Hakan Ezgi Kiziloz

The COVID‐19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID‐19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi‐threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG‐ELM) to predict the severity level of the COVID‐19 patients. We conduct a set of experiments on a recently published real‐world dataset. We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi‐threaded implementation with statistical analysis. In order to verify the efficiency of MG‐ELM, we compare our results with traditional and state‐of‐the‐art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy.

Wireless sensor networks (WSN) has been a prominent topic for the past decade. WSN consist of multiple sensor nodes, which collect and convey data to the base station(s). Sensor nodes are expected to run on batteries, and it makes energy the scarce resource for sensor nodes. Energy expenditure of a sensor node mainly depends on data transmission, which is exponentially affected by transmission distance. Consequently, if sensor nodes forward their data to the base station directly, distant sensor nodes will exhaust quickly. On contrary, minimization of transmission distance for each sensor node, i.e., each node transmits its data to the closest sensor node on its path to the base station, depletes the energy of sensor nodes that are closer to the base station fast. As a result, the flow balance in the network must be optimized. In this study, we investigate the effect of optimization of the base station location along with flow balance optimization. For this purpose, we compare five different localization methods on different topologies; three statically located linear programming approaches, a dynamically located nonlinear programming approach and a heuristic based hybrid approach. Experimental results indicate that lifetime improvement of up to 42% is possible in selected scenarios.

Natural Language Processing (NLP) is a prominent subject which includes various subcategories such as text classification, error correction, machine translation, etc. Unlike other languages, there are limited number of Turkish NLP studies in literature. In this study, we apply text classification on Turkish documents by using n-gram features. Our algorithm applies different preprocessing techniques, namely, n-gram choice (character level or word level, bigram or trigram models), stemming, and use of punctuation, and then determines the Turkish document's author and genre, and the gender of the author. For this purpose, Naive Bayes, Support Vector Machines and Random Forest are used as classification techniques. Finally, we discuss the effects of above mentioned preprocessing techniques to the performance of Turkish text classification.