Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques by Neha Singh, Umesh Chandra Jaiswal

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Physics Informed by Deep Learning: Numerical Solutions of Modified Korteweg-de Vries Equation by Yuexing Bai, Temuer Chaolu, Sudao Bilige

“…The method that we used in this paper had demonstrated the powerful mathematical and physical ability of deep learning to flexibly simulate the physical dynamic state represented by differential equations and also opens the way for us to understand more physical phenomena later.…”
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Enhancing thyroid disease prediction and comorbidity management through advanced machine learning frameworks by P. Sanju, N. Syed Siraj Ahmed, P. Ramachandran, P. Mohamed Sajid, R. Jayanthi

Subjects: “…Machine learning…”
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A deep reinforcement learning-based approach for cyber resilient demand response optimization by Ayush Sinha, Ranjana Vyas, Feras Alasali, William Holderbaum, O. P. Vyas

“…This research endeavors to advance peak load forecasting strategies and demand response optimization at the microgrid level, thereby enhancing grid reliability through the application of Deep Reinforcement Learning (DRL) techniques. Additionally, it investigates the ongoing threat of false data injection attacks. …”
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Electrical vehicle grid integration for demand response in distribution networks using reinforcement learning by Fayiz Alfaverh, Mouloud Denaï, Yichuang Sun

“…Here, an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) is proposed. Q‐learning, an RL strategy based on a reward mechanism, is used to make optimal decisions to charge or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. …”
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Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review by Aijing Luo, Wei Chen, Hongtao Zhu, Wenzhao Xie, Xi Chen, Zhenjiang Liu, Zirui Xin

“…In terms of model type, deep learning, represented by convolutional neural networks, was most frequently applied (14/23, 61%). …”
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Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling by Sianou Ezéckiel Houénafa, Olatunji Johnson, Erick K. Ronoh, Stephen E. Moore

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Research Trends in Project-Based Learning in Elementary Schools (2001-2024): Bibliometric Analysis by Purwandari Purwandari, Farida Huriawati, Viola Dea Amanda Sarry, Ita Susanti

Subjects: “…Bibliometrics; Elementary school; Project-based learning…”
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Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning by Guodong Sun, Ye Hu, Bo Wu, Hongyu Zhou

“…Addressing the problem that it is difficult to extract the features of vibration signal and diagnose the fault of rolling bearing, we propose a novel diagnosis method combining multisynchrosqueezing S transform and faster dictionary learning (MSSST-FDL). Firstly, MSSST is adopted to transform vibration signals into high-resolution time-frequency images. …”
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Teaching Quality Evaluation of Ideological and Political Courses in Colleges and Universities Based on Machine Learning by Lijun Qiao

“…Combined with the weighted Bayesian classification incremental learning principle, the performance of the classification model can be better than the traditional classification model.…”
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Deep Reinforcement Learning-Based Anti-Jamming Approach for Fast Frequency Hopping Systems by Sixi Cheng, Xiang Ling, Lidong Zhu

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Predicting the Link between Stock Prices and Indices with Machine Learning in R Programming Language by Mengya Cao

“…This paper provides an in-depth analysis machine study of the relationship between stock prices and indices through machine learning algorithms. Stock prices are difficult to predict by a single financial formula because there are too many factors that can affect stock prices. …”
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Selective Ensemble Learning Method for Belief-Rule-Base Classification System Based on PAES by Wanling Liu, Weikun Wu, Yingming Wang, Yanggeng Fu, Yanqing Lin

“…Traditional Belief-Rule-Based (BRB) ensemble learning methods integrate all of the trained sub-BRB systems to obtain better results than a single belief-rule-based system. …”
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PhyICNet: Physics-informed interactive learning convolutional recurrent network for spatiotemporal dynamics by Ruohan Cao, Jin Su, Jinqian Feng, Qin Guo

Subjects: “…physics-informed deep learning…”
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FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications by Ioannis V. Vondikakis, Ilias E. Panagiotopoulos, George J. Dimitrakopoulos

Subjects: “…Machine learning…”
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Data-driven evaluation of background radiation safety using machine learning and statistical analysis by Muhammad Abid, Muhammad Shahid

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Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction by Yue Peng, Junze Wu, Yi Sun, Yuanxing Zhang, Qiyao Wang, Shuai Shao

“…Here, we introduce a model named Contrastive-learning of Language Embedding and Biological Features (CLEF) leveraging contrastive learning to integrate PLM representations with supplementary biological features. …”
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Multimodal sentiment analysis based on multi-layer feature fusion and multi-task learning by Yujian Cai, Xingguang Li, Yingyu Zhang, Jinsong Li, Fazheng Zhu, Lin Rao

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Identification of depressive symptoms in adolescents using machine learning combining childhood and adolescence features by Xinzhu Liu, Rui Cang, Zihe Zhang, Ping Li, Hui Wu, Wei Liu, Shu Li

“…These symptoms may be linked to various factors experienced during both childhood and adolescence. Machine learning (ML) has attracted substantial attention in the field of adolescent depression; however, studies establishing prediction models have primarily considered childhood or adolescent features separately, resulting in a lack of analyses that incorporate factors from both stages. …”
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Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model by Yongyi Li, Ming Zhang, Yixing Ding, Zhenghua Zhou, Lingyu Xu

“…We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. …”
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