Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine Learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using deep machine Learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase Learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some state-of-the-art works.

Cerebellar Model Articulation Controller Neural Network is a computational model of cerebellum which acts as a lookup table. The advantages of CMAC are fast Learning convergence, and capability of mapping nonlinear functions due to its local generalization of weight updating, single structure and easy processing. In the training phase, the disadvantage of some CMAC models is unstable phenomenon or slower convergence speed due to larger fixed or smaller fixed Learning rate respectively. The present research deals with offering two solutions for this problem. The original idea of the present research is using changeable Learning rate at each state of training phase in the CMAC model. The first algorithm deals with a new Learning rate based on reviation of Learning rate. The second algorithm deals with number of training iteration and performance Learning, with respect to this fact that error is compatible with inverse training time. Simulation results show that this algorithms have faster convergence and better performance in comparison to conventional CMAC model in all training cycles.

Evaluating the cutting rate (CR) of stones is important in the cost estimation and the planning of the stone processing plants. This research used regression models to estimate the stones’ CR based on their physico-mechanical characteristics. Stone processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator models were developed. Results showed that multiple regression models are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression models was verified with the published data of one researcher. The findings indicated that these models are accurate enough for estimating the CR of stones. Consequently, the multiple regression models provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone processing factories.

Background: The current study is to evaluate cardiovascular effects of anesthetic medications and volatile anesthetics on cardiac stress using cardiac stress index (CSI) and rate pressure product (RPP) and to determine which of them in useful in evaluating cardiac stress after comparing results obtained from each method.Materials and methods: 40 patients, 60-80 years old, who were all performed trans abdominal prostatectomy were studied. Patients were divided into two groups, half of patients were placed in group A and the other half in group B. The study was carried out as a blinded study. CSI was measured and evaluated in group A and RPP changes were studied in group B.Results: The mean CSI were 60.25±5.57, 63.05±5.54, 55.75±4.78 and 67.65±4.88 before anesthesia induction, after induction, before surgical incision and in recovery respectively. There was no meaningful difference.RPP mean in four above mentioned stages was 10.15±0.44, 9.9±0.69, 6.8±0.36 and 9.2±0.61 respectively. There was a meaningful difference between RPP in stages before anesthesia induction and before surgical incision (P<0.0001).Conclusion: Considering the obtained results from this study, it can be seen that even in noncardiac surgery, the stress level is high in patients in the stage before surgery. This figure was not clear in RPP index case and was not in accordance with CSI, which means CSI has been able to illustrate existing stress level better and efficiently.