A smart city describes an urban setting which aims to effectively apply ICT technologies to help improve the well-being of its citizens and reduce the negative impacts of urbanization. The priority areas considered in the Global Smart City Index (SCI) by the Institute for Management Development’s (IMD) World Competitiveness Centre were key infrastructures and technologies in (1) health and safety, (2) mobility, (3) activities (e.g., recreational spaces), (4) opportunities (work and school), and (5) governance. A smart region is a term used to extend the concept of a smart city into both urban and rural settings to promote a sustainable planning approach at the regional level. A direction that must be considered is the adoption of a “Smart Region Mobility Framework” to effectively transform our urban and rural regional transportation networks. This research study focused on the development of the smart region mobility framework for an island region group in the Philippines. The smart region goal is to integrate intelligent transportation system (ITS) platforms such as advanced public transportation system (APTS), advanced traveler information system (ATIS), and advanced rural transportation system (ARTS) to the local public transportation route plans (LPTRP) of the region. The activities include the data collection, analysis, and evaluation of multimodal regional transportation networks and social services infrastructure. The transportation network modeling process follows the four-step transportation planning process of trip generation, trip distribution, modal-split analysis, and trip assignment. Based on the analysis of 6 provinces, 16 cities, and 114 municipalities included in the study, there are two cities identified as smart city candidates. One of the smart city candidates is designated as the smart city regional center. In the context of a smart region, the available social services (e.g., employment opportunities, education, and health services) in the designated smart cities can also be made accessible to connected cities/municipalities through ease of transportation and mobility services in the region. Lastly, the study presented the implementation of data flow architecture of the smart region mobility framework, and the regional traveler information system using mobile and web application services.

Incorrect diagnosis is still apparent especially in respiratory diseases. There is truly a need to extend the study with correct diagnosis as most of lung diseases affect children. Over-diagnosis is also a problem that is necessary to address. As an aid to health diagnostics and health professionals, this study constructed a low-cost diagnostic tool that classifies a chest x-ray image if it is under the normal or pneumonia category. Training, validation and cross-entropy were done by using MobileNetV2 as a pre-trained model and served as the general convolutional neural network system. Results yielded high accuracy based on percentage accuracy. Further validation is incorporated by showing the confusion matrix of the system.

Optimizing crop production is one of the significant advances in urban agriculture and biosystems engineering. This deals with providing possibilities of year-long plant-based food supply in an efficiently limited space or environment; to cater the increasing demand of population necessities. The study to be discussed contributes on a novel methodology on optimizing lettuce production in a smart aquaponics setup by predicting its phytomorphological features _ deemed to be a noteworthy factor for plant phenomics research. Pre-trained deep learning-based networks were customized to train the machine vision-based feature extracted data. The data comprised of the phytomorphological attributes of leaf lettuce, to be predicted by the model through regression, thus setting them as the system's response data. The phytomorphological features considered in the study are the area, centroid, major and minor axis length, and equivdiameter. The sole predictor data will be the raw and resized images gathered. Evaluation shows that DarkN et-53 was the best network used for modelling area prediction with an R2 value of 0.99. For x and y centroids, Xception was the best estimator, yielding R2 values of 0.64 and 0.72 respectively. InceptionResN etV2 derived the highest-performing axis length prediction models with R2 values of 0.82 for the major axis length while 0.75 for the minor. Equivdiameter with the highest R2 value of 0.98 was obtained with DarkN et-53.

Since many diseases and infections are dependent on the count and type of Red Blood Cells (RBCs) and White Blood Cells (WBCs) present in the blood stream, detection and classification pertaining to them is necessary and relevant. Based from existing related literature, ordinary Neural Networks are usually employed. Also, in existing researches, RBC types are the main focus. Hence, after observing research gaps, a Faster Region-based Convolutional Neural Network (Faster R-CNN) was utilized for this study, focusing not only on RBCs but also on the variants of WBCs. The aim is to have a fast and reliable system in order to achieve the goal of aiding the medical field in the classification of RBCs and WBCs.

The ability of renewable resources to provide energy has a very great potential. It can provide sustainability for the needs of the future, especially now that fossil fuel sources are gradually diminishing. Although renewable energy is very promising, it is still not yet fully integrated in teaching in schools. There are many renewable sources but almost all of it is dependent on solar energy and to increase awareness and knowledge of its potential, this study aims to provide a training module focusing on the renewable energy of the sun. The training module will be able to show how energy conversion works. Also, the training module uses developing innovations for harnessing solar energy to increase its efficiency. Because it will be used as a laboratory equipment, testing for efficiency and transportability were conducted. With planning, testing, and evaluation the researchers were able to develop a training module that is efficient with 0.15 percent - 7.40 percent coefficient of variance out of thirty (30) trials, and at the same time transportable and inexpensive that is ideal for the goal of this research to implement the teaching of renewable energy to schools.

Life being put into jeopardy when in public has always been Filipinos’ concern. While there are reinforcements of laws, and common practices taught, these are no more than just band-aid solutions to the problem. With the immediate detection and classification of common public security threats through the videos fed from CCTVs, it will be an immense help to protect Filipinos. In this study, the use of pre-trained R-CNN model inception v2 alongside tools for other phases such as annotation, training, and testing will be discussed. The process through which the study attained the goal of the system will be highlighted.