A novel hybrid machine learning framework for spatio-temporal analysis of reference evapotranspiration in India by Dolon Banerjee, Sayantan Ganguly, Wen-Ping Tsai

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Emotion-Aware Ensemble Learning (EAEL): Revolutionizing Mental Health Diagnosis of Corporate Professionals via Intelligent Integration of Multi-Modal Data Sources and Ensemble Tech... by Gaurav Yadav, Mohammad Ubaidullah Bokhari, Saleh I. Alzahrani, Shadab Alam, Mohammed Shuaib

“…EAEL integrates machine learning and deep learning paradigms to process multimodal data, including facial expression analysis and typing pattern recognition, offering a holistic evaluation of emotional well-being. …”
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Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory by Sergei Manzhos, Johann Lüder, Pavlo Golub, Manabu Ihara

“…Machine learning (ML) of kinetic energy functionals (KEF) for orbital-free density functional theory (DFT) holds the promise of addressing an important bottleneck in large-scale ab initio materials modeling where sufficiently accurate analytic KEFs are lacking. …”
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Land suitability assessment for sisal production: A machine learning and Analytical Hierarchy Process integrated approach by R.N. Mgohele, B.H. J. Massawe, M.J. Shitindi, H.G. Sanga, M.M. Omar

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Smart train control and monitoring system with predictive maintenance and secure communications features by Alfian Akbar Gozali, Muhammad Faris Ruriawan, Andry Alamsyah, Yudha Purwanto, Ade Romadhony, Febry Pandu Wijaya, Fifin Nugroho, Dewi Nala Husna, Agri Kridanto, Anang Fakhrudin, Mu’ammar Itqon, Sri Widiyanesti

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Mitigating Polarization Leakage in Gas Pixel Detectors through Hybrid Machine Learning and Analytic Event Reconstruction by Nicoló Cibrario, Michela Negro, Raffaella Bonino, Nikita Moriakov, Luca Baldini, Niccoló Di Lalla, Alessandro Di Marco, Sergio Fabiani, Andrea Frassá, Alessio Gorgi, Fabio La Monaca, Luca Latronico, Simone Maldera, Alberto Manfreda, Fabio Muleri, Nicola Omodei, John Rankin, Carmelo Sgró, Stefano Silvestri, Paolo Soffitta, Stefano Tugliani

“…To address this issue, we built a hybrid reconstruction algorithm, which combines machine learning and analytic techniques to improve the reconstruction of photoelectron tracks in the Gas Pixel Detector and to significantly mitigate polarization leakage. …”
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Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

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Assessing acoustic receiver detection efficiency using autocorrelation adjusted machine learning models by Devon A. Smith, James A. Crossman, Eduardo G. Martins

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Stacking data analysis method for Langmuir multi-probe payload by Jin Wang, Jin Wang, Duan Zhang, Qinghe Zhang, Qinghe Zhang, Xinyao Xie, Fangye Zou, Qingfu Du, Qingfu Du, V. Manu, Yanjv Sun

“…The integrated characteristics of the stacking model make full use of the advantages of various models such as multilayer perceptron (MLP), support vector regression (SVR), K-nearest neighbors (KNN), and light gradient boosting machine (LightGBM). The combination of integrated machine learning methods and IRI models greatly improves the accuracy of electron density measurements obtained by m-NLPs. …”
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Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies by Marissa Mock, Alex W. Jacobitz, Christopher James Langmead, Athena Sudom, Daniel Yoo, Sara C. Humphreys, Mai Alday, Larysa Alekseychyk, Nicolas Angell, Vivian Bi, Hannah Catterall, Chen-Chun Chen, Hui-Ting Chou, Kip P. Conner, Kevin D. Cook, Ana R. Correia, Andrew Dykstra, Sudipa Ghimire-Rijal, Kevin Graham, Peter Grandsard, Joon Huh, John O. Hui, Mani Jain, Victoria Jann, Lei Jia, Sheree Johnstone, Neelam Khanal, Carl Kolvenbach, Linda Narhi, Rupa Padaki, Emma M. Pelegri-O’Day, Wei Qi, Vladimir Razinkov, Austin J. Rice, Richard Smith, Christopher Spahr, Jennitte Stevens, Yax Sun, Veena A. Thomas, Sarah van Driesche, Robert Vernon, Victoria Wagner, Kenneth W. Walker, Yangjie Wei, Dwight Winters, Melissa Yang, Iain D. G. Campuzano

“…We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. …”
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D₂X: Depression Detection System Through X Using Hybrid Machine Learning by Thara Angskun, Suda Tipprasert, Atitthan Thippongtorn, Jitimon Angskun

“…The <inline-formula> <tex-math notation="LaTeX">$D_{2}\mathbb {X}$ </tex-math></inline-formula> model, utilizing all types of tweet data, achieved the highest F-measure compared to other machine learning techniques. …”
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Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables by Amir Shahcheraghian, Adrian Ilinca

“…Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. …”
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Spatiotemporal Analysis of the Termokarst Lakes Evolution Using Remote Sensing and Randomized Machine Learning and Forecasting by Dubnov Yuri, Popkov Alexey, Popkov Yuri, Kuprianov Matvey, Polyschuk Vladimir, Melnikov Andrey, Polyschuk Yuri

“…The approach proposed in the work is based on the Randomized Machine Learning method for constructing mathematical models of lake area from climate indicators, learning the model on real data and further forecasting. …”
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A Review of Data-Driven Models for Electromagnetic Devices Design and Analysis by Zihan Li, Mengyu Cheng, Andy Tyrrell, Xing Zhao

“…This review, focusing on recent deep learning advances, presents a comprehensive review on the application of data-driven models in the design and optimization of electromagnetic devices, summarizing the statistical models such as Response Surface Methodology (RSM) and recent popular machine learning (ML) methods in handling multiple variables, as well as the deep learning (DL) models, in predicting various electromagnetic device parameters and optimizing electromagnetic models. …”
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Infrared spectrum analysis of organic molecules with neural networks using standard reference data sets in combination with real-world data by Dev Punjabi, Yu-Chieh Huang, Laura Holzhauer, Pierre Tremouilhac, Pascal Friederich, Nicole Jung, Stefan Bräse

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Comparative Analysis of Automated Machine Learning and Optimized Conventional Machine Learning for Concrete’s Uniaxial Compressive Strength Prediction by Chukwuemeka Daniel

“…This study pioneers the application of automated machine learning (AutoML) and conventional ML techniques to unravel the intricate relationships between the UCS and six factors. …”
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Leveraging machine learning to extract insights and spatial patterns from hydrogeochemical datasets for major groundwater regions in the UAE by Khalid ElHaj, Dalal Alshamsi, Balqees Alblooshi, Fatima Haile, Shamma AlRashdi, Basant Elabyad

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Performance Analysis of Diabetes Detection Using Machine Learning Classifiers by Hung Huynh, Liu Hui, Ngoc Han Nguyen, Ruixuan Qiao

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Enhancing User Experiences in Digital Marketing Through Machine Learning: Cases, Trends, and Challenges by Alexios Kaponis, Manolis Maragoudakis, Konstantinos Chrysanthos Sofianos

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Machine learning and transfer learning techniques for accurate brain tumor classification by Seyed Matin Malakouti, Mohammad Bagher Menhaj, Amir Abolfazl Suratgar

“…This study leverages machine learning (ML) and transfer learning techniques to classify healthy and sick individuals using numerical data and MRI images. …”
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