Enhanced Mamba model with multi-head attention mechanism and learnable scaling parameters for remaining useful life prediction by Fugang Liu, Shenyang Liu, Yuan Chai, Yongtao Zhu

“…In this paper, we propose a remaining useful life (RUL) prediction model based on Mamba, which incorporates learnable parameters and a multi-head attention mechanism; to address the issues faced by traditional algorithms, which struggle to efficiently capture dependencies in long sequences and parallelize the processing of these sequences. …”
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Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry by Ari Primantara, Udisubakti Ciptomulyono, Berlian Al Kindhi

“…This study investigates the use of machine learning to predict weight deviations in the Urea Bagging Unit at PT Petrokimia Gresik. Four algorithms were used: an Artificial Neural Network (ANN), Random Forest Regression (RFR), Linear Regression (LR), and Support Vector Regression (SVR). …”
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Enhance differential privacy mechanisms for clinical data analysis using CNNs and reinforcement learning by Rakesh Batchala, Priyank Jain, Manasi Gyanchandani, Sanyam Shukla, Rajesh Wadhvani

“…We safeguard patient data in the MIMIC-III dataset while preserving analytical utility by integrating differential privacy techniques, such as Gaussian, Laplace, Exponential, Zero-Concentrated, and Rényi mechanisms. …”
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Development of Advanced Machine Learning Models for Predicting CO₂ Solubility in Brine by Xuejia Du, Ganesh C. Thakur

“…A feature importance analysis revealed that pressure has the most impactful effect and positively correlates with CO₂ solubility, while temperature generally exhibits a negative effect. …”
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DANC-Net: Dual-Attention and Negative Constraint Network for Point Cloud Classification by Hang Sun, Yuanyue Zhang, Jinmei Shi, Shuifa Sun, Guanqun Sheng, Yirong Wu

“…Experiments demonstrate that the DANC-Net achieves better classification performance than the existing point cloud classification algorithms on synthetic datasets ModelNet10 and ModelNet40 and real-scene dataset ScanObjectNN. …”
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Multi-level evidence reveals PANK2 as a potential target of PFOA/PFOS-induced bone metabolism disruption: From network toxicology to in vitro validation by Yiming Shao, Wenhao Fan

“…Pantothenate kinase 2 (PANK2) was identified as a key target through the intersection of multiple databases and cross-validation using three machine learning algorithms. Molecular docking and dynamics simulations demonstrated that PFOA and PFOS can stably bind to PANK2 protein. …”
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Capturing Preferences of New Users in Generative Tasks with Minimal Interactions Collaborative Filtering Using Siamese Networks and Soft Clustering by Subharag Sarkar, Manfred Huber

“…Many collaborative filtering algorithms exist but all of them require the availability of huge datasets of user information and expensive computations. …”
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Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data. by Enrico Glaab, Jaume Bacardit, Jonathan M Garibaldi, Natalio Krasnogor

“…Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. …”
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A hybrid extraction model for semantic knowledge discovery of water conservancy big data by Yanna Feng, Feng Zhang, Yongheng Zhang, Jiangang Dong, PengJu Wang

“…To address the growing demand for efficient public opinion analysis in water conservancy and related domains, as well as the inefficiencies and limited scalability of existing automated web data extraction algorithms for multi-source datasets, this research integrates advanced technologies including big data analytics, natural language processing, and deep learning. …”
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Racial Disparities in Conforming Mortgage Lending: A Comparative Study of Fintech and Traditional Lenders Under Regulatory Oversight by Zilong Liu, Hongyan Liang

“…Achieving equitable mortgage lending requires enhanced regulatory oversight, greater transparency in algorithmic decision-making, and stricter enforcement of fair lending practices.…”
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Carbon reduction analysis of power system based on carbon emission flow theory: a case study of Shenzhen power grid by Yinjie Li, Ningrui Zhou, Zeqi Zhang

“…To address these challenges, this study proposes an integrated framework combining carbon emission flow theory with a low carbon demand response mechanism, accompanied by corresponding computational algorithms. Utilizing operational data from the Shenzhen power grid, we conducted a comprehensive empirical study featuring three key innovations. …”
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Blockchain-driven framework for preventive maintenance management of aircraft hydraulic systems by Alaa Alqaryuti, Karim Moawad, Khaled Salah, Ahmad Mayyas, Raja Jayaraman

“…This paper details the system architecture, underlying algorithms, and rigorous testing and validation processes. …”
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Development and evaluation of accurate 3D human models using scan data: a comparison with SMPL and CLO models by Gyungin Jung, Minjoo Kang, Sungmin Kim

“…This study introduces a novel 3D human modeling framework that combines skeleton-driven deformation algorithms with high-resolution 3D body scan data to generate anatomically accurate virtual bodies. …”
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Aberrant mitochondrial homeostasis at the crossroad of musculoskeletal ageing and non-small cell lung cancer. by Konstantinos Prokopidis, Panagiotis Giannos, Oliver C Witard, Daniel Peckham, Theocharis Ispoglou

“…Overlapping module DEGs were ranked based on 11 topological algorithms and were correlated with prognosis, tissue expression, and tumour purity in NSCLC. …”
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A Literature Review on Security in the Internet of Things: Identifying and Analysing Critical Categories by Hannelore Sebestyen, Daniela Elena Popescu, Rodica Doina Zmaranda

“…From leveraging machine learning and blockchain for anomaly detection and real-time threat response to optimising lightweight algorithms for resource-limited devices, researchers propose innovative and adaptive solutions to address emerging threats. …”
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Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies by Wang Guoyuan, Fan Wenbo, Shi Qingbin, Luo Yingqi

“…For this purpose, the compressive strength (C-S) of foamcrete was assessed using two machine learning algorithms: gene expression programming (GEP) and multi-expression programming (MEP). …”
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“He is my savior, my guiding light in the dark”: imagination and domestication in Chinese women’s romantic relationships with AI companions by Liyao Huang, Wenxue Zou, Yanghao Huang

“…A thematic analysis was conducted to examine a total of 2,485 posts written by female users.ResultsThe analysis uncovers three distinct themes: exploring romantic communication styles, rethinking romantic relationship roles, and challenging dominant gender hierarchies. While AI companions appear to afford women new forms of relational agency, this empowerment is marked by what we term “compromised resistance,” a dialectical engagement wherein users deploy technology to contest traditional gendered power structures even as they become enmeshed in new forms of algorithmic dependency.DiscussionThe findings position AI companions as ambivalent disruptors of gendered social orders: their subversive potential does not reside in the technology itself, but in the ways it incites users to reimagine and enact more inclusive intimacies. …”
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Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making by Oreofeoluwa A. Akintan, Kifle G. Gebremedhin, Daniel Dooyum Uyeh

“…Leveraging advanced analytical techniques, such as machine learning and optimization algorithms, have created highly accurate feed formulations tailored to specific livestock needs. …”
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Role of Artificial Intelligence in Dentistry by V. Hysenaj-Cakolli, B. Kolgeci, V. Hysenaj-Hoxha, D. Kolgeci

“…Future research should focus on enhancing AI algorithms, expanding training datasets, and integrating AI seamlessly into dental practice. …”
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A Spatial–Temporal Time Series Decomposition for Improving Independent Channel Forecasting by Yue Yu, Pavel Loskot, Wenbin Zhang, Qi Zhang, Yu Gao

“…In this paper, we improve the performance of single-channel forecasting algorithms by designing an interpretable front-end that extracts the spatial–temporal components from the input multivariate time series. …”
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