Machine learning models in enhancing prediction of health-related indices among older adults: A scoping review by Raoof Nopour

“…Conclusion: This review demonstrated that various machine learning algorithms and data types significantly impact predictive ability and preventive strategies in clinical environments.…”
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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

“…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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Net buffer load during regional citrate anticoagulated continuous renal replacement therapy. by Innas Forsal, Dominique Pouchoulin, Viktoria Roos, Jorge Echeverri, Marcus Ewert Broman

“…This parameter is integrated as a static safety parameter in the Prismax version 3 software, and will guide the operator on a possible development of metabolic alkalosis with the current settings.<h4>Methods</h4>Algorithms for estimating the net buffer load were developed. …”
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Efficient IDS for IoT Networks Using Host-Based Data Aggregation and Multi-Entropy Analysis by Yusei Katsura, Arata Endo, Ismail Arai, Kazutoshi Fujikawa

“…This enables the reduction of computational resources during detection processing while maintaining detection accuracy, even when using fewer features and lightweight machine learning algorithms. The evaluation results demonstrate that the proposed method achieves a maximum reduction of 99.7% (2916 milliseconds) in processing time and 86.4% (633 MiB) in memory usage while maintaining an intrusion detection accuracy of 99.97%, proving its feasibility in constrained environments comparable to IoT gateways.…”
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Analysis of the Influence of Image Resolution in Traffic Lane Detection Using the CARLA Simulation Environment by Aron Csato, Florin Mariasiu, Gergely Csiki

“…Computer vision is one of the key technologies of advanced driver assistance systems (ADAS), but the incorporation of a vision-based driver assistance system (still) poses a great challenge due to the special characteristics of the algorithms, the neural network architecture, the constraints, and the strict hardware/software requirements that need to be met. …”
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Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis by Ahmed Alfakeeh, Mhd Saeed Sharif, Abin Daniel Zorto, Thiago Pillonetto

“…The study revealed that data balancing and augmentation on the ensemble machine learning algorithms boost the efficacy of clinical predictive models and can help clinics decide which data types are most important when examining patients and diagnosing sepsis early through intelligent human-machine interface.…”
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Cascaded Homography-Constrained Local Feature Matching for Optical and SAR Images by Xinhui Yuan, Zhi Li, Qingwu Hu, Jiayuan Li

“…Current deep learning-based methods for optical and SAR image matching typically enhance specific steps of traditional algorithms, such as feature detection or description, but lack a comprehensive end-to-end solution. …”
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Participatory science methods to monitor water quality and ground truth remote sensing of the Chesapeake Bay. by Patrick Neale, Shelby Brown, Tara Sill, Alison Cawood, Maria Tzortziou, Jieun Park, Min-Sun Lee, Beth Paquette

“…The Chesapeake Water Watch program seeks to enhance the monitoring of tributaries by developing and testing methods for volunteer scientists to easily measure chlorophyll, turbidity, and colored dissolved organic matter (CDOM) to inform Bay stakeholders and improve algorithms for analogous remote sensing (RS) products. …”
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Prediction of ischemic stroke in patients with H-type hypertension based on biomarker by Ke Chen, Jianxun He, Lan Fu, Xiaohua Song, Ning Cao, Hui Yuan

“…Logistic regression, least absolute shrinkage and selection operator regression, and best subset selection analysis were used to assess the contribution of variables to ischemic stroke, and models were derived using four machine learning algorithms. Area Under Curve (AUC), calibration plot and decision-curve analysis respectively evaluated the discrimination and calibration of four models, then external validation and visualization of the best-performing model. …”
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Bridging the digital readiness gap in palliative home care: A process to data approach by Yousra Odeh, Omar Shamieh

“…A data model from the first increment was algorithmically derived in the second increment. The King Hussein Cancer Center in Jordan's case study on the palliative home care process is used to demonstrate and evaluate the work. …”
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Research Progress on Sequence Recommendation Based on Deep Learning and Large Language Model by XU Fengru, LI Bohan, XU Shuai

“…Next, the main techniques in sequential recommendation are summarized in detail, including: traditional methods based on Markov chains, which model user behavior sequences by relying on state transition probabilities; deep learning-driven methods, which utilize neural network models to capture long-term dependencies and complex patterns; hybrid models, which combine multiple algorithms to enhance the accuracy and robustness of recommendation systems; and emerging methods based on large language models, which improve the understanding of user behavior and recommendation content through the integration of pre-trained large language models. …”
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Integration of Google Earth Engine, Sentinel-2 images, and machine learning for temporal mapping of total dissolved solids in river systems by Eric Ariel L. Salas, Sakthi S. Kumaran, Robert Bennett, Eric B. Partee, Jason Brownknight, Kellsie Schrack, Bryant Willis

“…This study highlights an advanced approach integrating Google Earth Engine (GEE), remote sensing techniques using Sentinel-2 imagery, and machine learning algorithms to map TDS in a spatially explicit manner. …”
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Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth by B. S. Saruk, G. Mokesh Rayalu

“…This study acknowledges the importance of sustainable agriculture practices and incorporates sophisticated machine learning algorithms as dynamic tools to forecast crop yields and enhance decision-making processes throughout the crop development cycle. …”
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Preliminary Study on Real-Time Phonocardiogram Signal Acquisition and Analysis Using Machine Learning and IoMT for Digital Stethoscope by M. Kalimuthu, C. Hemanth

“…Advanced signal segmentation and classification were performed using machine learning algorithms, including K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). …”
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On Complexities of Impact Simulation of Fiber Reinforced Polymer Composites: A Simplified Modeling Framework by M. Alemi-Ardakani, A. S. Milani, S. Yannacopoulos

“…Different numerical algorithms have been developed over the past decades for impact modeling of composites, yet a considerable gap often exists between predicted and experimental observations. …”
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METHODS OF APPROXIMATION OF SUMMABLE FUNCTIONS ON THE BASIS OF THE INTEGRAL OF STIELTJES WITH RESPECT TO APPLIED ANALYSIS by Igor Eduardovich Naats, Elena Pavlovna Yartseva

“…Thus, the computational schemes that are associated with practical carrying out-she described the method in applications in some cases can be much simpler and more eficient algorithms that are required for the implementation of integral representations of functions based on singular integrals. …”
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Tirzepatide safety in type 2 diabetes: a disproportionality analysis of adverse events using the FDA FAERS database by Zhenpo Zhang, Jiangxiong Li, Jingping Zheng, Yankun Liang, Lin Ma, Ling Su

“…Reports were deduplicated, normalized using standardized medical terminology, and analyzed using four disproportionality analysis algorithms. Significant signals required meeting all four methods’ criteria with at least three cases. …”
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Quadratic Regression Models for Profile Picture NFT Valuation by Geun-Cheol Lee, Hoon-Young Koo, Heejung Lee

“…For benchmarking purposes, we compare the proposed models against four machine learning algorithms: Random Forest, Support Vector Regression (SVR), XGBoost, and LightGBM. …”
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Intelligent Recognition and Automated Production of Chili Peppers: A Review Addressing Varietal Diversity and Technological Requirements by Sheng Tai, Zhong Tang, Bin Li, Shiguo Wang, Xiaohu Guo

“…., robotic systems incorporating diverse end-effectors like soft grippers or cutting mechanisms and motion planning algorithms such as RRT) as well as seed cleaning/separation techniques and simulations (e.g., CFD and DEM) for equipment optimization. …”
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A Study on an Improved Generative Adversarial Network Model for Super-Resolution Image Reconstruction by Ximin Qu, Cheng Peng

“…Experimental results show that the optimized model SRGAN-GBDB outperforms the traditional Bicubic interpolation method, SRGAN, and several other existing super-resolution algorithms on the Set5, Set14, BSD100, Urban100, and DIV2K datasets. …”
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