Journal ArticleDOI
Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction
TLDR
Six learning algorithms including biogeography-based optimization, particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, and population-based incremental learning are used to train a new dendritic neuron model (DNM) and are suggested to make DNM more powerful in solving classification, approximation, and prediction problems.Abstract:
An artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved a great success in many fields, e.g., classification, prediction, and control. However, traditional ANNs suffer from many problems, such as the hard understanding problem, the slow and difficult training problems, and the difficulty to scale them up. These problems motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses, not only for a better understanding of a biological neuronal system, but also for providing a more useful method for solving practical problems. To achieve its better performance for solving problems, six learning algorithms including biogeography-based optimization, particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, and population-based incremental learning are for the first time used to train it. The best combination of its user-defined parameters has been systemically investigated by using the Taguchi’s experimental design method. The experiments on 14 different problems involving classification, approximation, and prediction are conducted by using a multilayer perceptron and the proposed DNM. The results suggest that the proposed learning algorithms are effective and promising for training DNM and thus make DNM more powerful in solving classification, approximation, and prediction problems.read more
Citations
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Journal ArticleDOI
Automatic detection of COVID-19 infection using chest X-ray images through transfer learning
Elene Firmeza Ohata,Gabriel M. Bezerra,João Victor Souza das Chagas,Aloisio Vieira Lira Neto,Adriano Bessa Albuquerque,Victor Hugo C. de Albuquerque,Pedro Pedrosa Rebouças Filho +6 more
TL;DR: This work proposes an automatic detection method for COVID-19 infection based on chest X-ray images using different architectures of convolutional neural networks trained on ImageNet, and adapt them to behave as feature extractors for the X-Ray images.
Journal ArticleDOI
An embedded feature selection method for imbalanced data classification
Haoyue Liu,MengChu Zhou,Qing Liu +2 more
TL;DR: An embedded feature selection method using the proposed weighted Gini index ( WGI) is proposed, which shows that F-statistic and Chi2 reach the best performance when only a few features are selected, and as the number of selected features increases, the proposed method has the highest probability of achieving the highest performance.
Journal ArticleDOI
AI-based modeling and data-driven evaluation for smart manufacturing processes
TL;DR: The objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.
Journal ArticleDOI
A stochastic configuration network based on chaotic sparrow search algorithm
TL;DR: A stochastic configuration network based on chaotic sparrow search algorithm is first introduced, termed as CSSA-SCN, and experimental results demonstrate the feasibility and validity of CSSA -SCN compared with SCN and other contrast algorithms.
Journal ArticleDOI
Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing With Genetic Simulated-Annealing-Based Particle Swarm Optimization
TL;DR: In this paper, a hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSPO) was proposed to minimize the total energy consumed by mobile devices and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of mobile devices, allocated bandwidth of available channels, and transmission power of each mobile device in each time slot.
References
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Proceedings ArticleDOI
A new optimizer using particle swarm theory
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