“Human – Social Robot” Interaction: Through Overcoming Barriers to Hybrid Communication by V. I. Ignatyev, K. I. Spiridonova

“…In the process of reproducing the stable nature of communication in the peculiarities of the behavior of each of the parties, there were ways of responding to overcome the blockade, not foreseen either by the developers of the algorithms or by a person entering into communication with a robot with the expectation of programmed “machine behavior”. …”
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The transformative power of artificial intelligence in pharmaceutical manufacturing: Enhancing efficiency, product quality, and safety by Mukesh Vijayarangam Rajesh, Karthikeyan Elumalai

“…The combination of big data and AI applications through machine learning algorithms analyzes manufacturing inefficiencies and recommends improvements for both medicine formulation and packaging as well as quality control measures. …”
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Credit Scoring Prediction Using Deep Learning Models in the Financial Sector by Xi Shi, Dingfen Tang, Yike Yu

“…Existing approaches often struggle with integrating structured numerical records and unstructured user behavior signals, limiting their ability to capture meaningful temporal and non-linear patterns. In the swiftly transforming domain of computational science, the incorporation of sophisticated machine learning algorithms has emerged as a critical driver in addressing these challenges. …”
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Hybrid Intelligence approach to study post-processing impact on the mechanical performance of notched additively manufactured AlSi10Mg by Erfan Maleki, Sara Bagherifard, Okan Unal, Mario Guagliano

“…In parallel, Artificial Intelligence (AI), utilizing advanced machine learning (ML) algorithms, performed tasks related to prediction, sensitivity analysis, and parametric analysis. …”
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Research and predictive analysis of pyrolysis characteristics of multi-source organic solid wastes by ZHANG Zihang, XING Bo, MA Zhongqing, HU Yanjun, ZHANG Zhixiao, YUAN Shizhen, LU Rufei, CHEN Yingquan, WANG Shurong*

“…Descriptive statistical analysis, correlation analysis, and principal component analysis (PCA) were employed to uncover patterns within the dataset. Subsequently, the random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) algorithms were utilized to predict the high heating value (HHV) of organic solid waste, the distribution of fast pyrolysis products, and the thermogravimetric curves under various atmospheres. …”
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Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis by Dongxu Qin, Yongquan Zheng, Libo Wang, Zhenyi Lin, Yao Yao, Weidong Fei, Caihong Zheng

“…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify key genes. Machine learning algorithms, including Random Forest (RF) and XGBoost, were utilized to screen for shared diagnostic genes, which were subsequently validated through receiver operating characteristic (ROC) analysis and clinical prediction models. …”
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Convolutional Neural Decoder for Surface Codes by Hyunwoo Jung, Inayat Ali, Jeongseok Ha

“…Recently, various decoding algorithms based on machine learning have been proposed to improve the decoding performance and latency of QEC codes. …”
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Recent developments of artificial intelligence methods for sea ice concentration monitoring using high-resolution imaging datasets by Marzuraikah Mohd Stofa, Siti Raihanah Abdani, Asraf Mohamed Moubark, Muhammad Ammirrul Atiqi Mohd Zainuri, Ahmad Asrul Ibrahim, Nor Azwan Mohamed Kamari, Mohd Asyraf Zulkifley

“…Subsequently, relevant sea ice mapping techniques, including conventional algorithms, machine learning-based approaches and deep learning-based methods, are assessed. …”
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Socioeconomic status and lifestyle as factors of multimorbidity among older adults in China: results from the China Health and Retirement Longitudinal Survey by Wei Gong, Wei Gong, Wei Gong, Xiaoxiao Hu, Huimin Cui, Huimin Cui, Yuxin Zhao, Yuxin Zhao, Hong Lin, Hong Lin, Hong Lin, Peng Sun, Peng Sun, Jianjun Yang, Jianjun Yang

“…A total of 34,755 participants were included, and 17 features related to demographics, SES, and lifestyle were selected via LASSO regression. Eight machine learning algorithms including logistic regression, decision tree, naive Bayes, neural network, support vector machine, random forest, XGBoost and Bayesian Ridge Regression were applied to build predictive models. …”
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Comprehensive Analysis of Programmed Cell Death-Related Genes in Diagnosis and Synovitis During Osteoarthritis Development: Based on Bulk and Single-Cell RNA Sequencing Data by Zhou J, Jiao S, Huang J, Dai T, Xu Y, Xia D, Feng Z, Chen J, Li Z, Hu L, Meng Q

“…Diverse programmed cell death (PCD) pathways are closely linked to the pathogenesis of OA, but few studies have explored the relationship between PCD-related genes and synovitis.Methods: The transcriptome expression profiles of OA synovial samples were obtained from the Gene Expression Omnibus (GEO) database. Using machine learning algorithms, Hub PCD-related differentially expressed genes (Hub PCD-DEGs) were identified. …”
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AI‐Driven TENGs for Self‐Powered Smart Sensors and Intelligent Devices by Aiswarya Baburaj, Syamini Jayadevan, Akshaya Kumar Aliyana, Naveen Kumar SK, George K Stylios

“…This review explores the synergistic potential of AI‐driven TENG systems, from optimizing materials and fabrication to embedding machine learning and deep learning algorithms for intelligent real‐time sensing. …”
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Direct Discrimination and Growth Estimation of Foodborne Bacteria in Raw Meat Using Electronic Nose by Wellington Belarmino Gonçalves, Wanderson Sirley Reis Teixeira, Aryele Nunes da Cruz Encide Sampaio, Otávio Augusto Martins, Evelyn Perez Cervantes, Mateus de Souza Ribeiro Mioni, Jonas Gruber, Juliano Gonçalves Pereira

“…Concerning the machine learning algorithms, the accuracy varied from 93.8 to 100% for beef and chicken, while for pork, it varied from 75% to 100%. …”
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Quantum annealing feature selection on light-weight medical image datasets by Merlin A. Nau, Luca A. Nutricati, Bruno Camino, Paul A. Warburton, Andreas K. Maier

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Decoding Lung Cancer Radiogenomics: A Custom Clustering/Classification Methodology to Simultaneously Identify Important Imaging Features and Relevant Genes by Destie Provenzano, John P. Lichtenberger, Sharad Goyal, Yuan James Rao

“…Background: This study evaluated a custom algorithm that sought to perform a radiogenomic analysis on lung cancer genetic and imaging data, specifically by using machine learning to see whether a custom clustering/classification method could simultaneously identify features from imaging data that correspond to genetic markers. …”
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A comparative study of convolutional neural networks and traditional feature extraction techniques for adulteration detection in ground beef by Leila Bahmani, Saied Minaei, Alireza Mahdavian, Ahmad Banakar, Mahmoud Soltani Firouz

“…This shows the superiority of CNN algorithm over machine learning algorithms in identifying adulteration in minced meat. …”
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A study on the correlation between MBTI dimensions and driving behavior characteristics by Liwei Bai, Tao Wang, Jianyao Tu, Bozhezi Peng, Zhuoyu Wang

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Identifying common disease trajectories of Alzheimer’s disease with electronic health recordsResearch in context by Mingzhou Fu, Sriram Sankararaman, Bogdan Pasaniuc, Keith Vossel, Timothy S. Chang

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Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques by Xiao Hu, Jun Xie, Xiwei Li, Junzheng Han, Zhengquan Zhao, Hamzeh Ghorbani

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Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive by Elena Pastorelli, Alper Yegenoglu, Alper Yegenoglu, Nicole Kolodziej, Nicole Kolodziej, Nicole Kolodziej, Willem Wybo, Francesco Simula, Sandra Diaz-Pier, Johan Frederik Storm, Pier Stanislao Paolucci

“…This work provides the computational community with a two-compartment spiking neuron model that supports the proposed forms of brain-state-specific activity. A machine learning evolutionary algorithm, guided by a set of fitness functions, selected parameters defining neurons that express the desired apical dendritic mechanisms. …”
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FedPark: A Federated Learning Crowdsensing Solution for On-Street Parking Availability by Afraa Attiah, Shatha Alahmadi, Abeer Hakeem, Linda Mohaisen, Abeer Almakky, Reemah M. Alhebshi

“…FedPark employs a Federated Learning (FL) algorithm, enabling the creation of a global model that aggregates insights from multiple local models maintained on individual drivers’ devices. …”
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