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.

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.

scholarly journals Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm

2018 ◽  
Vol 13 (10) ◽  
pp. 146 ◽  
Author(s):  
Sujuan Jia ◽  
Yajing Pang
Keyword(s):  
Decision Tree ◽  
Quality Evaluation ◽  
Computation Time ◽  
Teaching Quality ◽  
Quality Data ◽  
Decision Tree Algorithm ◽  
Tree Model ◽  
Tree Algorithm ◽  
Higher Education System ◽  
C4.5 Decision Tree

Vast data in the higher education system are used to analyse and evaluate the teaching quality, so that the key factors that affect the quality of teaching can be predicted. Besides, the learner’s personalized behaviour can also become the data source for teaching result prediction. This paper proposes a decision tree model by taking the teaching quality data and the statistical analysis results of the learn-er’s personalized behaviour as inputs. This model was based on the improved C4.5 decision tree algorithm, which used the FAYYAD boundary point decision theorem for effectively reducing the computation time to the most threshold. In this algorithm, the iterative analysis mechanism was introduced in combination with the data change of the learner’s personalized behaviour, so as to dynamically adjust the final teaching evaluation result. Finally, according to the actual statisti-cal data of one academic year, the teaching quality evaluation was effectively completed and the direction of future teaching prediction was proposed.

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.

scholarly journals Spatial decision tree model for garlic land suitability evaluation

2021 ◽  
Vol 10 (3) ◽  
pp. 666
Author(s):  
Andi Nurkholis ◽  
Imas Sukaesih Sitanggang ◽  
Annisa Annisa ◽  
Sobir Sobir
Keyword(s):  
Decision Tree ◽  
Land Suitability ◽  
Decision Tree Algorithm ◽  
Tree Model ◽  
Spatial Join ◽  
Tree Algorithm ◽  
Suitability Evaluation ◽  
Root Node ◽  
Target Layer ◽  
Weather Characteristics

Predicting land and weather characteristics as indicators of land suitability is very important in increasing effectiveness in food production. This study aims to evaluate the suitability of garlic land using spatial decision tree algorithm. The algorithm is the improvement of the conventional decision tree algorithm in which spatial join relation is included to grow up spatial decision tree. The spatial dataset consists of a target layer that represents garlic land suitability and ten explanatory layers that represent land and weather characteristics in the study areas of Magetan and Solok district, Indonesia. This study generated the best spatial decision trees for each study area. On Magetan dataset, the best model has 33 rules with 94.34% accuracy and relief variable as the root node, whereas on Solok dataset, the best model has 66 rules with 60.29% accuracy and soil texture variable as the root node.

scholarly journals Applying Data Mining to Investigate Cancer Risk in Patients with Pyogenic Liver Abscess

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 141
Author(s):  
Jau-Shin Hon ◽  
Zhi-Yuan Shi ◽  
Chen-Yang Cheng ◽  
Zong-You Li
Keyword(s):  
Decision Tree ◽  
Liver Abscess ◽  
Biliary Tract Disease ◽  
Binary Logistic Regression ◽  
Pyogenic Liver Abscess ◽  
Incidence Rates ◽  
Decision Tree Algorithm ◽  
Clinical Tests ◽  
Tree Model ◽  
Tract Disease

Pyogenic liver abscess is usually a complication of biliary tract disease. Taiwan features among the countries with the highest incidence of colorectal cancer (CRC) and hepatocellular carcinoma (HCC). Few studies have investigated whether patients with pyogenic liver abscess (PLA) have higher incidence rates of CRC and HCC. However, these findings have been inconclusive. The risks of CRC and HCC in patients with PLA and the factors contributing to cancer development were assessed in these patients. The clinical tests significantly associated with cancers in these patients with PLA were determined to assist in the early diagnosis of these cancers. Odds ratios (ORs) and 95% confidence intervals (CIs) were determined using binary logistic regression Cancer classification models were constructed using the decision tree algorithm C5.0 to compare the accuracy among different models with those risk factors of cancers and then determine the optimal model. Thereafter, the rules were summarized using the decisi8on tree model to assist in the diagnosis. The results indicated that CRC and HCC (OR, 3.751; 95% CI, 1.149–12.253) and CRC (OR, 6.838; 95% CI, 2.679–17.455) risks were higher in patients with PLA than those without PLA. The decision tree analysis demonstrated that the model with the PLA variable had the highest accuracy, and that classification could be conducted using fewer factors, indicating that PLA is critical in HCC and CRC. Two rules were determined for assisting in the diagnosis of CRC and HCC using the decision tree model.

scholarly journals MOOC Learning Behavior Analysis and Teaching Intelligent Decision Support Method Based on Improved Decision Tree C4.5 Algorithm

2019 ◽  
Vol 14 (12) ◽  
pp. 29
Author(s):  
Qiang Wu
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Tree ◽  
Teaching Evaluation ◽  
Data Mining Algorithm ◽  
Decision Tree Algorithm ◽  
Tree Model ◽  
Digital Campus ◽  
Tree Algorithm ◽  
Score Analysis

to better carry out Massive Open Online Courses (MOOC) teaching evaluation and improve teaching effect, firstly, a teaching decision support system with evaluation function is designed by analyzing the actual situation of the college. Secondly, the decision tree data mining algorithm is introduced in the subsystem of student score analysis and evaluation. Finally, the decision tree model of student score analysis evaluation is constructed according to the decision tree algorithm. Through the practical exploration of applying the decision tree algorithm to the MOOC teaching evaluation management system of higher vocational colleges, it is found that the application of data mining technology to the construction of digital campus is not only reflected in the theoretical feasibility, but also reflected in its technical feasibility.

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.

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