ICARUS: An Android-Based Unmanned Aerial Vehicle (UAV) Search and Rescue Eye in the Sky

The purpose of this paper is to develop an unmanned aerial vehicle (UAV) using a quadcopter with the capability of video surveillance, map coordinates, a deployable parachute with a medicine kit or a food pack as a payload, a collision warning system, remotely controlled, integrated with an android application to assist in search and rescue operations. Applied research for the development of the functional prototype, quantitative and descriptive statistics to summarize data by describing the relationship between variables in a sample or population. The quadcopter underwent an evaluation using a survey instrument to test its acceptability using predefined variables to select respondents within Caloocan City and Quezon City, Philippines. Demographic profiles and known issues and concerns were answered by 30 respondents. The results were summarized and distributed in Tables 1 and 2. In terms of demographic profiles, the number of SAR operators within the specified areas is distributed equally, most are male, single, and within the age bracket of 31 and above. In issues and concerns, the most common type of search and rescue was ground search and rescue. Human error is the primary cause of most injuries in operating units. The prototype was useful and everyone agreed, in terms of acceptability, drone technology will improve search and rescue operations. The innovative way of utilizing Android and drone technology is a new step towards the improvement of SAR operations in the Philippines. The LiPo battery must be replaced with a higher capacity and the drone operator should undergo a training course and secure a permit from the Civil Aviation Authority of the Philippines (CAAP).

Smart Face Shield: A Sensor-Based Wearable Face Shield Utilizing Computer Vision Algorithms

The study aims the development of a wearable device to combat the onslaught of covid-19. Likewise, to enhance the regular face shield available in the market. Furthermore, to raise awareness of the health and safety protocols initiated by the government and its affiliates in the enforcement of social distancing with the integration of computer vision algorithms. The wearable device was composed of various hardware and software components such as a transparent polycarbonate face shield, microprocessor, sensors, camera, thin-film transistor on-screen display, jumper wires, power bank, and python programming language. The algorithm incorporated in the study was object detection under computer vision machine learning. The front camera with OpenCV technology determines the distance of a person in front of the user. Utilizing TensorFlow, the target object identifies and detects the image or live feed to get its bounding boxes. The focal length lens requires the determination of the distance from the camera to the target object. To get the focal length, multiply the pixel width by the known distance and divide it by the known width (Rosebrock, 2020). The deployment of unit testing ensures that the parameters are valid in terms of design and specifications.

Applied Computer Vision on 2-Dimensional Lung X-Ray Images for Assisted Medical Diagnosis of Pneumonia

This study focuses on the application of a specific subfield of artificial intelligence referred to as computer vision in the analysis of 2-dimensional lung x-ray images for the assisted medical diagnosis of ordinary pneumonia. A convolutional neural network algorithm was implemented in a Python-coded, Flask-based web application that can analyze x-ray images for the detection of ordinary pneumonia. Since convolutional neural network algorithms rely on machine learning for the identification and detection of patterns, a technique referred to as transfer learning was implemented to train the neural network in the identification and detection of patterns within the dataset. Open-source lung x-ray images were used as training data to create a knowledge base that served as the core element of the web application and the experimental design employed a 5-Trial Confirmatory Test for the validation of the web application. The results of the 5-Trial Confirmatory Test show the calculation of Diagnostic Precision Percentage per Trial, General Diagnostic Precision Percentage, and General Diagnostic Error Percentage while the Confusion Matrix further shows the relationship between the label and the corresponding diagnosis result of the web application on each test images. The developed web application can be used by medical practitioners in A.I.-assisted diagnosis of ordinary pneumonia, and by researchers in the fields of computer science and bioinformatics.

Predicting Pollution Level Using Random Forest: A Case Study of Marilao River in Bulacan Province, Philippines

This study aims to predict the pollution level that threatens the Marilao River, located in the province of Bulacan, Philippines. The inhabitants of this area are now being exposed to pollution. Contamination of this waterway comes from both formal and informal industries, such as a used lead-acid battery, open dumpsites metal refining, and other toxic metals. Using various water quality parameters like Dissolved Oxygen (DO), Potential of Hydrogen (pH), Biochemical Oxygen Demand (BOD) and Total Suspended Solids (TSS) were the basis for predicting the pollution level. This study used the Data Mining technique based on the sample data collected from January of 2013 to November of 2017. These were used as a training data and test results to predict the river condition with its corresponding pollution level classification indicated with the used of colors such as Green for Normal, Yellow for Average, Orange for Polluted and Red for Highly Polluted. The model got an accuracy of 91.75% with a Kappa value of 0.8115, interpreted as Strong in terms of the level of agreement.