Current Issue : July - September Volume : 2018 Issue Number : 3 Articles : 5 Articles
Several threats are propagated by malicious websites largely classified as\nphishing. Its function is important information for users with the purpose of\ncriminal practice. In summary, phishing is a technique used on the Internet\nby criminals for online fraud. The Artificial Neural Networks (ANN) are\ncomputational models inspired by the structure of the brain and aim to simulate\nhuman behavior, such as learning, association, generalization and abstraction\nwhen subjected to training. In this paper, an ANN Multilayer Perceptron\n(MLP) type was applied for websites classification with phishing characteristics.\nThe results obtained encourage the application of an ANN-MLP\nin the classification of websites with phishing characteristics....
Conventional models of motor control exploit the spatial representation of the controlled system to generate control commands.\nTypically, the control command is gained with the feedback state of a specific instant in time, which behaves like an optimal\nregulator or spatial filter to the feedback state. Yet, recent neuroscience studies found that the motor network may constitute an\nautonomous dynamical system and the temporal patterns of the control command can be contained in the dynamics of the motor\nnetwork, that is, the dynamical system hypothesis (DSH). Inspired by these findings, here we propose a computational model that\nincorporates this neural mechanism, in which the control command could be unfolded from a dynamical controller whose initial\nstate is specified with the task parameters. The model is trained in a trial-and-error manner in the framework of deep deterministic\npolicy gradient (DDPG). The experimental results show that the dynamical controller successfully learns the control policy for arm\nreaching movements, while the analysis of the internal activities of the dynamical controller provides the computational evidence\nto the DSH of the neural coding in motor cortices....
Structural system design is the process of giving form to a set of interconnected components\nsubjected to loads and design constraints while navigating a complex design space. While safe\ndesigns are relatively easy to develop, optimal designs are not. Modern computational optimization\napproaches employ population based metaheuristic algorithms to overcome challenges with the\nsystem design optimization landscape. However, the choice of the initial population, or ground\nstructure, can have an outsized impact on the resulting optimization. This paper presents a new\nmethod of generating such ground structures, using a combination of topology optimization (TO) and\na novel system extraction algorithm. Since TO generates monolithic structures, rather than systems,\nits use for structural system design and optimization has been limited. In this paper, truss systems\nare extracted from topologies through morphological analysis and artificial intelligence techniques.\nThis algorithm, and its assessment, constitutes the key contribution of this paper. The structural\nsystems obtained are compared with ground truth solutions to evaluate the performance of the\nalgorithms. The generated structures are also compared against benchmark designs from the literature.\nThe results indicate that the presented truss generation algorithm produces structures comparable\nto those generated through metaheuristic optimization, while mitigating the need for assumptions\nabout initial ground structures....
This study investigated whether parameters derived from hand motions of expert and novice surgeons accurately and objectively\nreflect laparoscopic surgical skill levels using an artificial intelligence system consisting of a three-layer chaos neural network. Sixtyseven\nsurgeons (23 experts and 44 novices) performed a laparoscopic skill assessment task while their hand motions were recorded\nusing a magnetic tracking sensor. Eight parameters evaluated as measures of skill in a previous study were used as inputs to the\nneural network. Optimization of the neural network was achieved after seven trials with a training dataset of 38 surgeons, with\na correct judgment ratio of 0.99. The neural network that prospectively worked with the remaining 29 surgeons had a correct\njudgment rate of 79% for distinguishing between expert and novice surgeons. In conclusion, our artificial intelligence system\ndistinguished between expert and novice surgeons among surgeons with unknown skill levels....
A new control system of a hand gesture-controlled wheelchair (EWC) is proposed. This smart control device is suitable for a large\nnumber of patients who cannot manipulate a standard joystick wheelchair. The movement control system uses a camera fixed on\nthe wheelchair. The patientââ?¬â?¢s hand movements are recognized using a visual recognition algorithm and artificial intelligence\nsoftware; the derived corresponding signals are thus used to control the EWC in real time. One of the main features of this\ncontrol technique is that it allows the patient to drive the wheelchair with a variable speed similar to that of a standard joystick.\nThe designed device ââ?¬Å?hand gesture-controlled wheelchairââ?¬Â is performed at low cost and has been tested on real patients and\nexhibits good results. Before testing the proposed control device, we have created a three-dimensional environment simulator to\ntest its performances with extreme security. These tests were performed on real patients with diverse hand pathologies in\nMohamed Kassab National Institute of Orthopedics, Physical and Functional Rehabilitation Hospital of Tunis, and the validity\nof this intelligent control system had been proved....
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