in efficient management teaching, students' results are important basis to evaluate teaching quality and teaching effect. Factors influencing students' results are complex and diversified. It is necessary to r...
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ISBN:
(纸本)9781510803121
in efficient management teaching, students' results are important basis to evaluate teaching quality and teaching effect. Factors influencing students' results are complex and diversified. It is necessary to reasonably utilize data mining technology and adopt decisiontree to analyze and predict students' results, correct bad behaviors influencing students' results in time in allusion to predication results and change teaching strategies. This paper studies application and value of decision tree algorithm in analyzing students' results in colleges. Teaching objective of higher education is to cultivate high-quality elites and inter-disciplinary talents and improve teaching quality. However, in teaching, students' results are an important indicator to evaluate students' knowledge mastery and also important basis to evaluate teaching quality. Reasonable analysis and prediction of students' results can provide important basis for enhancing teaching management, improving teaching environment, boosting teaching quality and deepening teaching reform. Data mining technology is the foundation and precondition of further deep-level data analysis in decision-making process. So, it is very significant to apply data mining technology in result analysis. It can comprehensively analyze the relationship between exam results and factors influencing results. When data mining technology is used to analyze students' results, relevant results can be gained in time and students, and bad behaviors can be corrected in time, too.
This paper discusses the basic operating principle and the development status of data mining technology,analyzes the insufficiency of the existing psychological management system,and proposes the development trend of ...
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This paper discusses the basic operating principle and the development status of data mining technology,analyzes the insufficiency of the existing psychological management system,and proposes the development trend of psychological health education in *** to an analysis on factors affecting college students' mental health and the deviation between the reality and the current number of students with psychological abnormality,this paper studies the application of data mining technology and puts forward a system based on data mining that combines the classified data mining technology with the existing psychological management system.
Middle East Respiratory Syndrome, which is a respiratory disease caused by MERS coronavirus, is known to be an endemic disease spread in Kingdom of Saudi Arabia, or KSA. On May 20, 2015, it has massively occurred in R...
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ISBN:
(纸本)9781509016969
Middle East Respiratory Syndrome, which is a respiratory disease caused by MERS coronavirus, is known to be an endemic disease spread in Kingdom of Saudi Arabia, or KSA. On May 20, 2015, it has massively occurred in Republic of Korea, with 186 confirmed cases, 36 deaths. In this paper, we analyzed some features of MERS-CoV's transmission route by a new molecular approach. We have collected DNA sequences of MERS-CoV from 15 different regions in the world, including some regions of KSA. We have converted the DNA sequences into amino acid sequences and used Apriori and decision tree algorithm to found the similarities and differences between different MERS-CoVs' amino acid sequences. Then, we drew some conclusions about MERS's transmission routes by using these results.
Groundwater potential mapping is an important prerequisite for evaluating the exploitation, utilization, and recharge of groundwater. The study uses BFT (best-first decisiontree classifier), CART (classification and ...
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Groundwater potential mapping is an important prerequisite for evaluating the exploitation, utilization, and recharge of groundwater. The study uses BFT (best-first decisiontree classifier), CART (classification and regression tree), FT (functional trees), EBF (evidential belief function) benchmark models, and RF-BFtree, RF-CART, and RF-FT ensemble models to map the groundwater potential of Wuqi County, China. Firstly, select sixteen groundwater spring-related variables, such as altitude, plan curvature, profile curvature, curvature, slope angle, slope aspect, stream power index, topographic wetness index, stream sediment transport index, normalized difference vegetation index, land use, soil, lithology, distance to roads, distance to rivers, and rainfall, and make a correlation analysis of these sixteen groundwater spring-related variables. Secondly, optimize the parameters of the seven models and select the optimal parameters for groundwater modeling in Wuqi County. The predictive performance of each model was evaluated by estimating the area under the receiver operating characteristic (ROC) curve (AUC) and statistical index (accuracy, sensitivity, and specificity). The results show that the seven models have good predictive capabilities, and the ensemble model has a larger AUC value. Among them, the RF-BFT model has the highest success rate (AUC = 0.911), followed by RF-FT (0.898), RF-CART (0.894), FT (0.852), EBF (0.824), CART (0.801), and BFtree (0.784), respectively. Groundwater potential maps of these 7 models were obtained, and four different classification methods (geometric interval, natural breaks, quantile, and equal interval) were used to reclassify the obtained GPM into 5 categories: very low (VLC), low (LC), moderate (MC), high (HC), and very high (VHC). The results show that the natural breaks method has the best classification performance, and the RF-BFT model is the most reliable. The study highlights that the proposed ensemble model has more effi
The selection of appropriate cutter types and configurations is crucial for effective Tunnel Boring Machine (TBM) operations. This study introduces a novel decisiontree methodology to develop a model for selecting pr...
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The selection of appropriate cutter types and configurations is crucial for effective Tunnel Boring Machine (TBM) operations. This study introduces a novel decisiontree methodology to develop a model for selecting primary cutter types (PCT) and cutter configurations based on actual data from 112 high-performing TBM projects. Key parameters, including geological conditions (Soft Soil, Hard Soil, Coarse Soil, Bouldery Ground, Soft Mixed, Hard Mixed), machine type, diameter, and cutter type, were analyzed. Various decision tree algorithms, including C4.5, CART, SVM, Random Forest, and ensemble methods such as Bagging, AdaBoost, XGBoost, and LightGBM, were applied and evaluated using performance metrics (accuracy, precision, recall, F1-score, and ROC-AUC). Results indicate that CART and KNN algorithms are the best performers, with accuracies of 89.3 % and 88.4 %, respectively, while AdaBoost was the least effective. decision rules from the CART model reveal geological conditions as the most significant predictor of PCT, followed by machine type and diameter. This study provides a systematic framework for cutter configuration selection in mechanized tunneling, offering practical guidelines for the industry.
In order to realize the scientific layout of underground roadway sensors, based on the information entropy theory, the decision tree algorithm is applied to the location selection of the underground wind speed sensor....
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In order to realize the scientific layout of underground roadway sensors, based on the information entropy theory, the decision tree algorithm is applied to the location selection of the underground wind speed sensor. Taking the air volume traversing the entire network branch as the goal, the node air volume balance and the characteristic relationship between air volume and wind pressure as the constraints, the conventional layout data of the mine wind speed sensor is selected as the training sample, and the reasonable decision node is selected by measuring the uncertainty of the characteristic attribute. The research shows that the smaller the information entropy of the layout elements, the greater the weight in determining the sensor location. The classification conditions obtained by the wind speed sensor are, in descending, order: airflow disturbance, roadway support, distance from the inlet (return) and outlet, and roadway type. algorithms are applied to effectively combine downhole sensor siting with historical data containing unambiguous results.
Manufacturing industries facing problem in optimal selection of process parameters in machining process. Finding optimum process parameters for achieving maximum Material Removal Rate and minimum Surface Roughness is ...
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ISBN:
(纸本)9781728185194
Manufacturing industries facing problem in optimal selection of process parameters in machining process. Finding optimum process parameters for achieving maximum Material Removal Rate and minimum Surface Roughness is a challenging task and it requires lot of time and energy for experimentation trails or experience. It wastes lot of resources and money, sometimes ends up with negative results. To overcome the above issue, this paper presents an algorithm for prediction of Surface Roughness and Material Removal rate using decision tree algorithm and Naive Bayes algorithm without experimentation. Lot of resources and time can be saved using these machine learning algorithms. In this paper, Material removal rate and Surface roughness of EDM machining of Aluminum composites is predicted using decision tree algorithm and Naive Bayes algorithm. Then the model can be used to predict the Material Removal Rate and Surface finish of any combination process parameters before machining process.
Prenatal diagnostics are vital for the woman as well as her unborn baby. The diagnostics help in the early identification of the possibility of complication and the initial measures that help to ameliorate the mother ...
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Prenatal diagnostics are vital for the woman as well as her unborn baby. The diagnostics help in the early identification of the possibility of complication and the initial measures that help to ameliorate the mother and the fetus health status are taken. Over the year's various techniques have been employed in diagnosing genetic disorders before birth that lack effectiveness in terms of cost, time, and places to access ultra-modern health facilities. To overcome these problems, this paper puts forward a diagnostic model that integrates Internet of Things innovation with a Machine Learning approach which is the decision tree algorithms. First, it implies the application of IOT devices in the collection of vital information like heart rate, blood pressure, glucose levels, and fetal movement. The data is structured in the form of a dataset and transmitted to a Big Data storage for warehousing and processing. Secondly, the DTA is employed to analyze the data and look for patterns and possibilities of future health complications. The DTA operates in that it divides the dataset into subsets considering specific features and formulates a tree-like model of decisions. At every node, the algorithm chooses the attribute which has the highest information gain, to partition the data into different classes. This process goes on until it reaches a decision node through which, it can decide probable health problems from the input data. To increase the reliability of the developed model this study fine-tunes the model by using a large database of pre-natal health records. The system is capable of collecting data in real-time and flagging data that needs attention in the case of any abnormality to the health professional. The above methodology was tested on a 1000-record database of pre-natal health records where the proposal achieved 95% possibility of potential health problems as against 85% by classical statistical analysis. Furthermore, the system scaled down the number of fals
In this study, in vitro regeneration protocol of sorghum (Sorghum bicolor) was successfully established by using direct organogenesis from a mature zygotic embryo explant. The used basal medium encompassed Murashige a...
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In this study, in vitro regeneration protocol of sorghum (Sorghum bicolor) was successfully established by using direct organogenesis from a mature zygotic embryo explant. The used basal medium encompassed Murashige and Skoog medium (MS) supplemented with 2-4 mg/L Benzylaminopurine (BAP) alone or with 0.25 mg/L Indole butyric acid (IBA) or Naphthalene acetic acid (NAA). Results demonstrated a significant impact of cytokinin-auxin on shoot count (1.24-3.46) and shoot length (2.80-3.47 cm). Maximum shoot count (3.46) and shoot length (3.97 cm) were achieved on the MS medium enriched with 2 mg/L BAP + 0.25 mg/L NAA and 2.0 mg/L BAP, respectively. To ascertain the impact of BAP alone, BAP + IBA, and BAP + NAA, the data were also analyzed by using a factorial regression model. Pareto chart and normal plots were used to check either the positive or negative impact of input variables on output variables. To further explore the association between BAP + IBA and BAP + NAA on shoot count and shoot length, contour and surface plots were also built. Three different artificial intelligence-based models along with four different performance metrics were utilized to validate the predicted results. Multilayer perceptron (MLP) model performed more efficiently (R-2 = 0.799 for shoot count and R-2 = 0.831 for shoot length) as compared to the decisiontree-based algorithms of random forest (RF) - (R-2 = 0.779 for shoot count and R-2 = 0.786 for shoot length) and extreme gradient boost (XGBoost) - (R-2 = 0.768 for shoot count and R-2 = 0.781 for shoot length). As plant tissue culture protocol is a powerful tool for genetic engineering and genome editing of crops, integration of different artificial intelligence-based models can lead to improvement of sorghum with the aid of biotechnological tools. [GRAPHICS]
This paper has conducted a study on the applications of track and field equipment training based on ID3 algorithm of decisiontree *** the selection of the elements used by decisiontree,this paper can be divided into...
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ISBN:
(纸本)9781510814585;1510814582
This paper has conducted a study on the applications of track and field equipment training based on ID3 algorithm of decisiontree *** the selection of the elements used by decisiontree,this paper can be divided into track training equipment,field events training equipment and auxiliary training equipment according to the properties of track and field *** decisiontree that regards track training equipment as root nodes has been obtained under the conditions of lowering computation cost through the selection of data as well as the application and optimization of ID3 algorithm model.
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