Cyber-Physical System Security for Manufacturing Industry 4.0 Using LSTM-CNN Parallel Orchestration by Salman Saeidlou, Nikdokht Ghadiminia, Kwadwo Oti-Sarpong

“…Interoperability among different machines, systems, and humans connected via the Internet of Things (IoT) has blessed Industry 4.0 with numerous advantages over the years. …”
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Spacecraft Intelligent Fault Diagnosis under Variable Working Conditions via Wasserstein Distance-Based Deep Adversarial Transfer Learning by Gang Xiang, Kun Tian

“…However, the problems that data in source and target domains usually have different probability distributions because of different working conditions and there are insufficient labeled or even unlabeled data in target domain significantly deteriorate the performance and generalization of deep fault diagnosis models. …”
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Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification by Yasir Salam Abdulghafoor, Auns Qusai Al-Neami, Ahmed Faeq Hussein

“…More than 70 state-of-the-art articles (from 2019 to 2024) were extensively explored to highlight the different machine learning and deep learning (DL) techniques of different models used for the detection, classification, and prediction of cancerous lung tumors. …”
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Application of artificial intelligence technologies for the detection of early childhood caries by Priyanka A, Rishi Sreekumar, S Namasivaya Naveen

“…This study mainly focuses on the different risk factors, dental caries indexes, and the importance of early caries prediction and treatment. …”
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Are baboons learning "orthographic" representations? Probably not. by Maja Linke, Franziska Bröker, Michael Ramscar, Harald Baayen

“…The ability of Baboons (papio papio) to distinguish between English words and nonwords has been modeled using a deep learning convolutional network model that simulates a ventral pathway in which lexical representations of different granularity develop. …”
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An example of the application of artificial intelligence models in human resources processes by Mustafa Kemal Aydın, Berk Küçük, Selim Sürücü

“…In the second stage, the resumes of the applicants are analyzed using three different deep learning models such as CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit), and LSTM (Long Short-Term Memory) for classification purposes. …”
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Bridging the Gap in Facial Age Progression: An Attention Mechanism Approach by Taoli Liu, Yubin Liang, Wenchen Wu, Yize Tang

“…We replace traditional convolutional networks with an attention mechanism-based backbone, integrating spatial and channel attention mechanisms to capture both spatial relationships and age-related feature importance. …”
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Near-real-time wildfire detection approach with Himawari-8/9 geostationary satellite data integrating multi-scale spatial–temporal feature by Lizhi Zhang, Qiang Zhang, Qianqian Yang, Linwei Yue, Jiang He, Xianyu Jin, Qiangqiang Yuan

“…Specifically, we design a multi-kernel attention-based convolution (MKAC) module for extracting spatial features representing the differences between fire and non-fire pixels within multi-scale receptive fields. …”
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Research on the Real-Time Scoring of Chest Ring Target Based on Transfer Learning and Improved Lightweight Neural Network by Minghui Meng, Chuande Zhou

“…Then, grouped shuffle convolution (GSConv) is used to reconstruct the standard down sampling convolutions in the PANFPN structure, promoting feature information extraction and fusion at bullet hole edges, reducing model computational complexity, and maintaining detection accuracy. …”
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Quantitative analysis of the nephron during human fetal kidney development by Daković-Bjelaković Marija Z., Vlajković Slobodan R., Čukuranović Rade E., Antić Svetlana, Bjelaković Goran B., Mitić Dejan

“…Glomeruli changed their size and shape, while the tubules changed their length and convolution. Renal cortex became wider and contained the more mature glomeruli and the more convoluted tubules. …”
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Retinal vascular segmentation network based on dual-scale morphological enhancement by Yunfeng Ni, Pei Wang, Wei Chen, Jie Qi

“…Subsequently, a feature aggregation module based on dynamic snake-shaped convolutions is introduced, which adapts convolution paths to achieve the fusion of features at different levels. …”
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Ultra-Short-Term Power Forecasting Method for Wind-Solar-Hydro Integration Based on Improved GRU-CNN by Xiaogang WU, Jie YAN, Chang GE, Yajie TANG, Chouwei NI, Qingfeng JI

“…The models of wind, solar, and hydro energy systems are very different, and there are multiple uncertainties among them. …”
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An efficient method for predicting temperature field of PC beams with CSWs using thermocouple numerical analysis and random forest algorithm by Haiping Zhang, Hao Long, Fanghuai Chen, Yuan Luo, Xinhui Xiao, Yang Deng, Naiwei Lu, Yang Liu

“…The significant difference in specific heat between concrete and steel results in non-uniform temperature fields in existing PC beam bridges with corrugated steel webs (CSWs) under combined environmental temperature, solar radiation, wind, and thermal radiation. …”
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SambaMixer: State of Health Prediction of Li-Ion Batteries Using Mamba State Space Models by Jose Ignacio Olalde-Verano, Sascha Kirch, Clara Perez-Molina, Sergio Martin

“…Approaches leveraging deep learning architectures have been proposed to predict the SOH using convolutional networks, recurrent networks, and transformers. …”
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Plant disease detection with generative adversarial networks by Garam Han, Derek Kwaku Pobi Asiedu, Kwabena Ebo Bennin

“…To empirically validate the effectiveness of GANs on the performance of binary and multi-class PDD, we train GANs on diverse plant species and disease symptoms, enabling the classification of different plant diseases. To achieve this, we trained two GAN models, namely Deep convolutional GAN (DCGAN) and alpha beta GAN (αβGAN), on different groups and numbers of plant species and disease classes to generate synthetic im-ages. …”
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Multi-Feature Facial Complexion Classification Algorithms Based on CNN by Xiyuan Cao, Delong Zhang, Chunyang Jin, Zhidong Zhang, Chenyang Xue

“…Three multi-feature facial complexion classification algorithms leveraging convolutional neural networks (CNNs) are proposed. They fuse, splice, or independently train the features extracted from distinct facial regions of interest (ROI), respectively. …”
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Fine-Grained Style Alignment and Class Balance for Unsupervised Domain Adaptation in Remote Sensing Image Segmentation by Yousheng Xu, Weiji Wang, Wei Yao, Shengzhou Xu

“…For instance, the widely used adaptive instance normalization (AdaIN) method reduces the distribution difference by transforming the style of source domain images to match that of the target domain. …”
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MSTNet: a multi-stage progressive network with local–global transformer fusion for image restoration by Ruyu Liu, Lin Wang, Jie He, Jiajia Wang, Jianhua Zhang, Xiufeng Liu, Chaochao Wang, Haoyu Zhang, Sheng Dai

“…Existing methods for image restoration mainly use convolutional neural networks (CNNs) or Transformer models, which have different advantages and limitations in capturing spatial and channel information of the image. …”
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Cumulative and offsetting effects of Streamflow Response to Climate change and Large Reservoir Group in the Jinsha River Basin, China by Ying Zhang, Zengxin Zhang, Qi Zhang, Xingnan Zhang, Yang Xu, Xiaoyang Liu, Jingqiao Mao, Chongyu Xu

“…Reservoir storage efficiency was more obvious in the midstream, especially after 2015 (with normalized difference (Sreconstruction – Sobservation)> 0 more frequent), while released more in the downstream after 2010 (with normalized difference<0 more frequent). …”
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Untrained perceptual loss for image denoising of line-like structures in MR images. by Elisabeth Pfaehler, Daniel Pflugfelder, Hanno Scharr

“…The uPL network's initialization is not important (e.g. for MR root images SSIM differences of 0.01 occur across initializations, while network depth and pooling operations impact denoising performance slightly more (SSIM of 0.83 for 5 convolutional layers and kernel size 3 vs. 0.86 for 5 convolutional layers and kernel size 5 for the root dataset). …”
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