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Article Citation - WoS: 1Citation - Scopus: 1Modeling of Kappa Factor Using Multivariate Adaptive Regression Splines: Application To the Western Türkiye Ground Motion Dataset(Springer, 2024) Kurtulmus, Tevfik Ozgur; Yerlikaya-Ozkurt, Fatma; Askan, AysegulThe recent seismic activity on Turkiye's west coast, especially in the Aegean Sea region, shows that this region requires further attention. The region has significant seismic hazards because of its location in an active tectonic regime of North-South extension with multiple basin structures on soft soil deposits. Recently, despite being 70 km from the earthquake source, the Samos event (with a moment magnitude of 7.0 on October 30, 2020) caused significant localized damage and collapse in the Izmir city center due to a combination of basin effects and structural susceptibility. Despite this activity, research on site characterization and site response modeling, such as local velocity models and kappa estimates, remains sparse in this region. Kappa values display regional characteristics, necessitating the use of local kappa estimations from previous earthquake data in region-specific applications. Kappa estimates are multivariate and incorporate several characteristics such as magnitude and distance. In this study, we assess and predict the trend in mean kappa values using three-component strong-ground motion data from accelerometer sites with known VS30 values throughout western Turkiye. Multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) were used to build the prediction models. The effects of epicentral distance Repi, magnitude Mw, and site class (VS30) were investigated, and the contributions of each parameter were examined using a large dataset containing recent seismic activity. The models were evaluated using well-known statistical accuracy criteria for kappa assessment. In all performance measures, the MARS model outperforms the MLR model across the selected sites.Article Citation - WoS: 6Citation - Scopus: 7A Simplified Method Based on Rssi Fingerprinting for Iot Device Localization in Smart Cities(Ieee-inst Electrical Electronics Engineers inc, 2024) Dogan, Deren; Dalveren, Yaser; Kara, Ali; Derawi, MohammadThe Internet of Things (IoT) has significantly improved location-based services in smart cities, such as automated public transportation and traffic management. Estimating the location of connected devices is a critical problem. Low Power Wide Area Network (LPWAN) technologies are used for localization due to their low power consumption and long communication range. Recent advances in Machine Learning have made Received Signal Strength Indicator (RSSI) fingerprinting with LPWAN technologies effective. However, this requires a connection between devices and gateways or base stations, which can increase network deployment, maintenance, and installation costs. This study proposes a cost-effective RSSI fingerprinting solution using IQRF technology for IoT device localization. The region of interest is divided into grids to provide training locations, and measurements are conducted to create a training dataset containing RSSI fingerprints. Pattern matching is performed to localize the device by comparing the fingerprint of the end device with the fingerprints in the created database. To evaluate the efficiency of the proposed solution, measurements were conducted in a short-range local area ( $80\times 30$ m) at 868 MHz. In the measurements, four IQRF nodes were utilized to receive the RSSIs from a transmitting IQRF node. The performances of well-known ML classifiers on the created dataset are then comparatively assessed in terms of test accuracy, prediction speed, and training time. According to the results, the Bagged Trees classifier demonstrated the highest accuracy with 96.87%. However, with an accuracy of 95.69%, the Weighted k-NN could also be a reasonable option for real-world implementations due to its faster prediction speed (37615 obs/s) and lower training time (28.1 s). To the best of the authors' knowledge, this is the first attempt to explore the feasibility of the IQRF networks to develop a RSSI fingerprinting-based IoT device localization in the literature. The promising results suggest that the proposed method could be used as a low-cost alternative for IoT device localization in short-range location-based smart city applications.Article Citation - WoS: 10Citation - Scopus: 16Student Engagement Research Trends of Past 10 Years: a Machine Learning-Based Analysis of 42,000 Research Articles(Springer, 2023) Gurcan, Fatih; Erdogdu, Fatih; Cagiltay, Nergiz Ercil; Cagiltay, KursatStudent engagement is critical for both academic achievement and learner satisfaction because it promotes successful learning outcomes. Despite its importance in various learning environments, research into the trends and themes of student engagement is scarce. In this regard, topic modeling, a machine learning technique, allows for the analysis of large amounts of content in any field. Thus, topic modeling provides a systematic methodology for identifying research themes, trends, and application areas in a comprehensive framework. In the literature, there is a lack of topic modeling-based studies that analyze the holistic landscape of student engagement research. Such research is important for identifying wide-ranging topics and trends in the field and guiding researchers and educators. Therefore, this study aimed to analyze student engagement research using a topic modeling approach and to reveal research interests and trends with their temporal development, thereby addressing a lack of research in this area. To this end, this study analyzed 42,517 peer-reviewed journal articles published from 2010 to 2019 using machine learning techniques. According to our findings, two new dimensions, "Community Engagement" and "School Engagement", were identified in addition to the existing ones. It is also envisaged that the next period of research and applications in student engagement will focus on the motivation-oriented tools and methods, dimensions of student engagement, such as social and behavioral engagement, and specific learning contexts such as English as a Foreign Language "EFL" and Science, Technology, Engineering and Math "STEM".Article Citation - WoS: 14Citation - Scopus: 17Enhancement of Quality and Quantity of Woody Biomass Produced in Forests Using Machine Learning Algorithms(Pergamon-elsevier Science Ltd, 2023) Peng, Wei; Sadaghiani, Omid KarimiForest is considered a significant source of woody biomass production. Sustainable production of wood, lower emittance of CO2 from burning, and lower amount of sulfur and heavy metals are the advantages of wood rather than fossil fuels. The quality and quantity of woody biomass production are a function of some operations including genetic modifications, high-quality forestry, evaluation, monitoring, storage, and transportation. Due to surveying numerous related works, it was found that there is a considerable reviewing gap in analyzing and collecting the applications of Machine Learning in the quality and quantity of woody biomass. To fill this gap in the current work, the above-mentioned operations are explained followed by the applications of Machine Learning algorithms. Conclusively, Machine Learning and Deep Learning can be employed in estimating main effective factors on trees growth, classification of seeds, trees, and regions, as well as providing decision-making tools for farmers or governors, evaluation of biomass, understanding the relation between the woody bimass internal structure and bio-fuel production, the ultimate and proximate analyses, prediction of wood contents and dimensions, determination of the proportion of mixed woody materials, monitoring for early disease identifi-cation and classification, classifying trees diseases, estimating evapotranspiration, collecting information about forest regions and its quality, nitrogen concentration in trees, choosing viable storage sites for storage depots and improving the solution, classifying different filling levels in silage, estimating acetic acid synthesis and aerobic reactions in silage, determining crop quantity in silo, estimating the methane production, and monitoring and predicting water content, quality and quantity of stored biomass, forecasting the demand, path way and on-time performance predicting, truck traffic predicting, and behavioral analysis and facility planning.Article Citation - WoS: 2Citation - Scopus: 3Real-Time Anomaly Detection System Within the Scope of Smart Factories(Springer, 2023) Bayraktar, Cihan; Karakaya, Ziya; Gokcen, HadiAnomaly detection is the process of identifying patterns that move differently from normal in a certain order. This process is considered one of the necessary measures for the safety of intelligent production systems. This study proposes a real-time anomaly detection system capable of using and analyzing data in smart production systems consisting of interconnected devices. Synthetic data were preferred in the study because it has difficulties such as high cost and a long time to obtain real anomaly data naturally for learning and testing processes. In order to obtain the necessary synthetic data, a simulation was developed by taking the popcorn production systems as an example. Multi-class anomalies were defined in the obtained data set, and the analysis performances were tested by creating learning models with AutoML libraries. In the field of production systems, while studies on anomaly detection generally focus on whether there is an anomaly in the system, it is aimed to determine which type of anomaly occurs in which device, together with the detection of anomaly by using multi-class tags in the data of this study. As a result of the tests, the Auto-Sklearn library presented the learning models with the highest performance on all data sets. As a result of the study, a real-time anomaly detection system was developed on dynamic data by using the obtained learning models.
