The need to treat illnesses or deterioration of the legs caused by accidents, rheumatism, muscular weakness, paralysis or bone diseases has increased. Paraplegic patients need physical therapy to mitigate their condition. This study developed a gamification smartphone application for leg physical therapy. The application was implemented for patients using Android Operating System smartphones. The smartphone was attached to the walking support equipment. The accelerometer sensor of the smartphone was utilized to measure distance, time, and number of steps. The application acquired data from the sensor, and processed and stored the data in a server, to enable assessment of critical conditions. Moreover, this system would report the evaluation of physical therapy on a weekly basis. Alerts of physiotherapy treatment could be set in this application. The advantages of the application include increasing the patient’s motivation for the therapy, performed by themselves at home, and the results could be used for planning treatments by a physician.

This research presented a system development approach for facility maintenance management system based on GIS and indoor map in the form of web applications that can be used with all devices and no worries about time limitations. The capabilities of GIS, indoor map, and geospatial data visualization help speeding up facility maintenance management process and create benefits to all concerned parties, i.e., users can notify and follow the data of facility errors at the time; or officers in charge can operate quickly because they can access real-time data. Indoor map display makes it easier to access locations or places of damaged facilities. In addition, the data from the model system presented in this research can also be applied to planning and decision-making of executives.

<span>Water bodies especially rivers are vital to existence of all lifeforms on Earth. Therefore, monitoring river areas and water bodies is essential. In the past, the monitoring relied essentially on manpower in surveying individual areas. However, there are limitations associated wih such surveys, e.g., tremendous amount of time and labour involved in expeditions. Presently, there have been accelerated development in remote sensing (RS) and artificial intelligence (AI) technology, particularly for change monitoring and detection in different areas globally. This research presents technical development of a toolbox for rivers classification and their change detection from Landsat images, by using water index analysis and four machine learning algorithms, which are K-Means, ISODATA, maximum likelihood classification (MLC), and support vector machine (SVM). Experimental findings indicated that all presented techniques were effective in detecting hydrological changes. The most accurate algorithm, nevertheless, for river classification was the SVM, with accuracy of 96.89%, precision of 98.61%, recall of 96.59%, and F-measure of 97.59%. Herein, it was demonstrated, in addition, that the developed toolbox was versatile and could be applied in rapid river change detection in other areas.</span>

<p class="0abstract">Public Health Office and the risk map created from the patient information. Many provincial hospitals currently have to admit a large number of patients to their emergency room. Each year, the number outgrow limited medical resources, causing tremendous operational delay, and thus undermining quality of medical services. In addition, existing ER flows remain lacking means of communicating with patients’ relatives and notifying them with treatment status of patients under their care. To addresses these concerns, registered nurses with experiences are required not only to make initial patient screening and prioritization, but also to serve as liaison between physicians and patients’ relatives. These double tasks impose great burden to already overloaded medical staffs. An emergency patient classification system, based on support vector machine was developed. It was implemented as a web application, written in PHP, and running on MySQL database. GIS technology was employed to analyze spatial data and producing relevant reports. The proposed system could classify emergency patient into different groups based on their severity, according to the government standard. The resultant recommendation, verified by a nurse on duty, as well as treatment status were presented to patients’ relatives on a digital screen. Moreover, the hospital was able to use the summarized reports, in both standard and spatial forms, for its managerial purposes. The develop system could help the hospital to make the most of their limit resources for treating emergency patients. The produced reports were useful for making relevant policies and executive planning.</p>

The data of impacts and damage caused by floods is necessary for manipulation to assist and relieve those impacts in each area. The main issue for data acquisition was acquisition methods that affect the durations, accuracy, and completeness of data obtained. Most data are currently obtained by field survey for data on impacts in each area. However, this method contains limitations, i.e., taking a long time, high cost, and no real-time data visualization. Thus, this research presented the study to develop an application for inspecting areas under impact and damage caused by floods using deep learning classification for flood classification and land use type classification in the affected areas using digital images, remote sensing data, and crowdsource data notified by users through the accuracy assessment application of classification. It was found that deep learning classification for flood classification had 97.50% accuracy, with Kappa = 0.95. Land use type classification had 93.71% accuracy, with Kappa = 0.91. Flood damage assessment process in this research was different from other previous research that used geospatial data for flood damage inspection, e.g., satellite images. In contrast, this research brought damage data notified by users for processing with flood data in each area by satellite image processing and land use types of classification. The proposed application can calculate damage in each area and visualize real-time results in maps and graphs on the dashboard via the application. Besides, the presented method can be used to verify and visualize data of areas under impact and damage caused by floods in different areas.