The aim of this research was to develop a learning recommendation component in an intelligent tutoring system (ITS) that dynamically predicts and adapts to a learner's style. In order to develop a proper ITS, we present an improved knowledge base supporting adaptive learning, which can be achieved by a suitable knowledge construction. This process is illustrated by implementing a web-based online tutor system. In addition, our knowledge structure provides adaptive presentation and personalized learning with the proposed adaptive algorithm, to retrieve content according to individual learner characteristics. To demonstrate the proposed adaptive algorithm, pre-test and post-test were used to evaluate suggestion accuracy of the course in a class for adapting to a learner's style. In addition, pre- and post-testing were also used with students in a real teaching/learning environment to evaluate the performance of the proposed model. The results show that the proposed system can be used to help students or learners achieve improved learning.
This paper proposes an algorithm for document plagiarism detection using the provided incremental knowledge construction with formal concept analysis (FCA). The incremental knowledge construction is presented to support document matching between the source document in storage and the suspect document. Thus, a new concept similarity measure is also proposed for retrieving formal concepts in the knowledge construction. The presented concept similarity employs appearance frequencies in the obtained knowledge construction. Our approach can be applied to retrieve relevant information because the obtained structure uses FCA in concept form that is definable by a conjunction of properties. This measure is mathematically proven to be a formal similarity metric. The performance of the proposed similarity measure is demonstrated in document plagiarism detection. Moreover, this paper provides an algorithm to build the information structure for document plagiarism detection. Thai text test collections are used for performance evaluation of the implemented web application.
A health or activity monitoring system is the most promising approach to assisting the elderly in their daily lives. The increase in the elderly population has increased the demand for health services so that the existing monitoring system is no longer able to meet the needs of sufficient care for the elderly. This paper proposes the development of an elderly tracking system using the integration of multiple technologies combined with machine learning to obtain a new elderly tracking system that covers aspects of activity tracking, geolocation, and personal information in an indoor and an outdoor environment. It also includes information and results from the collaboration of local agencies during the planning and development of the system. The results from testing devices and systems in a case study show that the k-nearest neighbor (k-NN) model with k = 5 was the most effective in classifying the nine activities of the elderly, with 96.40% accuracy. The developed system can monitor the elderly in real-time and can provide alerts. Furthermore, the system can display information of the elderly in a spatial format, and the elderly can use a messaging device to request help in an emergency. Our system supports elderly care with data collection, tracking and monitoring, and notification, as well as by providing supporting information to agencies relevant in elderly care.
The main goal of this study was to enable cultivating St. John's wort not only in Europe and West Asia, but also in Thailand, Southeast Asia despite warmer climate than in the natural growth regions. The challenge then was to control environmental factors with an automatic system. The Internet of Things (IoT) was applied in the sensor devices to control and collect relevant environmental data from the designed greenhouse. Moreover, data analysis by multiple linear regression was applied to enable the control of the designed greenhouse environment. It was used to discover interesting relationships between variables. The proposed system was implemented with hardware, a web application, and a mobile application. The hardware was designed to collect data and implement control of air temperature, air relative humidity, soil moisture, and light from sensors in the field. The web and mobile applications were developed to manipulate the obtained data, for intelligent control, and for real-time monitoring of environmental factors. The control system included an evaporative cooling system, fogging system, irrigation system, and artificial light system. The results show that the proposed system to assist and support the growth of St. John's wort was successful. Moreover, the results from this research can be used to culture St. John's wort, in order to produce medicines that are beneficial to human health in regions with the tropical climate.INDEX TERMS Temperature, multiple linear regression, Internet of Things (IoT), prediction, greenhouse.
The purpose of this study was to develop the prototype drying system for drying the Pisang-Awak (PA) banana. It was designed and implemented to study the drying kinetics in each drying mode: hot air drying (HA), infrared drying (IR), and hybrid drying (Hy). The experimental results were fit with simulation results from the finite element method (FEM). Two statistical parameters, the coefficient of determination R 2 and the root mean square error RMSE ð Þ, were used to assess the fit between experimental results and simulation results, and their respective ranges were 0.960-0.997 and 0.014-0.050. For PA banana drying the most suitable drying mode is hybrid, combining 1.5 m/s flow of hot air and IR heating at 60 C drying temperature. The axial and radial shrinkages of PA banana were the largest in Hy mode at 60 C, namely 18.2 and 27.3%, respectively, and this choice gave the best/lowest specific energy consumption (SEC) for drying performance and the best final dried PA banana. Practical ApplicationsThe understanding of the drying kinetics and shrinkage properties of the dried PA banana is important for the choice of drying process because these reflect the quality of the dried product and help an engineer to design the drying system. Therefore, the empirical mathematical modeling of the drying process serves a crucial role in the food industry sector. These results can be used to quantify temperature and moisture content as functions of drying time to monitor the drying process of PA banana, saving energy and time. | INTRODUCTIONIn the years 2017-2019, the top three produced bananas in Thailand were Pisang-Awak (PA) banana, Banana, and Pisang-Mas banana, at respectively 3,993, 903, and 670 ktons (Department of Agricultural Extension, n.d.). The PA banana is an economic plant widely used in Thai dessert recipes. Consuming two such bananas can give enough energy for up to 90 min of exercise, with also large doses of dietary fiber, vitamins, and minerals. Benefits of bananas to the human body include reducing bad breath, providing anti-oxidants, and relief to constipation, morning sickness, hemorrhoids, obesity, high blood pressure, broken capillaries, and so on (Robinson & Galan Sauco, 2011).Growing bananas gives year-round production in Thailand, but in some seasons the production can exceed the demand, while the shelf life of bananas is only 5 to 7 days before spoilage by the enzymes in them (Naknaen, Charoenthaikij, & Kerdsup, 2016;Sankat & Castaigne, 2004). Processing banana is an alternative way to address this problem.Drying is one way to preserve food products for prolonged storage. It removes water or moisture under controlled conditions by the use of heat. The low moisture level then inhibits the growth of harmful microbes, inactivates enzymes, and helps avoid toxins, parasites, and various insects (Bala, Mondol, Biswas, Das Chowdury, & Janjai, 2003). The design of a drying system requires the use of
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.