A Novel Approach for Optimization of Convolution Neural Network with Particle Swarm Optimization and Genetic Algorithm for Face Recognition

Convolutional neural networks are contemporary deep learning models that are employed for many various applications. In general, the filter size, number of filters, number of convolutional layers, number of fully connected layers, activation function and learning rate are some of the hyperparameters that significantly determine how well a CNN performs.. Generally, these hyperparameters are selected manually and varied for each CNN model depending on the application and dataset. During optimization, CNN could get stuck in local minima. To overcome this, metaheuristic algorithms are used for optimization. In this work, the CNN structure is first constructed with randomly chosen hyperparameters and these parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. A CNN with optimized hyperparameters is used for face recognition. CNNs optimized with these algorithms use RMSprop optimizer instead of stochastic gradient descent. This RMSprop optimizer helps the CNN reach global minimum quickly. It has been observed that optimizing with GA and PSO improves the performance of CNNs. It also reduces the time it takes for the CNN to reach the global minimum

Integrating water-energy-nexus in carbon footprint analysis in water utility company

The purpose of this paper is to highlight the water-energy-nexus within the context of carbon footprint methodology and water utility industry. In particular, the carbon management for water utility industry is crucial in reducing carbon emission within the upstream water distribution system. The concept of water-energy nexus alone however can be misleading due to exclusion of indirect and embodied energy involved in the water production. The study highlights the total energy use within water supply system as well as embedded carbon emission through carbon footprint methodology. The case study approach is used as a research method. The carbon footprint analysis includes data collection from water utility company; and data identification of direct and indirect carbon emission from corporation operation. The result indicates that the indirect and embodied energy may not be significant in certain operation area but the energy use may be ambiguous when these elements are excluded. Integrating carbon footprint methodology within the water supply system can improve the understanding on water-energy-nexus when direct and indirect energy use is included in the analysis. This paper aims to benefit academics, government agencies and particularly water utility companies in integrating carbon footprint analysis in water production