Infrared radiometric image classification and segmentation of cloud structures using a deep-learning framework from ground-based infrared thermal camera observations by K. Sommer, W. Kabalan, R. Brunet

“…Various methodologies for segmentation have been previously suggested. Most of them rely on color as the distinguishing criterion for cloud identification in the visible spectral domain and thus lack the ability to detect cloud structures in gray-scaled images with satisfying accuracy. …”
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Etiology of Late-Onset Alzheimer’s Disease, Biomarker Efficacy, and the Role of Machine Learning in Stage Diagnosis by Manash Sarma, Subarna Chatterjee

“…The broad objectives of our study are research gap identification, assessment of biomarker efficacy, and the most effective or prevalent ML technology used in AD diagnosis. …”
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Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm by Hanqing Hu, Tianmu Tian, Chengjing Liu, Xueyuan Bai

“…Second, an attention mechanism was applied to dynamically assign importance to different time steps and features, enabling the model to focus on the most critical information based on time periods and movie types. …”
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Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

“…Sensitivity and SHAP analyses identified equilibrium concentration and specific surface area as the most critical factors influencing adsorption. The findings underscore the effectiveness of machine learning methods, particularly XGBoost, LightGBM, and CatBoost, in forecasting adsorption levels with high precision while offering actionable insights into key variables driving adsorption mechanisms.…”
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Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap). by Reyhaneh Abbasi, Peter Balazs, Maria Adelaide Marconi, Doris Nicolakis, Sarah M Zala, Dustin J Penn

“…This study aims to 1) determine the most efficient USV detection tool among the existing methods, and 2) develop a classification model that is more generalizable than existing methods. …”
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Application of Artificial Intelligence Virtual Image Technology in Photography Art Creation Under Deep Learning by Qiong Yao

“…Additionally, user surveys reveal that most participants are highly satisfied with the images generated by the model, particularly regarding artistic perception and visual effects. …”
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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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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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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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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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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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TrioConvTomatoNet-BiLSTM: An Efficient Framework for the Classification of Tomato Leaf Diseases in Real Time Complex Background Images by S. Ledbin Vini, P. Rathika

“…Abstract Tomatoes are the most valuable vegetable worldwide that suffer from leaf diseases, which affect long-term tomato protection. …”
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