Journal of computer science research最新文献

A Multi-model Fusion Strategy for Android Malware Detection Based on Machine Learning Algorithms 基于机器学习算法的安卓恶意软件检测多模型融合策略

Journal of computer science research Pub Date : 2024-06-05 DOI: 10.30564/jcsr.v6i2.6632

Shuguang Xiong, Huitao Zhang

{"title":"A Multi-model Fusion Strategy for Android Malware Detection Based on Machine Learning Algorithms","authors":"Shuguang Xiong, Huitao Zhang","doi":"10.30564/jcsr.v6i2.6632","DOIUrl":"https://doi.org/10.30564/jcsr.v6i2.6632","url":null,"abstract":"In the digital age, the widespread use of Android devices has led to a surge in security threats, especially malware. Android, as the most popular mobile operating system, is a primary target for malicious actors. Conventional antivirus solutions often fall short in identifying new, modified, or zero-day attacks. To address this, researchers have explored various approaches for Android malware detection, including static and dynamic analysis, as well as machine learning (ML) techniques. However, traditional single-model ML approaches have limitations in generalizing across diverse malware behaviors. To overcome this, a multi-model fusion approach is proposed in this paper. The approach integrates multiple machine learning models, including logistic regression, decision tree, and K-nearest neighbors, to improve detection accuracy. Experimental results demonstrate that the fusion method outperforms individual models, offering a more balanced and robust approach to Android malware detection. This methodology showcases the potential of ensemble techniques in enhancing prediction accuracy, providing valuable insights for future research in cybersecurity.","PeriodicalId":479870,"journal":{"name":"Journal of computer science research","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141385351","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

Machine Learning Prediction of Fetal Health Status from Cardiotocography Examination in Developing Healthcare Contexts 在发展中医疗保健环境下，通过机器学习从心脏排畸检查预测胎儿健康状况

Journal of computer science research Pub Date : 2024-03-22 DOI: 10.30564/jcsr.v6i1.6242

O. C. Olayemi, O. O. Olasehinde

{"title":"Machine Learning Prediction of Fetal Health Status from Cardiotocography Examination in Developing Healthcare Contexts","authors":"O. C. Olayemi, O. O. Olasehinde","doi":"10.30564/jcsr.v6i1.6242","DOIUrl":"https://doi.org/10.30564/jcsr.v6i1.6242","url":null,"abstract":"Reducing neonatal mortality is a critical global health objective, especially in resource-constrained developing countries. This study employs machine learning (ML) techniques to predict fetal health status based on cardiotocography (CTG) examination findings, utilizing a dataset from the Kaggle repository due to the limited comprehensive healthcare data available in developing nations. Features such as baseline fetal heart rate, uterine contractions, and waveform characteristics were extracted using the RFE wrapper feature engineering technique and scaled with a standard scaler. Six ML models—Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Categorical Boosting (CB), and Extended Gradient Boosting (XGB)—are trained via cross-validation and evaluated using performance metrics. The developed models were trained via cross-validation and evaluated using ML performance metrics. Eight out of the 21 features selected by GB returned their maximum Matthews Correlation Coefficient (MCC) score of 0.6255, while CB, with 20 of the 21 features, returned the maximum and highest MCC score of 0.6321. The study demonstrated the ability of ML models to predict fetal health conditions from CTG exam results, facilitating early identification of high-risk pregnancies and enabling prompt treatment to prevent severe neonatal outcomes.","PeriodicalId":479870,"journal":{"name":"Journal of computer science research","volume":" 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140216390","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

Exploring Alternatives to Create Digital Twins from and for Process Simulation 探索从流程模拟中创建数字双胞胎的替代方案

Journal of computer science research Pub Date : 2024-01-19 DOI: 10.30564/jcsr.v6i1.6168

Jaime Barbero-Sánchez, Alicia Megía-Ortega, Víctor R. Ferro, Jose-Luis Valverde

引用次数: 0

Big Data 4.0: The Era of Big Intelligence 大数据 4.0：大智能时代

Journal of computer science research Pub Date : 2024-01-05 DOI: 10.30564/jcsr.v6i1.6054

Zhaohao Sun

引用次数: 0

Data, Analytics, and Intelligence 数据、分析和智能

Journal of computer science research Pub Date : 2023-12-15 DOI: 10.30564/jcsr.v5i4.6072

Zhaohao Sun

引用次数: 0

An Integrated Software Application for the Ancient Coptic Language 科普特古语综合应用软件

Journal of computer science research Pub Date : 2023-12-11 DOI: 10.30564/jcsr.v5i4.6068

Argyro Kontogianni, Evangelos C. Papakitsos, Theodoros Ganetsos

引用次数: 0