In silico exploration of novel EGFR-targeting compounds: integrative molecular modeling, docking, pharmacokinetics, and MD simulations for advancing anti-cervical cancer therapeuti... by Auwal Salisu Isa, Adamu Uzairu, Umar Meleh Umar, Muhammad Tukur Ibrahim, Abdullahi Bello Umar, Kamal Tabti, Abdussamad Mukhtar Mohammed

“…This study investigates the potential of 2,4-diphenyl indenol [1,2-b] pyridinol derivatives as inhibitors targeting the epidermal growth factor receptor (EGFR) through computational drug discovery methods. A genetic algorithm-multiple linear regression (GA-MLR) model was created, achieving strong predictive accuracy with R² = 0.9243, Q² = 0.8957, CCC = 0.9021, and MAE = 0.034. …”
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Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach by Illia Mushta, Sulev Koks, Anton Popov, Oleksandr Lysenko

“…To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. …”
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Learned Shrinkage Approach for Low-Dose Reconstruction in Computed Tomography by Joseph Shtok, Michael Elad, Michael Zibulevsky

“…Our numerical simulations indicate that the proposed algorithm can manage well with noisy measurements, allowing a dose reduction by a factor of 4, while reducing noise and streak artifacts in the FBP reconstruction, comparable to the performance of a statistically based iterative algorithm.…”
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Blind-Matched Filtering for Speech Enhancement with Distributed Microphones by Sebastian Stenzel, Jürgen Freudenberger

“…A multichannel noise reduction and equalization approach for distributed microphones is presented. …”
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QSAR, docking and pharmacokinetic studies of 2,4-diphenyl indenol [1,2-B] pyridinol derivatives targeting breast cancer receptors by Auwal Isa, Adamu Uzairu, umar Umar, Muhammad Tukur Ibrahim, Abdullahi Umar

“…A robust quantitative structure-activity relationship (QSAR) model, developed using genetic algorithms and multilinear regression analysis, predicts chemical activity (pGI50) against breast cancer receptors. …”
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Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning by Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama

“…By applying imitation learning techniques and comparing three algorithms – Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) – the research trains models to predict aircraft control inputs through sequences of state-action pairs. …”
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A continuous multi-night sleep database for exploring sleep structure similarity by Shaofei Ying, Junze Fu, Lin Wang, Haokai Zhang, Yue Yu, Hongqiong Wang, Jiaxin Xie, Tiejun Liu

“…It provides valuable opportunities to investigate the physiological mechanisms of sleep structure continuity, advancing sleep features recognition and prediction algorithms, and designing individualised templates for closed-loop sleep stimulation. …”
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Infra-Red Imaging to Detect Respirator Leak in Healthcare Workers During Fit-Testing Clinic by Darius Chapman, Campbell Strong, Kathryn D Tiver, Dhani Dharmaprani, Even Jenkins, Anand N Ganesan

“…Results: The study achieved high accuracy in predicting pass or fail outcomes of quantitative fit tests for flat-fold P2 FFRs. …”
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A Novel ANN-PSO Method for Optimizing a Small-Signal Equivalent Model of a Dual-Field-Plate GaN HEMT by Haowen Shen, Wenyong Zhou, Jinye Wang, Hangjiang Jin, Yifan Wu, Junchao Wang, Jun Liu

“…We initially train an ANN model to predict the S-parameters of the device, and subsequently utilize the PSO algorithm for parameter optimization. …”
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Role of Artificial Intelligence and Machine Learning to Create Predictors, Enhance Molecular Understanding, and Implement Purposeful Programs for Myocardial Recovery by Frederick M. Lang, Benjamin C. Lee, Dor Lotan, Mert. R. Sabuncu, Veli K. Topkara

“…By identifying novel patterns in high-dimensional data, artificial intelligence (AI) and machine learning (ML) algorithms can enhance the identification of key predictors and molecular drivers of myocardial recovery. …”
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WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting by Theodoros Theodoropoulos, Angelos-Christos Maroudis, Uwe Zdun, Antonios Makris, Konstantinos Tserpes

“…Furthermore, the aforementioned information is included via two novel dedicated algorithms, the Shared Borders Policy and the Adjustable Hops Policy. …”
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Digital Twin-Based Online Diagnosis Method for Inter-Turn Short Circuit Fault in Stator Windings of Induction Motors by Yujie Chen, Leiting Zhao, Liran Li, Kan Liu, Cunxin Ye

“…First, an advanced rapid-solving algorithm is employed to establish a real-time digital twin model of the motor under healthy conditions. …”
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Machine Learning Classification of First-Onset Drug-Naive MDD Using Structural MRI by Donghwa Kim, Pilsung Kang, Junhong Kim, Czang Yeob Kim, Jong-Ha Lee, Sangil Suh, Moon-Soo Lee

“…We aimed to determine whether it is possible to reliably train a highly accurate predictive classification algorithm, even with the first onset of drug-naive adolescent MDD, solely using structural magnetic resonance imaging and without using any other clinical data from the patients. …”
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Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data by Guihua Zhang, Jiayue Liu, Yuxin Wu, Guangxi Yue

“…The results demonstrate that the random forest algorithm represents the optimal choice when both training complexity and predictive performance are comprehensively considered. …”
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Aboveground biomass estimation using multimodal remote sensing observations and machine learning in mixed temperate forest by Shashika Himandi Gardeye Lamahewage, Chandi Witharana, Rachel Riemann, Robert Fahey, Thomas Worthley

“…The principal objective of this study is to utilize the random forest (RF) algorithm to build predictive AGB models. We utilized 67 explanatory variables, which were extracted from three RS sources resulting in nine RF models. …”
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Improved Electrochemical–Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models by Salvatore Scalzo, Davide Clerici, Francesca Pistorio, Aurelio Somà

“…Accurate and predictive models of lithium-ion batteries are essential for optimizing performance, extending lifespan, and ensuring safety. …”
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Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing by Mariam Assi, Julien Favre, Marcin Brykala, Franck Tancret, Anna Fraczkiewicz

“…This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multi-objective genetic algorithm. …”
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Machine Learning in Public Governance: A Systematic Review of Applications, Trends and Challenges by Y. Nuruly, G. N. Sansyzbayeva, L. Z. Ashirbekova, S. K. Tazhiyeva

“…Despite significant progress in the models’ technical implementation and predictive accuracy, in many cases, mechanisms for equity, transparency, and citizen participation have been poorly implemented. …”
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Advancing Agricultural Machinery Maintenance: Deep Learning-Enabled Motor Fault Diagnosis by Xusong Bai, Qian Chen, Xiangjin Song, Weihang Hong

“…These technologies offer reliable and efficient solutions for predictive maintenance in agricultural machinery, enabling proactive intervention in the early stages of catastrophic failures. …”
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IGWO-MALSTM: An Improved Grey Wolf-Optimized Hybrid LSTM with Multi-Head Attention for Financial Time Series Forecasting by Mingfu Zhu, Haoran Qi, Panke Qin

“…The present study investigates the potential of time series forecasting (TSF) in financial application scenarios, aiming to predict future spreads and inform investment decisions more effectively. …”
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