Background: Myocardial infarction (MI) occurs due to heart muscle death that costs like human life, which is higher than the treatment costs. This study aimed to present an MI prediction model using classification data mining me-thods, which consider the imbalance nature of the problem.Methods: We enrolled 455 healthy and 295 myocardial infarction cases of visitors to Shahid Madani Specialized Hos-pital, Khorramabad, Iran, in 2015. Then, a hybrid feature selection method included Weight by Relief and Genetic algorithm applied on the dataset to select the best features. After selection of the features, the metacost classifier ap-plied on the sampled dataset. Metacost made a cost sensitive J48 model by assigning different costs ratios for misclas-sified cases; include 1: 10, 1: 50, 1: 100, 1: 150 and 1: 200.Results: After applying the model on the imbalanced dataset, the cost ratio 1: 200 led to the best results in comparison to not using feature selection and cost sensitive model. The model achieved sensitivity, F-measure and accuracy of 86.67%, 80% and 82.67%, respectively.Conclusion: Experiments on the real dataset showed that using the cost-sensitive method along with the hybrid fea-ture selection method improved model performance. Therefore, the model considered a reliable Myocardial Infarction prediction model.

Making economic decisions and allocating resources optimally without the presence of valid and reliable data is not possible. Capital flows towards superior economic activities when capitalist decisions rely on timely, relevant and reliable information. In this regard, auditing plays a vital role in determining the validity of information; in other words, given the accountability requiring the presence of valid and reliable data, it can be stated that auditing is one of the fundamental accountability processes. In the current research, an optimum prediction method for independent auditor's report types is selected and two approaches of the J48 algorithm and random forest are compared. This research has been conducted on 84 corporates during 2008-2017. In order to train, test and investigate the research variables, Weka software was used. The dependent variable is the auditor's report type. Results indicated that the accuracy of the J48 algorithm has been 72.61% and 60.42% in training and test sections, respectively and the accuracy of the random forest has been 94.57% and 63.09% in training and test sections, respectively; so, the random forest model is more effective.

In the present study, classification trees (CTs-J48 algorithm) were used to study the occurrence of roach in rivers in Flanders (Belgium). The presence/absence of roach was modelled based on a set of river characteristics. The predictive performance of the CTs models was assessed based on the percentage of Correctly Classified Instances (CCI) and Cohen's kappa statistics. To find the best model performance, a 3- fold cross validation techniques was applied on the dataset. The effect of Pruning Confidence Factors (PCFs) was examined on the reliability and model complexity. Based on the obtained results, the induced model could predict well the presence/absence of roach in the rivers. The highest overall means of two model performances showed that the models were reliable. When analyzing the ecological relevance of CTs, it seemed that the structural-habitat variables were more the main predictors than the water quality ones to predict the occurrence of roach in rivers. In particular, the distance from the source and width contributed more to the prediction of roach while among water quality variables, only electric conductivity was relatively important in this regard.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic algorithm and multi-objective optimization goals. The genetic algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

One of the basic topics in hydrological and river engineering studies is flood routing.Flood flooding is common in multi-tributary rivers and rivers without intermediate basin statistics. Therefore, to achieve the determination of slopes and cross-sections in all sections of the river, the Muskingum hydrological model is a useful method that helps to save information on the depth and flow of the flood at any time by saving time and money. To specify. In this study, the nonlinear parameters of the new Muskingum model are optimized based on the fly algorithm (MA). In this non-linear model of Muskingum, which has eight parameters, the recovery coefficient γ is used, which has more or less values ​​than the number of peaks discharged in the output hydrograph.To evaluate the performance of Muskingum's new nonlinear model with the new MA algorithm, the Wilson and Weisman-Lewis case study has been used by many previous researchers for validation.The results of the MA algorithm for Wilson and Weissman-Lewis rivers show the minimization of the residual squares (SSQ) as the objective function, which is 3.21 for the Wilson River and 68722 for the Weissman River. The results of this study showed that the proposed model has high accuracy in estimating the output discharge values.

Todays, Intrusion Detection Systems (IDS) are considered as key components of security networks. However, high false positive and false negative rates are the important problems of these systems. On the other hand, many of the existing solutions in the articles are restricted to class datasets due to the use of a specific technique, but in real applications they may have multi-variant datasets. With the impetus of the facts, this paper presents a new anomaly based intrusion detection system using J48 Decision Tree, Support Vector Classifier (SVC) and k-means clustering algorithm in order to reduce false alarm rates and enhance the system performance. J48 decision tree algorithm is used to select the best features and optimize the dataset. Also, an SVM classifier and a modified k-means clustering algorithm are used to build a profile of normal and anomalous behaviors of dataset. Simulation results on benchmark NSL-KDD, CICIDS2017 and synthetic datasets confirm that the proposed method has significant performance in comparison with previous approaches.