Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process by Orsolya Péterfi, Nikolett Kállai-Szabó, Kincső Renáta Demeter, Ádám Tibor Barna, István Antal, Edina Szabó, Emese Sipos, Zsombor Kristóf Nagy, Dorián László Galata

“…After training the model, the performance of the developed system was assessed by analysing the particle size distribution of pellet cores with variable sizes within the 250–850 μm size range. The endoscopic system was tested in-line at a larger scale during the drug layering of inert pellet cores. …”
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Graph Neural Network Classification in EEG-Based Biometric Identification: Evaluation of Functional Connectivity Methods Using Time-Frequency Metric by Roghaieh Ashenaei, Ali Asghar Beheshti Shirazi

“…Despite reduced setup complexity, our GCNN achieves over 98% identification accuracy, comparable to CNN-based studies using 64 channels, with significantly lower computational cost and trainable variables reduced to less than 0.25 of those in a Convolutional Neural Network (CNN). …”
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Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models by Ziyang Zhang, Jiancheng Ye

“…Various ML models, including logistic regression, decision trees, and support vector machines, were compared with advanced DL models, including 1D convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. …”
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Detection of <i>Helicobacter pylori</i> Infection in Histopathological Gastric Biopsies Using Deep Learning Models by Rafael Parra-Medina, Carlos Zambrano-Betancourt, Sergio Peña-Rojas, Lina Quintero-Ortiz, Maria Victoria Caro, Ivan Romero, Javier Hernan Gil-Gómez, John Jaime Sprockel, Sandra Cancino, Andres Mosquera-Zamudio

“…Moreover, interobserver variability has been well documented in the traditional diagnostic approach, which may further complicate consistent interpretation. …”
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Revolutionizing Lung Segmentation with Machine Learning: A Critical Review of Techniques in Medical Imaging by Momina Aisha, Moazma Ijaz, Nimra Tariq, Sehar Anjum, Sidra Siddiqui, Usman Hashmi

“…Manual lung segmentation by radiologists, while adjustable, is time-consuming and subject to variability. Consequently, automated lung segmentation methods utilizing Machine Learning (ML) and Deep Learning (DL) have emerged as essential alternatives. …”
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PICT-Net: A Transformer-Based Network with Prior Information Correction for Hyperspectral Image Unmixing by Yiliang Zeng, Na Meng, Jinlin Zou, Wenbin Liu

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Integrated CNN‐LSTM for Photovoltaic Power Prediction based on Spatio‐Temporal Feature Fusion by Junwei Ma, Meiru Huo, Jinfeng Han, Yunfeng Liu, Shunfa Lu, Xiaokun Yu

“…This paper proposes a convolutional neural network‐long short‐term memory (CNN‐LSTM) network integration model based on spatio‐temporal feature fusion. …”
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Deep learning model for patient emotion recognition using EEG-tNIRS data by Mohan Raparthi, Nischay Reddy Mitta, Vinay Kumar Dunka, Sowmya Gudekota, Sandeep Pushyamitra Pattyam, Venkata Siva Prakash Nimmagadda

“…In cross-subject validation, the model attains a 55.53% accuracy, highlighting its robustness despite inter-subject variability. The findings illustrate that the proposed graph convolution fusion approach, combined with modality attention, effectively enhances emotion recognition accuracy and stability. …”
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Machine Learning for Chronic Kidney Disease Detection from Planar and SPECT Scintigraphy: A Scoping Review by Dunja Vrbaški, Boban Vesin, Katerina Mangaroska

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Automated classification of chest X-rays: a deep learning approach with attention mechanisms by Burcu Oltu, Selda Güney, Seniha Esen Yuksel, Berna Dengiz

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Improving Solar Radiation Forecasting in Cloudy Conditions by Integrating Satellite Observations by Qiangsheng Bu, Shuyi Zhuang, Fei Luo, Zhigang Ye, Yubo Yuan, Tianrui Ma, Tao Da

“…To alleviate this limitation, this study develops a hybrid network which relies on a convolutional neural network to extract cloud motion patterns from time series of satellite observations and a long short-term memory neural network to establish the relationship between future solar radiation and cloud information, as well as antecedent measurements. …”
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Real-Time Defect Detection for Fast-Moving Fabrics on Circular Knitting Machine Under Various Illumination Conditions by Yan-Qin Ni, Pei-Kai Huang, Ching-Han Yang, Chin-Chun Chang, Wei-Jen Wang, Deron Liang

“…First, to tackle the challenges of real-time detection, limited training data, and varying illumination conditions, we develop a lightweight semantic segmentation model, LBUnet, which leverages local binary (LB) convolution to effectively handle variable lighting conditions. …”
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Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model Selection by Julio Barzola-Monteses, Rosangela Caicedo-Quiroz, Franklin Parrales-Bravo, Cristhian Medina-Suarez, Wendy Yanez-Pazmino, David Zabala-Blanco, Maikel Y. Leyva-Vazquez

“…Experiments were conducted in two scenarios: one using a dataset that included 12 variables, and another in which the variables were reduced to those most significantly correlated with cardiovascular disease, i.e., 4 variables; both scenarios with 918 clinical records per variable. …”
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Deep learning method for cucumber disease detection in complex environments for new agricultural productivity by Jun Liu, Xuewei Wang, Qian Chen, Peng Yan, Xin Liu

“…The model effectively handles symptom variability and complex detection scenarios, outperforming mainstream detection algorithms in accuracy, speed, and compactness, making it ideal for embedded agricultural applications.…”
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LSEVGG: An attention mechanism and lightweight-improved VGG network for remote sensing landscape image classification by Yao Lu

“…Remote sensing landscape image classification is essential for environmental monitoring, land management, and ecological assessment, but presents critical challenges due to complex spatial distributions and high intra-class variability inherent in landscape scenes. Traditional deep convolutional neural networks, such as VGG16, though effective, are computationally intensive and unsuitable for deployment on resource-constrained platforms commonly used in landscape monitoring applications. …”
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A Comparative Study of a Deep Reinforcement Learning Solution and Alternative Deep Learning Models for Wildfire Prediction by Cristian Vidal-Silva, Roberto Pizarro, Miguel Castillo-Soto, Ben Ingram, Claudia de la Fuente, Vannessa Duarte, Claudia Sangüesa, Alfredo Ibañez

“…This study compared three deep learning models for wildfire prediction: Deep Reinforcement Learning (DRL) with Actor–Critic architecture, Convolutional Neural Network (CNN), and Transformer-based models. …”
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Diabetes diagnosis using a hybrid CNN LSTM MLP ensemble by Yanmin Fan

“…The second step involves employing two neural networks to retrieve features. Convolutional neural network (CNN) is the first neural network utilized for extracting the spatial characteristics of the data, while Long Short-Term Memory (LSTM) networks—more specifically, an LSTM Stack—are used to comprehend the time-dependent flow of the data based on medical information from patients. …”
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A Deep Learning Model for NOx Emissions Prediction of a 660 MW Coal-Fired Boiler Considering Multiscale Dynamic Characteristics by Jianrong Huang, Yanlong Ji, Haiquan Yu

“…MSGNet employs Fast Fourier Transform (FFT) for automatic periodic pattern recognition, adaptive graph convolution for dynamic inter-variable relationships, and a multihead attention mechanism to assess temporal dependencies comprehensively. …”
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A Lightweight Breast Cancer Mass Classification Model Utilizing Simplified Swarm Optimization and Knowledge Distillation by Wei-Chang Yeh, Wei-Chung Shia, Yun-Ting Hsu, Chun-Hui Huang, Yong-Shiuan Lee

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Hybrid CNN-Transformer-WOA model with XGBoost-SHAP feature selection for arrhythmia risk prediction in acute myocardial infarction patients by Li Li, Wenjun Ren, Yuying Lei, Lixia Xu, Xiaohui Ning

“…Methods We developed a novel hybrid model integrating convolutional neural network (CNN), Transformer, and Whale Optimization Algorithm (WOA) for arrhythmia prediction in AMI patients. …”
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