Yazar "Oguz, Abdulhalik" seçeneğine göre listele

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    A survey on applications of machine learning algorithms in water quality assessment and water supply and management
    (Iwa Publishing, 2023) Oguz, Abdulhalik; Ertugrul, Omer Faruk
    Managing water resources and determining the qualit y of surface and groundwater is one of the most significant issues fundamental to human and societal well-being. The process of maintaining water qualit y and managing water resources well involves complications due to human-induced errors. Therefore, applications that facilitate and enhance these processes have gained importance. In recent years, machine learning techniques have been applied successfully in the preservation of water quality and the management and planning of water resources. Water researchers have effectively used these techniques to integrate them into public management systems. In this study, data sources, pre-processing, and machine learning methods used in water research are briefly mentioned, and algorithms are cate-gorized. Then, a general summar y of the literature is presented on water qualit y determination and applications in water resources management. Finally, the study was detailed using machine learning investigations on two publicly shared datasets.
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    Emotion recognition by skeleton-based spatial and temporal analysis
    (Pergamon-Elsevier Science Ltd, 2024) Oguz, Abdulhalik; Ertugrul, Omer Faruk
    This study introduces an automatic emotion recognition system (AER) focusing on skeletal-based kinematic datasets for enhanced human-computer interaction. Departing from conventional approaches, it achieves realtime emotion recognition in real-life situations. The dataset covers seven emotions and undergoes assessment by eight diverse machine and deep learning algorithms. A thorough investigation is undertaken by varying window sizes and data states, including raw positions and feature-extracted data. The findings imply that incorporating advanced techniques like joint-related feature extraction and robust classifier models yields promising outcomes. Dataset augmentation via varying window sizes enriches insights into real-world scenarios. Evaluations exhibit classification accuracy surpassing 99% for small windows, 94% for medium, and exceeding 88% for larger windows, thereby confirming the robust nature of the approach. Furthermore, we highlight window size's impact on emotion detection and the benefits of combining coordinate axes for efficiency and accuracy. The analysis intricately examines the contributions of features at both the joint and axis levels, assisting in making well-informed selections. The study's contributions include carefully curated datasets, transparent code, and models, all of which ensure the possibility of replication. The paper establishes a benchmark that bridges theory and practicality, solidifying the proposed approach's effectiveness in balancing accuracy and efficiency. By pioneering advanced AER through kinematic data, it sets a new standard for efficacy while driving seamless human-computer interaction through rigorous analysis and strategic design.
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    Öğe
    Human identification based on accelerometer sensors obtained by mobile phone data
    (Elsevier Sci Ltd, 2022) Oguz, Abdulhalik; Ertugrul, Omer Faruk
    In order to achieve secure usage digitally, many different methodologies (i.e., pin code, fingerprint, face recognition) have been employed. In this study, a novel way of user identification, which can be expressed as a biometrical method, has been proposed. The proposed approach was based on the characteristics of mobile phone usage (position changes in carrying, talking, and other actions). To assess and validate the proposed method, a dataset, which consists of millions of data collected from users with the help of accelerometers for several months during their ordinary smartphone usage, was obtained. This large dataset was reduced by randomly taking 3000 samples from each of the 387 devices in the dataset. The arbitrarily selected signals were labeled according to one against all (or one vs. all) strategies. Extracted features were classified by the k nearest neighbor (kNN) and the randomized neural network (RNN), machine learning methods. It has been seen that behavior-based biometric recognition can be accomplished with mobile phone accelerometer data, with 99.994% success rates for kNN and 99.97% for RNN.