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.

In this study, digital image processing was incorporated to eliminate the Subjectiveness of manual inspection of diseases in rice plant and accurately identify the three common diseases to Philippine's farmlands: (1) Bacterial leaf blight, (2) Brown spot, and (3) Rice blast. The image processing section was built using MATLAB functions and it comprises techniques such as image enhancement, image segmentation, and feature extraction, where four features are extracted to analyze the disease: (1) fraction covered by the disease on the leaf; (2) mean values for the R, G, and B of the disease; (3) standard deviation of the R, G, and B of the disease and; (4) mean values of the H, S and V of the disease. The Backpropagation Neural Network was used in this project to enhance the accuracy and performance of the image processing. The database of the network involved 134 images of diseases and 70% of these were used for training the network, 15% for validation and 15% for testing. After the processing, the program will give the corresponding strategic options to consider with the disease detected. Overall, the program was proven 100^% accurate.

One of the biggest environmental problems right now is air pollution. Air quality is needed to be consistently monitored and assessed to ensure better living conditions. The U.S. Environmental Protection Agency (EPA) uses the air quality index (AQI) to standardize the air quality. However, AQI requires precise and accurate sensor readings and complex calculations, making it not feasible for portable air quality monitoring devices. The aim of this paper is to find an alternative way of monitoring and characterizing air quality through the use of integrated gas sensors and building predictive models using machine learning algorithms that can be used to obtain data-driven solutions to mitigate the risk of air pollution. The proposed methodology is implemented by building a prototype for the integrated sensors using DHT 11 temperature and relative humidity sensor, MQ2, MQ5 and MQ135 gas sensors. Five predictive models are developed in the study, k-nearest neighbors (KNN), support vector machine (SVM), Naïve-Bayesian classifier, random forest and neural network. Results show that the researchers are able to obtain an accuracy of 98.67%, 97.78%, 98.67%, 94.22%, and 99.56% for all the five models respectively, having the neural network to be the best performing model.

The study was about creating a device that can use colorimetry to determine the Nitrogen, Phosphorus and Potassium content and pH of the soil to be cultivated by using the chemicals of the Soil Test Kit in giving nutrient recommendations. Trials were done to produce the actual color reaction of the reactants which served as the color references. The RGB values were extracted using TCS3471 color sensor and an LDR color sensor. The device was tested using three samples each for every nutrient and pH with ten trials each. The benchmark value used was 0.05 to have a 95% level of confidence. The computed value of Chi-Square was equal to 0 using the table for the critical values 0.05 corresponds to the critical value of 3.841. Therefore, computed value was less than the critical value which can be concluded that there was no significant difference between the proposed device reading the soil pH and nutrient content and the human resource reading.

In the Philippines' banana export industry, the postharvest classification of Cavendish banana is an important factor which affects one of the country's revenue. The present set up uses a quality inspector and is solely dependent on its visual capability. This may result in an error and inconsistent results grading classification. This study explores the use of image processing with deep learning approach to classify Cavendish banana grade by the basis of its standard requirement. A total of 1116 bananas comprising of 279 images from each category such as Class A big-hand or small-hand, Class B big-hand or small-hand and Cluster class (part of hand) are used to develop algorithm and classification accuracy by using Phyton OpenCV and Tensorflow. The sample images follow four stages: Image Thresholding, Feature Extraction, Classification Prediction, and Testing. An analysis of predicted accuracy in all classes (A and B) are more significant in the finger size value extraction (small or big hand) than in surface defects value extraction. The final classification in all trained, validation, and test data showed above 90% accuracy in all four classes. Hence, the proposed CNN classification in Tensorflow model can be commercially developed as a field-based complete automatic postharvest classification system for grading a Cavendish banana.

Limited monitoring activities to assess data on heavy metal (HM) concentration contribute to worldwide concern for the environmental quality and the degree of toxicants in areas where there are elevated metals concentrations. Hence, this study used in-situ physicochemical parameters to the limited data on HM concentration in SW and GW. The site of the study was Marinduque Island Province in the Philippines, which experienced two mining disasters. Prediction model results showed that the SW models during the dry and wet seasons recorded a mean squared error (MSE) ranging from 6 × 10⁻⁷ to 0.070276. The GW models recorded a range from 5 × 10⁻⁸ to 0.045373, all of which were approaching the ideal MSE value of 0. Kling–Gupta efficiency values of developed models were all greater than 0.95. The developed neural network-particle swarm optimization (NN-PSO) models for SW and GW were compared to linear and support vector machine (SVM) models and previously published deterministic and artificial intelligence (AI) models. The findings indicated that the developed NN-PSO models are superior to the developed linear and SVM models, up to 1.60 and 1.40 times greater than the best model observed created by linear and SVM models for SW and GW, respectively. The developed models were also on par with previously published deterministic and AI-based models considering their prediction capability. Sensitivity analysis using Olden’s connection weights approach showed that pH influenced the concentration of HM significantly. Established on the research findings, it can be stated that the NN-PSO is an effective and practical approach in the prediction of HM concentration in water resources that contributes a solution to the limited HM concentration monitored data.