Investigation of deep learning approaches for automated damage diagnostics in fiber metal laminates using Detectron2 and SAM by Sanjeev Kumar, Stefan Bosse, Chirag Shah

“…This study proposes an automated approach to detect, segment, reconstruct, and characterize the damages in FML plates using state-of-the-art deep learning models: the Segment Anything Model (SAM) and the Mask Region-based Convolutional Neural Network (Mask R-CNN) implemented by the Detectron2 framework. …”
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Vision transformer-based diagnosis of lumbar disc herniation with grad-CAM interpretability in CT imaging by Qingsong Chu, Xingyu Wang, Hao Lv, Yao Zhou, Ting Jiang

“…Abstract Background In this study, a computed tomography (CT)-vision transformer (ViT) framework for diagnosing lumbar disc herniation (LDH) was proposed for the first time by taking advantage of the multidirectional advantages of CT and a ViT. …”
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A Multi-Scale Interpretability-Based PET-CT Tumor Segmentation Method by Dangui Yang, Yetong Wang, Yimeng Ma, Houqun Yang

“…To address this, we propose a tumor segmentation framework based on a multi-scale interpretability module (MSIM). …”
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Machine learning for base transceiver stations power failure prediction: A multivariate approach by Sofia Ahmed, Tsegamlak Terefe, Dereje Hailemariam

“…This paper proposes a machine-learning-based framework for preemptive BTS power failure prediction using multivariate time-series data from power and environmental monitoring systems. …”
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Micro-expression spotting based on multi-modal hierarchical semantic guided deep fusion and optical flow driven feature integration by Haolin Chang, Zhihua Xie, Fan Yang

“…To address this issue, this paper proposes a multi-scale hierarchical semantic-guided end-to-end multimodal fusion framework based on Convolutional Neural Network (CNN)-Transformer for MES, named MESFusion. …”
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Diagnosis of Alzheimer's disease using non-linear features of ERP signals through a hybrid attention-based CNN-LSTM model by Elias Mazrooei Rad, Sayyed Majid Mazinani, Seyyed Ali Zendehbad

“…In this study, a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model is proposed for the diagnosis of Alzheimer’s disease (AD) from the Event-Related Potential (ERP) signals obtained from the Electroencephalogram (EEG) data. …”
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AHN-YOLO: A Lightweight Tomato Detection Method for Dense Small-Sized Features Based on YOLO Architecture by Wenhui Zhang, Feng Jiang

“…Convolutional neural networks (CNNs) are increasingly applied in crop disease identification, yet most existing techniques are optimized solely for laboratory environments. …”
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Face Detection and Segmentation Based on Improved Mask R-CNN by Kaihan Lin, Huimin Zhao, Jujian Lv, Canyao Li, Xiaoyong Liu, Rongjun Chen, Ruoyan Zhao

“…Deep convolutional neural networks have been successfully applied to face detection recently. …”
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SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising by Xiuying Li, Haoze Li, Hongwei Liao, Zhufeng Suo, Xuesong Chen, Jiameng Han

“…In response to the challenges presented by traditional CS reconstruction methods, such as boundary artifacts and limited robustness, we propose a novel hierarchical deep learning framework, SwinTCS, for CS-aware image reconstruction. …”
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SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN by Shengfeng He, Wenhu Qin, Zhonghua Yun, Chao Wu, Chongbin Sun

“…Next, a SOH estimation framework based on the CGKAN model is developed, where 1-Dimensional-Convolutional Neural Networks (1D-CNN) are used to extract deep features from the original data, Bidirectional Gated Recurrent Unit (BiGRU) captures the bidirectional dependencies of the time series, and Kolmogorov–Arnold Networks (KAN) enhances the modeling of complex nonlinear features through its nonlinear mapping capabilities, thereby improving the accuracy of SOH estimation. …”
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RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture by Jinye Gao, Jun Sun, Xiaohong Wu, Chunxia Dai

“…This study presents RDM-YOLO, a computationally efficient deep learning framework derived from YOLOv5s architecture, specifically designed for the automated detection of three essential behaviors (resting, wriggling, and eating) in fourth instar silkworms. …”
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Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support Methods by Ruixiang Kan, Mei Wang, Tian Luo, Hongbing Qiu

“…This setup lays a solid foundation for subsequent methods and updating strategies. The core framework consists of enhanced ensemble learning methods and Dempster–Shafer Evidence Theory (D-SET). …”
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Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components by Romaine Byfield, Ahmed Shabaka, Milton Molina Vargas, Ibrahim Tansel

“…Signal data were analyzed using 1D and 2D convolutional neural networks (CNNs), long short-term memory (LSTM) models, and random forest classifiers to detect and classify load magnitudes. …”
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Artificial Intelligence Approaches for the Detection of Normal Pressure Hydrocephalus: A Systematic Review by Luis R. Mercado-Diaz, Neha Prakash, Gary X. Gong, Hugo F. Posada-Quintero

“…Challenges in implementing AI in clinical practice were identified, and the authors suggested that a hybrid deep-traditional ML framework could enhance NPH diagnosis. Further research is needed to maximize the benefits of AI while addressing limitations.…”
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Optimization Design of Indoor Substation Ventilation and Noise Reduction Based on Deep Reinforcement Learning by Jinhui TANG, Fayuan WU, Yanli ZHI, Mengting MAO, Xiaomin DAI

“…Then, based on a large number of simulation data, the convolutional neural network is used to establish the prediction model of temperature and noise. …”
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Enhanced Medical Image Classification Using LSA and PCA in CNN by Suhaifa S Thasneem, Mukthar Hussain S Faizal, R Karthikeyan, Yousuf T Sheik, Begum B Rasina, Uveise S A Mohammed

“…In this study, we present an enhanced approach that integrates Least Squares (LSA) alongside with Principal Component Analysis (PCA) within the Convolutional Neural Network (CNN) framework of deep learning to improve image processing and image resolution for medical diagnostics .Here LSA is employed to reduce the noise to the greater extent and to refine the feature for better clarity, while PCA employed in dimensionality reduction for efficient processing and preserving critical image details and at the same time CNN enables the automatic feature extraction and interpretation of image. …”
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A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background by Jianyong Chai, Limin Jia, Jianfeng Liu, Enguang Hou, Zhe Chen

“…This study introduces a hybrid spatial–temporal deep learning model, integrating graph convolutional network (GCN) and long short-term memory (LSTM) networks, to predict metro tunnel displacements under the imperatives of “dual carbon” goals. …”
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Cell-TRACTR: A transformer-based model for end-to-end segmentation and tracking of cells. by Owen M O'Connor, Mary J Dunlop

“…This work establishes a new framework for employing transformer-based models in cell segmentation and tracking.…”
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Approaches to Proxy Modeling of Gas Reservoirs by Alexander Perepelkin, Anar Sharifov, Daniil Titov, Zakhar Shandrygolov, Denis Derkach, Shamil Islamov

“…The methodology integrates graph neural networks to account for spatial interdependencies between wells with recurrent and convolutional neural networks for time-series analysis. …”
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Bridging the Gap in Facial Age Progression: An Attention Mechanism Approach by Taoli Liu, Yubin Liang, Wenchen Wu, Yize Tang

“…To address these issues, we propose a novel facial aging prediction framework that employs three independent encoders to model identity, texture features, and facial skeletal structure. …”
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