The relationship between the annual catch of bigeye tuna and climate factors and its prediction by Peng Ding, Hui Xu, Xiaorong Zou, Xiaorong Zou, Xiaorong Zou, Shuyi Ding, Siqi Bai

“…The SSA-XGBoost model have the highest prediction accuracy, followed by XGBoost, BP, LSTM, and RF. …”
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<p><strong>Evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of <em>Tetranychus</em> <em>urticae</em> (Acari: Tetranychidae) in cucumber field of Behbahan, Iran</strong></p> by Alireza Shabaninejad, Bahram Tafaghodinia, Nooshin Zandi-Sohani

“…In Geostatistics methods ordinary kriging, and ANN with imperialist competitive algorithm were evaluated. Comparison of ANN and geostatistical showed that ANN capability is more than ordinary kriging method so that the ANN predicts distribution of this pest dispersion with 0.98 coefficient of determination and 0.0038 mean squares errors lower than the Geostatistical methods. …”
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Mechanism of baricitinib supports artificial intelligence‐predicted testing in COVID‐19 patients by Justin Stebbing, Venkatesh Krishnan, Stephanie de Bono, Silvia Ottaviani, Giacomo Casalini, Peter J Richardson, Vanessa Monteil, Volker M Lauschke, Ali Mirazimi, Sonia Youhanna, Yee‐Joo Tan, Fausto Baldanti, Antonella Sarasini, Jorge A Ross Terres, Brian J Nickoloff, Richard E Higgs, Guilherme Rocha, Nicole L Byers, Douglas E Schlichting, Ajay Nirula, Anabela Cardoso, Mario Corbellino, the Sacco Baricitinib Study Group

“…Abstract Baricitinib is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis (RA) that was independently predicted, using artificial intelligence (AI) algorithms, to be useful for COVID‐19 infection via proposed anti‐cytokine effects and as an inhibitor of host cell viral propagation. …”
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Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique by Hao Huang, Zhaoli Wang, Yaoxing Liao, Weizhi Gao, Chengguang Lai, Xushu Wu, Zhaoyang Zeng

“…In order to reveal the intrinsic mechanism of prediction by such architectures, we adopted a coupled CNN-LSTM model based on the explainability technique SHapley Additive exPlanations (SHAP) to predict the rainfall-runoff process and identify key input feature factors, and took the Beijiang River Basin in China as an example, so as to improve the explainability and credibility of this black-box model. …”
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Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…The worldwide health epidemic of anaemia which is a condition with low levels of red blood cells or haemoglobin requires accurate prediction models to act promptly and improve patient outcomes because it is widespread and has different causes. …”
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Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation by Simone Costantini, Anna Falivene, Mattia Chiappini, Giorgia Malerba, Carla Dei, Silvia Bellazzecca, Fabio A. Storm, Giuseppe Andreoni, Emilia Ambrosini, Emilia Biffi

“…This study aimed at methodologically exploring the performance of artificial intelligence (AI) algorithms applied to structured datasets made of heart rate variability (HRV) and electrodermal activity (EDA) features to predict the level of patient engagement during RAGR. …”
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COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment by Lili Jiang, Liu Yang, Yang Huang, Yi Wu, Huixian Li, XiYan Shen, Meng Bi, Lin Hong, Yiting Yang, Zuping Ding, Wenjie Chen

“…Finally, from the experimental results of the CAWOA-ELM algorithm, it has excellent prediction effect and practical application value.…”
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Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis by Ana Maria Cristina Jura, Daniela Eugenia Popescu, Cosmin Cîtu, Marius Biriș, Corina Pienar, Corina Paul, Oana Maria Petrescu, Andreea Teodora Constantin, Alexandru Dinulescu, Ioana Roșca

“…While maternal and neonatal conditions are known contributors, few studies use advanced machine learning (ML) as predictive factors. This study assessed how maternal pathologies, medications, and neonatal factors affect the risk of PDA using traditional statistics and ML algorithms: Random Forest (RF) and XGBoost (XGB). …”
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Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis by Zhijian Ren, Minqiao Zhang, Pingping Wang, Kanan Chen, Jing Wang, Lingping Wu, Yue Hong, Yihui Qu, Qun Luo, Kedan Cai

“…Utilizing machine learning to predict blood pressure fluctuations during dialysis has become a viable predictive method. …”
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Machine learning in CTEPH: predicting the efficacy of BPA based on clinical and echocardiographic features by Qiumeng Xi, Juanni Gong, Jianfeng Wang, Xiaojuan Guo, Yuanhua Yang, Xiuzhang lv, Suqiao Yang, Yidan Li

“…By comparing the predictive performance of different algorithms, we aimed to establish a robust tool to identify patients most likely to benefit from BPA. …”
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Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting by Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Leen Al‐Omran, Duminda N. Wijeysundera, Mohamad A. Hussain, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Graham Roche‐Nagle, Mohammed Al‐Omran

“…We developed machine learning algorithms that predict 1‐year stroke or death following TFCAS. …”
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An integrated machine learning and fractional calculus approach to predicting diabetes risk in women by David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez

“…This study presents a novel dual approach for diabetes risk prediction in women, combining machine learning classification with fractional-order physiological modeling. …”
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IVIM-DWI-based radiomic model for preoperative prediction of hepatocellular carcinoma differentiation by ZHUANG Yuxiang, LI Xiaofeng, ZHOU Daiquan

“…Objective To construct a radiomic model based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for preoperative prediction of hepatocellular carcinoma (HCC) differentiation and validate its clinical value. …”
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Meteorological and satellite-based data for drought prediction using data-driven model by ALI H. AHMED SULIMAN

“…The newly developed model was tested for DDI prediction using PERSIANN, and compared with the calculated DDI original from WSs. …”
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The Value of PET/CT-Based Radiomics in Predicting Adrenal Metastases in Patients with Cancer by Qiujun He, Xiangxing Kong, Xiangxi Meng, Xiuling Shen, Nan Li

“…The AUC, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of XGBoost’s internal and external validation were 0.945, 0.932, 0.930, 0.960, 0.970, 0.890 and 0.910, 0.900, 0.860, 1, 1, 0.750. …”
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Prediction on rock strength by mineral composition from machine learning of ECS logs by Dongwen Li, Xinlong Li, Li Liu, Wenhao He, Yongxin Li, Shuowen Li, Huaizhong Shi, Gaojian Fan

“…This study proposes the use of Random Forest and Transformer algorithms to predict rock strength from Elemental Capture Spectroscopy (ECS) logs. …”
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Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization by Usharani Bhimavarapu, Gopi Battineni, Nalini Chintalapudi

“…The improved whale optimization (IWOA) algorithm was used for feature selection, which optimized weight functions to improve prediction accuracy. …”
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Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics by Hui Liu, Linlin Ying, Xing Song, Xueping Xiang, Shumei Wei

“…In this study, our objective is to develop a deep learning model utilizing pathological images to predict the metastasis and survival outcomes for breast cancer patients. …”
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Development and validation of a carotid plaque risk prediction model for coal miners by Yi-Chun Li, Yi-Chun Li, Yi-Chun Li, Tie-Ru Zhang, Tie-Ru Zhang, Tie-Ru Zhang, Fan Zhang, Fan Zhang, Fan Zhang, Chao-Qun Cui, Chao-Qun Cui, Chao-Qun Cui, Yu-Tong Yang, Yu-Tong Yang, Yu-Tong Yang, Jian-Guang Hao, Jian-Ru Wang, Jiao Wu, Hai-Wang Gao, Ying-Bo Liu, Ming-Zhong Luo, Li-Jian Lei, Li-Jian Lei, Li-Jian Lei

“…The area under the curve (AUC), sensitivity, and specificity of the model constructed based on the XGBoost algorithm were 0.846, 0.867, and 0.702, respectively.ConclusionsIt is possible to predict the presence of carotid plaque using machine learning. …”
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Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model by Beevi Fathima, N Santhi Dr, N Ramasamy Dr

“…Stroke is a critical condition marked by the death of brain cells due to inadequate blood flow, necessitating improved predictive models for stroke lesions. The accuracy and flexibility required to forecast and classify stroke lesions is lacking in current approaches, which compromise patient outcomes. …”
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