Selective Cleaning Enhances Machine Learning Accuracy for Drug Repurposing: Multiscale Discovery of MDM2 Inhibitors by Mohammad Firdaus Akmal, Ming Wah Wong

“…A key innovation of this work is the development and application of a selective cleaning algorithm that systematically filters assay data to mitigate noise and inconsistencies inherent in large-scale bioactivity datasets. …”
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Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments by Amin Taheri-Garavand, Mojgan Beiranvandi, Abdolreza Ahmadi, Nikolaos Nikoloudakis

“…On the other hand, POX had a lower predictive correlation (R = 0.8737), indicating a lower capacity of the ANN system in forecasting this parameter. …”
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The impact of environmental variables on reed stands of the intermittent Lake Cerknica, Slovenia: 40 years of change by Nik Ojdanič, Alenka Gaberščik, Igor Zelnik, Aleksandra Golob

“…Decision tree-based data mining using the M5P algorithm effectively identified significant changes in MSAVI2. …”
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Magnetic resonance imaging (MRI) radiomics in paediatric neuro-oncology: A systematic review of clinical applications, feature interpretation, and biological insights in the ch... by Teesta Mukherjee, Omid Pournik, Theodoros N Arvanitis

“…SVM was the most used classification algorithm ( n  = 9), with an AUC range of 0.858–0.977. …”
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Quantitative structure-properties relationship, molecular dynamic simulations and designs of some novel lubricant additives by Usman Abdulfatai, Adamu Uzairu, Sani Uba, Gideon Adamu Shallangwa

“…Quantitative structure-properties relationship (QSPR) method was used to design some novel antioxidant lubricant additives, while molecular dynamics simulations were used to calculate their dynamic binding energies on steel and to hydrogen-containing DLC (a-C: H) crystal surfaces. 29 synthesized antioxidant lubricant additives were collected from literature and geometrically optimized by Spartan’14 version 1.1.2 software while Genetic Function Algorithm (GFA) method of the material studio version 8.0 software was used to build the predictive QSPR model. …”
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Early Warning for Stepwise Landslides Based on Traffic Light System: A Case Study in China by Shuangshuang Wu, Zhigang Tao, Li Zhang, Song Chen

“…Furthermore, leveraging the C5.0 machine learning algorithm, a comparison between the predictive capabilities of the TLS model and a pure rate threshold model reveals that the TLS model achieves a 93% accuracy rate, outperforming the latter by 7 percentage points. …”
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Identification of priority conservation areas and potential corridors for jaguars in the Caatinga biome, Brazil. by Ronaldo Gonçalves Morato, Katia Maria Paschoaletto Micchi de Barros Ferraz, Rogério Cunha de Paula, Cláudia Bueno de Campos

“…A total of 62 points records of jaguar occurrence and 10 potential predictors were analyzed in a GIS environment. A predictive distributional map was obtained using Species Distribution Modeling (SDM) as performed by the Maximum Entropy (Maxent) algorithm. …”
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Revealing High‐Temporal‐Resolution Flood Evolution With Low Latency Using GRACE Follow‐On Ranging Data by Hao‐si Li, Shuang Yi, Zi‐ren Luo, Peng Xu

“…To address this problem, this study develops an improved algorithm accounting for peripheral signal sources and temporal correlations in mass variation. …”
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Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae by Hegazy Rezk, Ali Alahmer, Abdul Ghani Olabi, Enas Taha Sayed

“…Subsequently, the red-tailed hawk algorithm (RTH) is used to determine the optimal values for the process parameters, corresponding to maximum hydrogen yield. …”
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A Wind Power Density Forecasting Model Based on RF-DBO-VMD Feature Selection and BiGRU Optimized by the Attention Mechanism by Bixiong Luo, Peng Zuo, Lijun Zhu, Wei Hua

“…First, critical physical features relevant to WPD are identified using random forest (RF), effectively eliminating data redundancy and enhancing prediction efficiency. Second, the variational mode decomposition (VMD) parameters are optimized via the dung beetle optimizer (DBO) algorithm to extract independent intrinsic mode functions (IMFs), which, alongside the original data, serve as temporal feature inputs. …”
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Optimization of MXene-based aqueous ionic liquids for solar systems using conventional and AI-based techniques by Mohamed Bechir Ben Hamida, Ali B. M. Ali, Narinderjit Singh Sawaran Singh, Loghman Mostafa

“…Response surface methodology (RSM) is used for predictive modeling, while enhanced hill climbing (EHC), non-dominated sorting genetic algorithm II (NSGA-II), and the multi-objective generalized normal distribution optimizer (MOGNDO) are applied for multi-objective optimization. …”
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Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting. by Josefine Freiberg, Roshan A Welikala, Jens Rovelt, Christopher G Owen, Alicja R Rudnicka, Miriam Kolko, Sarah A Barman, FOREVER consortium

“…<h4>Method</h4>The dataset FOREVERP (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability, Pilot) contains retinal images obtained from a Danish high street optician chain. …”
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CT-based radiomics features for the differential diagnosis of nodular goiter and papillary thyroid carcinoma: an analysis employing propensity score matching by Haiming Zhang, Zhenyu Li, Fengtao Zhang, Hengguo Li

“…Feature selection was carried out utilizing the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to generate the radiomics score (Rad-score). …”
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Integrating explainable artificial intelligence and light gradient boosting machine for glioma grading by Teuku Rizky Noviandy, Ghalieb Mutig Idroes, Irsan Hardi

“…Methods: This study employs the Light Gradient Boosting Machine (LightGBM), an advanced ML algorithm, in combination with Explainable Artificial Intelligence (XAI) methodology to grade gliomas more effectively. …”
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Optimizing concrete strength: How nanomaterials and AI redefine mix design by Dan Huang, Guangshuai Han, Ziyang Tang

“…XGB was identified as the most effective ML algorithm for predicting compressive strength among others in this study (R2=0.974). …”
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Association between intraoperative lactate levels and acute kidney injury after on-pump cardiac surgery: a retrospective cohort study across two centers by Dong Xu Chen, Yu Yang Zhang, Xing Long Xiong, Leng Zhou, Jing Shi

“…The study established critical thresholds for AKI risk prediction: mean lactate (2.96 mmol/L), peak lactate (4.50 mmol/L), and TWA lactate (2.33 mmol/L). …”
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Glypican-3 regulated epithelial mesenchymal transformation-related genes in osteosarcoma: based on comprehensive tumor microenvironment profiling by Jiaming Zhang, Wei Wang

“…The model effectively predicted immune-related features and immunotherapy responses in high-risk and low-risk patient groups. …”
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Investigating the Prognostic Role of Telomerase-Related Cellular Senescence Gene Signatures in Breast Cancer Using Machine Learning by Qiong Li, Hongde Liu

“…The model demonstrated strong predictive accuracy and was successfully validated in multiple independent cohorts. …”
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High dietary live microbe intake associated with reduced depressive symptoms in gastrointestinal disease patients: findings from a cross-sectional study by Cheng Zhang, Ming-Hui Ma, Ke Li, Shu-Ning Xu, Ying Liu

“…Additionally, the eXtreme Gradient Boosting (XGBoost) algorithm was implemented to develop a predictive model for depression based on individual characteristics. …”
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TRAINING SYSTEM OF FUTURE SPECIALISTS: QUALITY CONTROL by Vladimir A. Romanov, Valentina N. Kormakova, Elena N. Musaelian

“…The strategy of the development of the process of quality control training in educational professional organizations and a predictive model of the system of quality control training for future engineers and workers have been created on the analysis and synthesis of a quantitative specification of the quality, the obtained experience and success in control training of future specialists in educational professional organizations in recent economic and educational conditions.Scientific novelty. …”
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