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

“…Forecast errors are related to cloud regimes, of which the cloud amount leads to a maximum relative RMSE difference of about 50% with an additional 5% from cloud variability. This study ascertains that multi-source data fusion contributes to a better simulation of cloud impacts and a combination of different deep learning techniques enables more reliable forecasts of solar radiation. …”
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Optimizing linear/non-linear Volterra-type integro-differential equations with Runge–Kutta 2 and 4 for time efficiency by Martin Ndi Azese

“…Additionally, a complex VTIDE is constructed featuring nonlinearities both within and outside the convolutions, as well as a derivative-of-dependent-variable integrant. …”
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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

“…The models were trained and evaluated using historical data from Chile (2000–2023), including wildfire occurrences, meteorological variables, topography, and vegetation indices. After preprocessing and class balancing, each model was tested over 100 experimental runs. …”
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Diabetes diagnosis using a hybrid CNN LSTM MLP ensemble by Yanmin Fan

“…To diagnose sickness, the MLP model serves as a meta-learner to combine and convert the data from the two feature extraction algorithms into the target variable. According to the implementation results, the suggested approach outperformed the compared approaches in terms of average accuracy and precision, achieving 98.28% and 0.99%, respectively, indicating a very great capacity to identify diabetes.…”
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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

“…A two-stage feature selection using XGBoost and SHAP identified the top 10 clinical predictors from 45 variables. The model was trained and validated using stratified 10-fold cross-validation on a retrospective cohort of 2,084 patients. …”
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Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India by Thamizh Vendan Tarun Kshatriya, Ramalingam Kumaraperumal, Sellaperumal Pazhanivelan, Nivas Raj Moorthi, Dhanaraju Muthumanickam, Kaliaperumal Ragunath, Jagadeeswaran Ramasamy

“…In this study, soil continuous (pH and OC) and categorical variables (order and suborder) were predicted using deep learning–multi layer perceptron (DL-MLP) and one-dimensional convolutional neural networks (1D-CNN) for the entire state of Tamil Nadu, India. …”
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AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation by Marta Narigina, Agris Vindecs, Dušanka Bošković, Yuri Merkuryev, Andrejs Romanovs

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Tumor segmentation in whole-slide histology images using deep learning by V. A. Kovalev, V. A. Liauchuk, A. A. Kalinovski, M. V. Fridman

“…The procedure capitalizes on convolutional neural networks and Deep Learning methods. …”
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Interpreting CNN models for musical instrument recognition using multi-spectrogram heatmap analysis: a preliminary study by Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

“…This task poses significant challenges due to the complexity and variability of musical signals.MethodsIn this study, we employed convolutional neural networks (CNNs) to analyze the contributions of various spectrogram representations—STFT, Log-Mel, MFCC, Chroma, Spectral Contrast, and Tonnetz—to the classification of ten different musical instruments. …”
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Mapping Peatlands Combing Deep Learning With Sparse Spectral Unmixing Based on Zhuhai-1 Hyperspectral Images by Yulin Xu, Xiaodong Na

“…The mixed pixel problem, arising from the complex vegetation types of peatlands, poses a significant challenge for remote sensing-based peatland mapping. A convolution and transformer-based reconstruction and sparse unmixing algorithm that integrates deep learning and sparse spectral unmixing is proposed to address the spectral variability and spatial heterogeneity of the endmembers in hyperspectral datasets. …”
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Artificial Intelligence Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis by Aikaterini Chatzara, Eirini Maliagkani, Dimitra Mitsopoulou, Andreas Katsimpris, Ioannis D. Apostolopoulos, Elpiniki Papageorgiou, Ilias Georgalas

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Early prediction of proton therapy dose distributions and DVHs for hepatocellular carcinoma using contour-based CNN models from diagnostic CT and MRI by Toshiya Rachi, Taku Tochinai

“…Despite anatomical variability between diagnostic and planning images, this approach provides timely insights into treatment feasibility, potentially supporting insurance pre-authorization, reducing unnecessary imaging, and optimizing clinical workflows for HCC proton therapy.…”
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Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism by Wangyong Guo, Shijin Liu, Liguo Weng, Xingyu Liang

“…Optimizing short-term load forecasting performance is a challenge due to the non-linearity and randomness of electrical load, as well as the variability of system operating patterns. Existing methods often fail to consider how to effectively combine their complementary advantages and fail to fully capture the internal information in the load sequence, leading to a decrease in accuracy. …”
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Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers by Johan Björkman, Sigrid Lagerroth, Jan Siarov, Filmon Yacob, Noora Neittaanmäki

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Causal inference-based graph neural network method for predicting asphalt pavement performance by CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde

“…The global feature extraction module employs attention mechanisms and gated recurrent units(GRU) to capture long-term temporal dependencies within variables. The local feature extraction module utilizes dilated convolutional neural networks(CNN) with various kernel sizes to extract short-term temporal patterns at different scales. …”
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Voltage Trajectory Prediction of Photovoltaic Power Station Based on CNN-GRU by Yuqi FENG, Hui LI, Lijuan LI, Yanbo ZHOU, Mao TAN, Hanmei PENG

“…Then, the autocorrelation coefficient of the voltage time series and its maximal information coefficient (MIC) relative to external variables are calculated, and the correlations of the voltage time series with external variables in timing are analyzed. …”
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Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet by Aaron E. Maxwell, Sarah Farhadpour, Muhammad Ali

“…Semantic segmentation algorithms, such as UNet, that rely on convolutional neural network (CNN)-based architectures, due to their ability to capture local textures and spatial context, have shown promise for anthropogenic geomorphic feature extraction when using land surface parameters (LSPs) derived from digital terrain models (DTMs) as input predictor variables. …”
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Addressing Distribution Discrepancies in Pulsar Candidate Identification via Bayesian-neural-network-based Multimodal Incremental Learning by Yi Liu, Jing Jin, Hongyang Zhao, Zhenyi Wang, Yi Shen

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