Machine-Learning-Guided Design of Nanostructured Metal Oxide Photoanodes for Photoelectrochemical Water Splitting: From Material Discovery to Performance Optimization by Xiongwei Liang, Shaopeng Yu, Bo Meng, Yongfu Ju, Shuai Wang, Yingning Wang

“…Particular attention is given to surrogate modeling, Bayesian optimization, convolutional neural networks, and explainable AI approaches that enable closed-loop synthesis-experiment-ML frameworks. …”
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Automatic Detection of Tiny Drainage Outlets and Ventilations on Flat Rooftops from Aerial Imagery by L. Arzoumanidis, W. Li, W. Li, J. Knechtel, Y. Kosmayadi, Y. Dehbi

“…We evaluated two different object detection methods, with FCOS (Fully Convolutional One-Stage Object Detection) outperforming Faster R-CNN in identifying these small utilities. …”
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Challenges and prospects of artificial intelligence in aviation: a ​bibliometric study by Nuno Moura Lopes, Manuela Aparicio, Fátima Trindade Neves

“…Burst keyword analysis identifies the leading-edge research on AI within predictive models, unmanned aerial vehicles, object detection, and convolutional neural networks. The primary objective is to bridge this knowledge gap and gain comprehensive insights into AI in the aviation sector. …”
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Deepfake Image Forensics for Privacy Protection and Authenticity Using Deep Learning by Saud Sohail, Syed Muhammad Sajjad, Adeel Zafar, Zafar Iqbal, Zia Muhammad, Muhammad Kazim

“…Key approaches include the use of CNNs, RNNs, and hybrid models like CNN-LSTM, CNN-GRU, and temporal convolutional networks (TCNs) to capture both spatial and temporal features during the detection of deepfake videos and images. …”
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Unsupervised Domain Adaptation via Contrastive Learning and Complementary Region-Class Mixing by Xiaojing Li, Wei Zhou, Mingjian Jiang

“…In semantic segmentation, current deep convolutional neural networks rely heavily on extensive data to achieve superior segmentation results. …”
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Artificial Intelligence in Diagnosis and Management of Nail Disorders: A Narrative Review by Vishal Gaurav, Chander Grover, Mehul Tyagi, Suman Saurabh

“…AI algorithms, particularly deep convolutional neural networks (CNNs), have demonstrated high sensitivity and specificity in interpreting nail images, aiding differential diagnosis as well as enhancing the efficiency of diagnostic processes in a busy clinical setting. …”
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Advances in Remote Sensing and Deep Learning in Coastal Boundary Extraction for Erosion Monitoring by Marc-André Blais, Moulay A. Akhloufi

“…The presented algorithms range from basic convolutional networks to encoder–decoder architectures and attention mechanisms. …”
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Transfer learning based hybrid feature learning framework for enhanced skin cancer diagnosis using deep feature integration by Maridu Bhargavi, Sivadi Balakrishna

“…To address these issues, this research proposes the DRMv2Net model, a feature fusion deep learning-based technique that integrates multiple pre-trained convolutional neural networks to enhance skin cancer diagnosis. …”
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DSGRec: dual-path selection graph for multimodal recommendation by Zihao Liu, Wen Qu

“…Although methods based on graph convolutional networks (GCNs) have achieved notable success, they still face two key limitations: (1) the narrow interpretation of interaction information, leading to incomplete modeling of user behavior, and (2) a lack of fine-grained collaboration between user behavior and multi-modal information. …”
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Clinical Applications of Artificial Intelligence in Periodontology: A Scoping Review by Georgios S. Chatzopoulos, Vasiliki P. Koidou, Lazaros Tsalikis, Eleftherios G. Kaklamanos

“…The review revealed a significant interest in utilizing AI, particularly convolutional neural networks (CNNs), for various periodontal applications. …”
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Monitoring Pine Wilt Disease Using High-Resolution Satellite Remote Sensing at the Single-Tree Scale with Integrated Self-Attention by Wenhao Lv, Junhao Zhao, Jixia Huang

“…The visualization of model inference results indicates that DANet, which integrates convolutional neural networks (CNNs) with self-attention mechanisms, achieved the highest overall accuracy at 94.43%. …”
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Integrating non-linear radon transformation for diabetic retinopathy grading by Farida Mohsen, Samir Belhaouari, Zubair Shah

“…We conducted extensive experiments on two benchmark datasets, APTOS-2019 and DDR, using three convolutional neural networks (CNNs): ResNeXt-50, MobileNetV2, and VGG19. …”
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Advances in machine learning for the detection and characterization of microplastics in the environment by M. Maksuda Khanam, M. Khabir Uddin, Julhash U. Kazi

“…In particular, algorithms such as support vector machines, random forests, and convolutional neural networks have demonstrated considerable success in classifying microplastics based on chemical signatures and visual characteristics. …”
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Advancements in artificial intelligence and machine learning for poultry farming: Applications, challenges, and future prospects by Muhammad Halim Natsir, Wayan Firdaus Mahmudy, Mochamad Tono, Yuli Frita Nuningtyas

“…The findings reveal that Convolutional Neural Networks (CNN), especially YOLOv8, offer superior performance in visual-based poultry health detection, achieving over 90 % accuracy for conditions like bumblefoot and woody breast. …”
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Impact of occupancy behavior on building energy efficiency: What’s next in detection and monitoring technologies? by Wenjie Song, John Calautit

“…Particular attention is paid to data-driven methods, including probabilistic models such as Hidden Markov Models (HMMs), classical machine learning algorithms such as Support Vector Machines (SVMs) and K-Nearest Neighbors (KNN), and deep learning architectures such as Convolutional Neural Networks (CNNs), all of which have demonstrated high accuracy in both laboratory and real-world settings. …”
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Machine learning for predicting resistance spot weld quality in automotive manufacturing by Nuttapong Chuenmee, Nattachai Phothi, Kontorn Chamniprasart, Sorada Khaengkarn, Jiraphon Srisertpol

“…Five distinct algorithms—Artificial Neural Network (ANN), Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), Random Forest Classifier (RFC), and Extreme Gradient Boosting (XGBoost)—were assessed. …”
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Efficient Side-Tuning for Remote Sensing: A Low-Memory Fine-Tuning Framework by Haichen Yu, Wenxin Yin, Hanbo Bi, Chongyang Li, Yingchao Feng, Wenhui Diao, Xian Sun

“…The proposed EST Block is the main component of the parallel network, which uses the multichannel adapter fusion module, gate layer and depthwise convolution to achieve feature selection and enhancement effects. …”
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Multi-Neighborhood Sparse Feature Selection for Semantic Segmentation of LiDAR Point Clouds by Rui Zhang, Guanlong Huang, Fengpu Bao, Xin Guo

“…To address these problems, a sparse feature dynamic graph convolutional neural network, abbreviated as SFDGNet, is constructed in this paper for LiDAR point clouds of complex scenes. …”
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YOLO-Helmet: A Novel Algorithm for Detecting Dense Small Safety Helmets in Construction Scenes by Guoliang Yang, Xinfang Hong, Yangyang Sheng, Liuyan Sun

“…Firstly, in order to solve the problem of difficult detection due to the small area of the helmet in the image, a small size detection layer was extended to improve the detection sensitivity of the network to small size targets. Secondly, in order to reduce the influence of occlusion on the accuracy of helmet detection, the C-ELAN module was constructed, and the receptive field is expanded by deformable convolution to provide rich contextual feature information for coordinate attention, so as to improve the accuracy of the network for the discrimination of target position information. …”
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Face Mesh for Dementia Detection: Evaluating Data Augmentation in Deep Learning and Traditional Machine Learning by Chuheng Zheng, Mondher Bouazizi, Taichi Okunishi, Tomoaki Ohtsuki, Momoko Kitazawa, Toshiro Horigome, Taishiro Kishimoto

“…We explored data augmentation techniques to address the challenges of over-fitting, particularly impactful in training neural networks on a small dataset. Deep learning methods, including transformer, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were employed to classify dementia patients and healthy subjects. …”
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