Dental shade matching for composite restoration to natural teeth color is a crucial aspect of dental treatment as it can significantly impact patient satisfaction and treatment outcomes. However, the subjective nature of manual shade selection often leads to shade mismatch, which leads to failure on the first visit. In addition, intraoral scanners are inaccessible to small enterprises dental clinic in the Philippines due to its unaffordable pricing. To address this problem, this study proposed a mobile application that utilizes image processing and deep learning techniques for objective and consistent dental shade matching. Exploring Convolutional Neural Network (CNN)-based MediaPipe for Facial Landmark Detection and Support Vector Machines (SVMs) to classify dental shades. The SVM model attained an overall accuracy of 68.5% during the experimental results while the implementation using the mobile application obtained an estimate of 90% during the user testing for A1 to A4 color shade. The findings have significant implications for clinical practice, empowering dental professionals with a reliable tool to improve patient care and satisfaction. This study emphasizes the importance of incorporating advanced technology into clinical practice, ultimately improving patient outcomes.

Optical character recognition (OCR) is the process of extracting the characters from a digital image. The concept behind OCR is to acquire a text in a video or image formats and extract the characters from that image and present it to the user in an editable format. In this study, a convolutional neural network (CNN) is applied, which is a mathematical representation of the functionality of the human brain, using back-propagation algorithm with test case files of English alphabets and numbers. The purpose of this study is to test systems capable of recognizing vehicle plate number English alphabets and numbers with different fonts, and to be familiar with CNN and digital image processing applied for character recognition. Scientific journals and reports were used to research the relevant information required for the thesis project. The chosen software was then trained and tested with both computer and video output files. The tests revealed that the OCR software can recognize both vehicular plate and computer alphabets and learns to do it better with each iteration. The study shows that although the system needs more training for vehicular plate characters than computerized fonts, and the use of CNN in OCR is of great benefit and allows for quicker and better character recognition.

Complete Blood Count is one of the most commonly performed medical laboratory procedure today. It is required to detect various types of diseases. Presently, some small-scale clinics in the country still does the tedious, manual method of counting the blood cell. With Complete Blood Count Analysis System through Image Processing, automated CBC can be performed by mounting the smart phone camera on the viewer of the microscope. The input image will go through several image processing algorithms such as: Binary Thresholding, Clustering, and Hough Circle Technique. The result will be computed through the formulas used in the manual method of the CBC process. Experimental results show the developed system gains 94% of accuracy for counting the Hematocrit, Hemoglobin, Red Blood Cell, and White Blood Cell values.

As people remain confined in their homes, more and more turn to the internet and social media daily for support, comfort, opportunities, and access to information. This presents an opportunity for businesses and e-commerce platforms to harness their own data and reach wide audiences through social media. An online store, Anywhere Fitness PH, took this opportunity which was launched to bring gym equipment to the comfort and safety of the homes of its consumers. However, the client, Anywhere Fitness PH, struggled in customer reviews and difficulties with its current e-commerce platforms. The researchers proposed a web application system that will provide their client an e-commerce platform that will utilize data analytics and sentiment analysis for its customer reviews and provide further improvements for the overall business operations of the client. The system passed for both evaluation of Customer Interface and Admin Interface with means of 4.27 and 4.49 respectively, making the Overall Evaluation have a mean of 4.3S. All means are interpreted as “Strongly Agree” which means that the admins, the non-IT, and the IT staff strongly agree that the system passed Functionality, Usability, Reliability, Performance, Security, pertaining that the system is now ready for the use of the client.

Flood is imminent when heavy rain occurs, identifying the level of water in plain sight is difficult to achieve. There are currently available ways to detect flood water but usually are very expensive and needs a huge equipment with sensors. The research has proposed an alternative solution to expensive ways on detecting flood and water levels. The study created an application to detect body of water by using image processing technique called Region-based segmentation algorithm to detect water on the image and Canny Edge Detection with computation using Pixel Ratio on a selected water region to determine the height of the water or flood. A CCTV camera was used to capture the image and was fed on the application through the network infrastructure. Once captured, the image was processed to detect the body of water and measurement of its level. The testing of the application was done on a controlled environment and the application was able to detect the water body on the picture. It was able to detect the edge of the water based on a selected region where the water is found. The measurement of the actual height of the water, closely matches the height of stated in the application. Thus, the research has found a way to detect body of water and gauge its water level using image processing, in which, have found a way to detect and measure water affordably. This research can be a step, in future research like monitoring the streets’ flood level when heavy rains occurs. This is a much more safe and affordable way to monitoring the increase and decrease of flood.