Comparing MEG and EEG measurement set-ups for a brain-computer interface based on selective auditory attention. by Dovilė Kurmanavičiūtė, Hanna Kataja, Lauri Parkkonen

“…Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. …”
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The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection by Pak Hung Chan, Boda Li, Gabriele Baris, Qasim Sadiq, Valentina Donzella

“…Assisted and automated driving functions will rely on machine learning algorithms, given their ability to cope with real-world variations, e.g. vehicles of different shapes, positions, colors, and so forth. …”
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Attention-Based Color Difference Perception for Photographic Images by Hua Qiang, Xuande Zhang, Jinliang Hou

“…Existing deep learning-based CD measurement algorithms only focus on local features and cannot accurately simulate the human perception of CD. …”
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Artificial intelligence in environmental monitoring: in-depth analysis by Emran Alotaibi, Nadia Nassif

“…Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. …”
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Damage Scene Change Detection Based on Infrared Polarization Imaging and Fast-PCANet by Min Yang, Jie Yang, Hongxia Mao, Chong Zheng

“…Comparisons with typical PCANet-based change detection algorithms are made on a dataset of infrared-polarized images. …”
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Identification of biomarkers for the diagnosis in colorectal polyps and metabolic dysfunction-associated steatohepatitis (MASH) by bioinformatics analysis and machine learning by Ying Geng, Yifang Li, Ge Liu, Jian Jiao

“…This study investigated the biomarkers of MASH and colorectal polyps, and the prediction of targeted drugs using an integrated bioinformatics analysis method. …”
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Countermeasuring Anti-Ship Missiles for Surface Naval Platforms: A Machine Learning Approach With Explainable Artificial Intelligence by Murat Ertop, Ali Oter, Ali Kara

“…This model has been used to predict target parameters. The simulator includes parameters for the ship, missile, and chaff/flare. …”
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Cooling Load Forecasting Method for Central Air Conditioning Systems in Manufacturing Plants Based on iTransformer-BiLSTM by Xiaofeng Huang, Xuan Zhou, Junwei Yan, Xiaofei Huang

“…Meanwhile, by integrating the SHAP sensitivity analysis method, the contributions of input variables to the cooling load prediction results are systematically evaluated, thereby enhancing the interpretability of the model. …”
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Raising Awareness of Acute Kidney Injury: Unfolding the Truth by Aida Lydia

“…Considering the substantial impacts of AKI in ICU patients, early implementation of preventive measures should be an essential program which consists of developing AKI risk stratification in the ICU and encouraging the use of novel AKI biomarkers (TIMP-2, IGFBP-7, Cystatin C, IL-18,  KIM-1 and NGAL) as well as other risk stratification tools (clinical risk prediction scores, computer algorithms, furosemide stress test). …”
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AI-driven precision diagnosis and treatment in Parkinson’s disease: a comprehensive review and experimental analysis by Bhekisipho Twala

“…The integration of multiple data modalities and advanced machine learning algorithms enables earlier detection, more accurate monitoring, and optimized therapeutic interventions. …”
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High-throughput image-based monitoring of cell aggregation and microspheroid formation. by Thomas Deckers, Toon Lambrechts, Stefano Viazzi, Gabriella Nilsson Hall, Ioannis Papantoniou, Veerle Bloemen, Jean-Marie Aerts

“…Studies on monolayer cultures and whole-animal models for the prediction of the response of native human tissue are associated with limitations. …”
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Data-driven modelling of a commercial cold storage system using subspace system identification by Adesola Temitope Bankole, Muhammed Bashir Mu’azu, Habeeb Bello-Salau, Zaharuddeen Haruna

“…The proposed model is stable, robust and suitable for testing linear control algorithms.…”
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Machine Learning Detection of Melting Layers From Radar Observations by Yan Xie, Fraser King, Claire Pettersen, Mark Flanner

“…Traditional detection algorithms based on fixed thresholds or a priori assumptions lack general robustness across diverse weather conditions, which can be addressed by leveraging machine learning techniques. …”
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Evaluation on nature-connected environment in building embedded landscape: theory, detection, and case design by Shu Zhong, Jiao Ren

“…Principal component analysis was used for verification, and the GBT algorithm surpassed MLR in regression performance. …”
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Ship Magnetic Signature Classification Using GRU-Based Recurrent Neural Networks by Kajetan Zielonacki, Jaroslaw Tarnawski, Miroslaw Woloszyn

“…Features, advantages and limitations of developed algorithms are derived strictly from the nature of neural networks.…”
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Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcinoma by Yun Zhao, Yun Zhao, Jing Yang, Yamei Jiang, Jingbiao Wu

“…The MSCC model, developed using the StepCox [both]+plsRcox algorithm, demonstrated superior prognostic performance compared to existing models. …”
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ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses by Yangyan Li, Tingting Chen

“…ISCCO integrates deep learning with advanced optimization algorithms. It focuses on minimizing transportation costs by accurately predicting customer behavior and dynamically allocating goods. …”
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REDalign: accurate RNA structural alignment using residual encoder-decoder network by Chun-Chi Chen, Yi-Ming Chan, Hyundoo Jeong

“…REDalign significantly reduces computational complexity compared to Sankoff-style algorithms and effectively handles non-nested structures, including pseudoknots, which are challenging for traditional alignment methods. …”
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Deep Learning in Power Systems: A Bibliometric Analysis and Future Trends by Seyed Mahdi Miraftabzadeh, Andrea Di Martino, Michela Longo, Dario Zaninelli

“…Advancements in deep learning enable sophisticated algorithms to learn from vast data sets, driving innovation and efficiency in power systems. …”
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A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network by Constantinos M. Mylonakis, Zaharias D. Zaharis

“…This article aims to constitute a noteworthy contribution to the domain of direction-of-arrival (DoA) estimation through the application of deep learning algorithms. We approach the DoA estimation challenge as a binary classification task, employing a novel grid in the output layer and a deep convolutional neural network (DCNN) as the classifier. …”
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