Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases by William DeGroat, Habiba Abdelhalim, Elizabeth Peker, Neev Sheth, Rishabh Narayanan, Saman Zeeshan, Bruce T. Liang, Zeeshan Ahmed

“…The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. …”
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Prediction of vibration in milling of thin-walled aluminum alloy parts using neural network model by Junming Hou, Baosheng Wang, Dongsheng Lv, Changhong Xu

“…In this study, a method for establishing a particle swarm optimization-back propagation (PSO-BP) neural network model is proposed to predict the modal parameters of thin-walled parts and the surface vibration of machined parts. …”
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The Nexus of UG-ESs in the Chinese Loess Plateau using CL-CA and Ecological Assessment Models by Liang Youjia, Su Zichong, Liu Lijun

“…Accurate evaluations of UG-ESs have become a challenge for the development of simulation models. To address long-term spatiotemporal dependencies in grid neighborhood interactions, this study enhances land-use simulation accuracy using a method combining machine learning algorithms and cellular automata (CL-CA) to model competitive relationship between urban growth and other land-use types during 2000-2050, and then, ESs supply was simulated with ecological assessment models under three landuse scenarios: business as usual, ecological priority, and economic priority. …”
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Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-Dimensional Tokens by Vittorio Erba, Emanuele Troiani, Luca Biggio, Antoine Maillard, Lenka Zdeborová

“…Statistical physics provides powerful tools to study the functioning of learning with neural networks and has played a recognized role in the development of modern machine learning. The statistical physics approach relies on simplified and analytically tractable models of data. …”
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A short investigation of the effect of the selection of human brain atlases on the performance of ASD's classification models by Naseer Ahmed Khan, Xuequn Shang

“…Our findings emphasize the need for standardized approaches to atlas selection and highlight future research directions, including the integration of novel atlases, advanced data augmentation techniques, and end-to-end deep-learning models. This study provides valuable insights into optimizing fMRI-based ASD diagnosis and underscores the importance of interpreting atlas-specific features for an improved understanding of brain connectivity in ASD.…”
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Hyperspectral Imaging Coupled with Random Frog and Calibration Models for Assessment of Total Soluble Solids in Mulberries by Yan-Ru Zhao, Ke-Qiang Yu, Yong He

“…TSS values in mulberry fruits were predicted by partial least squares regression (PLSR) and least-square support vector machine (LS-SVM) models based on full wavelengths and the selected important wavelengths. …”
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A New Mathematical Model for Economic Ordering with Preventive Maintenance and Reworking in a Supply Chain by Guolei Ding, Karthikeyan Kaliyaperumal, Xiaoguang Wang

“…In the proposed model, the defective items are considered, and the goal is to achieve an optimal amount of production in such a way that the costs of the entire system, including production cost, setup, maintenance, inspection, and rework costs, are minimized during a period per unit of time. …”
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Intelligent Fruit Localization and Grasping Method Based on YOLO VX Model and 3D Vision by Zhimin Mei, Yifan Li, Rongbo Zhu, Shucai Wang

“…The method comprises three key components: First, a fruit sample database containing varying maturity levels and morphological features is established, interfaced with an optimized YOLO VX model for target fruit identification. …”
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Data aggregation impacts on built environment-mode share models around public transit stations by Seyed Sajjad Abdollahpour, Huyen T. K. Le, Ralph Buehler, Steve Hankey

“… This study examines how data aggregation influences the relationship between the built environment (BE) and mode share around 2,794 rail and BRT stations in the United States, using both inferential and machine learning methods. The results indicate that data aggregation impacts the outcomes of BE-mode share models, regardless of the data analysis approach. …”
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Integrated Systemic Modeling of Production Scheduling, Maintenance, and Quality Control in Closed-Loop Supply Chains by Javad Rahim, Ali Morovati Sharifabadi, Davood Andalib Ardakani

“…The model categorizes machine failures into immediate and delayed modes, providing tailored strategies for each to maintain system performance. …”
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Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L. by Mahboobe Hojati, Ruhollah Naderi, Mohsen Edalat, Hamid Reza Pourghasemi

“…To identify ecological factors affecting the distribution and amount of silymarin in S. marianum three machine learning algorithms including boosted regression trees (BRT), random forest (RF), and support vector machines (SVM) have been applied in Fars Province, Iran. …”
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Predictive modeling of pediatric drug-induced liver injury: Dynamic classifier selection with clustering analysis by Zixin Shi, Linjun Huang, Haolin Wang

“…Results The Clustering-enhanced DCS-MCB framework demonstrated superior performance compared to conventional machine learning models across evaluation metrics. …”
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Random forest models highlight early Homo sapiens habitats and their relationship to lithic assemblage composition by Lucy Timbrell, James Blinkhorn, Matt Grove

“…We apply random forests, a powerful and highly flexible machine-learning tool for niche modelling, in combination with palaeoclimatic simulations at high temporal resolution. …”
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Site-specific prediction of O-GlcNAc modification in proteins using evolutionary scale model. by Ayesha Khalid, Afshan Kaleem, Wajahat Qazi, Roheena Abdullah, Mehwish Iqtedar, Shagufta Naz

“…However, compared to the traditional models which show an overfitting on the same data up to 99%, ESM-2 model outperforms in terms of optimal training and testing predictions. …”
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IMPLEMENTATION OF MEWMA CHART USING TIME SERIES MODEL FOR MONITORING THE WHITE CRISTAL SUGAR QUALITY by Donny Setya Pratama, Fachrur Rozi

“…Therefore, a time series model approach is needed to create an accurate control chart. …”
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Optimizing wind energy conversion system efficiency using advanced modified super-twisting direct power control: Real-time implementation on dSPACE 1104 board by Mourad Yessef, Habib Benbouhenni, Ahmed Lagrioui, Youness El Mourabit, Nicu Bizon, Ilhami Colak, Badre Bossoufi, Ayman Alhejji

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Advanced predictive modeling for enhanced mortality prediction in ICU stroke patients using clinical data. by Armin Abdollahi, Negin Ashrafi, Xinghong Ma, Jiahao Zhang, Daijia Wu, Tongshou Wu, Zizheng Ye, Maryam Pishgar

“…We developed a deep learning model to assess mortality risk and implemented several baseline machine learning models for comparison. …”
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Visual Detection on Aircraft Wing Icing Process Using a Lightweight Deep Learning Model by Yang Yan, Chao Tang, Jirong Huang, Zhixiong Cen, Zonghong Xie

“…Ghost Convolution and Atrous Spatial Pyramid Pooling modules are incorporated to reduce model parameters and computational complexity. The model is optimized using the transfer learning method, where pre-trained weights are utilized to accelerate convergence and enhance performance. …”
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Development of an AI-Based Image Analysis Model for Verifying Partial Defects in Nuclear Fuel Assemblies by Seulah Kim, Dayun Park, Hyung-Joo Choi, Chulhee Min, Jaejoon Ahn

“…By using emission tomography image data acquired from 3 × 3 nuclear fuel assemblies, we compare the performance of neural network models (AlexNet, ResNet, and the squeeze-and-excitation network (SENet)) and tree-based ensemble models (extreme gradient boosting (XGBoost), random forest model, and light gradient boosting machine (LightGBM)). …”
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Drought Stress Grading Model for Apple Rootstock Softwood Cuttings Based on the CU-ICA-Net by Xu Wang, Pengfei Wang, Jianping Li, Hongjie Liu, Xin Yang

“…In order to maintain adequate hydration of apple rootstock softwood cuttings during the initial stage of cutting, a drought stress grading model based on machine vision was designed. This model was optimized based on the U-Net (U-shaped Neural Network), and the petiole morphology of the cuttings was used as the basis for classifying the drought stress levels. …”
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