Pub Date : 2021-12-07DOI: 10.35377/saucis...1022600
Erhan Akbal, Ömer Faruk Yakut, S. Dogan, T. Tuncer, F. Ertam
{"title":"A Digital Forensics Approach for Lost Secondary Partition Analysis using Master Boot Record Structured Hard Disk Drives","authors":"Erhan Akbal, Ömer Faruk Yakut, S. Dogan, T. Tuncer, F. Ertam","doi":"10.35377/saucis...1022600","DOIUrl":"https://doi.org/10.35377/saucis...1022600","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114913069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-07DOI: 10.35377/saucis...863272
Khaled Elorbany, C. Bayilmis, Seda Balta
{"title":"A Smart Plug Equipped With IoT Technologies for Energy Management of Electrical Appliances","authors":"Khaled Elorbany, C. Bayilmis, Seda Balta","doi":"10.35377/saucis...863272","DOIUrl":"https://doi.org/10.35377/saucis...863272","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128282452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-08DOI: 10.35377/saucis...950746
E. Sönmez, Keziban Seçkin Codal
{"title":"Terrorism in Cyberspace : A Critical Review of Dark Web Studies under the Terrorism Landscape","authors":"E. Sönmez, Keziban Seçkin Codal","doi":"10.35377/saucis...950746","DOIUrl":"https://doi.org/10.35377/saucis...950746","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127875039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-04DOI: 10.35377/saucis...935353
Duygu Kabakçı, Emre Akbas
AUTOMATED LEARNING RATE SEARCH USING BATCH-LEVEL CROSS-VALIDATION Kabakcı, Duygu M.S., Department of Computer Engineering Supervisor: Assist. Prof. Dr. Emre Akbaş
基于批处理水平交叉验证的自动学习率搜索。计算机工程学系主管:协助。Emre akbakov教授
{"title":"Automated learning rate search using batch-level cross-validation","authors":"Duygu Kabakçı, Emre Akbas","doi":"10.35377/saucis...935353","DOIUrl":"https://doi.org/10.35377/saucis...935353","url":null,"abstract":"AUTOMATED LEARNING RATE SEARCH USING BATCH-LEVEL CROSS-VALIDATION Kabakcı, Duygu M.S., Department of Computer Engineering Supervisor: Assist. Prof. Dr. Emre Akbaş","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125301467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-03DOI: 10.35377/saucis...932620
Buket Kaya, Abdullah Günay
The coronavirus pandemic, which began to affect the whole world in early 2020, has become the most talked about agenda item by individuals. Individuals announce their feelings and thoughts through various communication channels and receive news from what is happening around them. One of the most important channels of communication is Twitter. Individuals express their feelings and thoughts by interacting with the tweets posted. This study aims to analyze the emotions of the comments made under the "daily coronavirus table" shared by the Republic of Turkey Ministry of Health and to measure their relationship with the daily number of cases and deaths. In the study, emotional classification of tweets was implemented using LSTM, GRU and BERT methods from deep learning algorithms. The results of all three algorithms were compared with the daily number of cases and deaths.
{"title":"Twitter Sentiment Analysis Based on Daily Covid-19 Table in Turkey","authors":"Buket Kaya, Abdullah Günay","doi":"10.35377/saucis...932620","DOIUrl":"https://doi.org/10.35377/saucis...932620","url":null,"abstract":"The coronavirus pandemic, which began to affect the whole world in early 2020, has become the most talked about agenda item by individuals. Individuals announce their feelings and thoughts through various communication channels and receive news from what is happening around them. One of the most important channels of communication is Twitter. Individuals express their feelings and thoughts by interacting with the tweets posted. This study aims to analyze the emotions of the comments made under the \"daily coronavirus table\" shared by the Republic of Turkey Ministry of Health and to measure their relationship with the daily number of cases and deaths. In the study, emotional classification of tweets was implemented using LSTM, GRU and BERT methods from deep learning algorithms. The results of all three algorithms were compared with the daily number of cases and deaths.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134118861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-19DOI: 10.35377/saucis...953348
Burcu DEMİRELLİ OKKALIOĞLU
{"title":"Effects of Binary Vectors Similarities on the Accuracy of Multi-Criteria Collaborative Filtering","authors":"Burcu DEMİRELLİ OKKALIOĞLU","doi":"10.35377/saucis...953348","DOIUrl":"https://doi.org/10.35377/saucis...953348","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134285812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-07DOI: 10.35377/saucis...901776
Onur Cihan
{"title":"GraParT: A MATLAB Toolbox for Partitioning Directed Graphs","authors":"Onur Cihan","doi":"10.35377/saucis...901776","DOIUrl":"https://doi.org/10.35377/saucis...901776","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121708138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-31DOI: 10.35377/SAUCIS.04.02.903208
M. Bicer
In recent years, microstrip antennas have become a popular research subject with the increasing use of mobile technologies. With the development of neural networks, the design and analysis of microstrip antennas are carried out quickly with high accuracy. However, optimizing the weight matrices and bias vectors of deep neural learning models is an important challenge for engineering problems. This study presents a deep neural network-based (DNN-based) neural model to estimate the gain and scattering parameter (S11) of C-shaped compact microstrip antennas (CCMAs). For this purpose, the S11 and gain values of 324 CCMAs with different physical and electrical properties were obtained using full-wave electromagnetic simulation software based on the finite integration technique (FIT). The data related to 324 CCMAs were used for the training and testing process. The improved manta ray foraging optimization (MRFO) algorithm based on the Lévy-flight (LF) mechanism was used to optimize the connection weights matrices and bias vectors. The MRFO-optimized model has estimation success for training and testing data as 0.925 and 0.922, in terms of R2 score, respectively. The estimated resonant frequencies using the trained model are compared with the studies in the literature, and an average percentage error (APE) of 0.933% is obtained.
{"title":"Analysis of C-shaped Compact Microstrip Antennas using Deep Neural Networks optimized by Manta Ray Foraging Optimization with Lévy-Flight Mechanism","authors":"M. Bicer","doi":"10.35377/SAUCIS.04.02.903208","DOIUrl":"https://doi.org/10.35377/SAUCIS.04.02.903208","url":null,"abstract":"In recent years, microstrip antennas have become a popular research subject with the increasing use of mobile technologies. With the development of neural networks, the design and analysis of microstrip antennas are carried out quickly with high accuracy. However, optimizing the weight matrices and bias vectors of deep neural learning models is an important challenge for engineering problems. This study presents a deep neural network-based (DNN-based) neural model to estimate the gain and scattering parameter (S11) of C-shaped compact microstrip antennas (CCMAs). For this purpose, the S11 and gain values of 324 CCMAs with different physical and electrical properties were obtained using full-wave electromagnetic simulation software based on the finite integration technique (FIT). The data related to 324 CCMAs were used for the training and testing process. The improved manta ray foraging optimization (MRFO) algorithm based on the Lévy-flight (LF) mechanism was used to optimize the connection weights matrices and bias vectors. The MRFO-optimized model has estimation success for training and testing data as 0.925 and 0.922, in terms of R2 score, respectively. The estimated resonant frequencies using the trained model are compared with the studies in the literature, and an average percentage error (APE) of 0.933% is obtained.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127325533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-31DOI: 10.35377/saucis.04.02.836375
Tuba Karagül, Nilüfer Yurtay, Birgül Öneç
Deciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.
{"title":"Diagnosing Hematological Disorders Using Deep Learning Method","authors":"Tuba Karagül, Nilüfer Yurtay, Birgül Öneç","doi":"10.35377/saucis.04.02.836375","DOIUrl":"https://doi.org/10.35377/saucis.04.02.836375","url":null,"abstract":"Deciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116938838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-31DOI: 10.35377/saucis.04.02.912154
A. Kara
Remaining useful life (RUL) prediction is of great significance for prognostic and health management (PHM) as it can achieve more reliable and effective maintenance strategies. With the advances in the field of deep learning, data-driven methods have provided promising prognostic prediction results. Hence, this research presents a data-driven prognostic approach based on deep learning models for predicting the RUL of mechanical systems effectively. Multiple separable convolution layers, a bidirectional Long Short-Term Memory (LSTM) layer, and fully-connected layers (FCL) are included in the proposed network, named the SC-BLSTM, to accomplish more accurate prognostic prediction from the raw degradation data acquired by different sensors. The proposed SC-BLSTM approach aims to learn complex and nonlinear features from the input data and capture temporal dependencies from the learned features. The presented approach in this research is tested and verified on the degradation data of turbofan engines (C-MAPSS dataset) from NASA. The result demonstrated that the SC-BLSTM is able to achieve more effective RUL prediction compared with some existing prognostic models. value. This shows that the performance of the RUL prediction improves when the testing turbofan engines are close to failure. The prognostic efficiency in the last periods of the mechanical systems is important to make effective maintenance decisions, ensure system reliability and availability, and decrease the overall cost. The proposed SC-BLSTM model is able to achieve more robust and effective prognostic prediction in the last stages.
{"title":"A Hybrid Prognostic Approach Based on Deep Learning for the Degradation Prediction of Machinery","authors":"A. Kara","doi":"10.35377/saucis.04.02.912154","DOIUrl":"https://doi.org/10.35377/saucis.04.02.912154","url":null,"abstract":"Remaining useful life (RUL) prediction is of great significance for prognostic and health management (PHM) as it can achieve more reliable and effective maintenance strategies. With the advances in the field of deep learning, data-driven methods have provided promising prognostic prediction results. Hence, this research presents a data-driven prognostic approach based on deep learning models for predicting the RUL of mechanical systems effectively. Multiple separable convolution layers, a bidirectional Long Short-Term Memory (LSTM) layer, and fully-connected layers (FCL) are included in the proposed network, named the SC-BLSTM, to accomplish more accurate prognostic prediction from the raw degradation data acquired by different sensors. The proposed SC-BLSTM approach aims to learn complex and nonlinear features from the input data and capture temporal dependencies from the learned features. The presented approach in this research is tested and verified on the degradation data of turbofan engines (C-MAPSS dataset) from NASA. The result demonstrated that the SC-BLSTM is able to achieve more effective RUL prediction compared with some existing prognostic models. value. This shows that the performance of the RUL prediction improves when the testing turbofan engines are close to failure. The prognostic efficiency in the last periods of the mechanical systems is important to make effective maintenance decisions, ensure system reliability and availability, and decrease the overall cost. The proposed SC-BLSTM model is able to achieve more robust and effective prognostic prediction in the last stages.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126780264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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