High-Frequency Cryptocurrency Price Forecasting Using Machine Learning Models: A Comparative Study by Fátima Rodrigues, Miguel Machado

“…We compare various machine learning models, including recurrent neural networks (RNNs), time series analysis (ARIMA), and conventional regression algorithms, using minute-step Bitcoin price data over a 30-day period to predict prices 60 min ahead. …”
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Performance-Enhancing Market Risk Calculation Through Gaussian Process Regression and Multi-Fidelity Modeling by N. Lehdili, P. Oswald, H. D. Nguyen

“…More precisely, multi-fidelity modeling combines models of different fidelity levels, defined as the degree of detail and precision offered by a predictive model or simulation, to achieve rapid yet precise prediction. …”
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Supervised filters for EEG signal in naturally occurring epilepsy forecasting. by Francisco Javier Muñoz-Almaraz, Francisco Zamora-Martínez, Paloma Botella-Rocamora, Juan Pardo

“…Such filters have been employed in a machine learning algorithm, such as the K-Nearest Neighbor (KNN), to improve the prediction of seizures. …”
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Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells by Yang Xu, Jin Yang, Zhiqiang Hu, Quanmin Zhao, Lei Li, Qishuai Yin

“…By quantitatively analyzing the main mechanisms such as undercompaction, high-temperature fluid expansion, and mud diapirism, the study addresses the complexities of overpressure prediction. This paper introduces an innovative analytical framework that combines hierarchical clustering algorithms with the LightGBM model. …”
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Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy by Zhenchang Wang, Yu Gu, Xiao Sun, Hao Huang

“…Driven by advancements in high-throughput sequencing technologies, mass spectrometry, and artificial intelligence, researchers have developed a growing interest in establishing more accurate neoantigen prediction algorithms. Here, we presented a comprehensive review of integrated neoantigen prediction algorithms, encompassing task definition, theoretical developments, benchmark datasets, cutting-edge applications, and future research directions. …”
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Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis by Pierre Heudel, Mashal Ahmed, Felix Renard, Arnaud Attye

“…Manifold learning and machine learning algorithms were applied to uncover complex data relationships and develop predictive models. …”
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Comparison of sample preparation methods for higher heating values in various sugarcane varieties using near-infrared spectroscopy by Kantisa Phoomwarin, Khwantri Saengprachatanarug, Jetsada Posom, Seree Wongpichet, Kittipong Laloon, Arthit Phuphaphud

“…Spectral data were pre-processed using seven techniques to minimize noise, and four variable selection algorithms–Variable Importance in Projection, Successive Projection Algorithm, Genetic Algorithm, and correlation-based selection via Partial Least Squares Regression–were employed to improve modelling accuracy.In parallel, four machine learning models–AdaBoost Regressor, Gradient Boosting, K-Nearest Neighbors, and Random Forest–were applied to the same dataset for Higher heating value prediction. …”
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Progress and perspectives on genomic selection models for crop breeding by Dongfeng Zhang, Feng Yang, Jinlong Li, Zhongqiang Liu, Yanyun Han, Qiusi Zhang, Shouhui Pan, Xiangyu Zhao, Kaiyi Wang

“…Genomic selection, a molecular breeding technique, is playing an increasingly important role in improving the efficiency of artificial selection and genetic gain in modern crop breeding programs. A series of algorithms have been proposed to improve the prediction accuracy of genomic selection. …”
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Using Explainable AI to Measure Feature Contribution to Uncertainty by Katherine Elizabeth Brown, Douglas A. Talbert

“…\textit{Uncertainty} measures the algorithm’s lack of trust in its predictions, and this information is important for practitioners using machine learning-based decision support. …”
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The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches by Lu Zhang, Lu Zhang, YuJing Lv, Mengqing Ma, Jile Lv, Jie Chen, Shang Lei, Yi Man, Guimei Xing, Yu Wang

“…Two machine learning algorithms were used to identify hub genes, which were used for drug prediction, immunological infiltration studies, nomogram construction, and regulatory network building. …”
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Active Learning for Medical Article Classification with Bag of Words and Bag of Concepts Embeddings by Radosław Pytlak, Paweł Cichosz, Bartłomiej Fajdek, Bogdan Jastrzębski

“…The proper choice of text representation for such algorithms may have a significant impact on their predictive performance. …”
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Efficient Learning of Long-Range and Equivariant Quantum Systems by Štěpán Šmíd, Roberto Bondesan

“…Further, we show that learning algorithms equivariant under the automorphism group of the interaction hypergraph achieve a sample complexity reduction, leading in particular to a constant number of samples for learning sums of local observables in systems with periodic boundary conditions. …”
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Application of Machine Learning for Real-Time Phishing Attack Detection by Akshay Shankar Agrawal, Sanketi Raut, Andrina Dsouza, Jimit Mehta, Prajwal Naik

“…The system uses machine learning algorithms to distinguish legitimate websites from phishing websites and generate a prediction to be used for the platform. …”
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Early Warning of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Multi-Omics Signature: A Machine Learning-Based Retrospective Study by Ke Z, Shen L, Shao J

“…The AUC of GLRM was 0.818 (95% CI: 0.757~0.879), significantly lower than that of RFM’s AUC 0.893 (95% CI: 0.836~0.950).Conclusion: The prediction models based on machine learning (ML) algorithms and multiomics have shown good performance in predicting ALN metastasis, and RFM shows greater advantages compared to traditional GLRM. …”
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A Triple-Optimized Extreme Learning Machine Model for Power Load Forecasting by Haoxiang Gao, Weixin Kang, Miao Fan

“…This approach disregards the pathological characteristics and overfitting that arise from the simultaneous optimization of the three, the challenges of calculation, and the deviation of the prediction results. This paper proposes a seamless enhanced incremental ELM triple optimization model (SBOA-SEI-MRU-ELM) based on the Secretary bird optimization algorithm and the MINres regularization under the U-curve method to solve the above problem. …”
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Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review by Manjusha Bhange, Darshan Telange

“…Simultaneously, AI contributes to improved diagnostic accuracy, predictive modeling, and the development of personalized treatment strategies. …”
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Dempster Shafer-Empowered Machine Learning-Based Scheme for Reducing Fire Risks in IoT-Enabled Industrial Environments by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Saurabh Singh, Byungun Yoon

“…This research proposes an advanced fire prediction approach aiming to enhance decision-making accuracy with uncertain or incomplete fire sensor data in an edge computing IoT complex industrial environment that integrates multiple supervised machine learning algorithms for each sensor types and Dempster-Shafer theory (DST) with multi-sensor fusion. …”
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Microarray-Based Cancer Diagnosis with Artificial Neural Networks by Markus Ringnér, Carsten Peterson

“…In recent years, the advent of experimental methods to probe gene expression profiles of cancer on a genome-wide scale has led to widespread use of supervised machine learning algorithms to characterize these profiles. The main applications of these analysis methods range from assigning functional classes of previously uncharacterized genes to classification and prediction of different cancer tissues. …”
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Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review by Sergio Agudelo-Pérez, Daniel Botero-Rosas, Laura Rodríguez-Alvarado, Julián Espitia-Angel, Lina Raigoso-Díaz

“…The thematic analysis revealed five major categories: 1. Prediction of exclusive breastfeeding patterns: AI models, such as decision trees and machine learning algorithms, identify factors influencing breastfeeding practices, including maternal experience, hospital policies, and social determinants, highlighting actionable predictors for intervention. 2. …”
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Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit. by Zheng Liu, Wenqi Shu, Hongyan Liu, Xuan Zhang, Wei Chong

“…Three models were compared: Model 1 based on Emergency Severity Index (ESI), Model 2 on vital signs, and Model 3 on vital signs, demographic characteristics, medical history, and chief complaints. Nine ML algorithms were employed. The area under the receiver operating characteristic curve (AUC), F1 Score, Positive Predictive Value, Negative Predictive Value, Brier score, calibration curves, and decision curves analysis were used to evaluate the performance of the models. …”
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