Fault Prediction of Centrifugal Pump Based on Improved KNN by YunFei Chen, Jianping Yuan, Yin Luo, Wenqi Zhang

“…To effectively predict the faults of centrifugal pumps, the idea of machine learning k-nearest neighbor algorithm (KNN) was introduced into the traditional Mahalanobis distance fault discrimination, and an improved centrifugal pump fault prediction model of KNN based on the Mahalanobis distance is proposed. …”
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AI-assisted discovery of quantitative and formal models in social science by Julia Balla, Sihao Huang, Owen Dugan, Rumen Dangovski, Marin Soljačić

“…Here, we demonstrate the use of a machine learning system to aid the discovery of symbolic models that capture non-linear and dynamical relationships in social science datasets. …”
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Customer service complaint work order classification based on matrix factorization and attention multi-task learning by Yong SONG, Zhiwei YAN, Yukun QIN, Dongming ZHAO, Xiaozhou YE, Yuanyuan CHAI, Ye OUYANG

“…The automatic classification of complaint work orders is the requirement of the digital and intelligent development of customer service of communication operators.The categories of customer service complaint work orders have multiple levels, each level has multiple labels, and the levels are related, which belongs to a typical hierarchical multi-label text classification (HMTC) problem.Most of the existing solutions are based on classifiers to process all classification labels at the same time, or use multiple classifiers for each level, ignoring the dependence between hierarchies.A matrix factorization and attention-based multi-task learning approach (MF-AMLA) to deal with hierarchical multi-label text classification tasks was proposed.Under the classification data of real complaint work orders in the customer service scenario of communication operators, the maximum Top1 F1 value of MF-AMLA is increased by 21.1% and 5.7% respectively compared with the commonly used machine learning algorithm and deep learning algorithm in this scenario.It has been launched in the customer service system of one mobile operator, the accuracy of model output is more than 97%, and the processing efficiency of customer service agent unit time has been improved by 22.1%.…”
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Validation and Calibration of an Agent-Based Model: A Surrogate Approach by Yi Zhang, Zhe Li, Yongchao Zhang

“…In this paper, we present a surrogate analysis method for calibration by combining supervised machine-learning and intelligent iterative sampling. Without any prior assumptions regarding the distribution of the parameter space, the proposed method can learn a surrogate model as the approximation of the original system with a relatively small number of training points, which will serve the needs of further sensitivity analysis and parameter calibration research. …”
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Few-Shot Contrail Segmentation in Remote Sensing Imagery With Loss Function in Hough Space by Junzi Sun, Esther Roosenbrand

“…Traditional computer vision techniques struggle with varying imagery conditions, and supervised machine learning approaches often require a large amount of hand-labeled images. …”
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Research and Model Prediction on the Performance of Recycled Brick Powder Foam Concrete by Hongyang Xie, Jianjun Dong, Yong Deng, Yiwen Dai

“…In this paper, the effect of air bubble swarm admixture, recycled brick powder admixture, water to material ratio, and HPMC content on the physical and mechanical properties of recycled brick powder foam concrete was investigated by conducting a 4-factor, 5-level orthogonal test with recycled brick powder as fine aggregate, and the effect of each factor on the physical and mechanical properties of recycled brick powder foam concrete was derived, and the optimum ratio of recycled brick powder foam concrete was determined by analysing the specific strength. Five machine learning models, namely, back propagation neural network improved by particle swarm optimization (PSO-BP), support vector machine (SVM), multiple linear regression (MLR), random forest (RF), and back propagation neural network (BP), were used to predict the compressive strength of recycled brick powder foam concrete, and the PSO-BP model was found to have obvious advantages in terms of prediction accuracy and model stability. …”
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Improving ocean reanalyses of observationally sparse regions with transfer learning by Simon Lentz, Sebastian Brune, Christopher Kadow, Johanna Baehr

“…Consequently, with infrequent input data, machine learning reconstructions exhibit similar physical structures, while correcting for known errors compared to state-of-the-art data assimilation products. …”
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Federated Learning Based on OPTICS Clustering Optimization by Chenyang Lu, Su Deng, Yahui Wu, Haohao Zhou, Wubin Ma

“…Federated learning (FL) has emerged for solving the problem of data fragmentation and isolation in machine learning based on privacy protection. Each client node uploads the trained model parameter information to the central server based on the local training data, and the central server aggregates the parameter information to achieve the purpose of common training. …”
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Multiobjective Prioritized Workflow Scheduling in Cloud Computing Using Cuckoo Search Algorithm by Babuli Sahu, Sangram Keshari Swain, Sudheer Mangalampalli, Satyasis Mishra

“…., Max–Min, first come first serve, minimum completion time, Min–Min, resource allocation security with efficient task scheduling in cloud computing-hybrid machine learning, and Round Robin. Our proposed approach is outperformed by minimizing energy consumption by 15% and reducing service level agreement violations by 22%.…”
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HARD-VOTING DAN SOFT-VOTING CLASSIFIER: MODEL KLASIFIKASI RISIKO KEMATIAN PADA PASIEN GAGAL GINJAL KRONIK by LUTHFATUL AMALIANA, ANI BUDI ASTUTI, ROSSANDA SEVIA GADIS, NAURAH ATIKAH RABBANI, NABILA AYUNDA SOVIA

“…This study aims to predict the risk of death in hospitalized chronic kidney failure patients using ensemble machine learning methods, specifically hard-voting and soft-voting. …”
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Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach by Sanaz Zamani, Minh Nguyen, Roopak Sinha

“…This study presents a groundbreaking IoT-based system that integrates big data analytics, fuzzy logic, and machine learning to revolutionise mental health monitoring. …”
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E-learning Platform and Pedagogical Monitoring in the Context of Digital Transformation by A. V. Khaperskaya, М. G. Minin

“…In particular, the implementation of an expert assessment method using image theory and machine learning is presented. The article argues that electronic didactics makes it possible to expand the functionalities of pedagogical monitoring in conditions of digitalization, while maintaining the principles of traditional pedagogy. …”
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Deep Learning-Based Dzongkha Handwritten Digit Classification by Yonten Jamtsho, Pema Yangden, Sonam Wangmo, Nima Dema

“…With the advancement in deep learning technology, many machine learning algorithms were developed to tackle the problem of pattern recognition. …”
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Deep phenotyping platform for microscopic plant-pathogen interactions by Stefanie Lück, Salim Bourras, Dimitar Douchkov

“…Building on accumulated experience and leveraging automated microscopy and software, we developed BluVision Micro, a modular, machine learning-aided system designed for high-throughput microscopic phenotyping. …”
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Deep learning for object recognition: A comprehensive review of models and algorithms by Paschalis Tsirtsakis, Georgios Zacharis, George S. Maraslidis, George F. Fragulis

“…The rapid advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced progress in computer vision, opening doors to innovative technological possibilities and enabling a range of real-world applications. …”
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Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing by Artur Samojluk, Piotr Artiemjew

“…An analysis of data mining and selected machine learning methods was carried out to develop an effective diagnosis algorithm. …”
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Enhancing Privacy While Preserving Context in Text Transformations by Large Language Models by Tymon Lesław Żarski, Artur Janicki

“…Our approach utilizes natural language processing methods, combining machine learning tools such as MITIE and spaCy with rule-based text analysis. …”
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Application Research of Intelligent Classification Technology in Enterprise Data Classification and Gradation System by Lina Yu, Chunwei Wang, Huixian Chang, Sheng Shen, Fang Hou, Yingwei Li

“…This paper introduces artificial intelligence classification, machine learning, and other means to learn and train enterprise documents according to the characteristics of enterprise sensitive data. …”
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Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks by Mo Yang, Dezhi Zhou

“…A comprehensive dataset of ignition delays was generated using a random sampling technique across different temperatures and pressures, focusing on hydrocarbon fuels with 1–4 carbon atoms. Two machine learning models, an artificial neural network and a graph convolutional network, are trained on this dataset, and their prediction performance was evaluated. …”
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Bibliometric Analysis of Studies on the Artificial Intelligence in Science Education with VOSviewer by Hayriye Nevin Genc, Nuriye Kocak

“…The most cited used keywords were "Machine Learning" and "Artificial Intelligence", followed by "Learning Analytics", "Data Science" and "Higher Education". …”
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