High-Precision Positioning Method for Robot Acoustic Ranging Based on Self-Optimization of Base Stations by Zekai Zhang, Jiayu Chen, Bishu Gao, Yefeng Sun, Xiaofeng Ling, Zheyuan Li, Liang Gong

“…Experiments demonstrate that the proposed method can effectively enhance the positioning accuracy of acoustic positioning systems compared to traditional four-base station weighted average positioning algorithms.…”
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Capsule Endoscopy Image Enhancement for Small Intestinal Villi Clarity by Shaojie Zhang, Yinghui Wang, Peixuan Liu, Yukai Wang, Liangyi Huang, Mingfeng Wang, Ibragim Atadjanov

“…Experimental results show that our proposed method achieved a 45.47% increase in PSNR compared to classical enhancement algorithms, a 12.63% improvement in IRMLE relative to the original images, and a 31.84% reduction in NIQE with respect to the original images.…”
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Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learning by Aveen K P, Ullal Vignesh Nayak, K M Pranesh Rao, Shivaramu H T, V Londhe Neelakantha, Shashikumar C M

“…A machine learning based multi-output random forest regression model with hyper parameter tuning was used to predict the T, F, and delamination factor (Fd). The algorithm showed that the most important parameter that influenced delamination was speed (s) followed by the feed rate (f) and filler content respectively. …”
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An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children by Mengyu Duan, Zhimin Geng, Lichao Gao, Yonggen Zhao, Zheming Li, Lindong Chen, Pekka Kuosmanen, Guoqiang Qi, Fangqi Gong, Gang Yu

“…Furthermore, its interpretability enhances model transparency, facilitating clinicians’ understanding of prediction reliability.…”
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A Machine Learning Platform for Isoform-Specific Identification and Profiling of Human Carbonic Anhydrase Inhibitors by Lisa Piazza, Miriana Di Stefano, Clarissa Poles, Giulia Bononi, Giulio Poli, Gioele Renzi, Salvatore Galati, Antonio Giordano, Marco Macchia, Fabrizio Carta, Claudiu T. Supuran, Tiziano Tuccinardi

“…<b>Methods:</b> By integrating four molecular representations with four ML algorithms, we built 64 classification models, each extensively optimized and validated. …”
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Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans by Sheikh Fahim Faysal Sowrav, Sujit Kumar Debsarma, Mohan Kumar Das, Khan Mohammad Ibtehal, Mahfujur Rahman, Noshin Tabassum Hridita, Atika Afia Broty, Muhammad Sajid Anam Hoque

“…Key water quality parameters—Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH—were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. …”
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Low-Cost, LiDAR-Based, Dynamic, Flood Risk Communication Viewer by Debra F. Laefer, Evan O’Keeffe, Kshitij Chandna, Kim Hertz, Jing Zhu, Raul Lejano, Anh Vu Vo, Michela Bertolotto, Ulrich Ofterdinger

“…The method couples a laser scanning point cloud with algorithms that produce textured floodwaters, achieved through compounding multiple sine functions in a graphics shader. …”
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Determining time since deposition of latent fingerprints on forensic adhesive tape using ultrafast DESI-MS and machine learning by Nora Rajs, Yinon Harush-Brosh, Ron Raisch, Ravit Yakobi Arancibia, Amani Zoabi, Guy Nevet Golan, Moshe Shpitzen, Sarena Wiesner, Michal Levin-Elad, Tommy Kaplan, Katherine Margulis

“…Data analysis using the XGBoost and SMOTE algorithms achieved a correlation of 0.54 (p-value < 1e−5) between TSD prediction and true TSD, achieving 83.3% accuracy in distinguishing between 0-4 days and 10–15 days old prints. …”
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RISKS OF IMPLEMENTING AND USING ARTIFICIAL INTELLIGENCE BY OIL AND GAS SECTOR ENTERPRISES by V.B. Kochkodan, M.Yu. Petryna

“…The most critical risks were identified as increased vulnerability to cyberattacks, unreliability of algorithms, data breaches, and social risks related to job reductions and the need for personnel retraining. …”
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Insights into optimization of oleaginous fungi – genome-scale metabolic reconstruction and analysis of Umbelopsis sp. WA50703 by Mikołaj Dziurzyński, Maksymilian E. Nowak, Maria Furman, Alicja Okrasińska, Julia Pawłowska, Marco Fondi

“…The model demonstrated a strong predictive accuracy correctly predicting metabolic capabilities in 81.05 % of cases when evaluated against experimental data. …”
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Classification of Lung Nodule Using Hybridized Deep Feature Technique by Malin Bruntha, Immanuel Alex Pandian, Siril Sam Abraham

“…The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. …”
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A Lightweight Neural Network for Cell Segmentation Based on Attention Enhancement by Shuang Xia, Qian Sun, Yiheng Zhou, Zhaoyuxuan Wang, Chaoxing You, Kainan Ma, Ming Liu

“…AttE-Unet enhances the network’s feature extraction capabilities through an attention mechanism and combines the strengths of deep learning with traditional image filtering algorithms, while substantially reducing computational and storage demands. …”
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Vehicle Painting Robot Path Planning Using Hierarchical Optimization by Yuya Nagai, Hiromitsu Nakamura, Narito Shinmachi, Yuta Higashizono, Satoshi Ono

“…This approach allows the use of different optimization algorithms at each layer, and permits flexible handling of constraints specific to the vehicle painting process through the design of variable representation, constraints, repair operators, and an initialization process at the upper and lower layers. …”
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Serum Lactate Dehydrogenase Levels Reflect the Lung Injury Extension in COVID‐19 Patients at Hospital Admission by Maria Sofia Bertilacchi, Giulia Vannucci, Rebecca Piccarducci, Lorenzo Germelli, Chiara Giacomelli, Chiara Romei, Brian Bartholmai, Greta Barbieri, Claudia Martini, Michela Baccini

“…Results High serum lactate dehydrogenase (LDH) values appeared to be predictive of high CALIPER ILD percentages at hospital admission in both retrospective and prospective datasets, even if the predictive performance of the algorithm was not optimal. …”
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Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update by Aniruddha Sen, Palani Selvam Mohanraj, Vijaya Laxmi, Sumel Ashique, Rajalakshimi Vasudevan, Afaf Aldahish, Anupriya Velu, Arani Das, Iman Ehsan, Anas Islam, Sabina Yasmin, Mohammad Yousuf Ansari

“…At the same time, the rapidly increasing role of AI in diabetes care is woven into the story, mainly targeting how insulin therapy can be modified and personalized through algorithms and predictive modelling. It leaves a deep review of their pre-existing synergies, which helps understand how collaborative opportunities will unlock the future of T2DM care. …”
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A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis by Bilal Hassan Ahmed Khattak, Imran Shafi, Abdul Saboor Khan, Emmanuel Soriano Flores, Roberto Garcia Lara, Md. Abdus Samad, Imran Ashraf

“…We observed that ensemble and hybrid models with long short-term memory (LSTM) and support vector machines (SVM) are being more adopted for financial trends and price prediction. Moreover, hybrid models employing AI algorithms for feature engineering have great potential at par with ensemble techniques. …”
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A Deep Learning Method for Pneumoconiosis Staging on Chest X-Ray Under Label Noise by Wenjian Sun, Dongsheng Wu, Jiang Shen, Yang Luo, Hao Wang, Li Min, Chunbo Luo

“…On the pneumoconiosis dataset containing 1,372 chest X-rays collected from West China Fourth Hospital, the proposed method achieves a sensitivity of 82.4% in identifying &#x2018;stage-1&#x2019; cases, which is higher than that of other algorithms (70.6%), and is especially beneficial for clinical radiologists in pneumoconiosis staging.…”
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Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model by Merjulah Roby, Juan C. Restrepo, Haehwan Park, Satish C. Muluk, Mark K. Eskandari, Seungik Baek, Ender A. Finol

“…Hence, there is a growing need for automated segmentation algorithms, particularly when these influence treatment planning. …”
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Toward causal artificial intelligence approach for PM2.5 interpretation: A discovery of structural causal models by Mallika Kliangkhlao, Apaporn Tipsavak, Thanathip Limna, Racha Dejchanchaiwong, Perapong Tekasakul, Kirttayoth Yeranee, Thanyabun Phutson, Bukhoree Sahoh

“…Understanding the causal mechanisms underlying PM2.5 generation is critical for developing effective prevention strategies, necessitating an approach that goes beyond prediction and seeks deeper causal explanations to support decision-making. …”
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Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context by Xueting Jin, Fangwu Wei, Srinivasa Srivatsav Kandala, Tejas Umesh, Kayleigh Steele, John N. Galgiani, Manfred D. Laubichler

“…Two models with different lengths of forecasting periods, 10 days and 30 days, are identified with good prediction. Interpretation: LSTM algorithms, combined with traditional statistical methods, could help with the forecasting of CM cases. …”
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