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More than just sentiment: Using social, cognitive, and behavioral information of social media to predict stock markets with artificial intelligence and big data

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Real-time emotions recognition system

da Silva, V. M. S., Soares, F., Esteves, J. S., Figueiredo, J., Leão, C. P., Santos, C. P., & Pereira, A. P. (2016) · International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) · doi:10.1109/icumt.2016.7765357 · Cited by 10

An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules

Gao, Y., Zhou, X., & Zhang, W. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00366 · Cited by 10

CrowdOptim: A Crowd-driven Neural Network Hyperparameter Optimization Approach to AI-based Smart Urban Sensing

Zhang, Y., Zong, R., Shang, L., Kou, Z., Zeng, H., & Wang, D. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555536 · Cited by 10

Nonlinear dimensionality reduction based visualization of single-cell RNA sequencing data

Yousuff, M., Babu, R., & Rathinam, A. (2024) · Journal of Analytical Science & Technology · doi:10.1186/s40543-023-00414-0 · Cited by 12

Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors

König, C., Cárdenas, M., Giraldo, J., Alquézar, R., & Vellido, A. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0731-9 · Cited by 10

Multivariate Bounded Asymmetric Gaussian Mixture Model

Azam, M., Alghabashi, B., & Bouguila, N. (2019) · Unsupervised and semi-supervised learning · doi:10.1007/978-3-030-23876-6_4 · Cited by 10

A machine learning algorithm for stock picking built on information based outliers

Barucci, E., Bonollo, M., Poli, F., & Rroji, E. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115497 · Cited by 10

Ranking Loss: A Ranking-Based Deep Neural Network for Colorectal Cancer Grading in Pathology Images

Vuong, T. T. L., Kim, K., Song, B., & Kwak, J. T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_52 · Cited by 10

On streaming disaster damage assessment in social sensing: A crowd-driven dynamic neural architecture searching approach

Zhang, Y., Zong, R., Kou, Z., Shang, L., & Wang, D. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107984 · Cited by 10

Material handling machine activity recognition by context ensemble with gated recurrent units

Chen, K., Rognvaldsson, T. S., Nowaczyk, S., Pashami, S., Klang, J., & Sternelöv, G. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106992 · Cited by 11

A unifying view for performance measures in multi-class predictionPreprint

Jurman, G., & Furlanello, C. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1008.2908 · Cited by 8

Enhancing Confusion Entropy (CEN) for binary and multiclass classification

Delgado, R., & Núñez-González, J. D. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210264 · Cited by 21

Probabilistic Confusion Entropy for Evaluating Classifiers

Wang, X., Wei, J., Han, J., Yu, G., & Zhang, H. (2013) · Entropy · doi:10.3390/e15114969 · Cited by 26

Why Cohen’s Kappa should be avoided as performance measure in classification

Delgado, R., & Tibau, X.-A. (2019) · PLOS ONE · doi:10.1371/journal.pone.0222916 · Cited by 324

Enhancing Confusion Entropy as Measure for Evaluating Classifiers

Delgado, R., & Núñez-González, J. D. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-94120-2_8 · Cited by 3