Stability of the Subaxial Spine after Penetrating Trauma: Do Classification Systems Apply? by Jackson Rucker Staggers, Thomas Elliot Niemeier, William E. Neway, Steven Michael Theiss

“…Blunt spinal trauma classification systems are well established and provide reliable treatment algorithms. To date, stability of the spine after civilian gunshot wounds (CGSWS) is poorly understood. …”
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Chemoreactomic study of fonturacetam effects: molecular mechanisms of influence on adipose tissue metabolism by O. A. Gromova, I. Yu. Torshin

“…The lipolytic effect is predicted specifically for fonturacetam as a result of activation by this molecule of β3-adrenoceptors, adenosine receptors, glucagon-like peptide, sphingosine phosphate and peroxisome proliferators, as well as specific inhibition of cannabinoid, opioid, histamine, glutamate, nociceptin, orexin and neuropeptide Y receptors. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…These challenges encompass defining a conceptual model outlining cause-and-effect relationships, addressing dissimilarities in data source quantity and information content, grappling with missing or noisy data, fine-tuning model optimization, achieving accurate predictions, and tackling the issue of imbalanced observations across factors. …”
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RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnosti... by Zhang Zhang, Fangfang Chen, Xiaoxiao Deng

“…Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. …”
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A combined immune and exosome-related risk signature as prognostic biomakers in acute myeloid leukemia by Zenghui Fang, Jiali Fu, Xin Chen

“…Notably, the risk model demonstrated significant associations with immune responses and the expression of immune checkpoints.Conclusions An immune-ERG-based risk model was developed to effectively predict prognostic outcomes for AML patients. There is potential for immune therapy in AML targeting the five hub genes.…”
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Unlocking the structural, vibrational, electronic, optical and thermoelectric properties of K2X (X=S, Se, Te) monolayers via DFT and ML by G. Sneha, R.D. Eithiraj

“…AFLOW-PLMF model was deployed for the electronic band gap value predictions. Machine learning algorithms namely Decision Tree Regressor and Gradient Boosting Regressor out-performs the other ML models with least root mean square error (RMSE) and accurate R2 values. …”
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Comparing MEG and EEG measurement set-ups for a brain-computer interface based on selective auditory attention. by Dovilė Kurmanavičiūtė, Hanna Kataja, Lauri Parkkonen

“…We then trained support vector machine classifiers to predict the target of attention from unaveraged trials of MEG/EEG. …”
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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

“…Finally, the false-detection points predicted by the Fast-PCANet are further corrected by region filling and filtering to obtain the final binary change map of the damage scene. …”
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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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AI-driven precision diagnosis and treatment in Parkinson’s disease: a comprehensive review and experimental analysis by Bhekisipho Twala

“…The integrated approach showed particular strength in identifying subtle motor fluctuations and predicting treatment response patterns.ConclusionAI-driven approaches demonstrate substantial potential for revolutionizing PD diagnosis and treatment personalization. …”
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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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Casualty Analysis of the Drivers in Traffic Accidents in Turkey: A CHAID Decision Tree Model by Zeliha Cagla Kuyumcu, Hakan Aslan, Nilufer Yurtay

“…This study aims to establish a model to predict the driver’s status (survived–injured–dead) as a result of the fatal-injury type of accident. …”
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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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REDalign: accurate RNA structural alignment using residual encoder-decoder network by Chun-Chi Chen, Yi-Ming Chan, Hyundoo Jeong

“…Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of $$O(L^6)$$ O ( L 6 ) for each RNA pair. …”
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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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Unveiling lipoprotein subfractions signature in high-FNPO PCOS: implications for PCOM diagnosis and risk assessment using advanced machine learning models by Xueqi Yan, Ziyi Yang, Hui Zhao, Gengchen Feng, Shumin Li, Yimeng Li, Yu Sun, Jinlong Ma, Han Zhao, Xueying Gao, Shigang Zhao

“…Ten machine learning algorithms and recursive feature elimination with logistic regression were used to construct the effective model to predict PCOM based on the new guideline. …”
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Monitoring of Thoracopulmonary Compliance During High-Frequency Jet Ventilation: Theoretical and Practical Aspects by M. B. Kontorovich, B. D Zislin, A. V. Chistyakov, A. V. Markov

“…The clinical tests of the developed algorithms permitted a new assessment of the physiological essence of thoracopulmonary compliance during HFJV. …”
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