Search results for: multi-scale feature extraction

Authors: H. G. C. P. Dinesh, G. Tharshini, M. P. B. Ekanayake, G. M. R. I. Godaliyadda

Abstract:

This paper presents a new spatial feature extraction method based on principle component analysis (PCA) and Fisher Discernment Analysis (FDA) for facial expression recognition. It not only extracts reliable features for classification, but also reduces the feature space dimensions of pattern samples. In this method, first each gray scale image is considered in its entirety as the measurement matrix. Then, principle components (PCs) of row vectors of this matrix and variance of these row vectors along PCs are estimated. Therefore, this method would ensure the preservation of spatial information of the facial image. Afterwards, by incorporating the spectral information of the eigen-filters derived from the PCs, a feature vector was constructed, for a given image. Finally, FDA was used to define a set of basis in a reduced dimension subspace such that the optimal clustering is achieved. The method of FDA defines an inter-class scatter matrix and intra-class scatter matrix to enhance the compactness of each cluster while maximizing the distance between cluster marginal points. In order to matching the test image with the training set, a cosine similarity based Bayesian classification was used. The proposed method was tested on the Cohn-Kanade database and JAFFE database. It was observed that the proposed method which incorporates spatial information to construct an optimal feature space outperforms the standard PCA and FDA based methods.

Keywords: facial expression recognition, principle component analysis (PCA), fisher discernment analysis (FDA), eigen-filter, cosine similarity, bayesian classifier, f-measure

Procedia PDF Downloads 404

Authors: Rosa Figueroa, Christopher Flores

Abstract:

Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

Procedia PDF Downloads 270

Authors: Rabab M. Ramadan, Elaraby A. Elgallad

Abstract:

With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

Procedia PDF Downloads 195

Authors: Fawaz S. Al-Anzi, Dia AbuZeina

Abstract:

Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit.

Keywords: speech recognition, acoustic features, mel frequency, cepstral coefficients

Procedia PDF Downloads 233

Authors: Lal Hussain, Wajid Aziz, Sajjad Ahmed Nadeem, Saeed Arif Shah, Abdul Majid

Abstract:

Brain electrical activity as reflected in Electroencephalography (EEG) have been analyzed and diagnosed using various techniques. Among them, complexity measure, nonlinearity, disorder, and unpredictability play vital role due to the nonlinear interconnection between functional and anatomical subsystem emerged in brain in healthy state and during various diseases. There are many social and economical issues of alcoholic abuse as memory weakness, decision making, impairments, and concentrations etc. Alcoholism not only defect the brains but also associated with emotional, behavior, and cognitive impairments damaging the white and gray brain matters. A recently developed signal analysis method i.e. Multiscale Permutation Entropy (MPE) is proposed to estimate the complexity of long-range temporal correlation time series EEG of Alcoholic and Control subjects acquired from University of California Machine Learning repository and results are compared with MSE. Using MPE, coarsed grained series is first generated and the PE is computed for each coarsed grained time series against the electrodes O1, O2, C3, C4, F2, F3, F4, F7, F8, Fp1, Fp2, P3, P4, T7, and T8. The results computed against each electrode using MPE gives higher significant values as compared to MSE as well as mean rank differences accordingly. Likewise, ROC and Area under the ROC also gives higher separation against each electrode using MPE in comparison to MSE.

Keywords: electroencephalogram (EEG), multiscale permutation entropy (MPE), multiscale sample entropy (MSE), permutation entropy (PE), mann whitney test (MMT), receiver operator curve (ROC), complexity measure

Procedia PDF Downloads 462

Authors: Annalakshmi G., Sakthivel Murugan S.

Abstract:

This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

Procedia PDF Downloads 138

Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

Abstract:

This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

Procedia PDF Downloads 455

Authors: L. S. Godlevsky, N. V. Kresyun, V. P. Martsenyuk, K. S. Shakun, T. V. Tatarchuk, K. O. Prybolovets, L. F. Kalinichenko, M. Karpinski, T. Gancarczyk

Abstract:

Structures of eye bottom were extracted using multiscale texture gradient method and color characteristics of macular zone and vessels were verified in CIELAB scale. The difference of average values of L*, a* and b* coordinates of CIE (International Commision of Illumination) scale in patients with diabetes and healthy volunteers was compared. The average value of L* in diabetic patients exceeded such one in the group of practically healthy persons by 2.71 times (P < 0.05), while the value of a* index was reduced by 3.8 times when compared with control one (P < 0.05). b* index exceeded such one in the control group by 12.4 times (P < 0.05). The integrated index on color difference (ΔE) exceeded control value by 2.87 times (P < 0.05). More pronounced differences with ΔE were followed by a shorter period of MA appearance with a correlation level at -0.56 (P < 0.05). The specificity of diagnostics raised by 2.17 times (P < 0.05) and negative prognostic index exceeded such one determined with the expert method by 2.26 times (P < 0.05).

Keywords: diabetic retinopathy, multiscale texture gradient, color spectrum analysis, medical diagnostics

Procedia PDF Downloads 93

Authors: Weiliang Wen, Xinyu Guo, Ying Zhang, Jianjun Du, Boxiang Xiao

Abstract:

Reconstructing and analyzing three-dimensional (3D) models from situ measured data is important for a number of researches and applications in plant science, including plant phenotyping, functional-structural plant modeling (FSPM), plant germplasm resources protection, agricultural technology popularization. It has many scales like cell, tissue, organ, plant and canopy from micro to macroscopic. The techniques currently used for data capture, feature analysis, and 3D reconstruction are quite different of different scales. In this context, morphological data acquisition, 3D analysis and modeling of plants on different scales are introduced systematically. The commonly used data capture equipment for these multiscale is introduced. Then hot issues and difficulties of different scales are described respectively. Some examples are also given, such as Micron-scale phenotyping quantification and 3D microstructure reconstruction of vascular bundles within maize stalks based on micro-CT scanning, 3D reconstruction of leaf surfaces and feature extraction from point cloud acquired by using 3D handheld scanner, plant modeling by combining parameter driven 3D organ templates. Several application examples by using the 3D models and analysis results of plants are also introduced. A 3D maize canopy was constructed, and light distribution was simulated within the canopy, which was used for the designation of ideal plant type. A grape tree model was constructed from 3D digital and point cloud data, which was used for the production of science content of 11th international conference on grapevine breeding and genetics. By using the tissue models of plants, a Google glass was used to look around visually inside the plant to understand the internal structure of plants. With the development of information technology, 3D data acquisition, and data processing techniques will play a greater role in plant science.

Keywords: plant, three dimensional modeling, multi-scale, plant phenotyping, three dimensional data acquisition

Procedia PDF Downloads 253

Authors: Girma Yohannis Bade, Olga Kolesnikova, Grigori Sidorov

Abstract:

Named entity recognition (NER) is a building block for information extraction. Though the information extraction process has been automated using a variety of techniques to find and extract a piece of relevant information from unstructured documents, the discovery of targeted knowledge still poses a number of research difficulties because of the variability and lack of structure in Web data. NER, a subtask of information extraction (IE), came to exist to smooth such difficulty. It deals with finding the proper names (named entities), such as the name of the person, country, location, organization, dates, and event in a document, and categorizing them as predetermined labels, which is an initial step in IE tasks. This survey paper presents the roles and importance of NER to IE from the perspective of different algorithms and application area domains. Thus, this paper well summarizes how researchers implemented NER in particular application areas like finance, medicine, defense, business, food science, archeology, and so on. It also outlines the three types of sequence labeling algorithms for NER such as feature-based, neural network-based, and rule-based. Finally, the state-of-the-art and evaluation metrics of NER were presented.

Keywords: the role of NER, named entity recognition, information extraction, sequence labeling algorithms, named entity application area

Procedia PDF Downloads 49

Authors: Raouf Mbarki, Fadi Al Khatib, Malek Adouni

Abstract:

Knee collateral ligaments play a significant role in restraining excessive frontal motion (varus/valgus rotations). In this investigation, a multiscale frame was developed based on structural hierarchies of the collateral ligaments starting from the bottom (tropocollagen molecule) to up where the fibred reinforced structure established. Experimental data of failure tensile test were considered as the principal driver of the developed model. This model was calibrated statistically using Bayesian calibration due to the high number of unknown parameters. Then the model is scaled up to fit the real structure of the collateral ligaments and simulated under realistic boundary conditions. Predications have been successful in describing the observed transient response of the collateral ligaments during tensile test under pre- and post-damage loading conditions. Collateral ligaments maximum stresses and strengths were observed near to the femoral insertions, a results that is in good agreement with experimental investigations. Also for the first time, damage initiation and propagation were documented with this model as a function of the cross-link density between tropocollagen molecules.

Keywords: multiscale model, tropocollagen, fibrils, ligaments commas

Procedia PDF Downloads 129

Authors: Sana Almi, Djamel Barkat

Abstract:

The solvent extraction of cobalt (II) and nickel (II) from aqueous sulfate solutions were investigated with the analytical methods of slope analysis using salicylidene aniline and the three isomeric o-, m- and p-salicylidene toluidine diluted with chloroform at 25°C. By a statistical analysis of the extraction data, it was concluded that the extracted species are CoL2 with CoL2(HL) and NiL2 (HL denotes HSA, HSOT, HSMT, and HSPT). The extraction efficiency of Co(II) was higher than Ni(II). This tendency is confirmed from numerical extraction constants for each metal cations. The best extraction was according to the following order: HSMT > HSPT > HSOT > HSA for Co2+ and Ni2+.

Keywords: solvent extraction, nickel(II), cobalt(II), salicylidene aniline, o-, m-, and p-salicylidene toluidine

Procedia PDF Downloads 455

Authors: Yuanchao Sang, Zhihao Gong, Longsheng Chen, Long Chen

Abstract:

Clothing fashion represents a human’s aesthetic appreciation towards everyday outfits and appetite for fashion, and it reflects the development of status in society, humanity, and economics. However, modelling fashion by machine is extremely challenging because fashion is too abstract to be efficiently described by machines. Even human beings can hardly reach a consensus about fashion. In this paper, we are dedicated to answering a fundamental fashion-related problem: what image feature best describes clothing fashion? To address this issue, we have designed and evaluated various image features, ranging from traditional low-level hand-crafted features to mid-level style awareness features to various current popular deep neural network-based features, which have shown state-of-the-art performance in various vision tasks. In summary, we tested the following 9 feature representations: color, texture, shape, style, convolutional neural networks (CNNs), CNNs with distance metric learning (CNNs&DML), AutoEncoder, CNNs with multiple layer combination (CNNs&MLC) and CNNs with dynamic feature clustering (CNNs&DFC). Finally, we validated the performance of these features on two publicly available datasets. Quantitative and qualitative experimental results on both intra-domain and inter-domain fashion clothing image retrieval showed that deep learning based feature representations far outweigh traditional hand-crafted feature representation. Additionally, among all deep learning based methods, CNNs with explicit feature clustering performs best, which shows feature clustering is essential for discriminative fashion feature representation.

Keywords: convolutional neural network, feature representation, image processing, machine modelling

Procedia PDF Downloads 112

Authors: A. A. Abid

Abstract:

The magnetospheric multiscale (MMS) satellite observations in the inner magnetosphere were used to detect the proton band of the electromagnetic ion cyclotron (EMIC) waves on December 14, 2015, which have been significantly contributing to the dynamics of the magnetosphere. It has been examined that the intensity of EMIC waves gradually increases by decreasing the L shell. The waves are triggered by hot proton thermal anisotropy. The low-energy cold protons (ions) can be activated by the EMIC waves when the EMIC wave intensity is high. As a result, these previously invisible protons are now visible. As a result, the EMC waves also excite the helium ions. The EMIC waves, whose frequency in the magnetosphere of the Earth ranges from 0.001 Hz to 5 Hz, have drawn a lot of attention for their ability to carry energy. Since these waves act as a mechanism for the loss of energetic electrons from the Van Allen radiation belt to the atmosphere, therefore, it is necessary to understand how and where they can be produced, as well as the direction of waves along the magnetic field lines. This work examines how the excitation of EMIC waves is affected by the energy of hot proton temperature anisotropy, and It has a minimum resonance energy of 6.9 keV and a range of 7 to 26 keV. On the hot protons, however, the reverse effect can be seen for energies below the minimum resonance energy. It is demonstrated that throughout the energy range of 1 eV to 100 eV, the number density and temperature anisotropy of the protons likewise rise as the intensity of the EMIC waves increases. Key Points: 1. The analysis of EMIC waves produced by hot proton temperature anisotropy using MMS data. 2. The number density and temperature anisotropy of the cold protons increases owing to high-intensity EMIC waves. 3. The cold protons with an energy range of 1-100eV are energized by EMIC waves using the Magnetospheric Multiscale (MMS) satellite not been discussed before

Keywords: EMIC waves, temperature anisotropy of hot protons, energization of the cold proton, magnetospheric multiscale (MMS) satellite observations

Procedia PDF Downloads 83

Authors: Chunying Fang, Haifeng Li, Lin Ma, Mancai Zhang

Abstract:

Speech intelligibility assessment is an important measure to evaluate the functional outcomes of surgical and non-surgical treatment, speech therapy and rehabilitation. The assessment of pathological speech plays an important role in assisting the experts. Pathological speech usually is non-stationary and mutational, in this paper, we describe a multi-granularity combined feature schemes, and which is optimized by hierarchical visual method. First of all, the difference granularity level pathological features are extracted which are BAFS (Basic acoustics feature set), local spectral characteristics MSCC (Mel s-transform cepstrum coefficients) and nonlinear dynamic characteristics based on chaotic analysis. Latterly, radar chart and F-score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96-dimensions.The experimental results denote that new features by support vector machine (SVM) has the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation.

Keywords: pathological speech, multi-granularity feature, MSCC (Mel s-transform cepstrum coefficients), F-score, radar chart

Procedia PDF Downloads 260

Authors: Ben Soltane Cheima, Ittansa Yonas Kelbesa

Abstract:

Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work.

Keywords: feature extraction, speaker modeling, feature matching, Mel frequency cepstrum coefficient (MFCC), Gaussian mixture model (GMM), vector quantization (VQ), Linde-Buzo-Gray (LBG), expectation maximization (EM), pre-processing, voice activity detection (VAD), short time energy (STE), background noise statistical modeling, closed-set tex-independent speaker identification system (CISI)

Procedia PDF Downloads 281

Authors: Aayush Bhisikar, Neha Rajas, Aditya Bhingare, Samarth Bhandare, Amruta Amrurkar

Abstract:

Orange peels are currently thrown away as garbage in India after orange fruits' edible components are consumed. However, the nation depends on important essential oils for usage in companies that produce goods, including food, beverages, cosmetics, and medicines. This study was conducted to show how to effectively use it. By using various extraction techniques, orange peel is used in the creation of essential oils. Stream distillation, water distillation, and solvent extraction were the techniques taken into consideration in this paper. Due to its relative prevalence among the extraction techniques, Design Expert 7.0 was used to plan an experimental run for solvent extraction. Oil was examined to ascertain its physical and chemical characteristics after extraction. It was determined from the outcomes that the orange peels.

Keywords: orange peels, extraction, essential oil, distillation

Procedia PDF Downloads 48

Authors: Neha Rajas, Aayush Bhisikar, Samarth Bhandare, Aditya Bhingare, Amruta Amrutkar

Abstract:

Orange peels are currently thrown away as garbage in India after orange fruits' edible components are consumed. However, the nation depends on important essential oils for usage in companies that produce goods, including food, beverages, cosmetics, and medicines. This study was conducted to show how to effectively use it. By using various extraction techniques, orange peel is used in the creation of essential oils. Stream distillation, water distillation, and solvent extraction were the techniques taken into consideration in this paper. Due to its relative prevalence among the extraction techniques, Design Expert 7.0 was used to plan an experimental run for solvent extraction. Oil was examined to ascertain its physical and chemical characteristics after extraction. It was determined from the outcomes that the orange peels.

Keywords: orange peels, extraction, distillation, essential oil

Procedia PDF Downloads 43

Authors: Jarek Krajewski, David Daxberger

Abstract:

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: Nimisha Elsa Koshy, Varun P. Gopi, V. I. Thajudin Ahamed

Abstract:

The entire visualization of GastroIntestinal (GI) tract is not possible with conventional endoscopic exams. Wireless Capsule Endoscopy (WCE) is a low risk, painless, noninvasive procedure for diagnosing diseases such as bleeding, polyps, ulcers, and Crohns disease within the human digestive tract, especially the small intestine that was unreachable using the traditional endoscopic methods. However, analysis of massive images of WCE detection is tedious and time consuming to physicians. Hence, researchers have developed software methods to detect these diseases automatically. Thus, the effectiveness of WCE can be improved. In this paper, a novel textural feature extraction method is proposed based on Contourlet transform and Log Gabor filter to distinguish ulcer regions from normal regions. The results show that the proposed method performs well with a high accuracy rate of 94.16% using Support Vector Machine (SVM) classifier in HSV colour space.

Keywords: contourlet transform, log gabor filter, ulcer, wireless capsule endoscopy

Procedia PDF Downloads 516

Authors: Chih-Fong Tsai, Ya-Han Hu

Abstract:

The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

Procedia PDF Downloads 635

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

Abstract:

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: Jigg L. Pelayo, Ricardo G. Villar, Einstine M. Opiso

Abstract:

The advances of aerial laser scanning in the Philippines has open-up entire fields of research in remote sensing and machine vision aspire to provide accurate timely information for the government and the public. Rapid mapping of polygonal roads and roof boundaries is one of its utilization offering application to disaster risk reduction, mitigation and development. The study uses low density LiDAR data and high resolution aerial imagery through object-oriented approach considering the theoretical concept of data analysis subjected to machine learning algorithm in minimizing the constraints of feature extraction. Since separating one class from another in distinct regions of a multi-dimensional feature-space, non-trivial computing for fitting distribution were implemented to formulate the learned ideal hyperplane. Generating customized hybrid feature which were then used in improving the classifier findings. Supplemental algorithms for filtering and reshaping object features are develop in the rule set for enhancing the final product. Several advantages in terms of simplicity, applicability, and process transferability is noticeable in the methodology. The algorithm was tested in the different random locations of Misamis Oriental province in the Philippines demonstrating robust performance in the overall accuracy with greater than 89% and potential to semi-automation. The extracted results will become a vital requirement for decision makers, urban planners and even the commercial sector in various assessment processes.

Keywords: feature extraction, machine learning, OBIA, remote sensing

Procedia PDF Downloads 337

Authors: Ahmed Boucherit, Hussein Khalaf, Eduardo Paredes, José Luis Todolí

Abstract:

Solvent extraction studies of molybdenum (VI) with two organophosphorus reagents namely TBP and D2EHPA have been carried out from aqueous acidic solutions of HCl, H2SO4 and H3PO4 under microwave irradiations. The extraction efficiencies of the investigated extractants in the extraction of molybdenum (Vl) were compared. Extraction yield was found unchanged when microwave power varied in the range 20-100 Watts from H2SO4 or H3PO4 but it decreases in the range 20-60 Watts and increases in the range 60-100 Watts when TBP is used for extraction of molybdenum (VI) from 1 M HCl solutions. Extraction yield of molybdenum (VI) was found higher with TBP for HCl molarities greater than 1 M than with D2EHPA for H3PO4 molarities lower than 1 M. Extraction yield increases with HCl molarities in the range 0.50 - 1.80 M but it decreases with the increase in H2SO4 and H3PO4 molarities in the range of 0.05 - 1 M and 0.50 - 1 M, respectively.

Keywords: extraction, molybdenum, microwave, solvent

Procedia PDF Downloads 612

Authors: Roukia Hammoudi, Chabrouk Farid, Dehak Karima, Mahfoud Hadj Mahammed, Mohamed Didi Ouldelhadj

Abstract:

The objective of this study was to optimise the extraction conditions for phenolic compounds from Deverra scoparia Coss and Dur. Apiaceae plant by ultrasound assisted extraction (UAE). The effects of solvent type (acetone, ethanol and methanol), solvent concentration (%), extraction time (mins) and extraction temperature (°C) on total phenolic content (TPC) were determined. The optimum extraction conditions were found to be acetone concentration of 80%, extraction time of 25 min and extraction temperature of 25°C. Under the optimized conditions, the value for TPC was 9.68 ± 1.05 mg GAE/g of extract. The study of the antioxidant power of these oils was performed by the method of DPPH. The results showed that antioxidant activity of the Deverra scoparia essential oil was more effective as compared to ascorbic acid and trolox.

Keywords: Deverra scoparia, phenolic compounds, ultrasound assisted extraction, total phenolic content, antioxidant activity

Procedia PDF Downloads 571

Authors: Roukia Hammoudi, Dehak Karima, Chabrouk Farid, Mahfoud Hadj Mahammed, Mohamed Didi Ouldelhadj

Abstract:

The objective of this study was to optimise the extraction conditions for phenolic compounds from Deverra scoparia Coss and Dur. Apiaceae plant by ultrasound assisted extraction (UAE). The effects of solvent type (Acetone, Ethanol and methanol), solvent concentration (%), extraction time (mins) and extraction temperature (°C) on total phenolic content (TPC) were determined. the optimum extraction conditions were found to be acetone concentration of 80%, extraction time of 25 min and extraction temperature of 25°C. Under the optimized conditions, the value for TPC was 9.68 ± 1.05 mg GAE/g of extract. The study of the antioxidant power of these oils was performed by the method of DPPH. The results showed that antioxidant activity of the Deverra scoparia essential oil was more effective as compared to ascorbic acid and trolox.

Keywords: Deverra scoparia, phenolic compounds, ultrasound assisted extraction, total phenolic content, antioxidant activity

Procedia PDF Downloads 567

Authors: Gyeong-Suk Jo, Soo-Hyun Ji, You-Seok Lee, Jeong-Hwa Kang

Abstract:

To develop an flavor oil using Sarcodon aspratus (Sarcodon aspratus Berk. S. Ito), infiltration extraction method was used to add dried mushroom flavor of Sarcodon aspratus to base olive oil. Edible base oil used during infiltration extraction was pressed olive oil, and infiltration extraction was done while varying extraction temperature to 20, 30, 40 and 50(℃) extraction time to 24 hours, 48 hours and 72 hours. Amount of Sarcodon aspratus added to base oil was 20% compared to 100% of base oil. Production yield of Sarcodon aspratus flavor oil decreased with increasing extraction frequency. Aroma intensity was 2195~2447 (A.U./1㎖), and it increased with increasing extraction temperature and extraction time. Chromaticity of Sarcodon aspratus flavor oil was bright pale yellow with pH of 4.5, sugar content of 71~72 (°Brix), and highest average turbidity of 16.74 (Haze %) shown by the 40℃ group. In the aromatic evaluation, increasing extraction temperature and extraction time resulted in increase of cheese aroma, savory sweet aroma and beef jerky aroma, as well as spicy taste comprised of slight bitter taste, savory taste and slight acrid taste, to make aromatic oil with unique flavor.

Keywords: Flavor Characteristics, Flavor Oil, Infiltration extraction method, mushroom, Sarcodon aspratus (Sarcodon aspratus Berk. S. Ito)

Procedia PDF Downloads 345

Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

Abstract:

Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

Procedia PDF Downloads 559

Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

Abstract:

In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

Procedia PDF Downloads 498

Authors: Saziye Ilgaz, Atilla Polat

Abstract:

There is a need for development of new processing techniques to ensure safety and quality of final product while minimizing the adverse impact of extraction solvents on environment and residue levels of these solvents in final product, decaffeinated black tea. In this study pilot scale supercritical carbon dioxide (SCCO₂) extraction was used to produce decaffeinated black tea in place of solvent extraction. Pressure (250, 375, 500 bar), extraction time (60, 180, 300 min), temperature (55, 62.5, 70 °C), CO₂ flow rate (1, 2 ,3 LPM) and co-solvent quantity (0, 2.5, 5 %mol) were selected as extraction parameters. The five factors BoxBehnken experimental design with three center points was performed to generate 46 different processing conditions for caffeine removal from black tea samples. As a result of these 46 experiments caffeine content of black tea samples were reduced from 2.16 % to 0 – 1.81 %. The experiments showed that extraction time, pressure, CO₂ flow rate and co-solvent quantity had great impact on decaffeination yield. Response surface methodology (RSM) was used to optimize the parameters of the supercritical carbon dioxide extraction. Optimum extraction parameters obtained of decaffeinated black tea were as follows: extraction temperature of 62,5 °C, extraction pressure of 375 bar, CO₂ flow rate of 3 LPM, extraction time of 176.5 min and co-solvent quantity of 5 %mol.

Keywords: supercritical carbon dioxide, decaffeination, black tea, extraction

Procedia PDF Downloads 336