Search results for: feature extraction method for tremor classification

Authors: Kamal Amarouche, Houda Benbrahim, Ismail Kassou

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Nowadays, e-commerce shopping websites have experienced noticeable growth. These websites have gained consumers’ trust. After purchasing a product, many consumers share comments where opinions are usually embedded about the given product. Research on the automatic management of opinions that gives suggestions to potential consumers and portrays an image of the product to manufactures has been growing recently. After launching the product in the market, the reviews generated around it do not usually contain helpful information or generic opinions about this product (e.g. telephone: great phone...); in the sense that the product is still in the launching phase in the market. Within time, the product becomes old. Therefore, consumers perceive the advantages/ disadvantages about each specific product feature. Therefore, they will generate comments that contain their sentiments about these features. In this paper, we present an unsupervised method to extract different product features hidden in the opinions which influence its purchase, and that combines Time Weighting (TW) which depends on the time opinions were expressed with Term Frequency-Inverse Document Frequency (TF-IDF). We conduct several experiments using two different datasets about cell phones and hotels. The results show the effectiveness of our automatic feature extraction, as well as its domain independent characteristic.

Keywords: opinion mining, product feature extraction, sentiment analysis, SentiWordNet

Procedia PDF Downloads 366

Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen

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Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.

Keywords: hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection

Procedia PDF Downloads 277

Authors: Yardfon Tanongkankit, Kanjana Narkprasom, Nukrob Narkprasom, Khwanruthai Saiupparat, Phatthareeya Siriwat

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Gac fruit (Momordica cochinchinensis (Lour.) Spreng) possesses high potential for health food as it contains high lycopene contents. The objective of this study was to optimize the extraction of lycopene from gac arils using the microwave extraction method. Response surface method was used to find the conditions that optimize the extraction of lycopene from gac arils. The parameters of extraction used in this study were extraction time (120-600 seconds), the solvent to sample ratio (10:1, 20:1, 30:1, 40:1 and 50:1 mL/g) and set microwave power (100-800 watts). The results showed that the microwave extraction condition at the extraction time of 360 seconds, the sample ratio of 30:1 mL/g and the microwave power of 450 watts were suggested since it exhibited the highest value of lycopene content of 9.86 mg/gDW. It was also observed that lycopene contents extracted from gac arils by microwave method were higher than that by the conventional method.

Keywords: conventional extraction, Gac arils, microwave-assisted extraction, Lycopene

Procedia PDF Downloads 357

Authors: Zyad Elkhadir, Khalid Chougdali, Mohammed Benattou

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Most of the existing intrusion detection systems works on quantitative network traffic data with many irrelevant and redundant features, which makes detection process more time’s consuming and inaccurate. A several feature extraction methods, such as linear discriminant analysis (LDA), have been proposed. However, LDA suffers from the small sample size (SSS) problem which occurs when the number of the training samples is small compared with the samples dimension. Hence, classical LDA cannot be applied directly for high dimensional data such as network traffic data. In this paper, we propose two solutions to solve SSS problem for LDA and apply them to a network IDS. The first method, reduce the original dimension data using principal component analysis (PCA) and then apply LDA. In the second solution, we propose to use the pseudo inverse to avoid singularity of within-class scatter matrix due to SSS problem. After that, the KNN algorithm is used for classification process. We have chosen two known datasets KDDcup99 and NSLKDD for testing the proposed approaches. Results showed that the classification accuracy of (PCA+LDA) method outperforms clearly the pseudo inverse LDA method when we have large training data.

Keywords: LDA, Pseudoinverse, PCA, IDS, NSL-KDD, KDDcup99

Procedia PDF Downloads 202

Authors: Jian Qu, Akira Shimazu

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OOV (Out Of Vocabulary) terms are terms that cannot be found in many dictionaries. Although it is possible to translate such OOV terms, the translations do not provide any real information for a user. We present an OOV term definition extraction method by using information available from the Internet. We use features such as occurrence of the synonyms and location distances. We apply machine learning method to find the correct definitions for OOV terms. We tested our method on both biomedical type and name type OOV terms, our work outperforms existing work with an accuracy of 86.5%.

Keywords: information retrieval, definition retrieval, OOV (out of vocabulary), biomedical information retrieval

Procedia PDF Downloads 466

Authors: Marjan Heidarzadeh

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High quality and purity of DNA isolated from canned tuna is essential for species identification. In this study, the efficiency of five different methods for DNA extraction was compared. Method of national standard in Iran, the CTAB precipitation method, Wizard DNA Clean Up system, Nucleospin and GenomicPrep were employed. DNA was extracted from two different canned tuna in brine and oil of the same tuna species. Three samples of each type of product were analyzed with the different methods. The quantity and quality of DNA extracted was evaluated using the 260 nm absorbance and ratio A260/A280 by spectrophotometer picodrop. Results showed that the DNA extraction from canned tuna preserved in different liquid media could be optimized by employing a specific DNA extraction method in each case. Best results were obtained with CTAB method for canned tuna in oil and with Wizard method for canned tuna in brine.

Keywords: canned tuna PCR, DNA, DNA extraction methods, species identification

Procedia PDF Downloads 628

Authors: Rohit Gupta, Ravinder Agarwal

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Surface electromyographic (sEMG) signal has the potential to identify the human activities and intention. This potential is further exploited to control the artificial limbs using the sEMG signal from residual limbs of amputees. The paper deals with the development of multichannel cost efficient sEMG signal interface for research application, along with evaluation of proposed class dependent statistical approach of the feature selection method. The sEMG signal acquisition interface was developed using ADS1298 of Texas Instruments, which is a front-end interface integrated circuit for ECG application. Further, the sEMG signal is recorded from two lower limb muscles for three locomotions namely: Plane Walk (PW), Stair Ascending (SA), Stair Descending (SD). A class dependent statistical approach is proposed for feature selection and also its performance is compared with 12 preexisting feature vectors. To make the study more extensive, performance of five different types of classifiers are compared. The outcome of the current piece of work proves the suitability of the proposed feature selection algorithm for locomotion recognition, as compared to other existing feature vectors. The SVM Classifier is found as the outperformed classifier among compared classifiers with an average recognition accuracy of 97.40%. Feature vector selection emerges as the most dominant factor affecting the classification performance as it holds 51.51% of the total variance in classification accuracy. The results demonstrate the potentials of the developed sEMG signal acquisition interface along with the proposed feature selection algorithm.

Keywords: classifiers, feature selection, locomotion, sEMG

Procedia PDF Downloads 267

Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

Procedia PDF Downloads 121

Authors: Syed Basit Ali Shah Bukhari, Yan Qiang, Saad Abdul Rauf, Syed Saqlaina Bukhari

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Text Classification is the methodology to classify any given text into the respective category from a given set of categories. It is highly important and vital to use proper set of pre-processing , feature selection and classification techniques to achieve this purpose. In this paper we have used different ensemble techniques along with variance in feature selection parameters to see the change in overall accuracy of the result and also on some other individual class based features which include precision value of each individual category of the text. After subjecting our data through pre-processing and feature selection techniques , different individual classifiers were tested first and after that classifiers were combined to form ensembles to increase their accuracy. Later we also studied the impact of decreasing the classification categories on over all accuracy of data. Text classification is highly used in sentiment analysis on social media sites such as twitter for realizing people’s opinions about any cause or it is also used to analyze customer’s reviews about certain products or services. Opinion mining is a vital task in data mining and text categorization is a back-bone to opinion mining.

Keywords: Natural Language Processing, Ensemble Classifier, Bagging Classifier, AdaBoost

Procedia PDF Downloads 206

Authors: Issa Qabaja, Fadi Thabtah

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Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set.

Keywords: data mining, email classification, phishing, online security

Procedia PDF Downloads 405

Authors: Jarek Krajewski, David Daxberger

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We propose a stress detection method based on an RGB camera using heart rate detection, also known as Photoplethysmography Imaging (PPGI). This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. A stationary lab setting with simulated video conferences is chosen using constant light conditions and a sampling rate of 30 fps. The ground truth measurement of heart rate is conducted with a common PPG system. The proposed approach for pulse peak detection is based on a machine learning-based approach, applying brute force feature extraction for the prediction of heart rate pulses. The statistical analysis showed good agreement (correlation r = .79, p<0.05) between the reference heart rate system and the proposed method. Based on these findings, the proposed method could provide a reliable, low-cost, and contactless way of measuring HR parameters in daily-life environments.

Keywords: heart rate, PPGI, machine learning, brute force feature extraction

Procedia PDF Downloads 102

Authors: Sang-Keun Moon, Hong-Rok Lim, Jin-O Kim

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This paper presents a Power Quality (PQ) modeling and filtering processes for the distribution system disturbances using recognition learning methods. Typical PQ waveforms with mathematical applications and gathered field data are applied to the proposed models. The objective of this paper is analyzing PQ data with respect to monitoring, discriminating, and evaluating the waveform of power disturbances to ensure the system preventative system failure protections and complex system problem estimations. Examined signal filtering techniques are used for the field waveform noises and feature extractions. Using extraction and learning classification techniques, the efficiency was verified for the recognition of the PQ disturbances with focusing on interactive modeling methods in this paper. The waveform of selected 8 disturbances is modeled with randomized parameters of IEEE 1159 PQ ranges. The range, parameters, and weights are updated regarding field waveform obtained. Along with voltages, currents have same process to obtain the waveform features as the voltage apart from some of ratings and filters. Changing loads are causing the distortion in the voltage waveform due to the drawing of the different patterns of current variation. In the conclusion, PQ disturbances in the voltage and current waveforms indicate different types of patterns of variations and disturbance, and a modified technique based on the symmetrical components in time domain was proposed in this paper for the PQ disturbances detection and then classification. Our method is based on the fact that obtained waveforms from suggested trigger conditions contain potential information for abnormality detections. The extracted features are sequentially applied to estimation and recognition learning modules for further studies.

Keywords: power quality recognition, PQ modeling, waveform feature extraction, disturbance trigger condition, PQ signal filtering

Procedia PDF Downloads 163

Authors: Jianfeng Hu

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Personal authentication based on electroencephalography (EEG) signals is one of the important field for the biometric technology. More and more researchers have used EEG signals as data source for biometric. However, there are some disadvantages for biometrics based on EEG signals. The proposed method employs entropy measures for feature extraction from EEG signals. Four type of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE) and spectral entropy (PE), were deployed as feature set. In a silhouettes calculation, the distance from each data point in a cluster to all another point within the same cluster and to all other data points in the closest cluster are determined. Thus silhouettes provide a measure of how well a data point was classified when it was assigned to a cluster and the separation between them. This feature renders silhouettes potentially well suited for assessing cluster quality in personal authentication methods. In this study, “silhouettes scores” was used for assessing the cluster quality of k-means clustering algorithm is well suited for comparing the performance of each EEG dataset. The main goals of this study are: (1) to represent each target as a tuple of multiple feature sets, (2) to assign a suitable measure to each feature set, (3) to combine different feature sets, (4) to determine the optimal feature weighting. Using precision/recall evaluations, the effectiveness of feature weighting in clustering was analyzed. EEG data from 22 subjects were collected. Results showed that: (1) It is possible to use fewer electrodes (3-4) for personal authentication. (2) There was the difference between each electrode for personal authentication (p<0.01). (3) There is no significant difference for authentication performance among feature sets (except feature PE). Conclusion: The combination of k-means clustering algorithm and silhouette approach proved to be an accurate method for personal authentication based on EEG signals.

Keywords: personal authentication, K-mean clustering, electroencephalogram, EEG, silhouettes

Procedia PDF Downloads 256

Authors: Muhammad Imran Ahmad, Ruzelita Ngadiran, Mohd Nazrin Md Isa, Nor Ashidi Mat Isa, Mohd ZaizuIlyas, Raja Abdullah Raja Ahmad, Said Amirul Anwar Ab Hamid, Muzammil Jusoh

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This paper presents two technique, local feature extraction using image spectrum and low frequency spectrum modelling using GMM to capture the underlying statistical information to improve the performance of face recognition system. Local spectrum features are extracted using overlap sub block window that are mapping on the face image. For each of this block, spatial domain is transformed to frequency domain using DFT. A low frequency coefficient is preserved by discarding high frequency coefficients by applying rectangular mask on the spectrum of the facial image. Low frequency information is non Gaussian in the feature space and by using combination of several Gaussian function that has different statistical properties, the best feature representation can be model using probability density function. The recognition process is performed using maximum likelihood value computed using pre-calculate GMM components. The method is tested using FERET data sets and is able to achieved 92% recognition rates.

Keywords: local features modelling, face recognition system, Gaussian mixture models, Feret

Procedia PDF Downloads 633

Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

Procedia PDF Downloads 66

Authors: Xiao Chen, Xiaoying Kong, Min Xu

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This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: vehicle classification, signal processing, road traffic model, magnetic sensing

Procedia PDF Downloads 293

Authors: Hala Alshamlan, Ghada Badr, Yousef Alohali

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Cancer is one of the dreadful diseases, which causes considerable death rate in humans. DNA microarray-based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. In recent years, a DNA microarray technique has gained more attraction in both scientific and in industrial fields. It is important to determine the informative genes that cause cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. In order to gain deep insight into the cancer classification problem, it is necessary to take a closer look at the proposed gene selection methods. We believe that they should be an integral preprocessing step for cancer classification. Furthermore, finding an accurate gene selection method is a very significant issue in a cancer classification area because it reduces the dimensionality of microarray dataset and selects informative genes. In this paper, we classify and review the state-of-art gene selection methods. We proceed by evaluating the performance of each gene selection approach based on their classification accuracy and number of informative genes. In our evaluation, we will use four benchmark microarray datasets for the cancer diagnosis (leukemia, colon, lung, and prostate). In addition, we compare the performance of gene selection method to investigate the effective gene selection method that has the ability to identify a small set of marker genes, and ensure high cancer classification accuracy. To the best of our knowledge, this is the first attempt to compare gene selection approaches for cancer classification using microarray gene expression profile.

Keywords: gene selection, feature selection, cancer classification, microarray, gene expression profile

Procedia PDF Downloads 423

Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the model-agnostic meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to an exponential growth of computation, this paper also proposes a key data extraction method that only extract part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: mutex task generation, data augmentation, meta-learning, text classification.

Procedia PDF Downloads 99

Authors: Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn

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Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality. This paper presents a feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) of the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a modified version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The proposed feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Nine classifiers have been employed to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy.

Keywords: binary shuffled frog leaping algorithm, feature selection, fuzzy-rough set, minimal reduct

Procedia PDF Downloads 186

Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

Procedia PDF Downloads 144

Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun

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Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.

Keywords: machine learning, pattern clustering, pooling, classification

Procedia PDF Downloads 423

Authors: Xiaoheng Tan, Xianfang Li, Tan Guo, Yuchuan Liu, Zhijun Yang, Hongye Li, Kai Fu, Yufang Wu, Heling Gong

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The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets.

Keywords: feature extraction, graph optimization local preserving projection, linear discriminant analysis, L₂, ₁ norm

Procedia PDF Downloads 124

Authors: Seyyedeh Faezeh Hassani Ziabari, Sadegh Eskandari, Maziar Salahi

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Infinite Feature Selection (IFS) algorithm is an efficient feature selection algorithm that selects a subset of features of all sizes (including infinity). In this paper, we present an improved version of it, called clustering IFS (CIFS), by clustering the dataset in advance. To do so, first, we apply the K-means algorithm to cluster the dataset, then we apply IFS. In the CIFS method, the spatial and temporal complexities are reduced compared to the IFS method. Experimental results on 6 datasets show the superiority of CIFS compared to IFS in terms of accuracy, running time, and memory consumption.

Keywords: feature selection, infinite feature selection, clustering, graph

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Authors: Nidal F. Shilbayeh, Remah W. Al-Khatib, Sameer A. Nooh

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Arabic offline handwriting recognition systems are considered as one of the most challenging topics. Arabic Handwritten Numeral Strings are used to automate systems that deal with numbers such as postal code, banking account numbers and numbers on car plates. Segmentation of connected numerals is the main bottleneck in the handwritten numeral recognition system.  This is in turn can increase the speed and efficiency of the recognition system. In this paper, we proposed algorithms for automatic segmentation and feature extraction of Arabic handwritten numeral strings based on Watershed approach. The algorithms have been designed and implemented to achieve the main goal of segmenting and extracting the string of numeral digits written by hand especially in a courtesy amount of bank checks. The segmentation algorithm partitions the string into multiple regions that can be associated with the properties of one or more criteria. The numeral extraction algorithm extracts the numeral string digits into separated individual digit. Both algorithms for segmentation and feature extraction have been tested successfully and efficiently for all types of numerals.

Keywords: handwritten numerals, segmentation, courtesy amount, feature extraction, numeral recognition

Procedia PDF Downloads 353

Authors: Ping Tan, Xiaomeng Su, Yi Shen

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The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods.

Keywords: non-uniform filter banks, motor imagery, brain-computer interface, minimum distance to Riemannian mean

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Authors: Rujia Chen, Ajit Narayanan

Abstract:

Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

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Authors: Benjawan Rangsikamol, Chutimet Srinilta

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This research paper deals with extraction of Thai character features using the proposed histogram projection so as to improve the recognition performance. The process starts with transformation of image files into binary files before thinning. After character thinning, the skeletons are entered into the proposed extraction using histogram projection (horizontal and vertical) to extract unique features which are inputs of the subsequent recognition step. The recognition rate with the proposed extraction technique is as high as 97 percent since the technique works very well with the idiosyncrasies of Thai characters.

Keywords: character recognition, histogram projection, multilayer perceptron, Thai character features extraction

Procedia PDF Downloads 434

Authors: S. Kherchaoui, A. Houacine

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This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.

Keywords: facial expression identification, curvelet coefficient, support vector machine (SVM), recognition system

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Authors: Dingyang Hu, Dan Liu

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DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.

Keywords: adversarial sample, gradient, probability, black box

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Authors: Kenan Morani, Esra Kaya Ayana

Abstract:

This study aimed to develop a novel pipeline for COVID-19 detection using a large and rigorously annotated database of computed tomography (CT) images. The pipeline consists of UNet-based segmentation, lung extraction, and a classification part, with the addition of optional slice removal techniques following the segmentation part. In this work, a batch normalization was added to the original UNet model to produce lighter and better localization, which is then utilized to build a full pipeline for COVID-19 diagnosis. To evaluate the effectiveness of the proposed pipeline, various segmentation methods were compared in terms of their performance and complexity. The proposed segmentation method with batch normalization outperformed traditional methods and other alternatives, resulting in a higher dice score on a publicly available dataset. Moreover, at the slice level, the proposed pipeline demonstrated high validation accuracy, indicating the efficiency of predicting 2D slices. At the patient level, the full approach exhibited higher validation accuracy and macro F1 score compared to other alternatives, surpassing the baseline. The classification component of the proposed pipeline utilizes a convolutional neural network (CNN) to make final diagnosis decisions. The COV19-CT-DB dataset, which contains a large number of CT scans with various types of slices and rigorously annotated for COVID-19 detection, was utilized for classification. The proposed pipeline outperformed many other alternatives on the dataset.

Keywords: classification, computed tomography, lung extraction, macro F1 score, UNet segmentation

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