Novel delayed binary time-series pattern based machine learning techniques for stock market forecasting by Zeqiye Zhan, Song-Kyoo Kim

“…This study proposes an innovative machine learning technique for stock market forecasting that leverages delayed binary time-series patterns to enhance prediction accuracy. …”
Get full text

Predicting the compressive strength of concrete incorporating waste powders exposed to elevated temperatures utilizing machine learning by Islam N. Fathy, Hany A. Dahish, Mohammed K. Alkharisi, Alaa A. Mahmoud, Hala Emad Elden Fouad

“…In this study, three machine learning approaches, extreme gradient boosting (XGBoost), random forest (RF), and M5P, were used for constructing the prediction model for the impact of elevated temperatures on the compressive strength of concrete modified by marble and granite construction waste powders as partial cement replacements in concrete. …”
Get full text

Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy by Mahadee Al Mobin

“…This study introduces a rigorous multivariate time series analysis, integrating meteorological factors with state-of-the-art machine learning (ML) models, to predict DENV case trends across different temporal scales. …”
Get full text

A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…QProteoML was experimentally tested by comparing accuracy, F1 score and AUC ROC between classical machine learning models such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN). …”
Get full text

A novel mechanism-guided residual network for accurate modelling of scroll expander under noisy and sparse data conditions by Xiaoshuang Lv, Xin Ma, Wei Peng, Ke Li, Chengdong Li

“…Even with a 90% reduction in training data, MGResNet maintains superior accuracy, showing up to 45.983% better performance than other models. …”
Get full text

BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs by Youngbin Kim, Kunlun Wang, Roberta I. Lock, Trevor R. Nash, Sharon Fleischer, Bryan Z. Wang, Barry M. Fine, Gordana Vunjak-Novakovic

“…We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. …”
Get full text

Machine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things by Nabeela Hasan, Saima Saleem, Mudassir Khan, Abdulatif Alabdultif, Mohammad Mazhar Nezami, Mansaf Alam

“…This research introduces ML-DCPP, a Machine Learning-based Dynamic Clustering and Privacy Preservation framework tailored to safeguard IIoT ecosystems. …”
Get full text

Integrating Bioengineering and Machine Learning: A Multi-Algorithm Approach to Enhance Agricultural Sustainability and Resource Efficiency by Senthil G.A., Prabha R., Asha R.M., Suganthi S.U., Sridevi S.

“…The novel research incorporates high-level machine learning algorithms for optimizing agricultural performance regarding sustainability and resource efficiencies. …”
Get full text

Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization by Kaitlyn M. Martinez, Kristen Wilding, Trent R. Llewellyn, Daniel E. Jacobsen, Makaela M. Montoya, Jessica Z. Kubicek-Sutherland, Sweta Batni, Carrie Manore, Harshini Mukundan

Get full text

Evaluation of machine learning approaches for large-scale agricultural drought forecasts to improve monitoring and preparedness in Brazil by J. W. Gallear, M. Valadares Galdos, M. Zeri, A. Hartley

“…Furthermore, we also determine spatio-temporal drivers of the VHI across the wide variation in climates and evaluate machine learning performance for El Niño–Southern Oscillation variation and forecasting of the onset of drought stress. …”
Get full text

A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings by Hafiz Muhammad Shakeel, Shamaila Iram, Richard Hill, Hafiz Muhammad Athar Farid, Akbar Sheikh-Akbari, Farrukh Saleem

“…Additionally, a customised machine learning interface was developed to visualise the multifaceted data analyses and model evaluations, promoting informed decision-making.…”
Get full text

Simulation of Ground Visibility Based on Atmospheric Boundary Layer Data Using K-Nearest Neighbors and Ensemble Model Algorithms by Ruolan Liu, Shujie Yuan, Duanyang Liu, Lin Han, Fan Zu, Hong Wu, Hongbin Wang

“…This study introduces a machine learning approach for simulating visibility, utilizing the K-Nearest Neighbors algorithm and an ensemble model, which incorporate data from atmospheric boundary layer detection and conventional ground meteorological observations as simulation inputs. …”
Get full text

Spatiotemporal dynamics and key drivers of carbon emissions in regional construction sectors: Insights from a Random Forest Model by Zhonghan Yu, Qudsia Kanwal, Menghan Wang, Anissa Nurdiawati, Sami G. Al-Ghamdi

“…This research utilizes the Random Forest Model, a sophisticated machine learning method, to examine the determinants of carbon emissions in China's construction sector at the regional scale. …”
Get full text

The predictive role of identifying frailty in assessing the need for palliative care in the elderly: the application of machine learning algorithm by Zahra Nejatifar, Ahad Alizadeh, Mohammad Amerzadeh, Shideh Omidian, Sima Rafiei

“…The super-learning model demonstrated higher accuracy (92%) than other machine-learning algorithms. …”
Get full text

Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine by Debendra Muduli, Rani Kumari, Adnan Akhunzada, Korhan Cengiz, Santosh Kumar Sharma, Rakesh Ranjan Kumar, Dinesh Kumar Sah

“…Lastly, a newly improved learning algorithm encompasses a modified pelican optimization algorithm (MOD-POA) and an extreme learning machine (ELM) for classification tasks. …”
Get full text

Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management by Vikram Pasupuleti, Bharadwaj Thuraka, Chandra Shikhi Kodete, Saiteja Malisetty

“…<b>Methods</b>: This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. …”
Get full text

Estimation of the air conditioning energy consumption of a classroom using machine learning in a tropical climate by Liliana Ortega-Diaz, Julian Jaramillo-Ibarra, German Osma-Pinto

“…Machine learning is the most widely used approach for prediction due to its speed, accuracy, and non-linear modeling. …”
Get full text

Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru by Lucia Enriquez, Kevin Ortega, Dennis Ccopi, Claudia Rios, Julio Urquizo, Solanch Patricio, Lidiana Alejandro, Manuel Oliva-Cruz, Elgar Barboza, Samuel Pizarro

“…Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). …”
Get full text

Increasing comprehensiveness and reducing workload in a systematic review of complex interventions using automated machine learning by Olalekan A Uthman, Rachel Court, Jodie Enderby, Lena Al-Khudairy, Chidozie Nduka, Hema Mistry, GJ Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…Background As part of our ongoing systematic review of complex interventions for the primary prevention of cardiovascular diseases, we have developed and evaluated automated machine-learning classifiers for title and abstract screening. …”
Get full text

CAMP-GNN: A Constraint-Aware Message-Passing Model for Optimal Resource Allocation in Software Projects by Muhammad Asif, Fahd M. Aldosari, Donia Y. Badawood, Abdu Salam

Get full text