Browsing by Author "Sezen,A."

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Citation - Scopus: 2
A 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.
Citation - Scopus: 2
Systematic Mapping Study on Natural Language Processing for Social Robots
(Prof.Dr. İskender AKKURT, 2024) Adem,A.İ.; Turhan,Ç.; Sezen,A.
Nowadays, social robots are becoming increasingly sophisticated in terms of their ability to interact with humans and possess social skills, and in this context, natural language processing (NLP) plays a critical role for robots to understand and communicate with human language. Natural Language Processing (NLP) is an interdisciplinary field used to help computers understand, interpret, and generate human language with a wide range of applications. The examination of the datasets, methods/techniques and tools, and usage of speech recognition or generation in the fields of NLP is important in understanding the developments in this field. In this study, 35 out of 92 studies in the literature collected from Web of Science were examined using a systematic mapping approach, and important findings on the use of NLP in social robots were identified. In particular, emphasis was placed on the effective evaluation of the research questions in the context of NLP in social robots. This study creates a starting point that will guide research in the field of NLP use in social robots and guide future studies. © 2024, Prof.Dr. İskender AKKURT. All rights reserved.