Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features

Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.

A Multi-Feature Weighting Based K-Means Algorithm for MOOC Learner Classification

Massive open online courses(MOOC)have recently gained worldwide attention in the field of education.The manner of MOOC provides a new option for learning various kinds of knowledge.A mass of data miming algorithms have been proposed to analyze the learner’s characteristics and classify the learners into different groups.However,most current algorithms mainly focus on the final grade of the learners,which may result in an improper classification.To overcome the shortages of the existing algorithms,a novel multi-feature weighting based K-means(MFWK-means)algorithm is proposed in this paper.Correlations between the widely used feature grade and other features are first investigated,and then the learners are classified based on their grades and weighted features with the proposed MFWK-means algorithm.Experimental results with the Canvas Network Person-Course(CNPC)dataset demonstrate the effectiveness of our method.Moreover,a comparison between the new MFWK-means and the traditional K-means clustering algorithm is implemented to show the superiority of the proposed method.

A classification method of building structures based on multi-feature fusion of UAV remote sensing images

In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.

A Multi-feature Fusion Apple Classification Method Based on Image Processing and Improved SVM

In order to achieve accurate classification of apple, a multi-feature fusion classification method based on image processing and improved SVM was proposed in this paper. The method was mainly divided into four parts, including image preprocessing, background segmentation, feature extraction and multi-feature fusion classification with improved SVM. Firstly, the homomorphic filtering algorithm was used to improve the quality of apple images. Secondly, the images were converted to HLS space. The background was segmented by the QTSU algorithm. Morphological processing was employed to remove fruit stem and surface defect areas. And apple contours were extracted with the Canny algorithm. Then, apples’ size, shape, color, defect and texture features were extracted. Finally, the cross verification method was used to optimize the penalty factor in SVM. A multi-feature fusion classification model was established. And the weight of each index was calculated by Fisher. In this study, 146 apple samples were selected for training and 61 apple samples were selected for testing. The test results showed that the accuracy of the classification method proposed in this paper was 96.72%, which can provide a reference for apple automatic classification.

Modified joint probabilistic data association with classification-aided for multitarget tracking

Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at risk.The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already few.Even established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the problem.These problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical examinations.These models help predict the effects of the disease outbreak and help detect the effects in the coming days.In this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan images.There are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia.The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results.The model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion Matrix.It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

Multi-Model Fusion Framework Using Deep Learning for Visual-Textual Sentiment Classification

Multimodal Sentiment Analysis(SA)is gaining popularity due to its broad application potential.The existing studies have focused on the SA of single modalities,such as texts or photos,posing challenges in effectively handling social media data with multiple modalities.Moreover,most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations,leading to unsatisfactory sentiment classification results.Motivated by this,we propose a new visualtextual sentiment classification model named Multi-Model Fusion(MMF),which uses a mixed fusion framework for SA to effectively capture the essential information and the intrinsic relationship between the visual and textual content.The proposed model comprises three deep neural networks.Two different neural networks are proposed to extract the most emotionally relevant aspects of image and text data.Thus,more discriminative features are gathered for accurate sentiment classification.Then,a multichannel joint fusion modelwith a self-attention technique is proposed to exploit the intrinsic correlation between visual and textual characteristics and obtain emotionally rich information for joint sentiment classification.Finally,the results of the three classifiers are integrated using a decision fusion scheme to improve the robustness and generalizability of the proposed model.An interpretable visual-textual sentiment classification model is further developed using the Local Interpretable Model-agnostic Explanation model(LIME)to ensure the model’s explainability and resilience.The proposed MMF model has been tested on four real-world sentiment datasets,achieving(99.78%)accuracy on Binary_Getty(BG),(99.12%)on Binary_iStock(BIS),(95.70%)on Twitter,and(79.06%)on the Multi-View Sentiment Analysis(MVSA)dataset.These results demonstrate the superior performance of our MMF model compared to single-model approaches and current state-of-the-art techniques based on model evaluation criteria.

A novel approach to structural anisotropy classification for jointed rock masses using theoretical rock quality designation formulation adjusted to joint spacing

Rock quality designation(RQD)has been considered as a one-dimensional jointing degree property since it should be determined by measuring the core lengths obtained from drilling.Anisotropy index of jointing degree(AI_(jd))was formulated by Zheng et al.(2018)by considering maximum and minimum values of RQD for a jointed rock medium in three-dimensional space.In accordance with spacing terminology by ISRM(1981),defining the jointing degree for the rock masses composed of extremely closely spaced joints as well as for the rock masses including widely to extremely widely spaced joints is practically impossible because of the use of 10 cm as a threshold value in the conventional form of RQD.To overcome this limitation,theoretical RQD(TRQD_(t))introduced by Priest and Hudson(1976)can be taken into consideration only when the statistical distribution of discontinuity spacing has a negative exponential distribution.Anisotropy index of the jointing degree was improved using TRQD_(t) which was adjusted to wider joint spacing by considering Priest(1993)’s recommendation on the use of variable threshold value(t)in TRQD_(t) formulation.After applications of the improved anisotropy index of a jointing degree(AI'_(jd))to hypothetical jointed rock mass cases,the effect of persistency of joints on structural anisotropy of rock mass was introduced to the improved AI'_(jd) formulation by considering the ratings of persistency of joints as proposed by Bieniawski(1989)’s rock mass rating(RMR)classification.Two real cases were assessed in the stratified marl and the columnar basalt using the weighted anisotropy index of jointing degree(W_AI'_(jd)).A structural anisotropy classification was developed using the RQD classification proposed by Deere(1963).The proposed methodology is capable of defining the structural anisotropy of a rock mass including joint pattern from extremely closely to extremely widely spaced joints.

Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of scholars.The biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this field.For a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or modules.However,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification problem.At the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word pairs.Finally,we use a biaffine predictor to assist in predicting the labels of word pairs for relation extraction.Our model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous approaches.Finally,we evaluated our model on two publicly accessible datasets.The experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal model.On the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal model.Our model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.

Methods of Classification of Ordnance Materials Military-Civilian Joint Support Categories Based on Multiple Attribute Decision

Ordnance material is the physical basis of ordnance equipment maintenance and support. With the increase of technology content and the enhancement of structural complexity of ordnance equipment,the traditional way of military self-independent support is unable to meet the troops' requirements. It has become an inevitable trend to integrate ordnance materials with the militarycivilian joint support. However, there is a problem demanding prompt solution,that is,to distinguish the categories of ordnance material that can be supported by civilian source. Based on the inherent properties of ordnance material, a method to classify ordnance materials military-civilian joint support categories based on multiple attribute decision was proposed. The effectiveness was validated through practical cases.

Data association based on target signal classification information

In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.

Building Classification Models with Combined Biomarker Tests： Application to Early Detection of Liver Cancer

Early detection of hepatocellular carcinoma （HCC） is critical for the effective treatment. Alpha fetoprotein （AFP） serum level is currently used for HCC screening, but the cutoff of the AFP test has limited sensitivity （-50%）, indicating a high false negative rate. We have successfully demonstrated that cancer derived DNA biomarkers can be detected in urine of patients with cancer and can be used for the early detection of cancer （Jain et al., 2015; Lin et al., 2011; Song et al., 2012; Su, Lin, Song, ＆ Jain, 2014; Su, Wang, Norton, Brenner, ＆ Block, 2008）. By combining urine biomarkers （uBMK） values and serum AFP （sAFP） level, a new classification model has been proposed for more efficient HCC screening. Several criterions have been discussed to optimal the cutoff for uBMK score and sAFP score. A joint distribution of sAFP and uBMK with point mass has been fitted using maximum likelihood method. Numerical results show that the sAFP data and uBMK data are very well described by proposed model. A tree-structured sequential test can be optimized by selecting the cutoffs. Bootstrap simulations also show the robust classification results with the optimal cuto~..

Viral infections in orthopedics: A systematic review and classification proposal

BACKGROUND Although the impact of microbial infections on orthopedic clinical outcomes is well recognized,the influence of viral infections on the musculoskeletal system might have been underestimated.AIM To systematically review the available evidence on risk factors and musculoskeletal manifestations following viral infections and to propose a pertinent classification scheme.METHODS We searched MEDLINE,Cochrane Central Register of Controlled Trials(CENTRAL),the Reference Citation Analysis(RCA),and Scopus for completed studies published before January 30,2021,to evaluate risk factors and bone and joint manifestations of viral infection in animal models and patient registries.Quality assessment was performed using SYRCLE's risk of bias tool for animal studies,Moga score for case series,Wylde score for registry studies,and Newcastle-Ottawa Scale for case-control studies.RESULTS Six human and four animal studies were eligible for inclusion in the qualitative synthesis.Hepatitis C virus was implicated in several peri-and post-operative complications in patients without cirrhosis after major orthopedic surgery.Herpes virus may affect the integrity of lumbar discs,whereas Ross River and Chikungunya viruses provoke viral arthritis and bone loss.CONCLUSION Evidence of moderate strength suggested that viruses can cause moderate to severe arthritis and osteitis.Risk factors such as pre-existing rheumatologic disease contributed to higher disease severity and duration of symptoms.Therefore,based on our literature search,the proposed clinical and pathogenetic classification scheme is as follows:(1)Viral infections of bone or joint;(2)Active bone and joint inflammatory diseases secondary to viral infections in other organs or tissues;and(3)Viral infection as a risk factor for post-surgical bacterial infection.

New Classification and Visual Analysis of the Patella Cartilage during Total Knee Replacement

Objective: The aim of this prospective study is to evaluate how much damage the patellar cartilage presents during a total knee replacement. Methods: The damage of the articular patellar surface was analysed by visual inspection and photographs in 354 primary total knee replacements. The authors graded the degree of cartilage lesion in five groups. The cartilage status was analyzed and correlated with age, gender, side, body mass index (BMI), Kellgren-Lawrence radiographic scale and axial deviation. Results: After statistical analysis, we concluded: there was no evidence of an association between patellar arthrosis and age gender, side, weight and deformity. Conclusions: Articular cartilage was damaged in all 354 knees. Important subchondral bone exposure occurred in 274 knees (77.4%). Obese patients had more severe patellar osteoarthritis.

颞下颌关节紊乱病的分类、诊断及治疗进展

近年来关于颞下颌关节紊乱病的分类和诊断已基本取得共识。颞下颌关节紊乱病是一种具有明显生物-心理-社会模式特点的心身疾病,除了临床诊断外,还要评价疼痛相关的功能丧失和心理状况。尽管外科手术治疗的水平有了很大的进步,但仍要强调治疗首选是非手术的保守治疗。相当一部分患者症状体征轻微或完全没有任何症状体征,颞下颌关节紊乱病治疗应充分考虑其症状体征对患者的影响程度,而不是仅仅根据临床诊断和影像检查结果而作出是否需要治疗的结论。

多区域注意力的细粒度图像分类网络

目前细粒度图像分类的难点在于如何精准定位图像中高度可辨的局部区域以及其他辅助判别特征。提出一种多区域注意力的细粒度图像分类网络来解决这个问题。首先使用Inception-V3对图像特征进行提取,通过重复使用注意力擦除的方法使模型关注次要特征;然后通过背景去除以及上采样的方法获取图像更精准的局部图像,对提取到的局部特征进行位置统计,并以矩形框的方式获取图像整体,减少细节信息丢失;最后对局部与整体图像进行更加细致的学习。此外,设计联合损失函数,通过动态平衡难易样本和缩小类内差距的方法改善模型的识别效果。实验结果表明,该方法在公开的细粒度图像数据集CUB-200-2011、Stanford-Cars和FGVC-Aircraft上的准确率分别达到89.2%、94.8%、94.0%,相较于对比方法性能更优。

基于CBAM和原型网络的小样本恶意软件分类模型

为解决小样本条件下恶意软件分类准确率低的问题,提出一种基于CBAM(convolutional block attention module)和原型网络的恶意软件分类模型。利用图像转换算法将恶意软件可执行文件转换为灰度图像;将残差连接和CBAM引入模型的特征嵌入模块,从通道和空间两个维度上增强关键特征表达,使得到的特征更具分辨性;提出联合损失函数,在距离交叉熵损失的基础上加入原型损失,通过减小类内距离的方式进一步扩增类间距离,使模型在样本数量有限的情况下取得良好的分类效果。实验结果表明,在每类恶意软件仅有5个样本的情况下,模型的分类准确率仍可达到83.12%。

单脉冲测角的二维分级主旁瓣干扰联合抑制方法

针对矩形平面阵列天线同时存在主、旁瓣干扰的单脉冲测角问题,该文设计了2维分级自适应单脉冲波束形成算法(TDHJ-ADBF)。TDHJ-ADBF算法将矩形平面阵分为方位维和俯仰维两个正交维度,采用2维分级处理架构:第1级处理在测角维进行,采用低运算量的压缩多重信号分类法对测角维主瓣干扰进行快速识别与方向估计,构造阻塞矩阵滤除主瓣干扰,获得仅含旁瓣干扰和噪声的协方差矩阵,进而对和、差波束方向图进行指向与鉴角曲线联合约束,完成测角维旁瓣干扰抑制与波束形成处理;第2级在非测角维对残留的测角维主瓣干扰进行抑制。通过2维分级处理实现主、旁瓣干扰联合对抗,并保持单脉冲测角的鉴角曲线线性度。仿真结果表明,TDHJ-ADBF算法实现了对主、旁瓣干扰联合抑制,具有高精度的单脉冲测角性能。

基于跨度和边界探测的实体关系联合抽取模型

针对大多数跨度模型将文本分割成跨度序列时,产生大量非实体跨度,导致了数据不平衡和计算复杂度高等问题,提出了基于跨度和边界探测的实体关系联合抽取模型(joint extraction model for entity relationships based on span and boundary detection,SBDM)。SBDM首先使用训练Transformer的双向编码器表征量(bidirectional encoder representations from Transformer,BERT)模型将文本转化为词向量,并融合了通过图卷积获取的句法依赖信息以形成文本的特征表示;接着通过局部信息和句子上下文信息去探测实体边界并进行标记,以减少非实体跨度;然后将实体边界标记形成的跨度序列进行实体识别;最后将局部上下文信息融合到1个跨度实体对中并使用sigmoid函数进行关系分类。实验表明,SBDM在SciERC(multi-task identification of entities,relations,and coreference for scientific knowledge graph construction)数据集、CoNLL04(the 2004 conference on natural language learning)数据集上的关系分类指标S F1分别达到52.86%、74.47%,取得了较好效果。SBDM用于关系分类任务中,能促进跨度分类方法在关系抽取上的研究。

基于自适应矩阵的核联合稀疏表示高光谱图像分类

针对高光谱图像丰富的空间信息和光谱信息未充分利用的问题,提出了基于自适应矩阵的核联合稀疏表示高光谱图像分类的方法。在特征表示阶段,定义了自适应矩阵特征,通过结合自适应邻域块策略与非线性相关熵度量构成的特征来描述原始光谱像素,充分融合了形状可变的空间信息与非线性光谱信息。在分类阶段,考虑自适应矩阵和高光谱图像非线性,采用对数欧式核函数,构建了核联合稀疏表示模型,以获得重构误差。同时利用字典空间信息构建了矩阵相关性,引入平衡参数实现了稀疏重构误差与矩阵相关性的联合分类。在两个数据集上的实验结果表明,该算法充分利用了高光谱图像的空间信息、光谱信息,能够有效提高分类精度。