Hybrid quantum neural networks show strongly reduced need for free parameters in entity matching by Lukas Bischof, Stefan Teodoropol, Rudolf M. Füchslin, Kurt Stockinger

“…In this paper, we evaluate quantum machine learning algorithms for entity matching on a hand-crafted data set and compare them to similar classical algorithms. …”
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CPP: a path planning method taking into account obstacle shadow hiding by Ruixin Zhang, Qing Xu, Youneng Su, Ruoxu Chen, Kai Sun, Fengchang Li, Guo Zhang

“…Abstract Path planning algorithms are crucial for the autonomous navigation and task execution of unmanned vehicles in battlefield environments. …”
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Interacting Large Language Model Agents Bayesian Social Learning Based Interpretable Models by Adit Jain, Vikram Krishnamurthy

“…This paper discusses the theory and algorithms for interacting large language model agents (LLMAs) using methods from statistical signal processing and microeconomics. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Nephelometer Sensitivities for the Determination of PM2.5 Mass Concentration in Ambient and Indoor Air by Dimitra Karali, Glykeria Loupa, Spyridon Rapsomanikis

“…Abstract Simple algorithms are presented for the relationships between the gravimetrically measured PM2.5 mass concentration and a nephelometer particle scattering coefficient, for three different environments: two outdoor locations (an urban and a suburban) and one indoors. …”
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A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease by K. M. Tawsik Jawad, Anusha Verma, Fathi Amsaad, Lamia Ashraf

“…Also, we have implemented the Explainable AI (XAI) algorithms to interpret the decision-making process of the ensemble models in terms of dominating features and the feature values. …”
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An Overview of Application of Machine Learning Models in Urban Flood Simulation and Forecasting by CHEN Zeming, FANG Xuhong, LI Jiaye, WANG Mengyao, CHEN Aifang, YIN Ling

“…Additionally, this paper also focused on the research on urban flood simulation and forecasting models based on machine learning algorithms published in the recent five years, summarized the technical processes for urban flood simulation and forecasting models based on machine learning algorithms, and proposed several current technological bottlenecks and potential solutions. …”
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Artificial intelligence for chest X-ray image enhancement by Liming Song, Hongfei Sun, Haonan Xiao, Sai Kit Lam, Yuefu Zhan, Ge Ren, Jing Cai

“…Recently, the successful application of deep learning (DL) algorithms in medical image analysis has prompted researchers to propose and design DL-based CXR image enhancement algorithms. …”
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Multimodal data integration in early-stage breast cancer by Arnau Llinas-Bertran, Maria Butjosa-Espín, Vittoria Barberi, Jose A. Seoane

“…We examined the primary challenges and opportunities of these multimodal data integration algorithms, including their advantages, limitations, and critical considerations for future research. …”
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Uncertainty-based quantization method for stable training of binary neural networks by A.V. Trusov, D.N. Putintsev, E.E. Limonova

“…However, training BNNs has proven to be challenging. Existing algorithms either fail to produce stable and high-quality results or are overly complex for practical use. …”
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Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Fatemeh Mohammadinia, Amirjavad Borhani

“…Input data for these algorithms include CO2, H2S, N2, C1, C2, C3, C4, C5, C6, C7+, MWC5+, MWC7+, T, alongside vol/int. …”
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Direct coupling analysis and the attention mechanism by Francesco Caredda, Andrea Pagnani

“…The model’s parameters, notably fewer than those in standard DCA-based algorithms, can be directly used for extracting structural determinants such as the contact map of the protein family under study. …”
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Models of idealized films and optimal networks relations between the problems and simulation results by Vydūnas Šaltenis

“…The simulation of wet films was used in algorithms and software for solving the Steiner prob­lem. …”
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Load recognition method based on convolutional neural network and attention mechanism by ZHAO Yitao, LI Zhao, LIU Xinglong, LUO Zhao, WANG Gang, SHEN Xin

“…Aiming at the problems of poor recognition performance of traditional algorithms and difficulty in adapting to the current complex electricity environment, a NILM load recognition method integrating convolutional neural network (CNN)-self-attention mechanism is proposed from the optimization idea of enhancing the feature extraction performance of classification algorithms. …”
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Bioinformatics insights into mitochondrial and immune gene regulation in Alzheimer's disease by Tian Meng, Yazhou Zhang, Yuan Ye, Hui Li, Yongsheng He

“…Then, using machine learning algorithms, biomarkers with good diagnostic value were selected, and a nomogram was constructed. …”
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Design and optimization of distributed energy management system based on edge computing and machine learning by Nan Feng, Conglin Ran

“…Future research can further explore how to integrate multiple machine learning algorithms to optimize energy scheduling strategies and improve the system’s adaptability in complex environments.…”
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Predicting child mortality determinants in Uttar Pradesh using Machine Learning: Insights from the National Family and Health Survey (2019–21) by Pinky Pandey, Sacheendra Shukla, Niraj Kumar Singh, Mukesh Kumar

“…Aim: This study aimed to delineate spatial variations in under-five mortality across Uttar Pradesh and evaluate the efficacy of various machine learning algorithms in identifying critical determinants influencing these mortality rates. …”
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Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC by Xiaoqin Luo, Chao Li, Gang Qin

“…Using 10 multi-omics consensus clustering algorithms via the MOVICS package, we identified two molecular subtypes (CS1 and CS2) and validated their stability. …”
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Ion suppression correction and normalization for non-targeted metabolomics by Iqbal Mahmud, Bo Wei, Lucas Veillon, Lin Tan, Sara Martinez, Bao Tran, Alexander Raskind, Felice de Jong, Yiwei Liu, Jibin Ding, Yun Xiong, Wai-kin Chan, Rehan Akbani, John N. Weinstein, Chris Beecher, Philip L. Lorenzi

“…Here we report a method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) library plus companion algorithms to: 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. …”
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DATABASES DESIGNED WITH PHP AND MYSQL REGARDING THE OPTIMIZATION OF THE AUTOMATION INSTALLATION IN WATER TREATMENT PLANTS by COSTACHE (BIRA) Lenuta

“…At the same time, the use of a database are directions for the development of new technologies, using as a tool the mathematical model and the optimization-solution algorithms. Scientific results will be published in collections of scientific papers at national and international conferences. …”
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