Sakarya university journal of computer and information sciences最新文献

Estimation of Uplink Channels for Multiple Users Using Tensor Modeling in RIS-Aided MISO Communication 基于张量建模的ris辅助MISO通信多用户上行信道估计

Sakarya university journal of computer and information sciences Pub Date : 2023-11-09 DOI: 10.35377/saucis...1356872

Rıfat Volkan ŞENYUVA

引用次数: 0

Conjoint Analysis of GPS Based Orbit Determination among Traditional Methods 基于GPS的传统定轨方法的综合分析

Sakarya university journal of computer and information sciences Pub Date : 2023-09-30 DOI: 10.35377/saucis...1215689

İbrahim ÖZ, Cevat ÖZARPA

{"title":"Conjoint Analysis of GPS Based Orbit Determination among Traditional Methods","authors":"İbrahim ÖZ, Cevat ÖZARPA","doi":"10.35377/saucis...1215689","DOIUrl":"https://doi.org/10.35377/saucis...1215689","url":null,"abstract":"Satellite orbits are subject to change due to external forces. Various data gathering and processing methods exist to determine a perturbed orbit. The operators need to estimate satellite orbits for safe orbital operations. Single station azimuth elevation and range, and range-to-range methods are two flight-proven commonly utilized methods among satellite operators. GPS signals in orbit determination of GEO communication satellite have become more popular recently. Much work validates GPS-based GEO orbit determination in different aspects. The validation of GPS-based orbit determination with flight-proven methods encourage the operator about fast switching utilization of the GPS method. This research evaluates performance of the GPS-based method by comparing it with flight-proven methods. The orbits of three communication satellites at different orbital slots were calculated using GPS-based, RNG-based, and AZEL-based methods. GPS-based determined orbit and RNG-based determined orbit RMSE of 3D differences are 75.887 m, 372.420m, and 768,223 m for Sat A, Sat B, and Sat C, respectively. Similarly, AZEL-based determşden orbit and GPS-based determined orbit RMSE of 3D position differences are 133.287 m, 242.076 m, and 764.866 m for Sat A, Sat B, and Sat C, respectively. The current study confirmed the finding's apparent support for GPS-based orbit determination. Flight-proven RNG and AZEL methods results in which satellite operators' well recognized, demonstrated evidence of the GPS-based orbit determination method. The results are in line with flight-proven AZEL and RNG method's orbit parameters. Finally, the result of our comparison of AZEL vs. GPS and RNG vs. GPS methods encourages the operators to utilize GPS-based navigation to determine communication satellite orbit precisely.","PeriodicalId":498230,"journal":{"name":"Sakarya university journal of computer and information sciences","volume":"30 31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135040885","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}

引用次数: 0

A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral 基于卷积神经网络的手写体文本和数字性别分类模型

Sakarya university journal of computer and information sciences Pub Date : 2023-09-28 DOI: 10.35377/saucis...1337649

Pakize ERDOĞMUŞ, Abdullah Talha KABAKUŞ, Enver KÜÇÜKKÜLAHLI, Büşra TAKGİL, Ezgi KARA TİMUÇİN

{"title":"A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral","authors":"Pakize ERDOĞMUŞ, Abdullah Talha KABAKUŞ, Enver KÜÇÜKKÜLAHLI, Büşra TAKGİL, Ezgi KARA TİMUÇİN","doi":"10.35377/saucis...1337649","DOIUrl":"https://doi.org/10.35377/saucis...1337649","url":null,"abstract":"Human handwriting is used to investigate human characteristics in various applications, including but not limited to biometric authentication, personality profiling, historical document analysis, and forensic investigations. Gender is one of the most distinguishing characteristics of human beings. From this point forth, we propose a novel end-to-end model based on Convolutional Neural Network (CNN) that automatically extracts features from a given handwritten sample, which contains both handwritten text and numerals unlike the related work that uses only handwritten text, and classifies its owner’s gender. In addition to proposing a novel model, we introduce a new dataset that consists of 530 gender-labeled Turkish handwritten samples since, to the best of our knowledge, there does not exist a public gender-labeled Turkish handwriting dataset. Following an exhaustive process of hyperparameter optimization, the proposed CNN featured the most optimal hyperparameters and was both trained and evaluated on this dataset. According to the experimental result, the proposed novel model obtained an accuracy as high as 74.46%, which overperformed the state-of-the-art baselines and is promising on such a task that even humans could not have achieved highly-accurate results for, as of yet.","PeriodicalId":498230,"journal":{"name":"Sakarya university journal of computer and information sciences","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135469663","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}

引用次数: 0

REAL-TIME INTELLIGENT ANOMALY DETECTION AND PREVENTION SYSTEM 实时智能异常检测和预防系统

Sakarya university journal of computer and information sciences Pub Date : 2023-09-24 DOI: 10.35377/saucis...1296210

Remzi GÜRFİDAN, Şerafettin ATMACA, Tuncay YİĞİT

{"title":"REAL-TIME INTELLIGENT ANOMALY DETECTION AND PREVENTION SYSTEM","authors":"Remzi GÜRFİDAN, Şerafettin ATMACA, Tuncay YİĞİT","doi":"10.35377/saucis...1296210","DOIUrl":"https://doi.org/10.35377/saucis...1296210","url":null,"abstract":"Real-time anomaly detection in network traffic is a method that detects unexpected and anomalous behaviour by identifying normal behaviour and statistical patterns in network traffic data. This method is used to detect potential attacks or other anomalous conditions in network traffic. Real-time anomaly detection uses different algorithms to detect abnormal activities in network traffic. These include statistical methods, machine learning and deep learning techniques. By learning the normal behaviour of network traffic, these methods can detect unexpected and anomalous situations. Attackers use various techniques to mimic normal patterns in network traffic, making it difficult to detect. Real-time anomaly detection allows network administrators to detect attacks faster and respond more effectively. Real-time anomaly detection can improve network performance by detecting abnormal conditions in network traffic. Abnormal traffic can overuse the network's resources and cause the network to slow down. Real-time anomaly detection detects abnormal traffic conditions, allowing network resources to be used more effectively. In this study, blockchain technology and machine learning algorithms are combined to propose a real-time prevention model that can detect anomalies in network traffic.","PeriodicalId":498230,"journal":{"name":"Sakarya university journal of computer and information sciences","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135927645","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}

引用次数: 0