Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. …”
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Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data by T. Radke, S. Fuchs, C. Wilms, I. Polkova, I. Polkova, I. Polkova, M. Rautenhaus, M. Rautenhaus

“…Recently, the feasibility of learning feature detection tasks using supervised learning with convolutional neural networks (CNNs) has been demonstrated. …”
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Assessment of Vegetation Indices Derived from UAV Imagery for Weed Detection in Vineyards by Fabrício Lopes Macedo, Humberto Nóbrega, José G. R. de Freitas, Miguel A. A. Pinheiro de Carvalho

“…Study limitations include lighting variability, reduced spatial coverage owing to low flight altitude, and a lack of spatial context in pixel-based methods. …”
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Scalable recurrence graph network for stratifying RhoB texture dynamics in rectal cancer biopsies by Tuan D. Pham

“…RhoB, a key biomarker assessed via immunohistochemistry, is crucial in predicting responses to radiotherapy (RT), but variability in staining techniques and tumor heterogeneity often complicate these assessments. …”
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Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification by Iftekhar Ahmed, Biggo Bushon Routh, Md. Saidur Rahman Kohinoor, Shadman Sakib, Md Mahfuzur Rahman, Farag Azzedin

“…Skin cancer, with its rising global prevalence, remains a crucial healthcare challenge, necessitating efficient and early detection for better patient outcomes. While deep convolutional neural networks have advanced image classification, current models struggle with diverse lesion types, variable image quality, and dataset imbalances. …”
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Low-Cost Hyperspectral Imaging in Macroalgae Monitoring by Marc C. Allentoft-Larsen, Joaquim Santos, Mihailo Azhar, Henrik C. Pedersen, Michael L. Jakobsen, Paul M. Petersen, Christian Pedersen, Hans H. Jakobsen

“…Using a one-dimensional convolutional neural network, we reached a high average classification precision, recall, and F1-score of 99.9%, 89.5%, and 94.4%, respectively, demonstrating the effectiveness of our custom low-cost HSI setup. …”
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Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions by Wenxin Xu, Jie Chen, Gerald Corzo, Chong‐Yu Xu, Xunchang John Zhang, Lihua Xiong, Dedi Liu, Jun Xia

“…The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN‐GRU) as the DL model, is applied to predict 1‐, 3‐, and 6‐month ahead reservoir inflows for the Danjiangkou Reservoir in China. …”
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Unmanned Aerial Vehicle-Based RGB Imaging and Lightweight Deep Learning for Downy Mildew Detection in Kimchi Cabbage by Yang Lyu, Xiongzhe Han, Pingan Wang, Jae-Yeong Shin, Min-Woong Ju

“…Among the evaluated models, Vision Transformer (ViT)-based architectures outperformed Convolutional Neural Network (CNN)-based models in terms of classification accuracy and generalization capability. …”
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Exploring Generative Pre-Trained Transformer-4-Vision for Nystagmus Classification: Development and Validation of a Pupil-Tracking Process by Masao Noda, Ryota Koshu, Reiko Tsunoda, Hirofumi Ogihara, Tomohiko Kamo, Makoto Ito, Hiroaki Fushiki

“… Abstract BackgroundConventional nystagmus classification methods often rely on subjective observation by specialists, which is time-consuming and variable among clinicians. Recently, deep learning techniques have been used to automate nystagmus classification using convolutional and recurrent neural networks. …”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…In 2023, Lopez et al. published a systematic review on how Artificial Intelligence could positively impact traditional anaesthesia practices.1 Various studies included in the review employed different models to achieve variable targets during the induction of anaesthesia. …”
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Fire and Smoke Detection Based on Improved YOLOV11 by Zhipeng Xue, Lingyun Kong, Haiyang Wu, Jiale Chen

“…In this paper, the core DCN2 (Deformable Convolutional Networks2) of the YOLOV11 Head is replaced with the DCN3 module to form a new detection head. …”
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Improved detection of air trapping on expiratory computed tomography using deep learning. by Sundaresh Ram, Benjamin A Hoff, Alexander J Bell, Stefanie Galban, Aleksa B Fortuna, Oliver Weinheimer, Mark O Wielpütz, Terry E Robinson, Beverley Newman, Dharshan Vummidi, Aamer Chughtai, Ella A Kazerooni, Timothy D Johnson, MeiLan K Han, Charles R Hatt, Craig J Galban

“…However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression.…”
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Development of interpretable intelligent frameworks for estimating river water turbidity by Amin Gharehbaghi, Salim Heddam, Saeid Mehdizadeh, Sungwon Kim

“…Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and a deep learning method named Convolutional Neural Networks (CNN). To evaluate the performance of proposed models, two gauging river stations situated in United States (i.e. …”
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Electrocardiographic sex index: a continuous representation of sex by Ibrahim Karabayir, Turgay Celik, Luke Patterson, Liam Butler, David Herrington, Oguz Akbilgic

“…Abstract Clinical risk calculators consider sex as a binary variable. However, sex is a complex trait with anatomic, physiologic, and metabolic attributes that are not easily summarized in this manner [1]. …”
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Research on Bearing Fault Diagnosis Method for Varying Operating Conditions Based on Spatiotemporal Feature Fusion by Jin Wang, Yan Wang, Junhui Yu, Qingping Li, Hailin Wang, Xinzhi Zhou

“…In real-world scenarios, the rotational speed of bearings is variable. Due to changes in operating conditions, the feature distribution of bearing vibration data becomes inconsistent, which leads to the inability to directly apply the training model built under one operating condition (source domain) to another condition (target domain). …”
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Wireless Channel Prediction Using Artificial Intelligence With Imperfect Datasets by Gowhar Javanmardi, Ramiro Samano Robles

“…Therefore, we consider sets of variable length (incomplete) to reflect the rapidly changing vehicular environment. …”
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From pixels to planning: scale-free active inference by Karl Friston, Karl Friston, Conor Heins, Tim Verbelen, Lancelot Da Costa, Lancelot Da Costa, Tommaso Salvatori, Dimitrije Markovic, Dimitrije Markovic, Alexander Tschantz, Magnus Koudahl, Christopher Buckley, Christopher Buckley, Thomas Parr

“…This model generalizes partially observed Markov decision processes to include paths as latent variables, rendering it suitable for active inference and learning in a dynamic setting. …”
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RE-YOLOv5: Enhancing Occluded Road Object Detection via Visual Receptive Field Improvements by Tianyu Li, Xuanrui Xiong, Yuan Zhang, Xiaolin Fan, Yushu Zhang, Haihong Huang, Dan Hu, Mengting He, Zhanjun Liu

“…The complexity and variability of real-world road environments make the detection of densely occluded objects more challenging in autonomous driving scenarios. …”
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Multi-scale U-like network with attention mechanism for automatic pancreas segmentation. by Yingjing Yan, Defu Zhang

“…The proposed network includes 2D convolutional layers and 3D convolutional layers, which means that it requires less computational resources than 3D segmentation models while it can capture more spatial information along the third dimension than 2D segmentation models. …”
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Modeling Temperature in the Ecuadorian Paramo Through Deep Learning by Marco Javier Castelo Cabay, Jose Antonio Piedra-Fernandez, Rosa Maria Ayala

“…The prediction integrates key variables such as humidity, precipitation, and wind speed through multivariate neural networks. …”
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