Exploring spatial association between residential and commercial urban spaces: A machine learning approach using taxi trajectory data by Lei Zhou, Weiye Xiao, Chen Wang, Haoran Wang

“…In the central urban region, increasing the number of high-level commercial centers and making the powers of commercial centers hierarchical can contribute to a polycentric mobility pattern of people's consumption. This research contributes to the literature by providing a novel framework to model, analyze and visualize people's mobility based on the trajectory big data, which is promising in future urban research. …”
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Exploring spatial association between residential and commercial urban spaces: A machine learning approach using taxi trajectory data by Lei Zhou, Weiye Xiao, Chen Wang, Haoran Wang

“…In the central urban region, increasing the number of high-level commercial centers and making the powers of commercial centers hierarchical can contribute to a polycentric mobility pattern of people's consumption. This research contributes to the literature by providing a novel framework to model, analyze and visualize people's mobility based on the trajectory big data, which is promising in future urban research. …”
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Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach by Nhat-Duc Hoang, Thanh-Canh Huynh, Van-Duc Tran

“…To characterize concrete surface condition, image texture descriptors of statistical measurement of color channels, gray-level run length, and center-symmetric local binary pattern are used. Based on these texture-based features, the support vector machine classifier optimized by the jellyfish search metaheuristic is put forward to construct a decision boundary that partitions the input data into two classes of shallow spalling and deep spalling. …”
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Machine learning assisted CFD optimization of fuel-staging natural gas burners for enhanced combustion efficiency and reduced NOx emissions by Muhammad Mubashir, Dekui Shen, Habib Kraiem, Aymen Flah, Nahar F. Alshammari, Muhammad Mubashar Hanif

“…This study presents a computational and data-driven approach to the design and optimization of a natural gas burner employing a folded flame pattern with fuel staging. Using Computational Fluid Dynamics (CFD) simulations combined with Machine Learning (ML)-assisted predictive modeling, the burner geometry, fuel–air mixing behavior, and heat transfer dynamics were systematically optimized. …”
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Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm by Yushuai Yuan, Li Yang, Rui Gao, Cheng Chen, Min Li, Jun Tang, Xiaoyi Lv, Ziwei Yan

“…The results demonstrate that FT-IR spectroscopy combined with a pattern recognition algorithm has great potential in screening patients with CRF.…”
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A Study on CNN-Based and Handcrafted Extraction Methods with Machine Learning for Automated Classification of Breast Tumors from Ultrasound Images by Mohamed Benaouali, Mohamed Bentoumi, Mansour Abed, Malika Mimi, Abdelmalik Taleb-Ahmed

“…We evaluated our approach using four openly available datasets and investigated two categories of feature extraction methods: handcrafted methods (Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG)) and methods based on convolutional neural network (CNN) models. …”
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Cryogenic and conventional machining using CVD-coated inserts to improve the surface characteristics of low-carbon steel alloy E350 by Sagar Vijayendra, Vikas Marakini, Raghavendra Pai K., Srinivasa Pai P., Gururaj Bolar

“…Furthermore, X-ray diffraction (XRD) pattern comparison provided more evidence for different levels of work hardening in milled surfaces compared to the polished as-cast material surface. …”
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Circadian rhythm related genes signature in glioma for drug resistance prediction: a comprehensive analysis integrating transcriptomics and machine learning by Junbo Liao, Yingxing Duan, Xiangwang Xu, Yaxue Liu, Chaohong Zhan, Gelei Xiao

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Mapping 30-m cotton areas based on an automatic sample selection and machine learning method using Landsat and MODIS images by Zhuting Tan, Zhengyu Tan, Juhua Luo, Hongtao Duan

“…The cotton spatial distribution pattern developed from dispersion to agglomeration. …”
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A missing data processing method for dam deformation monitoring data using spatiotemporal clustering and support vector machine model by Yan-tao Zhu, Chong-shi Gu, Mihai A. Diaconeasa

“…However, the deformation monitoring data are often incomplete due to environmental changes, monitoring instrument faults, and human operational errors, thereby often hindering the accurate assessment of actual deformation patterns. This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data. …”
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Research on Employee Innovation Ability in Human–Machine Collaborative Work Scenarios—Based on the Grounded Theory Construct of Chinese Innovative Enterprises by Baorong Guo, Xiaoning Liu, Shuai Liao, Jiayi Hu

“…Furthermore, we develop a theoretical model elucidating the formation mechanism of employees’ innovative capabilities in human–machine collaboration contexts, identifying four core dimensions—innovation drivers, human–AI collaboration patterns, knowledge conversion pathways, and technological breakthroughs—that dominantly shape these capabilities. …”
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Supervised and unsupervised machine learning approaches for prediction and geographical discrimination of Iranian saffron ecotypes based on flower-related and phytochemical attribu... by Seid Mohammad Alavi-Siney, Jalal Saba, Alireza Fotuhi Siahpirani, Jaber Nasiri

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OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction by Fangfang Jian, Haihua Cai, Qushuo Chen, Xiaoyong Pan, Weiwei Feng, Ye Yuan, Ye Yuan

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Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabol... by Pedro H. Godoy Sanches, Nicolly Clemente de Melo, Andreia M. Porcari, Lucas Miguel de Carvalho

“…By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.…”
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Balancing ethics and statistics: machine learning facilitates highly accurate classification of mice according to their trait anxiety with reduced sample sizes by Johannes Miedema, Beat Lutz, Susanne Gerber, Irina Kovlyagina, Hristo Todorov

“…Nevertheless, inter-individual variability and sex-specific patterns have been long disregarded in preclinical studies of anxiety and stress disorders. …”
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SEMP-TA: A Novel Stock Market Prediction Approach Based on Stacking Ensemble Machine Learning for Effective Trend Analysis by Chanda Raj Kumar, S. Manikandan

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Machine learning framework to estimate ridership loss in public transport during external crises: case study of bus network in Stockholm by Mahsa Movaghar, Erik Jenelius, David Hunter

“…And then introduces an approach to use Machine Learning algorithms and extract hidden patterns for predicting financial loss during any crisis, which is a novel perspective and application. …”
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Wear Prediction and Mechanism Study of Tunnel Boring Machine Disc Cutter Breaking in Hard–Soft Rock Considering Thermal Effect by Xiongfei Lyu, Youliang Chen, Shaoming Liao, Tomas Manuel Fernandez-Steeger

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Cost-effectiveness of the 3E model in diabetes management: a machine learning approach to assess long-term economic impact by Supriya Raghav, Santosh Kumar, Hamid Ashraf, Poonam Khanna

“…Natural language processing techniques revealed medication patterns associated with greater cost reductions. Long-term projections using ensemble methods (such as XG Boost, Exponential Smoothing, and Prophet) predicted that, on average, each year contributes approximately 20% to the total cumulative savings over 5 years. …”
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A Review of the Industry 4.0 to 5.0 Transition: Exploring the Intersection, Challenges, and Opportunities of Technology and Human–Machine Collaboration by Md Tariqul Islam, Kamelia Sepanloo, Seonho Woo, Seung Ho Woo, Young-Jun Son

“…Learning from historic patterns will enable us to navigate this era of change and mitigate any uncertainties in the future.…”
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