Limitations of Separating Athletes into High or Low-Risk Groups based on a Cut-Off. A Clinical Commentary by Justin M. Losciale, Linda K. Truong, Patrick Ward, Gary S. Collins, Garrett S. Bullock

“…A more accurate risk prediction will facilitate personalized approaches to injury risk mitigation and may lead to a decline in injury rates…”
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3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology by Isaac Y. Tian, Jason Liu, Michael C. Wong, Nisa N. Kelly, Yong E. Liu, Andrea K. Garber, Steven B. Heymsfield, Brian Curless, John A. Shepherd

“…Abstract Body composition prediction from 3D optical imagery has previously been studied with linear algorithms. …”
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Sistem Object Tracking pada Quadcopter Menggunakan Segmentasi Citra dengan Deteksi Warna HSV dan Metode Regresi Linier Berbasis Raspberry Pi by Fahmi Erza, Hurriyatul Fitriyah, Eko Setiawan

“…The accuracy of pitch motion prediction is 84%, yaw motion prediction is 94%, and up/down motion prediction is 91.5%. …”
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Forecasting Stock Prices with Artificial Intelligence by Zhao Danxuan

“…The objective is to enhance the accuracy of future stock price Prediction to support investment or trading decisions. …”
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ASA score is an independent predictor of 1-year outcome after moderate-to-severe traumatic brain injury by Olivia Kiwanuka, Philipp Lassarén, Alexander Fletcher-Sandersjöö, Charles Tatter, Jonathan Tjerkaski, David W. Nelson, Eric P. Thelin

“…Incorporating this simple comorbidity measure could enhance outcome prediction and support more personalized acute management. …”
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Extrinsic mortality and senescence: a guide for the perplexed by de Vries, Charlotte, Galipaud, Matthias, Kokko, Hanna

“…Here we provide heuristics as well as simple models that outline when the Williams prediction holds, why there is a ‘null model’ where extrinsic mortality does not change the evolution of senescence at all, and why it is also possible to expect the opposite of William’s prediction, where increased extrinsic mortality favours slower senescence. …”
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Research for SARIMA and PatchTSMixer Models on the IEA Monthly Statistics Dataset by Hu Yuwei

“…However, a significant gap exists in comparing the predictive performance of traditional machine learning methods with advanced deep learning models using real-world datasets. …”
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Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models by Lei Tianxing

“…Diabetes poses a significant threat to global health, making accurate prediction and effective treatment of the disease critical. …”
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Robustness of Big Language Modeling in Finance by Yao Mohan

“…In the financial field, people use financial big prediction models to solve problems such as stock prediction, risk assessment, etc., but the big language models can be incorrect due to model hallucination and adversarial attacks. …”
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Enhancing Medical Diagnostics with Machine Learning: A Study on Ensemble Methods and Transfer Learning by Zhang Jiaming

“…Specifically, these techniques are applied to large-scale datasets, to address tasks like disease detection, patient outcome prediction, and managing uncertainty in medical data. …”
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An optimized data analytics pipeline for improving healthcare diagnosis using ensemble learning by Lomat Haider Chowdhury, Shaira Tabassum, Swakkhar Shatabda, Ashir Ahmed

“…The medical doctors may make an early prediction by observing the physical signs and symptoms. …”
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Addressing Label Noise in Colorectal Cancer Classification Using Cross-Entropy Loss and pLOF Methods With Stacking-Ensemble Technique by Ishrat Zahan Tani, Kah Ong Michael Goh, Md Nazmul Islam, Md Tarek Aziz, S. M. Hasan Mahmud, Dip Nandi

“…This system not only predicts the disease but also generates the prediction probabilities of each class, which enhances both clarity and diagnostic insight. …”
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Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems by Abdulrahman Alruban, Hanan Abdullah Mengash, Majdy M. Eltahir, Nabil Sharaf Almalki, Ahmed Mahmud, Mohammed Assiri

“…The purpose of the AHOA-HDLTM technique is to predict traffic flow levels in smart cities, enabling effective traffic management. …”
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Multi-model Evaluation and Bayesian Model Averaging in Quantitative Air Quality Forecasting in Central China by Haixia Qi, Shuangliang Ma, Jing Chen, Junping Sun, Lingling Wang, Nan Wang, Weisi Wang, Xiefei Zhi, Hao Yang

“…We then established multi-model ensemble Bayesian model averaging (BMA). The prediction effect for PM2.5 and O3 was ranked as CUACE > CMAQ > NAQP and the prediction effect for SO2, NO2 and CO was CMAQ > NAQP > CUACE. …”
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Immune gene features and prognosis in colorectal cancer: insights from ssGSEA typing by Anwen Huang, Jinxiu Wu, Jiakuan Wang, Chengwen Jiao, Yunfei Yang, Huaiwen Xiao, Li Yao

“…Subsequently, a prognostic risk model of CRC was constructed based on 12 prognostic genes, and the accuracy and reliability of the model prediction were verified. Finally, Nomogram enabled accurate individual prediction of the survival prognosis of CRC patients. …”
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Autoencoder untuk Sistem Prediksi Berat Lahir Bayi by Fitra Septia Nugraha, Hilman Ferdinandus Pardede

“…This study aims to predict baby birth weight using the Deep Learning autoencoder method to predict baby birth weight. …”
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Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment by An Zichen, Li Fan

“…It summarizes the use of DL models in breast cancer screening and in preoperative prediction of molecular subtypes, response to neoadjuvant chemotherapy (NAC), and axillary lymph node (ALN) metastasis status. …”
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Spring phenological models combining the effects of temperature and photoperiod are successfully transferred to various spatial and temporal scales: a case study of Aesculus hippoc... by Korsakova Svetlana, Korsakov Pavel, Evstigneev Vladislav

“…Amongst models, those combining the effects of temperature and photoperiod showed the best phenophase prediction, suggesting the influence of photoperiod on the leaf unfolding and flowering of A. hippocastanum. …”
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An enhanced rainfall-induced landslide catalogue in Italy by Maria Teresa Brunetti, Stefano Luigi Gariano, Massimo Melillo, Mauro Rossi, Silvia Peruccacci

“…Abstract With the increasing use of data-driven landslide prediction models also based on artificial intelligence, the availability of accurate information on the occurrence of landslides and the rigorous reconstruction of their triggering rainfall conditions are crucial. …”
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Deep Learning of Temperature – Dependent Stress – Strain Hardening Curves by Nikolić, Filip, Čanađija, Marko

“…The ML model has shown excellent prediction accuracy in the temperature range from 20 °C to 250 °C. …”
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