Mineral Potential Mapping Using a Conjugate Gradient Logistic Regression Model

2019 ◽  
Vol 29 (1) ◽  
pp. 173-188 ◽  
Author(s):  
Nan Lin ◽  
Yongliang Chen ◽  
Laijun Lu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Conjugate Gradient ◽  
Logistic Regression Model ◽  
Mineral Potential Mapping ◽  
Mineral Potential ◽  
Potential Mapping

scholarly journals Application of singularity theory and logistic regression model for tungsten polymetallic potential mapping

2013 ◽  
Vol 20 (4) ◽  
pp. 445-453 ◽  
Author(s):  
Y. Liu ◽  
Q. Xia ◽  
Q. Cheng ◽  
X. Wang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Mineral Exploration ◽  
Singularity Theory ◽  
Forming Process ◽  
Singularity Method ◽  
Potential Mapping ◽  
Key Steps ◽  
Geological Background

Abstract. Geo-anomalies with complex structures in the earth's crust may be defined as preferable hydrothermal ore-forming targets. The separation and explanation of anomaly from geological background have a profound influence on the analysis of geological evolution and the ore-forming process. Usually one of the key steps to identify favorable exploration targets is to determine the threshold to separate anomaly from geological background. In this paper, the singularity theory and concentration–area (C–A) fractal method was applied in determination of the threshold of geo-anomalies. According to the thresholds, four singular maps can be each divided into two segments. Each of them is correlated to the anomaly and background of the geological objects (e.g., faults, fault intersections and contacts), which reveals that the various geo-anomalies can be characterized by the singularities. The results indicate that the local singularity method can be used to identify the weak anomalies in a low background. Logistic regression model was used to combine geo-singularity maps for mineral potential mapping, which provides a useful input for further mineral exploration in the Nanling tungsten polymetallic belt, South China.

Independent predictors for functional outcome after drainage of chronic subdural hematoma identified using a logistic regression model

2020 ◽  
Vol 64 (2) ◽  
Author(s):  
Sotirios Katsigiannis ◽  
Christina Hamisch ◽  
Boris Krischek ◽  
Marco Timmer ◽  
Anastasios Mpotsaris ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Functional Outcome ◽  
Subdural Hematoma ◽  
Logistic Regression Model ◽  
Chronic Subdural Hematoma

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

scholarly journals 5-Hydroxymethylcytosine profiles of cfDNA are highly predictive of R-CHOP treatment response in diffuse large B cell lymphoma patients

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Hang-Yu Chen ◽  
Wei-Long Zhang ◽  
Lei Zhang ◽  
Ping Yang ◽  
Fang Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
B Cell ◽  
Treatment Response ◽  
Logistic Regression Model ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Invasive Approach ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Background Although R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard chemotherapy regimen for diffuse large B cell lymphoma (DLBCL) patients, not all patients are responsive to the scheme, and there is no effective method to predict treatment response. Methods We utilized 5hmC-Seal to generate genome-wide 5hmC profiles in plasma cell-free DNA (cfDNA) from 86 DLBCL patients before they received R-CHOP chemotherapy. To investigate the correlation between 5hmC modifications and curative effectiveness, we separated patients into training (n = 56) and validation (n = 30) cohorts and developed a 5hmC-based logistic regression model from the training cohort to predict the treatment response in the validation cohort. Results In this study, we identified thirteen 5hmC markers associated with treatment response. The prediction performance of the logistic regression model, achieving 0.82 sensitivity and 0.75 specificity (AUC = 0.78), was superior to existing clinical indicators, such as LDH and stage. Conclusions Our findings suggest that the 5hmC modifications in cfDNA at the time before R-CHOP treatment are associated with treatment response and that 5hmC-Seal may potentially serve as a clinical-applicable, minimally invasive approach to predict R-CHOP treatment response for DLBCL patients.

Sign in / Sign up

Export Citation Format

Share Document