P. I. E. N. O.–Petrol-Filling Itinerary Estimation aNd Optimization by Marco Savarese, Antonio de Blasi, Carmine Zaccagnino, Carlo Augusto Grazia

“…Different domains are stressed to reach the goal: microcontroller and OEM to retrieve the fuel level from the car, national authorities to retrieve the daily fuel price, AI models to predict the price trend for the next days, and algorithms to compute the best fuel station and the best time to fill. …”
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IMU-LiDAR integrated SLAM technology for unmanned driving in mines by HU Qingsong, LI Jingwen, ZHANG Yuansheng, LI Shiyin, SUN Yanjing

“…IMU observation data was used to predict the motion state of point cloud and motion compensation was applied to reduce point cloud distortion caused by equipment movement. …”
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Volcano activity classification from synergy of EO data and machine learning: an application to Mount Etna volcano (Italy) by C. Petrucci, G. Romoli, A. Pignatelli, E. Trasatti, F. Zuccarello, F. Greco, M. Dozzo, G. Bilotta, F. Spina, G. Ganci

“…These findings are one of the first attempts of integrating satellite data with Artificial Intelligence (AI) to enhance the accuracy of volcanic state predictions and mitigate risks associated with eruptions, while emphasizing the need for rigorous validation against well-documented case studies.…”
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Differentiable Deep Learning Surrogate Models Applied to the Optimization of the IFMIF-DONES Facility by Galo Gallardo Romero, Guillermo Rodríguez-Llorente, Lucas Magariños Rodríguez, Rodrigo Morant Navascués, Nikita Khvatkin Petrovsky, Rubén Lorenzo Ortega, Roberto Gómez-Espinosa Martín

“…Specifically, neural operators are employed to predict deuteron beam envelopes along the longitudinal axis of the accelerator and neutron irradiation effects at the end, after the beam collision. …”
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Two-Way Feature Extraction Using Sequential and Multimodal Approach for Hateful Meme Classification by Apeksha Aggarwal, Vibhav Sharma, Anshul Trivedi, Mayank Yadav, Chirag Agrawal, Dilbag Singh, Vipul Mishra, Hassène Gritli

“…Both the approaches are implemented on the live challenge competition by Facebook and predicted quite acceptable results. Both approaches are tested on the validation dataset, and results are found to be promising for both models.…”
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Advancing clinical biochemistry: addressing gaps and driving future innovations by Haiou Cao, Enwa Felix Oghenemaro, Amaliya Latypova, Amaliya Latypova, Munthar Kadhim Abosaoda, Munthar Kadhim Abosaoda, Munthar Kadhim Abosaoda, Gaffar Sarwar Zaman, Anita Devi

“…Modern biosensor technology and wearable monitors facilitate continuous health tracking, Artificial Intelligence (AI)/machine learning (ML) applications enhance analytical capabilities, generating predictive insights for individualized treatment protocols. …”
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The clinical utility and safety of biomarker-guided immunosuppression withdrawal in liver transplantation: the LIFT prospective RCT by Julien Vionnet, Rosa Miquel, Juan G Abraldes, Juan-Jose Lozano, Pablo Ruiz, Miquel Navasa, Aileen Marshall, Frederik Nevens, William Gelson, Joanna Leithead, Steven Masson, Elmar Jaeckel, Richard Taubert, Phaedra Tachtatzis, Dennis Eurich, Kenneth Simpson, Eliano Bonaccorsi-Riani, James Ferguson, Alberto Quaglia, Maria Elstad, Marc Delord, Abdel Douiri, Alberto Sánchez-Fueyo

“…A previous clinical trial showed that a logistic regression algorithm including the transcript levels of a set of five genes in a liver biopsy could predict the success of immunosuppression withdrawal with high sensitivity and specificity. …”
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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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Deep learning-based temperature detection for group-raised pigs via thermal images by Xiaowen Liu, Xueting Zeng, Mengyuan Chu, Qian Li, Gang Liu

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Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain by Ali Asghar Rostami, Mohammad Taghi Sattari, Halit Apaydin, Adam Milewski

“…Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. …”
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AFQSeg: An Adaptive Feature Quantization Network for Instance-Level Surface Crack Segmentation by Shaoliang Fang, Lu Lu, Zhu Lin, Zhanyu Yang, Shaosheng Wang

“…The trainable crack post-processing module incorporates edge-guided post-processing algorithms to correct false predictions and refine segmentation results. …”
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A Novel Long Short-Term Memory-Based Approach for Microgrid Fault Detection and Classification Using the Wavelet Scattering Transform by Naema M. Mansour, Abdelazeem A. Abdelsalam, Ibrahim A. Awaad

“…These characteristics undermine the effectiveness of conventional protection schemes, particularly those based on overcurrent relays (OCRs) that are designed for systems with predictable and high fault currents. During islanded operation, a common mode in microgrids, fault currents are often reduced, making fault detection and isolation even more difficult. …”
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Fostemsavir resistance in clinical context: a narrative review by Jonathan M. Schapiro, Rolf Kaiser, Mark Krystal, Chris M. Parry, Allan R. Tenorio, Eugene Stewart, Bruce Gilliam, Margaret Gartland, Andrew Clark, Jose R. Castillo-Mancilla

“…Due to these factors and limited phenotypic clinical data, thus far, no relevant phenotypic cutoff or genotypic algorithms have been derived that reliably predict response to fostemsavir-based therapy in individuals who are HTE; therefore, pre-treatment temsavir resistance testing may be of limited benefit. …”
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Determination of high-confidence germline genetic variants in next-generation sequencing through machine learning models: an approach to reduce the burden of orthogonal confirmatio... by Muqing Yan, Qiandong Zeng, Zhenxi Zhang, Patricia Okamoto, Stanley Letovsky, Angela Kenyon, Natalia Leach, Jennifer Reiner

“…Results WES variant calls from Genome in a Bottle (GIAB) cell lines and their associated quality features were used to train five different machine learning models to predict whether a variant was a true positive or false positive based on quality metrics. …”
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Diagnostic framework to validate clinical machine learning models locally on temporally stamped data by Maximilian Schuessler, Scott Fleming, Shannon Meyer, Tina Seto, Tina Hernandez-Boussard

“…When applied to predicting ACU in cancer patients, the framework highlights fluctuations in features, labels, and data values over time. …”
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Porcine Ovarian piRNA Dynamics: A Comparative Study During Follicular Atresia by Jinbi Zhang, Long Huang, Xinxin Qin, Wenjie Li, Xiaolong Cheng, Zengxiang Pan

“…The target genes of differentially expressed piRNAs were predicted and subjected to functional analysis. A total of 452 piRNAs were identified across all samples, with 103 showing differential expression between HFs and AFs. …”
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Optimization of diagnostics of retinopathy of prematurity stages based on the integration of clinical data using the Key to Diagnosis I software by O. A. Pererva, M. A. Kovalevskaya

“…Despite the improvement of algorithms of preterm infants’ management, methods for predicting, diagnosing and treating ROP remains a vital issue.   …”
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Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets by Gonzalez Sergio Alberto, Rivarola Maximo, Ribone Andres, Lew Sergio, Paniego Norma

“…For this purpose, we simulated transcriptomic short-read sequence datasets from high-quality full-length predicted transcript models with varying degrees of complexity. …”
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Real Time Intrusion Detection System Based on Web Log File Analysis by Rawand Raouf Abdalla, Alaa Khalil Jumaa, Ahmad Freidoon Fadhil

“…In this paper, we developed a real time IDS based on weblog analysis which is used to predict if the user’s request is an attack, normal, or suspicious. …”
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Enhancing healthcare with AI: Sustainable AI and IoT-Powered ecosystem for patient aid and interpretability analysis using SHAP by Biplov Paneru, Bishwash Paneru, Sanjog Chhetri Sapkota, Ramhari Poudyal

“…The cloud-stored data can be harnessed to ascertain and supervise one's health and predict forthcoming perils. This study unveils an efficacious decision-making model custom-tailored for Internet of Things (IoT) ventures, and the proposed trained algorithm satiates these requirements, offering efficiency and precision, rendering it appropriate for numerous IoT applications. …”
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