Yazar "Karci, Ali" seçeneğine göre listele

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    Deep and Statistical Features Classification Model for Electroencephalography Signals
    (Int Information & Engineering Technology Assoc, 2022) Karaduman, Mucahit; Karci, Ali
    People strive to make sense of the complex electroencephalography (EEG) data generated by the brain. This study uses a prepared dataset to examine how easily people with alcohol use disorder (AUD) could be distinguished from healthy people. The signals from each electrode are connected to one another and are first represented as a single signal. The signal is then denoised through variation mode decomposition (VMD) during the preprocessing stage. The statistical and deep feature extraction phases are the two subsequent phases. The crucial step in the suggested strategy is to classify data using a combination of these two unique qualities. Deep and statistical feature performance was evaluated independently. Then, using the eigenvectors created by merging all of the collected features, classification was carried out using our DSFC (Deep - Statistical Features Classification) model. Although the classification accuracy rate using only statistical features was 81.2 percent and the classification accuracy rate using only deep learning was 95.71 percent, the classification accuracy rate utilizing hybrid features created using the suggested DSFC technique was 99.2%. Therefore, it can be proven that combining statistical and deep features can produce beneficial results.
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    Determining the Demands of Disabled People by Artificial Intelligence Methods
    (Ali KARCI, 2021) Karaduman, Mucahit; Karci, Ali
    Analysis of brain activities and remote control are among the current issues that are being studied. Analysis of signals arising during brain functions is electroencephalography (EEG). EEG signals have intellectual, visual stimulation, and motion resultant forms. Especially, EEG signals generated by visual stimulus are within the scope of this study. In this study, research was carried out on the classification of EEG signals formed in a person looking at visual figures. For these studies, first of all, EEG signals from the brain were recorded with images and filtered to remove noise. Then, the features were extracted from the signals. In this study, Moment 5 feature was also used in addition to the features used in many studies such as mean, median, standard deviation and entropy. Then, classification was made using Support Vector Machine (SVM), k Nearest Neighbor (KNN), and Decision Tree (DT) algorithms. Classification was made for 4 different visual shapes used, since these shapes are square, circle, triangle, and star, and the same categorical names were used in the classification stage. As a result of the classification of EEG signals; SVM and KNN algorithms have determined which shape is viewed with 99.99% accuracy. These results show that different signals are produced in the brain according to the structure of the shape viewed. This situation shows that it can be used as a method to give patients the opportunity to express their requests just by looking or thinking.
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    Social Trusty Algorithm: A New Algorithm for Computing the Trust Score Between All Entities in Social Networks Based on Linear Algebra
    (Mdpi, 2025) Karadeniz Kose, Esra; Karci, Ali
    The growing importance of social networks has led to increased research into trust estimation and interpretation among network entities. It is important to predict the trust score between users in order to minimize the risks in user interactions. This article enables the identification of the most reliable and least reliable entities in a network by expressing trust scores numerically. In this paper, the social network is modeled as a graph, and trust scores are calculated by taking the powers of the ratio matrix between entities and summing them. Taking the power of the proportion matrix based on the number of entities in the network requires a lot of arithmetic load. After taking the powers of the eigenvalues of the ratio matrix, these are multiplied by the eigenvector matrix to obtain the power of the ratio matrix. In this way, the arithmetic cost required for calculating trust between entities is reduced. This paper calculates the trust score between entities using linear algebra techniques to reduce the arithmetic load. Trust detection algorithms use shortest paths and similar methods to eliminate paths that are deemed unimportant, which makes the result questionable because of the loss of data. The novelty of this method is that it calculates the trust score without the need for explicit path numbering and without any data loss.
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    SSC: Clustering of Turkish Texts By Spectral Graph Partitioning
    (Gazi Univ, 2021) Uckan, Taner; Hark, Cengiz; Karci, Ali
    There is growing interest in studies on text classification as a result of the exponential increase in the amount of data available. Many studies have been conducted in the field of text clustering, using different approaches. This study introduces Spectral Sentence Clustering (SSC) for text clustering problems, which is an unsupervised method based on graph-partitioning. The study explains how the proposed model proposed can be used in natural language applications to successfully cluster texts. A spectral graph theory method is used to partition the graph into non-intersecting sub-graphs, and an unsupervised and efficient solution is offered for the text clustering problem by providing a physical representation of the texts. Finally, tests have been conducted demonstrating that SSC can be successfully used for text categorization. A clustering success rate of 97.08% was achieved in tests conducted using the TTC-3600 dataset, which contains open-access unstructured Turkish texts, classified into categories. The SSC model proposed performed better compared to a popular k-means clustering algorithm.