XAI Helps in Storm Surge Forecasts: A Case Study for the Southeastern Chinese Coasts by Lei Han, Wenfang Lu, Changming Dong

“…Guided by these insights, we introduce the surge time difference (ΔZ/Δt) as an explicit input feature to enhance the model’s physical representation. …”
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Multivariate Load Forecasting of Integrated Energy System Based on CEEMDAN-CSO-LSTM-MTL by WANG Yongli, LIU Zeqiang, DONG Huanran, LI Dexin, CHEN Xin, GUO Lu, WANG Jiarui

“…Firstly，preprocess the collected raw load data and calculate the actual load value considering system energy loss； Secondly，the maximum information coefficient （MIC） is used to analyze the correlation between multiple loads and between multiple loads and weather factors，and to extract strongly correlated variables of multiple loads； Once again，the strongly correlated variables of multiple loads are substituted into CEEMDAN，and the load data is decomposed into stationary subsequences； Then，the feature sequence is substituted into the LSTM-MTL shared layer and the CSO algorithm is used to optimize the prediction model，achieving collaborative prediction of multiple loads； Finally，the performance of the constructed model was validated using a multivariate load dataset from a chemical park in Jilin City，Jilin Province，China. …”
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Predictive Framework for Sustainable Engineering through Machine Learning and Cross-Sector Collaboration by Choudhary Abhik, Adhikari Upasana, Roy Dipankar, Gupta Subir, Roy Priyanka, Bhaduri Aparna

“…The model achieved 96% accuracy and 0.733 F1 score. Feature importance analysis corroborated interpretability of model predictions. …”
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Cross-Domain Fault Diagnosis of Rotating Machinery Under Time-Varying Rotational Speed and Asymmetric Domain Label Condition by Siyuan Liu, Jinying Huang, Peiyu Han, Zhenfang Fan, Jiancheng Ma

“…To maintain diagnostic performance and knowledge generalization across different speeds, cross-domain intelligent fault diagnosis (IFD) models are widely researched. …”
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Enhancing medical response efficiency in real-time large crowd environments via smart coverage and deep learning for stable ecological health monitoring by Asma A. Alhashmi, Ghada Moh. Samir Elhessewi, Mukhtar Ghaleb, Nazir Ahmad, Nojood O. Aljehane, Tareq M. Alkhaldi, Hamad Almansour, Samah Al Zanin

“…Finally, the parameter selection for the BiLSTM-AE model is performed by using the Hiking Optimisation Algorithm (HOA) model. …”
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Business Valuation with Machine learning by P. S. Koklev

“…The study also addresses the problem of the interpretability of the trained models. The most important features are identified —  the forms of financial statements and their specific items that have the greatest impact on market capitalization. …”
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A systematic review of effective data augmentation in cervical cancer detection by Betelhem Zewdu Wubineh, Andrzej Rusiecki, Krzysztof Halawa

“…Augmentation is vital for enhancing model performance in limited data scenarios.…”
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Estimation of Amino Acid and Tea Polyphenol Content of Tea Fresh Leaves Based on Fractional-Order Differential Spectroscopy by Shirui Li, Rui Sun, Xin Li, Yang Li, Liang Zhao, Xinyu Huang, Yufei Xu

“…In addition, CARS shows a better performance over the correlation coefficient (CC) method in model accuracy, contributing to more accurate selection of sensitive bands for the content prediction of tea ingredients. …”
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CFAR-DP-FW: A CFAR-Guided Dual-Polarization Fusion Framework for Large-Scene SAR Ship Detection by Tianjiao Zeng, Tianwen Zhang, Zikang Shao, Xiaowo Xu, Wensi Zhang, Jun Shi, Shunjun Wei, Xiaoling Zhang

“…Evaluated on the large-scale SAR ship detection dataset-v1.0, our framework demonstrates superior performance, surpassing 20 state-of-the-art models. …”
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A nomogram based on multiparametric magnetic resonance imaging radiomics for prediction of acute pancreatitis activity by Ting-Ting Liu, You-Qiang Hu, Ning-Jun Yu, Xue-Ying Zhang, Dong-Lin Jiang, Jiang Luo, Yong Chen, Di Tao, Xing-Hui Li, Xiao-Ming Zhang

“…The least absolute shrinkage and selection operator (LASSO) was used for feature screening, logistic regression was used to establish radiomic feature, and statistically significant laboratory parameters were incorporated to construct the nomogram. …”
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Decoding wisdom: Evaluating ChatGPT's accuracy and reproducibility in analyzing orthopantomographic images for third molar assessment by Ana Suárez, Stefania Arena, Alberto Herranz Calzada, Ana Isabel Castillo Varón, Victor Diaz-Flores García, Yolanda Freire

“…Further refinement and specialized training of AI models are needed to enhance their performance and ensure safe integration into dental practice, especially in patient-facing applications.…”
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Research and Application of a Multitarget Detection Algorithm Based on Improved YOLOv8 for Indoor Objects by Yanzhen Wang, Wei Wang, Xiaolong Zhou, Xubin Dong, Jianyong Li, Qi Zhao, Xinyu Yang, Yao Wang

“…A comparison with other popular models shows that YOLOv8 - CBW3 has strong generalizability, localization performance, detection ability and robustness. …”
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Context-Driven Automatic Target Detection With Cross-Modality Real-Synthetic Image Merging by Zhe Geng, Shiyu Zhang, Chongqi Xu, Haowen Zhou, Wei Li, Xiang Yu, Daiyin Zhu, Gong Zhang

“…After that, a novel Context-Aware Region Masking and Situation AWareness (CARMSAW) strategy is employed for target classification based on the inherent target properties and capabilities reflected by SAR and infrared (IR) imagery, and the cross-modality Real-synthetic Image Merging (CRIM) strategy is employed for feature enhancement. Specifically, to tackle with the random deviations of the real SAR imagery from the ideal ones, the synthetic SAR signature generated based on the target CAD model is treated as a “skeleton” with known structure for real-sync target feature alignment. …”
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Collaborative Static-Dynamic Teaching: A Semi-Supervised Framework for Stripe-like Space Target Detection by Zijian Zhu, Ali Zia, Xuesong Li, Bingbing Dan, Yuebo Ma, Hongfeng Long, Kaili Lu, Enhai Liu, Rujin Zhao

“…MRSA-Net incorporates multi-receptive field processing and multi-level feature fusion to effectively extract features of variable and low-SNR stripe-like targets. …”
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Numerical Solution to the Problem of Thermal Conductivity in a Porous Plate with a Topology of Triply Periodic Minimal Surfaces by K. V. Gubareva, A. V. Eremin

“…To achieve the stated objectives, mathematical modeling was performed, including the solution to the boundary value problem taking into account the identified correlations. …”
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Transfer of Periodic Phenomena in Multiphase Capillary Flows to a Quasi-Stationary Observation Using U-Net by Bastian Oldach, Philipp Wintermeyer, Norbert Kockmann

“…Once the model is trained sufficiently, it provides accurate segmentation for various flow conditions. …”
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Biomarkers of microvascularture by ultra Micro-angiography (UMA) assist to identify papillary thyroid carcinoma (PTC) with atypia of undetermined significance by Qingsong Wang, Zhewei Li, Jie Zhang, Sijie Zhang, Lijun Wang, Hongjian Yao, Hong Zhang, Jing Li, Shuo Wang, Jinglai Sun, Wenhui Zhang, Hui Yu

“…Then, 18 quantitative biomarkers were calculated and analyzed through Mann-Whitney test (U-test), while LASSO regression was utilized to remove collinear features. Finally, two different classification models were built using logistic regression through the selected biomarkers combined with Chinese TI-RADS (C TI-RADS) or American College of Radiology TI-RADS (ACR TI-RADS). …”
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Bangladeshi Vehicle Classification and Detection Using Deep Convolutional Neural Networks With Transfer Learning by Farid, Proshanta Kumer Das, Monirul Islam, Ebna Sina

“…To begin, we have implemented and tested the performance of the 11 pre-trained deep convolutional neural network (CNN) models: YOLOv8 Classify, MobileNetV2, GoogLeNet, AlexNet, ResNet-50, SqueezeNet, VGG19, DenseNet-121, Xception, InceptionV3, and NASNetMobile on the six vehicle classification and detection datasets: BIT-Vehicle, IDD, DhakaAI, Poribohon-BD, Sorokh-Poth, and VTID2. …”
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Enhancing AI microscopy for foodborne bacterial classification using adversarial domain adaptation to address optical and biological variability by Siddhartha Bhattacharya, Siddhartha Bhattacharya, Aarham Wasit, Aarham Wasit, J Mason Earles, J Mason Earles, Nitin Nitin, Nitin Nitin, Jiyoon Yi

“…MDANNs further improved accuracy in the BF domain from 73.3% to 76.7%. Feature visualizations by Grad-CAM and t-SNE validated the model's ability to learn domain-invariant features across conditions. …”
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Carotid plaque segmentation and classification using MRI-based plaque texture analysis and convolutional neural network by Zakarya Hasan Ahmed Abu Alregal, Gehad Abdullah Amran, Ali A. Al-Bakhrani, Saleh Abdul Amir Mohammad, Amerah Alabrah, Lubna Alkhalil, Abdalla Ibrahim, Maryam Ghaffar, Maryam Ghaffar

“…Mask R-CNN was fine-tuned with multi-task loss to address class imbalance, while a custom 13-layer CNN and Inception V3 were employed for classification, leveraging handcrafted texture features and deep hierarchical patterns. The custom CNN was evaluated via K10 cross-validation, and model performance was quantified using Dice Similarity Coefficient (DSC), Intersection over Union (IoU), accuracy, and ROC-AUC.ResultsThe Mask R-CNN achieved a mean DSC/IoU of 0.34, demonstrating robust segmentation despite anatomical complexity. …”
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