This study describes the development of a smart trash bin that separates and collects recyclables using a webcam and You Only Look Once (YOLO) real-time object detection in Raspberry Pi, to detect and classify these recyclables into their correct categories. The classification result rotates the trash bin lid and reveals the correct trash bin compartment for the user to throw away trash. The performance of the YOLO model was evaluated to measure its accuracy, which was 91% under an optimal computing environment and 75% when deployed in Raspberry Pi. Several Internet of Things hardware, such as ultrasonic sensors for measuring trash bin capacity and GPS for locating trash bin coordinates, are implemented to provide capacity monitoring controlled by Arduino Uno. The capacity and GPS information are uploaded to Firebase Database via theESP8266 Wi-Fi module. To deliver the capacity monitoring feature, the uploaded trash bin capacity information is displayed on the mobile application in the form of a bar level developed in the MIT App Inventor for the user to quickly take action if required. The system proposed in this study is intended to be implemented in a rural area, where it can potentially solve the recyclable waste separation problem.

This paper contains the development of a training service for foreigners to help them increase their ability to speak Korean. The service developed in this paper is implemented in the form of a mobile application that shows specific Korean sentences to the user for them to record themselves speaking the sentence. The objective is to generate the score automatically based on how similar the recorded voice with the actual sentence using Speech-To-Text (STT) engines and Sentence Transformers. The application is developed by selecting the four most commonly known STT engines with similar features, which are Google API, Microsoft Azure, Naver Clova, and IBM Watson, which are put into a Rest API along with the Sentence Transformer. The mobile application will record the user’s voice and send it to the Rest API. The STT engines will transcribe the file into a text and then feed it into a Sentence Transformer to generate the score based on their similarity. After measuring the response time and consistency as the performance evaluation by simulating a scenario using an Android emulator, Microsoft Azure with 1.13 s is found to be the fastest STT engine and Naver Clova is found to be the least consistent engine with nine different transcribe results.

This paper shows the comparison between several well‐known classification algorithms in Machine Learning with the purpose of finding the most suitable algorithm to predict the dwelling time, that is, how long a certain tourist should stay in a particular tourist spot. This dwelling time prediction can be adopted by tour and travel agents to provide optimal scheduling for their package tours. The algorithm in question is strictly for classification because in this case, the dwelling time does not require a very specific number of minutes; thus, the time can be classified and restricted into several time frames. The origin and features of the dataset are described in this paper as well as the comparison methodology to show the procedure of how the comparison was made. Lastly, the performance results will be used to determine which algorithm to use for this specific case and it will be shown in a form of a graph.

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learningbased prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.

This paper shows the comparison between several well-known classification algorithms in Machine Learning with the purpose to find the most suitable algorithm to predict the dwelling time i.e., how long a certain tourist should stay in a particular tourist spot. This dwelling time prediction can be adopted for tour and travel agents to provide optimal scheduling for their package tour. The algorithm in question is strictly for classification because in this case, the dwelling time does not require a very specific number of minutes, thus the time can be classified and restricted into several time frames. The origin and features of the dataset are described in this paper as well as the comparison methodology to show the procedure of how the comparison was made. Lastly, the performance results will be used to determine which algorithm to use for this specific case and it will be shown in a form of a graph