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Mohamad Abou Ali, Fadi Dornaika and Ignacio Arganda-Carreras
Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PB...
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Mohamad Abou Ali, Fadi Dornaika and Ignacio Arganda-Carreras
Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then ap...
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Jingying Zhang and Tengfei Bao
Crack detection is an important component of dam safety monitoring. Detection methods based on deep convolutional neural networks (DCNNs) are widely used for their high efficiency and safety. Most existing DCNNs with high accuracy are too complex for use...
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Sabah Abdulazeez Jebur, Khalid A. Hussein, Haider Kadhim Hoomod and Laith Alzubaidi
Detecting violence in various scenarios is a difficult task that requires a high degree of generalisation. This includes fights in different environments such as schools, streets, and football stadiums. However, most current research on violence detectio...
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Xiaoyu Han, Chenyu Li, Zifan Wang and Guohua Liu
Neural architecture search (NAS) has shown great potential in discovering powerful and flexible network models, becoming an important branch of automatic machine learning (AutoML). Although search methods based on reinforcement learning and evolutionary ...
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Yilun Qin, Qizhi Tang, Jingzhou Xin, Changxi Yang, Zixiang Zhang and Xianyi Yang
Rapid and accurate identification of moving load is crucial for bridge operation management and early warning of overload events. However, it is hard to obtain them rapidly via traditional machine learning methods, due to their massive model parameters a...
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Fei Yan, Hui Zhang, Yaogen Li, Yongjia Yang and Yinping Liu
Raw image classification datasets generally maintain a long-tailed distribution in the real world. Standard classification algorithms face a substantial issue because many labels only relate to a few categories. The model learning processes will tend tow...
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Raz Lapid, Zvika Haramaty and Moshe Sipper
Deep neural networks (DNNs) are sensitive to adversarial data in a variety of scenarios, including the black-box scenario, where the attacker is only allowed to query the trained model and receive an output. Existing black-box methods for creating advers...
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Dejian Guan, Wentao Zhao and Xiao Liu
Recent studies show that deep neural networks (DNNs)-based object recognition algorithms overly rely on object textures rather than global object shapes, and DNNs are also vulnerable to human-less perceptible adversarial perturbations. Based on these two...
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Tautvydas Fyleris, Andrius Kri?ciunas, Valentas Gru?auskas, Dalia Calneryte and Rimantas Barauskas
Urban change detection is an important part of sustainable urban planning, regional development, and socio-economic analysis, especially in regions with limited access to economic and demographic statistical data. The goal of this research is to create a...
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