Preoperative Prediction of Macrotrabecular-Massive Hepatocellular Carcinoma Using Machine Learning-Based Ultrasomics by Li Y, Duan S, Ren S, Li D, Ma Y, Bu D, Liu Y, Li X, Cai X, Zhang L

“…The model constructed based on the RF algorithm in the test set has a high accuracy rate and specificity, and the overall performance of the models is better than that of the other four algorithm models, the AUC, accuracy, specificity, and sensitivity of its combined model and ultrasomics model are significantly higher than the clinical model.Conclusion: ML-based ultrasomics model is an effective tool for predicting MTM-HCC before surgery. …”
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Wind speed and power forecasting using Bayesian optimized machine learning models in Gabal Al-Zayt, Egypt by Nehal Elshaboury, Haytham Elmousalami

“…For WPP, the LGBM and BDT algorithms demonstrate strong predictive performance across different time scales, with MAPE, MSE, EV, R, and CCC values ranging from 0.277 to 186.710%, 0.927 to 9444.576, 0.970 to 1.000, 0.985 to 1.000, and 0.985 to 1.000, respectively.…”
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Analysis of influencing factors and trend prediction based on quantile regression and GM(1, 1) model by Lianhui Li, Adham Manyara, Jie Liu

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Research on Real-Time Control Strategy of Air-Conditioning Water System Based on Model Predictive Control by Dehan Liu, Jing Zhao, Yibing Wu, Zhe Tian

“…This study proposes an online real-time control strategy for the air-conditioning water system based on the model predictive control (MPC) principle, implemented and validated on the integrated energy experimental platform. …”
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Different Approaches to Artificial Intelligence–Based Predictive Maintenance on an Axle Test Bench with Highly Varying Tests by Markus Siebert, Michael Fister, Christian Spieker, Daniel Stengler

“…The implementation of a machine learning and a deep learning algorithm for predictive maintenance through early damage detection on an electric rear axle test bench is presented in this paper. …”
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Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models by Jingmei Yang, Xinglong Ju, Feng Liu, Onur Asan, Timothy Church, Jeff Smith

“…However, few studies have been conducted on the predictive analysis of multiple chronic conditions (MCC) based on the working population. …”
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Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting by Sheng Li, Min Wang, Minghang Shi, Jiafeng Wang, Ran Cao

“…However, the inherent fuzziness and dynamism of ground-based clouds have hindered the development of effective prediction algorithms, resulting in low accuracy. This paper presents CloudPredRNN++, a novel method for predicting ground-based cloud dynamics, leveraging a deep spatiotemporal sequence prediction network enhanced with a self-attention mechanism. …”
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Potential analysis and energy prediction of photovoltaic power plants using satellite-based remote sensing and artificial intelligence techniques by Hadis Ghaedrahmati, Saeed Talebi, Amirmohammad Moradi, Aref Eskandari, Parviz Parvin, Mohammadreza Aghaei, Mohammadreza Aghaei

“…The evaluation, using various statistical metrics, shows that the XGBoost algorithm outperforms others, achieving up to 91% accuracy in predicting energy production from PV systems.…”
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Deep learning framework based on ITOC optimization for coal spontaneous combustion temperature prediction: a coupled CNN-BiGRU-CBAM model by Xuming Shao, Wenhao Liu, Gang Bai, Yan Chen, Yu Liu, Jiahe Guang

“…Accurate temperature prediction, crucial for assessing coal oxidation stages and combustion risk, underpins early warning systems. …”
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Predictive exploratory data analysis of shopfloor CNC machine operation through a machine learning model by Tarique Ameer, Omid Fatahi Valilai

“…This paper has focused on advanced analytic techniques and machine learning algorithms to predict and classify the problems of the manufacturing environment in connection with the supply chain. …”
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Video-Driven Artificial Intelligence for Predictive Modelling of Antimicrobial Peptide Generation: Literature Review on Advances and Challenges by Jielu Yan, Zhengli Chen, Jianxiu Cai, Weizhi Xian, Xuekai Wei, Yi Qin, Yifan Li

“…By integrating video analysis with computational modelling, researchers can visualise and quantify AMP–microbe interactions at unprecedented levels of detail, thereby informing both experimental design and the refinement of predictive algorithms. This review provides a comprehensive overview of these emerging techniques, highlights major breakthroughs, addresses critical challenges, and ultimately emphasises the powerful synergy between video-driven pattern recognition, AI-based modelling, and experimental validation in the pursuit of next-generation antimicrobial strategies.…”
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LPRLC: Linear predictive run length coding to improve energy consumption of wireless body area networks by Mahdieh Hajiloo Vakil, Zahra Shirmohammadi

“…First- Linear Predictive Coding (LPC) for data prediction, Second- Error calculation, Third- Error coding with Run Length Encoding (RLE) for data compression. …”
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Predicting Firefighter Injury and Entrapment in Urban Firefighting Operations: An Investigation Into the Effectiveness of Modified Fire Time Stages and Machine Learning by Mohammad Mahdi Barati Jozan, Hamed Khosravi, Aynaz Lotfata, Krzysztof J. Cios, Hamed Tabesh

“…Methods In this study, we compare the performance of eight machine learning algorithms in predicting the occurrence of firefighter injuries and entrapment during urban fire incidents. …”
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Heating, Ventilation, and Air Conditioning (HVAC) Temperature and Humidity Control Optimization Based on Large Language Models (LLMs) by Xuanrong Zhu, Hui Li

“…Existing HVAC control methods, such as Proportional–Integral–Derivative (PID) control or Model Predictive Control (MPC), face limitations in understanding high-level information, handling rare events, and optimizing control decisions. …”
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Backstepping Predictive Direct Power Control of Grid-Connected Photovoltaic System Considering Power Quality Issue by Brahim Elkhalil Youcefa, Ahmed Massoum, Said Barkat, Patrice Wira

“…Processor in the Loop (PIL) co-simulation results prove the performances efficiency of the implemented control algorithms under a nonlinear load operating condition.…”
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Enhancing three-phase induction motor reliability with health index and artificial intelligence-driven predictive maintenance by Felipe Lima Aires, Gabriel Dias Galeno, Fernando Nunes Belchior, Antonio Melo Oliveira, Julian David Hunt

“…The proposed approach is based on power quality concepts, the creation of an algebraic algorithm to determine the health index and the use of artificial intelligence algorithms for modelling time series, such as Autoregressive Integrated Moving Average and Facebook Prophet, to predict the future health of the motor based on its historical data. …”
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Predictive modeling of PMMA-based polymer composites reinforced with hydroxyapatite: a machine learning and FEM approach by Rohit Kumar Singh, Khyati Verma, G. C. Mohan Kumar

“…Various machine learning algorithms, such as Feedforward Neural Network (FFNN), Radial Basis Neural Network (RBNN), and Support Vector Machine (SVM), were used to predict the mechanical properties. …”
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A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction by Dabin Zhang, Yufeng Ye, Yongmei Fang, Jing Zhou

“…Finally, the final forecasting values of point prediction, interval prediction and comprehensive probability were obtained through the linear superposition of each subsequence, respectively. …”
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Optimization and predictive modelling for the diameter of nylon-6,6 nanofibers via electrospinning for coronavirus face masks by Malihe Zeraati, Rana Pourmohamad, Bahareh Baghchi, Narendra Pal Singh Chauhan, Ghasem Sargazi

“…The present study used artificial intelligence such as gene expression programming (GEP) and genetic algorithms (GA) were used to predict and optimize the diameter of Nylon-6,6 nanofibers via electrospinning for protection against coronavirus. …”
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Development of Machine Learning Prediction Models to Predict ICU Admission and the Length of Stay in ICU for COVID‑19 Patients Using a Clinical Dataset Including Chest Computed Tom... by Seyed Salman Zakariaee, Negar Naderi, Hadi Kazemi-Arpanahi

“…The imbalance in the data numbers of groups was resolved using the synthetic minority over-sampling technique algorithm. Two sets of prediction models were separately developed to predict ICU admission and ICU LOSs of COVID‑19 patients. …”
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