Understanding Social Relationships with Person-Pair Relations by Hang Zhao, Haicheng Chen, Leilai Li, Hai Wan

“…Extensive experiments on People In Social Context (PISC)- and People In Photo Album (PIPA)-relation datasets show the superiority of our method compared to other methods.…”
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Learning under label noise through few-shot human-in-the-loop refinement by Aaqib Saeed, Dimitris Spathis, Jungwoo Oh, Edward Choi, Ali Etemad

“…We evaluate our approach on four challenging tasks and datasets, and compare it against eight competitive baselines designed to deal with noisy labels. …”
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Brain state forecasting for precise brain stimulation: Current approaches and future perspectives by Matteo De Matola, Carlo Miniussi

“…Subsequently, we will review the evidence in favour of data-driven forecasting and discuss its potential contributions to TMS methodology and the scientific understanding of brain dynamics, highlighting the transformative potential of big open datasets.…”
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Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks by Lei Guo, Haoran Jiang, Xiyu Liu, Changming Xing

“…Experiments on two real-world datasets demonstrate the importance of the network embeddings and the effectiveness of our proposed method.…”
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Impact of Climate Change and Air Pollution Forecasting Using Machine Learning Techniques in Bishkek by Erkin Isaev, Boobek Ajikeev, Urmatbek Shamyrkanov, Kenjebek-uulu Kalnur, Karimov Maisalbek, Roy C. Sidle

“…We evaluate the performance of several machine learning algorithms applied to air quality and meteorology datasets and compare prediction accuracies of Bishkek air quality given its significant public importance. …”
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ADA-NAF: Semi-Supervised Anomaly Detection Based on the Neural Attention Forest by Andrey Ageev, Andrei Konstantinov, Lev Utkin

“…Through extensive experimentation across diverse datasets, ADA-NAF demonstrates superior performance compared to state-of-the-art methods. …”
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Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…The proposed method has been contrasted with conventional methods by applying a testing environment incorporating essential Meteorological Factors (MF) and historical SE datasets. The study of performance measurements (RMSE, MAE, and R2) demonstrates significant improvements in the accuracy of predictions. …”
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DVGEDR: a drug repositioning method based on dual-view fusion and graph enhancement mechanism in heterogeneous networks by Dongjiang Niu, Lianwei Zhang, Beiyi Zhang, Qiang Zhang, Shanyang Ding, Hai Wei, Zhen Li

“…Performance comparisons with baseline models on three public datasets validate the state-of-the-art performance of DVGEDR in the field of drug repositioning. …”
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Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance by Ciza Arsène Mushagalusa, Adandé Belarmain Fandohan, Romain Glèlè Kakaï

“…We also compared RF performance to that of classical generalized linear models (Poisson, negative binomial, and zero-inflated Poisson) and the linear model applied to log-transformed data. Two real datasets were analysed to demonstrate the usefulness of RF for overdispersed data modelling. …”
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The Direct Healthcare Expenditures of Prostate Cancer by Disease Severity: Evidence from US National Survey Data by Peter J. Mallow, Jie Chen, John A. Rizzo, John R. Penrod, Geralyn C. Trudel, Teresa M. Zyczynski

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Identification of the circRNA-miRNA-mRNA Regulatory Network in Bladder Cancer by Bioinformatics Analysis by Jiancheng Lv, Ping-an Chang, Xin Li, Xiao Yang, Jie Han, Hao Yu, Zijian Zhou, Haiwei Yang, Pengchao Li, Jiexiu Zhang, Qiang Lu

“…This study was aimed at exploring the molecular regulatory mechanism of circRNAs in BCa. We obtained four datasets of circRNA, microRNA (miRNA), and messenger (mRNA) expression profiles from the Gene Expression Omnibus and The Cancer Genome Atlas microarray databases and identified 434, 367, and 4799/4841 differentially expressed circRNAs, miRNAs, and mRNAs, respectively. …”
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Analysis and Prediction of Grouting Reinforcement Performance of Broken Rock Considering Joint Morphology Characteristics by Guanglin Liang, Linchong Huang, Chengyong Cao

“…Notably, the Random Forest (RF) algorithm excels in rapid and accurate predictions when handling similar training data, while the ANN-based MCD algorithm consistently delivers stable and precise results across diverse datasets.…”
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The Lawfulness of Using Copyrighted Works for Generative AI Training : A Case Study of a US Lawsuit against OpenAI and Perplexity AI by Stefany Ismantara, Wilma Silalahi

“…These regulations could include obligations for AI companies to release summaries of training datasets, include Uni EropaLAs that define the responsibilities of AI developers and users, and provide disclaimers regarding AI's limitations. …”
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Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm by Liping Yang, Chunxia Wang, Pan Zhou, Na Xie, Maozai Tian, Kai Wang

“…This study aims to bridge this gap by employing sequence decomposition and identifying significant change points, with datasets sourced from the Xinjiang Disease Prevention and Control Information System. …”
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Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling by Fazal E. Jalal, Mudassir Iqbal, Mohsin Ali Khan, Babatunde A. Salami, Shahid Ullah, Hayat Khan, Marwa Nabil

“…In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. …”
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Comparative Study of Statistical Features to Detect the Target Event During Disaster by Madichetty Sreenivasulu, M. Sridevi

“…The proposed algorithm is tested on Nepal earthquake and landslide datasets, 2015.…”
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CSTrans: cross-subdomain transformer for unsupervised domain adaptation by Junchi Liu, Xiang Zhang, Zhigang Luo

“…We perform extensive studies on five benchmarks, and the respective experimental results show that, as compared to existing UDA siblings, CSTrans attains remarkable results with average classification accuracy of 94.3%, 92.1%, and 85.4% on datasets Office-31, ImageCLEF-DA, and Office-Home, respectively.…”
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The Potential Diagnostic Value of Immune-Related Genes in Interstitial Fibrosis and Tubular Atrophy after Kidney Transplantation by Bin Yang, Dike Shi, Yahong Chen, Yi Zhu

“…Raw data of IF/TA samples after kidney transplantation and control samples after kidney transplantation were extracted from the Gene Expression Omnibus (GEO) database (GSE76882 and GSE120495 datasets), and genes that were differentially expressed between the two groups (DEGs) were screened. …”
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VulMPFF: A Vulnerability Detection Method for Fusing Code Features in Multiple Perspectives by Xiansheng Cao, Junfeng Wang, Peng Wu, Zhiyang Fang

“…We evaluate our approach on three datasets. Experiments show that VulMPFF outperforms existing state-of-the-art vulnerability detection methods (i.e., Rats, FlawFinder, VulDeePecker, SySeVR, Devign, and Reveal) in Acc and F1 score, with improvements ranging from 14.71% to 145.78% and 152.08% to 344.77%, respectively. …”
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Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC by Xiaoqin Luo, Chao Li, Gang Qin

“…Methods We integrated four HNSCC datasets (TCGA-HNSCC, GSE27020, GSE41613, and GSE65858) from TCGA and GEO databases. …”
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