Predictive Models of Patient Severity in Intensive Care Units Based on Serum Cytokine Profiles: Advancing Rapid Analysis by Cristiana P. Von Rekowski, Tiago A. H. Fonseca, Rúben Araújo, Ana Martins, Iola Pinto, M. Conceição Oliveira, Gonçalo C. Justino, Luís Bento, Cecília R. C. Calado

“…Naïve Bayes models were developed using the full dataset or with feature subsets selected either through an information gain algorithm or univariate data analysis. Strong predictive models were achieved for IMV (AUC = 0.891) and mortality within homogeneous (AUC = 0.774) or more heterogeneous (AUC = 0.887) populations utilizing two to nine features. …”
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Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate by Muhammad Ahtasham Mushtaq, Muhammad Ateeq, Muhammad Ikram, Shariq Mahmood Alam, Muhammad Mohsin Kaleem, Muhammad Atiq Ashraf, Muhammad Asim, Khalid F. Almutairi, Mahmoud F. Seleiman, Fareeha Shireen

“…AI integrated approaches such as stress prediction, irrigation optimization, and image-based phenotyping have enhanced agriculture, while machine learning models like Random Forest and Gradient Boosting improve stress management. …”
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XGBoost based enhanced predictive model for handling missing input parameters: A case study on gas turbine by Nagoor Basha Shaik, Kittiphong Jongkittinarukorn, Kishore Bingi

“…This study examines building a predictive model using the XGBoost algorithm, an ensemble learning approach known for handling huge datasets and producing robust predictions. …”
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A Computationally Efficient Model Predictive Control Energy Management Strategy for Hybrid Vehicles Considering Driving Style by Yalian Yang, Yuqi Chen, Changdong Liu

“…The driving-style adaptive Pontryagin’s minimum principle for model predictive control (DSA-PMP-MPC) algorithm was designed as a real-time energy management strategy for Hybrid Electric Vehicles (HEVs). …”
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Adaptive Virtual Inertia and Voltage Estimation: Enabled Model Predictive Control for Improved Performance in Islanded DC Microgrids by Salisu Abdullahi, Khaled Eltag, Lei Weining, Chen Xiaohu

“…To mitigate this issue, we propose an Adaptive Virtual Inertia and Voltage Estimation with Model Predictive Control (AVIE-MPC) approach, which enhances DC MGs performance while ensuring input-to-state stability (ISS). …”
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Enhancing predictive analytics in mandibular third molar extraction using artificial intelligence: A CBCT-Based study by Faezeh Khorshidi, Rasool Esmaeilyfard, Maryam Paknahad

“…This study aims to develop an AI-driven predictive model using CBCT reports, focusing specifically on predicting the difficulty of mandibular third molar extraction. …”
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Incorporating soil moisture data into a machine learning framework improved the predictive accuracy of corn yields in the U.S. by Bishwoyog Bhattarai, Zachary Leasor, André Fróes de Borja Reis

“…We hypothesize that incorporating soil moisture and temperature data from land surface models into a ML framework will enhance accuracy of corn yield predictions. To test this, we employed XGBoost algorithm using long-term (2010–2022) yield data collected from multi-environment corn trials conducted by the Variety Testing Program at the University of Missouri, along with weather data from Daymet database, soil data from North American Land Data Assimilation System (NLDAS-2), and POLARIS soil database. …”
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Identification and predictive machine learning models construction of gut microbiota associated with lymph node metastasis in colorectal cancer by Yongzhi Wu, Chengen Deng, Zigui Huang, Yongqi Huang, Chuanbin Chen, Mingjian Qin, Zhen Wang, Fuhai He, Shenghai Liu, Rumao Zhong, Jun Liu, Chenyan Long, Jungang Liu, Weizhong Tang, Xiaoliang Huang

“…The RF and MLP models constructed based on the LNM-associated gut microbiota showed good predictive efficacy in predicting LNM status in CRC. …”
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Prediction of China’s Sulfur Dioxide Emissions by Discrete Grey Model with Fractional Order Generation Operators by Wei Meng, Daoli Yang, Hui Huang

“…The model is then used to predict China’s sulfur dioxide emissions. The forecast result shows that the amount of sulfur dioxide emissions is steadily decreasing and the policies of sulfur dioxide reduction in China are effective. …”
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Predicting Travel Insurance Purchases in an Insurance Firm through Machine Learning Methods after COVID-19 by Shiuh Tong Lim, Joe Yee Yuan, Khai Wah Khaw, XinYing Chew

“…The findings of this study are a valuable guide for deploying machine learning algorithms in predicting travel insurance purchases, thus empowering insurance companies to target the most lucrative clientele and bolster revenue generation.…”
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Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation by Yimin Zhou, Xin Li, Zixiu Wang, Liqi Ng, Rong He, Chaozong Liu, Gang Liu, Xiao Fan, Xiaohong Mu, Yu Zhou, Yu Zhou

“…This study aimed to explore the relationship between SCI and cuproptosis and construct prediction models.MethodsGene expression data of SCI patient samples from the GSE151371 dataset were analyzed. …”
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An interpretable machine learning approach for predicting drug-resistant epilepsy in children with tuberous sclerosis complex by Jie Fu, Jie Fu, Genfu Zhang, Genfu Zhang, Zhixian Yang, Zhixian Yang, Jiong Qin, Jiong Qin

“…Clinical data were retrospectively collected from 88 pediatric patients with TSC-related epilepsy. 9 ML algorithms were applied, such as random forest (RF), to construct predictive models. …”
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Enhancing 4G/LTE Network Path Loss Prediction with PSO-GWO Hybrid Approach by Messaoud Garah, Nabil Boukhennoufa

“…Furthermore, a hybrid optimization model, PSO-GWO, is proposed to improve prediction accuracy. The performance of each optimization method is evaluated using various error metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Standard Deviation (STD), Mean Error (ME), and Mean Absolute Percentage Error (MAPE). …”
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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. …”
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A Comparative Analysis of the Effectiveness of Multiple Models for Predicting Heart Failure using Data Mining by Ahmed Sami Jaddoa, Juliet Kadum, Amaal Kadum

“…For forecasting, decision-making, and disease prediction, DM technologies are essential. This research predicts heart disease using DM algorithms. …”
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Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil) by Enner Alcântara, Cheila Flávia Baião, Yasmim Carvalho Guimarães, José Roberto Mantovani, José Antonio Marengo

“…This study employs machine learning techniques to map and predict landslide-prone areas in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and intense rainfall. …”
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Quantum-Enhanced Predictive Degradation Pathway Optimization for PV Storage Systems: A Hybrid Quantum–Classical Approach for Maximizing Longevity and Efficiency by Dawei Wang, Shuang Zeng, Liyong Wang, Baoqun Zhang, Cheng Gong, Zhengguo Piao, Fuming Zheng

“…The framework integrates a quantum-inspired predictive maintenance algorithm, which utilizes probabilistic modeling to forecast degradation states and dynamically adjust charge–discharge cycles in storage systems. …”
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Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing by Eslam Hussein Sayed, Amerah Alabrah, Kamel Hussein Rahouma, Muhammad Zohaib, Rasha M. Badry

“…These techniques can use various classification algorithms to predict which applicants will likely be eligible for loans. …”
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Early Breast Cancer Prediction Using Thermal Images and Hybrid Feature Extraction-Based System by Doaa Youssef, Hanan Atef, Shaimaa Gamal, Jala El-Azab, Tawfik Ismail

“…The principal component analysis (PCA) is utilized for dimensionality reduction. The back end of the methodology uses support vector machine (SVM) and extreme gradient boosting (XGB) classification algorithms to establish the relationship between the retrieved feature vector and breast functionality. …”
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A Hybrid Artificial Intelligence Approach for Down Syndrome Risk Prediction in First Trimester Screening by Emre Yalçın, Serpil Aslan, Mesut Toğaçar, Süleyman Cansun Demir

“…<b>Background/Objectives:</b> The aim of this study is to develop a hybrid artificial intelligence (AI) approach to improve the accuracy, efficiency, and reliability of Down Syndrome (DS) risk prediction during first trimester prenatal screening. …”
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