4 research outputs found

    Training Artificial Neural Networks by PSO to Perform Digital Circuits Using Xilinx FPGA

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    One of the major constraints on hardware implementations of Artificial Neural Networks (ANNs) is the amount of circuitry required to perform the multiplication process of each input by its corresponding weight and there subsequent addition. Field Programmable Gate Array (FPGA) is a suitable hardware IC for Neural Network (NN) implementation as it preserves the parallel architecture of the neurons in a layer and offers flexibility in reconfiguration and cost issues. In this paper the adaption of the ANN weights is proposed using Particle Swarm Optimization (PSO) as a mechanism to improve the performance of ANN and also for the reduction in the ANN hardware. For this purpose we modified the MATLAB PSO toolbox to be suitable for the taken application. In the proposed design training is done off chip then the fully trained design is download into the chip, in this way less circuitry is required. This paper executes four bit Arithmetic Logic Unit (ALU) implemented using Xilinx schematic design entry tools as an example for the implementation of digital circuits using ANN trained by PSO algorithm

    Grass Root Algorithm Optimize Neural Networks for Classification Problem

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    Artificial neural networks are computational models that trying to emulate the structure and functions of biological human networks. They have been extensively used in many applications include science, business, engineering, and data mining. Learning of an artificial neural network means how to adapt the weights of the network interconnections using suitable adaption algorithm. The training algorithms that is used to modify the weights of the network are considered the most important portion that influences the artificial networks performance. In the past few decade, many meta-heuristic algorithms have been used to optimize networks synaptic weights, in order to achieve better performance. This paper proposes a general network training method based on population-based algorithms, proposes a novel meta-heuristic algorithm that is inspired by the general grass plants root system to optimize the weights of the proposed artificial network to classify real data four classes XOR and Iris data comparing the obtained results of the proposed algorithm with other familiar evolutionary meta-heuristic algorithms.

    SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

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    Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population
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