Sakarya University Journal of Computer and Information Sciences最新文献

A NOVEL ADDITIVE INTERNET OF THINGS (IoT) FEATURES AND CONVOLUTIONAL NEURAL NETWORK FOR CLASSIFICATION AND SOURCE IDENTIFICATION OF IoT DEVICES 用于物联网设备分类和来源识别的新型附加物联网（IoT）特征和演化神经网络

Sakarya University Journal of Computer and Information Sciences Pub Date : 2023-11-15 DOI: 10.35377/saucis...1354791

A. Iorliam

引用次数: 0

Prediction of Cardiovascular Disease Based on Voting Ensemble Model and SHAP Analysis 基于投票集合模型和 SHAP 分析的心血管疾病预测

Sakarya University Journal of Computer and Information Sciences Pub Date : 2023-11-15 DOI: 10.35377/saucis...1367326

Erkan Akkur

引用次数: 0

High-Capacity Multiplier Design Using Look Up Table 使用查找表的高容量乘法器设计

Sakarya University Journal of Computer and Information Sciences Pub Date : 2023-11-07 DOI: 10.35377/saucis...1229353

Kenan Baysal, Deniz Taşkin

引用次数: 0

Sequential and Correlated Image Hash Code Generation with Deep Reinforcement Learning 基于深度强化学习的序列和相关图像哈希码生成

Sakarya University Journal of Computer and Information Sciences Pub Date : 2023-08-31 DOI: 10.35377/saucis...1339150

Can Yüzkollar

引用次数: 0

Price Prediction Using Web Scraping and Machine Learning Algorithms in the Used Car Market 二手车市场中使用Web抓取和机器学习算法的价格预测

Sakarya University Journal of Computer and Information Sciences Pub Date : 2023-08-28 DOI: 10.35377/saucis...1309103

Seda Yilmaz, Ihsan Hakan Selvi

{"title":"Price Prediction Using Web Scraping and Machine Learning Algorithms in the Used Car Market","authors":"Seda Yilmaz, Ihsan Hakan Selvi","doi":"10.35377/saucis...1309103","DOIUrl":"https://doi.org/10.35377/saucis...1309103","url":null,"abstract":"The development of technology increases data traffic and data size day by day. Therefore, it has become very important to collect and interpret data. This study, it is aimed to analyze the car sales data collected using web scraping techniques by using machine learning algorithms and to create a price estimation model. The data needed for analysis was collected using Selenium and BeautifulSoup and prepared for analysis by applying various data preprocessing steps. Lasso regression and PCA analysis were used for feature selection and size reduction, and the GridSearchCV method was used for hyperparameter tuning. The results were evaluated with machine learning algorithms. \u0000Random Forest, K-Nearest Neighbor, Gradient Boost, AdaBoost, Support Vector and XGBoost regression algorithms were used in the analysis. The obtained analysis results were evaluated together with Mean Square Error (MSE), Root Mean Square Error (RMSE) and Coefficient of Determination (R-square). When the results for data set 1 were examined, the model that gave the best results was XGBoost Regression with 0.973 R2, 0.026 MSE and 0.161 RMSE values. When the results for data set 2 were examined, the model that gave the best results was K-Nearest Neighbor Regression with 0.978 R2, 0.021 MSE and 0.145 RMSE values.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133094233","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

Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images 通过CT和x线图像的分割和分类快速准确地识别COVID-19

Sakarya University Journal of Computer and Information Sciences Pub Date : 2023-08-10 DOI: 10.35377/saucis...1309970

A. Saygılı

{"title":"Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images","authors":"A. Saygılı","doi":"10.35377/saucis...1309970","DOIUrl":"https://doi.org/10.35377/saucis...1309970","url":null,"abstract":"The COVID-19 pandemic, caused by a novel coronavirus, has become a global epidemic. Although the reverse transcription-polymerase chain reaction (RT-PCR) test is the current gold standard for detecting the virus, its low reliability has led to the use of CT and X-ray imaging in diagnostics. As limited vaccine availability necessitates rapid and accurate detection, this study applies k-means and fuzzy c-means segmentation to CT and X-ray images to classify COVID-19 cases as either diseased or healthy for CT scans and diseased, healthy, or non-COVID pneumonia for X-rays. Our research employs four open-access, widely-used datasets and is conducted in four stages: preprocessing, segmentation, feature extraction, and classification. During feature extraction, we employ the Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Histogram of Oriented Gradients (HOG). In the classification process, our approach involves utilizing k-Nearest Neighbor (kNN), Support Vector Machines (SVM), and Extreme Learning Machines (ELM) techniques. Our research achieved a sensitivity rate exceeding 99%, which is higher than the 60-70% sensitivity rate of PCR tests. As a result, our study can serve as a decision support system that can help medical professionals make rapid and precise diagnoses with a high level of sensitivity.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122216141","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

Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions 基于深度学习的皮肤损伤图像分类

Sakarya University Journal of Computer and Information Sciences Pub Date : 2023-07-26 DOI: 10.35377/saucis...1314638

Ahmet Furkan Sönmez, Serap Cakar, Feyza Cerezci, Muhammed Kotan, I. Delibasoglu, Guluzar Cit

{"title":"Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions","authors":"Ahmet Furkan Sönmez, Serap Cakar, Feyza Cerezci, Muhammed Kotan, I. Delibasoglu, Guluzar Cit","doi":"10.35377/saucis...1314638","DOIUrl":"https://doi.org/10.35377/saucis...1314638","url":null,"abstract":"Skin cancer has emerged as a grave health concern leading to significant mortality rates. Diagnosis of this disease traditionally relies on specialist dermatologists who interpret dermoscopy images using the ABCD rule. However, the integration of computer-aided diagnosis technologies is gaining popularity as a means to assist clinicians in accurate skin cancer diagnosis, overcoming potential challenges associated with human error. The objective of this research is to develop a robust system for the detection of skin cancer by employing machine learning algorithms for skin lesion classification and detection. The proposed system utilizes Convolutional Neural Network (CNN), a highly accurate and efficient deep learning technique well-suited for image classification tasks. By using the power of CNN, this system effectively classifies various skin diseases in dermoscopic images associated with skin cancer The MNIST HAM10000 dataset, comprising 10015 images, serves as the foundation for this study. The dataset encompasses seven distinct skin diseases falling within the realm of skin cancer. In this study, diverse transfer learning methods were used and evaluated to enhance the performance of the system. By comparing and analyzing these approaches, the study aimed to identify the most effective strategies for accurate skin disease classification in dermoscopic images.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124621106","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

Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach 预测环境可持续性引擎的有效效率:一种神经网络方法

Sakarya University Journal of Computer and Information Sciences Pub Date : 2023-07-17 DOI: 10.35377/saucis...1311014

B. Eren, İ. Cesur

{"title":"Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach","authors":"B. Eren, İ. Cesur","doi":"10.35377/saucis...1311014","DOIUrl":"https://doi.org/10.35377/saucis...1311014","url":null,"abstract":"Predicting engine efficiency for environmental sustainability is crucial in the automotive industry. Accurate estimation and optimization of engine efficiency aid in vehicle design decisions, fuel efficiency enhancement, and emission reduction. Traditional methods for predicting efficiency are challenging and time-consuming, leading to the adoption of artificial intelligence techniques like artificial neural networks (ANN). Neural networks can learn from complex datasets and model intricate relationships, making them promise for accurate predictions. By analyzing engine parameters such as fuel type, air-fuel ratio, speed, load, and temperature, neural networks can identify patterns influencing emission levels. These models enable engineers to optimize efficiency and reduce harmful emissions. ANN offers advantages in predicting efficiency by learning from vast amounts of data, extracting meaningful patterns, and identifying complex relationships. Accurate predictions result in better performance, fuel economy, and reduced environmental impacts. Studies have successfully employed ANN to estimate engine emissions and performance, showcasing its reliability in predicting engine characteristics. By leveraging ANN, informed decisions can be made regarding engine design, adjustments, and optimization techniques, leading to enhanced fuel efficiency and reduced emissions. Predicting engine efficiency using ANN holds promise for achieving environmental sustainability in the automotive sector.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125897256","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

Optimization of Several Deep CNN Models for Waste Classification 几种深度CNN垃圾分类模型的优化

Sakarya University Journal of Computer and Information Sciences Pub Date : 2023-07-17 DOI: 10.35377/saucis...1257100

Samet Ulutürk, Mahir Kaya, Yasemin ÇETİN KAYA, Onur Altintaş, B. Turan

{"title":"Optimization of Several Deep CNN Models for Waste Classification","authors":"Samet Ulutürk, Mahir Kaya, Yasemin ÇETİN KAYA, Onur Altintaş, B. Turan","doi":"10.35377/saucis...1257100","DOIUrl":"https://doi.org/10.35377/saucis...1257100","url":null,"abstract":"With urbanization, population, and consumption on the rise, urban waste generation is steadily increasing. Consequently, waste management systems have become integral to city life, playing a critical role in resource efficiency and environmental protection. Inadequate waste management systems can adversely affect the environment, human health, and the economy. Accurate and rapid automatic waste classification poses a significant challenge in recycling. Deep learning models have achieved successful image classification in various fields recently. However, the optimal determination of many hyperparameters is crucial in these models. In this study, we developed a deep learning model that achieves the best classification performance by optimizing the depth, width, and other hyperparameters. Our six-layer Convolutional Neural Network (CNN) model with the lowest depth and width produced a successful result with an accuracy value of 89% and an F1 score of 88%. Moreover, several state-of-the-art CNN models such as VGG19, DenseNet169, ResNet101, Xception, InceptionV3, RegnetX008, RegnetY008, EfficientNetV2S trained with transfer learning and fine-tuning. Extensive experimental work has been done to find the optimal hyperparameters with GridSearch. Our most comprehensive DenseNet169 model, which we trained with fine-tuning, provided an accuracy value of 96.42% and an F1 score of 96%. These models can be successfully used in a variety of waste classification automation.","PeriodicalId":257636,"journal":{"name":"Sakarya University Journal of Computer and Information Sciences","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126520471","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

Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm 基于朴素贝叶斯和遗传算法的恶意url分类

Sakarya University Journal of Computer and Information Sciences Pub Date : 2023-05-27 DOI: 10.35377/saucis...1273536

Murat Koca, İ. Avcı, Mohammed Abdulkareem Shakir AL-HAYANİ

引用次数: 1