The Role of Time in Facial Dynamics and Challenges in Automatic Emotion Recognition (2019–2024) by Williams Contreras-Higuera, Lucrezia Crescenzi-Lanna

“…While deep learning (DL) techniques like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks offer significant advances, they face challenges such as gradient vanishing and overfitting, particularly in long and complex sequences. …”
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Neural network-based symmetric encryption algorithm with encrypted traffic protocol identification by Jiakai Hao, Ming Jin, Yuting Li, Yuxin Yang

“…Special emphasis is placed on the performance of algorithms within both symmetric and asymmetric frameworks. Experimental results demonstrate the effectiveness of our proposed scheme, highlighting its potential for enhancing power grid security.…”
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AI-Driven Integration of Deep Learning With Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction by Kecheng Huang, Chujun Wu, Rongpeng Pi, Jieyu Fang

“…Traditional diagnostic methods such as radiological imaging and ABG analysis often lack integrated predictive accuracy. AI frameworks, particularly convolutional neural networks and hybrid models like TD-CNNLSTM-LungNet, demonstrate exceptional performance in detecting pulmonary inflammation and stratifying hypoxemia risk, achieving up to 96.57% accuracy in pneumonia subtype differentiation and an area under the curve of 0.96 for postoperative hypoxemia prediction. …”
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Ocular Disease Detection Using Fundus Images: A Hybrid Approach of Grad-CAM and Multiscale Retinex Preprocessing With VGG16 Deep Features and Fine KNN Classification by Shreemat Kumar Dash, Kante Satyanarayana, Santi Kumari Behera, Sudarson Jena, Ashoka Kumar Ratha, Prabira Kumar Sethy, Aziz Nanthaamornphong

“…Ethical issues and legal frameworks are also crucial for the safe and fair implementation of AI in medical services. …”
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Intelligent Testing Method for Multi-Point Vibration Acquisition of Pile Foundation Based on Machine Learning by Ke Wang, Weikai Zhao, Juntao Wu, Shuang Ma

“…The model’s performance was assessed using statistical error metrics, including the correlation coefficient R², mean absolute error (MAE), and variance accounted for (VAF), with comparative evaluations conducted across different model frameworks. Results show that both the convolutional neural network (CNN) and the long short-term memory neural network (LSTM) consistently achieved high accuracy in identifying the location of the first reflection point in the pile shaft, with R² values greater than 0.98, MAE below 0.41 (m), and VAF greater than 98%. …”
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Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends by Md Amran Hossen, Pg Emeroylariffion Abas

“…The review also discusses traditional machine learning approaches, including supervised and unsupervised learning, alongside cutting-edge advancements in deep learning, such as convolutional and recurrent neural networks, attention mechanisms, and reinforcement learning frameworks. …”
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Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation by Olamilekan Shobayo, Reza Saatchi

“…Future research directions emphasize optimising deep learning models for real-time applications, enhancing interpretability, and integrating deep learning with existing healthcare frameworks for improved patient outcomes.…”
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Brain Representation in Conscious and Unconscious Vision by Ning Mei, David Soto

“…The development of robust frameworks to understand how the human brain represents conscious and unconscious perceptual contents is paramount to make progress in the neuroscience of consciousness. …”
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Artificial Intelligence for data modeling in triboelectric nanogenerators by Chenjia Li, Ali Matin Nazar

“…Quantitative analyses of machine learning frameworks are presented, with CNNs and ANNs demonstrating up to 99.32 % accuracy in TENG signal processing tasks. …”
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Recent Advancements in Hyperspectral Image Reconstruction from a Compressive Measurement by Xian-Hua Han, Jian Wang, Huiyan Jiang

“…This survey presents a comprehensive overview of recent progress in HS image reconstruction, systematically categorized into three main paradigms: traditional model-based methods, deep learning-based approaches, and hybrid frameworks that integrate data-driven priors with the mathematical modeling of the degradation process. …”
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Mild autonomous cortisol secretion leads to reduced volumetric BMD at lumbar spine in patients with primary aldosteronism by Nabeel Mansour, Denise Bruedgam, Daniel Heinrich, Ulrich Dischinger, Nicole Reisch, Friederike Völter, Friederike Völter, Isabel Stüfchen, Elisabeth Nowak, Stephanie Zopp, Victoriya Vasileva, Osman Öcal, Moritz Wildgruber, Max Seidensticker, Jens Ricke, Martin Bidlingmaier, Martin Reincke, Juínia Ribeiro de Oliveira Longo Schweizer

“…Lumbar volumetric bone mineral density (vBMD) was extracted by a novel convolutional neural network (CNN)-based framework (SpineQ software v1.0) applied to routine CT data, incorporated into the diagnostic protocol for PA. …”
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A Survey on Data Mining for Data-Driven Industrial Assets Maintenance by Eduardo Coronel, Benjamín Barán, Pedro Gardel

“…Furthermore, the survey explores emerging trends in machine learning and related technologies, identifies future research directions, and underscores their critical role in advancing condition-based and predictive maintenance frameworks.…”
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The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection by Tarek Berghout

“…Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. …”
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Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology by Hilary S Tang, Joseph Ebriani, Matthew J Yan, Shannon Wongvibulsin, Mehdi Farshchian

“…Most studies employed convolutional neural networks (CNN) for image analysis, with accuracy rates ranging from 90.1% to 99.5%. …”
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The intelligent lift: Artificial Intelligence's growing role in plastic surgery - a comprehensive review by Amr Youssef Arkoubi

“…Advancing the field requires standardized protocols, multicenter collaborations, and ethical frameworks to ensure safe and equitable deployment of AI technologies.…”
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AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring by Venkatraman Manikandan, Suresh Neethirajan

“…The investigation reveals compelling evidence for edge computing deployment via TinyML frameworks, addressing critical scalability challenges in commercial poultry environments characterized by acoustic complexity and computational constraints. …”
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Symbol Detection and Channel Estimation for Space Optical Communications Using Neural Network and Autoencoder by Abdelrahman Elfikky, Zouheir Rezki

“…Additionally, with no fading and for both perfect and imperfect CSI with different code rates and fading channels, the proposed AE-based detection outperforms both benchmark learning frameworks and most popular convolutional codes.…”
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A multimodal functional structure-based graph neural network for fatigue detection by Dongrui Gao, Zhihong Zhou, Zongyao Peng, Haokai Zhang, Shihong Liu, Manqing Wang, Hongli Chang

“…An innovative intra- and inter-channel separable convolution module is designed to extract deep interaction patterns through parallel convolution operations within and across signal channels. …”
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A study on query terms proximity embedding for information retrieval by Ya-nan Qiao, Qinghe Du, Di-fang Wan

“…Moreover, term-field-convolutions frequency framework, which is an implementation of query term proximity embedding, is proposed in this article, and experimental results show that this framework can improve the performance effectively compared with traditional proximity retrieval models.…”
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Image Inpainting Algorithm Based on Structure-Guided Generative Adversarial Network by Li Zhao, Tongyang Zhu, Chuang Wang, Feng Tian, Hongge Yao

“…To address the challenges of image inpainting in scenarios with extensive or irregular missing regions—particularly detail oversmoothing, structural ambiguity, and textural incoherence—this paper proposes an Image Structure-Guided (ISG) framework that hierarchically integrates structural priors with semantic-aware texture synthesis. …”
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