Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicle multispectr... by Ao Zhang, Haibin Guan, Zhiheng Dong, Xin Jia, Yan Xue, Fengyu Han, Lingjiang Meng, Xiuling Yu, Xiaoqin Wang, Yang Cao

“…PIs collectively achieved high-precision predictions (mean 0.42 ≤ R2 ≤ 0.94), with the prediction of PH using green leaf index (GLI) in BP algorithm attaining peak accuracy (R² = 0.94). …”
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Sources and trends of black carbon aerosol in the megacity of Nanjing, eastern China, after the China Clean Action Plan and Three-Year Action Plan by A. Abulimiti, A. Abulimiti, Y. Zhang, Y. Zhang, M. Yu, M. Yu, Y. Hong, Y. Hong, Y.-C. Lin, Y.-C. Lin, C. Gul, F. Cao, F. Cao

“…Based on 3-year monitoring data, the random forest (RF) algorithm was employed to reconstruct BC concentrations in Nanjing from 2014 to 2021. …”
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Advancing lakes algal chlorophyll estimation in the contiguous USA: A comparative study of machine learning models and satellite data by Md Mamun, Xiao Yang

“…We assess the performance of four ML algorithms (random forest, extra tree regressor, bagging regressor, and xgboost model), discern the most influential spectral bands and indices, and compare these methods to established remote sensing techniques for CHL-a prediction. …”
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The Least Limiting Water Range to Estimate Soil Water Content Using Random Forest Integrated with GIS and Geostatistical Approaches by Orhan Dengiz, Pelin Alaboz

“…The present study determined the least limiting water range (LLWR) contents of alluvial lands with different soils distributed in the Bafra Plain, where intensive agricultural activities are carried out, and revealed the compression and aeration problems in the area with distribution maps. Also, the predictability of LLWR was evaluated with the random forest (RF) algorithm, one of the machine learning algorithms, and the usability of the prediction values distribution maps was revealed. …”
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Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka by Amila T. Peiris, Jeevani Jayasinghe, Upaka Rathnayake

“…Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE < 0.22, and BIAS < 1. …”
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Method of terminal control in ascent segment of unmanned aerial vehicle with ballistic phase by N. Y. Polovinchuk, S. V. Ivanov, M. Y. Zhukova, D. G. Belonozhko

“…Such algorithms have good convergence and injection accuracy due to the prediction of parameters during the flight at a shorter time interval.Discussion and Conclusions. …”
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Non-invasive ML methods for diagnosis of congenital heart disease associated with pulmonary arterial hypertension by Yuyang Gao, Pengyue Ma, Jiahua Pan, Hongbo Yang, Tao Guo, Weilian Wang

“…Pre-processing in both algorithms focuses on segmentation of heart sounds into discrete cardiac cycles. …”
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Personal Data Recognition Using a Deep Learning Model by Nikita Babak

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The Spatio-Temporal Equalization Sliding-Window Distribution Distance Maximization Based on Unsupervised Learning for Online Event-Related Potential-Based Brain–Computer Interfaces... by Haoye Wang, Jing Jin, Xinjie He, Shurui Li, Andrzej Cichocki

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Advancements and Optimisation Strategies in Building Integrated Photovoltaic Thermal (BIPVT) Systems by Mon Prakash Upadhyay, Arjun Deo, Ajitanshu Vedratnam

“…The paper covers the current algorithms for various optimisation algorithms such as Genetic Algorithms and Particle Swarm Optimisation that provide enhanced utilization improvements. …”
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Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review by Platon S. Papageorgiou, Rafail Christodoulou, Panagiotis Korfiatis, Dimitra P. Papagelopoulos, Olympia Papakonstantinou, Nancy Pham, Amanda Woodward, Panayiotis J. Papagelopoulos

“…This narrative review synthesizes the evolving applications of AI in PBTs, focusing on early tumor detection, imaging analysis, therapy response prediction, and histological classification. AI-driven radiomics and predictive models have yielded promising results in assessing chemotherapy efficacy, optimizing preoperative imaging, and predicting treatment outcomes, thereby advancing the field of precision medicine. …”
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Application of Artificial Intelligence Large Language Model in Power Equipment Operation and Maintenance by Xiaohong Chen, Wenrun Fu, Chaoming Liu, Zehong Liu, Junpeng Li, Zhiliang Hu, Dongbin Hu

“…This study aims to explore the enabling role of multimodal AI-LLM in health assessment, operational state prediction, fault diagnosis, life prediction, and maintenance strategy recommendation, among other specific scenarios of power equipment operation and maintenance. …”
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LSTM-MM: Efficient LSTM-Based Mobility Management for Power Inspection Vehicles in Smart Grids by Xiaoyan Wang, Xianfeng Xie, Shengnan Zhao

“…This scheme leverages Software-Defined Networking (SDN) technology to centrally manage vehicle communication and network traffic, integrating trajectory prediction algorithms to forecast the future movements of power inspection vehicles, optimizing route planning, and improving operational efficiency and network performance. …”
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Traffic Speed Forecast in Adjacent Region between Highway and Urban Expressway: Based on MFD and GRU Model by Yuan Gao, Jiandong Zhao, Ziyan Qin, Yingzi Feng, Zhenzhen Yang, Bin Jia

“…The proposed algorithm’s prediction performance is verified based on the GPS data collected from the adjacent region between Beijing Highways and Expressway.…”
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Scientists yet to consider spatial correlation in assessing uncertainty of spatial averages and totals by Alexandre M.J.-C. Wadoux, Gerard B.M. Heuvelink

“…The remote sensing data typically serve as covariates to deliver spatially explicit information using machine learning algorithms. Often the uncertainty associated with the maps is also quantified, for instance by prediction error variance maps or by maps of the lower and upper limits of a prediction interval. …”
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A Complex Network of MicroRNAs Expressed in Brain and Genes Associated with Amyotrophic Lateral Sclerosis by Santosh Shinde, Neelima Arora, Utpal Bhadra

“…Similar search was conducted using TargetScan and PicTar for prediction of target sites in 3′ UTR only. 1456 target sites were predicted using miRanda and more target sites were found in 5′ UTR and CDS region as compared to 3′ UTR. 11 target sites were predicted to be common by all the algorithms and, thus, these represent the most significant sites. …”
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Biomarker discovery by sparse canonical correlation analysis of complex clinical phenotypes of tuberculosis and malaria. by Juho Rousu, Daniel D Agranoff, Olugbemiro Sodeinde, John Shawe-Taylor, Delmiro Fernandez-Reyes

“…We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11% in tuberculosis and up to 5% in malaria. …”
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Artificial intelligence in diabetology by V. V. Klimontov, V. B. Berikov, O. V. Saik

“…Machine learning algorithms are applied for personalized prediction of glucose trends, in the closed-loop insulin delivery systems and decision support systems for lifestyle modification and diabetes treatment. …”
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AI Methods for New Psychoactive Substance (NPS) Design and Analysis by Enrico Greco

“…Special emphasis is placed on mass spectrometry (MS)-based techniques, where AI algorithms (e.g., for spectral prediction and pattern recognition) are revolutionizing the detection and characterization of unknown NPSs. …”
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WisdomModel: convert data into wisdom by Israa Mahmood, Hasanen Abdullah

“…Purpose – Traditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. …”
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