International Journal of Computing and Digital Systems最新文献_第6页

Enhanced Multipath TCP to Improve the Mobile Device Network Resiliency 增强型多路径 TCP，提高移动设备网络弹性

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160106

Hilal H. Nuha, Fazmah Arif Y., Hendrawan Hendrawan

引用次数: 1

Navigating the Software Symphony: A Review of Factors and Strategies for Software Development in Startups 驾驭软件交响乐：初创企业软件开发因素与策略综述

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160130

J. Anitha Gracy, S. Parthasarathy, S. Sivagurunathan

引用次数: 0

Fake News Detection Datasets: A Review and Research Opportunities 假新闻检测数据集：回顾与研究机会

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160104

Pummy Dhiman, Amandeep Kaur, Yasir Hamid, Nedal Ababneh

引用次数: 1

Deep Neural Networks for Classifying Nutrient Deficiencies in Rice Plants Using Leaf Images 利用叶片图像的深度神经网络对水稻植株养分缺乏症进行分类

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160124

Shrikrishna Kolhar, Jayant Jagtap, Rajveer Shastri

引用次数: 1

Evaluation of Deep Learning Models for Detection of Indonesian Rupiah 评估用于检测印尼盾的深度学习模型

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160125

Charleen Charleen, Gede Putra Kusuma

引用次数: 0

CFCM-SMOTE: A Robust Fetal Health Classification to Improve Precision Modeling in Multiclass Scenarios CFCM-SMOTE：在多类场景中改进精确建模的稳健胎儿健康分类法

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160137

Ahmad Ilham, Asdani Kindarto, Akhmad Fathurohman, L. Khikmah, Rima Dias Ramadhani, Syafrie Abdunnasir Jawad, Dhewi April Liana, Aura Amylia. AR, Ahmed Kareem Oleiwi, Astri Mutiar

引用次数: 0

Cloud Forensic Artefacts: Digital Forensics Registry Artefacts discovered from Cloud Storage Application 云取证人工制品：从云存储应用程序中发现的数字取证注册文物

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160102

Shailendra Mishra, Mohammed A. Bajahzar

{"title":"Cloud Forensic Artefacts: Digital Forensics Registry Artefacts discovered from Cloud Storage Application","authors":"Shailendra Mishra, Mohammed A. Bajahzar","doi":"10.12785/ijcds/160102","DOIUrl":"https://doi.org/10.12785/ijcds/160102","url":null,"abstract":": Cloud storage drives have become very popular around the world these days. In the traditional approach to computer forensics, the focus is on physically accessing the disks that contain the information that could contribute to the factors. Due to the data breaches that can occur through cloud-based applications, the research proposed in this paper focuses on collecting evidence from Windows 11 operating systems to discover and collect leftover registry artefacts from one of the major cloud storage applications, OneDrive. This research study examined Windows 11 artefacts and found distinct artefacts when the OneDrive program was deleted from the virtual machine and unlinked to an account. The results and lingering artefacts assist in determining the file path for each uploaded file in OneDrive as well as the email address that was linked to it. To assist digital forensic investigators in making an expedient determination regarding the use of cloud storage applications, a bash script was developed and appended to the document. Its purpose is to assemble the identified and discovered artefacts that were obtained throughout the practical simulations. Identifying the accounts and the chronology that were using OneDrive, may also be utilized as a lead to identify the attackers.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"44 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689736","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

Hybrid K-means and Principal Component Analysis (PCA) forDiabetes Prediction 用于糖尿病预测的混合 K 均值分析和主成分分析 (PCA)

International Journal of Computing and Digital Systems Pub Date : 2024-06-01 DOI: 10.12785/ijcds/1501121

Ahmed Abed Mohammed, Putra Sumari, K. Attabi

引用次数: 1

Reptile Search Algorithm for Association Rule Mining 关联规则挖掘的爬行动物搜索算法

International Journal of Computing and Digital Systems Pub Date : 2024-06-01 DOI: 10.12785/ijcds/1501122

Abderrahim Boukhalat, KamelEddine Heraguemi, Mohamed Benouis, Brahim Bouderah, Samir Akhrouf

引用次数: 0

Butterfly Image Identification Using Multilevel Thresholding Segmentasi and Convolution Neural Network Classification with Alexnet Architecture 利用 Alexnet 架构的多级阈值分割和卷积神经网络分类识别蝴蝶图像

International Journal of Computing and Digital Systems Pub Date : 2024-06-01 DOI: 10.12785/ijcds/1501125

Abdul Fadlil, Ainin Maftukhah, Sunardi, Tole Sutikno

{"title":"Butterfly Image Identification Using Multilevel Thresholding Segmentasi and Convolution Neural Network Classification with Alexnet Architecture","authors":"Abdul Fadlil, Ainin Maftukhah, Sunardi, Tole Sutikno","doi":"10.12785/ijcds/1501125","DOIUrl":"https://doi.org/10.12785/ijcds/1501125","url":null,"abstract":": Lepidoptera is the name for the broad group of butterflies. The ecology depends heavily on butterflies, thus it is problematic that so little is known about their many kinds. Understanding butterflies is a crucial part of education since they are a natural occurrence and may be used as teaching tools. A total of 419 butterfly photos were utilized in the data. The dataset is first input, and then it undergoes preprocessing steps like segmentation, scaling, and RGB to grayscale conversion. CNN with AlexNet architecture is used to classify the preprocessed dataset's output. The outcomes of the classification stage of the Alexnet architecture are Flatten, Danse, and ReLu (Convolution, Batch Normalization, Max_Pooling). The output data is assessed following the completion of the Alexnet CNN training process. The data's ultimate classification is based on species. High-accuracy picture classification can be achieved using the model without segmentation, however, this cannot be achieved with multilevel threshold segmentation. According to the test findings, the multilevel threshold segmentation model only attains 62% accuracy, but the segmentation-free model gets 83% accuracy. The test results demonstrate that combining AlexNet architecture with multilevel thresholding segmentation resulted in a classification model that is less accurate in identifying different species of butterflies. By comparing these test results, it is possible to draw the conclusion that the multilevel threshold segmentation model performs less well at information classification than the model without segmentation.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"12 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231898","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