Depression and Anxiety Screening for Pregnant Women via Free Conversational Speech in Naturalistic Condition by Rafael T. Sousa, Gustavo D. M. Silva, Paula L. L. Pinto, Juliano Backes, Amanda S. Mota, Thiago M. Paixao, Maria G. S. Teixeira, Wilian H. Hisatugu, Anilton S. Garcia, Kelly S. Prado, Rodrigo S. Dias, Marco A. Galletta, Hermano Tavares

“…This technological intervention could enhance maternal health by enabling early detection and timely care, ultimately improving pregnancy outcomes and child development.…”
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Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis by Ana Maria Cristina Jura, Daniela Eugenia Popescu, Cosmin Cîtu, Marius Biriș, Corina Pienar, Corina Paul, Oana Maria Petrescu, Andreea Teodora Constantin, Alexandru Dinulescu, Ioana Roșca

“…ML, particularly XGBoost, enhances predictive abilities, guiding targeted interventions and improving neonatal outcomes.…”
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Automatic location and recognition of horse freezing brand using rotational YOLOv5 deep learning network by Zhixin Hua, Yitao Jiao, Tianyu Zhang, Zheng Wang, Yuying Shang, Huaibo Song

“…Besides, Coordinate Attention (CA) was added to raise the attention to positional information in the network. These improvements enhanced the accuracy of detecting oriented brands. …”
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An experimental optimization environment for developing an intracycle pitch control in cross flow turbines by Stefan Hoerner, Roberto Leidhold, Shokoofeh Abbaszadeh, Karla Ruiz-Hussmann, Timo Bennecke, Zhao Zhao, Paul Joedecke, Christian-Thoralf Weber, Pierre-Luc Delafin, Cyrille Bonamy, Yves Delannoy

“… Cross-flow tidal turbines have not reached the efficiency of horizontal-axis turbines. Among various improvement approaches found in the literature, the intracycle pitch control is one of the most promising ones. …”
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Comparative analysis of the diagnostyc value of CT and MRI in the assessment of peritoneal carcinomatosis in ovarian cancer by Yu. A. Soloveva, N. A. Rubtsova, E. G. Novikova, D. O. Kabanov, S. A. Skugarev

“…The importance of diagnostic information on the prevalence of peritoneal carcinomatosis at the stage of planning treatment tactics in patients with AOC requires improvement of examination algorithms and unification of methodological approaches during CT and MRI. …”
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Mixed nontuberculous mycobacteria in an immunocompromised patient with progressive multifocal leukoencephalopathy by Dr Christoffel Opperman, Dr Janet Scott, Dr Aliasgar Esmail, Dr Sarishna Singh, Mr Yonas Ghebrekristos, Prof Rob warren, Dr Wynand Goosen

“…After initial clinical improvement, new neurological symptoms developed, including dysarthria and upper motor neuron weakness. …”
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Efficiency and safety of the Russian-made KERATOLINK device used to treat patients with stage I–II keratoconus and pellucid marginal corneal degeneration by A. T. Khandzhyan, E. N. Iomdina, A. V. Ivanova, A. S. Sklyarova, N. V. Khodzhabekyan, I. V. Manukyan

“…The use of local UVCL and accelerated algorithms with a significant reduced exposure time improves the comfort and tolerability of the procedure, and also reduces the risk of complications. …”
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HARMFUL CONDITIONS ON THE DENTAL STATUS OF POULTRY FARM WORKERS by T. Pupin, O. Kardashevska

“…In this regard, dental care improvement for the farm employees who meets harmful conditions becomes important. …”
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Optimum Combination of Spectral Variables for Crop Mapping in Heterogeneous Landscapes based on Sentinel-2 Time Series and Machine Learning by J. G. de Oliveira Júnior, J. C. D. M. Esquerdo, J. C. D. M. Esquerdo, R. A. C. Lamparelli, R. A. C. Lamparelli

“…Subsequently, the variables that showed the highest statistical correlation between each other were used in the spectro-temporal classification process, using the Random Forest, TempCNN, and LightTAE algorithms, following three different strategies: C1 (ALL), C2 (BE + IV _{(Red Edge)}) and C3 (BE + IV _{(without Red Edge)}), where ALL – All variables; BE – Spectral Bands; IV – Vegetation Indices. …”
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Machine learning for asphaltene polarizability: Evaluating molecular descriptors by Arun K. Sharma, Owen McMillan, Selsela Arsala, Supreet Gandhok, Rylend Young

“…Accurate prediction of polarizability is essential for modeling intermolecular forces and improving force field design in petroleum and materials chemistry, issues that are central to industrial and chemical applications.…”
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Integrating Machine Learning and Geospatial Data for Mapping Socioeconomic Vulnerability to Urban Natural Hazard by Esaie Dufitimana, Paterne Gahungu, Ernest Uwayezu, Emmy Mugisha, Jean Pierre Bizimana

“…We utilized a variety of data sources, such as demographic, environmental, and remotely sensing datasets, applying machine learning algorithms like Multilayer Perceptron (MLP), Random Forest, Support Vector Machine (SVM), and XGBoost. …”
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A Comparative Analysis of Machine Learning and Pedotransfer Functions Under Varying Data Availability in Two Greek Regions by Panagiotis Tziachris, Panagiota Louka, Eirini Metaxa, Miltiadis Iatrou, Konstantinos Tsiouplakis

“…The current study evaluates the performance of pedotransfer functions (PTFs) and machine learning (ML) algorithms in predicting the soil bulk density (BD) across two distinct regions in Greece—Kozani and Veroia—using both limited and extended sets of soil parameters. …”
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Automated interpretation of deep learning-based water quality assessment system for enhanced environmental management decisions by Javed Mallick, Saeed Alqadhi, Majed Alsubih, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…This study introduces an advanced computational approach integrating CNN and XAI techniques, improving water quality evaluation and supporting informed environmental management in the Aseer region.…”
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Disinformation in the Digital Age: Climate Change, Media Dynamics, and Strategies for Resilience by Andrea Tomassi, Andrea Falegnami, Elpidio Romano

“…Our results underscore that combating scientific disinformation requires an interdisciplinary, multi-pronged approach, combining improvements in science communication, education, and policy. …”
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Bayesian Model Prediction for Breast Cancer Survival: A Retrospective Analysis by Islam Bani Mohammad, Muayyad M. Ahmad

“…Objective: Over the recent years, machine learning (ML) models have been increasingly used in predicting breast cancer survival because of improvements in ML algorithms. However, cancer researchers still face a significant challenge in accurately predicting breast cancer patients’ survival rates. …”
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AI-powered approaches for enhancing remote sensing-based water contamination detection in ecological systems by Li Yang, Zhang Ziwen, Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…Recent advancements in artificial intelligence (AI) offer promising solutions to enhance water contamination detection, particularly by leveraging machine learning algorithms and sensor networks for continuous monitoring.MethodsThis paper presents a novel AI-powered approach for improving water contamination detection, which incorporates real-time data processing and predictive modeling to identify contamination events and optimize response strategies. …”
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Telecom Fraud Recognition Based on Large Language Model Neuron Selection by Lanlan Jiang, Cheng Zhang, Xingguo Qin, Ya Zhou, Guanglun Huang, Hui Li, Jun Li

“…By incorporating RoBERTa, a multi-head attention mechanism, and residual connections, the LENS-RMHR model augments the feature representation capabilities and improves training efficiency. Utilizing the CCL2023 telecommunications fraud dataset as a foundation, we have constructed an expanded dataset encompassing eight distinct categories that encapsulate a diverse array of fraud types. …”
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Prediction and validation of anoikis-related genes in neuropathic pain using machine learning. by Yufeng He, Ye Wei, Yongxin Wang, Chunyan Ling, Xiang Qi, Siyu Geng, Yingtong Meng, Hao Deng, Qisong Zhang, Xiaoling Qin, Guanghui Chen

“…<h4>Conclusion</h4>This study further explores the pathogenesis of NP and provides certain reference value for developing targeted therapeutic strategies, thereby improving NP management.…”
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Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis by XinPei Liu, SanXi Ai, RuiMing Yu, ChaoJi Zhang, Qi Miao

“…This study aims to create a machine learning model to predict AKI in this high-risk group, improving upon existing models by focusing specifically on endocarditis-related surgeries.MethodsWe analyzed medical records from 527 patients who underwent cardiac surgery for active infective endocarditis from January 2012 to December 2023. …”
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Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review by Florin-Stefan Zamfir, Madalina Carbureanu, Sanda Florentina Mihalache

“…Integrating advanced ML and DL technologies into the various processes involved in wastewater treatment improves the plant systems’ predictive capabilities and ensures a higher level of compliance with environmental standards.…”
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