GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction by Ishaan Seshukumar Pothapragada, Sujatha R

Subjects: “…Maize yield prediction…”
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Explainable artificial intelligence (XAI) for interpreting predictive models and key variables in flood susceptibility by Bahram Choubin, Abolfazl Jaafari, Jalal Henareh, Omid Karimi, Farzaneh Sajedi Hosseini

“…Various machine learning models were tested, with XGBoost achieving the best performance for flood susceptibility prediction (RMSE = 0.333, AUC = 0.890). …”
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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data by Rui Li MS, Xiaoyan Hao MS, Yanjun Diao MD, Liu Yang MS, Jiayun Liu MD

“…This study aims to develop machine learning (ML) models for CRC risk prediction using clinical laboratory data. …”
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Deep learning for algorithmic trading: A systematic review of predictive models and optimization strategies by MD Shahriar Mahmud Bhuiyan, MD AL Rafi, Gourab Nicholas Rodrigues, MD Nazmul Hossain Mir, Adit Ishraq, M.F. Mridha, Jungpil Shin

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Evaluating ensemble models for fair and interpretable prediction in higher education using multimodal data by Felipe Emiliano Arévalo-Cordovilla, Marta Peña

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Assessment of textile sludge pyrolysis behaviour through advance predictive models for bioenergy production by Arslan Khan, Asif Hussain Khoja, Salman Raza Naqvi, Waheed Miran, Imtiaz Ali

“…To optimize the pyrolysis of textile sludge, it is essential to comprehend the complex kinetics involved in its degradation. Machine learning models like Artificial Neural Networks (ANN), Classification and Regression Trees (C&RT), Boosted regression trees (BRT), and K Nearest Neighbors (KNN) employed to predict the activation energy (Ea) with ANN emerging as superior predicting capabilities (R2 = 0.999) over other models. …”
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Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models by Marta Terrados-Cristos, Marina Diaz-Piloneta, Francisco Ortega-Fernández, Gemma Marta Martinez-Huerta, José Valeriano Alvarez-Cabal

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Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction by Jesutofunmi Ayo Fajemisin, Glebys Gonzalez, Stephen A. Rosenberg, Ghanim Ullah, Gage Redler, Kujtim Latifi, Eduardo G. Moros, Issam El Naqa

“…Various feature selection techniques and machine models interrogate the relevant radiomics features for predicting cancer treatment outcomes. …”
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A methodological systematic review of validation and performance of sepsis real-time prediction models by Zichen Wang, Wen Wang, Che Sun, Jili Li, Shuangyi Xie, Jiayue Xu, Kang Zou, Yinghui Jin, Siyu Yan, Xuelian Liao, Yan Kang, Craig M. Coopersmith, Xin Sun

“…Abstract Sepsis real-time prediction models (SRPMs) provide timely alerts and may improve patient outcomes but face limited clinical adoption due to inconsistent validation methods and potential biases. …”
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MACHINE LEARNING MODELS FOR PERFORMANCE PREDICTION OF YOUNG PLAYERS IN FOOTBALL ACADEMIES – A REVIEW by Armand Florin Bertea, Cezar Honceriu

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LC-LLM: Explainable lane-change intention and trajectory predictions with Large Language Models by Mingxing Peng, Xusen Guo, Xianda Chen, Kehua Chen, Meixin Zhu, Long Chen, Fei-Yue Wang

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Using in silico models to predict lymphocyte activation and development in a data rich era by Salim I Khakoo, Jayajit Das

“…This presents us with a unique opportunity to leverage our access to such data to develop computational models that can generate usable predictions and mechanistic insights capable of seeding new ideas. …”
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Prediction models for prostate cancer to be used in the primary care setting: a systematic review by Artitaya Lophatananon, Mohammad Aladwani, William Ollier, Kenneth Muir

“…The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.Conclusion Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. …”
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Research on optimal selection of runoff prediction models based on coupled machine learning methods by Xing Wei, Mengen Chen, Yulin Zhou, Jianhua Zou, Libo Ran, Ruibo Shi

“…Abstract Runoff fluctuations under the influence of climate change and human activities present a significant challenge and valuable application in constructing high-accuracy runoff prediction models. This study aims to address this challenge by taking the Wanzhou station in the Three Gorges Reservoir area as a case study to optimize various prediction models. …”
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Risk factors and predictive models for sequelae of heat stroke primarily involving cerebellar dysfunction by Xiao-Xiao Ni, Gai-Ci Xue, Ye-Qun Guo, Xiao-Juan Xie, Jie Wang, Zhi-Feng Liu

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Evaluating predictive models for hemorrhagic transformation post-mechanical thrombectomy in acute ischemic stroke by Jiaxuan Wang, Jiaxuan Wang, Jianyou You, Hui Yang, Hui Yang, Zhongbin Xia, Xiangbin Wu, Moxin Wu, Moxin Wu, Xiaoping Yin, Xiaoping Yin, Zhiying Chen, Zhiying Chen

“…Therefore, assessing the relevant risks based on predictive models for post-MT HT is necessary. These predictive models incorporate a series of risk factors and conduct statistical analyses to generate corresponding assessment scales, which are then used to evaluate the risk of postoperative bleeding. …”
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Currency and Financial Crisis: A Review Study of Prediction Models and Crisis Management Strategies by Haitham Assi Kareem, Idress Mohammed Husien

“…Our review highlights that to improve prediction accuracy, a variety of datasets and models must be used. …”
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Machine Learning and Deep Learning Models for Dengue Diagnosis Prediction: A Systematic Review by Daniel Cristobal Andrade Girón, William Joel Marín Rodriguez, Flor de María Lioo-Jordan, Jose Luis Ausejo Sánchez

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Specificity and Areas of Usage of Cardiovascular Prediction Models Among Athletes—State-of-the-art Review by Tomasz Chomiuk, Przemysław Kasiak, Artur Mamcarz, Daniel Śliż

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Deep learning surrogate models for spatiotemporal prediction of coastal flooding inundations in Tianjin, China by Wanchao Bian, Jiayi Fang, Pin Wang, Qinke Sun, Jian Fang, Feng Kong, Tangao Hu

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