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BACKGROUND: Breast cancer (BC) is considered the world's most prevalent cancer. Early diagnosis of BC enables patients to receive better care and treatment, hence lowering patient mortality rates. Breast lesion identification and classification are challenging even for experienced radiologists due to the complexity of breast tissue and variations in lesion presentations. OBJECTIVE: This work aims to investigate appropriate features and classification techniques for accurate breast cancer detection in 336 Biglycan biomarker images. METHODS: The Biglycan biomarker images were retrieved from the Mendeley Data website (Repository name: Biglycan breast cancer dataset). Five features were extracted and compared based on shape characteristics (i.e., Harris Points and Minimum Eigenvalue (MinEigen) Points), frequency domain characteristics (i.e., The Two-dimensional Fourier Transform and the Wavelet Transform), and statistical characteristics (i.e., histogram). Six different commonly used classification algorithms were used; i.e., K-nearest neighbours (k-NN), Naïve Bayes (NB), Pseudo-Linear Discriminate Analysis (pl-DA), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). RESULTS: The histogram of greyscale images showed the best performance for the k-NN (97.6%), SVM (95.8%), and RF (95.3%) classifiers. Additionally, among the five features, the greyscale histogram feature achieved the best accuracy in all classifiers with a maximum accuracy of 97.6%, while the wavelet feature provided a promising accuracy in most classifiers (up to 94.6%). CONCLUSION: Machine learning demonstrates high accuracy in estimating cancer and such technology can assist doctors in the analysis of routine medical images and biopsy samples to improve early diagnosis and risk stratification. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most prevalent brain disorder which affects millions of people worldwide. Early detection is crucial for possible treatment. In this regard, machine learning (ML) approaches are widely utilized for AD detection. In this paper, we propose an ML-based method that drastically reduces the dimensionality of features while maintaining the relevant features and boosting the overall performance. To remove irrelevant features, first statistical feature extraction method is applied, and then further reduction among remaining features is applied by utilizing the harmony search method (HSM). The selected features are the most informative features that are fed to the different classifiers. To test the effectiveness of the proposed method, we deployed three classification techniques including support vector machine (SVM), k-nearest neighbor (k-NN), and decision tree (DT). The experimental results show that the proposed method has a higher performance while decreasing the dimensionality of feature space. To guarantee that the performance of the proposed method is accurate, we applied an ensemble of three classifiers (SVM, KNN, and DT) for classification. The results of the proposed method verify that this method can be successfully deployed for AD detection, due to its high performance and low dimensional features, and can help improve the accuracy and efficiency of Alzheimer's disease diagnosis. The proposed method demonstrated a significant improvement, achieving high performance in AD/HC classification, with accuracy, sensitivity, specificity, F1-score, MCC, and Cohen's Kappa rates reaching 95.5%, 97%, 94%, 95.56%, 0.9104, and 0.9109, respectively. AD/HC classification displayed the highest performance. Additionally, in the more challenging pMCI/sMCI classification, the method achieved an accuracy of 78.50%, sensitivity of 84.00%, specificity of 73.00%, F1-score of 79.62%, MCC of 0.57, and Cohen's Kappa of 0.59. [ABSTRACT FROM AUTHOR]

Due to the vast amount of patient health data, automated healthcare systems still struggle to classify and diagnose various ailments. Learning redundant data also reduces categorization accuracy. A Deep Belief Network (DBN) has been used to precisely extract the most important aspects from clinical data by ignoring irrelevant/redundant features. Due of many learning variables, training is complicated. Similarly, the hybrid model has been employed by ensemble Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) to categorize diseases. But, its efficiency depends on the proper choice of kernels and hyper-parameters. Therefore, this paper develops an efficient feature extraction and classification model for healthcare systems. First, several medical data related to the patient's health are collected. Then, an Optimized DBN (ODBN) model is presented for maximizing the accurateness of DBN by optimizing the learning variables depends on the Ant Lion Optimization (ALO) algorithm. With learning ODBN, the most relevant features are extracted with reduced computational complexity. After that, the CNN-LSTM with Unsupervised Fine-tuned Deep Self-Organizing Map (UFDSOM)-based classifier model is designed to categorize the extracted features into categories of illnesses. In this novel classifier, dropout normalization and parameter tuning processes are applied to avoid overfitting and optimize the hyper-parameters, which results in a less training period. In the end, studies utilizing publically accessible datasets show that the ODBN with CNN-LSTM-UFDSOM system outperforms classical models by 98.23%. [ABSTRACT FROM AUTHOR]

Meiotic recombination has a crucial role in the biological process involving double-strand DNA breaks. Recombination hotspots are regions with a size varying from 1 to 2 kb, which is closely related to the double-strand breaks. With the increasement of both sperm data and population data, it has been demonstrated that computational methods can help us to identify the recombination spots with the advantages of time-saving and cost-saving compared to experimental verification approaches. To obtain better identification performance and investigate the potential role of various DNA sequence-derived features in building computational models, we designed a computational model by extracting features including the position-specific trinucleotide propensity (PSTNP) information, the electron-ion interaction potential (EIIP) values, nucleotide composition (NC) and dinucleotide composition (DNC). Finally, the supporting vector machine (SVM) model was trained by using the 172-dimensional features selected by means of the F -score feature ranking mode, and the accuracy of the predictor reached 98.24% in the jackknife test, which elucidates this model is a potential way for identifying recombination spots. [ABSTRACT FROM AUTHOR]

OBJECTIVE: A representation of the sound recordings that are associated with the movement of the entire cardiac structure is termed the Phonocardiogram (PCG) signal. In diagnosing such diverse diseases of the heart, PCG signals are helpful. Nevertheless, as recording PCG signals are prone to several surrounding noises and other disturbing signals, it is a complex task. Thus, prior to being wielded for advanced processing, the PCG signal needs to be denoised. This work proposes an improved heart sound classification by utilizing two-stage Low pass filtering and Wavelet Threshold (WT) technique with subsequent Feature Extraction (FE) using Wavelet Scatter Transform and further classification utilizing the Cubic Polynomial Support Vector Machine (SVM) technique for CVD. METHOD: A computer-aided diagnosis system for CVD detection centered on PCG signal analysis is offered in this work. Initially, by heavily filtering the signal, the raw PCG signals obtained using the database were pre-processed. Then, to remove redundant information and noise, it is denoised via the WT technique. From the denoised PCG, wavelet time scattering features were extracted. After that, by employing SVMs, these features were classified for pathology. RESULTS: For the analysis, the PCG signal obtained from the Physionet dataset was considered. Heavy low-pass filtering utilizing a Low-Pass Butterworth Filter (LPBF) is entailed in the pre-processing step. This removed 98% of the noise inherently present in the signal. Further, the signal strength was ameliorated by denoising it utilizing the WT technique. Promising results with maximum noise removal of up to 99% are exhibited by the method. From the PCG, Wavelet Scattering (WS) features were extracted, which were later wielded to categorize the PCG utilizing SVMs with 99.72% accuracy for different sounds. DISCUSSION: The Classification accuracies are analogized with other classification techniques present in the literature. This technique exhibited propitious outcomes with a 3% improvement in the F1 score when weighed against the top-notch techniques. The improvement in the metrics is attributed to the usage of the pre-processing stage comprising of Low-pass filter and WT method, WS Transform (WST), and SVMs. CONCLUSION: The superiority of the proposed technique is advocated by the comparative investigation with prevailing methodologies. The system revealed that Coronary Artery Disease (CAD) can be implemented with superior methods to achieve high accuracy. [ABSTRACT FROM AUTHOR]

Background: In pursuit of diagnostic tools capable of targeting distinct stages of Alzheimer's disease (AD), this study explores the potential of electroencephalography (EEG) combined with machine learning (ML) algorithms to identify patients with mild or moderate AD (ADM) and advanced AD (ADA). Objective: This study aims to assess the classification accuracy of six classical ML algorithms using a dataset of 668 patients from multiple hospitals. Methods: The dataset comprised measurements obtained from 668 patients, distributed among control, ADM, and ADA groups, collected from five distinct hospitals between 2011 and 2022. For classification purposes, six classical ML algorithms were employed: support vector machine, Bayesian linear discriminant analysis, decision tree, Gaussian Naïve Bayes, K-nearest neighbor and random forest. Results: The RF algorithm exhibited outstanding performance, achieving a remarkable balanced accuracy of 93.55% for ADA classification and 93.25% for ADM classification. The consistent reliability in distinguishing ADA and ADM patients underscores the potential of the EEG-based approach for AD diagnosis. Conclusions: By leveraging a dataset sourced from multiple hospitals and encompassing a substantial patient cohort, coupled with the straightforwardness of the implemented models, it is feasible to attain notably robust results in AD classification. [ABSTRACT FROM AUTHOR]

Background: To date, the reliable detection of mild cognitive impairment (MCI) remains a significant challenge for clinicians. Very few studies investigated the sensitivity of acoustic features in detecting Mandarin-speaking elders at risk for MCI, defined as "putative MCI" (pMCI). Objective: This study sought to investigate the possibility of using automatically extracted speech acoustic features to detect elderly people with pMCI and reveal the potential acoustic markers of cognitive decline at an early stage. Methods: Forty-one older adults with pMCI and 41 healthy elderly controls completed four reading tasks (syllable utterance, tongue twister, diadochokinesis, and short sentence reading), from which acoustic features were extracted automatically to train machine learning classifiers. Correlation analysis was employed to evaluate the relationship between classifier predictions and participants' cognitive ability measured by Mini-Mental State Examination 2. Results: Classification results revealed that some temporal features (e.g., speech rate, utterance duration, and the number of silent pauses), spectral features (e.g., variability of F1 and F2), and energy features (e.g., SD of peak intensity and SD of intensity range) were effective predictors of pMCI. The best classification result was achieved in the Random Forest classifier (accuracy = 0.81, AUC = 0.81). Correlation analysis uncovered a strong negative correlation between participants' cognitive test scores and the probability estimates of pMCI in the Random Forest classifier, and a modest negative correlation in the Support Vector Machine classifier. Conclusions: The automatic acoustic analysis of speech could provide a promising non-invasive way to assess and monitor the early cognitive decline in Mandarin-speaking elders. [ABSTRACT FROM AUTHOR]

BACKGROUND: Hyperspectral brain tissue imaging has been recently utilized in medical research aiming to study brain science and obtain various biological phenomena of the different tissue types. However, processing high-dimensional data of hyperspectral images (HSI) is challenging due to the minimum availability of training samples. OBJECTIVE: To overcome this challenge, this study proposes applying a 3D-CNN (convolution neural network) model to process spatial and temporal features and thus improve performance of tumor image classification. METHODS: A 3D-CNN model is implemented as a testing method for dealing with high-dimensional problems. The HSI pre-processing is accomplished using distinct approaches such as hyperspectral cube creation, calibration, spectral correction, and normalization. Both spectral and spatial features are extracted from HSI. The Benchmark Vivo human brain HSI dataset is used to validate the performance of the proposed classification model. RESULTS: The proposed 3D-CNN model achieves a higher accuracy of 97% for brain tissue classification, whereas the existing linear conventional support vector machine (SVM) and 2D-CNN model yield 95% and 96% classification accuracy, respectively. Moreover, the maximum F1-score obtained by the proposed 3D-CNN model is 97.3%, which is 2.5% and 11.0% higher than the F1-scores obtained by 2D-CNN model and SVM model, respectively. CONCLUSION: A 3D-CNN model is developed for brain tissue classification by using HIS dataset. The study results demonstrate the advantages of using the new 3D-CNN model, which can achieve higher brain tissue classification accuracy than conventional 2D-CNN model and SVM model. [ABSTRACT FROM AUTHOR]

Traffic safety is directly affected by poor road conditions. Automating the detection of road defects allows improvements in the maintenance process. The identification of defects such as cracks and potholes can be done using computer vision techniques and supervised learning. In this paper, we propose the detection of cracks and potholes in images of paved roads using machine learning techniques. The images are subdivided into blocks, where Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor Filter's texture descriptors are used to extract features of the images. For the classification task, the Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Multi-Layer Perceptron (MLP) models are compared. We performed two experiments on a dataset built with images of Brazilian highways. In the first experiment, we obtained a F-measure of 75.16% when classifying blocks of images that have cracks and potholes, and 79.56% when comparing roads with defects and without defects. In the second experiment, a F-measure of 87.06% was obtained for the equivalent task. Thus, it is possible to state that the use of the techniques presented is feasible for locating faults in highways. [ABSTRACT FROM AUTHOR]

The diversity of attribute categories brings certain difficulties to data feature detection. In order to improve the accuracy and efficiency of feature detection, a hybrid attribute feature detection method for power system intelligent operation and maintenance big data based on improved random forest algorithm is proposed. Clustering processing power system intelligent operation and maintenance big data, based on data clustering results to reduce the characteristics of data mixed attributes, reduce the scale of data processing, and discretize the data mixed attributes; BP neural network is used to preprocess the results. Make corrections to improve the accuracy of feature detection, use the improved random forest algorithm to classify the data, and improve the convergence speed of the method. Finally, the support vector machine method is used to realize the feature detection of data mixed attributes. The experimental results show that the feature detection accuracy and efficiency of the method designed in this paper are high, and more features can be detected, which verifies its effectiveness. The method designed in this paper has the minimum RMSE value and the maximum value is only 0.12, which is far lower than the RMSE value of the improved spectral clustering algorithm and multi-domain feature extraction method, and has high detection accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the lack of thermal images and the difficulty of thermal feature extraction have led to low accuracy and efficiency in the fault diagnosis of circuit boards using thermal images. To address the problem, this paper presents a simple and efficient intelligent fault diagnosis method combined with computer vision, namely the bag-of-SURF-features support vector machine (BOSF-SVM). Firstly, an improved BOF feature extraction based on SURF is proposed. The preliminary fault features of the abnormally hot components are extracted by the speeded-up robust features algorithm (SURF). In order to extract the ultimate fault features, the preliminary fault features are clustered into K clusters by K-means and substituted into the bag-of-features model (BOF) to generate a bag-of-SURF-feature vector (BOSF) for each image. Then, all of the BOSF vectors are fed into SVM to train the fault classification model. Finally, extensive experiments are conducted on two homemade thermal image datasets of circuit board faults. Experimental results show that the proposed method is effective in extracting the thermal fault features of components and reducing misdiagnosis and underdiagnosis. Also, it is economical and fast, facilitating savings in labour costs and computing resources in industrial production. [ABSTRACT FROM AUTHOR]

A capability of cloud-based IDS in identifying complicated and anonymous attacks is rising in the current era. However, unwanted delays hinder the detection rate. A malicious user might utilize vast quantities of computational power. The cloud provides to perform attacks both within and without the cloud. Furthermore, there are major challenges for intrusion detection due to the ease of the cloud and also the continual restructuring and movement of cloud resources. Intruder detection, feature extraction, and data processing are all included in the novel optimization-based Intrusion Detection System (IDS) paradigm that will be presented in this study. Data normalization is used to first pre-process the input data. Then, appropriate feature extraction is carried out, including the extraction of (a) raw features, (b) statistical features, then (c) higher-order statistical features using suggested kurtosis. The detection phase is then applied to the retrieved features. A two-stage ensemble method is suggested for finding intruders in clouds. Random forest (RF), Support Vector Machine (SVM), optimal Neural Network (NN), and RNN make up the suggested ensemble technique. The RF, SVM, and Optimized NN algorithms are directly fed the collected features. The output of these classifiers is then provided to the RNN classifier (i.e.), RF output to RNN1, SVM output to RNN2, and optimized NN output to RNN3. Then, the weighted average of RNN 1, 2, and 3 is considered as the final output. A Self Adaptive Salp Swarm Optimization optimizes the weights of NN for exact detection (SA-SSO). Finally, a test is conducted to confirm the developed model's superiority. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cervical histopathology image classification is a crucial indicator in cervical biopsy results. OBJECTIVE: The objective of this study is to identify histopathology images of cervical cancer at an early stage by extracting texture and morphological features for the Support Vector Machine (SVM) classifier. METHODS: We extract three different texture features and one morphological feature of cervical histopathology images: first-order histogram, K-means clustering, Gray Level Co-occurrence Matrix (GLCM) and nucleus feature. The original dataset used in our experiment is obtained from 20 patients diagnosed with cervical cancer, including 135 whole slide images (WSIs). Given an entire WSI, the patches on its tissue region are extracted randomly. RESULTS: We finally obtain 3,000 patches, including 1,000 normal, 1,000 hysteromyoma and 1,000 cancer images. Among them, 80% of the entire data set is randomly selected as training set and the remaining 20% as test set. The accuracy of SVM classification using first-order histogram, K-means clustering, GLAM and nucleus feature for extracting features are respectively 87.4%, 90.6%, 91.6% and 93.5%. CONCLUSIONS: The classification accuracy of the SVM combining the four features is 96.8%, and the proposed nucleus feature plays a key role in the SVM classification of cervical histopathology images. [ABSTRACT FROM AUTHOR]

PURPOSE: To investigate the value of a CT-based radiomics model in identification of Crohn's disease (CD) active phase and remission phase. METHODS: CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS: Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION: This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance. [ABSTRACT FROM AUTHOR]

In the context of the explosive growth of online reviews in e-commerce, some merchants began to hire consumers to make fake purchases to increase sales, which caused the difficulty in identifying fake reviews. Therefore, from the perspective of consumers' shopping behavior, a feature recognition method based on comment text, reviewer attribute and reviewer's shopping behavior (referred to as TAB) is proposed to solve the problem of fake reviews caused by fake shopping behavior. Compared with BOW, TF-IDF, N-gram, FSP and other traditional feature extraction methods, experiments were carried out on Naive Bayes, SVM, Logistic Regression, Random Forest, CNN and other classification models. By changing the data dimension, the stability of each false comment recognition model was analyzed. The experimental results show that the TAB feature recognition method has better classification performance on logistic regression and support vector machine models, and the model is relatively stable, and the classification effect is not easily affected by dimension change. [ABSTRACT FROM AUTHOR]

Coronary artery diseases are one of the high-risk diseases, which occur due to the insufficient blood supply to the heart. The different types of plaques formed inside the artery leads to the blockage of the blood stream. Understanding the type of plaques along with the detection and classification of plaques supports in reducing the mortality of patients. The objective of this study is to present a novel clustering method of plaque segmentation followed by wavelet transform based feature extraction. The extracted features of all different kinds of calcified and sub calcified plaques are applied to first train and test three machine learning classifiers including support vector machine, random forest and decision tree classifiers. The bootstrap ensemble classifier then decides the best classification result through a voting method of three classifiers. A training dataset including 64 normal CTA images and 73 abnormal CTA images is used, while a testing dataset consists of 111 normal CTA images and 103 abnormal CTA images. The evaluation metrics shows better classification rate and accuracy of 97.7%. The Sensitivity and Specificity rates are 97.8% and 97.5%, respectively. As a result, our study results demonstrate the feasibility and advantages of developing and applying this new image processing and machine learning scheme to assist coronary artery plaque detection and classification. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is one of the most common diabetic diseases that affect the retina's blood vessels. Too much of the glucose level in blood leads to blockage of blood vessels in the retina, weakening and damaging the retina. Automatic classification of diabetic retinopathy is a challenging task in medical research. This work proposes a Mixture of Ensemble Classifiers (MEC) to classify and grade diabetic retinopathy images using hierarchical features. We use an ensemble of classifiers such as support vector machine, random forest, and Adaboost classifiers that use the hierarchical feature maps obtained at every pooling layer of a convolutional neural network (CNN) for training. The feature maps are generated by applying the filters to the output of the previous layer. Lastly, we predict the class label or the grade for the given test diabetic retinopathy image by considering the class labels of all the ensembled classifiers. We have tested our approaches on the E-ophtha dataset for the classification task and the Messidor dataset for the grading task. We achieved an accuracy of 95.8% and 96.2% for the E-ophtha and Messidor datasets, respectively. A comparison among prominent convolutional neural network architectures and the proposed approach is provided. [ABSTRACT FROM AUTHOR]

Brain-Computer Interface provides and simplifies the communication channel for the physically disabled individuals suffering from severe brain injury related to brain stroke and lost ability to speak. It helps these patients to connect with the outside world. In the proposed work, the electroencephalogram signal is used as an input source taken from Bonn University database that is divided into three class of data consisting of 247 samples each. It is further processed by Tunable Q-Wavelet Transform signal decomposition technique where the signals are subdivided into various sub-bands depending on the value of Q-factor, redundancy factor, and number of sub-bands. A novel custom technique uses Q-factor of 3, redundancy value of 3 & 12 number of sub-bands for high pass filtering as well as Q-factor of 1, redundancy value of 3 & 7 number of sub-bands for low pass filtering combined with nine statistical measures for feature extraction purpose. The classification is performed by using multi-class support vector machine giving the accuracy of 99.59%. The accuracy performs best when compared with the existing research results Furthermore, the comparative study has been performed on the same dataset by using deep neural network along with support vector machine giving an accuracy of 100%. Other evaluation parameters such as precision, sensitivity, specificity, and F1 score are also calculated. The classified data help transform the signal into three communication messages that will help solve the speech impairment of disabled individuals. [ABSTRACT FROM AUTHOR]

Computational aesthetics, which uses computers to learn human aesthetic habits and ultimately replace humans in scoring images, has become a hot topic in recent years due to its wide application. Most of the initial research is to manually extract features and use classifiers such as support vector machines to score images. With the development of deep learning, traditional manual feature extraction methods are gradually replaced by convolutional neural networks to extract more comprehensive features. However, it is a huge challenge to artificially design an aesthetic neural network. Recently, Neural Architecture Search has upsurged to find suitable neural networks for many tasks in deep learning. In this paper, we first attempt to combine Neural Architecture Search with computational aesthetics. We design and apply a customized progressive differentiable architecture search strategy to obtain a light-weighted and efficient aesthetic baseline model. In addition, we simulate the multi-person rating mechanism by outputting the distribution of the aesthetic value of the image, replacing the previous classification scheme of judging the beauty and unbeauty of the image by the threshold value, and propose a self-weighted Earth Mover's Distance loss to better fit human subjective scoring. Based on the baseline model, we further introduce several strategies including an attention mechanism, the dilated convolution, and adaptive pooling, to enhance the performance. Finally, we design several groups of comparative experiments to demonstrate the effectiveness of our baseline aesthetic model and the introduced improvement strategies. [ABSTRACT FROM AUTHOR]

The correct identification of gene recombination cold/hot spots is of great significance for studying meiotic recombination and genetic evolution. However, most of the existing recombination spots recognition methods ignore the global sequence information hidden in the DNA sequence, resulting in their low recognition accuracy. A computational predictor called iRSpot-DCC was proposed in this paper to improve the accuracy of cold/hot spots identification. In this approach, we propose a feature extraction method based on dinucleotide correlation coefficients that focus more on extracting potential DNA global sequence information. Then, 234 representative features vectors are filtered by SVM weight calculation. Finally, a convolutional neural network with better performance than SVM is selected as a classifier. The experimental results of 5-fold cross-validation test on two standard benchmark datasets showed that the prediction accuracy of our recognition method reached 95.11%, and the Mathew correlation coefficient (MCC) reaches 90.04%, outperforming most other methods. Therefore, iRspot-DCC is a high-precision cold/hot spots identification method for gene recombination, which effectively extracts potential global sequence information from DNA sequences. [ABSTRACT FROM AUTHOR]

The grade and stage of bladder tumors is an essential key for diagnosing and treating bladder cancer. This study proposed an automated bladder tumor prediction system to automatically assess the bladder tumor grade and stage automatically on Magnetic Resonance Imaging (MRI) images. The system included three modules: tumor segmentation, feature extraction and prediction. We proposed a U-ResNet network that automatically extracts morphological and texture features for detecting tumor regions. These features were used in support vector machine (SVM) classifiers to predict the grade and stage. Our proposed method segmented the tumor area and predicted the grade and stage more accurately compared to different methods in our experiments on MRI images. The accuracy of bladder tumor grade prediction was about 70%, and the accuracy of the data set was about 77.5%. The extensive experiments demonstrated the usefulness and effectiveness of our method. [ABSTRACT FROM AUTHOR]

Mixture production equipment is widely employed in road construction, and the quality of the produced mixture is the essential factor to ensure the quality of road construction. To detect the quality of the real-time produced mixture and solve the shortcomings of laboratory detection lag, a new fault detection method in the mixture production process is proposed, which is based on wavelet packet decomposition (WPD) and support vector machine (SVM). The proposed scheme includes feature extraction, feature selection, SVM classification, and optimization algorithm. During feature extraction, wavelet basis function is utilized to 4-layer decompose the aggregate and asphalt data mixed in real-time. The energy value calculated by wavelet packet coefficient is the extracted feature. During feature selection, a method combining the chi-square test and wrapper (CSW) is conducted to select the optimal feature subset from WPD features. Eventually, by adopting the optimal feature subset, SVM has been developed to classify various faults. Its parameters are optimized by differential evolution (DE) algorithm. In the test stage, multiple faults of different specifications of aggregates and asphalt are detected in the mixture production process. The results demonstrate that (1) accuracy produced by the CSW method with WPD features is 4.33% higher than the PCA method with statistical features; (2) SVM classification method optimized by DE algorithm brings an increase in recognition accuracy of identifying different types of mixture production faults produced by different equipment. Compared to other available methods, the proposed algorithm has a very outstanding detection performance. [ABSTRACT FROM AUTHOR]

Facial emotion recognition system (FERS) recognize the person's emotions based on various image processing stages including feature extraction as one of the major processing steps. In this study, we presented a hybrid approach for recognizing facial expressions by performing the feature level fusion of a local and a global feature descriptor that is classified by a support vector machine (SVM) classifier. Histogram of oriented gradients (HoG) is selected for the extraction of global facial features and local intensity order pattern (LIOP) to extract the local features. As HoG is a shape-based descriptor, with the help of edge information, it can extract the deformations caused in facial muscles due to changing emotions. On the contrary, LIOP works based on the information of pixels intensity order and is invariant to change in image viewpoint, illumination conditions, JPEG compression, and image blurring as well. Thus both the descriptors proved useful to recognize the emotions effectively in the images captured in both constrained and realistic scenarios. The performance of the proposed model is evaluated based on the lab-constrained datasets including CK+, TFEID, JAFFE as well as on realistic datasets including SFEW, RaF, and FER-2013 dataset. The optimal recognition accuracy of 99.8%, 98.2%, 93.5%, 78.1%, 63.0%, 56.0% achieved respectively for CK+, JAFFE, TFEID, RaF, FER-2013 and SFEW datasets respectively. [ABSTRACT FROM AUTHOR]

Sports athletes not only exercise fast, but also suffer from the surrounding complex environment. Therefore, the video needs to be sequenced to improve processing efficiency. From the perspective of machine learning, this paper designs a spatial feature extractor based on CNN to extract time series features of sports. Moreover, this paper uses the support vector machine as the basis of the construction model to construct a feature extraction model based on support vector machine and random forest based on different situations. At the same time, this paper collects test data through the sports database and uses the swimming project as an example to analyze the model performance. Finally, the paper verifies the validity of the model by comparing and verifying methods. The research indicates that the proposed method has certain effectiveness and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

This paper presents an optimized quantum Grey Wolf Optimization algorithm (qGWO), which is an enhanced version of the Grey Wolf optimization algorithm for feature selection of blood cells, which can further used for the detection of WBCs. White blood cells count in the human body determines the immune system of the human body. A deviation in the count of WBCs from the normal cell count in the human body may indicate an abnormal condition. The proposed model uses a quantum grey wolf optimization algorithm for the detection of White Blood cells among the dataset of various types of blood cells. Quantum Grey Wolf algorithm is used to find the minimal set of optimal features from the set of available features to detect the White Blood Cells optimally. As the ordinary Grey Wolf Optimization algorithm also used to find the minimal set of optimal features, but the features selected by qGWO are better in terms of computational time. Further, several classification algorithms such as Support Vector Machine (SVM), Random Forest algorithm, K Nearest Neighbor(KNN) algorithm are applied to the model to predict its accuracy for the selected subset of features after feature selection. The performance of several classifiers is compared, and the model attained the maximum accuracy of 97.8% using KNN with minimum computational time. The result obtained shows that the algorithm proposed is capable of finding an optimal subset of features and maximizing the accuracy. [ABSTRACT FROM AUTHOR]

Although much less fatal than the Ebola and previous SARS virus epidemics, the current coronavirus outbreak (COVID-19) has spread to more people in more countries in a much shorter time frame. With the rapid development of the Internet of things, it has played an important role to track/monitor transmission movements throughout the population. The technology infrastructure between mobile devices, wearable devices and sensors, smart home device makes it possible to readily deploy solutions to monitor and collect data and perform analysis to ensure policy make intelligent, rapid decisions. This research combines AOL and Support Vector Machine to form the Internet of things cycle through smart home. The parameters of Support Vector Machine model are optimized by ALO algorithm, which shortens the learning time and improves the performance of classifier. Then, the algorithm of ALO is used to optimize the Support Vector Machine intrusion detection method and agent technology, and the intrusion detection model is established. Experimental results show that the combination of these two can effectively reduce the false alarm rate of network intrusion. [ABSTRACT FROM AUTHOR]

Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects. [ABSTRACT FROM AUTHOR]

Image recognitionan is an important part of pattern recognition, It has been widely used in modern production and life. Using computer technology and modern information processing technology to complete human visual cognition and understanding. Thus, It have become a hot topic about how to study image recognition based on medical color feature extraction. This study firstly studied the existing literature, explored and analyzed relevant theories and technologies, and studied several algorithms related to current image recognition. Then the image recognition algorithm based on wavelet moment and support vector machine is combined with the artificial intelligence technology based on image feature extraction theory to establish the color medical image recognition algorithm based on wavelet moment and support vector machine. In order to verify the feasibility and advancedness of the new algorithm, practical experiments are carried out, and the experimental results are compared and analyzed by statistical method. The concrete chart proves the correctness of the conclusion. The final of the new algorithm is proved to be successful. [ABSTRACT FROM AUTHOR]

A fuzzy support vector machine kernel regression decision method of reliability probability distributions is presented aiming at the complexity of reliability probability distributions and disadvantage of the other regression model. The comprehensive decision model of probability distributions is built by the network design and feature extraction of the fuzzy support vector machine algorithm. A example is give for inward stress probability distribution type of a stem structural member by the model, the recognition result is Weibull distribution, the total recognition rate achieves 98.75%. The fuzzy support vector optimized algorithm has strong ability of nonlinear mapping and functional approach, it avoids availably partial minimum and overfitting, and gains high precision by comparing the numerical value of the network output with the numerical value of experiment. [ABSTRACT FROM AUTHOR]

Small-target categorization of butterflies suffers from large-scale search space of candidate target locations, subtler discriminations, camouflaged appearances, and complex backgrounds. Precise localization and domain-specific discrimination extraction are crucial for this issue. In this work, a novel hierarchical coarse-to-fine convolutional neural network (C-t-FCNN) was proposed. It consists of CoarseNet and FineNet, which incorporate object-level and part-level representations into framework. Specifically, the coarse-grained features containing the orientation description are generated by CoarseNet, while the fine-grained discriminations with semantic distinctiveness are captured by FineNet. Next, the correspondences are established to mark the target regions, background regions, and mismatched regions depending on the quantification of scale-invariant feature transform (SIFT) descriptors. Then, the features are subsampled via spatial pyramid pooling (SPP) for size uniformity and integration. Finally, the irrelevant background and mismatched regions are eliminated by the support vector machine (SVM) with a radial basis function (RBF) kernel, leaving only the target-specific patches for finer-scale extraction. Hence the numeracy can be economized from identifying irrelevant areas and can be rescheduled in feature extraction and final decision, which can suppress time complexity simultaneously. A total of 119,016 augmented butterfly images spanning 47 categories are utilized for model training, while 13,734 images are evaluated for effectiveness verification. The C-t-FCNN delivers impressive performance, i.e., it achieves a validation accuracy of 92.08% and a testing accuracy of 91.6%, which outperforms state-of-the-arts. [ABSTRACT FROM AUTHOR]

In this research work, machine learning techniques are used to classify magnetic resonance imaging brain scans of people with Alzheimer's disease. This work deals with binary classification between Alzheimer's disease and cognitively normal. Supervised learning algorithms were used to train classifiers in which the accuracies are being compared. The database used is from The Alzheimer's Disease Neuroimaging Initiative (ADNI). Histogram is used for all slices of all images. Based on the highest performance, specific slices were selected for further examination. Majority voting and weighted voting is applied in which the accuracy is calculated and the best result is 69.5% for majority voting. [ABSTRACT FROM AUTHOR]

Rich image information is one of the important means through which unmanned surface vehicles effectively and reliably identify targets during autonomous navigation. However, the adaptability of traditional artificial design feature methods in target representation and differentiation remains limited due to the diversity of ship target types, different scales, and complex and dynamic outdoor scenes. This study proposed a ship target recognition method based on single shot multi-box detector (SSD) deep learning. First, training and test sample sets were constructed by acquiring and creating a ship's target image and background image under different types and scenes in an actual river environment. Subsequently, the sample set was used to train and optimize the SSD depth model to achieve adaptive extraction and recognition of target features. Lastly, ship identification experiments with different background environments and foreground targets were performed to test the effectiveness of the proposed method. The support vector machine method based on artificial feature extraction was used for the comparative experiments. Experiment results showed that the SSD-based deep learning method achieved better results than the artificial design feature method in terms of recall and precision rates. [ABSTRACT FROM AUTHOR]

Several computer algorithms for recognition of visible human emotions are compared at the web camera scenario using CNN/MMOD face detector. The recognition refers to four face expressions: smile, surprise, anger, and neutral. At the feature extraction stage, the following three concepts of face description are confronted: (a) static 2D face geometry represented by its 68 characteristic landmarks (FP68); (b) dynamic 3D geometry defined by motion parameters for eight distinguished face parts (denoted as AU8) of personalized Candide-3 model; (c) static 2D visual description as 2D array of gray scale pixels (known as facial raw image). At the classification stage, the performance of two major models are analyzed: (a) support vector machine (SVM) with kernel options; (b) convolutional neural network (CNN) with variety of relevant tensor processing layers and blocks of them. The models are trained for frontal views of human faces while they are tested for arbitrary head poses. For geometric features, the success rate (accuracy) indicate nearly triple increase of performance of CNN with respect to SVM classifiers. For raw images, CNN outperforms in accuracy its best geometric counterpart (AU/CNN) by about 30 percent while the best SVM solutions are inferior. For F-score the high advantage of raw/CNN over geometric/CNN and geometric/SVM is observed, as well. We conclude that contrary to CNN based emotion classifiers, the generalization capability wrt human head pose for SVM based emotion classifiers, is worse too. [ABSTRACT FROM AUTHOR]

In recent years, action recognition techniques have played an increasingly important role in autonomous systems. However, the computational costs and precision of action recognition algorithms are still major challenges. Recently, a deep learning approach was proposed to obtain a higher accuracy, but large and deep neural networks have high computational costs. This paper presents a new approach that allows for a significant reduction in computational time while slightly increasing the accuracy. The contribution consists of two parts: a scalable feature extraction method (SFE) and a hybrid model of different classifiers. First, the SFE method is proposed for application to histogram orientation-based feature descriptors, such as the histogram of orientated gradient (HOG), histogram of optical flow (HOF), and the motion boundary histogram (MBH). An advantage of SFE is its ability to quickly compute features. Scalable feature extraction enables accurate approximation of features extracted from traditional image pyramids by efficiently using only the original image. Our method is inspired by a special data structure used for storing basic information of optical flow and image gradients, which are computed from the original image and then used to extract features across multiple scales of the feature region without recomputing the image gradients and optical flow. Second, we focus on a hybrid classification method based on a linear support vector machine (SVM) and hidden conditional random field (HCRF) model that improves the recognition precision. This effort shows that a combination of SVM and HCRF models provides a better accuracy than the traditional approaches. Experimental results illustrate that the proposed approach allows for both a significant reduction in computational time and an improved accuracy. [ABSTRACT FROM AUTHOR]

Human activity recognition emerges as one of the prominent research areas in the recent past. However, the activity recognition still encounters many challenges like reliability of sensor data and accuracy of prediction that severely affects the aspect of decision making. In this paper, a futuristic framework has been proposed and experimented to build a precision-centric activity recognition method by analyzing the data obtained from Environment Monitoring System (EMS) and Personalized Positions Detection System (PPDS) using machine learning methods such as AdaBoost, Support Vector Machine (SVM) and Probabilistic Neural Networks (PNN). Further, the proposed approach utilizes the Dempster-Shafer Theory (DST)-based complete sensor data fusion thereby improving the global activity recognition performance. Finally, the proposed approach is validated using a real-world dataset obtained from UCI machine learning repository. The results conclude that the proposed activity recognition framework outperforms its existing context/situation-awareness approaches in terms of reliability, efficiency, and accuracy. [ABSTRACT FROM AUTHOR]

Feature extraction is an important preprocessing step in many research areas. For anomaly detection, the purpose of feature extraction lies in not only extracting the most important features hidden in the datasets, but also discriminating different classes of samples. The latter is usually referred to as discriminative ability. The data collected from production systems usually do not follow Gaussian distribution. They may correspond to nonlinear mixture of independent components. In order to cope with non-Gaussian data and implement nonlinear feature extraction, this article proposes a feature extraction algorithm based on Supervised Independent Component Analysis with Kernel (termed SKICA). SKICA first adopts Kernel Principle Component Analysis (KPCA) to whiten the datasets. Further, by virtue of the within-cluster scatter matrix derived from Linear Discriminate Analysis (LDA), SKICA extends Independent Component Analysis (ICA) to supervised situation by introducing within-cluster information into solving independent components. The latter improvement makes SKICA obtain the independent components more beneficial to separating different classes of samples. In order to quantitatively measure discriminative ability of the feature extraction algorithms involved in experiments, this article defines three kinds of average square distance. This article conducts experiments on artificial datasets, Cloud datasets, and KDD Cup datasets to evaluate the effectiveness of SKICA. The experimental results show that SKICA outperforms several popular supervised feature extraction algorithms, including LDA, LDA with kernel (KDA), and supervised ICA (SICA). [ABSTRACT FROM AUTHOR]

This paper presents a novel binary classifier based on two best fitting hyperellipsoids in the feature space, called twin-hyperellipsoidal support vector machine (TESVM). The idea of TESVM is inspired by the minimum volume covering ellipsoid together with twin-hypersphere support vector machine (THSVM) which is a variant of the well-known support vector data description (SVDD). Following the concept of THSVM, TESVM constructs two hyperellipsoids where each hyperellipsoid is closest to one class but also as far as possible from the other class in order to form a decision boundary. The construction of hyperellipsoids in the feature space is also enabled through the use of empirical feature mapping. The experimental results on several artificial as well as standard real-world datasets are provided to demonstrate the performance of TESVM. Particularly, TESVM outperforms its spherical counterpart in term of classification accuracy. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel method to process crowds and partially occluded pedestrians during single-image pedestrian detection. First, two procedures are proposed and developed to extract features at different body parts of pedestrian from images. One procedure uses the multiscale block-based histogram of oriented gradients, which is preprocessed via Gabor filtering, to effectively enhance the descriptions of features of pedestrians’ heads and bodies. The other involves modifying the Haar-like features as parallelogram-based Haar-like features to suitably depict pedestrians’ legs and arms, which are typically not straight while walking. In addition, the computations of features are expedited through integral image acceleration for these two extraction methods. After the pedestrian features are acquired, a two-tier support vector machine (SVM) classifier is proposed for processing partially occluded pedestrians. The first-tier SVM classifier is used to judge if there are any occluded body parts. Next, classification probabilities of nonoccluded body parts in first-tier one are input into the second-tier classifier to determine if there are any pedestrians in the detection window. Compared with six state-of-the-art approaches, the experimental results indicate that the proposed method is more accurate and satisfactory in terms of the receiver operating characteristic curve and four other criteria. Additionally, our method effectively processes images in which a crowd is present or pedestrians are partially occluded and enables pedestrian detection in images of different scenes. [ABSTRACT FROM AUTHOR]

Fatigue will affect the normal work and even cause accidents. In order to reduce fatigue's impact on people, we proposes a method for providing a real-time fatigued detection.Specifically, this method comprise the following steps. Firstly of all, we use Active Shape Model (ASM) to detect face, extract the Histogram of Orientation Gradient (HOG) features of eyes and mouth. Secondly, we use Support Vector Machine (SVM) to classify the states and Pose from Orthography and Scaling with Iterations (POSIT) algorithm to estimate the poses of the head. Thirdly, based on the states of face, we obtain a fatigue decision index, wherein a weight of the fatigue decision index is calculated by the Entropy-weighting method. Finally, we apply Bayesian method to evaluate driver's fatigued level based on calculated fatigue decision index. The final mean accuracy of this method is 83.3%. [ABSTRACT FROM AUTHOR]

This paper presents the design and performance evaluation of a host-based misuse intrusion detection system for the Linux operating system. The proposed system employs a feature extraction technique based on principal component analysis, which is called Eigentraces, of operating system call trace data, and k-nearest neighbor algorithm for classification. The design is evaluated on the ADFA-LD dataset which entails one normal and six attack classes. Feature vectors are formed from fixed-size system call trace raw data through windowing and the principal component analysis, and serve as templates for the training phase. Classification of system call trace data that is in the form of feature vectors which are formulated through the Eigentraces procedure is accomplished using the k-nearest-neighbor algorithm. Two variants of the misuse intrusion detection system designs were evaluated through a simulation study on the ADFA-LD dataset: one design considered only two classes, namely normal and attack classes while the second design considered seven classes, namely one normal and six attack classes. In both cases the proposed design demonstrated very high performance. In overall, the misuse intrusion detection system was able to detect the attacks and predict the type of the attacks. [ABSTRACT FROM AUTHOR]

To overcome the low diagnosis accuracy caused by the scarcity of labeled training samples, a fault diagnosis method was proposed using orthogonal Semi-supervised linear local tangent space alignment (OSSLLTSA) for feature extraction and transductive support vector machine (TSVM) for fault identification. Through extracting the statistical features were extracted from the sub-bands of vibration signals decomposed by wavelet packet decomposition (WPD), the high-dimensional feature set could be obtained. Following that, the improved kernel space distance evaluation method was applied to remove non-sensitive fault features. Then, a semi-supervised manifold learning method (OSSLLTSA) was proposed to reduce the dimensionality of the fault feature set, and thus to extract fused fault features with high clustering performance. OSSLLTSA overcomes the over-learning of supervised manifold learning and projection aimlessness of unsupervised manifold learning. Finally, the low-dimensional feature set after dimension reduction was inputted into TSVM for fault diagnosis. TSVM was able to completely utilize the fault information contained in unlabelled samples to modify the model, and the trained fault diagnosis model has better generalization ability. The effectiveness of the proposed method was verified based on the case of gearbox fault. Experimental results showed that the proposed method is able to achieve very high fault diagnosis accuracy even when labeled samples were insufficient. [ABSTRACT FROM AUTHOR]

Eye detection plays an important role in many fields, because eyes provide prominent facial feature information. However, changes in face pose, illumination variation, with glasses, and eye occlusions can make it difficult to detect eyes well from facial images. This paper proposes a hybrid model for eye detection. The model is an integration of two classifiers: Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). In order to improve the speed of detection in the system, an eye variance filter (EVF) is constructed for eliminating most of noneye images to keep less candidate eye images. The CNN then works as a trainable feature extractor to explicitly extract various latent eye features. Finally, the trained SVM classifier is employed for eye verification instead of using the CNN classification function. Experiments applying the model have been conducted on the BioID, IMM, FERET and ORL face databases. Comparisons with other methods on the same databases indicate that this hybrid model has achieved a higher detection accuracy. Extensive experiments demonstrate the robustness and efficiency of our method by testing it on different facial images with varying eye conditions. [ABSTRACT FROM AUTHOR]

BACKGROUND: The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. OBJECTIVE: To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. METHODS: A window-based data acquisition method was presented to extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. RESULTS: The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. CONCLUSIONS: The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals' energy appropriately. The classical machine learning classifiers all performed well by using these features. [ABSTRACT FROM AUTHOR]

To develop the human-centric driver fatigue monitoring system for automatic understanding and charactering of driver's conditions, a novel, efficient feature extraction approach, named Local Multiresolution Derivative Pattern (LMDP), is proposed to describe the driver's fatigue expression images, and the Intersection Kernel Support Vector Machines classifier is then exploited to recognize three pre-defined classes of fatigue expressions, i.e., awake expressions, moderate fatigue expressions and severe fatigue expressions. With features extracted from a fatigue expressions dataset created at Southeast University, the holdout and crossvalidation experiments on fatigue expressions classification are conducted by the Intersection Kernel Support Vector Machines classifier, compared with three commonly used classification methods including the k-nearest neighbor classifier, the multilayer perception classifier and the dissimilarity-based classifier. The experimental results of holdout and cross-validation showed that LMDP offers the better performance than Local Derivative Pattern, and the second order LMDP exceeds other order LMDP.With the second order LMDP and the Intersection Kernel Support Vector Machines classifier, the classification accuracies of the severe fatigue are over 90% in the holdout and cross-validation experiments, thus demonstrating the effectiveness of the proposed feature extraction method in automatically understanding the driver's conditions towards the human-centric driver fatigue monitoring system. [ABSTRACT FROM AUTHOR]

Facial expressions classification is a fast growing research area. Lots of contribution has been made in this area by researchers from fields of computer science, computer vision, artificial intelligence and psychology. There are many applications that use facial expression classification to identify the behavior, emotion, feelings and opinion of a person. Facial expression classification is not a trivial task as there are many factors that need to be accounted like low quality of images, noise, and shape/color of image. In this article, we have proposed an efficient facial expression classification scheme. In the first step, we perform some pre-processing steps like face detection and histogram equalization inorder to reduce the data dimenions and normalize the illumination effects. Then, an efficient feature extraction technique is used to extract the relevant face features. In the last step, we train and test Support Vector Machine (SVM) classifier to classify the facial expressions. Emirical results obtained using the JAFFE database suggest that the proposed technique produces impressive results by utilzing the best facial features. [ABSTRACT FROM AUTHOR]

This paper studies pedestrian detection algorithm based on trapezoidal feature of local model, and applied to histogram intersection kernel support vector machine (HIKSVM) for verification. In comparison with traditional asymmetry, rectangle and triangle features, the experimental results from trapezoid feature of local model indicate that it can more effectively describe the pedestrian's postures and promote the accuracy and robustness of pedestrian detection. Experimental results show that the proposed algorithm is robust and accurate against cluttered dynamical background, occlusion and the object deformation, and tested in many pedestrian datasets and achieved good results. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is an important tool for assessing the quality of the insulation system in High Voltage (HV) power apparatus. In this work, four different PD sources namely corona and surface discharges in both air and oil are measured in the HV laboratory. Initially 3-D (φ-q-n) PD patterns are extracted from the PD data. Then it is subjected to two different fractal image compression techniques namely box counting method and semi variance method. For box counting method, the fractal dimensions like fractal dimension average, standard deviation and lacunarity are evaluated. For semi variance method, horizontal and vertical fractal dimension averages are evaluated. The extracted fractal features from 3-D PD patterns are used as input parameters for non linear Support Vector Machine (SVM) for PD recognition. The performance of non linear SVM is compared with Artificial Neural Network (ANN) and linear SVM classifiers. The non linear SVM with semi variance method provides outer performance as compared with other methods due to its gain flexibility and good out-of-sample generalization. [ABSTRACT FROM AUTHOR]

The characterisation and categorisation of weblogs and other short texts has become an important research theme in the areas of topic/trend detection, and pattern recognition, amongst others. The value of analysing and characterising short text is to understand and identify the features that can identify and distinguish them, thereby improving input to the classification process. In this research work, we analyse a large number of text features and establish which combinations are useful to discriminate between the different genres of short text. Having identified the most promising features, we then confirm our findings by performing the categorisation task using three approaches: the Gaussian and SVM classifiers and the K-means clustering algorithm. Several hundred combinations of features were analysed in order to identify the best combinations and the results confirmed the observations made. The novel aspect of our work is the detection of the best combination of individual metrics which are identified as potential features to be used for the categorisation process. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method for the detection of microcalcifications using mathematical morphology and a support vector machine (SVM). First, the contrast in the original mammogram was improved by gamma correction and two carefully designed structural elements were used to enhance any microcalcifications. Next, the potential regions were extracted using our proposed dual-threshold technique. Finally, a SVM classifier was used to reduce the number of false positives. The performance of the proposed method was evaluated using the MIAS database. The experimental results demonstrated the efficiency and effectiveness of our method. [ABSTRACT FROM AUTHOR]

In order to develop Human-centric Driver Fatigue Monitoring Systems (HDFMS) with aims to increase driving safety, an efficient combined features extraction from Curvelet transform and Gabor wavelet transform for fatigue expressions descriptions of vehicle drivers is proposed, and Random Subspace Ensemble (RSE) of Support Vector Machines (SVMs) with polynomial kernel as the base classifier is then exploited for classification of three predefined fatigue expressions classes, namely, awake expressions, moderate fatigue expressions, and severe fatigue expressions. The results of holdout and cross-validation experiments show that CF by RSE of SVMs with polynomial kernel outperforms other seven classifiers, i.e., Curvelet features by SVMs classifier, Gabor features by SVMs classifier, CF by five individual SVMs classifiers. With CF and RSE of SVMs with polynomial kernel, the classification accuracies of drivers' fatigue expressions are over 90% in both of the holdout and cross-validation experiments, which show the proposed approach of combined features extraction and RSE of SVMs can be used for developing Human-centric Driver Fatigue Monitoring Systems to increase driving safety. [ABSTRACT FROM AUTHOR]