Task-related population characteristics in handwriting analysis

An analysis of features extracted from handwriting samples according to writer demographics and writing task characteristics is presented. The individual demographics studied here include age, gender and handedness, while the handwriting tasks considered include writing the individual signature, form-filling, cheque-completion and constructing free-form written text. By analysing different features of handwriting, the authors establish a link between a writer's individual characteristics including demographic properties, the handwriting task being attempted and quantifiable features of handwriting such as pen velocity, acceleration and slant. Additionally, imitated or 'forged' handwriting is also analysed on exactly the same basis. The analysis is performed on a newly collected database of handwriting samples collected from a population of 150 writers, and which can be utilised in both forensic document inspection and automatic handwriting analysis research. All handwriting samples, including forgery attempts, were recorded both temporally as a series of pen positional coordinates and scanned at a resolution of 600 dpi to enable both dynamic and static processing.     

A Haze Feature Extraction and Pollution Level Identification Pre-Warning Algorithm

The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in fog and haze has been paid more and more attention, but the prediction accuracy of the results is not ideal. Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze. In order to improve the effects of prediction, this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning. Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze, and deep confidence network is utilized to extract high-level features. eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features, as well as predict haze. Establish PM2.5 concentration pollution grade classification index, and grade the forecast data. The expert experience knowledge is utilized to assist the optimization of the pre-warning results. The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine (SVM) and Back Propagation (BP) widely used at present, the accuracy has greatly improved compared with SVM and BP.     

Detection and classification of large bowel obstruction from X-ray images using machine learning algorithms

Large bowel obstruction (LBO) occurs when there is a blockage or twisting in the large bowel that prevents wastes and gas from passing through. If left untreated, the blockage cuts off blood supply to the colon, causing sections of it to die which results in high rates of morbidity and fatality. The examination of clinical symptoms of LBO involves careful inspection of the cecum and colon. Radiologists use X-rays to inspect the clinical signs. Some research has been done to automate the detection of related abdominal and intestinal diseases. However, all these studies concentrate only on detecting Crohn's, ulcerative colitis, Acute Appendicitis, colorectal cancer, celiac diseases, liver diseases, and chronic kidney diseases. Automatic detection and classification of LBO has not been given due attention so far to the best of the authors knowledge. To address this challenge, we have designed a model for the detection and classification of LBO. The models development comprises of stages such as preprocessing, detection, segmentation, feature extraction, and classification. We used YOLOv3 for detection and used a gray scale level co-occurrence matrix (GLCM), and a convolutional neural network for feature extraction, while support vector machine (SVM) and softmax were used for classification. The proposed model achieved a diagnostic accuracy of 89% when feature extraction methods such as CNN and median filter with softmax classifier were used. CNN and Gaussian filter with soft max classifier achieved 91%, while CNN and anisotropic filter with soft max classifier achieved 92%. GLCM with threshold segmentation and Gaussian filter with SVM classifier achieved 87%, while CNN with watershed segmentation and Gaussian filter with SVM classifier achieved 97% and CNN-GLCM with watershed segmentation and anisotropic diffusion filter with SVM classifier achieved 98% for detection and classification of LBO. Finally, this paper presented a performance analysis of various machine learning approaches for detection and classification of LBO. Hence, our model is designed to assist human experts (Radiologists) in diagnosing LBO.     

基于熵-流特征和樽海鞘群优化支持向量机的故障诊断方法

针对旋转机械设备故障特征提取困难的问题,提出一种熵-流特征和樽海鞘群优化支持向量机(salp swarm optimization support vector machine,SSO-SVM)的故障诊断方法。利用改进多尺度加权排列熵(improved multiscale weighted permutation entropy,IMWPE)提取机械设备不同工况下的故障特征;采用监督等度规映射(S-Isomap)流形学习进行降维处理,获取低维的熵-流特征集;将熵-流特征输入至SSO-SVM多故障分类器进行识别与诊断。行星齿轮箱故障诊断实验分析结果表明:IMWPE+S-Isomap熵-流特征提取方法优于现有的多尺度排列熵(multiscale permutation entropy,MPE)、多尺度加权排列熵(multiscale weighted permutation entropy,MWPE)和IMWPE等熵值特征提取方法以及IMWPE+等度规映射(Isomap)和IMWPE+线性局部切空间排列(linear local tangent space alignment,LLTSA)等熵-流特征提取方法;樽海鞘群算法对支持向量机参数寻优效果优于粒子群、灰狼群、人工蜂群和蝙蝠群等算法;所提故障诊断方法识别精度达到100%,能够有效诊断出行星齿轮箱各工况类型。     

一种基于核主成分分析和组合分类器的虹膜识别方法

提出了一种新的虹膜特征提取与识别方法,该方法利用核主成分分析(KPCA)在高维空间具有较强的特征选择能力来提取虹膜图像的纹理特征。采用了一种距离度量和支持向量机相结合的两级分类方法,前级采用欧式距离来度量图像间的相似性,若符合条件,给出分类结果,否则拒绝,并转入后一级分类器——支持向量机分类,以减少进入支持向量机的样本数目,该组合分类方法充分利用了支持向量机识别率高和距离度量速度快的优点。实验结果表明,该方法提高了虹膜识别率,是一种有效的虹膜识别方法。     

支持向量机集成和特征选择联合算法

提出了两种基于支持向量机集成和特征选择联合算法。联合算法的核心思想是在构建基础分类器的同时选择有效特征。通过对实测舰船数据和公共数据的识别实验,证明了两种算法都可以用于舰船目标识别。算法一更适用于冗余特征较多的情况。算法二在对舰船目标识别时,选择的特征数目降低为原来特征数目的30%,正确分类率比单个支持向量机高近10%。     

Fuzzy relevance vector machine based classification of lung nodules in computed tomography images

Lung cancer is a critical disease with growing death rate, hence, the faster identification and treatment of lung cancer is essential. In medical image processing, the traditional methods like support vector machine, relevance vector machine for classifying cancer tissues are less sensitive to false data and required optimal improvement in classification accuracy. The proposed system of accurate lung cancer classification is obtained by a hybrid fuzzy relevance vector machine (FRVM) classifier with correlation negation ant colony optimization (CNACO) algorithm. This system provides enhanced accuracy and sensitivity by implementing two stages of feature extraction, image thresholding, and tumor segmentation, with a novel feature selection and tumor classification algorithm. The best features are selected by the proposed CNACO algorithm. The selected features are labeled and classified by FRVM classifier. The proposed classification scheme is validated on lung image database consortium and image database resource initiative public database and obtained accuracy of about 98.75%.     

Automated diagnosis system for age‐related macular degeneration using hybrid features set from fundus images

Impairment to macula can cause loss of central vision. There are various macular disorders that can affect macular region and if not treated at an early stage can cause irreversible central vision loss. Age‐related macular degeneration (AMD) disorder is one of the most threading macular disorder. Bright lesion, drusens presence in macular region is known as the hallmark of AMD disorder. This bright lesion differentiation from other bright lesion like exudates is important for accurate diagnosis of AMD. Focus of this article is automated diagnosis of affected macular region by applying a hybrid features set containing textural, color, and structural/shape features for more accurate detection of AMD at an early stage using fundus images. These features also help to distinguish drusens from exudates. The proposed algorithm at first stage, detect macular region from input fundus image and then perform features extraction based on textural pattern, edge, and structural properties of macular region to classify abnormal macula from normal macula. For classification, we have used support vector machine (SVM), K‐nearest neighbor and neural networks but SVM classifier achieves high accuracy. The proposed algorithm is tested on publicly available STARE and locally available AFIO datasets. Attained sensitivity, specificity, and accuracy of our proposed system are 97.5%, 95% and 95.45%, respectively, when applied on STARE dataset. When we have applied our proposed system on AFIO dataset, we have attained sensitivity, specificity, and accuracy of 93.3%, 92% and 92.34%, respectively.     

Optimal channel and frequency band-based feature selection for motor imagery electroencephalogram classification

Common spatial pattern (CSP) is a widely adopted method for electroencephalogram (EEG) feature extraction in brain-computer interface (BCI) based on motor imagery. Bandpass-filtering EEG into several subbands related to brain activity tasks is an effective approach to improve the performance of CSP based algorithm. However, this approach tends to suffer the over-fitting problem because of the increase in feature dimension. Therefore, we proposed an optimal channel and frequency band-based CSP feature selection method in this paper. Firstly, the correlation coefficient was calculated to select the optimal channels, and these channels were bandpass-filtered into multiple overlapping subbands. The subbands with higher power spectrum density were chosen for CSP feature extraction. Next, the pair-wise relevance was utilized to remove subband features with less difference. And then the screened subband features were combined with features extracted from the broadband signal. The Fisher ratio was exploited to carry out further feature selection. Finally, a support vector machine (SVM) was trained to classify the selected CSP features. An experimental study was implemented on BCI competition III dataset IVa and BCI competition IV dataset 1. The average classification accuracy reached 89.33% and 84.08%, which indicated the rationality and effectiveness of the proposed method.     

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.     

基于SVM的高通量dPCR基因芯片荧光图像分类研究

目的 为了实现高通量dPCR基因芯片荧光图像的亮点分类与计数,提出一种基于支持向量机（SVM）的荧光图像分类与计数方法。方法 首先对荧光图像进行去噪、对比度增强等图像预处理,对预处理后荧光图像进行亮点区域提取标注,去除背景与暗点的冗余信息,利用方向梯度直方图（Histogram of Oriented Gradient, HOG）提取鉴别特征,计算合并所有样本的亮点特征得到HOG特征向量,根据已得到的HOG特征向量创建一个线性SVM分类器,利用训练好的SVM分类器对荧光图像亮点进行分类与计数。结果 对比传统算法,文中算法具有较高的分类识别精度,平均准确率高达98%以上,可以很好地实现荧光图像亮点分类与计数。结论 在有限的小样本标注数据下,文中算法具有良好的分类性能,能够有效识别荧光图像中的亮点,对其他荧光图像分类研究也具有一定参考价值。     

Comparative analysis of Alzheimer's disease classification by CDR level using CNN,feature selection,and machine-learning techniques

Magnetic resonance imaging (MRI) of brain needs an impeccable analysis to investigate all its structure and pattern. This analysis may be a sharp visual analysis by an experienced medical professional or by a computer aided diagnosis system that can help to predict, what may be the recent condition. Similarly, on the basis of various information and technique, a system can be designed to detect whether a patient is prone to Alzheimer's disease or not. And this task of detection of abnormalities at an initial stage from brain MRI is a major challenge in the field of neurosciences. The main idea behind our research is to utilize the deep layers feature extraction benefited from deep neural network architecture, without extensive hardware resource training, and classifying the image on a basis of simple machine-learning algorithm with selected best features in order to reduce work load, classification error and hardware utilization time. We have utilized convolution neural network (CNN) layer using similar architecture like that of Alexnet with some parametric change, for the automatic extraction of features of images obtained from slice extraction of whole brain MRI whereas 13 manual features based on gray level co-occurrence matrix were also extracted to test the impact of this features on ranking. If we had only classified using CNN network, the misclassification rate was much higher. So, feature selection is achieved with feature ranking algorithms like Mutinffs, ReliefF, Laplacian and UDFS and so on and also tested with different machine-learning techniques like Support Vector Machine, K-Nearest Neighbor and Subspace Ensemble under different testing condition. The performance of the result is satisfactory with classification accuracy around 98% to 99% with 7:3 ratio of random holdout partition of training to testing image sets and also with fivefolds of cross-validation on the same set using a standardized template.     

参数优化变分模态分解与多域流形学习的行星齿轮箱故障诊断

针对行星齿轮箱特征提取困难的问题,提出一种基于参数优化变分模态分解与多域流形学习的故障诊断方法.首先,利用樽海鞘群优化变分模态分解(SSO-VMD)对信号进行分解与重构,降低噪声干扰;然后,从多域提取故障特征,并采用改进监督型自组织增量学习神经网络界标点等度规映射(ISSL-Isomap)算法进行降维处理,获取低维故障...     

基于支持向量机的印品缺陷分类方法

目的研究印品图像的各类形状缺陷,建立基于支持向量机(Support vector machine,SVM)的印品形状缺陷分类模型。方法对印品进行符合人眼视觉特性的缺陷识别,并对提取缺陷进行特征分析。将特征数据导入支持向量机进行训练学习,SVM分类器对缺陷图像进行测试。结果分类器对点缺陷和面缺陷的识别率为100%,对线缺陷的分类准确率达93.94%。结论基于SVM的缺陷分类方法能较好地满足印品质量检测的需求。     

深度卷积神经网络在滑动轴承转子轴心轨迹识别中的应用

针对传统旋转机械智能识别方法需要人为提取特征及诊断精度低的问题,基于深度学习的强大学习能力,提出一种深度卷积神经网络故障诊断模型(Deep Convolutional Neural Network Fault Diagnosis Model,DCNN-FDM)用于轴心轨迹识别.该模型包括输入模块、特征提取模块及分类模块...     

基于主分量分析的声信号特征提取及识别研究

主分量分析(PCA)是统计学中分析数据的一种有效方法。研究了基于这种算法对四种战场目标的声信号进行特征提取,获得了低维的特征类器对声目标进行分类,分类结果准确率较高,均获得满意的实验效果