Cervical cancer demystified: exploring epidemiology, risk factors, screening, treatment modalities, preventive measures, and the role of artificial intelligence by N. Mohammad, M. Khan, M. Maqsood, A. H. K. Naseeb

“…AI-driven technologies, including deep learning algorithms and machine learning models, are emerging as valuable tools in cervical cancer detection, risk assessment, and treatment planning. …”
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Study on the mechanisms associating community outdoor public spaces with elderly behavior by Lei Wang, Wenqi He, Shan Wu, Bo Zhang, Xiaorui Zhang, Hu Yin

“…Subsequently, a set of quantifiable indicators for community outdoor public spaces is established, and nonlinear machine learning models (Random Forest, Gradient Boosting Decision Tree, and eXtreme Gradient Boosting) are employed to reveal the association mechanisms between these six behaviors and the four categories of spatial metrics. …”
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Land-cover classification in Addo Elephant National Park: Analyzing the impact of variables, classifiers, and object-based approach by Mohammad Safaei, Jane Southworth, Cerian Gibbes, Hannah V. Herrero, Mashoukur Rahaman, Bewuket B. Tefera, Jason K. Blackburn

“…Spectral mixture analysis was performed, and the resulting fraction layers were used as independent variables in the models. The study identified RF as the preferred classification algorithm using the optimal combination of these variables, achieving high accuracy of 89.1 %, 91.2 %, and 91.9 % for the years 2002, 2014, and 2022, respectively. …”
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Bioinformatics analysis for immune hub genes in BLIS subtype of triple-negative breast cancer by Hend Adel, Manal Abdel Wahed, Heba M. Afify

“…Conclusions Our findings provide a foundation for developing immune-targeted therapeutic approaches for BLIS-TNBC patients and provision the integration of machine learning models to predict treatment responses and optimize patient-specific strategies, potentially transforming the clinical management of this challenging breast cancer subtype.…”
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Dataset on Programming Competencies Development Using Scratch and a Recommender System in a Non-WEIRD Primary School Context by Jesennia Cárdenas-Cobo, Cristian Vidal-Silva, Nicolás Máquez

“…Furthermore, the dataset enables the application of machine learning techniques to predict educational outcomes and optimize personalized educational strategies. …”
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Availability and Use of Digital Technology Among Women With Polycystic Ovary Syndrome: Scoping Review by Pamela J Wright, Charlotte Burts, Carolyn Harmon, Cynthia F Corbett

“…ConclusionsThere is potential for digital technology to transform PCOS self-management, but further design and development are needed to optimize the technologies for women with PCOS. Future research should focus on including end users during the design phase of digital technology, refining predictive models, improving app inclusivity, conducting frequent reliability testing, and enhancing user engagement and support via additional features to promote more comprehensive self-management of PCOS.   …”
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No-Reference Error Detection in Color Difference Datasets: Application to Munsell Data by Valerii Timofeev, Galim Usaev, Mikhail Seliugin, Dmitry Bocharov, Anastasia Sarycheva, Ivan Konovalenko, Olga Basova, Mikhail Tchobanou, Valentina Bozhkova, Dmitry Nikolaev

“…However, substantial errors in the third version, the “Re-renotation”, compromise its reliability for optimizing color models. In this study, we identify and correct typographical errors in the “Re-renotation” dataset and introduce two semi-automatic methods for error detection. …”
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Artificial intelligence for predicting interstitial fibrosis and tubular atrophy using diagnostic ultrasound imaging and biomarkers by Yen-Chung Lin, Mai-Szu Wu, Chung-Yi Cheng, Cai-Mei Zheng, Chia-Te Liao, Ting-Wei Chang, Chang-Yu Tsai, Zhen-Yi Tang, Che-Chou Shen

“…This study aimed to leverage machine learning (ML) models to predict IFTA using a combination of ultrasonography (US) images and patient biomarkers.Methods We retrospectively collected US images and biomarkers from 632 patients with CKD across three hospitals. …”
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Study of the effect of cutting modes on output parameters under high-speed steel turn-milling by D. A. Matlygin, A. V. Savilov, A. S. Pyatykh, S. A. Timofeev

“…Statistical analysis methods based on the creation of a mathematical model for predicting microgeometric deviations of the treated surface were used. …”
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Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN) by Beytullah Erdoğan, Abdulsamed Güneş, İrfan Kılıç, Orhan Yaman

“…The average mean absolute error (MAE) for all hybrid nanofluids was 0.001, and the mean squared error (MSE) was 1.76 × 10⁻⁶. The proposed FFANN model provides a State-of-the-Art approach for predicting thermal conductivity, offering valuable insights into selecting optimal hybrid nanofluids based on thermal conductivity values and nanoparticle density.…”
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Human Pose Estimation and Event Recognition via Feature Extraction and Neuro-Fuzzy Classifier by Muhammad Hanzla, Naif S. Alshammari, Shuaa S. Alharbi, Wasim Wahid, Nouf Abdullah Almujally, Ahmad Jalal, Hui Liu

“…This paper introduces an advanced framework for Human Pose Estimation (HPE) and Semantic Event Classification (SEC), addressing the growing demand for sophisticated human skeleton modeling, context-aware feature extraction, and machine learning techniques for accurate event recognition in real-world scenarios. …”
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Identifying differences in evapotranspiration and ecosystem water use efficiency between different types of urban green spaces: A case study of the main urban area of Beijing by Rui Shao, Jiaqi Li, Weiwei Shao, Yicheng Wang

“…Study focus: Refined modeling is crucial for estimating carbon sequestration and water consumption in urban green spaces. …”
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Additive manufacturing of a 3D-segmented plastic scintillator detector for tracking and calorimetry of elementary particles by Tim Weber, Andrey Boyarintsev, Umut Kose, Botao Li, Davide Sgalaberna, Tetiana Sibilieva, Johannes Wüthrich, Siddartha Berns, Eric Boillat, Albert De Roeck, Till Dieminger, Matthew Franks, Boris Grynyov, Sylvain Hugon, Carsten Jaeschke, André Rubbia

“…By combining fused deposition modeling with an injection process optimized for fabricating scintillation geometries, we produced an additively manufactured fine-granularity plastic scintillator detector with performance comparable to the state of the art, and demonstrated its capabilities for 3D tracking of elementary particles and energy-loss measurement. …”
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Optimisation of the deployment of automated external defibrillators in public places in England by Terry P Brown, Lazaros Andronis, Asmaa El-Banna, Benjamin KH Leung, Theodoros Arvanitis, Charles Deakin, Aloysius N Siriwardena, John Long, Gareth Clegg, Steven Brooks, Timothy CY Chan, Steve Irving, Louise Walker, Craig Mortimer, Sandra Igbodo, Gavin D Perkins

“…We then used a computer to model the best locations for new defibrillators and calculate the optimal number needed. …”
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Designing and characterizing first Iranian study evaluating serum levels of lithium in patients for population pharmacokinetics (FIRELOLIPOP): baseline and first report by Mohammad Mahdi Khakshoor, Naser Pariz, Vahid Reza Askari, Mohammad Reza Fayyazi Bordbar, Sara Honari, Matin Shirazinia, Vafa Baradaran Rahimi, Mohammad Bagher Naghibi Sistani

“…Future research should incorporate pharmacokinetic modeling and machine learning to refine lithium therapy.…”
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Dataset on droplet spreading and rebound behavior of water and viscous water-glycerol mixtures on superhydrophobic surfaces with laser-made channelsMendeley Data by Matic Može, Samo Jereb, Robert Lovšin, Jure Berce, Matevž Zupančič, Iztok Golobič

“…It can help validate theoretical and numerical models of droplet spreading, retracting, and rebounding from poorly wettable surfaces, optimize superhydrophobic surfaces for applications such as self-cleaning and drag reduction, and contribute to machine learning models predicting droplet behavior. …”
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Lactate, an Essential Metabolic Marker in the Diagnosis and Management of Pediatric Conditions by Alina Belu, Nina Filip, Laura Mihaela Trandafir, Elena Lia Spoială, Elena Țarcă, Diana Zamosteanu, Gabriela Ghiga, Jana Bernic, Alina Jehac, Elena Cojocaru

“…Emerging technologies, such as machine learning models and small molecule inhibitors, show promise in advancing lactate regulation and predicting hemodynamic instability. …”
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Analysis of Selected Methods of Computer-Aided Design for Stage Structures by Szymon Wyrąbkiewicz, Marcin Zastempowski, Jurand Burczyński, Maciej Gajewski

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Autonomous Drilling and the Idea of Next-Generation Deep Mineral Exploration by George Nikolakopoulos, Anton Koval, Matteo Fumagalli, Martyna Konieczna-Fuławka, Laura Santas Moreu, Victor Vigara-Puche, Kashish Verma, Bob de Waard, René Deutsch

“…This article explores various aspects of automation, including the integration of advanced data collection methods that monitor the drilling parameters and facilitate the creation of 3D models of rock hardness. The shift toward machine automation involves transitioning from human-operated machines to systems powered by artificial intelligence, which are capable of making real-time decisions. …”
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An integrated method of selecting environmental covariates for predictive soil depth mapping by Yuan-yuan LU, Feng LIU, Yu-guo ZHAO, Xiao-dong SONG, Gan-lin ZHANG

“…A total of 129 soil sampling sites were collected using a representative sampling strategy, and RF and support vector machine (SVM) models were used to map soil depth. …”
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