Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling by Jing Zhang, Tingyi Tan, Yuhao Jiang, Congming Tan, Liangliang Hu, Daowen Xiong, Yikang Ding, Guowei Huang, Junjie Qin, Yin Tian

“…Due to its unique structural design, the proposed model proficiently extracts features in temporal, frequency, and spatial domains while its feature extraction capability is validated through post-hoc interpretability techniques. …”
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FSBNet: A Classifying Framework of Disaster Scene for Volcanic Lithology Through Deep-Learning Models by Lan Liu, Zhouyi Xiao, Jianpeng Hu, Jingxin Han, Jung Yoon Kim, Rohit Sharma, Chengfan Li

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Prediction of KRAS gene mutations in colorectal cancer using a CT-based radiomic model by Wenjing Wang, Qingbiao Zhang, Shimei Fan, Yuyin Wang, Xingyan Le, Min Ai, Chunqi Du, Junbang Feng, Chuanming Li

“…The Delong test was employed to assess the differences between the various models.ResultsAfter feature selection, the top 8 features with the highest mutual information scores were extracted to construct a prediction model. …”
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Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction by Chenglong Guo, Binyu Gao, Xuexue Han, Tianxing Zhang, Tianqi Tao, Jinggang Xia, Honglei Liu

“…A web platform ( https://prediction-killip-gby.streamlit.app/ ) was also developed to facilitate clinical application. Results Among the models, TabNet demonstrated the best performance, achieving an AUROC of 0.827 for KILLIP four-class classification and 0.831 for KILLIP binary classification. …”
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Individualized prediction of stent patency in malignant colonic obstruction: development and validation of a prognostic model by Yuan Wan, Meng-sha Zou, Zhao-fei Zeng, Xiao-zheng Cao, Huan-hua Wu, Bo Zhang

“…One retrospective set (N = 121) was used to develop and validate the predictive model. The clinical features were collected and subjected to Cox regression analyses. …”
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MNeuralTab: Integrating meta-modeling and neural networks for customer churn prediction in e-commerce by Arif Mohammad Asfe, Md. Rashadur Rahman, Md. Sabir Hossain

“…By effectively combining the strengths of individual base models, our proposed approach surpasses the limitations of traditional models and accurately captures both feature-specific and hierarchical relationships within the data. …”
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Deep learning for gender estimation using hand radiographs: a comparative evaluation of CNN models by Hilal Er Ulubaba, İpek Atik, Rukiye Çiftçi, Özgür Eken, Monira I. Aldhahi

“…While other models demonstrated acceptable performance, ResNet-50 consistently outperformed them across all metrics. …”
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A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning by Kailai Li, Junyi Liang, Nan Li, Jianbo Fang, Xinyi Zhou, Jian Zhang, Anqi Lin, Peng Luo, Hui Meng

“…By evaluating 113 machine learning algorithm combinations, the glmBoost+NaiveBayes model was selected to construct the NPC-RSS based on 18 key genes, which demonstrated good predictive performance in both public and in-house datasets. …”
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ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter content by Liying Cao, Dongjie Yin, Miao Sun, Yuzhu Yang, Musharaf Hassan, Yunpeng Duan

“…Through data smoothing and discretization in terms of fractions, the model is equipped to effectively repress noise as it enhances primary spectral features related to SOM, thus enhancing the robustness as well as explainability of the model. …”
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Federated Learning for Multimodal Sentiment Analysis: Advancing Global Models With an Enhanced LinkNet Architecture by P. Vasanthi, V. Madhu Viswanatham

“…The approach is structured in three stages: the initialization stage, where global parameters are established and shared among clients; the local training stage, where several local models independently handle preprocessing, feature extraction, and fusion of text (with Improved Aspect Term Extraction (IATE) and Term Frequency-Inverse Document Frequency (TF-IDF)), signals (using Modified Mel Frequency Cepstral Coefficient (MMFCC) and spectral features), and images (through Improved Active Appearance Model (IAAM) and Median Binary Pattern (MBP)) before training the EnLNet model; and the model aggregation stage, where updates from local models are collected and aggregated by the central server to refine the global model. …”
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Mathematical modeling and optimization of the active suspension system of a 6x6 electric vehicle by Berk Aydoğan, Ahmet Yildiz

“…In this study, mathematical modeling was performed for a vehicle with a 6 × 6 in-wheel electric motor. …”
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Enhancing Single-Cell and Bulk Hi-C Data Using a Generative Transformer Model by Ruoying Gao, Thomas N. Ferraro, Liang Chen, Shaoqiang Zhang, Yong Chen

“…To address these challenges, we developed a transformer-based deep learning model, HiCENT, to impute and enhance both scHi-C and Hi-C contact matrices. …”
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Smart indoor monitoring for disabled individuals using an ensemble of deep learning models in an IoT environment by Faisal S. Alsubaei, Abdulrahman A. Alshdadi, Mohammed Rizwanullah

“…Besides, the Improved Osprey Optimization Algorithm (IOOA)-based feature selection is employed to classify the most relevant features, enhancing the efficiency of the system by reducing dimensionality. …”
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Model Predictive Current Control with Asymmetric Stacked Multilevel Inverter and LCL-Filter Based STATCOM by Mostafa Q. Kasim, Raaed Faleh Hassan

“…Moreover, the work includes using a Finite Control Set Model Predictive Current Control (FCS-MPCC) to control the proposed structure. …”
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Identification and visualization of fusion gene subtypes in APL using spatial attention mechanisms in vision models by Peirou Yan, Guo Pu, Ping Wu, Lijun Wen, Suning Chen, Song Xue

“…Data collected from two hospitals and Kaggle, including bone marrow smear images of PML-RARA, TTMV-RARA, NPM1-RARA, STAT5B-RARA, and NUP98-RARG subtypes, were preprocessed to form a five-class dataset.ResultsThe model achieves an overall accuracy of 98.04% in five - class classification, with good performance in each category. …”
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Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions by Gabby B. Joseph, Charles E. McCulloch, Michael C. Nevitt, Nancy E. Lane, Sharmila Majumdar, Thomas M. Link

“…For MRI-based features, DL-based tools have been developed for automatic quantification of cartilage, bone marrow lesions, and subcutaneous fat; they have improved scalability and supported development of ML prediction models with cartilage loss outcomes. …”
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Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation by Khondker Fariha Hossain, Sharif Amit Kamran, Joshua Ong, Alireza Tavakkoli

“…The results demonstrate that our KD strategy reduces the model complexity and surpasses existing state-of-the-art methods to achieve superior performance. …”
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Physics field super-resolution reconstruction via enhanced diffusion model and fourier neural operator by Yanan Guo, Junqiang Song, Xiaoqun Cao, Chuanfeng Zhao, Hongze Leng

“…Furthermore, a residual-guided diffusion model is introduced to further improve reconstruction performance. …”
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Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

“…Input features are identified using the Partial Autocorrelation Function , and models are optimized using the Optuna algorithm. …”
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Developing an Interpretable Machine Learning Model for Early Prediction of Cardiovascular Involvement in Systemic Lupus Erythematosus by Deng Z, Liu H, Chen F, Liu Q, Wang X, Wang C, Lyu C, Li J, Li T

“…Among seven evaluated algorithms, the Gradient Boosting Machine (GBM) demonstrated the best performance on the test set. Model interpretability was assessed using the DALEX package, which generated feature importance plots and instance-level breakdown profiles to visualize decision-making logic.Results: Over a median follow-up of 3737 days, 192 (18.77%) patients developed cardiac involvement. …”
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