scholarly journals Decision Tree Model for Rockburst Prediction Based on Microseismic Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hongbo Zhao ◽  
Bingrui Chen ◽  
Changxing Zhu
Keyword(s):  
Decision Tree ◽  
Dynamic Instability ◽  
Microseismic Monitoring ◽  
Decision Tree Model ◽  
Monitoring Data ◽  
Underground Excavation ◽  
Decision Tree Algorithm ◽  
Complex Dynamic ◽  
Tree Model ◽  
The Relationship

Rockburst is an extremely complex dynamic instability phenomenon for rock underground excavation. It is difficult to predict and evaluate the rank level of rockburst in practice. Microseismic monitoring technology has been adopted to obtain microseismic events of microcrack in rock mass for rockburst. The possibility of rockburst can be reflected by microseismic monitoring data. In this study, a decision tree was used to extract the knowledge of rockburst from microseismic monitoring data. The predictive model of rockburst was built based on microseismic monitoring data using a decision tree algorithm. The predictive results were compared with the real rank of rockburst. The relationship between rockburst and microseismic feature data was investigated using the developed decision tree model. The results show that the decision tree can extract the rockburst feature from the microseismic monitoring data. The rockburst is predictable based on microseismic monitoring data. The decision tree provides a feasible and promising approach to predict and evaluate rockburst.

Sea-Battlefield Situation Assessment Based on Improved Decision Tree

2014 ◽  
Vol 574 ◽  
pp. 639-645
Author(s):  
Yong Tao Yu ◽  
Ying Ding
Keyword(s):  
Decision Tree ◽  
Decision Tree Model ◽  
Decision Tree Algorithm ◽  
Tree Model ◽  
Situation Assessment ◽  
Tree Algorithm ◽  
Initial Decision

How to efficiently evaluate the dynamic changing sea-battlefield is the key of command decision. According to research sea-battlefield situation assessment based on improved decision tree algorithm based on derived attributes. First is based on the decision tree algorithm to establish the sea-battlefield situation assessment initial decision tree. Second are dynamically generated derivative branches set based on derived attributes. Once again, it can be grafting and pruning derivative branches to form the sea-battlefield situation assessment derived decision tree model. Finally, it may use a derived dynamically decision tree model assess sea-battlefield in different time slices.

The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yejin Lee ◽  
Dae-Young Kim
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Tourism Industry ◽  
Decision Tree Model ◽  
Geographical Distance ◽  
Tree Model ◽  
Content Type ◽  
Hotel Booking ◽  
The Relationship

Purpose Using the decision tree model, this study aims to understand the online travelers booking behaviors on Expedia.com, by examining influential determinants of online hotel booking, especially for longer-stay travelers. The geographical distance is also considered in understanding the booking behaviors trisecting travel destinations (i.e. Americas, Europe and Asia). Design/methodology/approach The data were obtained from American Statistical Association DataFest and Expedia.com. Based on the US travelers who made hotel reservation on the website, the study used a machine learning algorithm, decision tree, to analyze the influential determinants on hotel booking considering the geographical distance between origin and destination. Findings The results of the findings demonstrate that the choice of package product is the prioritized determinant for longer-stay hotel guests. Several similarities and differences were found from the significant determinants of the decision tree, in accordance with the geographic distance among the Americas, Europe and Asia. Research limitations/implications This paper presents the extension to an existing machine learning environment, and especially to the decision tree model. The findings are anticipated to expand the understanding of online hotel booking and apprehend the influential determinants toward consumers’ decision-making process regarding the relationship between geographical distance and traveler’s hotel staying duration. Originality/value This research brings a meaningful understanding of the hospitality and tourism industry, especially to the realm of machine learning adapted to an online booking website. It provides a unique approach to comprehend and forecast consumer behavior with data mining.

scholarly journals Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults

2021 ◽  
Vol 10 (21) ◽  
pp. 5184
Author(s):  
Keitaro Makino ◽  
Sangyoon Lee ◽  
Seongryu Bae ◽  
Ippei Chiba ◽  
Kenji Harada ◽  
...  
Keyword(s):  
Older Adults ◽  
Decision Tree ◽  
Predictive Value ◽  
Community Dwelling ◽  
Decision Tree Model ◽  
Decision Tree Algorithm ◽  
Tree Model ◽  
Tree Algorithm ◽  
Fall Prediction ◽  
Community Dwelling Older Adults

The present study developed a simplified decision-tree algorithm for fall prediction with easily measurable predictors using data from a longitudinal cohort study: 2520 community-dwelling older adults aged 65 years or older participated. Fall history, age, sex, fear of falling, prescribed medication, knee osteoarthritis, lower limb pain, gait speed, and timed up and go test were assessed in the baseline survey as fall predictors. Moreover, recent falls were assessed in the follow-up survey. We created a fall-prediction algorithm using decision-tree analysis (C5.0) that included 14 nodes with six predictors, and the model could stratify the probabilities of fall incidence ranging from 30.4% to 71.9%. Additionally, the decision-tree model outperformed a logistic regression model with respect to the area under the curve (0.70 vs. 0.64), accuracy (0.65 vs. 0.62), sensitivity (0.62 vs. 0.50), positive predictive value (0.66 vs. 0.65), and negative predictive value (0.64 vs. 0.59). Our decision-tree model consists of common and easily measurable fall predictors, and its white-box algorithm can explain the reasons for risk stratification; therefore, it can be implemented in clinical practices. Our findings provide useful information for the early screening of fall risk and the promotion of timely strategies for fall prevention in community and clinical settings.

A novel enhanced decision tree model for detecting chronic kidney disease

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Avijit Kumar Chaudhuri ◽  
Deepankar Sinha ◽  
Dilip K. Banerjee ◽  
Anirban Das
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model

scholarly journals Decision Tree-Based Adaptive Reconfigurable Cache Scheme

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 176
Author(s):  
Wei Zhu ◽  
Xiaoyang Zeng
Keyword(s):  
Decision Tree ◽  
Adaptive Algorithms ◽  
Memory Access ◽  
Access Time ◽  
Decision Tree Algorithm ◽  
Verilog Hdl ◽  
Tree Model ◽  
Cache Associativity ◽  
Cache Scheme ◽  
Reconfigurable Cache

Applications have different preferences for caches, sometimes even within the different running phases. Caches with fixed parameters may compromise the performance of a system. To solve this problem, we propose a real-time adaptive reconfigurable cache based on the decision tree algorithm, which can optimize the average memory access time of cache without modifying the cache coherent protocol. By monitoring the application running state, the cache associativity is periodically tuned to the optimal cache associativity, which is determined by the decision tree model. This paper implements the proposed decision tree-based adaptive reconfigurable cache in the GEM5 simulator and designs the key modules using Verilog HDL. The simulation results show that the proposed decision tree-based adaptive reconfigurable cache reduces the average memory access time compared with other adaptive algorithms.

scholarly journals Risk of Pre-Malignancy or Malignancy in Postmenopausal Endometrial Polyps: A CHAID Decision Tree Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1094
Author(s):  
Michael Wong ◽  
Nikolaos Thanatsis ◽  
Federica Nardelli ◽  
Tejal Amin ◽  
Davor Jurkovic
Keyword(s):  
Decision Tree ◽  
Expectant Management ◽  
Decision Tree Model ◽  
Decision Tree Analysis ◽  
Focal Lesions ◽  
Tree Model ◽  
Normal Endometrium ◽  
Endometrial Polyps ◽  
Tree Analysis ◽  
Interaction Detection

Background and aims: Postmenopausal endometrial polyps are commonly managed by surgical resection; however, expectant management may be considered for some women due to the presence of medical co-morbidities, failed hysteroscopies or patient’s preference. This study aimed to identify patient characteristics and ultrasound morphological features of polyps that could aid in the prediction of underlying pre-malignancy or malignancy in postmenopausal polyps. Methods: Women with consecutive postmenopausal polyps diagnosed on ultrasound and removed surgically were recruited between October 2015 to October 2018 prospectively. Polyps were defined on ultrasound as focal lesions with a regular outline, surrounded by normal endometrium. On Doppler examination, there was either a single feeder vessel or no detectable vascularity. Polyps were classified histologically as benign (including hyperplasia without atypia), pre-malignant (atypical hyperplasia), or malignant. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed with a range of demographic, clinical, and ultrasound variables as independent, and the presence of pre-malignancy or malignancy in polyps as dependent variables. A 10-fold cross-validation method was used to estimate the model’s misclassification risk. Results: There were 240 women included, 181 of whom presented with postmenopausal bleeding. Their median age was 60 (range of 45–94); 18/240 (7.5%) women were diagnosed with pre-malignant or malignant polyps. In our decision tree model, the polyp mean diameter (≤13 mm or >13 mm) on ultrasound was the most important predictor of pre-malignancy or malignancy. If the tree was allowed to grow, the patient’s body mass index (BMI) and cystic/solid appearance of the polyp classified women further into low-risk (≤5%), intermediate-risk (>5%–≤20%), or high-risk (>20%) groups. Conclusions: Our decision tree model may serve as a guide to counsel women on the benefits and risks of surgery for postmenopausal endometrial polyps. It may also assist clinicians in prioritizing women for surgery according to their risk of malignancy.

scholarly journals Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Zhong Xin ◽  
Lin Hua ◽  
Xu-Hong Wang ◽  
Dong Zhao ◽  
Cai-Guo Yu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Predictive Value ◽  
Early Stage ◽  
Current Model ◽  
External Validation ◽  
Area Under The Curve ◽  
Decision Tree Model ◽  
Tree Model ◽  
Chinese Adult ◽  
Significant Difference

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.

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