Applications of machine learning to high temperature and high pressure environments: A literature review by Hengkai Wang, Zengtao Lv, Santosh Kumar, Qinglin Wang

“…In addition, its simulation process is very fast, and the cost is not as expensive as that of density functional theory, so machine learning in the field of HTHP computing has aroused great research interest. …”
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Using machine learning techniques for DSP software performance prediction at source code level by Weihua Liu, Erh-Wen Hu, Bogong Su, Jian Wang

“…To validate the new model, we measure experimentally the execution time of a set of functions selected from PHY DSP Benchmark and run them on TIC64 DSP processor. …”
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Industrial data-driven machine learning soft sensing for optimal operation of etching tools by Feiyang Ou, Henrik Wang, Chao Zhang, Matthew Tom, Sthitie Bom, James F. Davis, Panagiotis D. Christofides

“…Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. …”
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Wake Homing Torpedo Guidance Using a Hierarchical Deep Reinforcement Learning Framework by Kunchul Hwang, Jinwhan Kim

“…This paper proposes a novel Hierarchical Deep Reinforcement Learning (HRL) framework for wake homing torpedo guidance, applying the Discrete Event System Specification (DEVS) formalism to design high-level policies and reward shaping functions. …”
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Digital Transformation in Lesson Planning: Automating Semester Learning Plans to Improve Pedagogical Effectiveness by Uswatun Hasanah, Annas Ribab Sibilana

“…This study aims to develop the RTFAC application to enhance the efficiency of the Semester Learning Plan (Rencana Pembelajaran Semester) formulation. …”
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Investigation of the Application of Machine Learning Algorithms in Credit Risk Assessment of Medium and Micro Enterprises by Yujie Zhao

“…Micro and medium enterprises rely on small finance investments and returns to ensure smooth and prolonged functional processes. Due to improper planning and recovery, credit-based investments and lending sometimes lead to financial downfalls. …”
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Deep Learning-Based Remote Sensing Image Analysis for Wildfire Risk Evaluation and Monitoring by Shiying Yu, Minerva Singh

“…To effectively monitor and predict fires, this project aims to develop deep learning models capable of processing multivariate remotely sensed global data in real time. …”
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Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials by Aparna P. A. Subramanyam, Danny Perez

“…Abstract In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. …”
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Employing SAE-GRU deep learning for scalable botnet detection in smart city infrastructure by Usman Tariq, Tariq Ahamed Ahanger

“…These findings enhance the understanding of IoT security by offering a scalable and resource-efficient solution for botnet detection. The functional investigation establishes a foundation for future research into adaptive security mechanisms that address emerging threats and highlights the practical potential of advanced deep learning techniques in safeguarding next-generation smart city ecosystems.…”
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Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking by Nikolaos Pavlidis, Andreas Sendros, Theodoros Tsiolakis, Periklis Kostamis, Christos Karasoulas, Eleni Briola, Christos Chrysanthos Nikolaidis, Vasilis Perifanis, George Drosatos, Eleftheria Katsiri, Despoina Elisavet Filippidou, Anastasios Manos, Pavlos S. Efraimidis

“…Health applications often need to balance advanced AI functionalities with sufficient privacy measures to ensure user engagement. …”
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A Secure Data Collection Method Based on Deep Reinforcement Learning and Lightweight Authentication by Yunlong Wang, Jie Zhang, Guangjie Han, Dugui Chen

“…To address such challenges, we propose a lightweight chain authentication protocol for scalable IoT environments (LCAP-SIoT), which uses Physical Unclonable Functions (PUFs) and distributed authentication to secure communications, and a secure data collection algorithm, named LS-QMIX, which fuses the LCAP-SIoT and Q-learning Mixer (QMIX) algorithm to optimize the path planning and cooperation efficiency of the multi-UAV system. …”
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Power Communication Network Recovery from Large-Scale Failures Based on Reinforcement Learning by Huibin JIA, Yonghe GAI, Baogang LI, Hongda ZHENG

“…Regarding this model, a heuristic algorithm based on reinforcement learning is proposed, in which the link recovery resources and the degree of importance of the damaged link in the failed service are taken into account to set the reward and penalty functions as well as the selection rules. …”
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Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the Cell by Mehwish Faiz, Saad Jawaid Khan, Fahad Azim, Nazia Ejaz, Fahad Shamim

“…Their precise localization is crucial for understanding their functions. Existing protein subcellular localization predictors are predominantly trained on globular proteins; their performance diminishes for membrane proteins, explicitly via deep learning models. …”
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soundClass: An automatic sound classification tool for biodiversity monitoring using machine learning by Bruno Silva, Frederico Mestre, Sílvia Barreiro, Pedro J. Alves, José M. Herrera

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Revolutionizing Lung Segmentation with Machine Learning: A Critical Review of Techniques in Medical Imaging by Momina Aisha, Moazma Ijaz, Nimra Tariq, Sehar Anjum, Sidra Siddiqui, Usman Hashmi

“…Consequently, automated lung segmentation methods utilizing Machine Learning (ML) and Deep Learning (DL) have emerged as essential alternatives. …”
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scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…The critic learns state-value functions that quantify fate intensity for each cell, while the actor traces optimal developmental routes across the manifold. …”
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Integrated bioinformatics and machine learning reveal key genes and immune mechanisms associated with uremia by Zhiyue Sun, Zhiqiang Ding, Xiaoyang Guo, Jiangtao Li, Daoyuan Ding, Bo Wen

“…GSEA and immune infiltration analyses were conducted to explore functional pathways and immune relevance. ROC curves were used to evaluate the discriminatory power of the selected genes. …”
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Leveraging Digital Library to Enhance Research and Learning Experience of College Students: An In-Depth Study by Chunying Yang

“…Thus, the digital library provides a platform to assist students to develop an inclination towards learning and emotional shaping. Its functional system thus serves both comprehensive and harmonious development of students. …”
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Enhancing User Experiences in Digital Marketing Through Machine Learning: Cases, Trends, and Challenges by Alexios Kaponis, Manolis Maragoudakis, Konstantinos Chrysanthos Sofianos

“…The objective of the present study is to critically analyze the functional and ethical considerations of ML integration in DM and to evaluate its implications on data-driven personalization. …”
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Classifying software security requirements into confidentiality, integrity, and availability using machine learning approaches by Taghreed Bagies

“…Security requirements are considered one of the most important non-functional requirements of software. The CIA (confidentiality, integrity, and availability) triad forms the basis for the development of security systems. …”
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