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SeyedehRoksana Mirzaei, Hua Mao, Raid Rafi Omar Al-Nima and Wai Lok Woo
Explainable Artificial Intelligence (XAI) evaluation has grown significantly due to its extensive adoption, and the catastrophic consequence of misinterpreting sensitive data, especially in the medical field. However, the multidisciplinary nature of XAI ...
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MohammadMoein Shafi, Arash Habibi Lashkari, Vicente Rodriguez and Ron Nevo
The distributed denial of service attack poses a significant threat to network security. Despite the availability of various methods for detecting DDoS attacks, the challenge remains in creating real-time detectors with minimal computational overhead. Ad...
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Hassen Louati, Ali Louati, Rahma Lahyani, Elham Kariri and Abdullah Albanyan
Responding to the critical health crisis triggered by respiratory illnesses, notably COVID-19, this study introduces an innovative and resource-conscious methodology for analyzing chest X-ray images. We unveil a cutting-edge technique that marries neural...
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Wandile Nhlapho, Marcellin Atemkeng, Yusuf Brima and Jean-Claude Ndogmo
The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumors from Magnetic Resonance Imaging (M...
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Kedong Zhang, Wenhua Wang, Yihua Liu, Linlin Wang, Yazhen Du, Hongxia Li and Yi Huang
A new type of anti-rolling device denoted as a fluid momentum wheel (FMW) is proposed to address the limitations of traditional gyrostabilizers in reducing the roll responses of floating platforms in waves. The proposed device is based on the same gyrosc...
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