Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry by Chaowei Dong, Jianfei Xu, Nan Zhou, Jixiong Zhang, Hao Yan, Zejun Li, Yuzhe Zhang

“…Analysis demonstrates that the particle swarm optimal (PSO) algorithm based on adaptive adjustment strategy can effectively optimize the hyperparameters of support vector regression (SVR), and the MC-PSO-SVR model exhibits better predictive capability (R2> 0.88) and lower error coefficients (MAE, RSE, and RMSE values approaching 0) and narrower widths of 95 % confidence intervals for yield stress, plastic viscosity, fluidity, and UCS. …”
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Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm by Alok Kumar Shrivastav, Soham Dutta

“…Compared to conventional metaheuristic such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), the SMA achieves a power loss reduction of 12.3% and a levelized cost of energy (LCOE) improvement of 9.8%. …”
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Automated diabetes detection prediction system based on patients’ medical data by S.V. Pidopryhora, Yu.V. Bogoyavlenska

“…Given the continuous growth of medical data volumes, there is a clear need for modern information technologies capable of automating disease analysis and prediction processes. This paper examines the potential and benefits of implementing machine learning (ML) and artificial intelligence (AI) algorithms for medical data analysis aimed at diabetes detection. …”
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Post, Predict, and Rank: Exploring the Relationship Between Social Media Strategy and Higher Education Institution Rankings by Bruna Rocha, Álvaro Figueira

“…To better understand these strategies, we categorized the posts into five predefined topics—engagement, research, image, society, and education. This categorization, combined with Long Short-Term Memory (LSTM) and a Random Forest (RF) algorithm, was utilized to predict social media output in the last five days of each month, achieving successful results. …”
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Reference frame list optimization algorithm in video coding by quality enhancement of the nearest picture by Junyan HUO, Ruipeng QIU, Yanzhuo MA, Fuzheng YANG

“…Interframe prediction is a key module in video coding, which uses the samples in the reference frames to predict those in the current picture, thus helps to represent the complex video by transmitting a small amount of the prediction residual.In lossy video coding, the qualities of reference frames are affected by the quantization distortion, which lead to poor prediction accuracy and performance degradation.Targeted at the low latency video services, a reference frame list optimization algorithm was proposed, which enhanced the quality of the nearest reference frame by a deep learning-based convolutional neural network, and integrated the enhanced reference frame into the reference frame list to improve the accuracy of interframe prediction.Compared with H.265/HEVC reference software HM16.22, the proposed algorithm provides BD-rate savings of 9.06%, 14.92% and 13.19% for Y, Cb and Cr components, respectively.…”
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Study the Relationship between the University Student and Teacher using the Principal Component Analysis and Genetic Algorithms by Sahar Mahmood

Subjects: “…university education…”
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Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements by Pramod Kumar, Nagendra Kumar Swarnkar, Om Prakash Mahela, Baseem Khan, Divya Anand, Aman Singh, Juan Luis Vidal Mazon, Fahd S. Alharithi

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Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm by Javad Shahlaei Bagheri

“…Purpose: This study aimed to predict and manage physical injuries caused by gym equipment and facilities using the SVM algorithm.Method: This study was of a developmental-applied type. …”
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Impact of surrogate model accuracy on performance and model management strategy in surrogate-assisted evolutionary algorithms by Yuki Hanawa, Tomohiro Harada, Yukiya Miura

Subjects: “…Surrogate-assisted evolutionary algorithms…”
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Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics by Md Abu Sufian, Lujain Alsadder, Wahiba Hamzi, Sadia Zaman, A. S. M. Sharifuzzaman Sagar, Boumediene Hamzi

Subjects: “…cardiovascular risk prediction…”
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Enhanced zebra optimization algorithm for sustainable combined economic and emission dispatch in power systems by Ahmed Arkwazee, Rana Abttan

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Real-time freshness prediction for Apples and Lettuces using imaging recognition and advanced algorithms in a user-friendly mobile application by Chrysanthos Maraveas, George Kalitsios, Marianna I. Kotzabasaki, Dimitrios V. Giannopoulos, Kosmas Dimitropoulos, Anna Vatsanidou

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An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage prediction by Yin-bo He, Ke-ming Sheng, Ming-liang Du, Guan-cheng Jiang, Teng-fei Dong, Lei Guo, Bo-tao Xu

“…The proposed framework offers a versatile and reliable solution for precise predictive modeling in complex drilling and completion scenarios reliant on tabular data, thereby providing a robust theoretical foundation and algorithmic support for accurate forecasting in the oil and gas industry.…”
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Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms by Noémie N. Moreau, Noémie N. Moreau, Samuel Valable, Cyril Jaudet, Loïse Dessoude, Leleu Thomas, Romain Hérault, Romain Modzelewski, Romain Modzelewski, Dinu Stefan, Juliette Thariat, Juliette Thariat, Alexis Lechervy, Aurélien Corroyer-Dulmont, Aurélien Corroyer-Dulmont

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Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms by M. S. Aswathanrayan, N. Santhosh, Srikanth Holalu Venkataramana, Kurugundla Sunil Kumar, Sarfaraz Kamangar, Amir Ibrahim Ali Arabi, Sameer Algburi, Osamah J. Al-sareji, A. Bhowmik

“…The random forest model demonstrated the highest predictive accuracy for performance (test R2 = 0.9620, Test MAPE = 3.6795%), making it the most reliable statistical approach for predicting BSFC compared to linear regression and decision Tree models. …”
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Machine learning compared with rule‐in/rule‐out algorithms and logistic regression to predict acute myocardial infarction based on troponin T concentrations by Anders Björkelund, Mattias Ohlsson, Jakob Lundager Forberg, Arash Mokhtari, Pontus Olsson de Capretz, Ulf Ekelund, Jonas Björk

“…The primary aim was to assess the predictive accuracy of machine learning algorithms based on paired high‐sensitivity cardiac troponin T (hs‐cTnT) concentrations with varying sampling times, age, and sex in order to rule in or out AMI. …”
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Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study by Tao Thi Tran, Jeonghee Lee, Junetae Kim, Sun-Young Kim, Hyunsoon Cho, Jeongseon Kim

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Postpartum Haemorrhage Risk Prediction Model Developed by Machine Learning Algorithms: A Single-Centre Retrospective Analysis of Clinical Data by Wenhuan Wang, Chanchan Liao, Hongping Zhang, Yanjun Hu

“…This study used machine learning algorithms and new feature selection methods to build an efficient PPH risk prediction model and provided new ideas and reference methods for PPH risk management. …”
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Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients by Yuan Liu, Yuankun Liu, Yu Zhang, Pengpeng Zhang, Jiaheng Xie, Ning Zhao, Yi Xie, Chao Cheng, Songyun Zhao

“…The AUC value for the external validation set was 0.93, indicating robust extrapolative capabilities of the XGBoost prediction model. The HF prediction model post-CME, derived from the XGBoost machine learning algorithm in this study, attests to its elevated predictive accuracy and clinical utility.…”
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Development of a prediction model for chemotherapy and immunotherapy response in esophageal squamous cell carcinoma patients using machine learning algorithms by CHEN Jincheng, ZHANG Xiaoqin, LIU Jie

“…Objective‍　‍To develop models for predicting response to chemotherapy combined with immunotherapy in patients with esophageal squamous carcinoma with various machine learning algorithms, and then select the optimal model. …”
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