Hilal H. Nuha, Fazmah Arif Y., Hendrawan Hendrawan
: Mobile devices consume a significant amount of internet tra ffi c, and they can utilize multiple interfaces like Wi-Fi and cellular networks to share tra ffi c between networks, known as a multi-homed host. This approach enhances the resilience of the internet connection by allowing tra ffi c to flow through multiple paths. The Multipath Transmission Control Protocol (MPTCP) supports this type of connection, but a fairness issue emerges when a multi-path host shares a bottleneck link with regular single-path hosts. To deal with this issue, this paper proposes an enhanced MPTCP (eMPTCP) that uses a throughput adjustment to estimate bandwidth based on TCP Westwood + congestion control. By decreasing each subflow tra ffi c on the multi-path, the proposed eMPTCP achieves fairness in shared links. The simulation conducted using network simulator 2 (ns2) represents the mobile conditions of mobile data air interface, and the results demonstrate that eMPTCP outperforms standard congestion control in achieving connection resilience.
{"title":"Enhanced Multipath TCP to Improve the Mobile Device Network Resiliency","authors":"Hilal H. Nuha, Fazmah Arif Y., Hendrawan Hendrawan","doi":"10.12785/ijcds/160106","DOIUrl":"https://doi.org/10.12785/ijcds/160106","url":null,"abstract":": Mobile devices consume a significant amount of internet tra ffi c, and they can utilize multiple interfaces like Wi-Fi and cellular networks to share tra ffi c between networks, known as a multi-homed host. This approach enhances the resilience of the internet connection by allowing tra ffi c to flow through multiple paths. The Multipath Transmission Control Protocol (MPTCP) supports this type of connection, but a fairness issue emerges when a multi-path host shares a bottleneck link with regular single-path hosts. To deal with this issue, this paper proposes an enhanced MPTCP (eMPTCP) that uses a throughput adjustment to estimate bandwidth based on TCP Westwood + congestion control. By decreasing each subflow tra ffi c on the multi-path, the proposed eMPTCP achieves fairness in shared links. The simulation conducted using network simulator 2 (ns2) represents the mobile conditions of mobile data air interface, and the results demonstrate that eMPTCP outperforms standard congestion control in achieving connection resilience.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"2017 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141707064","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}
J. Anitha Gracy, S. Parthasarathy, S. Sivagurunathan
{"title":"Navigating the Software Symphony: A Review of Factors and Strategies for Software Development in Startups","authors":"J. Anitha Gracy, S. Parthasarathy, S. Sivagurunathan","doi":"10.12785/ijcds/160130","DOIUrl":"https://doi.org/10.12785/ijcds/160130","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689207","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}
: The impact of fake news is far-reaching, a ff ecting journalism, the economy, and democracy. In response, there has been a surge in research focused on detecting and combating fake news, resulting in the development of datasets, techniques, and fact-verification methods. One crucial aspect of this e ff ort is the creation of diverse and representative datasets for training and evaluating machine learning models for fake news detection. This review paper examines the available datasets relevant to detecting fake news, with a particular emphasis on those available in the Indian context, where few resources exist. By identifying research opportunities and highlighting existing corpora, this paper aims to assist researchers in improving their fake news detection studies and contributing to more comprehensive research on the topic. To the best of our knowledge, no survey has specifically focused on accessible corpora in the Indian context, making this review a valuable resource for researchers in the field.
{"title":"Fake News Detection Datasets: A Review and Research Opportunities","authors":"Pummy Dhiman, Amandeep Kaur, Yasir Hamid, Nedal Ababneh","doi":"10.12785/ijcds/160104","DOIUrl":"https://doi.org/10.12785/ijcds/160104","url":null,"abstract":": The impact of fake news is far-reaching, a ff ecting journalism, the economy, and democracy. In response, there has been a surge in research focused on detecting and combating fake news, resulting in the development of datasets, techniques, and fact-verification methods. One crucial aspect of this e ff ort is the creation of diverse and representative datasets for training and evaluating machine learning models for fake news detection. This review paper examines the available datasets relevant to detecting fake news, with a particular emphasis on those available in the Indian context, where few resources exist. By identifying research opportunities and highlighting existing corpora, this paper aims to assist researchers in improving their fake news detection studies and contributing to more comprehensive research on the topic. To the best of our knowledge, no survey has specifically focused on accessible corpora in the Indian context, making this review a valuable resource for researchers in the field.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"341 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691646","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}
{"title":"Deep Neural Networks for Classifying Nutrient Deficiencies in Rice Plants Using Leaf Images","authors":"Shrikrishna Kolhar, Jayant Jagtap, Rajveer Shastri","doi":"10.12785/ijcds/160124","DOIUrl":"https://doi.org/10.12785/ijcds/160124","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"22 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141703983","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}
{"title":"Evaluation of Deep Learning Models for Detection of Indonesian Rupiah","authors":"Charleen Charleen, Gede Putra Kusuma","doi":"10.12785/ijcds/160125","DOIUrl":"https://doi.org/10.12785/ijcds/160125","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700405","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}
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
{"title":"CFCM-SMOTE: A Robust Fetal Health Classification to Improve Precision Modeling in Multiclass Scenarios","authors":"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","doi":"10.12785/ijcds/160137","DOIUrl":"https://doi.org/10.12785/ijcds/160137","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"68 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141701783","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}
: 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.
:如今，云存储驱动器在世界各地都非常流行。在传统的计算机取证方法中，重点是物理访问包含可能导致因素的信息的磁盘。由于基于云的应用程序可能会发生数据泄露，本文提出的研究侧重于从 Windows 11 操作系统中收集证据，以发现和收集主要云存储应用程序之一 OneDrive 中遗留的注册表人工制品。本研究对 Windows 11 的人工制品进行了检查，发现当 OneDrive 程序从虚拟机中删除并与账户解除链接时，会出现明显的人工制品。这些结果和残留的人工制品有助于确定 OneDrive 中每个上传文件的文件路径以及与之链接的电子邮件地址。为了帮助数字取证调查人员快速确定云存储应用程序的使用情况，我们开发了一个 bash 脚本并将其附在文件中。该脚本的目的是汇总在实际模拟过程中获得的已识别和已发现的人工制品。识别使用 OneDrive 的账户和时间顺序也可作为识别攻击者的线索。
{"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}
{"title":"Hybrid K-means and Principal Component Analysis (PCA) for\u0000Diabetes Prediction","authors":"Ahmed Abed Mohammed, Putra Sumari, K. Attabi","doi":"10.12785/ijcds/1501121","DOIUrl":"https://doi.org/10.12785/ijcds/1501121","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"9 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141228918","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}
{"title":"Reptile Search Algorithm for Association Rule Mining","authors":"Abderrahim Boukhalat, KamelEddine Heraguemi, Mohamed Benouis, Brahim Bouderah, Samir Akhrouf","doi":"10.12785/ijcds/1501122","DOIUrl":"https://doi.org/10.12785/ijcds/1501122","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"28 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141233555","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}
Abdul Fadlil, Ainin Maftukhah, Sunardi, Tole Sutikno
: 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.
{"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}
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