Performance Evaluation of Four Deep Learning-Based CAD Systems and Manual Reading for Pulmonary Nodules Detection, Volume Measurement, and Lung-RADS Classification Under Varying Ra... by Sifan Chen, Lingqi Gao, Maolu Tan, Ke Zhang, Fajin Lv

“…<b>Background:</b> Optimization of pulmonary nodule detection across varied imaging protocols remains challenging. …”
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Robust development of data-driven models for methane and hydrogen mixture solubility in brine by Kashif Saleem, Abhinav Kumar, K. D. V. Prasad, Ahmad Alkhayyat, T. Ramachandran, Protyay Dey, Navdeep Kaur, R. Sivaranjani, I. B. Sapaev, Mehrdad Mottaghi

“…The results indicate that Ensemble Learning and AdaBoost yield the highest accuracy algorithms in prediction capability as they tend to illustrate the lowest values of mean squared error and mean absolute relative error (%) and highest R-squared values. …”
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Structure-guided deep learning for back acupoint localization via bone-measuring constraints by Yulong Wang, Tian Lan, Wenjian Dou, Zhi Chen, Song Zhang, Gong Chen, Gong Chen

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On-Road Evaluation of an Unobtrusive In-Vehicle Pressure-Based Driver Respiration Monitoring System by Sparsh Jain, Miguel A. Perez

“…These findings support the potential integration of unobtrusive physiological monitoring into driver state monitoring systems, which can aid in the early detection of fatigue and impairment, enhance post-crash triage through timely vital sign transmission, and extend to monitoring other vehicle occupants. …”
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Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

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Macroepidemiological trends of Influenza A virus detection through reverse transcription real-time polymerase chain reaction (RT-rtPCR) in porcine samples in the United States over... by Daniel C. A. Moraes, Guilherme A. Cezar, Edison S. Magalhães, Rafael R. Nicolino, Kinath Rupasinghe, Srijita Chandra, Gustavo S. Silva, Marcelo N. Almeida, Bret Crim, Eric R. Burrough, Phillip C. Gauger, Darin Madson, Joseph Thomas, Michael A. Zeller, Rodger Main, Mary Thurn, Paulo Lages, Cezar A. Corzo, Mattew Sturos, Hemant Naikare, Rob McGaughey, Franco Matias Ferreyra, Jamie Retallick, Jordan Gebhardt, Sara McReynolds, Jon Greseth, Darren Kersey, Travis Clement, Angela Pillatzki, Jane Christopher-Hennings, Beth S. Thompson, Melanie Prarat, Dennis Summers, Craig Bowen, Joseph Boyle, Kenitra Hendrix, James Lyons, Kelli Werling, Andreia G. Arruda, Mark Schwartz, Mark Schwartz, Paul Yeske, Deborah Murray, Brigitte Mason, Peter Schneider, Samuel Copeland, Luc Dufresne, Daniel Boykin, Corrine Fruge, William Hollis, Rebecca C. Robbins, Thomas Petznick, Kurt Kuecker, Lauren Glowzenski, Megan Niederwerder, Daniel C. L. Linhares, Giovani Trevisan

“…Of the total of 118,490 samples tested for IAV subtyping using RT-rtPCR, the most frequently detected subtypes were H1N1 (33.1%), H3N2 (25.5%), H1N2 (24.3%), H3N1 (0.2%), mixed subtypes (5.4%), and partial subtype detection (11.5%). …”
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Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery by Reda Amer

“…., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. …”
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Deepfakes in Visual Art: Differentiating AI-Generated Art From Human Art Using Convolutional Neural Networks (CNN) by Ngonidzashe Tinago, Silas Formunyuy Verkijika, Kelibone Eva Mamabolo

“…This study explores the use of Convolutional Neural Networks (CNNs) to differentiate AI-generated art from human-created art. By employing Error Level Analysis (ELA), an image forensic technique for detecting fake and real images, this study develops a robust CNN classifier. …”
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Assessing Geometry Perception of Direct Time-of-Flight Sensors for Robotic Safety by Jakob Gimpelj, Marko Munih

“…Quantitative metrics including the root mean square error, mean absolute error, area difference, and others were used to evaluate measurement accuracy. …”
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Hydrological insights: Comparative analysis of gridded potential evapotranspiration products for hydrological simulations and drought assessment by Mohammed Abdallah, Ke Zhang, Sudhir Kumar Singh, Mukhtar Jibril Abdi, Ayman M. Elameen, Almutaz A.A. Mohammed, Hussam Eldin Elzain

“…Seasonally, GLEAM PET exhibits the highest accuracy and lowest error in streamflow simulation across all three regions. …”
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Comprehensive analysis on soil nitrogen prediction using near-infrared spectroscopy: Models, methods, and insights for precision agriculture by Xiaoyu Wang, Zhiyou Zhang, Yujiao Li, Jie Li, Qing Yang, Qiqi Liu, Zhijie Gong, Mengling Zhang, Xu Wang, Guanghui Xie

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DeepLASD countermeasure for logical access audio spoofing by Hamed Al-Tairi, Ali Javed, Tasawer Khan, Abdul Khader Jilani Saudagar

“…Extensive experimentation was conducted on the large-scale and diverse ASVspoof 2019 and 2021 datasets. Achieving an Equal Error Rate as low as $$4.98\%$$ and a minimum Tandem Detection Cost Function of 0.1208, along with strong generalization to both VC and TTS spoof types, demonstrate the competency of the proposed method for LA spoofing detection. …”
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From Accuracy to Vulnerability: Quantifying the Impact of Adversarial Perturbations on Healthcare AI Models by Sarfraz Brohi, Qurat-ul-ain Mastoi

“…Unlike prior studies, we conducted a quantitative evaluation on the impact of a Fast Gradient Sign Method (FGSM) attack on an optimized DL model designed for breast cancer detection to demonstrate how minor perturbations reduced the model’s accuracy from 98% to 53%, and led to a substantial increase in the classification errors, as revealed by the confusion matrix. …”
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An Obstacle Perception Algorithm Based on Multi-Sensor Fusion for Autonomous-Rail Rapid Transit by JIANG Liangyu, PAN Wenbo, HUANG Ruipeng

“…This paper presents an obstacle perception algorithm based on multi-sensor fusion, aimed at addressing omissions and errors, as well as low accuracy in object detection for autonomous-rail rapid transit (ART). …”
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Model Updating of Bridges Using Measured Influence Lines by Doron Hekič, Jan Kalin, Aleš Žnidarič, Peter Češarek, Andrej Anžlin

“…In developing a digital twin of a real structure, finite element model updating (FEMU) is essential for refining the model’s response based on measured data, enabling the detection of structural damage or hidden reserves over time. …”
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Study on infrasonic leakage monitoring and signal processing for product oil pipeline by Yuanbo YIN, Yuxing LI, Wen YANG, Shu LU, Chen ZHANG, Cuiwei LIU, Kai YANG, Wuchang WANG

“…At a 91 km monitoring interval along the product oil pipeline, the positioning error was about 800 m, facilitating reliable monitoring up to a leak rate of 0.001 6 m3/s, with the minimum detectable leak rate recorded at 0.000 46 m3/s. …”
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Predictive identification of oral cancer using AI and machine learning by Saraswati Patel, Dheeraj Kumar

“…These findings underscore the importance of normalization in preprocessing for machine learning models, highlighting its role in achieving superior performance in oral cancer detection. …”
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Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes by Neha Irfan, Sherin Zafar, Kashish Ara Shakil, Mudasir Ahmad Wani, S. N. Kumar, A. Jaiganesh, K. M. Abubeker

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A comparative study of ultra-massive MIMO intelligent receivers with adversarial robustness and energy efficiency for 6G applications by Pushkar Nidagundi, Advesh Darvekar, Malik Amber, Ramesh R

“…Simulation results demonstrate that the optimized Minimum Mean Square Error (MMSE) detector achieves up to 95 % reduction in Bit Error Rate (BER) compared to Zero-Forcing (ZF) in small-to-medium UM-MIMO configurations (2 × 2 to 32 × 32), as shown in Tables (4 and 5), while maintaining computational feasibility. …”
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Tightly coupled integration of vector HD map, LiDAR, GNSS, and INS for precise vehicle navigation in GNSS-challenging environment by Hongjuan Zhang, Chuang Qian, Wenzhuo Li, Bijun Li, Hui Liu

“…But it suffers from severe signal reflections and blockages of GNSS signals and error accumulation of INS with MEMS-IMU in GNSS-challenging environment. …”
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