The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks by Sunku V.S., Namboodiri V., Mukkamala R.

“…The objective is to develop an innovative deep learning (DL) model that integrates a convolutional neural network (CNN) with a gated recurrent unit (GRU) to enhance forecasting precision for day-ahead applications. …”
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Building Damage Detection Using Deep Learning Architecture with Satellite Images: The Case of the 6 February 2023 Kahramanmaraş Earthquake by Zeynep Aygün, Merve Kocaman, Salih Aydemir, Berkant Konakoğlu

“…The Kahramanmaraş earthquake on February 6, 2023, was one of the most devastating in recent years, causing extensive damage and loss. …”
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A Novel Transformer-Based Object Detection Method With Geometric and Object Co-Occurrence Prior Knowledge for Remote Sensing Images by Nan Mo, Ruixi Zhu

“…Last, we design a graph convolutional reference module with co-occurrence prior knowledge to improve the inferential ability of the detector. …”
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Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition by Sk Mahmudul Hassan, Kumar Sekhar Roy, Ruhul Amin Hazarika, Mehbub Alam, Mithun Mukherjee

“…To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years. Most of these solutions with the standard convolutional neural network (CNN) approaches use uniform background laboratory setup leaf images to identify the diseases. …”
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Cyberbullying detection of resource constrained language from social media using transformer-based approach by Syed Sihab-Us-Sakib, Md. Rashadur Rahman, Md. Shafiul Alam Forhad, Md. Atiq Aziz

“…After rigorous experimentation, XLM-RoBERTa emerged as the most effective model, achieving a significant F1-score of 0.83 and an accuracy of 82.61%, outperforming all other models. …”
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Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos by Lisa Boucret, Floris Chabrun, Magalie Boguenet, Pascal Reynier, Pierre-Emmanuel Bouet, Pascale May-Panloup

“…Abstract Time-lapse imaging and deep-learning algorithms are promising tools to assess the most viable embryos and improve embryo selection in IVF laboratories. …”
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A Voxelized Transformer-Based Neural Network for 3D Reconstruction From Multi-Energy SEM Backscattered Electrons by Caizhi Zheng, Ronghan Hong, Hao-Jie Hu, Qing Huo Liu

“…However, the traditional methods are difficult to obtain high-precision reconstruction results in the longitudinal direction, and the reconstruction results of most methods contain a large number of artifacts. …”
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Optical Flow Magnification and Cosine Similarity Feature Fusion Network for Micro-Expression Recognition by Heyou Chang, Jiazheng Yang, Kai Huang, Wei Xu, Jian Zhang, Hao Zheng

“…Recent advances in deep learning have significantly advanced micro-expression recognition, yet most existing methods process the entire facial region holistically, struggling to capture subtle variations in facial action units, which limits recognition performance. …”
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Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions by Ömer Akgüller, Larissa M. Batrancea, Mehmet Ali Balcı, Gökhan Tuna, Anca Nichita

“…Among the fractional dimension methods, Genton and Hall–Wood reveal the most significant differences in anomaly detection performance between the models, while Box Counting and Wavelet yield more consistent outcomes across sectors. …”
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PlantCareNet: an advanced system to recognize plant diseases with dual-mode recommendations for prevention by Muhaiminul Islam, AKM Azad, Shifat E. Arman, Salem A. Alyami, Md Mehedi Hasan

“…The proposed architecture utilizes a convolutional neural network (CNN) to examine images of plant leaves, with the final block flattened and subsequently forwarded to Dense-100 and ultimately Dense-35 for the precise classification of various plant diseases. …”
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Automated assessment of simulated laparoscopic surgical skill performance using deep learning by David Power, Cathy Burke, Michael G. Madden, Ihsan Ullah

“…Lack of labeled data is a particular problem in surgery considering its complexity, as human annotation and manual assessment are both expensive in time and cost, and in most cases rely on direct intervention of clinical expertise. …”
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Full-Scale Piano Score Recognition by Xiang-Yi Zhang, Jia-Lien Hsu

“…Sheet music is one of the most efficient methods for storing music. Meanwhile, a large amount of sheet music-image data is stored in paper form, but not in a computer-readable format. …”
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Using Deep Learning (CNN, RNN, LSTM, GRU) methods for the prediction of Protein Secondary Structure by Ezgi Çakmak, İhsan Hakan Selvi

“…The goal of this study is to compare the results generated by predictive models that were created using the four most frequently utilized deep learning methods: convolutional neural networks (CNN), recurrent neural networks (RNN), long short term memory networks (LSTM), and gated recurrent units (GRU). …”
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An Effective Feature Extraction Method for Tomato Leafminer - Tuta Absoluta (Meyrick) (Lepidoptera: Gelechiidae) Classification by Tahsin Uygun, Serhat Kiliçarslan, Cemil Közkurt, Mehmet Metin Ozguven

“…Insecticides are commonly used to combat pests. However, most of the time, farmers' lack of knowledge in recognizing pests and understanding their effects results in incorrect and excessive spray applications. …”
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A Destination Prediction Network Based on Spatiotemporal Data for Bike-Sharing by Jian Jiang, Fei Lin, Jin Fan, Hang Lv, Jia Wu

“…In this paper, we propose an innovative deep learning model to predict the most probable destination for each user. The model, called destination prediction network based on spatiotemporal data (DPNst), comprises three steps. …”
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CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features by Wenxuan Ge, Xubing Yang, Rui Jiang, Wei Shao, Li Zhang

“…Hence, cloud detection is a necessary preprocessing procedure. However, most existing methods have numerous calculations and parameters. …”
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Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah, Seifeddine Bouallegue, Ridha Bouallegue

“…The main goals consist of creating an AI-based framework to monitor and analyze student interactions, evaluating the influence of online learning platforms on student understanding using advanced algorithms, and determining the most efficient methods for blended learning systems. …”
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Machine Learning in Acute Stroke Neuroimaging. A Systematic Literature Review by D. Matuliauskas, I. Stražnickaitė, A. Samuilis, D. Jatužis

“…The training set sizes consisted of minimum 28 CT scans, maximum – 24214, mean – 1279, median – 153, standard deviation – ±5006.7. Most popular software used in the studies were Brainomix (n=12, 20% of studies) and RAPID (n=12, 20%), 6 studies (10%) used convolutional neural networks, and 6 studies did not iden- tify the model or name of software used. …”
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Boosting Degradation Representation Learning for Blind Image Super-Resolution by YUAN Jiang, MA Ji, ZHOU Dengwen

“…In most convolutional neural networks-based super-resolution (SR) methods, the degradation assumptions are fixed and known (e.g., bicubic degradation). …”
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Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation by Hyun Bin Kim, Hong Qi Tan, Wen Long Nei, Ying Cong Ryan Shea Tan, Yiyu Cai, Fuqiang Wang

“…For CT scans, two different approaches with convolutional neural network were utilized to tackle the 3D scan entirely or tackle it slice by slice. …”
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