A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

“…In addition, both the XGBoost classifier and the neural network classifier showed high accuracy and reliability at each prediction stage. …”
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Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model by Yohei Kakimoto, Yuto Omae, Hirotaka Takahashi

“…We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…This study employed the Informer model to forecast drought and conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), and Convolutional Neural Network (CNN) models. The findings indicate that the Informer model outperforms the other three models in terms of drought forecasting accuracy across all time scales. …”
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Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipel... by Nalinda D. Liyanagedera, Corinne A. Bareham, Heather Kempton, Hans W. Guesgen

“…While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. …”
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Advancing Horticultural Crop Loss Reduction Through Robotic and AI Technologies: Innovations, Applications, and Practical Implications by H. W. Gammanpila, M. A. Nethmini Sashika, S. V. G. N. Priyadarshani

“…For instance, Ji et al. in 2007 developed an artificial neural network (ANN)-based system for rice yield prediction in Fujian, China, improving accuracy over traditional models. …”
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New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret by T. Netzel, A. Miebach, T. Litt, A. Hense

“…For the models and biome distributions used, a simple feedforward neural network provides the optimal choice of the classification problem.…”
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Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models by Xiaoxue Yang, Yajie Zou, Jinjun Tang, Jian Liang, Muhammad Ijaz

“…., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. …”
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Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV h... by Anqi He, Zhanghua Xu, Yifan Li, Bin Li, Xuying Huang, Huafeng Zhang, Xiaoyu Guo, Zenglu Li

“…We analyzed the impact of on-year and off-year phenological characteristics on the accuracy of hazard extraction and developed detection models for P. phyllostachysae hazard levels in on-year and off-year Moso bamboo using Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and one-dimensional Convolutional Neural Network (1D-CNN). The results demonstrate that classical machine learning and deep learning models can effectively detect P. phyllostachysae damage, with the 1D-CNN algorithm achieving the best performance. …”
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Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques by C. Zhang, X. Niu, H. Wu, Z. Ding, K. L. Chan, J. Kim, T. Wagner, C. Liu, C. Liu, C. Liu

“…In this study, we successfully incorporate geostationary satellite observations into a neural network model (GeoNet) to forecast full-coverage surface nitrogen dioxide (NO<span class="inline-formula">₂</span>) concentrations over eastern China at 4 h intervals for the next 24 h. …”
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Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin by Marco Bianchini, Mohamed Tarhouni, Matteo Francioni, Marco Fiorentini, Chiara Rivosecchi, Jamila Msadek, Abderrazak Tlili, Farah Chouikhi, Marina Allegrezza, Giulio Tesei, Paola Antonia Deligios, Roberto Orsini, Luigi Ledda, Maria Karatassiou, Athanasios Ragkos, Paride D'Ottavio

“…A set of 33 environmental variables (topography, soil, and bioclimatic) was screened using Pearson correlation matrices, and predictive models were built using four algorithms: MaxEnt, Random Forest, XG Boost, and Neural Network. Results revealed increasing temperatures and declining precipitation in both regions, confirming Mediterranean climate trends. …”
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Identification and preliminary validation of biomarkers associated with mitochondrial and programmed cell death in pre-eclampsia by Rong Lin, Rong Lin, XiaoYing Weng, XiaoYing Weng, Liang Lin, Liang Lin, XuYang Hu, XuYang Hu, ZhiYan Liu, ZhiYan Liu, Jing Zheng, Jing Zheng, FenFang Shen, FenFang Shen, Rui Li, Rui Li

“…Their performance was assessed through nomogram and artificial neural network models. Biomarkers were subjected to localization, functional annotation, regulatory network analysis, and drug prediction. …”
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Modelling of a new form of nitrogen doped activated carbon for adsorption of various dyes and hexavalent chromium ions by Mohamed A. El-Nemr, Uyiosa Osagie Aigbe, Kingsley Eghonghon Ukhurebor, Kingsley Obodo, Adetunji Ajibola Awe, Mohamed A. Hassaan, Safaa Ragab, Ahmed El Nemr

“…AB14 and AO7 dyes and Cr6+ ions adsorption to synthesised AC5-600 was predicted employing the response surface methodology (RSM) and artificial neural network (ANN) models. The ANN model was more effective in predicting AB14 and AO7 dyes and Cr6+ ions adsorption than the RSM, and it was highly applicable in the sorption process.…”
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Deep Transfer Learning for Classification of Late Gadolinium Enhancement Cardiac MRI Images into Myocardial Infarction, Myocarditis, and Healthy Classes: Comparison with Subjective... by Amani Ben Khalifa, Manel Mili, Mezri Maatouk, Asma Ben Abdallah, Mabrouk Abdellali, Sofiene Gaied, Azza Ben Ali, Yassir Lahouel, Mohamed Hedi Bedoui, Ahmed Zrig

“…A spatial attention mechanism was implemented as a part of the neural network architecture. The MLP architecture was used for the classification. …”
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Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder by Yilu Zhao, Zhao Fu, Eric J. Barnett, Ning Wang, Kangfuxi Zhang, Xuping Gao, Xiangyu Zheng, Junbin Tian, Hui Zhang, XueTong Ding, Shaoxian Li, Shuyu Li, Qingjiu Cao, Suhua Chang, Yufeng Wang, Stephen V. Faraone, Li Yang

“…The convolutional neural network (CNN) model, using variants with genome-wide P values less than E-02 (5516 SNPs), demonstrated the best performance with mean squared error (MSE) equals 0.012 (Accuracy = 0.83; Sensitivity = 0.90; Specificity = 0.75) in the validation dataset, 0.081 in an independent test dataset (Acc = 0.61, Sensitivity = 0.81; Specificity = 0.26). …”
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Multiview Multimodal Feature Fusion for Breast Cancer Classification Using Deep Learning by Sadam Hussain, Mansoor Ali Teevno, Usman Naseem, Daly Betzabeth Avendano Avalos, Servando Cardona-Huerta, Jose Gerardo Tamez-Pena

“…Imaging features were extracted using a Squeeze-and-Excitation (SE) network-based ResNet50 model, while textual features were extracted using an artificial neural network (ANN). Afterwards, extracted features from both modalities were fused using a late feature fusion strategy. …”
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Inflammation biomarkers and neurobehavioral performance in rural adolescentsKey PointsRelevance by Beemnet Amdemicael, Kun Yang, Briana N.C. Chronister, Caroline Mackey, Xin Tu, Sheila Gahagan, Danilo Martinez, Harvey Checkoway, David R. Jacobs, Jr., Jose Suarez-Torres, Suzi Hong, Jose R. Suarez-Lopez

“…Cytokines can alter neural network activity to induce neurocognitive changes. …”
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Advancing arabic dialect detection with hybrid stacked transformer models by Hager Saleh, Hager Saleh, Hager Saleh, Abdulaziz AlMohimeed, Rasha Hassan, Mandour M. Ibrahim, Saeed Hamood Alsamhi, Moatamad Refaat Hassan, Sherif Mostafa

“…The stacking model compares various models, including long-short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network (CNN), and two transformer models using different word embedding. …”
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Disproportionality analysis of upadacitinib-related adverse events in inflammatory bowel disease using the FDA adverse event reporting system by Shiyi Wang, Xiaojian Wang, Jing Ding, Xudong Zhang, Hongmei Zhu, Yihong Fan, Changbo Sun

“…This study evaluates upadacitinib-related adverse events (AEs) utilizing data from the US Food and Drug Administration Adverse Event Reporting System (FAERS).MethodsWe employed disproportionality analyses, including the proportional reporting ratio (PRR), reporting odds ratio (ROR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayesian geometric mean (EBGM) algorithms to identify signals of upadacitinib-associated AEs for treating inflammatory bowel disease (IBD).ResultsFrom a total of 7,037,004 adverse event reports sourced from the FAERS database, 37,822 identified upadacitinib as the primary suspect drug in adverse drug events (ADEs), including 1,917 reports specifically related to the treatment of inflammatory bowel disease (IBD). …”
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Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective coho... by Stefanie Aeschbacher, David Conen, Diederick E Grobbee, Raphael Twerenbold, Thomas Lung, Theo Rispens, Jakob Kjellberg, Lorenz Risch, Martin Risch, Marianna Mitratza, Harald Renz, Spiros Denaxas, Billy Franks, Diederick Grobbee, Martina Rothenbühler, Janneke Wijgert, Santiago Montes, Richard Dobson, Hans Reitsma, Christian Simon, Titia Leurink, Charisma Hehakaya, Patricia Bruijning, Kirsten Grossmann, Ornella C Weideli, Marc Kovac, Fiona Pereira, Nadia Wohlwend, Corina Risch, Dorothea Hillmann, Daniel Leibovitz, Vladimir Kovacevic, Andjela Markovic, Paul Klaver, Timo B Brakenhoff, George S Downward, Ariel Dowling, Maureen Cronin, Brianna M Goodale, Brianna Goodale, Ornella Weideli, Regien Stokman, Hans Van Dijk, Eric Houtman, Jon Bouwman, Kay Hage, Lotte Smets, Marcel van Willigen, Maui Chodura, Niki de Vink, Tessa Heikamp, Timo Brakenhoff, Wendy van Scherpenzeel, Wout Aarts, Alison Kuchta, Antonella Chiucchiuini, Steve Emby, Annemarijn Douwes, George Downward, Nathalie Vigot, Pieter Stolk, Duco Veen, Daniel Oberski, Amos Folarin, Pablo Fernandez Medina, Eskild Fredslund

“…The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO.Conclusion Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. …”
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Artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model by Xing Tu, Zixing Zou, Jiahui Li, Simiao Zeng, Zhengchao Luo, Gen Li, Yuanxu Gao, Kang Zhang, Jing Ni

“…We employed a series of AI methods, including large language and graph neural network models, to identify the target compounds of RIPK3. …”
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