, RTS Rough-Set theory
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 30001572 Realization of Design Features for Linear Flow Splitting in NX 6
Authors: Anselm L. Schüle, Thomas Rollmann, Reiner Anderl
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Within the collaborative research center 666 a new product development approach and the innovative manufacturing method of linear flow splitting are being developed. So far the design process is supported by 3D-CAD models utilizing User Defined Features in standard CAD-Systems. This paper now presents new functions for generating 3D-models of integral sheet metal products with bifurcations using Siemens PLM NX 6. The emphasis is placed on design and semi-automated insertion of User Defined Features. Therefore User Defined Features for both, linear flow splitting and its derivative linear bend splitting, were developed. In order to facilitate the modeling process, an application was developed that guides through the insertion process. Its usability and dialog layout adapt known standard features. The work presented here has significant implications on the quality, accurateness and efficiency of the product generation process of sheet metal products with higher order bifurcations.Keywords: Linear Flow Splitting, CRC 666, User Defined Features.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24751571 A New Ridge Orientation based Method of Computation for Feature Extraction from Fingerprint Images
Authors: Jayadevan R., Jayant V. Kulkarni, Suresh N. Mali, Hemant K. Abhyankar
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An important step in studying the statistics of fingerprint minutia features is to reliably extract minutia features from the fingerprint images. A new reliable method of computation for minutiae feature extraction from fingerprint images is presented. A fingerprint image is treated as a textured image. An orientation flow field of the ridges is computed for the fingerprint image. To accurately locate ridges, a new ridge orientation based computation method is proposed. After ridge segmentation a new method of computation is proposed for smoothing the ridges. The ridge skeleton image is obtained and then smoothed using morphological operators to detect the features. A post processing stage eliminates a large number of false features from the detected set of minutiae features. The detected features are observed to be reliable and accurate.Keywords: Minutia, orientation field, ridge segmentation, textured image.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18441570 Predicting Protein Function using Decision Tree
Authors: Manpreet Singh, Parminder Kaur Wadhwa, Surinder Kaur
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The drug discovery process starts with protein identification because proteins are responsible for many functions required for maintenance of life. Protein identification further needs determination of protein function. Proposed method develops a classifier for human protein function prediction. The model uses decision tree for classification process. The protein function is predicted on the basis of matched sequence derived features per each protein function. The research work includes the development of a tool which determines sequence derived features by analyzing different parameters. The other sequence derived features are determined using various web based tools.Keywords: Sequence Derived Features, decision tree.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19391569 Mining Image Features in an Automatic Two-Dimensional Shape Recognition System
Authors: R. A. Salam, M.A. Rodrigues
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The number of features required to represent an image can be very huge. Using all available features to recognize objects can suffer from curse dimensionality. Feature selection and extraction is the pre-processing step of image mining. Main issues in analyzing images is the effective identification of features and another one is extracting them. The mining problem that has been focused is the grouping of features for different shapes. Experiments have been conducted by using shape outline as the features. Shape outline readings are put through normalization and dimensionality reduction process using an eigenvector based method to produce a new set of readings. After this pre-processing step data will be grouped through their shapes. Through statistical analysis, these readings together with peak measures a robust classification and recognition process is achieved. Tests showed that the suggested methods are able to automatically recognize objects through their shapes. Finally, experiments also demonstrate the system invariance to rotation, translation, scale, reflection and to a small degree of distortion.Keywords: Image mining, feature selection, shape recognition, peak measures.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14481568 Identifying New Sequence Features for Exon-Intron Discrimination by Rescaled-Range Frameshift Analysis
Authors: Sing-Wu Liou, Yin-Fu Huang
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For identifying the discriminative sequence features between exons and introns, a new paradigm, rescaled-range frameshift analysis (RRFA), was proposed. By RRFA, two new sequence features, the frameshift sensitivity (FS) and the accumulative penta-mer complexity (APC), were discovered which were further integrated into a new feature of larger scale, the persistency in anti-mutation (PAM). The feature-validation experiments were performed on six model organisms to test the power of discrimination. All the experimental results highly support that FS, APC and PAM were all distinguishing features between exons and introns. These identified new sequence features provide new insights into the sequence composition of genes and they have great potentials of forming a new basis for recognizing the exonintron boundaries in gene sequences.Keywords: Exon-Intron Discrimination, Rescaled-Range Frameshift Analysis, Frameshift Sensitivity, Accumulative Sequence Complexity
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11671567 Emotion Recognition Using Neural Network: A Comparative Study
Authors: Nermine Ahmed Hendy, Hania Farag
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Emotion recognition is an important research field that finds lots of applications nowadays. This work emphasizes on recognizing different emotions from speech signal. The extracted features are related to statistics of pitch, formants, and energy contours, as well as spectral, perceptual and temporal features, jitter, and shimmer. The Artificial Neural Networks (ANN) was chosen as the classifier. Working on finding a robust and fast ANN classifier suitable for different real life application is our concern. Several experiments were carried out on different ANN to investigate the different factors that impact the classification success rate. Using a database containing 7 different emotions, it will be shown that with a proper and careful adjustment of features format, training data sorting, number of features selected and even the ANN type and architecture used, a success rate of 85% or even more can be achieved without increasing the system complicity and the computation time
Keywords: Classification, emotion recognition, features extraction, feature selection, neural network
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 46811566 Robust Features for Impulsive Noisy Speech Recognition Using Relative Spectral Analysis
Authors: Hajer Rahali, Zied Hajaiej, Noureddine Ellouze
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The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC called Modified Function Cepstral Coefficients (MODFCC) were tested and compared against the original MFCC and RASTA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases.
Keywords: Auditory filter, impulsive noise, MFCC, prosodic features, RASTA filter.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23121565 Content-Based Image Retrieval Using HSV Color Space Features
Authors: Hamed Qazanfari, Hamid Hassanpour, Kazem Qazanfari
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In this paper, a method is provided for content-based image retrieval. Content-based image retrieval system searches query an image based on its visual content in an image database to retrieve similar images. In this paper, with the aim of simulating the human visual system sensitivity to image's edges and color features, the concept of color difference histogram (CDH) is used. CDH includes the perceptually color difference between two neighboring pixels with regard to colors and edge orientations. Since the HSV color space is close to the human visual system, the CDH is calculated in this color space. In addition, to improve the color features, the color histogram in HSV color space is also used as a feature. Among the extracted features, efficient features are selected using entropy and correlation criteria. The final features extract the content of images most efficiently. The proposed method has been evaluated on three standard databases Corel 5k, Corel 10k and UKBench. Experimental results show that the accuracy of the proposed image retrieval method is significantly improved compared to the recently developed methods.
Keywords: Content-based image retrieval, color difference histogram, efficient features selection, entropy, correlation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6461564 Systems Versioning: A Features-Based Meta-Modeling Approach
Authors: Ola A. Younis, Said Ghoul
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Systems running these days are huge, complex and exist in many versions. Controlling these versions and tracking their changes became a very hard process as some versions are created using meaningless names or specifications. Many versions of a system are created with no clear difference between them. This leads to mismatching between a user’s request and the version he gets. In this paper, we present a system versions meta-modeling approach that produces versions based on system’s features. This model reduced the number of steps needed to configure a release and gave each version its unique specifications. This approach is applicable for systems that use features in its specification.
Keywords: Features, Meta-modeling, Semantic Modeling, SPL, VCS, Versioning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14261563 Computer Aided Classification of Architectural Distortion in Mammograms Using Texture Features
Authors: Birmohan Singh, V. K. Jain
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Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Architectural distortions, masses and microcalcifications are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support vector machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and an accuracy of 96% for mammogram images collected from digital database for screening mammography database.Keywords: Architecture Distortion, GLCM Texture features, GLRLM Texture Features, Mammograms, Support Vector Machine.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22511562 Identify Features and Parameters to Devise an Accurate Intrusion Detection System Using Artificial Neural Network
Authors: Saman M. Abdulla, Najla B. Al-Dabagh, Omar Zakaria
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The aim of this article is to explain how features of attacks could be extracted from the packets. It also explains how vectors could be built and then applied to the input of any analysis stage. For analyzing, the work deploys the Feedforward-Back propagation neural network to act as misuse intrusion detection system. It uses ten types if attacks as example for training and testing the neural network. It explains how the packets are analyzed to extract features. The work shows how selecting the right features, building correct vectors and how correct identification of the training methods with nodes- number in hidden layer of any neural network affecting the accuracy of system. In addition, the work shows how to get values of optimal weights and use them to initialize the Artificial Neural Network.
Keywords: Artificial Neural Network, Attack Features, MisuseIntrusion Detection System, Training Parameters.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22721561 Fabricating Protruded Micro-features on AA6061 Substrates by Hot Embossing Method
Authors: Nhat Khoa Tran, Yee Cheong Lam, Chee Yoon Yue, Ming Jen Tan
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Metallic micro parts are playing an important role in micro-fabrication industry. Recently, we have demonstrated a new deformation mechanism for micro-formability of polycrystalline materials. Different depressed micro-features smaller than the grain size have been successfully fabricated on 6061 aluminum alloy (AA6061) substrates with good fidelity. To further verify this proposed deformation mechanism that grain size is not a limiting factor, we demonstrate here that in addition of depressed features, protruded micro-features on a polycrystalline substrate can similarly be fabricated.
Keywords: Deformation mechanism, grain size, microfabrication, polycrystalline materials.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17581560 Exploring the Correlation between Population Distribution and Urban Heat Island under Urban Data: Taking Shenzhen Urban Heat Island as an Example
Authors: Wang Yang
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Shenzhen is a modern city of China's reform and opening-up policy, the development of urban morphology has been established on the administration of the Chinese government. This city`s planning paradigm is primarily affected by the spatial structure and human behavior. The subjective urban agglomeration center is divided into several groups and centers. In comparisons of this effect, the city development law has better to be neglected. With the continuous development of the internet, extensive data technology has been introduced in China. Data mining and data analysis has become important tools in municipal research. Data mining has been utilized to improve data cleaning such as receiving business data, traffic data and population data. Prior to data mining, government data were collected by traditional means, then were analyzed using city-relationship research, delaying the timeliness of urban development, especially for the contemporary city. Data update speed is very fast and based on the Internet. The city's point of interest (POI) in the excavation serves as data source affecting the city design, while satellite remote sensing is used as a reference object, city analysis is conducted in both directions, the administrative paradigm of government is broken and urban research is restored. Therefore, the use of data mining in urban analysis is very important. The satellite remote sensing data of the Shenzhen city in July 2018 were measured by the satellite Modis sensor and can be utilized to perform land surface temperature inversion, and analyze city heat island distribution of Shenzhen. This article acquired and classified the data from Shenzhen by using Data crawler technology. Data of Shenzhen heat island and interest points were simulated and analyzed in the GIS platform to discover the main features of functional equivalent distribution influence. Shenzhen is located in the east-west area of China. The city’s main streets are also determined according to the direction of city development. Therefore, it is determined that the functional area of the city is also distributed in the east-west direction. The urban heat island can express the heat map according to the functional urban area. Regional POI has correspondence. The research result clearly explains that the distribution of the urban heat island and the distribution of urban POIs are one-to-one correspondence. Urban heat island is primarily influenced by the properties of the underlying surface, avoiding the impact of urban climate. Using urban POIs as analysis object, the distribution of municipal POIs and population aggregation are closely connected, so that the distribution of the population corresponded with the distribution of the urban heat island.
Keywords: POI, satellite remote sensing, the population distribution, urban heat island thermal map.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9071559 Human Fall Detection by FMCW Radar Based on Time-Varying Range-Doppler Features
Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou
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The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.
Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-Doppler features.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4921558 An Automatic Feature Extraction Technique for 2D Punch Shapes
Authors: Awais Ahmad Khan, Emad Abouel Nasr, H. M. A. Hussein, Abdulrahman Al-Ahmari
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Sheet-metal parts have been widely applied in electronics, communication and mechanical industries in recent decades; but the advancement in sheet-metal part design and manufacturing is still behind in comparison with the increasing importance of sheet-metal parts in modern industry. This paper presents a methodology for automatic extraction of some common 2D internal sheet metal features. The features used in this study are taken from Unipunch ™ catalogue. The extraction process starts with the data extraction from STEP file using an object oriented approach and with the application of suitable algorithms and rules, all features contained in the catalogue are automatically extracted. Since the extracted features include geometry and engineering information, they will be effective for downstream application such as feature rebuilding and process planning.
Keywords: Feature Extraction, Internal Features, Punch Shapes, Sheet metal, STEP.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20851557 Feature Reduction of Nearest Neighbor Classifiers using Genetic Algorithm
Authors: M. Analoui, M. Fadavi Amiri
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The design of a pattern classifier includes an attempt to select, among a set of possible features, a minimum subset of weakly correlated features that better discriminate the pattern classes. This is usually a difficult task in practice, normally requiring the application of heuristic knowledge about the specific problem domain. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency, and allow higher classification accuracy. Many current feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and increasing classification efficiency, it does not necessarily reduce the number of features that must be measured since each new feature may be a linear combination of all of the features in the original pattern vector. In this paper a new approach is presented to feature extraction in which feature selection, feature extraction, and classifier training are performed simultaneously using a genetic algorithm. In this approach each feature value is first normalized by a linear equation, then scaled by the associated weight prior to training, testing, and classification. A knn classifier is used to evaluate each set of feature weights. The genetic algorithm optimizes a vector of feature weights, which are used to scale the individual features in the original pattern vectors in either a linear or a nonlinear fashion. By this approach, the number of features used in classifying can be finely reduced.Keywords: Feature reduction, genetic algorithm, pattern classification, nearest neighbor rule classifiers (k-NNR).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17561556 Image Retrieval Using Fused Features
Authors: K. Sakthivel, R. Nallusamy, C. Kavitha
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The system is designed to show images which are related to the query image. Extracting color, texture, and shape features from an image plays a vital role in content-based image retrieval (CBIR). Initially RGB image is converted into HSV color space due to its perceptual uniformity. From the HSV image, Color features are extracted using block color histogram, texture features using Haar transform and shape feature using Fuzzy C-means Algorithm. Then, the characteristics of the global and local color histogram, texture features through co-occurrence matrix and Haar wavelet transform and shape are compared and analyzed for CBIR. Finally, the best method of each feature is fused during similarity measure to improve image retrieval effectiveness and accuracy.
Keywords: Color Histogram, Haar Wavelet Transform, Fuzzy C-means, Co-occurrence matrix; Similarity measure.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21211555 Over-Height Vehicle Detection in Low Headroom Roads Using Digital Video Processing
Authors: Vahid Khorramshahi, Alireza Behrad, Neeraj K. Kanhere
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In this paper we present a new method for over-height vehicle detection in low headroom streets and highways using digital video possessing. The accuracy and the lower price comparing to present detectors like laser radars and the capability of providing extra information like speed and height measurement make this method more reliable and efficient. In this algorithm the features are selected and tracked using KLT algorithm. A blob extraction algorithm is also applied using background estimation and subtraction. Then the world coordinates of features that are inside the blobs are estimated using a noble calibration method. As, the heights of the features are calculated, we apply a threshold to select overheight features and eliminate others. The over-height features are segmented using some association criteria and grouped using an undirected graph. Then they are tracked through sequential frames. The obtained groups refer to over-height vehicles in a scene.Keywords: Feature extraction, over-height vehicle detection, traffic monitoring, vehicle tracking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28121554 GA Based Optimal Feature Extraction Method for Functional Data Classification
Authors: Jun Wan, Zehua Chen, Yingwu Chen, Zhidong Bai
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Classification is an interesting problem in functional data analysis (FDA), because many science and application problems end up with classification problems, such as recognition, prediction, control, decision making, management, etc. As the high dimension and high correlation in functional data (FD), it is a key problem to extract features from FD whereas keeping its global characters, which relates to the classification efficiency and precision to heavens. In this paper, a novel automatic method which combined Genetic Algorithm (GA) and classification algorithm to extract classification features is proposed. In this method, the optimal features and classification model are approached via evolutional study step by step. It is proved by theory analysis and experiment test that this method has advantages in improving classification efficiency, precision and robustness whereas using less features and the dimension of extracted classification features can be controlled.Keywords: Classification, functional data, feature extraction, genetic algorithm, wavelet.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1546