K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems by Djamel Guessoum, Maen Takrouri, Mohammad Rabih, Maissa Farhat, Sufian A. Badawi

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Advanced Color Edge Detection Using Clifford Algebra in Satellite Images by Uzair Aslam Bhatti, Zhou Ming-Quan, Qingsong Huo, Sajid Ali, Aamir Hussain, Yan Yuhuan, Zhaoyuan Yu, Linwang Yuan, Saqib Ali Nawaz

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A novel deep learning approach for investigating liquid fuel injection in combustion system by Syed Azeem Inam, Abdullah Ayub Khan, Noor Ahmed, Tehseen Mazhar, Tariq Shahzad, Sunawar Khan, Mamoon M. Saeed, Habib Hamam

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Forestry climate adaptation with HarvesterSeasons service—a gradient boosting model to forecast soil water index SWI from a comprehensive set of predictors in Destination Earth by Mikko Strahlendorff, Anni Kröger, Golda Prakasam, Miriam Kosmale, Mikko Moisander, Heikki Ovaskainen, Asko Poikela

“…The Copernicus Global Land Monitoring Service’s Soil Water Index (SWI) satellite-based observations from 2015 to 2023 at 10,000 locations in Europe were used as the predictand (target parameter) to train an artificial intelligence (AI) model to predict soil wetness with XGBoost (eXtreme Gradient Boosting) and LightGBM (Light Gradient Boosting Machine) implementations of gradient boosting algorithms. …”
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Machine learning applications in forecasting patient satisfaction and clinical outcomes after carpal tunnel release: a retrospective study by Zohreh Manoochehri, Sara Manoochehri, Seyed Reza Bagheri, Alireza Abdi, Ehsan Alimohammadi

“…Four machine learning algorithms—random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and k-nearest neighbors (k-NN)—were used to predict these outcomes. …”
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Physics-informed deep learning model for line-integral diagnostics across fusion devices by Cong Wang, Weizhe Yang, Haiping Wang, Renjie Yang, Jing Li, Zhijun Wang, Yixiong Wei, Xianli Huang, Chenshu Hu, Zhaoyang Liu, Xinyao Yu, Changqing Zou, Zhifeng Zhao

“…The incorporation of the PILF has been shown to correct the model’s predictions, bringing the back-projections closer to the actual inputs and reducing the errors associated with inversion algorithms. …”
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Discovering New Tyrosinase Inhibitors by Using In Silico Modelling, Molecular Docking, and Molecular Dynamics by Kevin A. OréMaldonado, Sebastián A. Cuesta, José R. Mora, Marcos A. Loroño, José L. Paz

“…<b>Background/Objectives:</b> This study was used in silico modelling to search for potential tyrosinase protein inhibitors from a database of different core structures for IC₅₀ prediction. <b>Methods</b>: Four machine learning algorithms and topographical descriptors were tested for model construction. …”
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Intelligent Data Reduction for IoT: A Context-Driven Framework by Laercio Pioli, Douglas D. J. De Macedo, Daniel G. Costa, Mario A. R. Dantas

“…With these predictions based on existing datasets, a selector algorithm module is adopted to identify the most suitable data reduction approach for specific IoT applications. …”
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The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK by Solomon White, Tiago Silva, Laurent O. Amoudry, Evangelos Spyrakos, Adrien Martin, Adrien Martin, Encarni Medina-Lopez

“…Feature importance analysis identified the critical roles of infrared bands in SST prediction and blue/green colour bands in SSS estimation. …”
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Goal-oriented autonomous decision-making for social robots via collaborative interactive inverse reinforcement learning approach by Mingyue Luo, Hui Li, Wanbo Luo, Hewei Li, Jianan Li

“…Moreover, trajectory prediction at longer distances also poses significant challenges for such trajectory planning methods. …”
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Automated Calibration of SWMM for Improved Stormwater Model Development and Application by Hossein Ahmadi, Durelle Scott, David J. Sample, Mina Shahed Behrouz

“…Hydrologic models are commonly used to predict flows and assess the performance of stormwater controls, often based on a hypothetical yet standardized design storm. …”
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Accuracy is not enough: a heterogeneous ensemble model versus FGSM attack by Reham A. Elsheikh, M. A. Mohamed, Ahmed Mohamed Abou-Taleb, Mohamed Maher Ata

“…Firstly, the three individual deep learning classifier-based Facial Emotion Recognition (FER) classifications have been developed; the predictions from all three classifiers are then merged using majority voting to develop the HEM_Net-based ensemble model. …”
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Programmed cell death signatures-driven microglial transformation in Alzheimer’s disease: single-cell transcriptomics and functional validation by Mi-Mi Li, Ying-Xia Yang, Ya-Li Huang, Shu-Juan Wu, Wan-Li Huang, Li-Chao Ye, Ying-Ying Xu

“…BackgroundThis study aims to develop and validate a programmed cell death signature (PCDS) for predicting and classifying Alzheimer’s disease (AD) using an integrated machine learning framework. …”
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Distress-Based Pavement Condition Assessment Using Artificial Intelligence: A Case Study of Egyptian Roads by Mostafa M. Radwan, Sundus A. Faris, Ahmed Y. Barakat, Ahmad Mousa

“…The ANN model exhibited greater prediction accuracy than the other developed models and can also reliably forecast PCI values by using only measured distress data.…”
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Point-of-care prognostication in moderate Covid-19: Analytical validation and prognostic accuracy of a soluble urokinase plasminogen activator receptor (suPAR) rapid test. by Arjun Chandna, Raman Mahajan, Priyanka Gautam, Lazaro Mwandigha, Sabine Dittrich, Vikash Kumar, Jennifer Osborn, Pragya Kumar, Constantinos Koshiaris, George M Varghese, Yoel Lubell, Sakib Burza

“…AUC of ELISA = 0.70 [95% CI = 0.63 to 0.76]; p = 0.12) or as part of a published multivariable prediction model (AUC of RDT-based model = 0.74 [95% CI = 0.66 to 0.83] vs. …”
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Cotton Crop Classification using Optical and Microwave Remote Sensing Datasets in Google Earth Engine by B. Meerasha, M. Sagayam

“…To make well-informed decisions, farmers, governments, scientists, and agricultural organizations need accurate information on crop prediction. However, automated crop type mapping remains challenging due to the limited availability of field-level crop labels required to train supervised classification models. …”
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Accurate and affordable multi-cancer early detection and localization via plasma cfDNA multi-omic profiling by Pin Cui, Weihuang He, Mingji Feng, Hanming Lai

“…By combining cfDNA cleavage profile of CpG sites with machine learning algorithms, we have identified specific CpG cleavage profile as biomarkers to predict the methylation status of individual CpG sites, based on which we built in silico classifiers for prediction of each of the four groups previously mentioned, achieving considerable performance of AUC ranging from 0.8896 to 0.959. …”
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Green Video Transcoding in Cloud Environments Using Kubernetes: A Framework With Dynamic Renewable Energy Allocation and Priority Scheduling by B. M. Beena, Prashanth Cheluvasai Ranga, A. Vinitha Chowdary, Rohan Gamidi, M. Hemasri, Tejaswi Muppala

“…The integration of predictive analytics with Kubernetes&#x2019; Horizontal Pod Autoscaler (HPA) allows dynamic workload distribution, ensuring optimal energy utilization. …”
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Deep reinforcement learning Lane-Change Decision-Making for autonomous vehicles based on motion primitives library in hierarchical action space by Guizhe Jin, Zhuoren Li, Bo Leng, Minhua Shao

“…In addition, model predictive control method (MPC) based on the vehicle kinematics model is used to optimize the motion primitives instantly. …”
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Concise and Accessible Representations for Multidimensional Datasets: Introducing a Framework Based on the nD-EVM and Kohonen Networks by Ricardo Pérez-Aguila, Ricardo Ruiz-Rodríguez

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