METHODS AND SYSTEMS FOR PROCESSING DATA HAVING VARIED TEMPORAL CHARACTERISTICS TO GENERATE PREDICTIONS RELATED TO MANAGEMENT ARRANGEMENTS USING RANDOM FOREST CLASSIFIERS

• 1. 用于使用相变材料来生成随机数据的方法以及相关的设备和系统 [P] . 中国专利： CN107077324B . 2021.06.29
• 2. 用于使用相变材料来生成随机数据的方法以及相关的设备和系统 [P] . 中国专利： CN107077324A . 2017-08-18
• 3. METHODS AND SYSTEMS FOR PROCESSING DATA HAVING VARIED TEMPORAL CHARACTERISTICS TO GENERATE PREDICTIONS RELATED TO MANAGEMENT ARRANGEMENTS USING RANDOM FOREST CLASSIFIERS [P] . US2021279598A1 . 2021-09-09

  机译： 处理具有多种时间特征的数据的方法和系统，以使用随机林分类器生成与管理安排相关的预测

• 4. Random Forest based Prediction Method and System of Road Surface Condition Using Spatio-Temporal Features [P] . KR20210030047A . 2021-03-17

  机译： 基于随机的森林基于森林的预测方法和道路表面条件系统使用时空特征

• 5. Random Forest based Prediction Method and System of Road Surface Condition Using Spatio-Temporal Features [P] . KR102309583B1 . 2021-10-07

  机译： 基于时空特征的随机森林的预测方法和道路表面状况系统

• 1. Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations [J] . Xiao Wang, Guosheng Su, Dan Hao, 畜牧与生物技术杂志：英文版 . 2020,第002期
• 2. Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations [J] . Xiao Wang, Guosheng Su, Dan Hao, 畜牧与生物技术杂志(英文版) . 2020,第002期
• 3. A Geographic Information Systems approach for classifying and mapping forest management category in Baihe Forestry Bureau, Northeast China [J] . WANG, Shun-zhong, SHAO, 林业研究：英文版 . 2006,第003期
• 4. Modifying Cleaning Method in Big Data Analytics Process using Random Forest Classifier [C] . J. Hossen, M. Z. Jesmeen H, Shohel Sayeed International Conference on Computer and Communication Engineering . 2018
• 5. Secure Training of Random Forest Classifiers over Continuous Data [D] . Shen, Jianwei. 2020
• 6. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy sensitivity and specificity of linear discriminant analysis logistic regression neural networks support vector machines classification trees and random forests [O] . João Maroco, Dina Silva, Ana Rodrigues, 2011
• 7. ABSTRACT Various body parts or organs can be analysed to identify the different diseases in the human body. Fingernail analysis is one of the ways to identify disease in the human body. Nails are the body part which are farthest from the heart and therefore receive oxygen at last. As a result the nails are the first who show the symptoms of a disease in the human body. Fingernails can be easily captured for diagnosis and there are no heavy equipment or no specific conditions required to use nail image for disease diagnosis, like in other tests and scanning processes. Human nails deliver beneficial information about complaints or any nutritive imbalances in the human body depending upon their shape, texture and colour. In human beings, numerous systemic and skin diseases can be easily analyzed through careful examination of nails of both the limbs. A lot of nail illnesses have been found to be primary signs of numerous underlying systemic illnesses. The colour, texture or shape changes in nails are signs of many diseases mainly affecting nails. Considering all these properties of nails a system is proposed that uses digital image processing (DIP) methods for identifying such changes in the human nail to get more precise results and predict numerous diseases effortlessly. With the emerging Internet of Things (IOT) concept the generated report is made available remotely, this will help users to reduce transportation efforts. As the system has to deal with large and private data, the security of data must be ensured. To keep the data confidential, the Blockchain concept which is one of the most emerging concepts in the field of data management is used. The paper contains the implementation of the digital image processing for feature extraction of nail images, usage of IOT (ThingSpeak cloud) for data storage and implementation of Blockchain to keep the system secured and theft free. KEY WORDS: Int ernet of thin gs (IOT), Image proc essin g, Thin gSpeak, RG B vavalues, Mean pi xel vavalues, Bloc kchain , Hash key. Disease Diagnostic System: Abnormalities in Human Nail [O] . Pranav S. Wazarkar 2020

• 1. Computational Intensity Prediction Model of Vector Data Overlay with Random Forest Method [J] . Qian Wang1 ,Han Cao1 ,Yan-Hui Guo12 . 国际计算机前沿大会会议论文集 . 2017,第001期
• 2. BACKGROUND： Vibrio vulnifi cus inside the body could activate the NF-!B signaling pathwayand initiate the inflammatory cascade. The lung is one of the earliest organs affected by sepsisassociated with acute lung injury. High mobility group protein B1 （HMGB1） is an important late-actingpro-infl ammatory cytokine involving in the pathophysiology of sepsis. It is also involved in the injuryprocess in the lung, liver and intestine. There has been no report on the involvement of HMGB1 inVibrio vulnifi cus sepsis-induced lung injury.METHODS： Sixty rats were randomly divided into a normal control group （group A, n=10） anda Vibrio vulnificus sepsis group （group B, n=50）. Sepsis was induced in the rats by subcutaneousinjection of Vibrio vulnificus （concentration 6×108 cfu/mL, volume 0.1 mL/100g）） into the left lowerlimbs. The rats in group B were sacrifi ced separately 1, 6, 12, 24, and 48 hours after the infection.Their lungs were stored as specimens, lung water content was measured, and lung pathology wasobserved under a light microscope. The expressions of the HMGB1 gene and protein in the lungswere detected by RT-PCR and Western blot. Data were analyzed with one-way analysis of variance（ANOVA） and the LSD method for pair-wise comparison between the two groups. P〈0.05 wasconsidered statistically signifi cant.RESULTS： Compared to group A （0.652±0.177）, HMGB1 mRNA expression in the lungs ofgroup B was signifi cantly higher at 0 hour （1.161±0.358, P=0.013）, 24 hours （1.679±0.235, P=0.000）,and 48 hours （1.258±0.274, P=0.004） （P〈0.05）, and peaked at 24 hours. Compared to group A（0.594±0.190）, HMGB1 protein expression at 6 hours （1.408±0.567, P=0.026） after infection wassignificantly increased （P〈0. 05）, and peaked at 24 hours （2.415±1.064, P=0.000） after infection.Compared to group A （0.699±0.054）, lung water content was significantly increased at 6 hours（0.759±0.030, P=0.001）,12 hours （0.767±0.023, P=0.000）, 24 hours （0.771±0.043, P=0.000） and 48hours （0.789±0.137, P=0.000） after infection （P〈0.05）. Compared to group A, pathological changesat 12 hours in group B indicate marked pulmonary vascular congestion, interstitial edema andinfl ammatory infi ltration. Alveolar cavity collapse and boundaries of the alveolar septum could not beclearly identifi ed.CONCLUSION： Vibrio vulnifi cus sepsis can lead to injury in rat lungs, and increased HMGB1expression in lung tissue may be one of the mechanisms for injury from Vibrio vulnifi cus sepsis. [J] . Qiao-meng Qiu ,Zhong-wang Li ,Lu-ming Tang . 世界急诊医学杂志(英文) . 2011,第4期
• 3. 具有控制输入滤波器的随机系统的多维条件滤波方法 [C] . 吴森堂 . 第五届飞行控制及操纵学术交流会 . 1993
• 4. 具有隐私保护意识的大样本双盲随机对照试验数据管理系统的设计与实现 [A] . 高超 . 2015