scholarly journals Application of Haralick’s Texture Features for Rapid Detection of Windthrow Hotspots in Orthophotos

Forests ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 763
Author(s):  
Hans-Joachim Klemmt ◽  
Rudolf Seitz ◽  
Christoph Straub
Keyword(s):  
Rapid Detection ◽  
Texture Features ◽  
Training Data ◽  
Future Research ◽  
Storm Damage ◽  
Random Forest Algorithm ◽  
Data Set ◽  
Validation Set ◽  
Fine Distinction

Windthrow and storm damage are crucial issues in practical forestry. We propose a method for rapid detection of windthrow hotspots in airborne digital orthophotos. Therefore, we apply Haralick’s texture features on 50 × 50 m cells of the orthophotos and classify the cells with a random forest algorithm. We apply the classification results from a training data set on a validation set. The overall classification accuracy of the proposed method varies between 76% for fine distinction of the cells and 96% for a distinction level that tried to detect only severe damaged cells. The proposed method enables the rapid detection of windthrow hotspots in forests immediately after their occurrence in single-date data. It is not adequate for the determination of areas with only single fallen trees. Future research will investigate the possibilities and limitations when applying the method on other data sources (e.g., optical satellite data).

scholarly journals Genome-Wide Identification of a Novel Autophagy-Related Signature for Colorectal Cancer

Dose-Response ◽  
2019 ◽  
Vol 17 (4) ◽  
pp. 155932581989417 ◽  
Author(s):  
Zhi Huang ◽  
Jie Liu ◽  
Liang Luo ◽  
Pan Sheng ◽  
Biao Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Signaling Pathway ◽  
Risk Score ◽  
Low Risk ◽  
Training Data ◽  
The Cancer Genome Atlas ◽  
Training Set ◽  
Data Set ◽  
Validation Set ◽  
Cox Analysis

Background: Plenty of evidence has suggested that autophagy plays a crucial role in the biological processes of cancers. This study aimed to screen autophagy-related genes (ARGs) and establish a novel a scoring system for colorectal cancer (CRC). Methods: Autophagy-related genes sequencing data and the corresponding clinical data of CRC in The Cancer Genome Atlas were used as training data set. The GSE39582 data set from the Gene Expression Omnibus was used as validation set. An autophagy-related signature was developed in training set using univariate Cox analysis followed by stepwise multivariate Cox analysis and assessed in the validation set. Then we analyzed the function and pathways of ARGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, a prognostic nomogram combining the autophagy-related risk score and clinicopathological characteristics was developed according to multivariate Cox analysis. Results: After univariate and multivariate analysis, 3 ARGs were used to construct autophagy-related signature. The KEGG pathway analyses showed several significantly enriched oncological signatures, such as p53 signaling pathway, apoptosis, human cytomegalovirus infection, platinum drug resistance, necroptosis, and ErbB signaling pathway. Patients were divided into high- and low-risk groups, and patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both training set and validation set. Furthermore, the nomogram for predicting 3- and 5-year OS was established based on autophagy-based risk score and clinicopathologic factors. The area under the curve and calibration curves indicated that the nomogram showed well accuracy of prediction. Conclusions: Our proposed autophagy-based signature has important prognostic value and may provide a promising tool for the development of personalized therapy.

scholarly journals Prediction of Lung Cancer Risk using Random Forest Algorithm Based on Kaggle Data Set

2020 ◽  
Vol 8 (6) ◽  
pp. 1623-1630
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Random Forest ◽  
Naive Bayes ◽  
Early Stage ◽  
Naïve Bayes ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Data Set ◽  
Wide Range

As huge amount of data accumulating currently, Challenges to draw out the required amount of data from available information is needed. Machine learning contributes to various fields. The fast-growing population caused the evolution of a wide range of diseases. This intern resulted in the need for the machine learning model that uses the patient's datasets. From different sources of datasets analysis, cancer is the most hazardous disease, it may cause the death of the forbearer. The outcome of the conducted surveys states cancer can be nearly cured in the initial stages and it may also cause the death of an affected person in later stages. One of the major types of cancer is lung cancer. It highly depends on the past data which requires detection in early stages. The recommended work is based on the machine learning algorithm for grouping the individual details into categories to predict whether they are going to expose to cancer in the early stage itself. Random forest algorithm is implemented, it results in more efficiency of 97% compare to KNN and Naive Bayes. Further, the KNN algorithm doesn't learn anything from training data but uses it for classification. Naive Bayes results in the inaccuracy of prediction. The proposed system is for predicting the chances of lung cancer by displaying three levels namely low, medium, and high. Thus, mortality rates can be reduced significantly.

Application of Support Vector Machine in Determination of Real Estate Price

2012 ◽  
Vol 461 ◽  
pp. 818-821
Author(s):  
Shi Hu Zhang
Keyword(s):  
Support Vector Machine ◽  
Real Estate ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Training Data ◽  
Small Samples ◽  
Support Vector ◽  
Data Set ◽  
Real Estate Price

The problem of real estate prices are the current focus of the community's concern. Support Vector Machine is a new machine learning algorithm, as its excellent performance of the study, and in small samples to identify many ways, and so has its unique advantages, is now used in many areas. Determination of real estate price is a complicated problem due to its non-linearity and the small quantity of training data. In this study, support vector machine (SVM) is proposed to forecast the price of real estate price in China. The experimental results indicate that the SVM method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. It was also found that the predictive ability of the SVM outperformed those of some traditional pattern recognition methods for the data set used here.

scholarly journals Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Karthik Kalyan ◽  
Binal Jakhia ◽  
Ramachandra Dattatraya Lele ◽  
Mukund Joshi ◽  
Abhay Chowdhary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Texture Features ◽  
Speckle Noise ◽  
Training Data ◽  
Ultrasound Images ◽  
Data Set ◽  
Imaging Artifacts ◽  
Artificial Neural ◽  
Ultrasonic Images

The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.

scholarly journals Development and validation of an algorithm to estimate the risk of severe complications of COVID-19: a retrospective cohort study in primary care in the Netherlands

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e050059
Author(s):  
Ron M C Herings ◽  
Karin M A Swart ◽  
Bernard A M van der Zeijst ◽  
Amber A van der Heijden ◽  
Koos van der Velden ◽  
...  
Keyword(s):  
Cohort Study ◽  
Target Population ◽  
Population Based ◽  
Community Dwelling ◽  
Training Data ◽  
Prediction Algorithm ◽  
Data Set ◽  
C Statistic ◽  
Confirmation Test ◽  
Validation Set

ObjectiveTo develop an algorithm (sCOVID) to predict the risk of severe complications of COVID-19 in a community-dwelling population to optimise vaccination scenarios.DesignPopulation-based cohort study.Setting264 Dutch general practices contributing to the NL-COVID database.Participants6074 people aged 0–99 diagnosed with COVID-19.Main outcomesSevere complications (hospitalisation, institutionalisation, death). The algorithm was developed from a training data set comprising 70% of the patients and validated in the remaining 30%. Potential predictor variables included age, sex, chronic comorbidity score (CCS) based on risk factors for COVID-19 complications, obesity, neighbourhood deprivation score (NDS), first or second COVID-19 wave and confirmation test. Six population vaccination scenarios were explored: (1) random (naive), (2) random for persons above 60 years (60plus), (3) oldest patients first in age band of 5 years (oldest first), (4) target population of the annual influenza vaccination programme (influenza), (5) those 25–65 years of age first (worker), and (6) risk based using the prediction algorithm (sCOVID).ResultsSevere complications were reported in 243 (4.8%) people with 59 (20.3%) nursing home admissions, 181 (62.2%) hospitalisations and 51 (17.5%) deaths. The algorithm included age, sex, CCS, NDS, wave and confirmation test (c-statistic=0.91, 95% CI 0.88 to 0.94) in the validation set. Applied to different vaccination scenarios, the proportion of people needed to be vaccinated to reach a 50% reduction of severe complications was 67.5%, 50.0%, 26.1%, 16.0%, 10.0% and 8.4% for the worker, naive, influenza, 60plus, oldest first and sCOVID scenarios, respectively.ConclusionThe sCOVID algorithm performed well to predict the risk of severe complications of COVID-19 in the first and second waves of COVID-19 infections in this Dutch population. The regression estimates can and need to be adjusted for future predictions. The algorithm can be applied to identify persons with highest risks from data in the electronic health records of general practitioners (GPs).

A Prognostic System Predictive of Outcomes in Persons Undergoing Allogeneic Hematopoietic Cell Transplantation for Myelodysplastic Syndrome

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 64-64
Author(s):  
Brian C Shaffer ◽  
Kwang Woo Ahn ◽  
Zhen-Huan Hu ◽  
Uday R. Popat ◽  
Matt Kalaycio ◽  
...  
Keyword(s):  
Risk Score ◽  
Scoring System ◽  
Hematopoietic Cell Transplantation ◽  
Proportional Hazards ◽  
Training Data ◽  
Validation Data ◽  
Data Set ◽  
Training Cohort ◽  
Validation Set ◽  
Significant Covariates

Abstract Allogeneic hematopoietic cell transplantation (allo HCT) is a curative therapy for myelodysplastic syndrome (MDS) that may result in toxicity and mortality, limiting its efficacy in patients with this disease. There is no standardized criterion to guide selection of patients with MDS for allo HCT. In order to address this problem we examined outcomes in 2,133 patients undergoing HLA matched or mismatched allo HCT for MDS reported to the Center for International Blood and Marrow Transplant Research from 2000-2012. The primary aim of this study is to develop a prognostic scoring system predictive of overall survival (OS) in this population. We additionally addressed whether the model is predictive of transplant related mortality (TRM), relapse, and disease free survival (DFS). Patients undergoing haploidentical, syngeneic, umbilical cord blood, or with missing donor data (N = 663) and pediatric patients (N = 262) were excluded from this analysis. An additional 84 patients were removed due to missing date of diagnosis, unknown graft versus host disease (GVHD) prophylaxis, or who were missing any 100-day follow up data. 1,728 patients met these criteria and underwent HLA-matched allo HCT. An additional 405 patients underwent HLA mismatched allo HCT and formed the HLA-mismatched set. The HLA-matched allo HCT set were randomly divided into a training data set comprising 67% (N = 1,151) of the cohort and a validation data set using the remaining 33% (N = 577). The training data set was used to develop a prognostic scoring system and the validation data set was used to assess the predictive ability of the scoring system. A Cox proportional hazards model with the stepwise selection procedure was used to select significant covariates for OS. Interactions between significant covariates were examined and proportional hazards assumption was examined. Based on the magnitude of the hazard ratios (HR) associated with variables a weighted score was assigned to factors that were positively associated with OS in the training cohort. Scores were grouped based on associated HRs into good, intermediate, high, and very high risk groups. Patients with missing data were included in the multivariate Cox model analysis but were excluded from the analysis of the final risk score in the training and validation set. This analysis identified five factors predictive of mortality in the HLA matched allo HCT training set: Peripheral blood blasts ≥ 3% or platelet count < 50 × 109/μL at transplantation, IPSS-R cytogenetic risk score, poor Karnofsky performance status, and older age at transplantation (Table 1). Using these variables we developed a MDS prognostic score (Table 2). We then used the scoring system defined in Table 2 to calculate a risk score for individuals in the training cohort that had complete data on all five variables (N = 839). Increasing score was associated with greater HR for death (p-overall < 0.0001). Based on these data we applied the score to the HLA-matched validation cohort, where increasing score was predictive of overall survival (p < 0.001). Because the training set was developed based on OS and not other outcomes, we combined the 839 cases from the training cohort with the 427 cases from the validation cohort for the analyses of the secondary endpoints. In the combined HLA-matched cohort the scoring system was associated with relapse (p < 0.0001), TRM (p < 0.0001), and DFS (p < 0.0001). We then tested this model in an additional set of individuals undergoing HLA-mismatched allo HCT (N = 405). Here, the score was predictive of relapse (p < 0.0001) but not OS, DFS, or TRM. In order to determine if the proposed scoring system is superior to the IPSS or IPSS-R prognostic tools we compared the three scoring systems in the HLA-matched validation set using concordance probabilities and Brier scores in 384 patients that had complete data for all three prognostic systems. The proposed scoring system was more predictive of OS when compared to the IPSS and IPSS-R using Brier (0.241, 0.252, and 0.249, respectively) and concordance probability tools (0.575, 0.538, and 0.554, respectively). In summary, we propose a system for prediction of outcomes in transplant recipients for MDS. Such a tool may be used to inform clinical decisions and to standardize mortality risk index in clinical trials examining transplantations in this patient population. Table 1. Table 1. Table 2. Table 2. Disclosures Maziarz: Athersys: Consultancy, Patents & Royalties, Research Funding; Novartis: Consultancy.

scholarly journals Machine Learning Algorithms in Fraud Detection: Case Study on Retail Consumer Financing Company

2021 ◽  
Vol 6 (2) ◽  
pp. 213
Author(s):  
Nadya Intan Mustika ◽  
Bagus Nenda ◽  
Dona Ramadhan
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Historical Data ◽  
Learning Algorithm ◽  
Fraud Detection ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Data Set

This study aims to implement a machine learning algorithm in detecting fraud based on historical data set in a retail consumer financing company. The outcome of machine learning is used as samples for the fraud detection team. Data analysis is performed through data processing, feature selection, hold-on methods, and accuracy testing. There are five machine learning methods applied in this study: Logistic Regression, K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Historical data are divided into two groups: training data and test data. The results show that the Random Forest algorithm has the highest accuracy with a training score of 0.994999 and a test score of 0.745437. This means that the Random Forest algorithm is the most accurate method for detecting fraud. Further research is suggested to add more predictor variables to increase the accuracy value and apply this method to different financial institutions and different industries.

scholarly journals Texture Feature Extraction of Lumbar Spine Trabecular Bone Radiograph Image using Laplacian of Gaussian Filter with KNN Classification to Diagnose Osteoporosis

2021 ◽  
Vol 2070 (1) ◽  
pp. 012137
Author(s):  
Kavita Avinash Patil ◽  
K V Mahendra Prashanth ◽  
A Ramalingaiah
Keyword(s):  
Lumbar Spine ◽  
Trabecular Bone ◽  
Bone Fractures ◽  
Directional Derivative ◽  
Texture Feature ◽  
Texture Features ◽  
Training Data ◽  
Gaussian Filter ◽  
Data Set ◽  
Laplacian Of Gaussian Filter

Abstract The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy whereas the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. The detection of Osteoporosis in Lumbar Spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. The paper is systematized in two different sections to classify normal (non-osteoporosis) and abnormal(osteoporosis)Lumbar spine trabecular bone. In this method, the first section is based on discriminating the lumbar spine trabecular bone micro-architecture predisposing by means of first and second order directional derivative of Laplacian of Gaussian filter with different standard deviation to acquire the minimum and maximum responses. The dimension reduction of texture features, quantization and adjacent scale coding with weighted multipliers are used to lessen the intensity variations of texture features. The second section is based on the reduction of histogram features as a training data set for classification of normal and osteoporotic images of lumbar spine (L1-L4) using K-Nearest Neighborhood (KNN) classifier. The tested dataset result gives effective classification accuracy of 97.22% with lesser texture feature dimension. The usage of weight multiplier as well as quantization technique plays a major role for the improvement of accuracy to diagnose osteoporosis for an input noisy and noiseless image.

scholarly journals An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Bo Huang ◽  
Wei Tan ◽  
Zhou Li ◽  
Lei Jin
Keyword(s):  
Artificial Intelligence ◽  
Time Lapse ◽  
Training Data ◽  
Prediction Algorithm ◽  
Preliminary Evaluation ◽  
Training Set ◽  
Data Set ◽  
Test Set ◽  
Validation Set ◽  
Ploidy Status

Abstract Background For the association between time-lapse technology (TLT) and embryo ploidy status, there has not yet been fully understood. TLT has the characteristics of large amount of data and non-invasiveness. If we want to accurately predict embryo ploidy status from TLT, artificial intelligence (AI) technology is a good choice. However, the current work of AI in this field needs to be strengthened. Methods A total of 469 preimplantation genetic testing (PGT) cycles and 1803 blastocysts from April 2018 to November 2019 were included in the study. All embryo images are captured during 5 or 6 days after fertilization before biopsy by time-lapse microscope system. All euploid embryos or aneuploid embryos are used as data sets. The data set is divided into training set, validation set and test set. The training set is mainly used for model training, the validation set is mainly used to adjust the hyperparameters of the model and the preliminary evaluation of the model, and the test set is used to evaluate the generalization ability of the model. For better verification, we used data other than the training data for external verification. A total of 155 PGT cycles from December 2019 to December 2020 and 523 blastocysts were included in the verification process. Results The euploid prediction algorithm (EPA) was able to predict euploid on the testing dataset with an area under curve (AUC) of 0.80. Conclusions The TLT incubator has gradually become the choice of reproductive centers. Our AI model named EPA that can predict embryo ploidy well based on TLT data. We hope that this system can serve all in vitro fertilization and embryo transfer (IVF-ET) patients in the future, allowing embryologists to have more non-invasive aids when selecting the best embryo to transfer.

scholarly journals Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors

2021 ◽  
Vol 11 ◽  
Author(s):  
Meihua Shao ◽  
Zhongfeng Niu ◽  
Linyang He ◽  
Zhaoxing Fang ◽  
Jie He ◽  
...  
Keyword(s):  
Clinical Features ◽  
Gastrointestinal Stromal Tumors ◽  
Roc Curves ◽  
Ct Images ◽  
Training Data ◽  
Training Set ◽  
Data Set ◽  
Stromal Tumors ◽  
Risk Rating ◽  
Validation Set

We aimed to build radiomics models based on triple-phase CT images combining clinical features to predict the risk rating of gastrointestinal stromal tumors (GISTs). A total of 231 patients with pathologically diagnosed GISTs from July 2012 to July 2020 were categorized into a training data set (82 patients with high risk, 80 patients with low risk) and a validation data set (35 patients with high risk, 34 patients with low risk) with a ratio of 7:3. Four diagnostic models were constructed by assessing 20 clinical characteristics and 18 radiomic features that were extracted from a lesion mask based on triple-phase CT images. The receiver operating characteristic (ROC) curves were applied to calculate the diagnostic performance of these models, and ROC curves of these models were compared using Delong test in different data sets. The results of ROC analyses showed that areas under ROC curves (AUC) of model 4 [Clinic + CT value of unenhanced (CTU) + CT value of arterial phase (CTA) + value of venous phase (CTV)], model 1 (Clinic + CTU), model 2 (Clinic + CTA), and model 3 (Clinic + CTV) were 0.925, 0.894, 0.909, and 0.914 in the training set and 0.897, 0.866, 0,892, and 0.892 in the validation set, respectively. Model 4, model 1, model 2, and model 3 yielded an accuracy of 88.3%, 85.8%, 86.4%, and 84.6%, a sensitivity of 85.4%, 84.2%, 76.8%, and 78.0%, and a specificity of 91.2%, 87.5%, 96.2%, and 91.2% in the training set and an accuracy of 88.4%, 84.1%, 82.6%, and 82.6%, a sensitivity of 88.6%, 77.1%, 74.3%, and 85.7%, and a specificity of 88.2%, 91.2%, 91.2%, and 79.4% in the validation set, respectively. There was a significant difference between model 4 and model 1 in discriminating the risk rating in gastrointestinal stromal tumors in the training data set (Delong test, p &lt; 0.05). The radiomic models based on clinical features and triple-phase CT images manifested excellent accuracy for the discrimination of risk rating of GISTs.

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