Machine learning-based prediction of physical parameters in heterogeneous carbonate reservoirs using well log data by Fuyong Wang, Xianmu Hou

“…Six machine learning algorithms are utilized: support vector machine (SVM), backpropagation (BP) neural network, gaussian process regression (GPR), extreme gradient boosting (XGBoost), K-nearest neighbor (KNN), and random forest (RF). …”
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Incorporating Contextual Factors into a Comprehensive Analysis of Operational Efficiency and Service Quality in Healthcare Sector by Utsav Pandey, Sanjeet Singh

“…In the first stage, an output-oriented Data Envelopment Analysis is employed to model the interdependency between operational efficiency and service quality by assessing the allocation of the input resources for achieving these two objectives. While accounting for the external influences and avoiding the ‘best practice trap’ in the healthcare sector, a set of classification algorithms are used to quantify the impact of external factors on efficiency levels. …”
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Detection and Prediction of Wind and Solar Photovoltaic Power Ramp Events Based on Data-Driven Methods: A Critical Review by Jie Zhang, Xinchun Zhu, Yigong Xie, Guo Chen, Shuangquan Liu

“…This review systematically examines recent advancements in the field, focusing on three principal areas: the definition and detection of ramp event characteristics, innovations in predictive model architectures, and strategies for precision optimization. Our analysis reveals that while detection algorithms for ramp events have matured and the overall predictive performance of power forecasting models has improved, existing approaches often struggle to capture localized ramp phenomena, resulting in persistent deviations. …”
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The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease by Mohammed A. Chowdhury, Rodrigue Rizk, Conroy Chiu, Jing J. Zhang, Jamie L. Scholl, Taylor J. Bosch, Arun Singh, Lee A. Baugh, Jeffrey S. McGough, KC Santosh, William C.W. Chen

“…We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. …”
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Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion by Yice Cao, Chenchen Liu, Zhenhua Wu, Lei Zhang, Lixia Yang

“…Although segmentation algorithms based on convolutional neural networks (CNNs) or Transformers have achieved significant performance improvements, the trade-off between segmentation precision and computational complexity remains a key limitation for practical applications. …”
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On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data Analytics by Andreas Andreou, Constandinos X. Mavromoustakis, Evangelos K. Markakis, Athina Bourdena, George Mastorakis

“…Specifically, Twin-Delayed Deep Deterministic Policy Gradients (TD3) and Soft Actor-Critic (SAC) algorithms are adopted to optimize task scheduling and system decisions in dynamic, resource-constrained settings. …”
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An Exceptional Responder to Nivolumab in Metastatic Non-Small-Cell Lung Cancer: A Case Report and Literature Review of Long-Term Survivors by Babak Baseri, Bachar Samra, Eric Tam, Edwin Chiu, Andrea Leaf

“…Studying the clinical course of exceptional responders can help identify potential predictors of response to immunotherapy and further fine-tune our management algorithms in the absence of standard of care in challenging scenarios. …”
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A DSP–FPGA Heterogeneous Accelerator for On-Board Pose Estimation of Non-Cooperative Targets by Qiuyu Song, Kai Liu, Shangrong Li, Mengyuan Wang, Junyi Wang

“…Instruction-level control supports model generalization, while a weight concatenation strategy improves resource utilization during convolution. …”
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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…However, persistent challenges include data scarcity in developing regions (only 12% of Sub-Saharan African monitoring stations provide real-time data), algorithmic bias (65% sensitivity loss in cross-regional models), and regulatory skepticism toward "black-box" systems. …”
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Inflow Prediction for Agricultural Reservoirs Using Disaster Prevention Measurement Data: A Comparison of TANK Model and Machine Learning by Bong-Kuk Lee, Joonyoung Choi, Kyoung Jae Lim, Jeongho Han

“…The results of this study demonstrate the potential of machine learning techniques for inflow prediction in agricultural reservoirs and suggest the need for further research on model improvement using various algorithms and input variables.…”
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Deep Temporal Clustering of Pathological Gait Patterns in Post-Stroke Patients Using Joint Angle Trajectories: A Cross-Sectional Study by Gyeongmin Kim, Hyungtai Kim, Yun-Hee Kim, Seung-Jong Kim, Mun-Taek Choi

“…Through this method, six optimal clusters were selected with a silhouette score of 0.2831, which is a relatively higher value compared to other clustering algorithms. …”
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Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations by Martina Casari, Piotr A. Kowalski, Laura Po

“…Notably, incorporating only PM2.5, relative humidity and temperature as features yields optimal performance while mitigating overfitting issues. …”
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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…IntroductionIn the field of materials science, the prediction of material properties plays a critical role in designing new materials and optimizing existing ones. Traditional experimental approaches, while effective, are resource-intensive and time-consuming, often requiring extensive trial-and-error methods. …”
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Efficient base metal exploration in northern New Brunswick, Canada through a hybrid ANN integrated with ABC and PSO methods by Claire Dusabemariya, Wei Qian, Romuald Bagaragaza, Ajibola Richard Faruwa, Ali Mossad

“…By integrating Artificial Neural Networks (ANN) with metaheuristic algorithms, specifically Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO), the research optimizes resistivity inversion techniques to enhance geophysical exploration. …”
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Empirical Evaluation and Analysis of YOLO Models in Smart Transportation by Lan Anh Nguyen, Manh Dat Tran, Yongseok Son

“…By examining the performance of various YOLO models across datasets, IoU thresholds, and object classes, we make six observations on these three aspects while aiming to identify optimal models for road transportation scenarios. …”
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Deepmol: an automated machine and deep learning framework for computational chemistry by João Correia, João Capela, Miguel Rocha

“…Despite its potential to revolutionize the field, researchers are often encumbered by obstacles, such as the complexity of selecting optimal algorithms, the automation of data pre-processing steps, the necessity for adaptive feature engineering, and the assurance of model performance consistency across different datasets. …”
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Design and Analysis of a Serial Manipulator for Pick and Drop Objects for Material Handling at Uiri Metal Forming Workshop. by Behangana, Abert

“…A-36 steel emerged as the optimal choice due to its superior weight-to-structural stiffness ratio, while 6061 aluminum proved advantageous for weight-sensitive applications. …”
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Application of machine learning for predicting the incubation period of water droplet erosion in metals by Khaled AlHammad, Mamoun Medraj, Moussa Tembely

“…The performance of various ML algorithms is evaluated while investigating the effect of data transformation techniques on prediction accuracy. …”
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Automatic diagnosis of extraocular muscle palsy based on machine learning and diplopia images by Xiao-Lu Jin, Xue-Mei Li, Tie-Juan Liu, Ling-Yun Zhou

“…Diagnostic models were constructed using logistic regression (LR), decision tree (DT), support vector machine (SVM), extreme gradient boosting (XGBoost), and deep learning (DL) algorithms. A total of 2757 diplopia images were randomly selected as training data, while the test dataset contained 487 diplopia images. …”
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Simulating the Deterioration Behavior of Tunnel Elements Using Amalgamation of Regression Trees and State-of-the-Art Metaheuristics by Eslam Mohammed Abdelkader, Abobakr Al-Sakkaf, Moaaz Elkabalawy, Abdelhady Omar, Ghasan Alfalah

“…Leveraging metaheuristic algorithms’ strengths, the developed method jointly optimizes critical regression tree hyperparameters and identifies the most relevant features for deterioration prediction. …”
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