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Identifying and classifying malware has become a critical task in ensuring the security and resilience of computer systems and networks. Traditional techniques for malware assessment often rely on signature-based methods, which struggle to keep up with the constantly evolving landscape of malware variations. Recently, the application of advanced deep learning methods has shown promising results in automating the malware classification process. This study presents an innovative strategy for classifying malware images using the Vision Transformer (ViT) architecture. The ViT model is adapted to the domain of malware analysis by representing malware images as input tokens. A comprehensive dataset of 14,226 malware samples from 26 families was used to evaluate the effectiveness of this approach. A comparative analysis was performed between the performance of the ViT-based classifier, traditional machine learning approaches and other deep learning architectures. Our experimental results demonstrate the potential of ViT in handling malware images, achieving a classification accuracy of 98.80%. The presented approach establishes a strong foundation for further research in utilizing cutting-edge deep learning architectures for enhanced malware analysis and detection techniques. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has helped to obtain accurate, fast, robust results without any human errors. Hence, it has been used in various applications in our daily lives. The Turing test has been a fundamental problem that AI systems aim to overcome. Recently developed various natural language processing (NLP) models have shown significant performances. AI language models, used in translation, digital assistant, and sentiment analysis, have improved the quality of our lives. It can perform scans on thousands of documents in seconds and report them by establishing appropriate sentence structures. Generative pre-trained transformer (ChatGPT) is a popular model developed recently has been used for many applications. Users of this model have obtained surprising results on various applications and shared them on various social media platforms. This study aims to evaluate the performance of the GPT-3 model in writing an academic article. Hence, we chose the subject of the article as tools based on artificial intelligence in academic article writing. The organized queries on ChatGPT created the flow of this article. In this article, we have made an effort to highlight the advantages and limitations of using ChatGPT-3 for academic research paper writing. As a result of the findings, it has been observed that ChatGPT can be used as an auxiliary tool while writing academic research articles. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Intelligent Transportation Systems & Applications is the property of Journal of Intelligent Transportation Systems & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji is the property of Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of computer vision methods, the number of areas where autonomous systems are used has also increased. Among these areas is the transportation sector. Autonomous systems in the transportation sector are mostly developed for road vehicles, but highway rules and standards different between countries. In this study, models capable of semantic segmentation have been developed for autonomous railway vehicles with the help of the public dataset. Four different U-Net models were trained with 8500 images for four different scenarios. The model trained for binary semantic segmentation reached mean Intersection over Union (mIoU) value of 89.1%, while the models trained for multi-class semantic segmentation reached 83.2% mIoU, 79.7% mIoU and 29.6% mIoU. Information about the inclusion of high-resolution images in model training and performance metrics in semantic segmentation studies shared. [ABSTRACT FROM AUTHOR]

The brain, which consists of nerve cells called neurons, is the center of the nervous system. The rapid and abnormal growth of nerve cells by interacting with each other is called a brain tumor. Undiagnosed or delayed diagnosis of brain tumors lead to death. Although it depends on experience, manually diagnosing and classifying brain tumors is challenging for physicians. Artificial intelligence-based computer systems can help doctors detect brain tumors using the developments in hardware technology and the amount of data increasing daily. This study proposes a deep learning-based system to classify brain MRI images as tumorous or normal using the pre-trained EfficientNet-B0 model. Our radiologist validated a public dataset containing 3000 brain MRI images. The dataset is divided into 70% train, 20% validation, and 10% test. In the test phase after the training, the pre-trained EfficientNet-B0 model achieved high performance with 99.33% accuracy, 99.33% sensitivity, and 99.33% F1 score. In addition, in the evaluation of the test images, the heat maps obtained by the Grad-CAM method were examined by our radiology specialist. The result of evaluations shows that the pre-trained EfficientNet-B0 deep model chooses the right focus areas in its predictions and can be used for clinical tumor detection due to its explainable structure. [ABSTRACT FROM AUTHOR]

Objectives: Expansion sphincter pharyngoplasty is a well-known palatal correction surgery applied for obstructive sleep apnea patients. Even though high success rates are reported, in some patients, the desired outcome cannot be achieved. Therefore, patient selection is crucial to avoid undesirable outcomes. In this study we present our results with expansion sphincter pharyngoplasty, and we aim to show the difference between supine and non-supine apnea in terms of their response to the surgery, in order to utilize position dependency for selection of surgical candidates.Methods: Pre- and post-operative polysomnography results of patients who underwent expansion sphincter pharyngoplasty were analyzed retrospectively. Total AHI, supine AHI, non-supine AHI, supine/non-supine AHI ratio and surgical success values are compared.Results: 85 patients were included to the study. Mean AHI significantly decreased from 48.7 ± 27.99 to 26.37 ± 21.16 with the surgery. Surgical success rate was found to be 51.8%. Both supine and non-supine apnea decreased significantly with the surgery, but the decrease was significantly higher in non-supine apnea (20.6% to 39.1% respectively, p = 0.016). There was significant negative correlation between pre-operative supine to non-supine AHI ratio and the change in AHI, showing that supine dominant patients had less improvement with the surgery (r = 0.274, p = 0.01).Conclusion: Expansion sphincter pharyngoplasty is an effective surgery which achieves significant improvement in AHI. Non-supine respiratory events respond better to the surgery than supine events. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to determine the effectiveness of coblation midline glossectomy for obstructive sleep apnea (OSA) when used as an isolated procedure. We also aim to compare the effect of this surgical procedure on supine and non-supine apnea.Materials and Methods: The medical records of patients who underwent isolated tongue base surgery as a part of step-wise surgeries between January 2014 and February 2019 are retrospectively reviewed. Pre-operative and post-operative Epworth sleepiness score (ESS), body mass index (BMI), and polysomnographic data, including the apnea-hypopnea index (AHI), oxygen desaturation index (ODI), supine AHI, and non-supine AHI of the patients were compared.Results: The study included 29 patients (26 male and 3 female). AHI improved significantly, decreasing from 34.9 ± 20.9 to 25.8 ± 17.6. Supine AHI decreased from 62.55 ± 28.23 to 55.18 ± 31.67 post-operatively, but this decrease was not significant. Non-supine AHI decreased significantly from 22.49 ± 24.02 to 14.08 ± 17.46. ESS and ODI also improved significantly.Conclusion: Coblation midline glossectomy is an effective surgical procedure when applied solely, with a success rate of 52%. Non-supine apnea benefits to a greater degree than supine apnea from this surgical procedure. [ABSTRACT FROM AUTHOR]

We discuss a case of lower lip carcinoma which presented with atypical symptoms; facial paralysis, conductive type hearing loss, and ophthalmoplegia. Due to an earlier resection, no mass was evident on the primary examination. Diagnostic imaging revealed a mass originating from the lower lip, the perineural spread of the tumor along the left inferior alveolar nerve to the left infratemporal fossa and the left foramen ovale. Through a retrograde course from the foramen ovale, the tumor extended the ipsilateral cavernous sinus, Meckel's cave, and cisternal portion of the CN V. This atypical spread pattern of the tumor caused symptoms that may be attributed to a diagnosis related to the ear. The biopsy confirmed squamous cell carcinoma, and the patient was referred for chemotherapy and radiotherapy. [ABSTRACT FROM AUTHOR]