Indonesian Journal of Computer Science最新文献

{"title":"Challenges and Outcomes of Combining Machine Learning with Software-Defined Networking for Network Security and management Purpose: A Review","authors":"Noura Bilal, Shavan K. Askar, K. Muheden, Mariwan Ahmed","doi":"10.33022/ijcs.v13i2.3845","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3845","url":null,"abstract":"Current research in data dissemination in Vehicular Ad Hoc Networks (VANETs) has examined different approaches and frameworks to enhance the effectiveness and dependability of information sharing between vehicles on the road. The integration of Machine Learning (ML) with Software-Defined Networking (SDN) has fundamentally transformed the field of network administration and security. This paper specifically addresses the challenges faced by traditional network architectures in effectively handling the increasing amount of data and complex applications. Software-Defined Networking (SDN), a cutting-edge framework, separates the control of network operations from the actual forwarding of data, offering a versatile and cost-effective solution. The combination of Software-Defined Networking (SDN) and Machine Learning (ML) allows for the extraction of valuable information from network data, leading to enhanced network management and the facilitation of predictive analytics. The aim of this study is to examine the feasibility and challenges of incorporating machine learning into software-defined networking (SDN), focusing particularly on practical applications. Integrating Machine Learning (ML) into Software-Defined Networking (SDN) presents challenges, including the requirement for robust algorithms to detect patterns and ensure security. It is crucial to carry out the tasks of developing and implementing machine learning models for real-time predictions and ensuring the robustness of the system. Research is essential to strike a balance between the transformative abilities of ML-SDN and the practical network environments. This helps to improve the resilience, security, and adaptability of network infrastructures in the digital age.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"17 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140728452","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":"A Monte Carlo Simulation study on the Gamma Radiation Shielding Properties of Concrete with PET Plastic Composite using the PHITS Code","authors":"Myat Mon Aye","doi":"10.33022/ijcs.v13i2.3838","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3838","url":null,"abstract":"The gamma radiation shielding properties of four different types of PET concretes, containing 0 %, 5 %, 10 %, and 15 % PET additives, were simulated using the PHITS code. The simulation covered photon energy levels ranging from 0.01 to 1.5 MeV and employed a NaI (Tl) scintillation detector. Parameters such as the linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), half-value layer (HVL), and mean free path (MFP) were calculated to evaluate the gamma-ray attenuation for each photon energy level. The effectiveness of PET plastics as a radiation shield depends on factors like material thickness, the type of radiation, and specific application requirements. However, this research provides valuable insights into repurposing waste PET plastics to enhance the radiation-shielding properties of concrete, contributing to improved waste management practices and the development of radiation-shielding materials. The results obtained from the PHITS code align satisfactorily with both the simulation results and the theoretical XCOM data.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"30 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732296","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":"Enhancing Problem-Solving Learning Models: A Review from the Lens of Independent Learning in the Post-Pandemic Era","authors":"Elsa Sabrina, Ambiyar, Rizky Ema Wulansari","doi":"10.33022/ijcs.v13i2.3868","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3868","url":null,"abstract":"This research aims to explore the optimization of the problem-solving learning model within the context of independent learning in the post-pandemic era. Utilizing a systematic literature review method and the PRISMA model, the study identifies 25 pertinent articles concerning the implementation of the problem-solving learning model in independent learning. The analysis indicates that applying this model positively impacts students' critical thinking abilities, enhances creativity, and reinforces communication and collaboration skills. From an independent learning standpoint, the problem-solving learning model grants students the autonomy to cultivate creative thinking patterns and fosters heightened engagement in the learning process. The study also highlights adapting the model to online learning, with teachers as facilitators. In conclusion, these findings underscore the effectiveness of the problem-solving learning model in independent learning, especially in the post-pandemic era. They also offer valuable insights for educators and policymakers to develop adaptive learning strategies suited to the current educational environment.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"10 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729274","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":"Fog Computing in Next Generation Networks: A Review","authors":"Dilshad Khosnawi, Shavan K. Askar, Zhala Soran, Hasan Saeed","doi":"10.33022/ijcs.v13i2.3851","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3851","url":null,"abstract":"Cloud, Edge, and Fog computing has recently attracted significant attention in both industry and academia. However, finding their definition in computing paradigms and the correlation between them is difficult. In order to support modern computing systems, the cloud, edge devices, and fog computing offer high-quality services, lower latency, multi-tenancy, mobility support, and many other features. Fog/edge computing is an emerging computing paradigm that uses decentralized resources at the edge of a network to process data closer to user devices, like smartphones and tablets, as an alternative to using remote and centralized cloud data center resources. Fog networking or fogging is one of the best used models recently. By addressing this issue, this work serves as a valuable resource for those who will come after. Initially, we present an overview modern computing models and associated areas of interest research. After that, we discuss each paradigm. After that, we go into great detail about fog computing, highlighting its exceptional function as the link between edge, cloud, and IoT computing. Finally, we briefly outline open research questions and future directions in Edge, Fog, Cloud, and IoT computing. ","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"131 s215","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731467","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":"The Industrial Internet of Things (IIoT) and its roles in the Fourth Industrial Revolution: A review","authors":"Mohammed Saleem, Shavan K. Askar, Media Ali Ibrahim, Mina Othman, Nihad Abdullah","doi":"10.33022/ijcs.v13i2.3841","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3841","url":null,"abstract":"The Industrial Internet of Things and Industry 4.0 are now two highly sought-after areas of research and development, attracting significant interest from both academic and industrial sectors. The two ideas, Industry 4.0 and IIoT, share significant similarities, with Industry 4.0 being seen as the use of IIoT specifically in the automation and manufacturing sectors. Within the framework of the present Industry 4.0 paradigm, many growth pathways have emerged, collectively leading to notable enhancements in terms of efficiency, flexibility, communication, adaptability, customization, and modularity in the industrial sector. The Industry 4.0 is rapidly evolving within the framework of the Industrial Internet of Things (IIoT), and the authors are recognizing the necessity for a comprehensive and in-depth overview of the many research areas that are currently expanding. The area will remain intriguing in the foreseeable future due to its significant potential for enhancing the existing industrial technologies. An exhaustive evaluation of the current systems in the automotive sector, emergency response, and chain management on IIoT has been conducted, revealing that IIoT has been widely adopted across several technological domains. Industry 4.0 is the term used to describe the present automation and data sharing trend in businesses. Presently, there is a dearth of agreement about the assessment of an organization's readiness for Industry 4.0. Industry 4.0 encompasses a diverse array of digital technologies that profoundly influence industrial enterprises. The literature on Industry 4.0 has had significant exponential growth during the previous decade. The results of our research confirm the idea of Industry 4.0 as a concept that goes beyond the Smart Manufacturing sector, hence opening up possibilities for collaboration with other interconnected disciplines.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"57 S4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731924","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 Learning Based Security Schemes for IoT Applications: A Review","authors":"Mina Othman, Shavan K. Askar, Daban Ali, Media Ali Ibrahim","doi":"10.33022/ijcs.v13i2.3839","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3839","url":null,"abstract":"Due to its widespread perception as a crucial element of the Internet of the future, the Internet of Things (IoT) has garnered a lot of attention in recent years. The Internet of Things (IoT) is made up of billions of sentients, communicative \"things\" that expand the boundaries of the physical and virtual worlds. Every day, such widely used smart gadgets generate enormous amounts of data, creating an urgent need for rapid data analysis across a range of smart mobile devices. Thankfully, current developments in deep learning have made it possible for us to solve the issue tastefully. Deep models may be built to handle large amounts of sensor data and rapidly and effectively learn underlying properties for a variety of Internet of Things applications on smart devices. We review the research on applying deep learning to several Internet of Things applications in this post. Our goal is to provide insights into the many ways in which deep learning techniques may be used to support Internet of Things applications in four typical domains: smart industrial, smart home, smart healthcare, and smart transportation. One of the main goals is to seamlessly integrate deep learning and IoT, leading to a variety of novel ideas in IoT applications, including autonomous driving, manufacture inspection, intelligent control, indoor localization, health monitoring, disease analysis, and home robotics. We also go over a number of problems, difficulties, and potential avenues for future study that make use of deep learning (DL), which is turning out to be one of the most effective and appropriate methods for dealing with various IoT security concerns. The goal of recent research has been to enhance deep learning algorithms for better Internet of Things security. This study examines deep learning-based intrusion detection techniques, evaluates the effectiveness of several deep learning techniques, and determines the most effective approach for deploying intrusion detection in the Internet of Things. This study uses Deep Learning (DL) approaches to better expand intelligence and application skills by using the large quantity of data generated or acquired. The many IoT domains have drawn the attention of several academics, and both DL and IoT approaches have been explored. Because DL was designed to handle a variety of data in huge volumes and required processing in virtually real-time, it was indicated by several studies as a workable method for handling data generated by IoT.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"18 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140728438","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":"Feature Selection using Extra Trees for Breast Cancer Prediction","authors":"Shahad Awadelkarim","doi":"10.33022/ijcs.v13i2.3874","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3874","url":null,"abstract":"Breast cancer is a disease that seriously threatens women's health. It is considering a common death cause in women. Machine learning has made significant progress in recent years to improve the effectiveness of early diagnosis of various diseases. Accurate predication and detection help decrease the death rate of breast cancer. This paper aims to predict breast cancer using several machine-learning techniques. The idea is to lower the number of features in the Wisconsin Breast Cancer Dataset (WCDB) and use it for prediction. The study used the extra trees method for feature selection and Random forest, Logistic regression, and Support Vector Machine (SVM) for testing the dataset. According to the results, SVM achieved the best performance among the other models with 98% accuracy. The proposed method in this study proved its effectiveness in breast cancer prediction.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"71 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729527","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 Learning Algorithms for IoT Based Crop Yield Optimization","authors":"Souzan Maghdid, Shavan K. Askar, Farah Xoshibi, Soran Hamad","doi":"10.33022/ijcs.v13i2.3846","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3846","url":null,"abstract":"Precision agriculture, with its objectives of optimizing crop yields, decreasing resource waste, and enhancing overall farm management, has emerged as a revolutionary technology in modern agricultural practices. The advent of deep learning techniques and the Internet of Things (IoT) has brought about a paradigm shift in monitoring, decision-making, and predictive analysis within the agriculture industry. This review paper investigates the relationship between deep learning, the (IoT), and agriculture, with an emphasis on how these three domains might work together to optimize crop yields through intelligent decision-making. The integration of deep learning techniques with  (IoT) technology for precision agriculture is thoroughly analyzed in this study, covering recent developments, obstacles, and possible solutions. The paper investigates the role of deep learning algorithms in analyzing the vast amounts of data generated by IoT devices in agriculture. It scrutinizes various deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants applied for crop disease detection, yield prediction, weed identification, and other crucial tasks. Furthermore, this review critically examines the integration of IoT-generated data with deep learning models, highlighting the synergistic benefits in enhancing agricultural decision-making, resource allocation, and predictive analytics. This review underscores the pivotal role of IoT and deep learning techniques in revolutionizing precision agriculture. It emphasizes the need for interdisciplinary collaboration among agronomists, data scientists, and engineers to harness the full potential of these technologies for sustainable and efficient farming practices.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"61 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729567","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":"Distributed Architectures for Big Data Analytics in Cloud Computing: A Review of Data-Intensive Computing Paradigm","authors":"Chiai Al-Atroshi, Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3812","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3812","url":null,"abstract":"Big Data challenges are prevalent in various fields, including economics, business, public administration, national security, and scientific research. While it offers opportunities for productivity and scientific breakthroughs, it also presents challenges in data capture, storage, analysis, and visualization. This paper provides a comprehensive overview of Big Data applications, opportunities, challenges, and current techniques and technologies to address these issues. This study presents a system for managing big data resources using cloud for the development of data-intensive applications. It addresses even the challenges related to technologies that combine cloud computing with other allied technologies and devices.  In addition, the increasing volume, velocity, and variety of data in the era of Big Data necessitate advanced methods for data processing and management. This study delves into the intricacies of data scalability, real-time processing, and the integration of diverse data types. Furthermore, it explores the role of machine learning algorithms and artificial intelligence in extracting meaningful insights from massive datasets.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"113 S142","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731915","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":"Proactive Fault Tolerance in Distributed Cloud Systems: A Review of Predictive and Preventive Techniques","authors":"D. Hasan, Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3808","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3808","url":null,"abstract":"In a cloud computing environment, various hardware and software services are provided to the users across multiple servers and data centers. These servers are communicated to each other to allow greater scalability, flexibility, and reliability. Reliability is a vital factor in cloud computing that ensures that the requested services will be delivered to the users whenever they request them. However, different hardware or software faults may occur in cloud servers or data centers that prevent the users from receiving the service. Fault tolerance is defined as the ability of the system to provide services to the users even with the presence of faults or failures. In this review, we focused on some of the emerging fault tolerance techniques researchers have proposed to tackle the fault issues in cloud computing. We divided these techniques into three main categories: proactive and reactive techniques. Proactive techniques involve protecting the system defects by proposing certain procedures to prevent reaching the defective condition. Reactive techniques refer to the ability of the cloud system to recover the defective server or framework to continue working and providing the service.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140789619","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}