An Interpretable Machine Learning Framework for Unraveling the Dynamics of Surface Soil Moisture Drivers by Zahir Nikraftar, Esmaeel Parizi, Mohsen Saber, Mahboubeh Boueshagh, Mortaza Tavakoli, Abazar Esmaeili Mahmoudabadi, Mohammad Hassan Ekradi, Rendani Mbuvha, Seiyed Mossa Hosseini

“…This study introduces an interpretable machine learning framework that combines the SHapley Additive exPlanations (SHAP) method with two-step clustering to unravel the spatial drivers of SSM across Iran. …”
Get full text

Astronomical Image Superresolution Reconstruction with Deep Learning for Better Identification of Interacting Galaxies by Jiawei Miao, Liangping Tu, Hao Liu, Jian Zhao

Get full text

Field scale wheat yield prediction using ensemble machine learning techniques by Sandeep Gawdiya, Dinesh Kumar, Bulbul Ahmed, Ramandeep Kumar Sharma, Pankaj Das, Manoj Choudhary, Mohamed A. Mattar

“…These findings are a promising step towards future research focused on creating more advanced ensemble methods with finely tuned hyperparameters for improving the accuracy of large-scale wheat yield prediction.…”
Get full text

Methodical aspects of using of Web 2.0 services in the process of blended learning by Diana V. Moglan

“…As a theoretical basis for the organization and maintenance of blended learning using Web 2.0 services was used a five-step model of moderating online learning by J. …”
Get full text

A lightweight deep-learning model for parasite egg detection in microscopy images by Wenbin Xu, Qiang Zhai, Jizhong Liu, Xingyu Xu, Jing Hua

“…Therefore, we have designed a lightweight deep-learning model, YAC-Net, to achieve rapid and accurate detection of parasitic eggs and reduce the cost of automation. …”
Get full text

Generating learning guides for medical education with LLMs and statistical analysis of test results by Iván Roselló Atanet, Mihaela Tomova, Miriam Sieg, Victoria Sehy, Patrick Mäder, Maren März

“…Methods We have developed a seven-step approach based on large language models and statistical analysis to fulfil the purpose of this study. …”
Get full text

Design of cross-reactive antigens with machine learning and high-throughput experimental evaluation by Chelsy Chesterman, Thomas Desautels, Luz-Jeannette Sierra, Kathryn T. Arrildt, Adam Zemla, Edmond Y. Lau, Shivshankar Sundaram, Jason Laliberte, Lynn Chen, Aaron Ruby, Mark Mednikov, Sylvie Bertholet, Dong Yu, Kate Luisi, Enrico Malito, Corey P. Mallett, Matthew J. Bottomley, Robert A. van den Berg, Daniel Faissol

“…Selecting an optimal antigen is a crucial step in vaccine development, significantly influencing both the vaccine’s effectiveness and the breadth of protection it provides. …”
Get full text

Multi-modal remote sensory learning for multi-objects over autonomous devices by Aysha Naseer, Naif Almudawi, Hanan Aljuaid, Abdulwahab Alazeb, Yahay AlQahtani, Asaad Algarni, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

“…Using a synergistic combination of Markov Random Field (MRF) for accurate labeling and Alex Net model for robust scene recognition, this work presents a novel method for the identification of remote sensing objects. During the labeling step, the use of MRF guarantees precise spatial contextual modeling, which improves comprehension of intricate interactions between nearby aerial objects. …”
Get full text

An explainable Machine Learning model for Large-Scale Travelling Ionospheric Disturbances forecasting by Ventriglia Vincenzo, Guerra Marco, Cesaroni Claudio, Spogli Luca, Altadill David, Segarra Antoni, Galkin Ivan, Barta Veronika, Verhulst Tobias G.W., de Paula Víctor, Navas-Portella Víctor, Berényi Kitti A., Belehaki Anna

“…In this work, we present a machine learning model able to forecast the occurrence of LSTIDs over the European continent up to three hours in advance. …”
Get full text

Integrating Hybrid Team-Based and Case-Based Learning in Dental Education Transformation by Sidra Abid, Humna Rafiq

“… In the context of dental education, the adoption of Hybrid team based learning (TBL) and case based learning (CBL) is a promising approach towards meeting the needs of whether competencies-based or outcome-based medical education by promoting active, collaborative, and self-directed learning. …”
Get full text

Predictive survival modelings for HIV-related cryptococcosis: comparing machine learning approaches by Xuemin Fu, Luling Wu, Luling Wu, Jingna Xun, Benno Pütz, Zhihang Zheng, Yanpeng Li, Yanpeng Li, Yinzhong Shen, Hongzhou Lu, Jun Chen, Bertram Müller-Myhsok

“…In parallel, our penalized survival model provides a step forward in personalized risk assessment, supporting informed clinical decisions and customized patient management. …”
Get full text

Decoding methane concentration in Alberta oil sands: A machine learning exploration by Liubov Sysoeva, Ilhem Bouderbala, Miles H. Kent, Esha Saha, B.A. Zambrano-Luna, Russell Milne, Hao Wang

“…We introduce a multi-step framework for finding the primary factors associated with higher methane concentrations, powered by machine learning models (such as random forest) trained on high dimensional datasets sourced from multiple weather monitoring stations located in the Lower Athabasca region. …”
Get full text

The Effectiveness of Self-Directed Learning on The Professional Development of Pre-Registration Nursing Students: by Yusra Al-Nasseri

“…SDL does not only provide a means for effective learning of knowledge and skills but also serves as a vehicle for enabling nursing students to acquire and develop independent learning skills; as well as develop a sense of accountability and responsibility that equips them with the essential attributes they require for their future nursing career. …”
Get full text

Risk assessment of oral leukoplakia by DNA content enhanced by machine learning models by Matheus Henrique Lopes Dominguete, Vera Cavalcanti de Araújo, Fernanda Viviane Mariano, Carmen Silvia Passos Lima, João Figueira Scarini, Paulo de Camargo Moraes, Victor Angelo Martins Montalli, Larissa Agatti, Zuraiza Mohamad Zaini, Luíza Hellmeister, Marcelo Sperandio

Get full text

The MECOVMA Framework: Implementing Machine Learning Under Macroeconomic Volatility for Marketing Predictions by Manuel Muth

“…This leads to the final framework with four overarching P_MECOVMA process steps, guiding the Machine Learning application process in this context with specific tasks. …”
Get full text

ACE2: accurately learning subseasonal to decadal atmospheric variability and forced responses by Oliver Watt-Meyer, Brian Henn, Jeremy McGibbon, Spencer K. Clark, Anna Kwa, W. Andre Perkins, Elynn Wu, Lucas Harris, Christopher S. Bretherton

“…It exactly conserves global dry air mass and moisture and can be stepped forward stably for arbitrarily many steps with a throughput of about 1500 simulated years per wall clock day. …”
Get full text

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems by Alireza Jahandoost, Razieh Dashti, Mahboobeh Houshmand, Seyyed Abed Hosseini

“…Abstract Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. …”
Get full text

Research on Path Planning of Agricultural UAV Based on Improved Deep Reinforcement Learning by Haitao Fu, Zheng Li, Weijian Zhang, Yuxuan Feng, Li Zhu, Xu Fang, Jian Li

“…Traditional manual or semi-mechanized pesticide spraying methods often suffer from issues such as redundant coverage and cumbersome operational steps, which fail to meet current pest and disease control requirements. …”
Get full text

International Capacity Building for Sustainable Growth: What Role for the World’s Learned Societies? by Elizabeth Bell

“…The author therefore proposes a number of steps that should help to build strong relationships between Learned Societies and various funding bodies including governments. …”
Get full text

Factor Implicit Model of Machine Classification Learning Based on Factor Space Theory by Ying Wang, Fanhui Zeng, Hui Sun, Xiaotong Liu, Kaile Lin, Kaijie Zhang

“…The conclusion of factor implicit research on multi-classification learning expands the theory and application of factor space.…”
Get full text