Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance by Bala Subramanian C, Bharathi ST, Shanmugapriya S

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Comparative performance analysis of ensemble learning methods for fetal health classification by Tasnim Bill Zannah, Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Afjal Hossan Sarower, Mahbuba Begum

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A study on the application of the latent dirichlet allocation model in production optimization by Youbin Chen, Da Huang, Lifei Wang

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Advanced Zero-Shot Learning (AZSL) Framework for Secure Model Generalization in Federated Learning by Muhammad Asif, Surayya Naz, Faheem Ali, Amerah Alabrah, Abdu Salam, Farhan Amin, Faizan Ullah

“…Federated learning (FL) introduces new perspectives in machine learning (ML) by enabling model training across decentralized devices. …”
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One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study by Davide Paolini, Pierpaolo Dini, Ettore Soldaini, Sergio Saponara

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Study on the Impact of Input Parameters on Seawater Dissolved Oxygen Prediction Models by Wenqing Li, Jing Lv, Yuhang Wang, Xiangfeng Kong

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Cyber epidemic spread forecasting based on the entropy-extremal dynamic interpretation of the SIR model by Viacheslav Kovtun, Krzysztof Grochla, Mohammed Al-Maitah, Saad Aldosary, Tetiana Gryshchuk

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The development of a multimodal prediction model based on CT and MRI for the prognosis of pancreatic cancer by Zheng Dou, Jiaxi Lin, Chenghao Lu, Xiaoting Ma, Ruoxu Zhang, Jinzhou Zhu, Songbing Qin, Chao Xu, Jinli Li

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A Device for the Rapid Detection of Benzodiazepines and Synthetic Cannabinoids via Fluorescence Spectroscopy and Machine Learning by A. Power, M. Gardner, C. Pudney

“…In the UK, the abuse of Benzodiazepines and Synthetic Cannabinoids is particularly prevalent, especially in healthcare and custodial settings, and there is currently no solution to quickly detect these substances for harm reduction. Methods: We are developing a portable and rapid device that utilizes Fluorescence Spectroscopy and Machine Learning to detect Benzodiazepines and Synthetic Cannabinoids in a variety of media, including saliva. …”
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Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning by Y. Qiu, X. Li, X. Li, W. Chai, Y. Liu, M. Song, X. Tian, Q. Zou, W. Lou, W. Zhang, J. Li, Y. Zhang

“…Primarily due to adverse changes in meteorological conditions, the effects of emission reduction are masked. In this study, we integrated a machine learning model, an observation-based model, and a positive matrix factorization model based on 4 years of continuous observation data from a typical urban site. …”
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Ensemble machine learning algorithm for cost-effective and timely detection of diabetes in Maiduguri, Borno State by Emmanuel Gbenga Dada, Aishatu Ibrahim Birma, Abdulkarim Abbas Gora

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Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning by Navita, Pooja Mittal, Yogesh Kumar Sharma, Anjani Kumar Rai, Sarita Simaiya, Umesh Kumar Lilhore, Vimal Kumar

“…Abstract A dual-stage model for classifying Parkinson’s disease severity, through a detailed analysis of Gait signals using force sensors and machine learning approaches, is proposed in this study. …”
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Towards generalizable machine learning prediction of downskin surface roughness in laser powder bed fusion by Jigar Patel, Mihaela Vlasea, Sagar Patel

“…While numerical or experimental approaches alone can be significantly resource intensive, data-driven approaches such as machine learning (ML) have the potential to be more practical. …”
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Energy Management and Edge-Driven Trading in Fractal-Structured Microgrids: A Machine Learning Approach by Mostafa Pasandideh, Jason Kurz, Mark Apperley

“…Leveraging incremental learning capabilities, the proposed model continuously updates, achieving robust predictive performance with mean absolute errors (MAE) across individual households and the community of less than 10% of typical hourly consumption values. …”
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CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction by Rui Chen, Hu Zhang, Xingwen Huang, Haitao Han, Jinbo Jian

“…Objective: To develop and validate a machine learning model based on CT radiomics to improve the ability to differentiate pathological subtypes of pulmonary ground-glass nodules (GGN). …”
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Dynamic demand response strategies for load management using machine learning across consumer segments by Ravi Kumar Goli, Nazeer Shaik, Manju Sree Yalamanchili

“…These systems efficiently support load adjustment tactics, such as load shifting and curtailment, to achieve notable peak load reductions by utilizing sophisticated prediction approaches, such as machine learning, statistical methods, and reinforcement learning. …”
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Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study by Yaxuan Wang, Shiyang Xie, Jiayun Liu, He Wang, Jiangang Yu, Wenya Li, Aika Guan, Shun Xu, Yong Cui, Wenfei Tan

“…All the net benefits of the five machine-learning models in the training and validation sets demonstrated excellent clinical applicability, and the calibration curves showed good agreement between the predicted and observed risks.Conclusion The combination of machine-learning models and nomograms may contribute to the early prediction and reduction in the incidence of PCNC.…”
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Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development by Lilia Lazli

“…While not curable, earlier detection can help improve symptoms substantially. Machine learning (ML) models are popular and well suited for medical image processing tasks such as computer-aided diagnosis. …”
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A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset by Ghadeer Jasim Mahdi

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Interpretable material descriptors for critical pitting temperature in austenitic stainless steel via machine learning by Faguo Hou, Hong-Hui Wu, Dexin Zhu, Jinyong Zhang, Liudong Hou, Shuize Wang, Guilin Wu, Junheng Gao, Jing Ma, Xinping Mao

“…Utilizing interpretable machine learning techniques, a predictive model for CPT is developed and confirmed via cross-validation, demonstrating superior predictive accuracy. …”
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