Graphic Perception System for Visually Impaired Groups by Jingzi Wen

“…In recent years, deep neural networks have promoted the development of image object recognition. …”
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Adversarial Robust Modulation Recognition Guided by Attention Mechanisms by Quanhai Zhan, Xiongwei Zhang, Meng Sun, Lei Song, Zhenji Zhou

“…Deep neural networks have demonstrated considerable effectiveness in recognizing complex communications signals through their applications in the tasks of automatic modulation recognition. …”
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Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks by Workeneh Geleta Negassa, Demissie J. Gelmecha, Ram Sewak Singh, Davinder Singh Rathee

“…The hybrid framework combined the strengths of Graph Neural Networks (GNN) for feature aggregation, Deep Neural Networks (DNN) for efficient resource allocation, and Double Deep Q-Networks (DDQN) for distributed decision-making. …”
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Efficient nonlinear function approximation in analog resistive crossbars for recurrent neural networks by Junyi Yang, Ruibin Mao, Mingrui Jiang, Yichuan Cheng, Pao-Sheng Vincent Sun, Shuai Dong, Giacomo Pedretti, Xia Sheng, Jim Ignowski, Haoliang Li, Can Li, Arindam Basu

“…Abstract Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. …”
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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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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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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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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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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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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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STA-HAR: A Spatiotemporal Attention-Based Framework for Human Activity Recognition by Md. Khaliluzzaman, Md. Furquan, Mohammod Sazid Zaman Khan, Md. Jiabul Hoque

“…Furthermore, the utilization of an attention mechanism serves the purpose of dynamically selecting the significant segments within the sequence, thereby improving the model’s comprehension of context and enhancing the efficacy of deep neural networks (DNNs) in the domain of human activity recognition (HAR). …”
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AI-Based Screening Method for Early Identification of Invasive Ductal Carcinoma in Breast Cancer by Dominik Jánošík, Sila Yavuz

“…Next, by leveraging deep neural networks, we extracted effective features, and through a majority vote method, we performed data classification to establish a screening structure for the diagnosis of invasive ductal carcinoma of breast tumors. …”
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Contrastive Dual-Pool Feature Adaption for Domain Incremental Remote Sensing Scene Classification by Yingzhao Shao, Yunsong Li, Xiaodong Han

“…Remote sensing image classification has achieved remarkable success in environmental monitoring and urban planning using deep neural networks (DNNs). However, the performance of these models is significantly impacted by domain shifts due to seasonal changes, varying atmospheric conditions, and different geographical locations. …”
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Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment by Soronzonbold Otgonbaatar, Dieter Kranzlmuller

“…Our quantum resource estimation showed that quantum machine learning (QML) models, with a sufficient number of T-gates, provide the quantum advantage if and only if they generalize on unseen data points better than their classical counterparts deployed on the HPC system and they break the symmetry in their weights at each learning iteration like in conventional deep neural networks. We also estimated the quantum resources required for some QML models as an initial innovation. …”
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