Exploring the typhoon intensity forecasting through integrating AI weather forecasting with regional numerical weather model by Hongxiong Xu, Yang Zhao, Zhao Dajun, Yihong Duan, Xiangde Xu

“…This is largely due to constraints inherent in regression algorithm properties including deep neural networks and inability of coarse resolution to capture the finer-scale weather processes. …”
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MAL-Net: Model-Adaptive Learned Network for Slow-Time Ambiguity Function Shaping by Jun Wang, Xiangqing Xiao, Jinfeng Hu, Ziwei Zhao, Kai Zhong, Chaohai Li

“…Existing methods typically involve model-based approaches with relaxation and data-driven Deep Neural Networks (DNNs) methods, which face the challenge of dataimitation. …”
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Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks by Ángel Morera, Ángel Sánchez, José Francisco Vélez, Ana Belén Moreno

“…This work describes an experimental study on the suitability of deep neural networks to three automatic demographic problems: gender, handedness, and combined gender-and-handedness classifications, respectively. …”
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Transfer Learning-Empowered Physical Layer Security in Aerial Reconfigurable Intelligent Surfaces-Based Mobile Networks by Yosefine Triwidyastuti, Tri Nhu Do, Ridho Hendra Yoga Perdana, Kyusung Shim, Beongku An

“…Additionally, we employ Artificial Intelligence (AI) and Machine Learning (ML) techniques, specifically Deep Neural Networks (DNN), for performance prediction of PHY security metrics. …”
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Can the number of confirmed COVID-19 cases be predicted more accurately by including lifestyle data? An exploratory study for data-driven prediction of COVID-19 cases in metropolit... by Sungwook Jung

“…The deep learning algorithms used in the analysis are deep neural networks (DNNs) and recurrent neural networks (RNNs). …”
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Automated recognition and segmentation of lung cancer cytological images based on deep learning. by Qingyang Wang, Yazhi Luo, Ying Zhao, Shuhao Wang, Yiru Niu, Jinxi Di, Jia Guo, Guorong Lan, Lei Yang, Yu Shan Mao, Yuan Tu, Dingrong Zhong, Pei Zhang

“…With the development of deep neural networks, the You Only Look Once (YOLO) object-detection model has been recognized for its impressive speed and accuracy. …”
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LazyAct: Lazy actor with dynamic state skip based on constrained MDP. by Hongjie Zhang, Zhenyu Chen, Hourui Deng, Chaosheng Feng

“…However, the high computational cost of policies based on deep neural networks restricts their practical application. …”
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Retracted: The Application of Edge Computing Technology in the Collaborative Optimization of Intelligent Transportation System Based on Information Physical Fusion by Gongxing Yan, Qi Qin

“…It plays a key role in multi-system fusion and intelligent manufacturing, and can play a key role in training and testing of deep neural networks. The purpose of this paper is to study the application of edge computing technology in the collaborative optimization of intelligent transportation systems based on information and physical fusion. …”
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DNNobfus: a study on obfuscation-based edge-side model protection framework by SONG Feiyang, ZHAO Xinmiao, YAN Fei, CHENG Binlin, ZHANG Liqiang, YANG Xiaolin, WANG Yang

“…Given the analogous structural characteristics of deep neural networks, adversaries can employ decompilation tactics to extract model structural details and parameters, facilitating the reconstruction of these models. …”
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Noise-attention-based forgery face detection method by Bolin ZHANG, Chuntao ZHU, Qilin YIN, Jingqiao FU, Lingyi LIU, Jiarui LIU, Hongmei LIU, Wei LU

“…With the advancement of artificial intelligence and deep neural networks, the ease of image generation and editing has increased significantly.Consequently, the occurrence of malicious tampering and forgery using image generation tools is on the rise, posing a significant threat to multimedia security and social stability.Therefore, it is crucial to research detection methods for forged faces.Face tampering and forgery can occur through various means and tools, leaving different levels of forgery traces during the tampering process.These traces can be partly reflected in the image noise.From the perspective of image noise, the noise components reflecting tampering traces of forged faces were extracted through a noise removal module.Furthermore, noise attention was generated to guide the backbone network in the detection of forged faces.The training of the noise removal module was supervised using SRM filters.In order to strengthen the guidance of the noise removal module, the noise obtained by the noise removal module was added back to the real face image, forming a pair of supervised training samples in a self-supervised manner.The experimental results illustrate that the noise features obtained by the noise removal module have a good degree of discrimination.Experiments were also conducted on several public datasets, and the proposed method achieves an accuracy of 98.32% on the Celeb-DF dataset, 92.61% on the DFDC dataset, and more than 94% on the FaceForensics++ dataset, thus proving the effectiveness of the proposed method.…”
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Feature Representations Using the Reflected Rectified Linear Unit (RReLU) Activation by Chaity Banerjee, Tathagata Mukherjee, Eduardo Pasiliao Jr.

“…Deep Neural Networks (DNNs) have become the tool of choice for machine learning practitioners today. …”
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An integrated toolbox for creating neuromorphic edge applications by Lars Niedermeier, Nikil Dutt, Jeffrey L Krichmar

“…spiking neural networks (SNNs) and neuromorphic models are believed to be more efficient in general and have more biological realism than the activation functions typically used in deep neural networks, transformer models and generative AI. …”
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Opening the Black Box of the Radiation Belt Machine Learning Model by Donglai Ma, Jacob Bortnik, Xiangning Chu, Seth G. Claudepierre, Qianli Ma, Adam Kellerman

“…Abstract Many Machine Learning (ML) systems, especially deep neural networks, are fundamentally regarded as black boxes since it is difficult to fully grasp how they function once they have been trained. …”
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Real-Time Diagnostic Technique for AI-Enabled System by Hiroaki Itsuji, Takumi Uezono, Tadanobu Toba, Subrata Kumar Kundu

“…The last few decades have witnessed a dramatic evolution of Artificial Intelligence (AI) algorithms, represented by Deep Neural Networks (DNNs), resulting in AI-enabled systems being significantly dominant in various fields, including robotics, healthcare, and mobility. …”
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Explainable machine learning framework for cataracts recognition using visual features by Xiao Wu, Lingxi Hu, Zunjie Xiao, Xiaoqing Zhang, Risa Higashita, Jiang Liu

“…Abstract Cataract is the leading ocular disease of blindness and visual impairment globally. Deep neural networks (DNNs) have achieved promising cataracts recognition performance based on anterior segment optical coherence tomography (AS-OCT) images; however, they have poor explanations, limiting their clinical applications. …”
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Feature fusion-based collaborative learning for knowledge distillation by Yiting Li, Liyuan Sun, Jianping Gou, Lan Du, Weihua Ou

“…Deep neural networks have achieved a great success in a variety of applications, such as self-driving cars and intelligent robotics. …”
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Task offloading optimization in mobile edge computing based on a deep reinforcement learning algorithm using density clustering and ensemble learning by Yi Qin, Junyan Chen, Lei Jin, Rui Yao, Zidan Gong

“…Firstly, DCEDRL utilizes multiple deep neural networks to explore the environment. It trains multiple models using ensemble learning methods to obtain a combination of prediction results. …”
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Optimisation of sparse deep autoencoders for dynamic network embedding by Huimei Tang, Yutao Zhang, Lijia Ma, Qiuzhen Lin, Liping Huang, Jianqiang Li, Maoguo Gong

“…However, the existing deep learning‐based NE methods are time‐consuming as they need to train a dense architecture for deep neural networks with extensive unknown weight parameters. …”
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Chemical Process Fault Diagnosis Based on Improved ResNet Fusing CBAM and SPP by Xiaochen Yan, Yang Zhang, Qibing Jin

“…Firstly, 1D convolution is introduced in the construction of the model to reduce the number of parameters and training time, and shortcut connections are used to alleviate the network degradation problem of traditional deep neural networks. Second, a residual-CBAM module is proposed by combining residual networks with Convolutional Block Attention Module (CBAM). …”
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A Joint Deep Recommendation Framework for Location-Based Social Networks by Omer Tal, Yang Liu

“…To make best use of these inputs, we utilize multiple types of deep neural networks that are best suited for each type of data. …”
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