AI-Driven LOPCOW-AROMAN Framework and Topological Data Analysis Using Circular Intuitionistic Fuzzy Information: Healthcare Supply Chain Innovation by Muhammad Riaz, Freeha Qamar, Sehrish Tariq, Kholood Alsager

“…Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such as big data analysis, supply chain management, energy efficiency, sustainable development, etc. …”
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Enhancing LiDAR Data Positioning Accuracy in National Forest Surveys through Multi-Source Point Cloud Matching in Terrasolid software by A. Shcherbacheva, A. Puttonen, A. Soininen

“…National Land Surveys (NLS) worldwide extensively utilize LiDAR (Light Detection and Ranging) technology for forest inventory, integrating airborne (ALS) and terrestrial/mobile (TLS/MLS) LiDAR to obtain detailed 3D forest structure data. Efficient multi-modal data co-registration is essential for applications such as biomass estimation, forest volume assessment, growth monitoring, and tree mapping. …”
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Surface Classification from Robot Internal Measurement Unit Time-Series Data Using Cascaded and Parallel Deep Learning Fusion Models by Ghaith Al-refai, Dina Karasneh, Hisham Elmoaqet, Mutaz Ryalat, Natheer Almtireen

“…Two feature fusion models were introduced to classify the surface type using time-series data from an IMU sensor mounted on a ground robot. …”
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A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological data by Xiao Zhang, Yuxin Tao, Chenglong Zhang

“…The Penman Monteith 56 (P-M 56) formula is considered as the standard method for estimating ETo but requires extensive meteorological data, limiting its use in data-scarce regions. In response to this concern, this study proposed two integrated deep learning models, i.e., CNN-Transformer and CNN-Informer, to estimate ETo based on three meteorological factor input combinations (temperature-based, radiation-based, and mass transfer-based). …”
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Measuring Retiming Responses of Passengers to a Prepeak Discount Fare by Tracing Smart Card Data: A Practical Experiment in the Beijing Subway by Qingru Zou, Xiangming Yao, Peng Zhao, Zijia Wang, Taoyuan Yang

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Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study by Ryo Hidaka, Kenta Matsuda, Takashi Igari, Shu Takeuchi, Yuichi Imoto, Satoshi Yagi, Hirotaka Kawano

“…Conclusions The proposed AI model performed adequately in predicting hip OA progression and may be clinically applicable with additional datasets and validation.…”
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External validation of and improvement upon a model for the prediction of placenta accreta spectrum severity using prospectively collected multicenter ultrasound data by Magdalena Kolak, Stephen Gerry, Hubert Huras, Ammar Al Naimi, Karin A. Fox, Thorsten Braun, Vedran Stefanovic, Heleen vanBeekhuizen, Olivier Morel, Alexander Paping, Charline Bertholdt, Pavel Calda, Zdenek Lastuvka, Andrzej Jaworowski, Egle Savukyne, Sally Collins, IS‐PAS group

“…Abstract Introduction This study aimed to validate the Sargent risk stratification algorithm for the prediction of placenta accreta spectrum (PAS) severity using data collected from multiple centers and using the multicenter data to improve the model. …”
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Estimating net energy for activity for grazing beef cattle by integrating GPS tracking data, in-pasture weighing technology, and animal nutrition models by Logan Riley Vandermark, Jameson R. Brennan, Krista Ann Ehlert, Hector M. Menendez

“…As the rates of precision technology and virtual fencing are adopted, the applications of the algorithm developed in this study may be used to quantify these differences at larger landscape scales across western rangelands.…”
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Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach by Olamilekan Shobayo, Sidikat Adeyemi-Longe, Olusogo Popoola, Bayode Ogunleye

“…The GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was resource-demanding and yielded a moderate performance. …”
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Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations by Brendan D. Adkinson, Matthew Rosenblatt, Javid Dadashkarimi, Link Tejavibulya, Rongtao Jiang, Stephanie Noble, Dustin Scheinost

“…This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of brain-phenotype associations in real-world scenarios and clinical settings.…”
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C-HIDE: A Steganographic Framework for Robust Data Hiding and Advanced Security Using Coverless Hybrid Image Encryption With AES and ECC by Sahar A. El-Rahman, Ahmed E. Mansour, Leila Jamel, Manal Abdullah Alohali, Mohamed Seifeldin, Yasmin Alkady

“…Furthermore, it enhances security by eliminating metadata transmission, achieving a zero additional information ratio, unlike conventional methods requiring up to 25% extra data. By integrating encryption, minimizing detection, and removing metadata transmission, C-HIDE provides a secure, efficient, and scalable solution for covert communication in real-world applications.…”
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A General Model for Large-Scale Paddy Rice Mapping by Combining Biological Characteristics, Deep Learning, and Multisource Remote Sensing Data by Zhenjie Liu, Jialin Liu, Yingyue Su, Xiangming Xiao, Jingwei Dong, Luo Liu

“…Currently, many approaches for paddy rice mapping rely on the prior knowledge of paddy rice phenology or require widely distributed ground samples of paddy rice, which are limited for large-scale applications. In this work, we propose a general paddy rice mapping (GPRM) model by combining biological characteristics, deep learning, and multisource remote sensing data. …”
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Web-based platform to collect, share and manage technical data of historical systemic architectures: the Telegraphic Towers along the Madrid-Valencia path by Margherita Lasorella, Pasquale de-Dato, Elena Cantatore

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How HR analytics catalyzes bank competitiveness: Investigating the mediating role of data-driven decision-making and the moderating effect of organizational agility by Amro Alzghoul

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Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study by Audêncio Victor, Francielly Almeida, Sancho Pedro Xavier, Patrícia H.C. Rondó

“…This study aimed to develop and evaluate predictive models for LBW using machine learning algorithms, including Random Forest, XGBoost, Catboost, and LightGBM. Methods We analyzed data from 1,579 pregnant women enrolled in the Araraquara Cohort, a population-based longitudinal study. …”
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Predicting Methods for Analyzing Data on Fatal Outcome Possibility in the Combination of Acute Coronary Syndrome and Atrial Fibrillation According to the Krasnodar Region Registry by Z. G. Tatarintseva, E. D. Kosmacheva, S. V. Kruchinova, V. A. Akinshina, A. A. Khalafyan

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Limited Performance of Machine Learning Models Developed Based on Demographic and Laboratory Data Obtained Before Primary Treatment to Predict Coronary Aneurysms by Mi-Jin Kim, Gi-Beom Kim, Dongha Yang, Yeon-Jin Jang, Jeong-Jin Yu

“…Future studies should focus on enhancing predictive models by incorporating additional clinical data, such as acute-phase coronary artery diameter measurements, to improve accuracy and clinical utility.…”
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Generating 1 km Seamless Land Surface Temperature from China FY3C Satellite Data Using Machine Learning by Xinhan Liu, Weiwei Zhu, Qifeng Zhuang, Tao Sun, Ziliang Chen

“…This method successfully reconstructed the FY-3C satellite’s 1 km level all-weather LST time series, providing reliable technical support for the use of domestic satellite data in remote sensing applications such as ecological drought monitoring and urban heat island tracking.…”
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Effects of azithromycin in young adults with cystic fibrosis: a protocol for emulating a published randomised controlled trial using registry data by Bin Huang, Rhonda Szczesniak, Lutz Naehrlich, Freddy Frost, Gwyneth Davies, Sanja Stanojevic, Susan Charman, Nicole Mayer-Hamblett, Emily Granger, Alex Gifford, Josh Ostrenga, Jonathan Todd, Susan Christine Charman, Elizabeth Cromwell, Nicole Mayer Hamblett, Ruth Keogh, Josh Osttrenga

“…This protocol describes a study which aims to assess the applicability of target trial emulation in CF. We aim to emulate an existing trial in CF and assess to what extent the results from the trial can be replicated using registry data.Methods and analysis The target trial is a published randomised controlled trial which found evidence for beneficial effects of azithromycin use on lung function in young adults with CF. …”
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Predicting errors in accident hotspots and investigating satiotemporal, weather, and behavioral factors using interpretable machine learning: An analysis of telematics big data. by Ali Golestani, Nazila Rezaei, Mohammad-Reza Malekpour, Naser Ahmadi, Seyed Mohammad-Navid Ataei, Sepehr Khosravi, Ayyoob Jafari, Saeid Shahraz, Farshad Farzadfar

“…While machine learning (ML) has been increasingly used to predict RTAs, the lack of interpretability limits its applicability in policymaking. This study aimed to utilize interpretable ML models to predict the occurrence of errors in road accident hotspots using telematics data in Iran and interpret the most influential predictors.…”
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