Computer Engineering / Bilgisayar Mühendisliğihttp://openacccess.atu.edu.tr:8080/xmlui/handle/123456789/5042024-03-29T13:37:10Z2024-03-29T13:37:10ZAn overview of deep learning techniques for COVID-19 detection: methods, challenges, and future worksGursoy, ErcanKaya, Yasinhttp://openacccess.atu.edu.tr:8080/xmlui/handle/123456789/42212023-07-26T08:16:22Z2023-06-01T00:00:00ZAn overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works
Gursoy, Ercan; Kaya, Yasin
The World Health Organization (WHO) declared a pandemic in response to the coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the disease appears to have lost its impact, millions of people have been affected by this virus, and new infections still occur. Identifying COVID-19 requires a reverse transcription-polymerase chain reaction test (RT-PCR) or analysis of medical data. Due to the high cost and time required to scan and analyze medical data, researchers are focusing on using automated computer-aided methods. This review examines the applications of deep learning (DL) and machine learning (ML) in detecting COVID-19 using medical data such as CT scans, X-rays, cough sounds, MRIs, ultrasound, and clinical markers. First, the data preprocessing, the features used, and the current COVID-19 detection methods are divided into two subsections, and the studies are discussed. Second, the reported publicly available datasets, their characteristics, and the potential comparison materials mentioned in the literature are presented. Third, a comprehensive comparison is made by contrasting the similar and different aspects of the studies. Finally, the results, gaps, and limitations are summarized to stimulate the improvement of COVID-19 detection methods, and the study concludes by listing some future research directions for COVID-19 classification.
WOS indeksli yayınlar koleksiyonu. / WOS indexed publications collection.
2023-06-01T00:00:00ZThe New Prediction Methodology for CO2 Emission to Ensure Energy Sustainability with the Hybrid Artificial Neural Network ApproachAksu, Inayet OzgeDemirdelen, Tugcehttp://openacccess.atu.edu.tr:8080/xmlui/handle/123456789/41842023-04-14T08:54:43Z2022-12-01T00:00:00ZThe New Prediction Methodology for CO2 Emission to Ensure Energy Sustainability with the Hybrid Artificial Neural Network Approach
Aksu, Inayet Ozge; Demirdelen, Tugce
Energy is one of the most fundamental elements of today's economy. It is becoming more important day by day with technological developments. In order to plan the energy policies of the countries and to prevent the climate change crisis, CO2 emissions must be under control. For this reason, the estimation of CO2 emissions has become an important factor for researchers and scientists. In this study, a new hybrid method was developed using optimization methods. The Shuffled Frog-Leaping Algorithm (SFLA) algorithm has recently become the preferred method for solving many optimization problems. SFLA, a swarm-based heuristic method, was developed in this study using the Levy flight method. Thus, the speed of reaching the optimum result of the algorithm has been improved. This method, which was developed later, was used in a hybrid structure of the Firefly Algorithm (FA). In the next step, a new Artificial Neural Network (ANN)-based estimation method is proposed using the hybrid optimization method. The method was used to estimate the amount of CO2 emissions in Turkiye. The proposed hybrid model had the RMSE error 5.1107 and the R2 0.9904 for a testing dataset, respectively. In the last stage, Turkiye's future CO2 emission estimation is examined in three different scenarios. The obtained results show that the proposed estimation method can be successfully applied in areas requiring future estimation.
WOS indeksli yayınlar koleksiyonu. / WOS indexed publications collection.
2022-12-01T00:00:00ZAutomatic Short Answer Grading With SemSpace Sense Vectors and MaLSTMTulu, Cagatay NeftaliOzkaya, OzgeOrhan, Umuthttp://openacccess.atu.edu.tr:8080/xmlui/handle/123456789/41092023-01-09T07:31:13Z2021-01-01T00:00:00ZAutomatic Short Answer Grading With SemSpace Sense Vectors and MaLSTM
Tulu, Cagatay Neftali; Ozkaya, Ozge; Orhan, Umut
Automatic assessment of exams is widely preferred by educators than multiple-choice exams because of its efficiency in measuring student performance, lack of subjectivity when evaluating student response, and faster evaluation time than the time consuming manual evaluation. In this study, a new approach for the Automatic Short Answer Grading (ASAG) is proposed using MaLSTM and the sense vectors obtained by SemSpace, a synset based sense embedding method built leveraging WordNet. Synset representations of the Student's answers and reference answers are given as input into parallel LSTM architecture, they are transformed into sentence representations in the hidden layer and the vectorial similarity of these two representation vectors are computed with Manhattan Similarity in the output layer. The proposed approach has been tested using the Mohler ASAG dataset and successful results are obtained in terms of Pearson (r) correlation and RMSE. Also, the proposed approach has been tested as a case study using a specific dataset (CU-NLP) created from the exam of the Natural Language Processing course in the Computer Engineering Department of Cukurova University. And it has achieved a successful correlation. The results obtained in the experiments show that the proposed system can be used efficiently and effectively in context-dependent ASAG tasks.
WOS indeksli yayınlar koleksiyonu. / WOS indexed publications collection.
2021-01-01T00:00:00ZDroidClone: Attack of the Android Malware Clones - A Step Towards Stopping ThemAlam, ShahidSogukpinar, Ibrahimhttp://openacccess.atu.edu.tr:8080/xmlui/handle/123456789/41082023-01-09T07:18:21Z2021-01-01T00:00:00ZDroidClone: Attack of the Android Malware Clones - A Step Towards Stopping Them
Alam, Shahid; Sogukpinar, Ibrahim
Code clones are frequent in use because they can be created fast with little effort and expense. Especially for malware writers, it is easier to create a clone of the original than writing a new malware. According to the recent Symantec threat reports, Android continues to be the most targeted mobile platform, and the number of new mobile malware clones grew by 54%. There is a need to develop techniques and tools to stop this attack of Android malware clones. To stop this attack, we propose DroidClone that exposes code clones (segments of code that are similar) in Android applications to help detect malware. DroidClone is the first such effort uses specific control flow patterns for reducing the effect of obfuscations and detect clones that are syntactically different but semantically similar up to a threshold. DroidClone is independent of the programming language of the code clones. When evaluated with real malware and benign Android applications, DroidClone obtained a detection rate of 94.2% and false positive rate of 5.6%. DroidClone, when tested against various obfuscations, was able to successfully provide resistance against all the trivial (Renaming methods, parameters, and nop insertion, etc) and some non-trivial (Call graph manipulation and function indirection, etc.) obfuscations.
WOS indeksli yayınlar koleksiyonu. / WOS indexed publications collection.
2021-01-01T00:00:00Z