Hybridising Neurofuzzy Model the Seasonal Autoregressive Models for Electricity Price Forecasting on Germany’s Spot Market by Dorel Mihai Paraschiv, Narciz Bălășoiu, Souhir Ben-Amor, Raul Cristian

“…Despite the growing interest in predictive algorithms, the challenges are difficult to overcome given the restricted access to relevant data series and the lack of accurate metrics. …”
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High-resolution Annual Dynamic dataset of Curve Number from 2008 to 2021 over Conterminous United States by Qiong Wu, John J. Ramirez Avila, Jia Yang, Cunxiong Ji, Shanmin Fang

“…This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales.…”
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Temporal-Sequence Offline Reinforcement Learning for Transition Control of a Novel Tilt-Wing Unmanned Aerial Vehicle by Shiji Jin, Wenjie Zhao

“…Although online reinforcement learning algorithms offer adaptability, they depend on real-world exploration, posing considerable safety and cost risks for safety-critical UAV applications. …”
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Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations by Keping Li, Hangfei Huang, Paul Schonfeld

“…Branch-and-bound and frequency determining algorithms are designed to solve OST. A novel rolling optimization algorithm is designed to solve OH. …”
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Hybrid LSTM–Seq2Seq Models: Improved Patient Interaction for Healthcare Chatbots by saba abdalmnam, Suha Mohammed Hadi, Mustafa Musa

“…The capability of the model for sequence prediction is attributed to its robustness. Other methods of evaluation employing measures such as the Nash–Sutcliffe efficiency coefficient, correlation coefficient, and normalized root mean square error demonstrate that the model is superior to other machine learning algorithms utilizing linear regression and GP regression. …”
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Transcriptome associated with single-cell analysis reveal the role of S-palmitoylation in coronary artery disease by Yiming Xing, Xianhe Lin

“…Nomogram and ROC curve analyses revealed robust diagnostic predictive capabilities grounded in the three hub genes. …”
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A Novel Approach for Improving Cloud Liquid Water Content Profiling with Machine Learning by Anas Amaireh, Yan (Rockee) Zhang, Pak Wai Chan, Dusan Zrnic

“…The methodology was tested across multiple ML algorithms, with ensemble models such as Bagged Trees demonstrating superior predictive accuracy and robustness. …”
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A Multi-Model Machine Learning Framework for Identifying Raloxifene as a Novel RNA Polymerase Inhibitor from FDA-Approved Drugs by Nhung Thi Hong Van, Minh Tuan Nguyen

“…We developed a multi-model machine learning framework combining five traditional algorithms (ExtraTreesClassifier, RandomForestClassifier, LGBMClassifier, BernoulliNB, and BaggingClassifier) with a CNN deep learning model to identify potential RdRP inhibitors among FDA-approved drugs. …”
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Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202 by Amit Kaimkuriya, S. Balaguru

“…WOA outperformed FEA in predictive accuracy, achieving error margins as low as 1.87% for minimum load and 2.31% for compressive strength. …”
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TSCAPE: time series clustering with curve analysis and projection on an Euclidean space by Jeremy Renaud, Raphaël Couturier, Christophe Guyeux, Benoit Courjal

“…The results demonstrate the method's superiority over “only distance comparison clustering techniques”, like dynamic time warping and K-Means, with future prospects aimed at predictive applications and refining the clustering process by exploring alternative, more powerful clustering algorithms.…”
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The Future of Minimally Invasive GI and Capsule Diagnostics (REFLECT), October 2024 by Lea Østergaard Hansen, Alexandra Agache, Anastasios Koulaouzidis

“…The symposium also highlighted the significance of predictive models for patient selection and developments in panenteric CE. …”
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Epigenetic modifications of cfDNA in liquid biopsy for the cancer care continuum by Jodie Wong, Rohit Muralidhar, Liang Wang, Chiang-Ching Huang

“…It advocates for the integration of multi-omic data through machine learning algorithms to refine predictive models and stresses the importance of collaboration among clinicians, researchers, and data scientists. …”
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Innovative data techniques for centrifugal pump optimization with machine learning and AI model. by Gaurav Sandeep Dave, Amar Pradeep Pandhare, Atul Prabhakar Kulkarni, Dhananjay Vasant Khankal

“…Additionally, it decreases model training time by 180 seconds, facilitating predictive maintenance of critical performance metrics and minimizing downtime. …”
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Explicit and Implicit Knowledge in Large-Scale Linguistic Data and Digital Footprints from Social Networks by Maria Pilgun

“…This research aims to develop and test algorithms for analyzing both explicit and implicit information in user-generated content and digital interactions. …”
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Artificial intelligence-augmented smart grid architecture for cyber intrusion detection and mitigation in electric vehicle charging infrastructure by Ankita Sharma, Shalli Rani, Mohammad Shabaz

“…It incorporates AI-driven security models and anomaly detection algorithms to enhance grid resilience and optimize energy utilization. …”
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MFBCE: A Multi-Focal Bionic Compound Eye for Distance Measurement by Qiwei Liu, Xia Wang, Jiaan Xue, Shuaijun Lv, Ranfeng Wei

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Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions by Yiqun Pang, Kaiqi Zhang, Fengmei Li

“…Abstract How to utilize modern technological means to provide both accurate scoring and objective feedback for martial arts movements has become an issue that needs to be addressed in the field of physical education. This study proposes an intelligent scoring method based on machine learning. …”
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Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM by Fanyu ZENG, Yaning HAN, Hongyuan YANG, Dapeng YANG, Fan ZHENG

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Boosting brain-computer interface performance through cognitive training: A brain-centric approach by Ziyuan Zhang, Ziyu Wang, Kaitai Guo, Yang Zheng, Minghao Dong, Jimin Liang

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Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China by Lei-Lei Liu, Aasim Danish, Xiao-Mi Wang, Wen-Qing Zhu

“…In this study, we evaluate and compare the effectiveness of six machine learning algorithms for predicting landslide susceptibility in Anhua County, Hunan Province, China. …”
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