Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies by Renata Rozovsky, Maria Wolfe, Halimah Abdul‐waalee, Mariah Chobany, Greeshma Malgireddy, Jonathan A. Hart, Brianna Lepore, Farzan Vahedifard, Mary L. Phillips, Boris Birmaher, Alex Skeba, Rasim S. Diler, Michele A. Bertocci

“…Conclusions These findings indicate that pattern recognition models focusing on GMVs in regions associated with movement, sensory processing, and cognitive control can effectively distinguish well‐characterized BD‐I/II from other forms of psychopathology, including other specified BD, in a pediatric population. …”
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Regression Analysis of Heat Release Rate for Box-Type Power Bank Based on Experimental and Machine Learning Methods by Shihan Luo, Hua Chen, Xiaobing Mao, Wenbing Zhu, Yuanyi Xie, Wenbin Wei, Mengmeng Jiang, Chenyang Zhang, Chaozhe Jiang

“…This study uses experimental testing and machine learning regression analysis to explore the heat release rate (HRR) characteristics and influencing factors of box-type power banks under fire conditions. …”
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REVOLUTIONIZING LUXURY: THE ROLE OF AI AND MACHINE LEARNING IN ENHANCING MARKETING STRATEGIES WITHIN THE TOURISM AND HOSPITALITY LUXURY SECTORS by Maria Nascimento CUNHA, Manuel PEREIRA, António CARDOSO, Jorge FIGUEIREDO, Isabel OLIVEIRA

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Multi-omic and machine learning analysis of mitochondrial RNA modification genes in lung adenocarcinoma for prognostic and therapeutic implications by Xiao Zhang, Jiatao Liu, Yaolin Cao, Wei Wang, Haoran Lin, Yue Yu

“…Integrating multi-omic datasets, we systematically explored the molecular features of MRM-related genes across various cancers and identified distinct expression patterns and prognostic associations. Single-cell analysis further reveals MRM-driven cell-cell interactions and pathway activation, particularly in cycling and epithelial cells. …”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…The clinical community has a lot of diabetes diagnostic data. Machine learning algorithms may simplify finding hidden patterns, retrieving data from databases, and predicting outcomes. …”
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Prognostic model identification of ribosome biogenesis-related genes in pancreatic cancer based on multiple machine learning analyses by Yuan Sun, Yan Li, Anlan Zhang, Tao Hu, Ming Li

“…Single-cell RNA sequencing analysis (GSE155698 dataset) was performed to assess gene expression patterns and module scores. Results Sixty ribosome biogenesis-related prognostic genes were identified in pancreatic cancer. …”
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Incorporating soil moisture data into a machine learning framework improved the predictive accuracy of corn yields in the U.S. by Bishwoyog Bhattarai, Zachary Leasor, André Fróes de Borja Reis

“…Understanding environmental factors that influence corn yield is crucial for improving crop management and designing more resilient cropping systems. Leveraging machine learning (ML) techniques capable of handling large-scale datasets offers a promising alternative for uncovering hidden patterns and generating actionable insights to improve crop yield. …”
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Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

“…Introduction: Urban growth has accelerated in recent decades, therefore, predicting the future growth pattern of the city is very important to prevent environmental, economic, and social problems. …”
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Integrating sequencing methods with machine learning for antimicrobial susceptibility testing in pediatric infections: current advances and future insights by Zhuan Zou, Zhuan Zou, Fajuan Tang, Fajuan Tang, Lina Qiao, Lina Qiao, Sisi Wang, Sisi Wang, Haiyang Zhang, Haiyang Zhang

“…These inconsistencies may arise from factors such as genetic mutations or variants in resistance genes, differences in the phenotypic expression of resistance, and the influence of environmental conditions on resistance levels, which can lead to variations in the observed resistance patterns. Machine learning (ML) provides a promising solution by integrating large-scale resistance data with sequencing outcomes, enabling more accurate predictions of pathogen drug susceptibility. …”
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Identification of palmitoylated biomarkers in non-alcoholic fatty liver disease via integrated bioinformatics analysis and machine learning by Zheng Liu, Xiaohong Wang, Mingzhu Xiu, Rui Luo, Xiaomin Shi, Yizhou Wang, Yusong Ye, Ruiyu Wang, Sha Liu, Muhan Lv, Xiaowei Tang

“…This study integrated bioinformatics analysis and machine learning to identify palmitoylation-related biomarkers for NAFLD. …”
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Machine-learning approaches to identify determining factors of happiness during the COVID-19 pandemic: retrospective cohort study by Takahiro Tabuchi, Yusuke Tsugawa, Tadahiro Goto, Itsuki Osawa, Hayami K Koga

“…Among 6965 subjects who responded to questionnaires both before and during the COVID-19 pandemic, there was no systemic difference in the patterns as to determinants of declined happiness during the pandemic.Conclusion Using machine-learning methods on data from large online surveys in Japan, we found that interventions that have a positive impact on social capital as well as successful pandemic control and economic stimuli may effectively improve the population-level psychological well-being during the COVID-19 pandemic.…”
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Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors by Heba-Allah Ibrahim El-Azab, R. A. Swief, Noha H. El-Amary, H. K. Temraz

“…Where the whole database is split into four seasons based on demand patterns. This article’s integrated model is built on techniques for machine and deep learning methods: Adaptive Neural-based Fuzzy Inference System, Long Short-Term Memory, Gated Recurrent Units, and Artificial Neural Networks. …”
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Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction by Sazzli Kasim, Sorayya Malek, JunJie Tang, Xue Ning Kiew, Song Cheen, Bryan Liew, Norashikin Saidon, Raja Ezman, Raja Shariff

“…MLP-based models also achieved strong results, effectively capturing non-linear patterns in the data. In contrast, ResNet50 exhibited limitations, likely due to overfitting caused by the small dataset. …”
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Contribution of Scalp Regions to Machine Learning-Based Classification of Dementia Utilizing Resting-State qEEG Signals by Simfukwe C, An SSA, Youn YC

“…The processed PSD data, representing 19 scalp regions, were then input into a Random Forest (RF) machine learning classifier to identify distinctive EEG patterns across the groups. …”
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Exploring shared pathogenic mechanisms and biomarkers in hepatic fibrosis and inflammatory bowel disease through bioinformatics and machine learning by Shangkun Li, Haoyu Li, Mingran Qi

“…The four key diagnostic gene expression patterns across diverse cell subpopulations were visualized by single-cell sequencing analysis.ConclusionMMP2, COL1A2, CXCL1, and STAT1 were identified as shared biomarkers for IBD and HF, providing a molecular basis for early diagnosis and precision medicine approaches. …”
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A Novel Framework for Saraiki Script Recognition Using Advanced Machine Learning Models (YOLOv8 and CNN) by Hafiz Muhammad Raza Ur Rehman, Syed Arfan Haider, Hiba Faisal, Kook-Yeol Yoo, M. Z. Jhandir, Gyu Sang Choi

“…By combining these two domains, machine learning has emerged as a potent instrument in linguistics, improving our capacity to comprehend semantics, analyze verbal patterns, and even simulate human-like replies. …”
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Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy by Tobias Schraut, Anne Schützenberger, Tomás Arias-Vergara, Melda Kunduk, Matthias Echternach, Stephan Dürr, Julia Werz, Michael Döllinger

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Integration of UAV-sensed features using machine learning methods to assess species richness in wet grassland ecosystems by Clara Oliva Gonçalves Bazzo, Bahareh Kamali, Murilo dos Santos Vianna, Dominik Behrend, Hubert Hueging, Inga Schleip, Paul Mosebach, Almut Haub, Axel Behrendt, Thomas Gaiser

“…These findings underscore the potential of spectral and textural data to effectively capture the ecological dynamics of wet grasslands, providing valuable insights into biodiversity patterns.…”
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Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning by Yuanyuan Wang, Yu Ji, Jie Liu, Lianjiang Lv, Zihe Xu, Meimei Yan, Jialu Chen, Zhijun Luo, Xianjun Zeng

“…Employing the sliding time window analysis and K-means clustering method, we sought to identify dynamic functional connectivity (dFC) variability patterns in both groups. The investigation into the topological structure of whole-brain functional networks utilized a graph theoretical approach. …”
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Integrating proteomics and machine learning reveals characteristics and risks of lymph node-independent distant metastasis in colorectal cancer by Chenxiao Zheng, Baiwang Zhu, Yanyu Chen, Numan Shahid, Yiwang Hu, Hajar Mansoor Ahmed Ali Husain, Binbin Ou, Qiongying Zhang, Haobo Jin, Yating Zheng, Peng Li, Yifei Pan, Xiaodong Zhang, Xiaodong Zhang

“…Immunohistochemistry (IHC) confirmed its expression pattern, while wound healing and transwell assays elucidated the role of ITGA11 in CRC metastasis.ResultsThe LIMGs signature demonstrated strong predictive performance of lymph node-independent synchronous metastasis across cohorts. …”
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