Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine by Hojin Moon, Lauren Tran, Andrew Lee, Taeksoo Kwon, Minho Lee

“…Objectives: The primary goal of this research is to develop treatment-related genomic predictive markers for non-small cell lung cancer by integrating various machine learning algorithms that recommends near-optimal individualized patient treatment for chemotherapy in an effort to maximize efficacy or minimize treatment-related toxicity. …”
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Optimizing IoT Intrusion Detection—A Graph Neural Network Approach with Attribute-Based Graph Construction by Tien Ngo, Jiao Yin, Yong-Feng Ge, Hua Wang

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Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer by Junyi Li, Shixin Li, Dongpo Zhang, Yibing Zhu, Yue Wang, Xiaoxiao Xing, Juefei Mo, Yong Zhang, Daixiang Liao, Jun Li

“…To address these limitations, this study systematically analyzed RNA-seq high-throughput data and combined 10 machine learning algorithms to construct 117 models. …”
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Robotic Positioning Accuracy Enhancement via Memory Red Billed Blue Magpie Optimizer and Adaptive Momentum PSO Tuned Graph Neural Network by Jian Liu, Xiaona Huang, Yonghong Deng, Canjun Xiao, Zhibin Li

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Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods by Ankita Lawarde, Ankita Lawarde, Masuma Khatun, Prakash Lingasamy, Prakash Lingasamy, Andres Salumets, Andres Salumets, Andres Salumets, Vijayachitra Modhukur, Vijayachitra Modhukur

“…Ensemble ML algorithms were trained and validated with stratified five-fold cross-validation for robust performance assessment across class distributions.ResultsOur models achieved an overall 99% classification accuracy, distinguishing 14 cancer types with high robustness and generalizability. …”
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Variance-based sensitivity analysis of climate variability impact on crop yield using machine learning: A case study in Jordan by Yingqiang Xu, Abeer Albalawneh, Maysoon Al-Zoubi, Hiba Baroud

“…Using meteorological, environmental, and demographic datasets, we predict the yields of four major crops – wheat, barley, date palm, and olive – and evaluate the relative importance of input variables, including drought indices, using the stratified first-order Sobol’ index. Machine learning models, particularly eXtreme Gradient Boosting, outperformed traditional methods, achieving out-of-sample R2 values of 0.79 for wheat, 0.92 for date palm, 0.83 for olive, and 0.48 for barley yield prediction. …”
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A Data-Driven Framework for Optimizing Propranolol Dosage Using Support Vector Regression and Reinforcement Learning by Felix Anayo Njoku, Sunday Olajide Awofisayo, Frank Edughom Ekpar, Simeon Ozuomba

“…This research attempts to model a hybrid machine learning framework combining Support Vector Regression (SVR) and Reinforcement Learning (RL) for individualized Propranolol dosage optimization using patient-specific clinical, enzymatic, and lifestyle data. …”
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Evaluation of a fully automated 2-dimensional imaging system for real-time cattle lameness detection using machine learning by N. Siachos, B.E. Griffiths, J.P. Wilson, C. Bedford, A. Anagnostopoulos, J.M. Neary, R.F. Smith, G. Oikonomou

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Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network by Xia Li, Feng Guo

“…This can significantly improve computational efficiency. The optimized model showed a coefficient of determination (R²) of 0.938 and a mean square error (MSE) of 0.0025 on the test dataset, indicating its ability for accurate prediction. …”
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Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism. by Meshari Alazmi, Nasir Ayub

“…Furthermore, the EffiXNet value of AUC amounting to 0.99, a 25% reduction of logarithmic loss relative to the baseline models, precision of 97.8%, F1-score of 98.1%, and reliable optimization of memory usage. …”
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Predicting water-based drilling fluid filtrate volume in close to real time from routine fluid property measurements by Shadfar Davoodi, Mohammed Ba Geri, David A. Wood, Mohammed Al-Shargabi, Mohammad Mehrad, Alireza Soleimanian

“…This study adapts machine and deep learning (ML/DL) models to predict FV in almost real-time based on more easily measured fluid properties. …”
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Opportunities and challenges of quantum computing for climate modeling by Mierk Schwabe, Lorenzo Pastori, Inés de Vega, Pierre Gentine, Luigi Iapichino, Valtteri Lahtinen, Martin Leib, Jeanette Miriam Lorenz, Veronika Eyring

“…Building on the work of hybrid (physics + AI) ESMs, we here discuss the additional potential of further improving and accelerating climate models with quantum computing. We discuss how quantum computers could accelerate climate models by solving the underlying differential equations faster, how quantum machine learning could better represent subgrid-scale phenomena in ESMs even with currently available noisy intermediate-scale quantum devices, how quantum algorithms aimed at solving optimization problems could assist in tuning the many parameters in ESMs, a currently time-consuming and challenging process, and how quantum computers could aid in the analysis of climate models. …”
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Leveraging Bayesian optimization and multilayer artificial neural network (MLANN) for fault prediction in oil-immersed transformers by Elahe Moradi

“…The proposed multilayer artificial neural network model, optimized using the Bayesian optimization method, achieved an outstanding accuracy of 97.99 %, pointedly outperforming benchmark machine learning classifiers. …”
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Optimizing 5G resource allocation with attention-based CNN-BiLSTM and squeeze-and-excitation architecture by Anfal Musadaq Rayyis, Mohammad Maftoun, Maryam Khademi, Emrah Arslan, Silvia Gaftandzhieva

“…In 5G networks, efficient resource allocation is crucial for optimizing performance and minimizing latency. Traditional machine learning models struggle to capture intricate temporal dependencies and handle imbalanced data distributions, limiting their effectiveness in real-world applications.MethodsTo overcome these limitations, this study presents an innovative deep learning-based framework that combines a convolutional layer with squeeze-and-excitation block, bidirectional long short-term memory, and a self-attention mechanism for resource allocation prediction. …”
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Optimization of Reservoir Water Quality Parameters Retrieval and Treatment Using Remote Sensing and Artificial Neural Networks by Alice Nureen Adhiambo Omondi, Yashon Ouma, Simon Njoroge Mburu, Cleophas Mecha Achisa

“…The study concluded that spectral reflectance from medium resolution satellite data products can be used to estimate WQPs from inland water bodies. Further, the ANN models demonstrate that extracted water quality data from satellite images can be used to inform ANN models for water quality predictions, and for the optimization of water treatment plant operations. …”
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Towards precision in IoT-based healthcare systems: a hybrid optimized framework for big data classification by Satheeshkumar Palanisamy, Vigneshwaran Thangaraju, Jeevitha Kandasamy, Ayodeji Olalekan Salau

“…However, traditional machine learning models often struggle with issues such as data imbalance, redundant features, and noise, limiting their reliability in clinical diagnostics. …”
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Milling-Force Prediction Model for 304 Stainless Steel Considering Tool Wear by Changxu Wang, Yan Li, Feng Gao, Kejun Wu, Kan Yin, Peng He, Yunjiao Xu

“…However, its material properties lead to severe tool wear during milling processes, significantly increasing milling force and adversely impacting machining quality and efficiency. Consequently, an accurate milling-force model is crucial for guiding the formulation and optimization of machining parameters. …”
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Optimization of Bus Dispatching in Public Transportation Through a Heuristic Approach Based on Passenger Demand Forecasting by Javier Esteban Barrera Hernandez, Luis Enrique Tarazona Torres, Alejandra Tabares, David Álvarez-Martínez

“…It reduces computation times by up to 98% compared to the optimization model, making it practically viable for daily scheduling where solving large-scale models exactly can be prohibitively time-consuming. …”
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Prediction of stock market using sentiment analysis and ensemble learning by Dr․ Archana Y. Chaudhari, Dr․ Smita Mahajan

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Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performance by Aneel Manan, Zhang Pu, Ali Majdi, Wael Alattyih, S K Elagan, Jawad Ahmad

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