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Article Citation - WoS: 32Citation - Scopus: 39A Computationally Efficient Method for Hybrid Eeg-Fnirs Bci Based on the Pearson Correlation(Hindawi Ltd, 2020) Hasan, Mustafa A. H.; Khan, Muhammad U.; Mishra, DeeptiA hybrid brain computer interface (BCI) system considered here is a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG-fNIRS signals are simultaneously recorded to achieve high motor imagery task classification. This integration helps to achieve better system performance, but at the cost of an increase in system complexity and computational time. In hybrid BCI studies, channel selection is recognized as the key element that directly affects the system's performance. In this paper, we propose a novel channel selection approach using the Pearson product-moment correlation coefficient, where only highly correlated channels are selected from each hemisphere. Then, four different statistical features are extracted, and their different combinations are used for the classification through KNN and Tree classifiers. As far as we know, there is no report available that explored the Pearson product-moment correlation coefficient for hybrid EEG-fNIRS BCI channel selection. The results demonstrate that our hybrid system significantly reduces computational burden while achieving a classification accuracy with high reliability comparable to the existing literature.Article Citation - WoS: 6Citation - Scopus: 9Experimental Simulation-Based Performance Evaluation of an Sms-Based Emergency Geolocation Notification System(Hindawi Ltd, 2017) Osebor, Isibor; Misra, Sanjay; Omoregbe, Nicholas; Adewumi, Adewole; Fernandez-Sanz, LuisIn an emergency, a prompt response can save the lives of victims. This statement generates an imperative issue in emergency medical services (EMS). Designing a system that brings simplicity in locating emergency scenes is a step towards improving response time. This paper therefore implemented and evaluated the performance of an SMS-based emergency geolocation notification system with emphasis on its SMS delivery time and the system's geolocation and dispatch time. Using the RAS metrics recommended by IEEE for evaluation, the designed system was found to be efficient and effective as its reliability stood within 62.7% to 70.0% while its availability stood at 99% with a downtime of 3.65 days/year.Article Citation - WoS: 5Citation - Scopus: 7Deadline-Aware Energy-Efficient Query Scheduling in Wireless Sensor Networks With Mobile Sink(Hindawi Ltd, 2013) Karakaya, MuratMobile sinks are proposed to save sensor energy spent for multihop communication in transferring data to a base station (sink) in Wireless Sensor Networks. Due to relative low speed of mobile sinks, these approaches are mostly suitable for delay-tolerant applications. In this paper, we study the design of a query scheduling algorithmfor query-based data gathering applications using mobile sinks. However, these kinds of applications are sensitive to delays due to specified query deadlines. Thus, the proposed scheduling algorithm aims to minimize the number of missed deadlines while keeping the level of energy consumption at the minimum.Article Citation - WoS: 12Citation - Scopus: 32Sentimental Analysis of Twitter Users From Turkish Content With Natural Language Processing(Hindawi Ltd, 2022) Balli, Cagla; Guzel, Mehmet Serdar; Bostanci, Erkan; Mishra, AlokArtificial Intelligence has guided technological progress in recent years; it has shown significant development with increased academic studies on Machine Learning and the high demand for this field in the sector. In addition to the advancement of technology day by day, the pandemic, which has become a part of our lives since early 2020, has led to social media occupying a larger place in the lives of individuals. Therefore, social media posts have become an excellent data source for the field of sentiment analysis. The main contribution of this study is based on the Natural Language Processing method, which is one of the machine learning topics in the literature. Sentiment analysis classification is a solid example for machine learning tasks that belongs to human-machine interaction. It is essential to make the computer understand people emotional situation with classifiers. There are a limited number of Turkish language studies in the literature. Turkish language has different types of linguistic features from English. Since Turkish is an agglutinative language, it is challenging to make sentiment analysis with that language. This paper aims to perform sentiment analysis of several machine learning algorithms on Turkish language datasets that are collected from Twitter. In this research, besides using public dataset that belongs to Beyaz (2021) to get more general results, another dataset is created to understand the impact of the pandemic on people and to learn about public opinions. Therefore, a custom dataset, namely, SentimentSet (Balli 2021), was created, consisting of Turkish tweets that were filtered with words such as pandemic and corona by manually marking as positive, negative, or neutral. Besides, SentimentSet could be used in future researches as benchmark dataset. Results show classification accuracy of not only up to similar to 87% with test data from datasets of both datasets and trained models, but also up to similar to 84% with small "Sample Test Data" generated by the same methods as SentimentSet dataset. These research results contributed to indicating Turkish language specific sentiment analysis that is dependent on language specifications.
