GIS-Based Red Soil Resources Classification andEvaluation

A small scale red soil resources information system (RSRIS) with applied mathematical models wasdeveloped and applied in red soil resources (RSR) classification and evaluation, taking Zhejiang Province,a typical distribution area of red soil, as the study area. Computer-aided overlay was conducted to classifyRSR types. The evaluation was carried out by using three methods, i.e., index summation, square root ofindex multiplication and fuzzy comprehensive assessment, with almost identical results. The result of indexsummation could represent the basic qualitative condition of RSR, that of square root of index multiplicationreflected the real condition of RSR qualitative rank, while fuzzy comprehensive assessment could satisfactorilyhandle the relationship between the evaluation factors and the qualitative rank of RSR, and therefore it is afeasible method for RSR evaluation.

Evaluation of the Sensitivity and Specificity of the New Clinical Diagnostic and Classification Criteria for Kashin-Beck Disease,an Endemic Osteoarthritis,in China

This study aimed to evaluate the sensitivity and specificity of the new clinical diagnostic and classification criteria for Kashin-Beck disease （KBD） using six clinical markers： flexion of the distal part of fingers, deformed fingers, enlarged finger joints, shortened fingers, squat down, and dwarfism. One-third of the total population in Linyou County was sampled by stratified random sampling.

Land ecosystems classification and eco－economic evaluation in Wuding River Basin of Yulin Region

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Grading,Classification and Evaluation Technique of Farmland Based on GIS

This study gives a brief introduction to the significance,theoretical and technique programs of grading,classification and evaluation technique of farmland.At the same time,the application of spatial database,space target buffer,spatial data adding and many other GIS techniques in the grading,classification and evaluation of farmland is explored.And the obtained achievements are analyzed.It is thought that the use of GIS for the grading,classification and evaluation of farmland has full expressed its advantages of integration of figures and attributes,thus improving the scientificity,reliability and objectivity of this work to some extents.

Evaluation and classification of seawater corrosiveness by environmental factors

According to the data of main environmental factors and the depth of localized corrosion of carbon steel and low alloy steels in China seas, combined with the result of grey interrelation analysis, double-factor method was proposed to evaluate and classify seawater corrosiveness. According to the temperature of seawater and the biologically adhesive area on steels, the corrosiveness of seawater from low to high level is classified into five levels （C l-C5）, which was identified by the data of corrosion depth of carbon steel immersed in water for one year.

Evaluation of Tourist Resources in Liuchong and Longtan Scenic Spots of Longtan National Forest Park

To further explore characteristics of tourist resources in Liuchong and Longtan Scenic spots in Longtan National Forest Park,Classification,Investigation and Evaluation of Tourist Resources(GB/T18972-2003) and Quality Grading of Scenic Resources in Forest Parks of China(GB/T18005-1999) were adopted to analyze composition and distribution features of tourist resources in Liuchong and Longtan Scenic Spots,qualitative evaluation of scenic resources in the 2 study scenic spots,quantitative evaluation of resource units and quality evaluation of tourist resources were respectively carried out.The results showed that the park has diversified tourist resources such as peaks,valleys,cliffs,brooks,waterfalls and forests,as well as rich animal and plant species and an excellent natural eco-environment;5 Grade-IV tourist landscapes,6 Grade-III tourist landscapes,14 Grade-II ones,4 Grade-I ones;quality of tourist resources was scored as 41.3,up to the scenic resource standards of Grade-I Forest Park.

Improved Collaborative Filtering Recommendation Based on Classification and User Trust

When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation.

Evaluation and Improvement Measures of Rural Human Settlements in Licheng District of Jinan City

Based on the data of rural human settlements in Licheng District of Jinan City in 2016,the evaluation system of rural human settlements composed of four subsystems（ social economy,infrastructure,public environment,and construction management） was constructed. According to the comprehensive scores of various administrative villages calculated by means of multi-index comprehensive evaluation method and fuzzy comprehensive evaluation method,the human settlements of 521 administrative villages in Licheng District were divided into four types： excellent,good,average,and poor. Moreover,the spatial differences in the evaluation results of rural human settlements were analyzed using GIS spatial analysis technology. Finally,based on the evaluation results of rural human settlements in Licheng District,some measures to improve and control rural human settlements in different types of villages at various development stages were proposed to fully improve the quality of rural human settlements.

Evaluation Criteria Based on Mutual Information for Classifications Including Rejected Class

与用表演措施的常规评估标准不同，信息理论基于在场的标准在机器学习的应用的一个唯一的有益的特征。然而，我们仍然远非正在拥有熵类型标准的深入的理解，说，在与常规基于表演的标准的关系。这份报纸学习通用分类问题，它包括一拒绝，或未知，班。我们在场基本公式和分类基于信息学习的图解的图理论。一个靠近形式的方程为通用分类问题在规范的相互的信息和扩充混乱矩阵之间被导出。敏感方程的三个定理和定理集合为学习在相互的信息和常规表演索引之间的关系被给。我们也与常规标准比较举与相互的信息标准的优点和限制有关的数字例子和几讨论。

Accuracies and Training Times of Data Mining Classification Algorithms:An Empirical Comparative Study

Two important performance indicators for data mining algorithms are accuracy of classification/ prediction and time taken for training. These indicators are useful for selecting best algorithms for classification/prediction tasks in data mining. Empirical studies on these performance indicators in data mining are few. Therefore, this study was designed to determine how data mining classification algorithm perform with increase in input data sizes. Three data mining classification algorithms—Decision Tree, Multi-Layer Perceptron (MLP) Neural Network and Naïve Bayes— were subjected to varying simulated data sizes. The time taken by the algorithms for trainings and accuracies of their classifications were analyzed for the different data sizes. Results show that Naïve Bayes takes least time to train data but with least accuracy as compared to MLP and Decision Tree algorithms.

Evaluations on social network analysis (SNA) research in China's Mainland:A critical assessment of journal literatures

Social network analysis(SNA) has been introduced to China's Mainland since the end of last century. It is often stated that SNA research has experienced rapid growth in China over these years, but few studies have been conducted to prove the statement. This paper aims at exploring the research status and development of SNA in China by a critical assessment of journal articles. Our findings show that SNA is an evolving and diversified research area which has rich themes and topics, and can be applied to those studies on different levels, context and disciplines, and attract researchers and scholars from various fields and domains. In addition, the information community(Library & Information Science and Information Systems) plays a leading role in the SNA related researches. The paper also points out the research on SNA in China has some limitations, so it proposes several implications for the future development of SNA research from perspectives of information science.

OBJECTIVE IMAGE QUALITY MEASURES AND THEIR APPLICATIONS IN SEGMENTATION EVALUATION

Image segmentation is a critical step of image analysis. Segmentation evaluation is an effective procedure for studying the performance of segmentation techniques, in which quality measure plays an important role. This paper presents a group of new objective quality measures for segmentation evaluation and compares their performances. In addition, to verify the effectiveness of these new measures, an appropriate classification of segmentation is proposed. According to this classification, several representative algorithms from different categories are selected for comparison testing. Some valuable results are obtained and presented.

Evaluation and reasonable exploitation on the natural view resources of Lushan Mountain scenic spot

The paper carried on the classified and rating evaluation primarily on natural landscape resources in Lushan Mountain. According to the evaluation, exploiting and utilizing the situation of scenic spot natural landscape resources, some reasonable advices were given on further exploiting Lushan Mountain natural scenic spot, expecting that it could supply some theoretical references for the natural landscape resources sustainable development in Lushan Mountain in the future.

Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications

Many medical diagnosis applications are characterized by datasets that contain under-represented classes due to the fact that the disease is much rarer than the normal case. In such a situation classifiers such as decision trees and Na?ve Bayesian that generalize over the data are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far are more appropriate for such a task. We propose to calculate the contingency table and class specific evaluation measures despite the overall accuracy for evaluation purposes of classifiers for these specific data characteristics. We evaluate the different options of our case-based classifier and compare the perform-ance to decision trees and Na?ve Bayesian. Finally, we give an outlook for further work.

Weed Classification Using Particle Swarm Optimization and Deep Learning Models

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity.To address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image classification.Throughout this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed datasets.The Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed images.An effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification accuracy.The proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed classification.This work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

Classification Management on Plant by Use of a Fuzzy Comprehensive Evaluation Method

Applying fuzzy comprehensive evaluation principle, the authors put forward a new classification management method on plant, and developed a computer-aided system of classification management on plant.

基于《国际功能、残疾和健康分类》评价中国智能化辅盲设备的补偿功能

背景:辅盲设备及技术的应用是当今视力障碍人群常用的干预手段,可提高日常生活活动的参与及工作学习能力,帮助其回归家庭、回归社会。数字信息和智能化时代下辅盲设备的形式、技术和功能各有不同,其分类目前暂未能得到有效统一的讨论和评价。目的:基于《国际功能、残疾和健康分类》评价国内视力障碍人群智能化辅盲设备的补偿功能。方法:通过计算机检索中国知网、维普和万方数据库2013-01-01/2023-12-31收录的相关文献。基于《国际功能、残疾和健康分类》理论模式和框架结构,应用术语结构和编码程序归纳相关视力障碍评估类目,整理与分析国内视力障碍的智能化辅盲设备补偿功能研究与分类。结果与结论:①共纳入197篇文献:其中身体功能1篇,包含b2(b210);身体结构1篇,包含s2(s220);活动与参与共119篇,包含d1(10篇,d110、d115、d120、d140、d166),d3(4篇,d315、d325、d345、d360),d4(102篇,d465、d470),d8(3篇,d820、d825);环境因素共76篇,包含e1(72篇,e115、e120、e125、e130、e140、e150、e155、e160),e2(4篇,e210、e240);②基于《国际功能、残疾和健康分类》的视力障碍智能化辅盲设备补偿功能研究分类包含4个部分、8个分类、25个类目,领域涉及身体补偿类、生活用品类、教育学习类、出行导盲类、布局规划类。

Landsat 7 ETM+影像的融合和自动分类研究

利用SFIM、MLT、HPF和修改的Brovey(MB)等遥感影像融合算法对Landsat7ETM+影像进行融合和自动分类研究,并就融合影像的光谱保真度、高频空间信息融入度和分类精度对这些方法进行评价。结果表明SFIM变换几乎完全保持了原始影像的光谱特点,并具有最高的平均分类精度;MB变换具有最高的高频空间信息融入度;MLT变换也具有较高的分类精度;只有HPF变换的各项指标都不突出。所有4种融合影像的分类精度都较原始影像的分类精度有明显的提高。这表明,源于同一传感器系统的不同分辨率影像的融合可以避免异源传感器融合影像所常见的各种参数、时相和配准误差,所以能够明显地提高影像的自动分类精度。

基于Landsat-8的遥感影像分类研究

遥感影像分类在专题信息提取、地表动态监测以及专题地图制作等应用中具有重要作用,传统的分类方法可以分为监督分类和非监督分类,因算法成熟、操作简单,这两类方法仍然是当前使用较广泛的分类方法,但从理论、过程以及使用范围条件上二者都不相同,各有其优缺点。鉴于这种现状,本文采用Landsat-8 OLI焦作地区遥感数据分别基于监督与非监督中的各种算法进行土地覆盖分类,并对分类结果进行比较分析和精度评价,以期为实际工作中根据不同需求选取适当分类器提供依据。研究结果表明:监督分类中最大似然法分类精度相对较高,漏分错分最少,总体分类精度达到87.152%;非监督分类中ISODATA算法从聚类效果、漏分错分以及计算时间上综合分析要优于K-均值分类;另外,不同分类算法对不同地物类型的解译效果不同。

APPLYING PPE MODEL BASED ON RAGA TO CLASSIFYAND EVALUATE SOIL GRADE

The research of soil classification and soil grade evaluation is often based on fuzzy theory. So, the traditional method has an inevitable problem about weight matrix which given by some experts, and the final result can be influenced by artificial factors. The essentials of fuzzy synthetically judge is to handle the data of high dimension. That is to reducing the dimension number. The weight matrix in fuzzy theory is corresponding to low dimension projection value of each index. But we can′t define whether the weight matrix given by experts is the best projection value or not. So, the authors apply a new technique of falling dimension named projection pursuit to soil study, through using the improved real coding based accelerating genetic algorithm to optimize the projection direction. Thus, it can transfer multi dimension data into one dimension data, through searching for the optimum projection direction to realize the soil classification and its grade evaluation. The method can avoid the artificial disturbance, and acquire preferably effect. Thus, the paper provides a new method to the research of soil classification and grade evaluation.