Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci

م.د. فاطمة حميد ،أ.م.د وفاء صباح محمد الخفاجي, International Journal of Psychosocial Rehabilitation,, 2020 - Cited by 1

Emotional exhaustion considered one of the critical factors in the formation and composition of organizational behavior of individuals within organizations, as well as social behavior and psychological, and emotional exhaustion is one of the three components of burnout, as well as depersonalization (cynicism) and low achievement, the emergence of research relevant to this concept began at the beginning of the seventies of the twentieth century, then started to become clear features in the eighties it. This research aims to build intellectual framework for draining emotional exhaustion through highlight on most important philosophical contents, as well as review and analysis of some models associated with this concept, and then a

Enzyme activity were studied in the sera of children with leukemia than healthy children, where 31 cases were studied, including 21 cases of patients with acute lymphatic leukemia

Due to the numerous motor and performance skills required of female students in futsal refereeing during college lessons, along with the constant need to reposition themselves and move within the playing field, continuous monitoring and repetition are essential to minimize potential errors during performance. These errors may arise from failing to assume the correct position or delays in signalling officiating gestures. Addressing this issue necessitated the implementation of an electronic program and supplementary tools—such as electronic games, scientific posters illustrating officiating signals, and other instructional aids—since these serve as guiding tools that direct individuals toward correct performance by distinguishing