Rice is one of the essential food crops consumed by every Filipino due to it being a good source of energy and protein. However, well-milled rice mostly encounters rice weevil (Sitophilus oryzae) invasion during their prolonged storage, causing severe damage to rice grains. Several solutions have been proposed to address the problem, but these solutions may harm the consumers and oftentimes, lengthy, and tedious. This study developed an automated rice sieving device (ARSD) with a heat-controlled system for the reduction of Sitophilus oryzae, programmed in Arduino IDE (C++) for automation and genetic algorithm using Python for optimum angle of inclination for sieving. Results showed that the device can effectively and efficiently separate rice weevils and rice grains at sieving inclination of 15 degrees and can eliminate rice weevils within 1 minute at constant temperature of 54°C, exhibiting a 100% mortality rate. The optimum angle of inclination at 15.7° is obtained from the genetic algorithm using Python. Furthermore, at optimum angle of inclination and 1 minute heating time, the device is reliable in sieving 1 to 3 kilograms in four rice varieties namely Malagkit, Denorado, Angelica and Sinandomeng with an average percentage value of 97.9167, 96.1458 and 92.6042 with respect to their respective rice weights (1 kg, 2 kg, and 3kg), proving that ARSD is a potential solution in addressing rice weevil problems.

The transcription of music is essential since it preserves the originality of the music and the compositional technique used by the composers. In this manner, native music can be reproduced, and documents of certain tunes can be passed on to succeeding generations without hearing the original music. A very limited amount of research has been conducted on the application of automation in music transcription using deep learning, particularly in native music instruments. This research is an effort to preserve and conserve Filipino culture in the context of native music and musical instruments particularly the Diwdiw-as, a native Filipino bamboo flute. Using signal processing and deep neural networks, the proposed study aims to automate music transcription. The system is capable of classifying pitches based on the fundamental frequency and is also capable of classifying notes based on their duration. Diwdiw-as pitch can be transcribable to the music sheet using the following pitches: CS, DS, ES, FS, GS, AS, BS, C6, while the note values are whole, eighth, quarter, half, dotted eighth, dotted half, dotted quarter. A web-based application has been developed to assist in the automatic music transcription (AMT) of Diwdiw-as. A pdf file of the transcribed music sheet can then be downloaded. According to the confusion matrices, the system's accuracy is high in terms of the transcription of pitches and notes in the music sheet.

Hearing is one of the most basic human senses. Research shows that 97 percent of the total population of elderly people suffer from hearing loss. This project is designed to address problems that the existing hearing aid users face throughout their use of the product which includes prices, high maintenance, sensitivity to noise, limited functional range and low-quality sound produced. This project aims to provide a hearing aid with noise filtration using Noise Reduction (NR) algorithm. NR algorithm which uses Multi-channel Wiener Filter is implemented to reduce noise and improve speech intelligibility. The proponents create a program for the Intel compute stick using NR algorithm to function as the filter and for the Raspberry Pi as the user interface of the device. The signal gathered by the microphone was processed by the Intel compute stick then outputted through the earphone audio output. The system is tested through simulation in terms of filtering the noise.

Pork meat is a very important source of proteins and other nutrients, so it requires a high level of quality. There is a serious health risk associated with the consumption of spoiled or contaminated pork meat, which is why it is extremely important to monitor its freshness. In this study, sensor arrays consisting of RGB IR sensors, thermal sensors, electronic noses (gas sensors) for detecting the color, temperature, and carbon dioxide and ammonia level of the pork meat were used to evaluate pork meat quality and estimate shelf life. The use of various supervised machine learning approaches has been applied with optimization to perform classification as to whether the meat was fresh or not, as well as regression analysis to predict the amount of exposure time for the meat that can be used in computing shelf-life estimates. Several high-performance algorithms were then tested, evaluated, and compared after hyperparameters of each model were optimized using grid search. As a result of a comparative analysis of the machine learning used, gentle boost ensembles outperformed other machine learning methods in detecting pork meat quality with 92.8% accuracy, while gaussian process regression predicted shelf life with the lowest RMSE, MSE and MAE.