Integrating Proximal and Remote Sensing with Machine Learning for Pasture Biomass Estimation by Bernardo Cândido, Ushasree Mindala, Hamid Ebrahimy, Zhou Zhang, Robert Kallenbach

“…We applied the Boruta algorithm for feature selection to identify influential biophysical predictors and evaluated four machine learning models—Linear Regression, Decision Tree, Random Forest, and XGBoost—for biomass prediction. …”
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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…By building on our work, researchers can develop better algorithms to improve the diagnosis of AFB, reducing the burden on laboratory staff and improving diagnostic speed and accuracy of these medically important organisms.…”
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Preoperative ternary classification using DCE-MRI radiomics and machine learning for HCC, ICC, and HIPT by Peng Xie, Zhong-Jian Liao, Lu Xie, Junyuan Zhong, Xiaodong Zhang, Wei Yuan, Yujin Yin, Tianxian Chen, Huizhen Lv, Xinglin Wen, Xiaochun Wang, Ling Zhang

“…Critical relevance statement This study develops a novel preoperative imaging-based machine learning model to differentiate hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and hepatic inflammatory pseudotumor (HIPT), improving diagnostic accuracy and advancing personalized treatment strategies in clinical radiology. …”
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SEPDNet: simple and effective PCB surface defect detection method by Du Lang, Zhenzhen Lv

“…SEPDNet uses RepConv (Re-parameterizable Convolution) to improve the backbone representation ability, and FPN (Feature Pyramid Network) is used in the neck part to simplify the model. …”
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Link Prediction Based on the Derivation of Mapping Entropy by Hefei Hu, Yanan Wang, Zheng Li, Yang Tian, Yuemei Ren

“…Abundant experiments on nine real networks suggest that the model can improve precision in link prediction and perform better than traditional algorithms obviously with no increase in time complexity.…”
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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. …”
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Analysis of Machine Learning Performance in Spatial Interpolation of Rainfall Data by Alexandre E. L. Nobrega, Itamir M. Barroca Filho

“…After testing different models for data spatialization and conducting a thorough statistical analysis, we conclude that machine learning models outperformed the Inverse Distance Weighting method, yielding greater variability in accumulated Annual Maximum Daily Precipitation values and improved overall results.…”
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A Reinforcement Learning Approach to Personalized Asthma Exacerbation Prediction Using Proximal Policy Optimization by Dahiru Adamu Aliyu, Emelia Akashah Patah Akhir, Maryam Omar Abdullah Sawad, Jameel Shehu Yalli, Yahaya Saidu

“…Future work will focus on training the model on larger, multi-source datasets to improve generalization across diverse populations. …”
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Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety by Olly Kravchenko, Julia Bäckman, David Mataix-Cols, James J. Crowley, Matthew Halvorsen, Patrick F. Sullivan, John Wallert, Christian Rück

“…Employing machine learning algorithms capable of capturing complex non-linear associations and interactions is a viable next step to improve prediction of post-ICBT symptom severity. …”
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Mf-net: multi-feature fusion network based on two-stream extraction and multi-scale enhancement for face forgery detection by Hanxian Duan, Qian Jiang, Xin Jin, Michal Wozniak, Yi Zhao, Liwen Wu, Shaowen Yao, Wei Zhou

“…However, it is difficult to achieve satisfactory generalization performance in cross-dataset scenarios. In order to improve the cross-dataset detection performance of the model, this paper proposes a multi-feature fusion network based on two-stream extraction and multi-scale enhancement. …”
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Artificial Intelligence-Based Prediction of Bloodstream Infections Using Standard Hematological and Biochemical Markers by Ferhat DEMİRCİ, Murat AKŞİT, Aylin DEMİRCİ

“…The model’s strong performance and interpretability suggest its potential application in clinical decision support systems to improve diagnostic stewardship, reduce unnecessary cultures, and optimize resource use in suspected BSI cases.…”
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Transfer learning for securing electric vehicle charging infrastructure from cyber-physical attacks by Ahmad Almadhor, Shtwai Alsubai, Imen Bouazzi, Vincent Karovic, Monika Davidekova, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…This paper proposes a Transfer learning (TL) framework for cyber-physical attack detection in EVCS in order to overcome these difficulties and improve both accuracy and scalability. The weights preserved from the Deep Neural Network (DNN) model after implementing data normalization and min-max scaling techniques utilized for training are used to initialize a new model termed Transfer Learning. …”
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Scheduling Approach for the Simulation of a Sustainable Resource Supply Chain by Henning Strubelt, Sebastian Trojahn, Sebastian Lang, Abdulrahman Nahhas

“…This paper discusses the improvement of a logistical system’s performance using machine scheduling approaches with the support of a plant simulation model. …”
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Prediction of Myocardial Infarction Based on Non-ECG Sleep Data Combined With Domain Knowledge by Changyun Li, Yonghan Zhao, Qihui Mo, Zhibing Wang, Xi Xu

“…The experiments demonstrate that the model’s accuracy reaches its optimal level by combining the age rule, improving to 73.1%. …”
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Emotion-Based Music Recommendation System Integrating Facial Expression Recognition and Lyrics Sentiment Analysis by V. S. G. S. Phaneendra Bottu, K. Ragavan

“…This study introduces a robust Convolutional Neural Network (CNN)-based model specifically designed for accurate FER, complemented by an innovative music recommendation system. …”
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Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques by Mustafa Muthanna Najm Shahrabani, Rasa Apanaviciene

“…Six optimised machine learning algorithms (K-Nearest Neighbours (KNNs), Support Vector Regression (SVR), Random Forest, Adaptive Boosting (AdaBoost), Decision Tree (DT), and Extra Tree (ET)) were employed to train the model and perform feature engineering and permutation importance analysis. …”
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An Experimental Study of GPR Near-Field Problem in Depth Ranging of Buried Objects by Rex Yui Kei Cham, Wallace Wai Lok Lai

“…Then, the same antenna was used to estimate the cover depth by measuring the time of flight and velocity based on the two velocity algorithms. The results reveal that the ranging accuracy is highly in doubt in reactive near-field and Fresnel regions but is largely improved when the object locates in far-field region where GPR wave propagation becomes plane-type. …”
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Clock factorized quantum Monte Carlo method for long-range interacting systems by Zhijie Fan, Chao Zhang, Youjin Deng

“…Next, we show how the clock factorized quantum Monte Carlo method can be flexibly implemented in various update strategies, like the Metropolis and worm-type algorithms. Finally, we demonstrate the high efficiency of the clock factorized quantum Monte Carlo algorithms using examples of three typical long-range interacting quantum systems, including the transverse field Ising model with long-range $z$-$z$ interaction, the extended Bose-Hubbard model with long-range density-density interactions, and the XXZ Heisenberg model with long-range spin interactions. …”
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Dynamic Optimization of Tunnel Construction Scheduling in a Reverse Construction Scenario by Jianying Wei, Yuming Liu, Xiaochun Lu, Rong Zhao, Gan Wang

“…The Mixed-Integer Programming (MIP) approach is used to build the mathematical model, solved with both exact algorithms and Genetic Algorithms (GA), and implemented in Python 3.12.7. …”
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Artificial intelligence in hospital infection prevention: an integrative review by Rabie Adel El Arab, Zainab Almoosa, May Alkhunaizi, May Alkhunaizi, Fuad H. Abuadas, Joel Somerville, Joel Somerville

“…Advanced algorithms, including neural networks, decision trees, and random forests, significantly improved detection rates when integrated with EHRs, enabling real-time surveillance and timely interventions. …”
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