Application of Cascade Forward Backpropagation Neural Networks for Selecting Mining Methods

Decision-making is very important in many fields, such as mining engineering. In addition, there has been a growth of computer applications in all fields, especially mining operations. One of these application fields is mine design and the selection of suitable mining methods, and computer applications can help mine engineers to decide upon and choose more satisfactory methods. The selection of mining methods depends on the rock-layer specification. All rock characteristics should be classified in terms of technical and economic concerns related to mining rock specifications, such as mechanical and physical properties, and evaluated according to their weights and ratings. Methodologically, in this study, the criteria considered in the University of British Columbia (UBC) method were used as references to establish general criteria. These criteria consist of general shape, ore thickness, ore plunge, and grade distribution, in addition to the rock quality designation (ore zone, hanging wall, and foot wall) and rock substance strength (ore zone, hanging wall, and foot wall). The technique for order of preference by similarity to ideal solution (TOPSIS) was adopted, and an improved TOPSIS method was developed based on experimental testing and checked by means of the application of cascade forward backpropagation neural networks in mining method selection. The results provide indicators that decision makers can use to choose between different mining methods based on the total points given to all ore properties. The best mining method is cut and fill stopping, with a rank of 0.70, and the second is top slicing, with a rank of 0.67.

PRAKIRAAN HARGA MEAT BONE MEAL (MBM) MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION

XYZ is a poultry feed producer in Banyuwangi Regency, East Java. The problem in developing poultry feed at PT XYZ was the fluctuating price of poultry feed. Meat bone meal (MBM) or what is called meat flour is one of the raw materials for poultry feed that affects the final price of poultry feed products. The price of MBM was greatly influenced by the exchange rate of the rupiah against the dollar. Forecasting is one way that needs to be done in dealing with MBM price fluctuations. The aim of this study was to estimate the price of MBM using backpropagation neural networks (BNN). The data used in this study was the price of MBM in the period January 2016-October 2018. Based on the results of the study, the best BNN architecture for the estimated MBM price was12-10-1 (12 input nodes, 10 hidden nodes, and 1 output node). This architecture has reached the training target of 0.002 with a MAPE test value of 13.93%. Based on forecasts with the BNN the highest MBM price in May 2019 and the lowest MBM price in January 2019.

Activity Classification Using Backpropagation Neural Networks for the Daily Lives of the Elderly

Activity Analysis Systems or Activity Recognition Systems for the elderly is recently a part of the smart home systems design. This assisted system normally helps the senior people to live alone in a house, safely and improve a quality of life. Therefore, learning to recognize which activities are safe is necessary for classifying the activities of the elderly. This information will give the researchers in the assistive technology some insights to understand the basic daily lives of the elderly. Moreover, it is also help the caregivers to monitor activities of the senior people while they live alone in the house. In this paper, the novel method for detecting and recognizing the activities using Backpropagation Neural Networks has been proposed. The proposed model was tested on a set of basic daily activities (lie, stand, sit, walk and dine). The proposed model was trained to construct the Backpropagation Neural Networks model and used the trained model to classify basic daily activities of the elderly. The proposed model gives the results of 0.78, 0.72 and 0.74 of precision, recall and F1 score, respectively. The discussion and future extension are also given in this paper.

Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.