Establishing an accurate count and classification of leukocytes commonly known as WBC (white blood cells) is crucial in the assessment and detection of illness of an individual, which involves complications on the immune system that leads to various types of diseases including infections, anemia, leukemia, cancer, AIDS (Acquired Immune Deficiency Syndrome) etc. The two widely used methods to count WBC is with the use of hematology analyzer and manual counting. Currently, in the age of modernization there has been numerous research in the field of image processing incorporated with various segmentation and classification techniques to be able to generate alternatives for WBC classification and counting. However, the accuracy of these existing methods could still be improved. Thus, in this paper we proposed a new method that could segment various types of WBCs: monocytes, lymphocytes, eosinophils, basophils, and neutrophils from a microscopic blood image using HSV (Hue, Saturation, Value) saturation component with blob analysis for segmentation and incorporate CNN (Convolutional Neural Network) for counting which in turn generates more accurate results.

The study focuses on monitoring freshness and determining food spoilage inside the refrigerator. The objective is to design an electronic nose system that will be sensitive to the gases emitted by spoiled food samples namely banana, pechay, carrots and grapes operating in low level temperature particularly the refrigerator and then determine food spoilage using Principal Component Analysis – K Nearest Neighbors, however, it will not take any corrective actions. The system will gather readings from MQ gas sensors and will be subjected to PCA and KNN. PCA is implemented to minimize data and for feature projection represented in form of graphs. Whereas, KNN is applied for clusters formed by the PCA transformation to classify the grouping of the food. The results from the combined approach produced an overall accuracy rate of 92%, thus, the electronic nose system is capable of sensing food gases and accurately determine spoilage inside the refrigerator.

Every person has different perspective on whether a food is spoiled, and this may lead to wrong assumptions on the food's condition. Misinterpretation on food condition could lead to food-borne illness. The need for electronic nose system for classifying if a food is spoiled is still in demand due to this prediction confusion. This study aims to detect food spoilage of tomato-based Filipino cuisines using an electronic nose. Specifically, this research aims to develop a device with an array of sensors to detect the gases emitted by spoiled tomato-based Filipino cuisines and implement Artificial Neural Network as an algorithm for the classification of the data reading of the sensor. The hardware part of the e-nose system makes use of Gizduino 1281, Raspberry Pi 3 model B, 20×4 LCD Screen, 7 MQ gas sensors, and 1 temperature/humidity sensor. Stochastic Gradient Descent together with Back propagation algorithm is used for training the Artificial Neural Network data. The tomato-based Filipino cuisine is placed below the sensor chamber for easy recognition of the gas sensors emitted by the cuisine itself. The study could be useful in classifying whether food is spoiled. This electronic nose system could determine spoilage level of a specific tomato-based Filipino cuisine. The researchers assigned level 0 to 12 as the spoilage level. These levels correspond to the time when the food is observed every four hours starting 7:00 AM of Day 1. As the experiment continued, it was also that the food often spoiled between levels 5 and 6. Based on the confusion matrix, the error rate of the electronic nose system is 3.85%.

Determining the accurate count of leukocytes commonly known as WBC (white blood cells) in a blood test is critical in evaluating and diagnosing an individual's health particularly on a wide range of diseases which includes infections, anemia, leukemia etc. There are two widely used methods to determine leukocyte count. First, is through the use of hematology analyzer and the other is done manually. Nowadays, with the advent of technology leukocyte counting was improved through the use of digital image processing. However, the algorithm of the existing methods involves too many steps which complicates the image processing phase. Thus, we considered counting leukocytes by incorporating the HSV (Hue, Saturation, Value) saturation component with blob analysis on microscopic blood images which highlights the eccentricity and area features for counting to simplify existing methodologies which in turn generates faster and more accurate results.

Air Pollution in the Philippines is already a serious problem especially to the people living in the cities. The factors such as smoke coming from vehicles and factories as well as dust and debris contribute to air pollution which also leads to people having health problems. This design aims to create a prototype which will detect the air pollution and particulate matter in a barangay at Pandacan, Manila. The design hardware will send an email notification to the registered email addresses of barangay officials whenever the sensors detect high reading on either the particulate matter or carbon monoxide. The components included in the hardware is a Dust Sensor which will measure the particulate matter in the area, Carbon Monoxide Sensor for measuring the gas detected, microcontroller, and a Raspberry Pi in order to send the data through email.