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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Turbulence-Resilient Object Classification in Remote Sensing Using a Single-Pixel Image-Free Approach by Yin Cheng, Yusen Liao, Jun Ke

“…In this work, we propose a novel image-free classification framework using single-pixel imaging (SPI), which directly classifies targets from 1D measurements without reconstructing the image. …”
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Estimation of Potato Growth Parameters Under Limited Field Data Availability by Integrating Few-Shot Learning and Multi-Task Learning by Sen Yang, Quan Feng, Faxu Guo, Wenwei Zhou

“…These results collectively demonstrated that the proposed FSLGP framework could achieve reliable estimation of crop growth parameters using only a very limited number of in-field samples (approximately 80 samples). …”
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Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach by Shu Xu, Tao Wang, Zenghui Ding, Yu Wang, Tongsheng Wan, Dezhang Xu, Xianjun Yang, Ting Sun, Meng Li

“…In view of this, this study proposes a cost-effective and user-friendly approach that integrates inertial measurement units (IMUs) with a novel deep learning framework for real-time lower limb joint torque estimation. …”
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DDoS Attack Detection in SDN-Assisted Federated Learning Environment Based on Contrastive Learning by Minghong Fan, Jinghua Lan, Yiyi Zhou, Mengshuang Pan, Junrong Li, Daqiang Zhang

“…The framework features a two-layer classification structure. …”
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Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…In this context, we propose a comprehensive framework that applies machine learning methods to enhance evidence collection and malware activity analysis. …”
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GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults by Proma Dutta, Kanchon Kanti Podder, Md. Shaheenur Islam Sumon, Muhammad E. H. Chowdhury, Amith Khandakar, Nasser Al-Emadi, Moajjem Hossain Chowdhury, M. Murugappan, Mohamed Arselene Ayari, Sakib Mahmud, S. M. Muyeen

“…The overall accuracy achieved by this framework is 94.04%. This shallow network can also be applied to estimate other mechanical faults in different machinery.…”
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Design of an Iterative Method for Time Series Forecasting Using Temporal Attention and Hybrid Deep Learning Architectures by Yuvaraja Boddu, A. Manimaran

“…Addressing these challenges, this paper introduces the Temporal Graph Attention Model for Time Series Analysis (TGAMTSA), a novel deep learning framework designed to enhance prediction accuracy and model adaptability in complex time series contexts and scenarios. …”
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Leveraging Prior Knowledge in Semi-Supervised Learning for Precise Target Recognition by Guohao Xie, Zhe Chen, Yaan Li, Mingsong Chen, Feng Chen, Yuxin Zhang, Hongyan Jiang, Hongbing Qiu

“…This study proposes DART-MT, a semi-supervised framework that integrates a Dual Attention Parallel Residual Network Transformer with a mean teacher paradigm, enhanced by domain-specific prior knowledge. …”
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The application of suitable sports games for junior high school students based on deep learning and artificial intelligence by Xueyan Ji, Shamsulariffin Bin Samsudin, Muhammad Zarif Bin Hassan, Noor Hamzani Farizan, Yubin Yuan, Wang Chen

“…This study intends to develop a Spatial Temporal-Graph Convolutional Network (ST-GCN) action detection algorithm based on the MediaPipe framework. …”
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DeepAir: deep learning and satellite imagery to estimate high-resolution PM2.5 at scale by Wenxuan Guo, Zhaoping Hu, Ling Jin, Yanyan Xu, Marta C Gonzalez

“…DeepAir integrates a pre-trained convolutional neural network with the LightGBM method. …”
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Transferring Learned ECG Representations for Deep Neural Network Classification of Atrial Fibrillation with Photoplethysmography by Jayroop Ramesh, Zahra Solatidehkordi, Raafat Aburukba, Assim Sagahyroon, Fadi Aloul

“…In this work, we present a deep learning framework that leverages convolutional layers with a bidirectional long short-term memory (CNN-BiLSTM) network and an attention mechanism for effectively classifying raw AF rhythms from normal sinus rhythms (NSR). …”
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Maize and soybean yield prediction using machine learning methods: a systematic literature review by Ramandeep Kumar Sharma, Jasleen Kaur, Gary Feng, Yanbo Huang, Chandan Kumar, Yi Wang, Sandhir Sharma, Johnie Jenkins, Jagmandeep Dhillon

“…Numerous ML models are used, yet systemized framework guiding the crop-targeted selection of models, features, accuracy measures, and addressing associated challenges is lacking, specifically for soybean and maize, world’s vital crops. …”
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