An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks by Qasem Abu Al-Haija, Saleh Zein-Sabatto

“…In particular, the proposed system is composed of three subsystems: a feature engineering subsystem, a feature learning subsystem, and a traffic classification subsystem. …”
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Integration of Multi-Head Self-Attention and Convolution for Person Re-Identification by Yalei Zhou, Peng Liu, Yue Cui, Chunguang Liu, Wenli Duan

“…We propose an improved multi-scale feature learning structure, DM-OSNet, with better performance than the original OSNet. …”
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Classification of Typical Static Objects in Road Scenes Based on LO-Net by Yongqiang Li, Jiale Wu, Huiyun Liu, Jingzhi Ren, Zhihua Xu, Jian Zhang, Zhiyao Wang

“…Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. …”
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A Review of Deep Learning in Multiscale Agricultural Sensing by Dashuai Wang, Wujing Cao, Fan Zhang, Zhuolin Li, Sheng Xu, Xinyu Wu

“…In the past few years, deep learning (DL) has shown great potential to reshape many industries because of its powerful capabilities of feature learning from massive datasets, and the agriculture industry is no exception. …”
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EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5 by Shisong Zhu, Wanli Ma, Jianlong Wang, Meijuan Yang, Yongmao Wang, Chunyang Wang

“…Therefore, an efficient and accurate model for apple leaf disease detection based on YOLOv5 is proposed and named EADD-YOLO.MethodsIn the EADD-YOLO, the lightweight shufflenet inverted residual module is utilized to reconstruct the backbone network, and an efficient feature learning module designed through depthwise convolution is proposed and introduced to the neck network. …”
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Domain-Generalized EEG Classification With Category-Oriented Feature Decorrelation and Cross-View Consistency Learning by Shuang Liang, Changsheng Xuan, Wenlong Hang, Baiying Lei, Jun Wang, Jing Qin, Kup-Sze Choi, Yu Zhang

“…To this end, we propose a novel domain-generalized EEG classification framework, named FDCL, to generalize EEG decoding through category-relevant and -irrelevant Feature Decorrelation and Cross-view invariant feature Learning. Specifically, we first devise data augmented regularization through mixing the segments of same-category features from multiple subjects, which increases the diversity of EEG data by spanning the space of subjects. …”
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Semantic Segmentation of Agricultural Images Based on Style Transfer Using Conditional and Unconditional Generative Adversarial Networks by Hirokazu Madokoro, Kota Takahashi, Satoshi Yamamoto, Stephanie Nix, Shun Chiyonobu, Kazuki Saruta, Takashi K. Saito, Yo Nishimura, Kazuhito Sato

“…Using these networks, the proposed framework provides not only image generation for data augmentation, but also automatic labeling based on distinctive feature learning among domains. The Fréchet inception distance (FID) and mean intersection over union (mIoU) were used, respectively, as evaluation metrics for GANs and semantic segmentation. …”
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Multiscale Adjacency Matrix CNN: Learning on Multispectral LiDAR Point Cloud via Multiscale Local Graph Convolution by Jian Yang, Binhan Luo, Ruilin Gan, Ao Wang, Shuo Shi, Lin Du

“…The network effectively captures global and local representative features of the point cloud by harnessing the capabilities of convolutional neural networks in local feature modeling and the self-attention mechanism in global semantic feature learning. Experimental results on the Titan dataset demonstrate that the proposed MS-AMCNN network achieves a promising multispectral LiDAR point cloud segmentation performance with an overall accuracy of 94.39% and a mean intersection over union (MIoU) of 86.57%. …”
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Exploring Deep Physiological Models for Nociceptive Pain Recognition by Patrick Thiam, Peter Bellmann, Hans A. Kestler, Friedhelm Schwenker

“…In contrast to previous works, which rely on carefully designed sets of hand-crafted features, the current work aims at building competitive pain intensity inference models through autonomous feature learning, based on deep neural networks. The assessment of the designed deep learning architectures is based on the <i>BioVid Heat Pain Database (Part A)</i> and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous methods reported in the literature, with respective average performances of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>84.57</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mn>84.40</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> for the binary classification experiment consisting of the discrimination between the baseline and the pain tolerance level (<inline-formula> <math display="inline"> <semantics> <msub> <mi>T</mi> <mn>0</mn> </msub> </semantics> </math> </inline-formula> vs. …”
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Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image by Parisa Soleimani, Navid Farezi

“…In this research, a deep learning approach is introduced for segmenting acute and sub-acute stroke lesions from MRI images. To enhance feature learning through brain hemisphere symmetry, pre-processing techniques are applied to the data. …”
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Detail Enhanced Change Detection in VHR Images Using a Self-Supervised Multiscale Hybrid Network by Dalong Zheng, Zebin Wu, Jia Liu, Chih-Cheng Hung, Zhihui Wei

“…In addition, we design a mixed feature pyramid within the DST, which provides interlayer interaction information and intralayer multiscale information for a more complete feature learning within the new network. Furthermore, we impose a self-supervised strategy to guide the VGG16 provide the semantic change information from the output features of the encoder. …”
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Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection by Yang Liu, Huaiqing Zhang, Zeyu Cui, Kexin Lei, Yuanqing Zuo, Jiansen Wang, Xingtao Hu, Hanqing Qiu

“…However, the traditional image classification methods often show low robustness when extracting complex objects from VHR images, with insufficient feature learning, object edge blur and noise. Our objective was to develop a repeatable method—superpixel-enhanced deep neural forests (SDNF)—to detect the UTC distribution from VHR images. …”
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An Optimized Smoke Segmentation Method for Forest and Grassland Fire Based on the UNet Framework by Xinyu Hu, Feng Jiang, Xianlin Qin, Shuisheng Huang, Xinyuan Yang, Fangxin Meng

“…GFUNet exhibited robust smoke feature learning capability and segmentation stability. …”
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A Novel Fully Convolutional Auto-Encoder Based on Dual Clustering and Latent Feature Adversarial Consistency for Hyperspectral Anomaly Detection by Rui Zhao, Zhiwei Yang, Xiangchao Meng, Feng Shao

“…However, existing methods proposed in recent years still suffer from certain limitations: (1) Constraints are lacking in the deep feature learning process in terms of the issue of the absence of prior background and anomaly information. (2) Hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. (3) The architecture of fully connected deep networks in hyperspectral anomaly detectors leads to low utilization of spatial information and the destruction of the original spatial relationship in hyperspectral imagery and disregards the spectral correlation between adjacent pixels. (4) Hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. …”
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Early diagnosis and clinical score prediction of Parkinson's disease based on longitudinal neuroimaging data by Lei, Haijun, Lei, Yukang, Chen, Zihao, Li, Shiqi, Huang, Zhongwei, Zhou, Feng, Tan, Ee-Leng, Xiao, Xiaohua, Lei, Yi, Hu, Huoyou, Huang, Yaohui, Liu, Chien-Hung, Lei, Baiying

“…Differing from previous studies on PD, we propose a network combining feature selection method with feature learning to obtain the most discriminative feature representation for longitudinal early diagnosis and clinical score prediction. …”
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Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis by Qiankun Zuo, Qiankun Zuo, Libin Lu, Lin Wang, Lin Wang, Jiahui Zuo, Tao Ouyang

“…By incorporating the volume and location of anatomical brain regions, the region-guided feature learning network can roughly focus on local features for each brain region. …”
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Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy by Ankita Singh, Shayok Chakraborty, Zhe He, Zhe He, Shubo Tian, Shenghao Zhang, Mia Liza A. Lustria, Neil Charness, Nelson A. Roque, Erin R. Harrell, Walter R. Boot

“…We leveraged the feature learning capabilities of deep neural networks to predict patterns of adherence for a given participant, based on their past behavior. …”
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Deep Learning-Based Forearm Subcutaneous Veins Segmentation by Zaineb Shah, Syed Ayaz Ali Shah, Aamir Shahzad, Ahmad Fayyaz, Shoaib Khaliq, Ali Zahir, Goh Chuan Meng

“…These are used for unsupervised feature learning and image-to-image translation applications. …”
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DEEPFAKE Image Synthesis for Data Augmentation by Nawaf Waqas, Sairul Izwan Safie, Kushsairy Abdul Kadir, Sheroz Khan, Muhammad Haris Kaka Khel

“…In this work, a subjective self-attention layer has been added before <inline-formula> <tex-math notation="LaTeX">$256 \times 256$ </tex-math></inline-formula> convolution layer for efficient feature learning and the use of spectral normalization in the discriminator and pixel normalization in the generator for training stabilization - the two tasks resulting into what is referred to as Enhanced-GAN. …”
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Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT Images by Francisco Silva, Tania Pereira, Julieta Frade, José Mendes, Claudia Freitas, Venceslau Hespanhol, José Luis Costa, António Cunha, Hélder P. Oliveira

“…The results reported in this comparison suggested that the feature learning achieved when reconstructing the input with an encoder-decoder based architecture can be considered an useful knowledge that might allow overcoming labelling constraints.…”
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