Pub Date : 2022-06-07DOI: 10.35377/saucis...1088304
Gulsum Akkuzukaya
In recent days, the metaverse which is defined as virtual-reality space in which people can interact with each other in a computer-generated environment, has attracted people’s attention. People have posted their opinions about the metaverse on social media platforms. Twitter is one of those platforms in which people have tweeted about the metaverse. Tweets help researchers to understand public attitudes on a subject. This research focuses on two analyses: The first one used sentiment analysis on Turkish tweets about various alternative words of the metaverse, such as karma evren, misal evren, ote evren, sahte dunya, sanal evren and internet otesi. The second focus of our research was on an analysis of a questionnaire that aimed to understand whether people are aware of the metaverse and willing to experience it or not. The result of sentiment analysis showed that the most of the collected tweets were positive about the collected tweets. The questionnaire analysis showed that the majority of participants were aware of the metaverse and would like to experience that virtual space
{"title":"Sentiment Analysis on the Metaverse: Twitter Data","authors":"Gulsum Akkuzukaya","doi":"10.35377/saucis...1088304","DOIUrl":"https://doi.org/10.35377/saucis...1088304","url":null,"abstract":"In recent days, the metaverse which is defined as virtual-reality space in which people can interact with each other in a computer-generated environment, has attracted people’s attention. People have posted their opinions about the metaverse on social media platforms. Twitter is one of those platforms in which people have tweeted about the metaverse. Tweets help researchers to understand public attitudes on a subject. This research focuses on two analyses: The first one used sentiment analysis on Turkish tweets about various alternative words of the metaverse, such as karma evren, misal evren, ote evren, sahte dunya, sanal evren and internet otesi. The second focus of our research was on an analysis of a questionnaire that aimed to understand whether people are aware of the metaverse and willing to experience it or not. The result of sentiment analysis showed that the most of the collected tweets were positive about the collected tweets. The questionnaire analysis showed that the majority of participants were aware of the metaverse and would like to experience that virtual space","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"394 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132541690","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 : 2022-05-25DOI: 10.35377/saucis...1049798
Ömer Aslan, Erdal Akin
Malware (Malicious Software) is any software which performs malicious activities on computer-based systems without the user's consent. The number, severity, and complexity of malware have been increasing recently. The detection of malware becomes challenging because new malware variants are using obfuscation techniques to hide themselves from the malware detection systems. In this paper, a new behavioral-based malware detection method is proposed based on file-registry operations. When malware features are generated, only the operations which are performed on specific file and registry locations are considered. The file-registry operations divided into five groups: autostart file locations, temporary file locations, specific system file locations, autostart registry locations, and DLLs related registry locations. Based on the file-registry operations and where they performed, the malware features are generated. These features are seen in malware samples with high frequencies, while rarely seen in benign samples. The proposed method is tested on malware and benign samples in a virtual environment, and a dataset is created. Well-known machine learning algorithms including C4.5 (J48), RF (Random Forest), SLR (Simple Logistic Regression), AdaBoost (Adaptive Boosting), SMO (Sequential Minimal Optimization), and KNN (K-Nearest Neighbors) are used for classification. In the best case, we obtained 98.8% true positive rate, 0% false positive rate, 100% precision and 99.05% accuracy which is quite high when compared with leading methods in the literature.
{"title":"Malware Detection Method Based on File and Registry Operations Using Machine Learning","authors":"Ömer Aslan, Erdal Akin","doi":"10.35377/saucis...1049798","DOIUrl":"https://doi.org/10.35377/saucis...1049798","url":null,"abstract":"Malware (Malicious Software) is any software which performs malicious activities on computer-based systems without the user's consent. The number, severity, and complexity of malware have been increasing recently. The detection of malware becomes challenging because new malware variants are using obfuscation techniques to hide themselves from the malware detection systems. In this paper, a new behavioral-based malware detection method is proposed based on file-registry operations. When malware features are generated, only the operations which are performed on specific file and registry locations are considered. The file-registry operations divided into five groups: autostart file locations, temporary file locations, specific system file locations, autostart registry locations, and DLLs related registry locations. Based on the file-registry operations and where they performed, the malware features are generated. These features are seen in malware samples with high frequencies, while rarely seen in benign samples. The proposed method is tested on malware and benign samples in a virtual environment, and a dataset is created. Well-known machine learning algorithms including C4.5 (J48), RF (Random Forest), SLR (Simple Logistic Regression), AdaBoost (Adaptive Boosting), SMO (Sequential Minimal Optimization), and KNN (K-Nearest Neighbors) are used for classification. In the best case, we obtained 98.8% true positive rate, 0% false positive rate, 100% precision and 99.05% accuracy which is quite high when compared with leading methods in the literature.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131205972","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 : 2022-04-30DOI: 10.35377/saucis.5.69696.1019187
Enes Ayan
{"title":"Using a Convolutional Neural Network as Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia","authors":"Enes Ayan","doi":"10.35377/saucis.5.69696.1019187","DOIUrl":"https://doi.org/10.35377/saucis.5.69696.1019187","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128602078","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 : 2022-04-18DOI: 10.35377/saucis...1070822
Ekin Ekinci
{"title":"Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM","authors":"Ekin Ekinci","doi":"10.35377/saucis...1070822","DOIUrl":"https://doi.org/10.35377/saucis...1070822","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115932566","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 : 2022-04-14DOI: 10.35377/saucis...978409
Vafa Radpour, Farhad Soleimanian Gharehchopogh
{"title":"BFFA-NB: Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease","authors":"Vafa Radpour, Farhad Soleimanian Gharehchopogh","doi":"10.35377/saucis...978409","DOIUrl":"https://doi.org/10.35377/saucis...978409","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130794067","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 : 2022-04-04DOI: 10.35377/saucis...1094043
Ö. Özer, Nazli Arda
{"title":"Comparision of Different Machine Learning Algorithms to Predict the Diagnostic Accuracy Parameters of Celiac Serological Tests","authors":"Ö. Özer, Nazli Arda","doi":"10.35377/saucis...1094043","DOIUrl":"https://doi.org/10.35377/saucis...1094043","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114905076","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 : 2022-03-29DOI: 10.35377/saucis...932400
H. Arslan, Orhan Er
{"title":"A comparative study on COVID-19 prediction using deep learning and machine learning algorithms: A case study on performance analysis","authors":"H. Arslan, Orhan Er","doi":"10.35377/saucis...932400","DOIUrl":"https://doi.org/10.35377/saucis...932400","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130447303","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 : 2022-03-10DOI: 10.35377/saucis...932969
M. Akçay, Abdurrahim Akgundogdu
{"title":"Calculation of Driving Parameters for GOA4 Signaling System using Machine Learning Methods","authors":"M. Akçay, Abdurrahim Akgundogdu","doi":"10.35377/saucis...932969","DOIUrl":"https://doi.org/10.35377/saucis...932969","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114599455","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 : 2022-02-23DOI: 10.35377/saucis...1002582
Sefa Tunçer, C. Karakuzu
{"title":"Performance Analysis of Chaotic Neural Network and Chaotic Cat Map Based Image Encryption","authors":"Sefa Tunçer, C. Karakuzu","doi":"10.35377/saucis...1002582","DOIUrl":"https://doi.org/10.35377/saucis...1002582","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124260207","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 : 2021-12-13DOI: 10.35377/saucis...978803
Y. Kaya, Ü. Yilmaz
{"title":"Rain Rate and Rain Attenuation Prediction For Satellite Communication in Ku Band Beacon Over TURKSAT Golbası","authors":"Y. Kaya, Ü. Yilmaz","doi":"10.35377/saucis...978803","DOIUrl":"https://doi.org/10.35377/saucis...978803","url":null,"abstract":"","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129476114","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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