Deep Learning-Based Systems for Evaluating and Enhancing Child-Friendliness of Urban Streets—A Case of Shanghai Urban Street by Huijun Tu, Xudong Miao, Shitao Jin, Jiayi Yang, Xinyue Miao, Jiale Qi

“…The system extracts spatial features of streets based on urban street environmental information, and incorporates evaluation inputs from intergenerational user groups, including children and their caregivers. …”
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A Comprehensive Method for Anomaly Detection in Complex Dynamic IoT Systems by Andrii Liashenko, Larysa Globa

“…These results underline the practical relevance of our approach for real-time monitoring of transportation networks, while also contributing theoretically to the integration of spatial and temporal features in anomaly detection.…”
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Translation of computed tomography images to T2-Weighted magnetic resonance images of lumbar spine using generative adversarial networks on sagittal images by Kwang Hyeon Kim, Eun-Chong Lee, Yeo Dong Yoon, Dong-Won Shin, Hae-Won Koo, Byung-Jou Lee

“…Abstract This study aims to develop a generative adversarial networks (GAN)-based image translation model for synthesizing lumbar spine Computed Tomography (CT) to Magnetic Resonance (MR) images, focusing on sagittal images, and to evaluate its performance. …”
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3D medical image segmentation using the serial–parallel convolutional neural network and transformer based on cross‐window self‐attention by Bin Yu, Quan Zhou, Li Yuan, Huageng Liang, Pavel Shcherbakov, Xuming Zhang

“…Abstract Convolutional neural network (CNN) with the encoder–decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global feature. …”
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Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog by Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang

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CochleaSpecNet: An Attention-Based Dual Branch Hybrid CNN-GRU Network for Speech Emotion Recognition Using Cochleagram and Spectrogram by Atkia Anika Namey, Khadija Akter, Md. Azad Hossain, M. Ali Akber Dewan

“…The network integrates a hybrid model that combines Convolutional Neural Networks (CNN) for feature extraction with Gated Recurrent Units (GRU) to handle temporal dependencies. …”
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Using Segmentation to Boost Classification Performance and Explainability in CapsNets by Dominik Vranay, Maroš Hliboký, László Kovács, Peter Sinčák

“…In this paper, we present Combined-CapsNet (C-CapsNet), a novel approach aimed at enhancing the performance and explainability of Capsule Neural Networks (CapsNets) in image classification tasks. …”
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MKD-Net: A Novel Neuro Evolutionary Approach for Blockchain-Based Secure Medical Image Classification Using Multi-Kernel DLM by Virendra P. Vishwakarma, Abhay Kumar Yadav

“…In the proposed methodology, we develop a non-iterative learning-based neural network that can handle class imbalance with the help of class-specific regularizations, named multi-kernel deep neural networks (MKD-Net). …”
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ImmunoCheckDB: A Comprehensive Platform for Evaluating Cancer Immunotherapy Biomarkers Through Meta‐Analyses and Multiomic Profiling by Chongxuan Lu, Mingxiao Li, Hong Yang, Zaoqu Liu, Jian Zhang, Quan Cheng, Anqi Lin, Shixiang Wang, Peng Luo

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An Effective Res-Progressive Growing Generative Adversarial Network-Based Cross-Platform Super-Resolution Reconstruction Method for Drone and Satellite Images by Hao Han, Wen Du, Ziyi Feng, Zhonghui Guo, Tongyu Xu

“…First, the residual module facilitates the training of deep networks, as well as the extraction of deep features. …”
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CBAM-DeepConvNet: Convolutional Block Attention Module-Deep Convolutional Neural Network for asymmetric visual evoked potentials recognition by Zhouyu Ji, Shuran Li, Hongfei Zhang, Chuangquan Chen, Qian Xu, Junhua Li, Hongtao Wang

“…Purpose: This study aimed to improve the accuracy and the ITR in the stimulative paradigm of character spelling systems based on asymmetric Visual Evoked Potentials (aVEPs) by utilizing EEG signal and an improved Convolutional Block Attention Module-Deep Convolutional Neural Network. Methods: This study proposed a deep-learning analysis framework called Convolutional Block Attention Module-Deep Convolutional Neural Network (CBAM-DeepConvNet) to decode aVEPs-based characters. …”
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Integration of Graph Neural Networks and multi-omics analysis identify the predictive factor and key gene for immunotherapy response and prognosis of bladder cancer by Shuai Ren, Yongjian Lu, Guangping Zhang, Ke Xie, Danni Chen, Xiangna Cai, Maodong Ye

“…Abstract Objective The evaluation of the efficacy of immunotherapy is of great value for the clinical treatment of bladder cancer. …”
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Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors by Zhangli Lu, Huiying Zhou, Honghao Lyu, Haiteng Wu, Shaohua Tian, Geng Yang

“…Thus, to address the limitations of manual scoring and complexities of capturing gait features, we proposed an automated BBS assessment system using an attention-based deep learning algorithm with IMU data, integrating convolutional neural networks (CNNs) for spatial feature extraction, bidirectional long short-term memory (Bi-LSTM) networks for temporal modeling, and attention mechanisms to emphasize informative features. …”
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Enhanced visibility graph for EEG classification by Asma Belhadi, Pedro G. Lind, Pedro G. Lind, Pedro G. Lind, Youcef Djenouri, Youcef Djenouri, Anis Yazidi, Anis Yazidi

“…Our framework offers a holistic approach for capturing both frequency-domain characteristics and temporal dynamics of EEG signals. We evaluate four DL architectures, namely multilayer perceptron (MLP), long short-term memory (LSTM) networks, InceptionTime and ChronoNet, applied to several datasets and in different experimental conditions. …”
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Few-shot Remote Sensing Imagery Recognition with Compositionality Inductive Bias in Hierarchical Representation Space by Shichao Zhou, Zhuowei Wang, Zekai Zhang, Wenzheng Wang, Yingrui Zhao, Yunpu Zhang

“…Different from the naive data-driven strategies mentioned above, we alternatively devote to delicate feature modeling by constraining the mapping behavior of deep neural networks. …”
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Evaluation of Morphometric Factors on the Distribution of Debris: A Case Study on the Attica Ripples on the South of Paveh- Kermanshah Province, Iran by Mohammad Ahmadi

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MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing images by Fang Chen, Zhihui Ou, Congrong Li, Lei Wang, Bo Yu

“…It features a multi-scale feature interaction module that integrates feature maps across different resolutions in the encoder and enhances the importance of these fused features using a coordinate attention module. …”
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Multi-scale attention patching encoder network: a deployable model for continuous estimation of hand kinematics from surface electromyographic signals by Chuang Lin, Qiong Xiao, Penghui Zhao

“…The MAF module adaptively captures the local spatiotemporal features at multiple scales, the PE module acquires the global spatiotemporal features of sEMG, and the smoothing layer further improves prediction stability. …”
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Rolling Bearing Fault Diagnosis Model Based on Multi-Scale Depthwise Separable Convolutional Neural Network Integrated with Spatial Attention Mechanism by Zhixin Jin, Xudong Hu, Hongli Wang, Shengyu Guan, Kaiman Liu, Zhiwen Fang, Hongwei Wang, Xuesong Wang, Lijie Wang, Qun Zhang

“…Guided by the attention mechanism to concentrate on discriminative feature regions and to suppress noise, the convolutional component efficiently extracts hierarchical features in parallel through the multi-scale receptive fields. …”
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Tampered text detection via RGB and frequency relationship modeling by Yuxin WANG, Boqiang ZHANG, Hongtao XIE, Yongdong ZHANG

“…In recent years, the widespread dissemination of tampered text images on the Internet constitutes an important threat to the security of text images.However, the corresponding tampered text detection (TTD) methods have not been sufficiently explored.The TTD task aims to locate all text regions in an image while judging whether the text regions have been tampered with according to the authenticity of the texture.Thus, different from the general text detection task, TTD task further needs to perceive the fine-grained information for real-world and tampered text classification.TTD task has two main challenges.One the one hand, due to the high similarity in texture between real-world texts and tampered texts, TTD methods that only learn from RGB domain features have limited capability to distinguish these two-category texts well.On the other hand, as the different detecting difficulty exists in real-world texts and tampered texts, the network cannot well balance the learning process of the two-category texts, resulting in the imbalance detection performance between real-world and tampered texts.Compared with RGB domain features, the discontinuity of text texture in frequency domain can help the network to identify the authenticity of text instances.Accordingly, a new TTD method based on RGB and frequency information relationship modeling was proposed.The features in the RGB and frequency domains were extracted by independent feature extractors respectively.Thus, the identification ability of tampered texture can be enhanced by introducing frequency information during the texture perception.Then, a global RGB-frequency relationship module (GRM) was introduced to model the texture authenticity relationship between different text instances.GRM referred to the RGB-frequency features of other text instances in the same image to assist in judging the authenticity of the current text instance, which solved the problem of imbalanced detection performance.Furthermore, a new TTD dataset (Tampered-SROIE) was proposed to evaluate the effectiveness of proposed method, which contains 986 images (626 training images and 360 test images).By evaluating on the Tampered-SROIE, the proposed method obtains 95.97% and 96.80% in F-measure for real-world and tampered texts respectively and reduces the imbalanced detection accuracy by 1.13%.The proposed method will give new insights to the TTD community from the perspective of network structure and detection strategy.Tampered-SROIE also provides an evaluation benchmark for future TTD methods.…”
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