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.

In Kant and the Problem of Metaphysics (KPM), Heidegger shows how the initial image-making power of the imagination is pictorial in the pure image of time. He further clarifies the recognized link between the syntheses and the time-visualizing power of images which is pictorial formative power, which can visualize time. The three syntheses of imagination are replaced with the modes of existence of Dasein. Three kinds of photographs resemble the images produced when the transcendental imagination functions as Dasein. We can address the ways to visualize time by considering Dasein with a photographic preoccupation through 1. A possible look in the form of presentation (a photograph shows an immediate look of a house or person as intuitable this-here); 2. An immediate look of the thing, house, or deceased person, as well as a copy or its reproduction as a death mask; and, 3. The manner of contemplation of a likeness is an immediate look of a being present in the form of representation.

Face recognition from digital images is used for surveillance and authentication in cities, organizations, and personal devices. Internet of Things (IoT)-powered face recognition systems use multiple sensors and one or more servers to process data. All sensor data from initial methods was sent to the central server for processing, raising concerns about sensitive data disclosure. The main concern was that all data from all sectors that could contain confidential information was placed in a central server. Federated Learning can solve this Problem by using several local model training servers for each region and a central aggregation server to form a global model in IoT networks. This article presents a novel approach to optimize data transfer and convergence time in federated Learning for a face recognition task using Non-dominated Sorting Genetic Algorithm II (NSGA II). The aim of the study is to balance the trade-off between training time and model accuracy in a federated Learning environment. The results demonstrate the effectiveness of the proposed approach in reducing data transfer and convergence time, leading to improved performance in face recognition accuracy. This research provides insights for researchers and practitioners to enhance the efficiency of federated Learning in real-world applications.

The purpose of the present study was to investigate the effect of generative Learning teaching based to pattern cognitive Load on the memorization math of 7th grade students of in the academic year of 2021- 2022. The research method is quasi-experimental with pre-test and post-test with four groups: two control group and two experimental groups. The statistical population of the study consisted of all 7th grade male and female students of District 2 of Mashhad city that 112 person sample was selected by single stage cluster sampling method. The subjects in the experimental group involved in generative Learning based to cognitive Load in 16 sessions and the control group did not receive any interventions. The research instrument consisted of a 20-questions researcher-made test. To analyze the obtained data, Multivariate analysis of covariance and SPSS version 24 software were used. The results showed that generative Learning teaching based to approach cognitive Load have a positive and significant effect on 7th grade students' memorization math. based on the findings, it can be concluded that one of the effective training on math memorization students is the use of generative Learning on the based load cognitive pattern.

The origins of Problem-based Learning (PBL) probably go back to 1920s. Celestin Freinet, a primary school teacher, returned from World War I to his village in south-east France. Due to his injuries, he was too breathless to speak to the class for more than a few minutes, so he created a new system of Learning. This new system encouraged his pupils to take control of their own studies, communicate effectively, be more cooperative and evaluate their own progress. PBL, as currently employed in medical education, was first used and recognized at McMaster University, in Canada, in the late 1960s. PBL is an active form of Learning stimulated by, and focused around a clinical, community or scientific Problem….

Background: Problem-based Learning (PBL) is one of the most commonly used educational methods in medical schools of different countries. By working through this method, students think critically, generate ideas, and acquire the knowledge and skills required to become a doctor.Objectives: This study aimed to compare Problem-based Learning with lecture-based Learning in the education of medical students.Materials and Methods: This crossover interventional study was conducted on 40 medical students in pediatric ward of Zanjan University of Medical Sciences. All of the students were enrolled in the study and divided into two groups by simple randomization. Then two topics in pediatric courses were chosen. One of the topics was presented as LBL for the first group and as PBL for the second group. The other topic was presented as PBL for the first group and as LBL for the second group.Results: The median score of the exam was higher in the intervention group compare to the control group for both topics. However, the difference was not statistically significant. Students preferred Problem-based Learning over lecture-based Learning because of motivation boost, a higher quality of education, knowledge retention, class attractiveness, and practical use.Conclusions: Students’ knowledge was similar in both methods.