Yazar "Ari A." seçeneğine göre listele

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    Computer-aided tumor detection system using brain MR images
    (Institute of Electrical and Electronics Engineers Inc., 2016) Ari A.; Alpaslan N.; Hanbay D.
    In today's technology, computer assisted detection applications have managed to make great contributions to the field of medicine. Computer assisted detection systems aim to help radiologist about mass detection by using image processing systems. In this study, it's aimed to carry out mass detection process on the images of 3D brain MRI (Magnetic Resonance Imaging). The steps followed in this study are the stage of pre-processing the stage of segmentation, identification of the areas of interests and identification of tumor. As a result of processing's in the stages of preprocessing and segmentation, obtained areas of interests are labelled and attributes of these areas of interests are extracted during the stage of attributes extraction and in the last stage, the areas of interests are identified as whether they are mass or not according to these attributes. With this method applied on 845 number of magnetic resonance image sections belonging to 13 patients, it has been achieved classification success with 86.39%. © 2015 IEEE.
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    Generation of Substitution Box Structures Based on Blum Blum Shub Random Number Outputs
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ari A.; Ozkaynak F.
    One of the most important milestones in our transformation into a digital society is the effective adoption of Internet technologies by all stakeholders of the society. The critical factor in this adoption has been the trust model provided through cryptographic protocols. However, the secure environment that tries to be created with cryptographic algorithms is constantly under attack threat with technological developments. Therefore, it is necessary to develop new algorithms resistant to new attacks. In this study, a new approach is proposed for substitution box structures, which is a cryptographic component that can be used as a precaution against side channel attacks. The success of the obtained component has been analyzed for five basic substitution box design metrics. Successful experimental results indicate that the proposed approach have various potentials for new practical applications that can be designed in the future. © 2022 IEEE.
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    A NEW ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEM FOR DIAGNOSIS OF MAJOR PSYCHIATRIC DISEASES BASED ON VOICE ANALYSIS
    (Medicinska Naklada Zagreb, 2023) Cansel N.; Alcin Ö.F.; Yılmaz Ö.F.; Ari A.; Akan M.; Ucuz İ.
    Background: Speech features are essential components of psychiatric examinations, serving as important markers in the recognition and monitoring of mental illnesses. This study aims to develop a new clinical decision support system based on artificial intelligence, utilizing speech signals to distinguish between bipolar, depressive, anxiety and schizophrenia spectrum disorders. Subjects and methods: A total of 79 patients, who were admitted to the psychiatry clinic between 2020-2021, including 15 with schizophrenia spectrum disorders, 24 with anxiety disorders, 25 with depressive disorders, and 15 with bipolar affective disorder, alongside with 25 healthy individuals were included in the study. The speech signal dataset was created by recording participants’ readings of two texts determined by the Russell emotion model. The number of speech samples was increased by using random sampling in speech signals. The sample audio signals were decomposed into time-frequency coe?cients using Wavelet Packet Transform (WPT). Feature extraction was performed using each coe?cient obtained from both Mel-Frequency Cepstral Coe?cients (MFCC) and Gammatone Cepstral Coe?cient (GTCC) methods. The disorder classification was carried out using k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. Results: The success rate of the developed model in distinguishing the disorders was 96.943%. While the kNN model exhibited the highest performance in diagnosing bipolar disorder, it performed the least effectively in detecting depressive disorders. Whereas, the SVM model demonstrated close and high performance in detecting anxiety and psychosis, but its performance was low in identifying bipolar disorder. The findings support the utilization of speech analysis for distinguishing major psychiatric disorders. In this regard, the future development of artificial intelligence-based systems has the potential to enhance the psychiatric diagnosis process. © Medicinska naklada – Zagreb, Croatia.