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All Journal The Indonesian Journal of Computer Science
Hamad, Soran
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : The Indonesian Journal of Computer Science

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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 (IJCS)
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 (IJCS)
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 (IJCS)
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.
Co-Authors Abdullah, Nihad askar, shavan Maghdid, Souzan Maghdid, Sozan Sami Khoshaba, Farah