Cultivation of Plants Harnessing an Ontology-Based Expert System and A Wireless Sensor Network

In this research an expert system was developed for taking adequate care of plants through the use of Android smart phones based on ontology for data collection and analysis. A Wireless Sensor Network has been applied to collect and transmit environmental data such as soil moisture, soil pH, and sunlight. The Expert System was developed to control the watering of plants, which consists of three parts: 1) data acquisition: Provided by the Thai Meteorological Department, the data were used according to the rules from the Plant Ontology; 2) watering control system: The self-activating system controls the watering of the plants using the expert system and an automated dispensing mechanism; and 3) decision making process: The Expert System applies the data and suggests a particular way to adequately provide the requirements of each plant, amount of water, and amount and type of fertilizer. The system has the ability to notify users by sending messages to their smart phones in a sufficient and timely way to ensure optimum cultivation activities and efficient and effective plant husbandry

HOME: Hybrid Ontology Mapping Evaluation Tool for Computer Science Curricula

This paper presents a hybrid ontology mapping tool for evaluating the standard of computer science subjects against the Thailand Qualification Framework for Higher Education (HQF: HEd). This can improve the standard of curriculum of universities in Thailand with higher accuracy and enable the decrease of processing time. Three ontologies have been designed: course, TQF: HEd and the standard curriculum of computer science. They were used for comparing course contents by applying a combination of ontology mapping techniques (semantic-based using extended Wu & Palmer’s algorithm and structure-based using SKOS features). Test with the sample data show that the tool based on a hybrid ontology mapping worked sufficiently well and can inform the efforts for curriculum improvement

Shopping Navigation Assistance System on Android Using RFID Applications and Dijkstra's Algorithm

This research aims at developing a model of indoor route guidance with RFID technology to identify users (customers) and product locations by using a mall as a prototype for a Data Warehouse. The researchers have designed a set of RFID devices, which store and read product locations and also communicate with applications. The route guidance system was developed in the form of applications on Android operating system, where users can search for desired products. The system then guides the customer using Web Server transmission. The system gets a product list from the customer and displays the path, which guides the user to the locations of selected products. Results from the application tests of the RFID technology for navigation on Android within a local mall showed that the system can display the selected items and recommended routes to the various points correctly. However, this is only a prototype. For realistic operation, it needs better performance by modifying the set of RFID devices to improve the speed and distance of reading and transmission of data

Towards an “Internet of Food”: Food Ontologies for the Internet of Things

Automated food and drink recognition methods connect to cloud-based lookup databases (e.g., food item barcodes, previously identified food images, or previously classified NIR (Near Infrared) spectra of food and drink items databases) to match and identify a scanned food or drink item, and report the results back to the user. However, these methods remain of limited value if we cannot further reason with the identified food and drink items, ingredients and quantities/portion sizes in a proposed meal in various contexts; i.e., understand from a semantic perspective their types, properties, and interrelationships in the context of a given user’s health condition and preferences. In this paper, we review a number of “food ontologies”, such as the Food Products Ontology/FOODpedia (by Kolchin and Zamula), Open Food Facts (by Gigandet et al.), FoodWiki (Ontology-driven Mobile Safe Food Consumption System by Celik), FOODS-Diabetes Edition (A Food-Oriented Ontology-Driven System by Snae Namahoot and Bruckner), and AGROVOC multilingual agricultural thesaurus (by the UN Food and Agriculture Organization—FAO). These food ontologies, with appropriate modifications (or as a basis, to be added to and further expanded) and together with other relevant non-food ontologies (e.g., about diet-sensitive disease conditions), can supplement the aforementioned lookup databases to enable progression from the mere automated identification of food and drinks in our meals to a more useful application whereby we can automatically reason with the identified food and drink items and their details (quantities and ingredients/bromatological composition) in order to better assist users in making the correct, healthy food and drink choices for their particular health condition, age, body weight/BMI (Body Mass Index), lifestyle and preferences, etc