Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects by Nandito Davy, Ammar El-Husseiny, Umair bin Waheed, Korhan Ayranci, Manzar Fawad, Mohamed Mahmoud, Nicholas B. Harris

“…Petrophysical constraints were derived from triple combo well logs (gamma ray, bulk density, neutron porosity), while geological constraints included stratigraphic data or spatial distance between training and target wells—petrophysical constraints most improved predictions, while stratigraphic and spatial constraints had progressively less impact. …”
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Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment by Ebrahimpourboura Zahra, Mosalpuri Manish, Jonas Baltrusaitis, Dubey Pallavi, Mba Wright Mark

“…Key case studies demonstrate the transformative potential of ML in improving economic viability and environmental sustainability, highlighting its role in predicting system performance, optimizing configurations, and reducing costs and impacts. …”
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Development and evaluation of customized software to automatically align macula and optic disc centered scanning laser ophthalmoscope fundus images by M. Elena Martinez-Perez, Franziska G. Rauscher, Pingping Zhao, Tobias Elze

“…BloodVesselReg implements an image registration and mosaicing algorithm based on retinal blood vessels. OCTFundusReg optimizes a general-purpose image registration toolkit to operate on SLO images. …”
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Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control by Aziz Watil, Hamid Chojaa

“…It also provides flexibility in BEV power sizing, optimizing the use of power electronics converters to reduce costs and complexity. …”
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FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images by Liang Zhou, Achin Jain, Arun Kumar Dubey, Sunil K. Singh, Neha Gupta, Arvind Panwar, Sudhakar Kumar, Turki A. Althaqafi, Varsha Arya, Wadee Alhalabi, Brij B. Gupta

“…Abstract Cancer is among the most dangerous diseases contributing to rising global mortality rates. …”
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Predicting biomarkers of progressive pulmonary fibrosis: morphological, cytokine profile, and clinical portrait by Nicol Bernardinello, Federica Pezzuto, Lauren D’Sa, Luca Vedovelli, Chiara Giraudo, Anamaria Chelu, Anamaria Chelu, Cecilia de Chellis, Francesca Lunardi, Francesco Fortarezza, Francesca Boscaro, Elisabetta Cocconcelli, Paolo Spagnolo, Elisabetta Balestro, Fiorella Calabrese

“…Lung specimens revealed a significant overexpression of IL9 in the PPF compared to the nPPF group (p=0.049). Boruta algorithm analysis showed that lymphoid aggregates and traction bronchiectasis at diagnosis are the most important variables in determining the PPF status.ConclusionsThe present results increase the understanding of the pathological mechanisms of PPF, offering potential avenues for improved prognostication and therapeutic intervention.…”
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Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting by Ubaid Ahmed, Anzar Mahmood, Ahsan Raza Khan, Levin Kuhlmann, Khurram Saleem Alimgeer, Sohail Razzaq, Imran Aziz, Amin Hammad

“…The mutual information (MI) algorithm is implemented as a feature selection technique to identify the most important features for forecasting. …”
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SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals by Davide Lillini, Carlo Aironi, Lucia Migliorelli, Leonardo Gabrielli, Stefano Squartini

“…The final detection of <i>apnea</i> events is performed using an unsupervised clustering algorithm, specifically <i>k-means</i>. Multiple experimental runs were carried out to determine the optimal network configuration and the most suitable type and frequency range for the input data. …”
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Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario by Jingxuan Zhang, Xiaodong Xu, Kangjie Zhang, Bufang Zhang, Xiaofeng Tao, Ping Zhang

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The impact of Poyang Lake water level changes on the landscape pattern of wintering wading bird habitats by Feihong Yu, Jiancheng Zhai, Zhiqiang Huang, Jimei Chen, Fuqiang Han, Liaobo Wang

“…The cyclical rhythm of water level changes determines the dynamic variations in the wetland landscape pattern of Poyang Lake, directly impacting the habitat and survival of wintering migratory birds, particularly wading birds, which are most sensitive to these changes. This study employs an Artificial Neural Network (ANN) algorithm to interpret wetland landscapes using the Gao-Fen Satellite Images across 14 different water levels. …”
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Postmarketing safety evaluation of pemetrexed using FAERS and JADER databases by Luo Lv, Xiangyang Wu, Yubo Ren, Yuli Guo, Haixiong Wang, Xiaofang Li

“…Continuous pharmacovigilance is essential to optimize its clinical use and improve patient safety.…”
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YOLORM: An Advanced Key Point Detection Method for Accurate and Efficient Rotameter Reading in Low Flow Environments by Huang Yong, Xia Xing, Xiao Shengwang

“…Moreover, YOLORM exhibited significant reductions in parameter count and computational cost while maintaining or enhancing detection performance relative to state-of-the-art algorithms. …”
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Empowering Sustainability: The Crucial Role of IoT-Enabled Distributed Learning Systems in Reducing Carbon Footprints by Anjana M S, Aryadevi Remanidevi Devidas, Maneesha Vinodini Ramesh

“…Transitioning to cleaner energy sources and improving energy efficiency are essential steps to reduce the environmental impact of electricity generation. …”
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Accelerated development of multi-component alloys in discrete design space using Bayesian multi-objective optimisation by Osman Mamun, Markus Bause, Bhuiyan Shameem Mahmood Ebna Hai

“…Our findings highlight the superior performance of the qEHVI acquisition function in identifying the optimal Pareto front across 1-, 2-, and 3-objective aluminum alloy optimisation problems, all within a constrained evaluation budget and reasonable computational cost. …”
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Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study by Emanuele Frassini, Teddy S. Vijfvinkel, Rick M. Butler, Maarten van der Elst, Benno H. W. Hendriks, John J. van den Dobbelsteen

“…We employed only the clinical phases derived from video analysis as input to the algorithms. Our results show that InceptionTime and LSTM-FCN yielded the most accurate predictions. …”
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ABL-SMOTE: A Novel Resampling Method by Handling Noisy and Borderline Challenge for Imbalanced Dataset for Software Defect Prediction by Kamal Bashir, Sara Abdelwahab Ghorashi, Ali Ahmed, Abdolraheem Khader

“…Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. …”
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Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction by Xidi Yang, Liangyu Zhu, Wenyu Jiang, Yiting Yang, Mailin Gan, Linyuan Shen, Li Zhu

“…Feed conversion ratio (FCR) is a critical indicator of production efficiency in livestock husbandry. Improving FCR is essential for optimizing resource utilization and enhancing productivity. …”
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Efficient spatio-temporal modeling for sign language recognition using CNN and RNN architectures by Kasian Myagila, Kasian Myagila, Devotha Godfrey Nyambo, Mussa Ally Dida

“…These results show that more effort is required to improve signer independence performance, including the challenges of hand dominance by optimizing spatial features.…”
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The artificial intelligence revolution in gastric cancer management: clinical applications by Runze Li, Jingfan Li, Yuman Wang, Xiaoyu Liu, Weichao Xu, Runxue Sun, Binqing Xue, Xinqian Zhang, Yikun Ai, Yanru Du, Jianming Jiang

“…This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. …”
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Machine learning-based prediction of physical parameters in heterogeneous carbonate reservoirs using well log data by Fuyong Wang, Xianmu Hou

“…The results demonstrate that GPR achieves the highest accuracy in porosity prediction, with a coefficient of determination (R2) value of 0.7342, while RF proves to be the most accurate for permeability prediction. Despite these improvements, accurately predicting low-permeability zones in heterogeneous carbonate rocks remains a significant challenge. …”
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