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.
The common problem that is mostly found in urban area and the organizations service numbers of people such as government places, university, department store, and hospital, is the insufficient car parking. This problem is the result of the continuing increasing number of vehicle. Further, the car parking management is insufficient so the service users waste their time and fuel trying to look for the available parking. The objective of this research was to develop mobile application for smart car parking using Radio-Frequency Identification (RFID) and Internet of Thing (IoT) which can detect the available parking lot so it is time saving for people. Moreover, the parking area management is more efficient as it minimizes the limitation of traditional system which the users have to access web application which is unable to automatically alert when the parking lot status has changed. Further, the data can be applied to the management and planning such as analyzing numbers of vehicle daily to compare with the parking lot if it is sufficient or not in order to improve and provide more parking space appropriately.
<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>
The amount and frequency of shopping in department stores reduce due to COVID-19 outbreak all over the world. Thus, consumers have to give precedence to planning to go shopping in department stores in order to reduce social interaction time so as to reduce the risk of COVID-19 infection. This research introduced the development of an application prototype for product location search based on augmented reality (AR) for facilitating customers to search for desirable products with no need to ask salespersons and no need to go for product search on their own. Besides, the introduced application could also present statistical data related to behavior and interest in shopping. Organizations can use the data to make plans for decision making on selling, and to use it as a supportive tool to increase their competitiveness.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.