Contrastive cross-domain sequential recommendation with attention-aware mechanism by Wei Zhao, Bo Li, Xian Mo

“…Abstract Cross-domain sequential recommendation (CDSR) aims to predict future sequential interactions in a target domain by analyzing historical sequence data from different domains. …”
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Mantle Flow and Anisotropy in Subduction Zones: Modeling and Clustering of Olivine Textures by Yijun Wang, Ágnes Király, Clinton P. Conrad, Valerie Maupin

“…We compare olivine texture evolution predicted using different methods in both retreating and stationary‐trench settings. …”
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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

“…The TLICS, SLIC, and three-column classification systems cannot be applied to CSGSW to quantify injury severity, predict outcomes, or guide treatment decision-making. …”
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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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A Hybrid Machine Learning Approach for Estimating Aboveground Biomass and Carbon Stock in Tanzania’s Miombo Woodlands by Emmanuel F. Chifunda, Ramkumar T. Balan, Peter J. Kirigiti

“…A model (ANN-RF) was developed using a combination of ANN and RF models. Initially, the RF algorithm combined the predictions from the ANN models. …”
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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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Collaborative Optimization Planning Method for Distribution Network Considering “Hydropower, Photovoltaic, Storage, and Charging” by Jinlin Liao, Jia Lin, Guilian Wu, Sudan Lai

“…Secondly, the trip chain method is used to predict the charging demand of electric vehicles. …”
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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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Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L. by Mahboobe Hojati, Ruhollah Naderi, Mohsen Edalat, Hamid Reza Pourghasemi

“…Results showed that The RF (ROC: 0.99), BRT (ROC: 0.98), and SVM (ROC: 0.96) models were highly accurate in predicting the habitat suitability of S. marianum. The results of the RF algorithm also revealed that factors such as distance from roads, elevation, and mean annual rainfall had the most significant influence on the habitat suitability of S. marianum. …”
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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

“…The results clearly show that network performance is affected by ground truth variations, and removing clear errors is beneficial for predicting real-world objects only for some networks. …”
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Attention-Based Color Difference Perception for Photographic Images by Hua Qiang, Xuande Zhang, Jinliang Hou

“…Finally, a dual-branch network for high-frequency and low-frequency predictions is used for score estimation, and the final score is obtained through a weighted sum. …”
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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

“…Furthermore, the U‐Net model provides additional information of detection uncertainty based on ensemble predictions. The U‐Net model and the data extraction tool can be applied to similar profiling radar instruments in different regions of the world, contributing to an enhanced understanding of the distribution and variations of melting layers.…”
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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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