Fault analysis and fault degree evaluation via an improved ResNet method for aircraft hydraulic system by Kenan Shen, Dongbiao Zhao

“…After that the evaluate results are given and analyzed, moreover the comparison between the SE-ResNet method with the other machine learning methods are given. The results show that the method in this paper has the best accuracy and shortest test time, therefore the method proposed in this paper has effective measures to improve the reliability of the aircraft hydraulic system.…”
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Utilising causal inference methods to estimate effects and strategise interventions in observational health data. by Bao Duong, Manisha Senadeera, Toan Nguyen, Melanie Nichols, Kathryn Backholer, Steven Allender, Thin Nguyen

“…This gap is bridged by the latest advancements in causal inference methods, building upon machine learning-based approaches to investigate not only population-level effects but also the heterogeneous effects of interventions across population subgroups. …”
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OryzaGP: rice gene and protein dataset for named-entity recognition by Pierre Larmande, Huy Do, Yue Wang

“…Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. …”
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Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine by Lei Jiang, Jiaming Li, Suhuai Luo, Sam West, Glenn Platt

“…This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. …”
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Short-term prediction of trimaran load based on data driven technology by Haoyun Tang, Rui Zhu, Qian Wan, Deyuan Ren

“…According to the machine learning theory, a short-term prediction method towards trimaran loads is studied by using an optimized data-driven model. …”
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Clustering Large-Scale Biomedical Data to Model Dynamic Accumulation Processes in Disease Progression and Anti-Microbial Resistance Evolution by Kazeem A. Dauda, Olav N. L. Aga, Iain G. Johnston

“…Accumulation modelling uses machine learning to discover the dynamics by which systems acquire discrete features over time. …”
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Conditional similarity triplets enable covariate-informed representations of single-cell data by Chi-Jane Chen, Haidong Yi, Natalie Stanley

“…In order to translate immune signatures assayed from blood or tissue into powerful diagnostics, machine learning approaches are often employed to compute immunological summaries or per-sample featurizations, which can be used as inputs to models for outcomes of interest. …”
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Revolutionizing Ophthalmic Care: The Impact of Artificial Intelligence by L. Subha, Atul Sharma, Anita Misra

“…Deep learning, an emerging facet of machine learning (ML), can uncover complex data structures without explicit rule specifications. …”
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Research status and development trends of dust concentration monitoring technology by ZHANG Yongqi, WANG Jie, ZHOU Yuhao, YANG Junni, DENG Bin

“…It is proposed that future efforts should be dedicated to integrating dust concentration monitoring technologies with emerging technologies such as machine learning, deep learning, computer vision, and big data analysis and prediction. …”
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Portable solutions for plant pathogen diagnostics: development, usage, and future potential by Anurag Yadav, Kusum Yadav

“…Furthermore, the integration of these devices with digital technologies, including the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), is transforming disease surveillance and management. …”
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A Distribution Agnostic Rank-Based Measure for Proximity Search by Mayur Garg, Ashutosh Nayak, Rajasekhara Reddy Duvvuru Muni

“…Proximity search is extensively used in modern machine learning algorithms across various applications. …”
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Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction. by Akash Bahai, Chee Keong Kwoh, Yuguang Mu, Yinghui Li

“…Computational approaches have emerged as valuable tools, leveraging physics-based-principles and machine learning to predict RNA structures rapidly. Despite advancements, the accuracy of computational methods remains modest, especially when compared to protein structure prediction. …”
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Conceptual Structure and Trend Topics of the Education and Information Technologies Journal: A bibliometrics analysis by Yousef Mahdavinasab, Davoud Haseli

“…The analysis revealed the presence of nine subject areas of EAIT: role of ICT and technology in education, blended learning and teacher training, e-learning and mobile learning, engaging and motivating learning environments, educational data mining and machine learning, distance education in different academic settings, education during the COVID-19 pandemic, educational technology in higher education and developing countries, and the use of social media in education. …”
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Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design by Akiyasu Yamamoto, Shinnosuke Tokuta, Akimitsu Ishii, Akinori Yamanaka, Yusuke Shimada, Mark D. Ainslie

“…In this study, we employ machine learning to manipulate intricate polycrystalline microstructures through a process design that integrates researcher- and data-driven approaches via tailored software. …”
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An Overview of Pavement Degradation Prediction Models by Amir Shtayat, Sara Moridpour, Berthold Best, Shahriar Rumi

“…The findings show that most previous studies preferred machine learning approaches and artificial neural networks forecasting and estimating the road pavement conditions because of their ability to deal with massive data, their higher accuracy, and them being worthwhile in solving time-series problems.…”
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ECP-IEM: Enhancing seasonal crop productivity with deep integrated models. by Ghulam Mustafa, Muhammad Ali Moazzam, Asif Nawaz, Tariq Ali, Deema Mohammed Alsekait, Ahmed Saleh Alattas, Diaa Salama AbdElminaam

“…Artificial Intelligence (AI) has increased the yield accuracy significantly. The existing Machine Learning (ML) methods are using statistical measures as regression, correlation and chi square test for predicting crop yield, all such model's leads to low accuracy when the number of factors (variables) such as the weather and soil conditions, the wind, fertilizer quantity, and the seed quality and climate are increased. …”
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Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction by Mohammed Abufouda

“…Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive subwebsites. …”
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Analysis of Concrete Dam Deformation Prediction Based on the ResNet-GRU-SGWO Model by Ning Ma, Xiubo Niu, Xunhui Chen, Wenxiu Wei, Ye Zhang, Xinyu Kang, Wen Zhong, Jing Wu

“…Concrete dam deformation prediction plays a crucial role in ensuring dam safety monitoring. Machine learning, especially recurrent neural networks, plays an important role in this, yet its predictive accuracy is not high. …”
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The media framing dataset: Analyzing news narratives in Mexico and ColombiaZenodo by Juan Cuadrado, Elizabeth Martinez, Juan Carlos Martinez-Santos, Edwin Puertas

“…Additionally, it provides a foundation for linguistic analysis and machine learning projects, enabling researchers and practitioners to explore media framing dynamics and develop innovative tools for media analysis.…”
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AI driven decision support reduces antibiotic mismatches and inappropriate use in outpatient urinary tract infections by Shirley Shapiro Ben David, Roni Romano, Daniella Rahamim-Cohen, Joseph Azuri, Shira Greenfeld, Ben Gedassi, Uri Lerner

“…UTIS integrates machine learning forecasts of antibiotic resistance, patient data, and guidelines into a user-friendly order set for UTI management. …”
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