Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factors by Yan-Qun Sun, Xiao-Yan Zhu, Tian-Ci Fan, Tian Ma, Hong-Han Ge, Rui-Fang Shi, Xu Wang, Wei Li, Jie-Yun Yin, Ye Tian

“…The COVID-19 pandemic severely disrupted reporting dynamics, with 2020 and 2021 cases falling 43.9 % (95 % CI: 41.2–46.7 %) and 22.0 % (95 % CI: 19.5–24.5 %) below predictions, respectively—a decline most pronounced in the Northeast and linked to reduced healthcare access and outdoor activity during lockdowns. …”
Get full text

Assessing concordance between RNA-Seq and NanoString technologies in Ebola-infected nonhuman primates using machine learning by Mostafa Rezapour, Aarthi Narayanan, Wyatt H. Mowery, Metin Nafi Gurcan

“…Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, OAS1 was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. …”
Get full text

Toward Culturally and Linguistically Responsive E-Learning in Post-COVID-19 Higher Education: Perspectives from the United Arab Emirates by Sarah Hopkyns

“…This article presents empirical data from a qualitative phenomenological case study investigating male and female Emirati university students’ (n = 107) perspectives on access, interaction, and engagement during Zoom classes in the fall of 2020 and spring of 2021. Students’ reflective essays and researcher observations revealed that intersecting factors, such as gender, religion, culture, linguistic challenges in English-medium universities, and fear of judgment, affected participants’ comfort levels and learning effectiveness in online classes. …”
Get full text

Comparison of Classical Arima Forecasting Methods to the Machine Learning LSTM Method: a Case Study on DAX® 50 ESG Index by Rosinus, Manuel

“…Background: Traditional econometric models like ARIMA, while foundational for time series forecasting, often rely on assumptions of linearity and stationarity. These models can fall short in capturing the complex, nonlinear dynamics frequently present in financial markets. …”
Get full text

A machine learning approach to carbon emissions prediction of the top eleven emitters by 2030 and their prospects for meeting Paris agreement targets by Arju Manara Begum, Mahadee Al Mobin

“…Results indicate that Russia is on track to exceed its reduction targets, while Germany and the United States will fall slightly short. China, India, Japan, Canada, South Korea, and Indonesia are projected to miss their commitments by significant margins. …”
Get full text

On Robustness of the Explanatory Power of Machine Learning Models: Insights From a New Explainable AI Approach Using Sensitivity Analysis by Banamali Panigrahi, Saman Razavi, Lorne E. Doig, Blanchard Cordell, Hoshin V. Gupta, Karsten Liber

“…Abstract Machine learning (ML) is increasingly considered the solution to environmental problems where limited or no physico‐chemical process understanding exists. …”
Get full text

Machine Learning-Augmented Triage for Sepsis: Real-Time ICU Mortality Prediction Using SHAP-Explained Meta-Ensemble Models by Hülya Yilmaz Başer, Turan Evran, Mehmet Akif Cifci

“…Traditional scoring systems, including qSOFA, SIRS, and NEWS, often fall short of delivering the precision necessary for timely and effective clinical decision-making. …”
Get full text

Developing an Equitable Machine Learning–Based Music Intervention for Older Adults At Risk for Alzheimer Disease: Protocol for Algorithm Development and Validation by Chelsea S Brown, Luna Dziewietin, Virginia Partridge, Jennifer Rae Myers

“…Data analysis for the results of aims 1, 2, and 3 are underway and results are expected to be published in the fall of 2025. ConclusionsThis protocol seeks to use ML to improve the equitability and accessibility of a digital lifestyle intervention for AD. …”
Get full text

Crack-Based Estimation of Seismic Damage Level in Confined Masonry Walls in the Lima Metropolitan Area Using Deep Learning Techniques by Miguel Diaz, Luis Lopez, Michel Amancio, Italo Inocente, Jhianpiere Salinas, Sergio Isuhuaylas, Erika Flores, Edisson Moscoso

Get full text

A Multi-Agent and Attention-Aware Enhanced CNN-BiLSTM Model for Human Activity Recognition for Enhanced Disability Assistance by Mst Alema Khatun, Mohammad Abu Yousuf, Taskin Noor Turna, AKM Azad, Salem A. Alyami, Mohammad Ali Moni

“…<b>Methods:</b> This paper proposes a novel strategy using a three-stage feature ensemble combining deep learning (DL) and machine learning (ML) for accurate and automatic classification of activity recognition. …”
Get full text

E-learning success evaluation in Lebanon during wartime: An extension of Delone and McLean IS success model [version 1; peer review: 2 approved] by Elie Yammine, Layal Rabih

“…Due to the impact of the war on education in Lebanon during fall 2024-2025 and the suspension of traditional learning, e-learning adoption was the answer to the ministry of education and higher education call for learning continuity. …”
Get full text

The Development of a Reading Book on House Construction Civilization Based on Project-Based Learning to Improve Children's Social Intelligence Character by Gregorius Ari Nugrahanta, Hilary Relita Vertikasari Sekarningrum, Brigita Sherlyta Erlansa Putri

“… The present study discusses the need to improve social intelligence character in elementary schools, which has so far been limited to lecture-based methods and not integrated into formal learning. The study aims to develop a reading book on the civilization of building houses based on Project-Based Learning (PjBL) to foster children's social intelligence character. …”
Get full text

English As A Medium Of Instruction (emi): Non-english Major Scientific Teachers’ Expectancy & Requirement Vs. Language Learning Challenges by Fatima Zohra Malki, Amina Kimouche, Amina Ayadi

“…The findings show that the reality of teaching English to scientific instructors falls far short of expectations. Many challenges were found to hinder the success of this process, including selecting appropriate content and adopting suitable methods for heterogeneous groups in terms of English proficiency level, discipline, availability, professional background, and age. …”
Get full text

Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle by Shing-Hong Liu, Alok Kumar Sharma, Bo-Yan Wu, Xin Zhu, Chun-Ju Chang, Jia-Jung Wang

“…Abstract The gait analysis has been applied in many fields, such as the assessment of falling, force evaluation in sports, and gait disorder detection for neuromuscular diseases. …”
Get full text

Landslide Susceptibility Level Mapping in Kozhikode, Kerala, Using Machine Learning-Based Random Forest, Remote Sensing, and GIS Techniques by Pradeep Kumar Badapalli, Anusha Boya Nakkala, Raghu Babu Kottala, Sakram Gugulothu, Fahdah Falah Ben Hasher, Varun Narayan Mishra, Mohamed Zhran

“…Results indicate that approximately 17.82% of the study area falls under high to very high susceptibility, predominantly in the steep, weathered, and high rainfall zones of the Western Ghats. …”
Get full text

A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning by Forrest Corcoran, Christopher E. Parrish, Lori A. Magruder, J. P. Swinski

Get full text

Machine-Learning-Based Analysis of Internal Forces in Reinforced Concrete Conical and Cylindrical Tanks Under Hydrostatic Pressure Considering Material Nonlinearity by May Haggag, Mohamed K. Ismail, Ahmed Elansary

“…Accordingly, the present paper introduces a machine learning (ML) framework as an effective predictive tool for RC conical and cylindrical tanks under hydrostatic pressure. …”
Get full text

A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE by Md. Shamshuzzoha, Tazkia Tasnim Bahar Audry, Md. Jahangir Alam, Zaheed Ahmed Bhuiyan, Md Motaharul Islam, Mohammad Mehedi Hassan

“…Seasonal Affective Disorder (SAD) is a mood disorder characterized by recurring depressive episodes during specific seasons, particularly in Fall and Winter. With the rise of social media as a platform for self-expression, user-generated content offers valuable insights into mental health trends, presenting an opportunity for data-driven SAD detection. …”
Get full text

SmartTrust: a hybrid deep learning framework for real-time threat detection in cloud environments using Zero-Trust Architecture by Umesh Kumar Lilhore, Sarita Simaiya, Roobaea Alroobaea, Abdullah M. Baqasah, Majed Alsafyani, Afnan Alhazmi, Md Monish Khan

“…Traditional security mechanisms, such as static rule-based systems and Multi-Factor Authentication (MFA), often fall short of identifying advanced attacks like insider threats, privilege escalation, and data breaches. …”
Get full text

A machine learning-based methodology for short-term kinetic energy forecasting with real-time application: Nordic Power System case by Jose Miguel Riquelme-Dominguez, Manuel Carranza-García, Pedro Lara-Benítez, Francisco M. González-Longatt

“…Therefore, transmission system operators (TSOs) need tools to forecast the inertia or the kinetic energy available in the systems in the very short term (from minutes to hours) in order to take appropriate actions if the values fall below the one that ensures secure operation. …”
Get full text