2 results
Search Results
Now showing 1 - 2 of 2
Conference Object Citation - Scopus: 4Distance Laboratory Applications Errl: a Study on Radio Communication in Electronic Field(2008) Aydin,C.Ç.; Türkmen,G.; Özyurt,E.; Aydm,E.U.; Çaǧiltay,N.E.; Özbek,M.E.; Kara,A.In the last decade, the effect of internet usage in education is gradually increased. When we look from academic perspective, the new technologies provided alternatives for students learning. As distance education becomes important everyday, the indispensable elements of teaching and education, laboratories must be reachable via remote connection. Consequently, the education that is going to be given to the students will be more flexible with respect to place and time constraints and students can reach laboratory facilities at any time and anywhere not only in lectures and practical hours. In this study, European Remote Radio Laboratory (ERRL) which is a distance remote Radio Frequency (RF) laboratory designed for electrical-electronics students, is described generally. The software architecture, infrastructure and experiment that can be done with a remote connection have been described.Article Citation - Scopus: 3A Comparative Analysis of Xgboost and Lightgbm Approaches for Human Activity Recognition: Speed and Accuracy Evaluation(Prof.Dr. İskender AKKURT, 2024) Sezen,A.; Türkmen,G.Human activity recognition is the process of automatically identifying and classifying human activities based on data collected from different modalities such as wearable sensors, smartphones, or similar devices having necessary sensors or cameras capturing the behavior of the individuals. In this study, XGBoost and LightGBM approaches for human activity recognition are proposed and the performance and execution times of the proposed approaches are compared. The proposed methods on a dataset including accelerometer and gyroscope data acquired using a smartphone for six activities. The activities are laying, sitting, standing, walking, walking downstairs, and walking upstairs. The available dataset is divided into training and test sets, and proposed methods are trained using the training set, and tested on the test sets. At the end of the study, 97.23% accuracy using the LightGBM approach, and 96.67% accuracy using XGBoost is achieved. It is also found that XGBoost is faster than the LightGBM, whenever the execution times are compared. © IJCESEN.
