Learning Modeling Research Articles

A high-resolution GIS and machine learning approach for targeted disease management and localized risk assessment in an urban setup: A case study from Bhopal, Central India.

A high-resolution GIS and machine learning approach for targeted disease management and localized risk assessment in an urban setup: A case study from Bhopal, Central India.

Advancing urban solar assessment: A deep learning and atmospheric modelling framework for quantifying PV yield and carbon reduction

Advancing urban solar assessment: A deep learning and atmospheric modelling framework for quantifying PV yield and carbon reduction

Integrating machine learning and discrete choice modeling for enhanced shopping destination choice model

Integrating machine learning and discrete choice modeling for enhanced shopping destination choice model

Identification of key feature variables and prediction of harmful algal blooms in a water diversion lake based on interpretable machine learning.

Identification of key feature variables and prediction of harmful algal blooms in a water diversion lake based on interpretable machine learning.

Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price.

Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price.

Research on Machine Learning Optimization Algorithm Based on QUBO Model

This paper deals with the research of QUBO model optimization machine learning algorithm, including the following: first, discretize the continuous parameters of the machine learning model into binary variables, and construct the QUBO objective function; then optimize the QUBO model by using the quantum annealing algorithm; and finally solve the globally optimal solution by adjusting the annealing parameters, which include the initial temperature, the coefficient of temperature reduction, and the number of iterations. QUBO model can discretize continuous variables (including parameters such as weights and bias) in AR, SVM, CNN and other models in machine learning into binary variables, so as to better deal with nonlinear relationships and reduce the computational complexity in the training process. With the QUBO model, the regularization term can be better controlled to avoid the overfitting problem. Meanwhile, the QUBO model can be solved in parallel by quantum computing or simulated annealing algorithm, which significantly improves the computational efficiency under large-scale data.

Islamic Religious Education in the Formation of Moral and Religious Values

This study aims to analyse the role of Islamic Religious Education (IRE) in shaping the religious and tolerant character of students through a literature review approach. In the context of a multicultural society and the digital age, religious values and tolerance are important elements in character education. Based on a review of several literature sources (Arifim 2020, Rizkia and Zaedi 2019, Bangun and Ndoana 2024, Putra and Fauzi 2024 dan Aviatin, Robandi, and Komalasari 2023), it was found that PAI plays a strategic role in instilling values of faith, worship, and morality through value-based learning, teacher role modelling, digital media, and intercultural dialogue. These strategies have proven effective in fostering inclusive religious character and a tolerant attitude toward diversity. This article recommends strengthening the PAI curriculum with a reflective and contextual approach to support the development of students who are not only religiously devout but also capable of living harmoniously in a pluralistic societyHighlight : Islamic Religious Education fosters religious and tolerant character through value-based learning and role modelling. Digital media and intercultural dialogue enhance students' understanding and empathy in a diverse society. A holistic, reflective, and contextual curriculum strengthens spiritual values and social harmony. Keywords : Islamic Religious Education, Religious Character, Tolerance, Digital Media, Teacher Role

Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region

Ozone (O3) and fine particulate matter (PM2.5) are critical atmospheric pollutants whose complex chemical coupling presents significant challenges for multi-pollutant control strategies. This study investigated the spatiotemporal variations and driving mechanisms of O3 and PM2.5 in Jiaxing, China, during different COVID-19 lockdown periods from November 2019 to January 2024. Using high-resolution monitoring data, random forest modeling, and HYSPLIT backward trajectory analysis, we quantified the relative contributions of anthropogenic emissions, meteorological conditions, and regional transport to the formation and variation of O3 and PM2.5 concentrations. The results revealed a distinct inverse relationship between O3 and PM2.5, with meteorologically normalized PM2.5 decreasing significantly (−5.0 μg/m3 compared to the pre-lockdown baseline of 0.6 μg/m3), while O3 increased substantially (15.2 μg/m3 compared to the baseline of 5.3 μg/m3). Partial dependency analysis revealed that PM2.5-O3 relationships evolved from linear to non-linear patterns across lockdown periods, while NO2-O3 interactions indicated shifts from VOC-limited to NOx-limited regimes. Regional transport patterns exhibited significant temporal variations, with source regions shifting from predominantly northern areas pre-lockdown to more diverse directional contributions afterward. Notably, the partial lockdown period demonstrated the most balanced pollution control outcomes, maintaining reduced PM2.5 levels while avoiding O3 increases. These findings provide critical insights for developing targeted multi-pollutant control strategies in the Yangtze River Delta region and similar urban environments.

ActBeCalf: Accelerometer-based multivariate time-series dataset for calf behavior classification.

ActBeCalf: Accelerometer-based multivariate time-series dataset for calf behavior classification.

A deep learning and statistical shape modeling-based method for assessing intercondylar notch volume in anterior cruciate ligament reconstruction.

A deep learning and statistical shape modeling-based method for assessing intercondylar notch volume in anterior cruciate ligament reconstruction.

Coronary artery disease severity and location detection using deep-mining-based magnetocardiography pattern features.

Coronary artery disease severity and location detection using deep-mining-based magnetocardiography pattern features.

Predicting pregnancy-related pelvic girdle pain using machine learning.

Predicting pregnancy-related pelvic girdle pain using machine learning.

Potential and limitations of machine learning modeling for forecasting Acute Food Insecurity

Potential and limitations of machine learning modeling for forecasting Acute Food Insecurity

Characteristics and spatial distribution of marine litter on the Moroccan Mediterranean coasts: Insights from Al Hoceima region in the central southern Alboran Sea.

Characteristics and spatial distribution of marine litter on the Moroccan Mediterranean coasts: Insights from Al Hoceima region in the central southern Alboran Sea.

Machine learning modeling of supercritical water gasification for predictive hydrogen production from waste biomass

Machine learning modeling of supercritical water gasification for predictive hydrogen production from waste biomass

Evaluating polymer influence on resuspendability of indomethacin suspensions produced by microfluidization.

Evaluating polymer influence on resuspendability of indomethacin suspensions produced by microfluidization.

Hybrid machine learning modeling of strength in sustainable basalt fiber-reinforced concrete

Hybrid machine learning modeling of strength in sustainable basalt fiber-reinforced concrete

Ionic liquid electrolytes for metal-air batteries: High-throughput screening and machine learning modeling

Ionic liquid electrolytes for metal-air batteries: High-throughput screening and machine learning modeling

CO2 adsorption on NaOH and acid modified montmorillonite: Response surface methodology and machine learning modeling

CO2 adsorption on NaOH and acid modified montmorillonite: Response surface methodology and machine learning modeling

Development of field compaction curves for asphalt mixtures based on laboratory workability tests and machine learning modeling

Development of field compaction curves for asphalt mixtures based on laboratory workability tests and machine learning modeling