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Rockburst is a complex phenomenon of dynamic instability in the underground excavation of rock. Owing to the complex and unclear rockburst mechanism, it is difficult to accurately predict and reasonably assess the rockburst potential. With the increasing availability of case histories from rock engineering and the advancement of data science, the data mining algorithms provide a good way to predict complex phenomena, like rockburst potential. This paper investigates the potential of J48 and random tree algorithms to predict the rockburst classification ranks using 165 cases, with four parameters, namely maximum tangential stress of surrounding rock, uniaxial compressive strength, uniaxial tensile strength, and strain energy storage index. A comparison of developed models’ performances reveals that the random tree gives more reliable predictions than J48 and other empirical models (Russenes criterion, rock brittleness coefficient criterion, and artificial neural networks). Similar comparisons with convolutional neural network resulted at par performance in modeling the rockburst hazard data.

In the era of globalization and the Internet, due to the unprecedented business pressure from competition, in order to make long-term benefits and achieve sustainable development, enterprises should select competitive markets for their products to maximize the benefits of limited resources. The key to corporate survival and development is to find the most profitable customer bases all over the world and develop products to meet their demands. Traditionally, market targeting relies on the decisions of a small number of senior managers in enterprises; however, due to novel and changing customer demands, the business environment has become increasingly complex. If previous traditional methods are adopted, enterprises may select the wrong markets, which can lead to complete destruction. Therefore, this study proposes a new market targeting method and replaces human decisions with artificial intelligence (AI) algorithms, in order to render market targeting more scientific and systematic, improve the quality of marketing decisions, maximize corporate profits, occupy the optimal market with limited resources, and achieve the goal of sustainable business. This study applied three AI algorithms, the naive Bayes algorithm, J48 algorithm, and OneR algorithm, for model training and analytical prediction of the testing datasets. According to the results, the model accuracies of the naive Bayes algorithm, J48 algorithm, and OneR algorithm are 100, 91.7, and 83.3%, respectively; the F-measures of the naive Bayes algorithm, J48 algorithm, and OneR algorithm are 1, 0.909, and 0.8, respectively, which indicates that the three algorithms have reliable predictions. The results show that AI algorithms can help enterprises in market targeting.

Automatic Modulation Recognition (AMR) has a significant impact in the military as well as civil applications. Recognizing the modulation of the received signal has been considered as an intermediate step between the detection and demodulation of the signal. Which is why, in many military and communication systems, the AMR is considered as part of the system. Presently, due to increasing digital modulations in military and civil applications. Digital modulation recognition is especially important. Usually for the AMR a small number of the received signal features are obtained and utilized. The choice of the suitable feature plays an important part in the increase of AMR efficiency. The presented paper indicates hybrid intelligent system for the recognitions of digital signal types, consisting of 3 major modules: classifier module, feature extraction module and J48 Classifier that was used for the first time in our research in the field of classification of modulated signals and optimization module by Chicken Swarm Optimization (CSO). To get better results of the system suggested optimization the features to discard weak or irrelevant features in the system and keep only strong relevant features Chicken Swarm Optimization. The results of simulation confirm the high accuracy of recognition that is related to the suggested system even at low SNR.

Due to the huge amount of data obtained from students’ academic results in most tertiary institutions such as the colleges, polytechnics and universities, data mining has become one of the most effective tools for discovering vital knowledge from students’ dataset. The discovered knowledge can be productive in understanding numerous challenges in the scope of education and providing possible solutions to these challenges. The main objective of this research is to utilize the J48 decision algorithm model to test, classify and predict the students’ dataset by identifying some important attributes and instances. The analysis was conducted on the final year students’ academic results in C# programming amongst five universities which was imported in csv excel file dataset in WEKA environment. These training datasets contained the scores obtained in the examinations, grade remarks, grades, gender, and department. The knowledge extracted for the prediction model will help both the tutors and students to determine the success grade performance in the future. Flow lines, J48 decision trees, confusion matrices and a program flowchart were generated from the students’ dataset. The KAPPA value obtained from the prediction in this research ranges from 0.9070–0.9582 which perfectly agrees with the standard for an ideal analysis on datasets.

Background: The etiologies of kidney disease are sequential and interlinked. They are known as disease markers. The Decision Tree representing initial symptoms needs to be popularized to seek prophylactic measures and educate the community health workers. Material and Methods'. Chronic Kidney Disease dataset containing two classes, namely CKD and not CKD was retrieved from UC Irvine Machine Learning Repository available from https://archive.ics.uci.edu/ml/index.php. This dataset includes 400 instances and 25 attributes related to the CKD. The CKD dataset was saved in ARFF format. J48 algorithm was chosen from the WEKA software tool to develop the Decision Tree. Results: The Decision Tree comprises root nodes, branches, internal nodes, and leaf nodes. The root node began in J48 classifier with 'sc' (serum creatinine). This node gave two branches, of which one branch led to the internal node 'pe' (pedal edema) (sc<=1.2), and the other (sc>1.2) ended with a leaf node showing the condition CKD. The internal node 'pe' further yielded two branches, the branch with pedal edema terminated with CKD and the branch with no pedal edema led to the internal node. Internal node with 'dm' (diabetes mellitus) showed two more branches namely 'yes', which lead to CKD, and branch 'no' 'dm' led to another internal node namely hemo (haemoglobin). In continuation, the nodes for yet another two disease markers, namely hemoglobin and specific gravity of serum of people prone to kidney diseases, were shown at the terminal end of the Decision Tree. Conclusion: The Decision Tree developed for the CKD dataset by using J48 classifier would guide prospective patients with their clinical data reports.

The hypersaline wetland of Meighan located in western Iran is an important habitat for Artemia parthenogenetica . The habitat condition of this native zooplankton is facing with various problems in the wetland, so its abundance has been reduced in the wetland in the recent years. The study aimed to optimize decision tree model with an optimizer (greedy stepwise) to predict the species abundance in 10 different sampling sites over one-year study period (2017–2018). The model output was the species abundance categorized into 4 classes (poor: 5–20; fair: 21–50; good: 51–100; very good:101–255 individuals) and measured with abiotic variables. The optimizer method improved the model performance leading to easy interpretation of the model. According to the model’s prediction, high abundance of species in the wetland is associated with high concentration of specific conductivity, dissolved oxygen and total dissolved solids. In contrast, increased concentration of chloride, total suspended solids, nitrate and precipitation might decrease the abundance of zooplankton. Chi-square test showed a significant difference between the species abundance and spatio-temporal patterns in the wetland ( x 2  = 160.2, p  = 0.001) so that warm seasons (spring and summer) had more contribution to the zooplankton sampling than cold seasons (autumn and winter).

To predict and make an early diagnosis of diabetes is a critical approach in a population with high risk of diabetes, one of the devastating diseases globally. Traditional and conventional blood tests are recommended for screening the suspected patients; however, applying these tests could have health side effects and expensive cost. The goal of this study was to establish a simple and reliable predictive model based on the risk factors associated with diabetes using a decision tree algorithm. A retrospective cross-sectional study was used in this study. A total of 10,436 participants who had a health check-up from January 2017 to July 2017 were recruited. With appropriate data mining approaches, 3454 participants remained in the final dataset for further analysis. Seventy percent of these participants (2420 cases) were then randomly allocated to either the training dataset for the construction of the decision tree or the testing dataset (30%, 1034 cases) for evaluation of the performance of the decision tree. For this purpose, the cost-sensitive J48 algorithm was used to develop the decision tree model. Utilizing all the key features of the dataset consisting of 14 input variables and two output variables, the constructed decision tree model identified several key factors that are closely linked to the development of diabetes and are also modifiable. Furthermore, our model achieved an accuracy of classification of 90.3% with a precision of 89.7% and a recall of 90.3%. By applying simple and cost-effective classification rules, our decision tree model estimates the development of diabetes in a high-risk adult Chinese population with strong potential for implementation of diabetes management.

The microalga Dunaliella salina has been broadly studied for different purposes such as beta-carotene production, toxicity assessment and salinity tolerance, yet research on the habitat suitability of this alga has rarely been reported. The present research aims to apply a suitable monitoring and modelling methods (two critical steps in ecological researches) to predict the abundance of D. salina . The abundance of D. salina was predicted by decision tree model (J48 algorithm) in 10 different monitoring sites during 1-year study period (2016–2017) in the Meighan wetland, one of the valuable hypersaline wetlands in Iran. The abundance of alga (as output of model) together with various water quality and physical-habitat wetland characteristics (as inputs of model) were monthly and repeatedly monitored in two different depths (one from the surface layer and another one from the depth of maximum 50 cm) which in total resulted in 240 instances (120 instances for each depth). Based on trial and error, a sevenfold cross-validation resulted in the highest predictive performances of the model (CCI > 75% and Cohen’s Kappa > 0.65). According to the model’s prediction, the number of sunny hours might be one of the most important driving parameters to meet the habitat requirements of alga in the hypersaline wetland. Model also predicted that an increase in dissolved oxygen and sodium concentrations might increase the abundance of D. salina in the salt wetland. In contrast, an increase in total suspended solids concentration and monthly precipitation might lead to a decrease in the abundance of alga. Chi-square test of independence showed a significant difference between the abundance of the D. salina and spatio-temporal patterns in the wetland (Pearson chi-square statistic = 221.7, p  = 0.001) so warm seasons (spring and summer) had more contribution to the sampling of the species than cold seasons (autumn and winter). The difference in the abundance of the species in different sampling sites can be attributed due to the various anthropogenic activities.

In Colombia, all undergraduate students, regardless of the professional training program they take, must complete the general competencies sections of the Saber Pro exam that include Critical Reading, Quantitative Reasoning, Citizen Competencies, Written Communication, and English. This paper presents the application of the classification technique based on decision trees in the prediction of the performance in the Critical Reading section presented by the students of the Pontificia Universidad Javeriana Cali in the years 2017 and 2018. The CRISP methodology was used. From the socioeconomic, academic and institutional data stored in the ICFES databases, a data repository was built, cleaned and transformed. A mineable view composed of 2052 records and 17 attributes was obtained. The J48 algorithm of the Weka tool was used to build the decision tree. The score obtained in the Critical Reading section of the Saber Pro exam was taken as a class. According to the results obtained, the Philosophy, Applied Mathematics, and Medicine programs stood out for having the best performance in this test. Among the predictive variables associated with performance in the Critical Reading skill are the faculty, the age group and the student's transportation index, as three important variables related to the good or low academic performance of the students of the Universidad Javeriana Cali. The knowledge generated in this research is constituted in quality information to support the decision-making process of the university directives in order to improve the quality of the higher education offered in this institution.

Nonalcoholic fatty liver disease (NAFLD) is the commonest form of chronic liver disease worldwide and its prevalence is rapidly increasing. Screening and early diagnosis of high-risk groups are important for the prevention and treatment of NAFLD; however, traditional imaging examinations are expensive and difficult to perform on a large scale. This study aimed to develop a simple and reliable predictive model based on the risk factors for NAFLD using a decision tree algorithm for the diagnosis of NAFLD and reduction of healthcare costs. This retrospective cross-sectional study included 22,819 participants who underwent annual health examinations between January 2019 and December 2019 at Physical Examination Center in Shengjing Hospital of China Medical University. After rigorous data screening, data of 9190 participants were retained in the final dataset for use in the J48 decision tree algorithm for the construction of predictive models. Approximately 66% of these patients (n=6065) were randomly assigned to the training dataset for the construction of the decision tree, while 34% of the patients (n=3125) were assigned to the test dataset to evaluate the performance of the decision tree. The results showed that the J48 decision tree classifier exhibited good performance (accuracy=0.830, precision=0.837, recall=0.830, F-measure=0.830, and area under the curve=0.905). The decision tree structure revealed waist circumference as the most significant attribute, followed by triglyceride levels, systolic blood pressure, sex, age, and total cholesterol level. Our study suggests that a decision tree analysis can be used to screen high-risk individuals for NAFLD. The key attributes in the tree structure can further contribute to the prevention of NAFLD by suggesting implementable targeted community interventions, which can help improve the outcome of NAFLD and reduce the burden on the healthcare system.