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All Journal Buletin Ilmiah Sarjana Teknik Elektro Control Systems and Optimization Letters
Abdel-Jaber, Gamal T.
Unknown Affiliation
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Ensuring Safety in Human-Robot Cooperation: Key Issues and Future Challenges Sharkawy, Abdel-Nasser; Mahmoud, Khaled H.; Abdel-Jaber, Gamal T.
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.154

Abstract

Human-robot cooperation (HRC) is becoming increasingly essential in many different sectors such as industry, healthcare, agriculture, and education. This cooperation between robot and human has many advantages such as increasing and boosting productivity and efficiency, executing the task easily, effectively, and in a fast time, and minimizing the efforts and time. Therefore, ensuring safety issues during this cooperation are critical and must be considered to avoid or minimize any risk or danger whether for the robot, human, or environment. Risks may be such as accidents or system failures. In this paper, an overview of the safety issues of human-robot cooperation is discussed. The main key challenges in robotics safety are outlined and presented such as collision detection and avoidance, adapting to unpredictable human behaviors, and implementing effective risk mitigation strategies. The difference between industrial robots and cobots is illustrated. Their features and safety issues are also provided. The problem of collision detection or avoidance between the robot and environment is defined and discussed in detail. The result of this paper can be a guideline or framework to future researchers during the design and the development of their safety methods in human-robot cooperation tasks. In addition, it shapes future research directions in safety measures.
A Systematic Review of Machine Learning and Deep Learning Approaches in MRI-Based Brain Tumour Analysis, Detection and Classification Omran, Hanan M.; Ibrahim, Khalil; Abdel-Jaber, Gamal T.; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.14673

Abstract

A brain tumour develops when abnormal cell growth happens in or near the brain. These tumours can grow slowly and not be cancerous, or they can grow quickly and spread, which is known as malignancy. Brain tumours put pressure on the surrounding brain tissues, causing symptoms like memory loss, migraines, movement dysfunction, and vision impairment. Brain tumours are often divided into two groups: primary tumours, which start in the brain, and secondary tumours, which are caused by cancers that spread to other regions of the body. Although brain tumours provide a significant medical challenge, patient outcomes have improved thanks to recent advancements in diagnostic and treatment methods. Because of its better soft-tissue contrast and noninvasive nature, magnetic resonance imaging (MRI) is one of the most important medical imaging modalities for the early identification and precise localization of brain tumours. Clinical practice also makes use of other imaging methods such as PET-CT and functional MRI (fMRI). Artificial intelligence and deep learning techniques have demonstrated significant promise in automated brain cancer analysis in recent years. These methods enable precise cancer diagnosis, classification, and segmentation by identifying intricate patterns from MRI data that are challenging to recognize through manual examination. A thorough study of current deep learning and machine learning techniques for MRI-based brain tumour analysis is provided in this paper. The current thorough literature search includes papers released between 2019 and 2024. 67 pertinent articles are chosen for in-depth analysis after predetermined inclusion and exclusion criteria is used. Many of these studies make use of publicly accessible datasets like Figshare, TCIA, and BraTS. The results show that deep learning models frequently outperform traditional machine learning methods in terms of accuracy and robustness, especially convolutional neural network-based designs. However, there are still issues with clinical generalisation, model interpretability, and data heterogeneity.
Co-Authors Ibrahim, Khalil Mahmoud, Khaled H. Omran, Hanan M. Sharkawy, Abdel-Nasser