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Bull service is the natural copulation by a purebred male carabao with a female counterpart. This is part of the bull loan agenda of the Philippine Carabao Center-Visayas State University (PCC-VSU), one of the 12 regional centers of PCC. For the past years, PCC-VSU used averaging of bull services count of previous years and sometimes did a little tweaking of the formulas in setting annual targets. Some targets fell short in the actual accomplishment that may have led to achieving lesser animal products. There is a need for resource optimization. Resource optimization through Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) forecasting can be used in the setting of goals of future number of copulations. This study deals with the comparative assessment of ARIMA and ANN in 4-step and 12-step ahead time series forecasting of bull services.

This study presents an application that employs a machine-learning algorithm to identify fish species found in Leyte Gulf. It aims to help students and marine scientists with their identification and data collection. The application supports 467 fish species in which 6,918 fish images are used for training, validating, and testing the generated model. The model is trained for a total of 4,000 epochs. Using convolutional neural network (CNN) algorithm, the best model during training is observed at epoch 3,661 with an accuracy rate of 96.49% and a loss value of 0.1359. It obtains 82.81% with a loss value of 1.868 during validation and 80.58% precision during testing. The result shows that the model performs well in predicting Malatindok and Sapsap species, after obtaining the highest precision of 100%. However, Hangit is sometimes misclassified by the model after attaining 55% accuracy rate from the testing results because of its feature similarity to other species.

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