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

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Solving Data Overlapping Problem Using A Class‐Separable Extreme Learning Machine Auto‐Encoder by Ekkarat Boonchieng, Wanchaloem Nadda

“…Data overlapping and imbalanced data are significant challenges in data classification. Extreme learning machine auto‐encoding (ELM‐AE) is a feature reduction method that transforms original features into a new set of features capturing essential information in the data. …”
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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. …”
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Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques by Oana Vîrgolici, Daniela Lixandru, Andrada Mihai, Diana Simona Ștefan, Cristian Guja, Horia Vîrgolici, Bogdana Virgolici

“…<b>Methods</b>: The dataset includes medical records from patients in Bucharest hospitals, collected between 2012 and 2016. Several machine learning models, namely linear regression, polynomial regression, Gradient Boosting, and Extreme Gradient Boosting, were employed to predict changes in medical parameters as a function of body weight variation. …”
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Machine-Learning-Assisted Identification of Steam Channeling after Cyclic Steam Stimulation in Heavy-Oil Reservoirs by Yu Li, Huiqing Liu, Peng Jiao, Qing Wang, Dong Liu, Liangyu Ma, Zhipeng Wang, Hao Peng

“…To solve the issues of steam breakthrough, it is essentially important and necessary to recognize steam channeling. In this work, a machine-learning-assisted identification model, based on a random-forest ensemble algorithm, is developed to predict the occurrence of steam channeling during steam huff-and-puff processes. …”
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Data-Driven Fault Detection and Diagnosis in Cooling Units Using Sensor-Based Machine Learning Classification by Amilcar Quispe-Astorga, Roger Jesus Coaquira-Castillo, L. Walter Utrilla Mego, Julio Cesar Herrera-Levano, Yesenia Concha-Ramos, Erwin J. Sacoto-Cabrera, Edison Moreno-Cardenas

“…This research is based on data-driven models and machine learning, where a specific strategy is proposed for five types of system failures. …”
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Integrated approach of extreme learning machines and locally weighted linear regression for improved discharge coefficient prediction by Mohammed Majeed Hameed, Mohamed Khalid Alomar, Siti Fatin Mohd Razali, Ali Salem

“…Overall, the findings demonstrate that the ELM-LWLR model is a practical and robust tool for Cd modeling, offering significant advantages in cost reduction and enhanced hydraulic modeling for complex engineering applications.…”
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Machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributes by Jiaxin Liu, Shuo Yang, Qichao Li, Leiming Ji, Xuefeng Hou, Liudong Hou, Jing Ma

“…Therefore, this study presents the outcomes of predictive activities for NOx emissions using machine learning. We employed a vector autoregression (VAR) model that considers the influence of other pollutants on NOx emissions. …”
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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

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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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Real‐Time Self‐Optimization of Quantum Dot Laser Emissions During Machine Learning‐Assisted Epitaxy by Chao Shen, Wenkang Zhan, Shujie Pan, Hongyue Hao, Ning Zhuo, Kaiyao Xin, Hui Cong, Chi Xu, Bo Xu, Tien Khee Ng, Siming Chen, Chunlai Xue, Zhanguo Wang, Chao Zhao

“…In this work, in situ reflection high‐energy electron diffraction (RHEED) is integrated with machine learning (ML) to correlate the surface reconstruction with the photoluminescence (PL) of InAs/GaAs quantum dots (QDs), which serve as the active region of lasers. …”
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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). …”
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Environmental regulations and eco-innovation as catalysts for green agricultural practices: insights from Pakistan–China agricultural cooperation by Ali Raza, Lu Hongliang, Zhu Yue, Tingyu Yang, Nian Wei

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How artificial intelligence reduces human bias in diagnostics? by Artur Luczak

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DNS over HTTPS Tunneling Detection System Based on Selected Features via Ant Colony Optimization by Hardi Sabah Talabani, Zrar Khalid Abdul, Hardi Mohammed Mohammed Saleh

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Assessment and Modeling of Green Roof System Hydrological Effectiveness in Runoff Control: A Case Study in Dublin by Mehdi Gholamnia, Payam Sajadi, Salman Khan, Srikanta Sannigrahi, Saman Ghaffarian, Himan Shahabi, Francesco Pilla

“…The comprehensive dataset enabled detailed modeling of runoff hydrograph parameters using rainfall hyetographs, which were subsequently analyzed through sophisticated machine learning algorithms. …”
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