Machine Learning Applications in Road Pavement Management: A Review, Challenges and Future Directions by Tiago Tamagusko, Matheus Gomes Correia, Adelino Ferreira

“…We discuss the limitations of conventional PMS and explore how Artificial Intelligence (AI) algorithms can overcome these shortcomings by improving the accuracy of pavement condition assessments, enhancing performance prediction, and optimizing maintenance and rehabilitation decisions. …”
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Why are Some Recommendation Systems Preferred? by Gaofeng Yi

“…The majority of the existing researches are concerned with improving the accuracy and effectiveness of the recommendation algorithms, or focusing on how to limit perceived risks, with the aim of increasing consumer satisfaction. …”
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Enhancing Breast Cancer Diagnosis With Multi-Resolution Vision Transformers and Robust Decision-Making by Margo Sabry, Hossam Magdy Balaha, Khadiga M. Ali, Tayseer Hassan A. Soliman, Dibson Gondim, Mohammed Ghazal, Norah Saleh Alghamdi, Ayman El-Baz

“…A stacking ensemble method combines predictions from ViT models trained at these levels, improving classification robustness. …”
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MC21 v.10 – coupled-radiation Monte Carlo transport solver with support for multiphysics simulations by Griesheimer David P., Gill Daniel F., Nease Brian R., Pavlou Andrew, Stedry Mark H., Thompson Jason T., Burke Paul E., Dobreff Peter S.

“…Over the past decade, the development of the MC21 Monte Carlo radiation transport solver has focused on extending the functionality of the code beyond static calculations of reactivity and reaction rate distributions, as well as improving accuracy, performance, and scalability. Notable improvements include enhanced interaction physics models, efficient model representation and tracking algorithms, support for coupled physics calculations using both in-line and externally coupled feedback modules, and the development of native visualization and results post-processing tools. …”
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Impact of imbalanced features on large datasets by Waleed Albattah, Rehan Ullah Khan

“…Distributed Gaussian (D-GA) and Distributed Poisson (D-PO) are found to be the most effective techniques, especially in improving Random Forest (RF) and SVM models. The deep learning experiments also show an improvement as such.…”
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Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets by Catarina Lopes, Andreia Brandão, Manuel R. Teixeira, Mário Dinis-Ribeiro, Carina Pereira

“…Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. …”
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Literature Review of Prognostic Factors in Secondary Generalized Peritonitis by Valerii Luțenco, Adrian Beznea, Raul Mihailov, George Țocu, Verginia Luțenco, Oana Mariana Mihailov, Mihaela Patriciu, Grigore Pascaru, Liliana Baroiu

“…Emerging evidence suggests that machine learning algorithms may improve early risk stratification and individualized outcome prediction when integrated with conventional scoring systems. …”
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Assessing reading fluency in elementary grades: A machine learning approach by Gabriel Candido da Silva, Rodrigo Lins Rodrigues, Américo N. Amorim, Lieny Jeon, Emilia X.S. Albuquerque, Vanessa C. Silva, Vinícius F. da Silva, André L.A. Pinheiro, João P.J.R. Nunes, Suzana X.M.G. de Souza, Maxsuel S. Silva, Igor Mauro, Alexandre Magno Andrade Maciel

“…This study compares eleven widely used machine learning algorithms to identify the most accurate and comprehensive method for assessing children's reading fluency. …”
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Review of Demand Response-based Optimal Scheduling of Electric and Thermal Integrated Energy Systems by Jingshan MO, Guangxian YAN, Na SONG, Mingyang YUAN

“…This study provides an overview of the current research status, classification of scheduling models, and model solution methods for demand response-based scheduling of electric and thermal integrated energy systems in recent years. …”
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A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space by Zheng Luo, Jiahao Mai, Caihong Feng, Deyao Kong, Jingyu Liu, Yunhong Ding, Bo Qi, Zhanbo Zhu

“…Nevertheless, machine learning algorithms possess significant advantages in handling data complexity and nonlinearity. …”
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Enhanced 3-D Reconstruction Method for Polarimetric Coherent Optimal-Based Tomographic SAR by Shuhang Dong, Zekun Jiao, Liangjiang Zhou, Xiaolan Qiu, Qiancheng Yan

“…However, in the practical application, there are relatively few models and algorithms for full-polarized SAR joint 3-D imaging. …”
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Towards representation learning of radar altimeter waveforms for sea ice surface classification by L. Happ, L. Happ, S. Patil, S. Hendricks, R. Fellegara, L. Kaleschke, A. Gerndt, A. Gerndt

“…Moreover, machine learning models for sea ice classification often depend on supervised training, which is vulnerable to uncertainties in labeled data, especially in polar regions. …”
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Machine Learning Introduces Electrophysiology Assessment as the Best Predictor for the Recovery Prognosis of Spinal Cord Injury Patients for Personalized Rehabilitation Approaches by Dionysia Chrysanthakopoulou, Charalampos Matzaroglou, Eftychia Trachani, Constantinos Koutsojannis

“…Machine learning’s (ML’s) increasing importance in medicine is driven by the growing availability of health data and improved algorithms. It enables the creation of predictive models for disease diagnosis, progression prediction, personalized treatment, and improved healthcare efficiency. …”
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Applications of artificial intelligence and computational intelligence in hydraulic optimization of centrifugal pumps: a comprehensive review by Yuanhui Xu, Xingcheng Gan, Ji Pei, Wenjie Wang, Jia Chen, Shouqi Yuan

“…Ultimately, such progress will contribute to more sustainable and reliable energy utilisation in diverse industrial applications.Highlights Comprehensive review of AI and CI applications in centrifugal pump hydraulic optimisation.Analysis of machine learning methods for predictive modelling and optimisation.Insights into integrating intelligent algorithms with CFD for high-dimensional, multi-objective optimisation.Identification of future research directions to enhance precision and efficiency in pump design methodologies.…”
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Exploring Gender Bias in AI for Personalized Medicine: Focus Group Study With Trans Community Members by Nataly Buslón, Davide Cirillo, Oriol Rios, Simón Perera del Rosario

“…Participants expressed apprehensions about potential misdiagnoses or inappropriate treatments due to cisnormative data models. However, they also identified opportunities for AI to enhance health care outcomes, advocating for community-led data collection initiatives and improved algorithmic transparency. …”
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Inferring Mechanical Properties of Wire Rods via Transfer Learning Using Pre-Trained Neural Networks by Adriany A. F. Eduardo, Gustavo A. S. Martinez, Ted W. Grant, Lucas B. S. Da Silva, Wei-Liang Qian

“…By proper calibration and fine-tuning, observed improvements over the standard CNN models are reflected by good training and test accuracies in order to predict the material’s mechanical properties, with efficiency demonstrated by the loss function’s rapid convergence. …”
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Comparison of light gradient boosting and logistic regression for interactomic hub genes in Porphyromonas gingivalis and Fusobacterium nucleatum-induced periodontitis with Alzheime... by Pradeep Kumar Yadalam, Shubhangini Chatterjee, Prabhu Manickam Natarajan, Carlos M. Ardila, Carlos M. Ardila

“…The accuracy of logistic regression and light gradient boosting was 67% and 60%, respectively.DiscussionThe logistic regression model demonstrated superior accuracy and balance compared to the light gradient boosting model, indicating its potential for future improvements in predicting hub genes in periodontal and Alzheimer's diseases.…”
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Exploring the capabilities of hyperspectral remote sensing for soil texture evaluation by Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker, Reza Alimardani, Pouya Bohlol

“…Additionally, we compared the performance of random forest (RF) algorithms with partial least squares regression (PLSR), multiple linear regression (MLR), support vector machine regression (SVR), decision trees (DTs), and multilayer perceptron (MLP) neural networks, addressing the effects of feature selection and irregular soil data on the modeling procedure. …”
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Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain by Jay Gopal, Jonathan Bao, Tessa Harland, Julie G. Pilitsis, Steven Paniccioli, Rachael Grey, Michael Briotte, Kevin McCarthy, Ilknur Telkes

“…Our findings suggest that combination of subjective self-reports, intraoperatively obtained EEGs, and well-designed machine learning algorithms might be potentially used to distinguish responders and nonresponders. …”
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Cervical cancer demystified: exploring epidemiology, risk factors, screening, treatment modalities, preventive measures, and the role of artificial intelligence by N. Mohammad, M. Khan, M. Maqsood, A. H. K. Naseeb

“…AI-driven technologies, including deep learning algorithms and machine learning models, are emerging as valuable tools in cervical cancer detection, risk assessment, and treatment planning. …”
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