Dergiler / Sakarya University Journal of Computer and Information Sciences

Sakarya University Journal of Computer and Information Sciences (SAUCIS) is a double-blind , peer-reviewed international scientific journal which has an open access policy. The SAUCIS was founded in 2018 by Sakarya University, Faculty of Computer and Information Sciences. It is published regularly three times a year. The journal does not charge submission and publication fee. The SAUCIS is devoted to publishing original research on computer and information sciences. The journal publishes original research articles on computer science, computer engineering, software engineering, and information systems engineering. The journal accepts the submission of manuscripts in English languages. An article with a high overall similarity rate (maximum 20%) or with more than 3% similarity rate from a single source may be rejected or sent back to the author to reduce the similarity rate. The manuscript template can be downloaded from the following link: Manuscript Template (.docx) Copyright form of the manuscript can be downloaded from the following link: Copyright Form The first round review and publication receive-publish process times are 33.46 and 98.52 days. Detailed statistics of review time are given here , and daily statistics of the journal are given here . License Type: CC BY-NC (Creative Commons Attribution-NonCommercial). Click for more information.

2023 · Cilt: 6 Sayı: 2

MakaleYazarSayfa
Classification of Malicious URLs Using Naive Bayes and Genetic AlgorithmMurat KOCA, İsa AVCI, Mohammed Abdulkareem Shakir AL-HAYANİ80–90
Optimization of Several Deep CNN Models for Waste ClassificationMahir KAYA, Samet ULUTÜRK, Yasemin ÇETİN KAYA, Onur ALTINTAŞ, Bülent TURAN91–104
Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network ApproachBeytullah EREN, İdris CESUR105–113
Deep Learning-Based Classification of Dermoscopic Images for Skin LesionsAhmet Furkan SÖNMEZ, Serap ÇAKAR, Feyza CEREZCİ, Muhammed KOTAN, İbrahim DELİBAŞOĞLU, Gülüzar ÇİT114–122
Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray ImagesAhmet SAYGILI123–139