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All Journal Jurnal Informatika Upgris International Journal of Research and Applied Technology (INJURATECH) Bulletin of Social Informatics Theory and Application The Indonesian Journal of Computer Science
askar, shavan
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Social Sciences

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The Industrial Internet of Things (IIoT) and its roles in the Fourth Industrial Revolution: A review Saleem, Mohammed; askar, shavan; Ibrahim, Media Ali; Othman, Mina; Abdullah, Nihad
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3841

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.
Deep Learning in Medical Image Analysis Article Review Ibrahim, Media Ali; askar, shavan; saleem, Mohammad; Ali, Daban; Abdullah, Nihad
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3842

Abstract

Transfer learning, in evaluation to common deep studying strategies which include convolutional neural networks (CNNs), stands proud due to its simplicity, efficiency, and coffee education value, efficaciously addressing the venture of restricted datasets. The importance of scientific picture analysis in both scientific research and medical prognosis can't be overstated, with image techniques like Computer Tomography (CT), Magnetic Resonance Image (MRI), Ultrasound (US), and X-Ray playing a crucial function. Despite their utility in non-invasive analysis, the scarcity of categorized medical images poses a completely unique challenge in comparison to datasets in other pc imaginative and prescient domains, like facial reputation. Given this shortage, switch getting to know has won reputation amongst researchers for medical photo processing. This complete evaluation draws on one hundred amazing papers from IEEE, Elsevier, Google Scholar, Web of Science, and diverse sources spanning 2000 to 2023 It covers vital components, which includes the (i) shape of CNNs, (ii) foundational know-how of switch learning, (iii) numerous techniques for enforcing transfer mastering, (iv) the utility of switch gaining knowledge of throughout numerous sub-fields of medical photo analysis, and (v) a dialogue at the future potentialities of transfer studying within the realm of medical image analysis. This evaluate no longer handiest equips beginners with a scientific understanding of transfer mastering applications in medical image analysis but additionally serves policymakers by means of summarizing the evolving trends in transfer learning within the scientific image domain. This insight might also encourage policymakers to formulate advantageous rules that support the continued development of Transfer learning knowledge of in medical image analysis.
Exploring the Landscape of Smart Cities: A Comprehensive Review of IoT and Cyber-Physical Systems Muheden, Karwan; askar, shavan; Mohammed, Mariwan; Bilal, Noura
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3843

Abstract

They focus on housing, well-being, equality, clean energy and fair conditions. The cyber-physical approach involves the development of IoT and Cyber-Things. Smart cities have a variety of use cases, including electricity and transportation. Automating is used for efficiency in industrial manufacturing. An integrated supply and demand side management system is required for the reliability, security and ability to manage the power grid. This paper introduces an integrated energy approach, enhances existing standards, and establishes a shared basis for multidisciplinary planning. It also introduces new semantic network ontologies to provide a comprehensive framework for solving resource-related challenges. This new approach aims to fill the gaps in current standards and create an integrated environment for multi-stakeholder collaboration, using a semantic web ontology for communication and improved decision making in energy systems Provides information integrated, including various forms of smart cities With flexibility for flexibility and inclusion in the energy industry, can accommodate the specific characteristics and needs of various smart city applications In this study, computing -physical system (CPS), software-defined network (SDN), internet (IoT). ), and analyze how smart cities are connected. CPS combines physical channels with electronic systems to provide increased network management efficiency and flexibility. SDN improves dynamic capacity and flexibility, while IoT is more connected for real-time data exchange and decision-making.
Enhancing Educational Paradigms: A Comprehensive Review of Virtual Desktop Infrastructure (VDI) Applications in Learning Environments Ahmed, Mariwan; Askar, Shavan; Muheden, Karwan; Bilal, Noura
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3844

Abstract

This article comprehensively evaluates Virtual Desktop Infrastructure (VDI) in academic environments. It explores the role of VDI in transforming and gaining knowledge via offering more advantageous accessibility and flexibility, addressing the digital divide, and adapting to various learning patterns. The paper examines case studies throughout one-of-a-kind educational settings, discusses the technical components, and evaluates VDI's effect on mastering and teaching. It additionally highlights the challenges and potential risks related to VDI implementation. Synthesizing the outcomes from various case studies and study papers lays a stable foundation for understanding the multifaceted nature of VDI's implementation and its effect on instructional paradigms. The technical limitations of reviewed cases play a significant function in determining the fulfillment of VDI implementations in instructional environments. Well-structured planning and evaluation of these elements are vital to ensure that the selected VDI efficiently meets the goals of instructional concerns and their participants. Future research instructions are cautioned to deal with diagnosed gaps, including their application in various educational contexts and lengthy-term impacts. The article is valuable for educators, policymakers, and era providers.
Challenges and Outcomes of Combining Machine Learning with Software-Defined Networking for Network Security and management Purpose: A Review Bilal, Noura; Askar, Shavan; Muheden, Karwan; ahmed, Mariwan
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3845

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.
Deep Learning Algorithms for IoT Based Crop Yield Optimization Maghdid, Souzan; Askar, Shavan; Sami Khoshaba, Farah; Hamad, Soran
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3846

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.
Deep Learning Algorithms for Detecting and Mitigating DDoS Attacks Hamad, Soran; Askar, Shavan; Sami Khoshaba, Farah; Maghdid, Sozan; Abdullah, Nihad
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3847

Abstract

Raising the threat of Distributed Denial of Service (DDoS) attacks means that high and adapted detection tools are required now more than ever. This research focuses on exploring the latest solutions in preventing DDoS attacks and emphasizes how Artificial Intelligence (AI) is involved in enhancing end-to-end detection techniques. Through the analysis of several key approaches, this work notes that AI-guided models quickly identify and counteract any unusual traffic patterns that may indicate an oncoming DDoS attack. Essential aspects towards creating more resilient networks against such attacks include machine learning algorithms, sophisticated data analytics together with AI based detection systems for traffic pattern recognition. Importantly, AI does well in behavioral analysis because it can distinguish and adapt to changing attack vectors. Additionally, it puts AI into perspective as making positive mitigation strategies possible that contain quick interferences such as temporary halt of traffic, rerouting and targeted block listing with real time control panel operations. On the contrary, current DDoS detection prevention techniques remain critically addressed of persistent challenges and limitations fundamental to them. From what emerges, they should always be ready for innovation and improvement because of how attacks might evolve over time. This paper aligns itself with the position that AI-driven detection mechanisms are natural to network security against DDoS attacks. It underlines the importance of integrating AI-based solutions with conventional practices in order to enhance network resilience and efficiently counteract cyber threats that are evolving all the time.
Cyber Security Challenges in Industry 4.0: A Review Sami Khoshaba, Farah; Askar, Shavan; Hamad, Soran; Maghdid, Sozan
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3848

Abstract

In the era of Industry 4.0, when smart factories and networked systems are reshaping the landscape of industrial production, the protection of important data and information security is a top priority. Cyber-physical systems and the technology that supports it are the keys to Industry 4.0. It is founded on four essential design principles: interoperability, availability of information, technological assistance, and decentralized decision-making. These design principles, however, provide new weaknesses that could be exploited by bad people. To protect these systems from emerging dangers, great consideration should be given to the proactive and adaptive security measures, which will consequently enable the continuing growth and success of Industry 4.0 technologies. This paper will delve into the multifaceted challenges that Industry 4.0 presents in terms of data security and the emerging solutions and strategies required protecting vital information in this brave new world of manufacturing. The exploration of these challenges and the proposed solutions are essential for businesses and policymakers alike to navigate the complexities of data security and ensure the resilience of critical information in the digital age of Industry 4.0.
Signal Propagation and Path- Loss in 6G Mobile Telecommunication System Soran, Zhala; Askar, Shavan; Khosnawi, Dilshad; Saeed, Hasan
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3850

Abstract

As previous we know about many types of generation like (2G,3G and 4G network), and also it has been developed to the fifth – generation mobile communication system. Compare with 5G, 6G will be faster and lower latency, in 6G era, the theoretical network speed will reach to 1Tbps. That is 100 times the speed of the 5G, which is 10,000 times the speed of the current 4G. 5G is officially commercialized in 2019, but 5G has no yet covered a large area and have problems with high tariffs and incomplete network coverage ,6G network makes up for these problems. The 6G is through the integration of ground base stations and satellite communications, thus covering the whole world, it is also the real coverage. But also, there’s some issues with 6G networks, like here in this article I will explain the propagation signal for 6G and I will talk about path loss signals because it uses sub-millimeter waves or tera-hertz waves to make faster connection but it will cause so much issues in propagation filed. And explained how to address path-loss challenge.
Fog Computing in Next Generation Networks: A Review Khosnawi, Dilshad; Askar, Shavan; Soran, Zhala; Saeed, Hasan
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3851

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.
Co-Authors Abdulkhaleq, Ibrahim Abdullah, Nihad Abdullah, Rebwar Abdulwahab, Sara Ahmed Al-Zakhali, Omar Ahmed, Kosrat Dlshad Ahmed, Mariwan Ali, Daban Aziz Mohammed, Marwan Bilal, Noura Hamad, Soran Hawezi, Roojwan Sc. Hussein, Diana Hussien, Diana Ibrahim, Media Ibrahim, Media Ali Kareem, Shahab Kareem, Shahab Wahhab Khosnawi, Dilshad Maghdid, Souzan Maghdid, Sozan Mohammed , Marwan Aziz Mohammed, Mariwan Muheden, Karwan Omer, Saman M. Othman, Mina Saeed, Hasan Saleem, Mohammad saleem, mohammed Sami Khoshaba, Farah Soran, Zhala Zeebaree, Subhi