A data-parallelism approach for PSO-ANN based medical image reconstruction on a multi-core system

Designing Artificial Neural Network Using Particle Swarm Optimization: A Survey

Neural network modeling has become a special interest for many engineers and scientists to be utilized in different types of data as time series, regression, and classification and have been used to solve complicated practical problems in different areas, such as medicine, engineering, manufacturing, military, business. To utilize a prediction model that is based upon artificial neural network (ANN), some challenges should be addressed that optimal designing and training of ANN are major ones. ANN can be defined as an optimization task because it has many hyper parameters and weights that can be optimized. Metaheuristic algorithms such as swarm intelligence-based methods are a category of optimization methods that aim to find an optimal structure of ANN and to train the network by optimizing the weights. One of the commonly used swarm intelligence-based algorithms is particle swarm optimization (PSO) that can be used for optimizing ANN. In this study, we review the conducted research works on optimizing the ANNs using PSO. All studies are reviewed from two different perspectives: optimization of weights and optimization of structure and hyper parameters.

A Survey of Soft Computing Approaches in Biomedical Imaging

Medical imaging is an essential technique for the diagnosis and treatment of diseases in modern clinics. Soft computing plays a major role in the recent advances in medical imaging. It handles uncertainties and improves the qualities of an image. Until now, various soft computing approaches have been proposed for medical applications. This paper discusses various medical imaging modalities and presents a short review of soft computing approaches such as fuzzy logic, artificial neural network, genetic algorithm, machine learning, and deep learning. We also studied and compared each approach used for other imaging modalities based on the certain parameter used for the system evaluation. Finally, based on comparative analysis, the possible research strategies for further development are proposed. As far as we know, no previous work examined this issue.

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.