Search Results - "Information fusion"

A review of uncertainty quantification in deep learning: Techniques, applications and challenges by Abdar, Moloud, Pourpanah, Farhad, Hussain, Sadiq, Rezazadegan, Dana, Liu, Li, Ghavamzadeh, Mohammad, Fieguth, Paul, Cao, Xiaochun, Khosravi, Abbas, Acharya, U. Rajendra, Makarenkov, Vladimir, Nahavandi, Saeid

“…Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes…”
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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI by Barredo Arrieta, Alejandro, Díaz-Rodríguez, Natalia, Del Ser, Javier, Bennetot, Adrien, Tabik, Siham, Barbado, Alberto, Garcia, Salvador, Gil-Lopez, Sergio, Molina, Daniel, Benjamins, Richard, Chatila, Raja, Herrera, Francisco

“…•We review concepts related to the explainability of AI methods (XAI).•We comprehensive analyze the XAI literature organized in two taxonomies.•We identify…”
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Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence by Ali, Sajid, Abuhmed, Tamer, El-Sappagh, Shaker, Muhammad, Khan, Alonso-Moral, Jose M., Confalonieri, Roberto, Guidotti, Riccardo, Del Ser, Javier, Díaz-Rodríguez, Natalia, Herrera, Francisco

“…Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to…”
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ChatGPT: Jack of all trades, master of none by Kocoń, Jan, Cichecki, Igor, Kaszyca, Oliwier, Kochanek, Mateusz, Szydło, Dominika, Baran, Joanna, Bielaniewicz, Julita, Gruza, Marcin, Janz, Arkadiusz, Kanclerz, Kamil, Kocoń, Anna, Koptyra, Bartłomiej, Mieleszczenko-Kowszewicz, Wiktoria, Miłkowski, Piotr, Oleksy, Marcin, Piasecki, Maciej, Radliński, Łukasz, Wojtasik, Konrad, Woźniak, Stanisław, Kazienko, Przemysław

“…OpenAI has released the Chat Generative Pre-trained Transformer (ChatGPT) and revolutionized the approach in artificial intelligence to human-model…”
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Tabular data: Deep learning is not all you need by Shwartz-Ziv, Ravid, Armon, Amitai

“…A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually…”
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IFCNN: A general image fusion framework based on convolutional neural network by Zhang, Yu, Liu, Yu, Sun, Peng, Yan, Han, Zhao, Xiaolin, Zhang, Li

“…•Propose a CNN based general image fusion framework.•Demonstrate good generalization ability for fusing various types of images.•Perform comparably or even…”
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Image fusion meets deep learning: A survey and perspective by Zhang, Hao, Xu, Han, Tian, Xin, Jiang, Junjun, Ma, Jiayi

“…Image fusion, which refers to extracting and then combining the most meaningful information from different source images, aims to generate a single image that…”
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Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond by Yang, Guang, Ye, Qinghao, Xia, Jun

“…Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems’ black-box choices are made. This…”
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A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion by Ali, Farman, El-Sappagh, Shaker, Islam, S.M. Riazul, Kwak, Daehan, Ali, Amjad, Imran, Muhammad, Kwak, Kyung-Sup

“…•The accurate prediction of heart disease is essential to treat the patient efficiently.•Feature fusion can provide rich healthcare data for heart disease…”
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FusionGAN: A generative adversarial network for infrared and visible image fusion by Ma, Jiayi, Yu, Wei, Liang, Pengwei, Li, Chang, Jiang, Junjun

“…•We propose a new IR/VIS fusion method based on Generative Adversarial Networks.•It can keep both the thermal radiation and the texture details in the source…”
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RFN-Nest: An end-to-end residual fusion network for infrared and visible images by Li, Hui, Wu, Xiao-Jun, Kittler, Josef

“…In the image fusion field, the design of deep learning-based fusion methods is far from routine. It is invariably fusion-task specific and requires a careful…”
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Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network by Tang, Linfeng, Yuan, Jiteng, Ma, Jiayi

“…Infrared and visible image fusion aims to synthesize a single fused image that not only contains salient targets and abundant texture details but also…”
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Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges by Qiu, Sen, Zhao, Hongkai, Jiang, Nan, Wang, Zhelong, Liu, Long, An, Yi, Zhao, Hongyu, Miao, Xin, Liu, Ruichen, Fortino, Giancarlo

“…This paper firstly introduces common wearable sensors, smart wearable devices and the key application areas. Since multi-sensor is defined by the presence of…”
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PIAFusion: A progressive infrared and visible image fusion network based on illumination aware by Tang, Linfeng, Yuan, Jiteng, Zhang, Hao, Jiang, Xingyu, Ma, Jiayi

“…Infrared and visible image fusion aims to synthesize a single fused image containing salient targets and abundant texture details even under extreme…”
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Infrared and visible image fusion methods and applications: A survey by Ma, Jiayi, Ma, Yong, Li, Chang

“…•The IR and VIS image fusion methods and applications are comprehensively reviewed.•The IR and VIS image registration, as a prerequisite of fusion, is briefly…”
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Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges by Lesort, Timothée, Lomonaco, Vincenzo, Stoian, Andrei, Maltoni, Davide, Filliat, David, Díaz-Rodríguez, Natalia

“…•State of the art on continual / lifelong learning and its implications for robotics.•Proposal of a framework to present Continual Learning algorithms.•Summary…”
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A survey on machine learning for data fusion by Meng, Tong, Jing, Xuyang, Yan, Zheng, Pedrycz, Witold

“…•We sum up a group of main challenges that data fusion might face.•We propose a thorough list of requirements to evaluate data fusion methods.•We review the…”
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Deep learning in food category recognition by Zhang, Yudong, Deng, Lijia, Zhu, Hengde, Wang, Wei, Ren, Zeyu, Zhou, Qinghua, Lu, Siyuan, Sun, Shiting, Zhu, Ziquan, Gorriz, Juan Manuel, Wang, Shuihua

“…•We analysed over 350 references from all well-famed databases.•We provided a comprehensive survey on deep learning in food category recognition.•This review…”
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Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review by Zhang, Jianhua, Yin, Zhong, Chen, Peng, Nichele, Stefano

“…•Several major EEG feature extraction methods are introduced.•Several major EEG feature reduction methods are introduced.•Different types of machine learning…”
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Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI by Zhu, Zhiqin, He, Xianyu, Qi, Guanqiu, Li, Yuanyuan, Cong, Baisen, Liu, Yu

“…Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and treatment. The utilization of multimodal information plays a…”
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