Enhancing chemical reaction search through contrastive representation learning and human-in-the-loop by Youngchun Kwon, Hyunjeong Jeon, Joonhyuk Choi, Youn-Suk Choi, Seokho Kang

“…Scientific contribution This study seeks to enhance the search functionality of chemical reaction databases by drawing inspiration from recommender systems. …”
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Crafting desirable climate trajectories with reinforcement learning explored socio-environmental simulations by James Rudd-Jones, Fiona Thendean, María Pérez-Ortiz

“…However, upon introducing competition between agents, for instance by using opposing reward functions, desirable climate futures are rarely reached. …”
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Self-supervised multi-stage deep learning network for seismic data denoising by Omar M. Saad, Matteo Ravasi, Tariq Alkhalifah

“…In this study, we introduce a multi-stage deep learning model, trained in a self-supervised manner, designed specifically to suppress seismic noise while minimizing signal leakage. …”
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Disease Phenotypes in Refractory Musculoskeletal Pain Syndromes Identified by Unsupervised Machine Learning by Thomas Hügle, Tiffany Prétat, Marc Suter, Chris Lovejoy, Pedro Ming Azevedo

“…Three of five clusters responded to the multimodal treatment in terms of pain (BPI intensity), one cluster responded in terms of functional improvement (BPI interference), and one cluster notably responded to the virtual reality intervention. …”
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Machine learning projection of climate and technology impacts on crops key to food security by Dan Li, Vassili Kitsios, David Newth, Terence John O’Kane

“…Our study implies that integrated assessment and other economic models that use oversimplified climate damage functions can compound inaccuracies in production estimates with adverse repercussions on policy decisions.…”
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Lessons learned: Chronic idiopathic constipation patient experiences with over-the-counter medications. by Brian E Lacy, Elizabeth P Shea, Machelle Manuel, Jessica L Abel, Hancheng Jiang, Douglas C A Taylor

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Mismatch response to polysyllabic nonwords: a neurophysiological signature of language learning capacity. by Johanna G Barry, Mervyn J Hardiman, Dorothy V M Bishop

“…<h4>Background</h4>The ability to repeat polysyllabic nonwords such as "blonterstaping" has frequently been shown to correlate with language learning ability but it is not clear why such a correlation should exist. …”
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Multivariate machine learning algorithms for energy demand forecasting and load behavior analysis by Farhan Hussain, M. Hasanuzzaman, Nasrudin Abd Rahim

“…Critical hyperparameters, such as the number of hidden neurons in ANN and the number of input membership functions in ANFIS, are optimized to effectively capture uncertainties in load patterns. …”
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Machine learning identifies KRT8 dysregulation and endothelial remodeling in Moyamoya disease by Zhiguang Han, Jialong Yuan, Zhenyu Zhou, Yutong Liu, Chengxu Lei, Xun Ye, Yuanli Zhao, Shihao He

“…Key genes were identified through differential analysis and WGCNA. The functions of potential biomarkers were explored by methods such as correlation analysis, KEGG analysis, PPI network, and tube formation experiments. …”
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Fine-Grained Aircraft Recognition Based on Dynamic Feature Synthesis and Contrastive Learning by Huiyao Wan, Pazlat Nurmamat, Jie Chen, Yice Cao, Shuai Wang, Yan Zhang, Zhixiang Huang

“…However, methods based on deep learning are confronted with several challenges: (1) the inherent limitations of activation functions and downsampling operations in convolutional networks lead to frequency deviations and loss of local detail information, affecting fine-grained object recognition; (2) class imbalance and long-tail distributions further degrade the performance of minority categories; (3) large intra-class variations and small inter-class differences make it difficult for traditional deep learning methods to effectively extract fine-grained discriminative features. …”
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Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach by Tahereh Vasei, Harshil Gediya, Maryam Ravan, Anand Santhanakrishnan, David Mayor, Tony Steffert

“…Notably, gamma band activity showed the highest sensitivity to TEAS, suggesting significant effects on higher cognitive functions. Saliency mapping revealed that a subset of electrodes (Fp1, Fp2, Fz, F7, F8, T3, T4) could achieve accurate classification, indicating potential for more efficient EEG setups.…”
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Machine Learning-Driven GLCM Analysis of Structural MRI for Alzheimer’s Disease Diagnosis by Maria João Oliveira, Pedro Ribeiro, Pedro Miguel Rodrigues

“…Different combinations of features and planes were used to feed classical machine learning (cML) algorithms to analyze their discrimination power between the groups. …”
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Prediction and validation of anoikis-related genes in neuropathic pain using machine learning. by Yufeng He, Ye Wei, Yongxin Wang, Chunyan Ling, Xiang Qi, Siyu Geng, Yingtong Meng, Hao Deng, Qisong Zhang, Xiaoling Qin, Guanghui Chen

“…Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), alongside Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis, were performed on these hub genes. …”
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Two Machine-learning Hybrid Models for Predicting Type 2 Diabetes Mellitus by Rahman Farnoosh, Karlo Abnoosian, Rasha Abbas Isewid

“…Our proposed hybrid models demonstrated superior performance in two scenarios, handling and rejecting outliers, compared to other machine-learning models in this study, including support vector machines (with radial-based, polynomial, linear, and sigmoid kernel functions), decision trees (J48), and GNB classifiers for diabetes prediction. …”
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An integrative approach to the methods of parallel learning of native and foreign languages in conditions of digitalization by Klyuchkovska Iryna, Kozlovska Iryna, Savka Iryna

“…There is applied the classification of the integration of educational courses: extended subject (monodisciplinary); combined subject (interdisciplinary, transdisciplinary); core (problematic, thematic). The stages of learning a native and foreign language in conditions of digitalization are distinguished as: familiarization-visual (observational); mechanical and reproductive (work with texts, elementary operations); basic-mastery (thorough acquaintance with the main functions of digital technologies and the possibilities of their use); life-practical (use of digital technologies for professional needs); professional and creative (development of abilities, skills in a certain field of application of digital technologies); personal and creative (level of self-improvement). …”
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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…<h4>Background</h4>The unique anatomical characteristics and blood supply of the rectosigmoid junction confer particular significance to its physiological functions and clinical surgeries. However, research on the prognosis of rectosigmoid junction cancer (RSC) is scarce, and reliable clinical prediction models are lacking.…”
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Optimizing Space Heating in Buildings: A Deep Learning Approach for Energy Efficiency by Fernando Almeida, Mauro Castelli, Nadine Corte-Real, Luca Manzoni

“…This study investigates the role of deep learning models in optimizing space heating while maintaining thermal comfort across multiple building zones. …”
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Learning science from museums Museus e o aprendizado da ciência by John H. Falk, Martin Storksdieck

“…The best available evidence indicates that if you want to understand learning at the level of individuals within the real world, learning does functionally differ depending upon the conditions, i. e., the context, under which it occurs. …”
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Data mining methods application in reflexive adaptation realization in e-learning systems by A. S. Bozhday, Y. I. Evseeva, A. A. Gudkov

“…In recent years, e-learning technologies are rapidly gaining momentum in their evolution. …”
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Automatic summarization of cooking videos using transfer learning and transformer-based models by P. M. Alen Sadique, R. V. Aswiga

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