Efficient Classification Approach Research Articles

Dynamic mutation based glowworm swarm optimization with long short-term memory approaches for thyroid nodule classification

Objectives: To design an efficient approach for thyroid nodule classification with higher true positive rate. Methodology and statistical analysis: The proposed system designed as a Dynamic Mutation based Glowworm Swarm Optimization with Long-Short Term Memory (DMGSO with LSTM) scheme for thyroid nodule classification. In this proposed research work, input thyroid images are preprocessed by using Dynamically Weighted Median Filter (DWMF). The preprocessed images are segmented with the help of Region based Active Contour scheme. An Improved Local Binary Pattern (ILBP), Grey Level Cooccurrence Matrix (GLCM) and Histogram of Oriented Gradient (HOG) features are extracted from segmented image. Then the optimal features are selected by using Dynamic Mutation based Glowworm Swarm Optimization (DMGSO) algorithm. Finally, the Long-Short Term Memory (LSTM) scheme is utilized for classifying the thyroid nodule. Findings: The experimental results show that the proposed system achieves better performance compared with the existing system in terms of accuracy, precision, recall and f-measure. Keywords: Thyroid nodule; Histogram of Oriented Gradient (HOG); Long Short-Term Memory (LSTM); Dynamic Mutation based Glow worm Swarm Optimization (DMGSO)

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.

An efficient approach for physical actions classification using surface EMG signals.

Physical actions classification of surface electromyography (sEMG) signal is required in applications like prosthesis, and robotic control etc. In this paper, tunable-Q factor wavelet transform (TQWT) based algorithm is proposed for the classification of physical actions such as clapping, hugging, bowing, handshaking, standing, running, jumping, waving, seating, and walking. sEMG signal is decomposed into sub-bands by TQWT. Various features are extracted from each different band and statistical analysis is performed. These features are fed into multi-class least squares support vector machine classifier using two non-linear kernel functions, morlet wavelet function, and radial basis function. The proposed method is an attempt for classifying physical actions using TQWT and its performance and results are promising and have high classification accuracy of 97.74% for sub-band eight with morlet kernel function.

Convolutional neural networks based efficient approach for classification of lung diseases

Treatment of lung diseases, which are the third most common cause of death in the world, is of great importance in the medical field. Many studies using lung sounds recorded with stethoscope have been conducted in the literature in order to diagnose the lung diseases with artificial intelligence-compatible devices and to assist the experts in their diagnosis. In this paper, ICBHI 2017 database which includes different sample frequencies, noise and background sounds was used for the classification of lung sounds. The lung sound signals were initially converted to spectrogram images by using time–frequency method. The short time Fourier transform (STFT) method was considered as time–frequency transformation. Two deep learning based approaches were used for lung sound classification. In the first approach, a pre-trained deep convolutional neural networks (CNN) model was used for feature extraction and a support vector machine (SVM) classifier was used in classification of the lung sounds. In the second approach, the pre-trained deep CNN model was fine-tuned (transfer learning) via spectrogram images for lung sound classification. The accuracies of the proposed methods were tested by using the ten-fold cross validation. The accuracies for the first and second proposed methods were 65.5% and 63.09%, respectively. The obtained accuracies were then compared with some of the existing results and it was seen that obtained scores were better than the other results.

Statistical-Hypothesis-Aided Tests for Epilepsy Classification

In this paper, an efficient, accurate, and nonparametric epilepsy detection and classification approach based on electroencephalogram (EEG) signals is proposed. The proposed approach mainly depends on a feature extraction process that is conducted using a set of statistical tests. Among the many existing tests, those fit with processed data and for the purpose of the proposed approach were used. From each test, various output scalars were extracted and used as features in the proposed detection and classification task. Experiments that were conducted on the basis of a Bonn University dataset showed that the proposed approach had very accurate results ( 98.4 % ) in the detection task and outperformed state-of-the-art methods in a similar task on the same dataset. The proposed approach also had accurate results ( 94.0 % ) in the classification task, but it did not outperform state-of-the-art methods in a similar task on the same dataset. However, the proposed approach had less time complexity in comparison with those methods that achieved better results.

An efficient approach for dimension selection and classification in HSI images

ABSTRACTIn this paper, we have proposed a divide and select approach for dimension selection (DSCS) and classification of hyperspectral images (HSI). The DSCS algorithm has been offered using a two-stage framework. In stage-1, we have generated the intermediate channels for every pair of spectral dimensions that are weakly correlated and highly significant. These channels have derived by splitting the adjacent dimensions without any loss of physical meaning. Intermediated channels are concatenated with original HSI in a better sense to transform the image into a more informative datacube. However, this leads to an increase in the dimensionality of the image. In stage-2, a trace and determinant based local feature response approach has applied to select the most informative dimensions of transformed HSI. We have exploited the local feature and scale selection methods to obtain significant channels. Finally, classification experiments have conducted for selected bands with SVM (Support Vector Machine) and LDA (Linear Discriminant Analysis). An expansion graph cut optimization has applied to improve the classification accuracy. This method has demonstrated as state-of-art for target detection in hyperspectral scenes.

Efficient local-region approach for high-resolution remote-sensing image retrieval and classification

Efficient local-region approach for high-resolution remote-sensing image retrieval and classification

On Runtime and Classification Performance of the Discretize-Optimize (DISCO) Classification Approach

Using machine learning in high-speed networks for tasks such as flow classification typically requires either very resource efficient classification approaches, large amounts of computational resources, or specialized hardware. Here we provide a sketch of the discretize-optimize (DISCO) approach which can construct an extremely efficient classifier for low dimensional problems by combining feature selection, efficient discretization, novel bin placement, and lookup. As feature selection and discretization parameters are crucial, appropriate combinatorial optimization is an important aspect of the approach. A performance evaluation is performed for a YouTube classification task using a cellular traffic data set. The initial evaluation results show that the DISCO approach can move the Pareto boundary in the classification performance versus runtime trade-off by up to an order of magnitude compared to runtime optimized random forest and decision tree classifiers.

Robust Sparse Representation and Multiclass Support Matrix Machines for the Classification of Motor Imagery EEG Signals.

Background: EEG signals are extremely complex in comparison to other biomedical signals, thus require an efficient feature selection as well as classification approach. Traditional feature extraction and classification methods require to reshape the data into vectors that results in losing the structural information exist in the original featured matrix. Aim: The aim of this work is to design an efficient approach for robust feature extraction and classification for the classification of EEG signals. Method: In order to extract robust feature matrix and reduce the dimensionality of from original epileptic EEG data, in this paper, we have applied robust joint sparse PCA (RJSPCA), Outliers Robust PCA (ORPCA) and compare their performance with different matrix base feature extraction methods, followed by classification through support matrix machine. The combination of joint sparse PCA with robust support matrix machine showed good generalization performance for classification of EEG data due to their convex optimization. Results: A comprehensive experimental study on the publicly available EEG datasets is carried out to validate the robustness of the proposed approach against outliers. Conclusion: The experiment results, supported by the theoretical analysis and statistical test, show the effectiveness of the proposed framework for solving classification of EEG signals.

Empirical Performance Analysis of Decision Tree and Support Vector Machine based Classifiers on Biological Databases

The classification and prediction of medical diseases is a cutting edge research problem in the medical field. The experts of machine learning are continuously proposing new classification methods for the prediction of diseases. The discovery of classification rules from medical databases for classification and prediction of diseases is a challenging and non-trivial task. It is very significant to investigate the more promising and efficient classification approaches for the discovery of classification rules from the medical databases. This paper focuses on the problem of selection of more efficient, promising and suitable classifier for the prediction of specific diseases by performing empirical studies on bunch mark medical databases. The research work under the focus concentrates on the benchmark medical data sets i.e. arrhythmia, breast-cancer, diabetes, hepatitis, mammography, lymph, liver-disorders, sick, cardiotocography, heart-statlog, breast-w, and lung-cancer. The medical data sets are obtained from the open-source UCI machine learning repository. The research work will be investigating the performance of Decision Tree (i.e. AdaBoost.NC, C45-C, CART, and ID3-C) and Support Vector Machines. For experimentation, Knowledge Extraction based on Evolutionary Learning (KEEL), a data mining tool will be used. This research work provides the empirical performance analysis of decision tree-based classifiers and SVM on a specific dataset. Moreover, this article provides a comparative performance analysis of classification approaches in terms of statistics.

Novel Method for Lung Tumour Detection Using Wavelet Shrinkage-based Double Classifier Analysis

Medical image detection and classification play an important role in medical research. Tumour detection is a process of crucial importance in oncology. The aim of this paper is to devise an approach for efficient image enhancement, tumour detection, and classification on computed tomographic lung images. Three different application-related methods on computed tomography (CT) images have been designed and implemented in this work. The first method, which was based on image quality, enhances the performance of the medical images. Generally, the CT images are very sensitive to noise and are difficult to handle. Proper care may be taken by introducing some preprocessing algorithms like enhancement algorithms and filters. According to this, an image enhancement algorithm, Wavelet Shrinkage adaptive histogram Equalization (WSAHE), with anisotropic diffusion filter (ADF) was introduced to improve the contrast of the CT images and denoising. In the second method Seed Region growing with Random walk segmentation algorithm (SRGWRWS) is proposed for lung tumour segmentation. In the third method GLCM features, FOS features, structural features, and combinational features are calculated from the segmented lung images. After segmentation, the tumour classification is done by Multi-class support vector machine (MCSVM) and Descend RUSBOOST classifiers. Here, MCSVM classifier was used to find whether the input CT tumour image is suspicious, benign or malignant. Overall accuracy of 92% is obtained using Descend RUSBOOST classifier with GLCM features, FOS features, structural features, combinational features and overall accuracy of 97% is obtained using MCSVM classifier with GLCM feature in MATLAB 2017a software.

Efficient email classification approach based on semantic methods

Abstract Emails have become one of the major applications in daily life. The continuous growth in the number of email users has led to a massive increase of unsolicited emails, which are also known as spam emails. Managing and classifying this huge number of emails is an important challenge. Most of the approaches introduced to solve this problem handled the high dimensionality of emails by using syntactic feature selection. In this paper, an efficient email filtering approach based on semantic methods is addressed. The proposed approach employs the WordNet ontology and applies different semantic based methods and similarity measures for reducing the huge number of extracted textual features, and hence the space and time complexities are reduced. Moreover, to get the minimal optimal features’ set, feature dimensionality reduction has been integrated using feature selection techniques such as the Principal Component Analysis (PCA) and the Correlation Feature Selection (CFS). Experimental results on the standard benchmark Enron Dataset showed that the proposed semantic filtering approach combined with the feature selection achieves high computational performance at high space and time reduction rates. A comparative study for several classification algorithms indicated that the Logistic Regression achieves the highest accuracy compared to Naive Bayes, Support Vector Machine, J48, Random Forest, and radial basis function networks. By integrating the CFS feature selection technique, the average recorded accuracy for the all used algorithms is above 90%, with more than 90% feature reduction. Besides, the conducted experiments showed that the proposed work has a highly significant performance with higher accuracy and less time compared to other related works.

An efficient approach for land record classification and information retrieval in data warehouse

Data warehouse collects recent and old land record data used to generate analytical reports. Depending on factors such as historical evolution and local traditions, the system of land records differs from states. The survey maps, textual data, and registration records are matched with each other and updated for the registration and maintenance of such land records. In addition, citizens must get access to multiple agencies to get full information on land records. In order to eliminate such limitation, we propose a novel Artificial Neural Network (ANN)–FUZZY–cat swarm optimization (CSO) approach to accurately predict and retrieve the information. First, the ANN classifies the input data for ordering the information to construct a database of different classes. Then, the mongo database store a large amount of land record data for facilitating easy maintenance, prompt updating of land records and security. The accurate results for the user query are retrieved using CSO algorithm. Optimal rules allow users to access their posts easily. Finally, better performance results are for the information retrieval in terms of accuracy, precision, and recall.

Big earth observation time series analysis for monitoring Brazilian agriculture

This paper presents innovative methods for using satellite image time series to produce land use and land cover classification over large areas in Brazil from 2001 to 2016. We used Moderate Resolution Imaging Spectroradiometer (MODIS) time series data to classify natural and human-transformed land areas in the state of Mato Grosso, Brazil’s agricultural frontier. Our hypothesis is that building high-dimensional spaces using all values of the time series, coupled with advanced statistical learning methods, is a robust and efficient approach for land cover classification of large data sets. We used the full depth of satellite image time series to create large dimensional spaces for statistical classification. The data consist of MODIS MOD13Q1 time series with 23 samples per year per pixel, and 4 bands (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), near-infrared (nir) and mid-infrared (mir)). By taking a series of labelled time series, we fed a 92-dimensional attribute space into a support vector machine model. Using a 5-fold cross validation, we obtained an overall accuracy of 94% for discriminating among nine land cover classes: forest, cerrado, pasture, soybean-fallow, fallow-cotton, soybean-cotton, soybean-corn, soybean-millet, and soybean-sunflower. Producer and user accuracies for all classes were close to or better than 90%. The results highlight important trends in agricultural intensification in Mato Grosso. Double crop systems are now the most common production system in the state, sparing land from agricultural production. Pasture expansion and intensification has been less studied than crop expansion, although it has a stronger impact on deforestation and greenhouse gas (GHG) emissions. Our results point to a significant increase in the stocking rate in Mato Grosso and to the possible abandonment of pasture areas opened in the state’s frontier. The detailed land cover maps contribute to an assessment of the interplay between production and protection in the Brazilian Amazon and Cerrado biomes.

Mammographic mass classification using filter response patches

Considering the importance of early diagnosis of breast cancer, a supervised patch‐wise texton‐based approach has been developed for the classification of mass abnormalities in mammograms. The proposed method is based on texture‐based classification of masses in mammograms and does not require segmentation of the mass region. In this approach, patches from filter bank responses are utilised for generating the texton dictionary. The methodology is evaluated on the publicly available Digital Database for Screening Mammography database. Using a naive Bayes classifier, a classification accuracy of 83% with an area under the receiver operating characteristic curve of 0.89 was obtained. Experimental results demonstrated that the patch‐wise texton‐based approach in conjunction with the naive Bayes classifier constructs an efficient and alternative approach for automatic mammographic mass classification.

An efficient classification approach in imbalanced datasets for intrinsic plagiarism detection

The ever increasing volume of information due to the widespread use of computers and the web has made effective plagiarism detection methods a necessity. Plagiarism can be found in many settings and forms, in literature, in academic papers, even in programming code. Intrinsic plagiarism detection is the task that deals with the discovery of plagiarized passages in a text document, by identifying the stylistic changes and inconsistencies within the document itself, given that no reference corpus is available. The main idea consists in profiling the style of the original author and marking the passages that seem to differ significantly. In this work, we follow a supervised machine learning classification approach. We consider, for the first time, the fact of imbalanced data as a crucial parameter of the problem and experiment with various balancing techniques. Apart from this, we propose some novel stylistic features. We combine our features and imbalanced dataset treatment with various classification methods. Our detection system is tested on the data corpora of PAN Webis intrinsic plagiarism detection shared tasks. It is compared to the best performing detection systems on these datasets, and succeeds the best resulting scores.

An efficient classification approach for detection of Alzheimer’s disease from biomedical imaging modalities

The complex patterns of the neuroimaging data are analyzed successfully with bio-medical imaging applications. The patients with/without AD can be discriminated effectively through several biomedical imaging modalities such as, sMRI, fMRI, PET and so on. In this paper, brain images from structural MRI (sMRI) are used for better categorization of 3 subjects namely, NC (Normal Control), MCI (Mild Cognitive Impairment) and AD (Alzheimer’s disease). Moreover, ambiguous training data employed for the discrimination of subjects may mislead the classifier to take incorrect decisions and in turn degrades the classification performance. In order to recover these hurdles, we propose an automated reliable system for the detection of AD affected patients accurately from the brain images of sMRI. The proposed system is a multi-stage system comprising four key phases namely, i) pre-processing, ii) feature extraction, iii) feature selection and iv) detection phase. In the initial phase, ROI regions related to Hippocampus (HC) and Posterior Cingulate Cortex (PCC) from the brain images are extracted using Automated Anatomical Labeling (AAL) method. In the feature extraction stage, important texture and shape features are extracted from HC and PCC involved in three brain planes. Nearly, 19 highly relevant AD related features are selected through a multiple-criterion feature selection method. It should be noted that, the class labels when explored manually consumes more time and turns to be an expensive process. Therefore, it is essential to construct an automatic method to identify irrelevant samples in the training data to enhance the decision-making process. With this in mind, at the detection phase, a novel classification technique is proposed by combining the Kernel fuzzy c-means clustering (i.e. unsupervised learning technique) and Back-propagation artificial neural network (i.e. supervised learning technique) to categorize NC, MCI and AD from the brain images of sMRI. This proposed KFCM based BPANN algorithm can improve the classification performance by removing the suspicious training samples. The proposed frameworks efficiency is evaluated with the ADNI subset and then to the Bordeaux-3 city dataset. The experimental validation of our proposed approach attains an accuracy of 97.63%, 95.4%, 96.4% for the most challenging classification tasks AD vs NC, MCI vs NC and AD vs MCI, respectively.

Lane marking detection and classification with combined deep neural network for driver assistance

An efficient approach for lane marking detection and classification by the combination of convolution neural network and recurrent neural network is proposed in this paper. First, convolution neural network is trained for lane marking features extraction, and then these convolution neural network features of continuous frames are transferred to recurrent neural network model for lane boundary detection and classification in the time domain. At last, a lane boundary fitting method based on dynamic programming is proposed to improve the lane detection accuracy and robustness. The method presented generates satisfactory results of lane detection and type classification under various traffic conditions according to the experimental results, which show that the approach provided in this paper outperforms traditional methods and the total lane markings classification reached 95.21% accuracy.

An efficient approach for imputation and classification of medical data values using class-based clustering of medical records

Medical data is usually not free from missing values and this is also true when data is collected and sampled through various clinical trials. Existing Imputation techniques do not address the problem of high dimensionality and apply distance functions that also have the curse of high dimensionality. There is a need to turn up with innovative approaches and methods for accurate and efficient analysis of medical records. This research proposes an improved imputation approach called IM-CBC (Imputation based on class-based clustering) and a classifier termed as the Class-Based-Clustering Classifier(CBCC-IM). Experiments are performed on nine benchmark datasets and the recorded results using IM-CBC imputation approach are compared to ten imputation approaches using classifiers KNN, SVM and C4.5 and to the CBCC classifier using Euclidean distance and fuzzy gaussian similarity functions. Results obtained prove that the performance of classifiers is improved or atleast nearer to the existing approaches. CBCC-IM classifier records highest accuracy when compared to all other classifiers on benchmark datasets such as Cleveland, Ecoli, Iris, Pima, Wine and Wisconsin.

Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover

Reliable representations of global urban extent remain limited, hindering scientific progress across a range of disciplines that study functionality of sustainable cities. We present an efficient and low-cost machine-learning approach for pixel-based image classification of built-up areas at a large geographic scale using Landsat data. Our methodology combines nighttime-lights data and Landsat 8 and overcomes the lack of extensive ground-reference data. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30m resolution maps that characterize built-up land cover in three geographically diverse countries: India, Mexico, and the US. Our approach highlights the usefulness of data fusion techniques for studying the built environment and is a first step towards the creation of an accurate global-scale map of urban land cover over time.