Machine learning discovery of the dielectric properties of strontium-containing condensed matter by Dongyang Huang, Jiaxing Fu, Chenghao Yu

“…Strontium-containing dielectrics constitute a diverse class of materials, some of which exhibit exceptionally high dielectric constants, thereby showing great potential for practical applications. In this work, machine learning models were successfully developed to capture the relationship between composition and dielectric properties of strontium-containing dielectrics using different algorithms, with hyperparameter optimization performed via grid search. …”
Get full text

Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing by Müge Sinem Çağlayan, Aslı Aksoy

“…The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. …”
Get full text

Hysteretic curve characteristics in rectangular shear walls predicted by machine learning by Jungui Dong, Ce Chen

“…Trained on a self-built dataset of 184 samples, IEG-ML accuracy and efficiency are enhanced using a population optimization algorithm. The model identifies the importance of feature points and component factors, providing a dominant explicable formula. …”
Get full text

A three-stage machine learning and inference approach for educational data by Ting Da

“…While existing studies have utilized meticulous regression designs, it is challenging to select appropriate controls. Machine learning, however, offers a solution whereby the entire variable set can be inspected and filtered by different optimization schemes. …”
Get full text

Optimal Forecast Combination for Japanese Tourism Demand by Yongmei Fang, Emmanuel Sirimal Silva, Bo Guan, Hossein Hassani, Saeed Heravi

Get full text

Stochastic black-box optimization using multi-fidelity score function estimator by Atul Agrawal, Kislaya Ravi, Phaedon-Stelios Koutsourelakis, Hans-Joachim Bungartz

Get full text

Multi-source Transformer for Automatic Post-Editing of Machine Translation Output by Amirhossein Tebbifakhr, Matteo Negri, Marco Turchi

“…In particular, we adapt reinforcement learning (RL) techniques to optimize our models by considering task-specific metrics (i.e. …”
Get full text

Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms by Mintae Kim, Muharrem A. Senturk, Liang Li

“…The algorithms are trained and evaluated using metrics such as the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Hyperparameter optimization is performed to enhance the model performance. …”
Get full text

Mitigating Online Banking Fraud Using Machine Learning and Anomaly Detection by Sheunesu Makura, Caden Dobson, Seani Rananga

“…The key contribution is an ensemble model combining Isolation Forest and K-means clustering, which achieves 98% accuracy and 98% F1-score in anomaly detection while reducing false positives to 2% which is a critical improvement for operational deployment in banking systems. …”
Get full text

Improving EEG based brain computer interface emotion detection with EKO ALSTM model by R. Kishore Kanna, Preety Shoran, Meenakshi Yadav, Mohammad Nadeem Ahmed, Shrikant Burje, Garima Shukla, Anurag Sinha, Mohammad Rashid Hussain, Saifullah Khalid

Get full text

Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

“…The developed model can be considered an accurate an easy-to-use tool for the prediction of crude oil/N2 IFT values for enhance oil recovery study optimization and upstream reservoir investigations.…”
Get full text

Industrial-scale prediction of cement clinker phases using machine learning by Sheikh Junaid Fayaz, Néstor Montiel-Bohórquez, Shashank Bishnoi, Matteo Romano, Manuele Gatti, N. M. Anoop Krishnan

“…Here, using a comprehensive two-year industrial dataset, we develop machine learning models that outperform conventional Bogue equations with mean absolute percentage errors of 1.24%, 6.77%, and 2.53% for alite, belite, and ferrite prediction respectively, compared to 7.79%, 22.68%, and 24.54% for Bogue calculations. …”
Get full text

Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning by Sangwoo Park, Osvaldo Simeone

“…Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate-scale quantum computers. …”
Get full text

Harnessing Predictive Modeling to Advance HIV Self-Testing in SubSaharan Africa: A Viewpoint on Equity-Driven Implementation by Felix Emeka Anyiam, Maureen Nokuthula Sibiya, Olanrewaju Oladimeji

“…While machine learning techniques such as Random Forest (RF) and Classification and Regression Trees (CART) offer powerful tools for identifying high-risk populations and optimizing HIVST distribution, their adoption in public health remains limited. …”
Get full text

An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer by Nan Yi, Shuangyang Mo, Yan Zhang, Qi Jiang, Yingwei Wang, Cheng Huang, Shanyu Qin, Haixing Jiang

“…The retained nonzero coefficient features were subsequently applied to develop predictive eight DL models based on distinct machine learning algorithms. …”
Get full text

Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions by Khaled Chahine

“…This review then suggests future research directions to overcome these limitations, including lightweight ML models for faster and more efficient control, federated learning for decentralized optimization, and digital twins for real-time system monitoring. …”
Get full text

Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning by Adrienn VARGA-BALOGH, Ádám LEELŐSSY, Róbert MÉSZÁROS

Get full text

Machine learning–enhanced screening funnel for clinical trials in Alzheimer's disease by Scott Gladstein, Liuqing Yang, Dustin Wooten, Xin Huang, Robert Comley, Qi Guo, the Alzheimer's Disease Neuroimaging Initiative

“…The funnel incorporates machine learning (ML)–based disease progression models. …”
Get full text

The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines by Christos Emexidis, Panagiotis Gkonis

“…After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their capabilities and responses to preprocessing, as well as each model’s reaction in terms of statistical performance deviation indicators. …”
Get full text

Optimal Design and Analysis of Conical Magnetic Gear by Seyed Ahamdreza Afsari Kashani

“…To compare the proposed topology, the optimal design of the model is compared with the conventional radial flux structure using the genetic algorithm and 3-D finite element method to obtain maximum torque density. …”
Get full text