iMESc – an interactive machine learning app for environmental sciences by Danilo Cândido Vieira, Danilo Cândido Vieira, Fabiana S. Paula, Luciana Erika Yaginuma, Gustavo Fonseca

“…Finally, a hybrid model combining an unsupervised SOM and followed by the supervised Random Forest model returned an accuracy of 83.47% for the training and 80.77% for the test, with Bathymetry, Chlorophyll, and Coarse Sand as key predictive variables. …”
Get full text

Multi-crop plant counting and geolocation using a YOLO-Powered GUI System by Renato Herrig Furlanetto, Nathan Schawn Boyd, Ana Claudia Buzanini

“…Crop counting has traditionally relied on manual field assessments or complex machine learning algorithms, which often struggle to identify small objects or underestimate the total count of objects present in the field. …”
Get full text

Architectural Exploration and Performance Enhancement of the CVA6 RISC-V Core Using the Gem5 Simulation Framework by Umer Shahid, Muhammad Tahir, Bilal Zafar, Ayesha Ahmad, Shanzay Wasim, Bisal Saeed

“…This approach resulted in two distinct optimized models: a resource-efficient variant that reduced resource consumption by 12% with only a 0.2% decrease in RMS micro-benchmark IPC, and a performance-optimized model that improved performance by 29.5% while increasing resource usage by just 4.20%. …”
Get full text

Modulating gene expression as a strategy to investigate thyroid cancer biology by Diego Claro de Mello, Joice Moraes Menezes, Antonio Tarelo Freitas de Oliveira, Marcella Maringolo Cristovão, Edna Teruko Kimura, Cesar Seigi Fuziwara

“…This strategy has evolved methodologically due to advances in cloning approaches, modeling/algorithms in short hairpin RNA (shRNA) design for knockdown efficiency, and biochemical modifications in RNA synthesis, among other developments. …”
Get full text

An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition by Nguyen Hai Thanh, Truong Nguyen Nhat, Pham Linh Thuy Thi, Pham Ngoc Huynh

“…The models were trained and evaluated on a dataset of 3939 images of 10 yoga poses. …”
Get full text

Fusing LiDAR and Photogrammetry for Accurate 3D Data: A Hybrid Approach by Rytis Maskeliūnas, Sarmad Maqsood, Mantas Vaškevičius, Julius Gelšvartas

“…The main contribution of this paper is the development of an innovative fusion system that combines classical algorithms, such as Structure from Motion (SfM), with advanced machine learning techniques, like Coherent Point Drift (CPD) and Feature-Metric Registration (FMR), to improve point cloud alignment and fusion. …”
Get full text

Advancing road maintenance with EfficientDet-based pothole monitoring by Jenefa Archpaul, Taurshia Antony, Kuriakose Bessy Mani, Kumar Edward Naveen Vijaya, Lincy Archpaul

“…Our experimental evaluations demonstrate that the EfficientDet-based model achieves an impressive average precision of 0.90 and a robust recall of 0.92, highlighting its capacity for accurate and swift pothole detection-an essential component for improving road maintenance. …”
Get full text

Architectural Gaps in Generative AI: Quantifying Cognitive Risks for Safety Applications by He Wen, Pingfan Hu

“…<b>Methods</b>: This study investigates whether architectural design elements in Transformer-based generative models contribute to a measurable divergence from human reasoning. …”
Get full text

Comparison of Bioelectric Signals and Their Applications in Artificial Intelligence: A Review by Juarez-Castro Flavio Alfonso, Toledo-Rios Juan Salvador, Aceves-Fernández Marco Antonio, Tovar-Arriaga Saul

“…AI-driven approaches are shown to have the potential to enhance diagnostic precision and improve patient outcomes. However, further research is needed to optimize these models for diverse clinical environments and fully exploit the interaction between bioelectrical signals and AI technologies.…”
Get full text

Neural representation of consciously seen and unseen information by Pablo Rodríguez-San Esteban, Jose A. Gonzalez-Lopez, Ana B. Chica

“…Furthermore, we conducted a comparative analysis of the performance of the classifier when employing either raw voltage signals or time-frequency representations, finding a substantial improvement when the latter was used to train the model, particularly in the theta and alpha bands. …”
Get full text

Analysis of the Impact of Rain on Perception in Automated Vehicle Applications by Tim Brophy, Darragh Mullins, Ashkan Parsi, Jonathan Horgan, Enda Ward, Patrick Denny, Ciaran Eising, Brian Deegan, Martin Glavin, Edward Jones

“…The results reveal that rain is not detrimental to object detection performance, and in some cases, better performance is observed. For some models, medium-sized objects experience improved detection and classification under rain conditions, while large objects experience a slight decline in performance. …”
Get full text

Hybrid Machine Learning in Hydrological Runoff Forecasting: An Exploration of Extreme Gradient-Boosting and Categorical Gradient Boosting Optimization in the Russian River Basin by Reza Seifi Majdar, Ali Rahnamaei, Vahid Babazadeh

“…Recent advances in machine learning (ML) have opened new opportunities to improve prediction accuracy. This study focuses on evaluating commonly used ML methods for runoff prediction, with an emphasis on simplicity and comparability to more advanced models. …”
Get full text

Pre-Filtering SCADA Data for Enhanced Machine Learning-Based Multivariate Power Estimation in Wind Turbines by Bubin Wang, Bin Zhou, Denghao Zhu, Mingheng Zou, Haoxuan Luo

“…Finally, the performance of the power estimation model is validated using two wind turbine datasets and two machine learning algorithms, with results compared with and without filtering. …”
Get full text

Marine Voyage Optimization and Weather Routing with Deep Reinforcement Learning by Charilaos Latinopoulos, Efstathios Zavvos, Dimitrios Kaklis, Veerle Leemen, Aristides Halatsis

“…This study explores two model-free Deep Reinforcement Learning (DRL) algorithms: (i) a Double Deep Q Network (DDQN) and (ii) a Deep Deterministic Policy Gradient (DDPG). …”
Get full text

Genetic fuzzy system for prediction of respiratory rate of chicks subject to thermal challenges by Patrícia F. P. Ferraz, Tadayuki Yanagi Junior, Yamid F. Hernandez-Julio, Gabriel A. e S. Ferraz, Maria A. J. G. Silva, Flavio A. Damasceno

“…The Genetic Fuzzy Rule Based System interacted well with the Fuzzy Inference System model previously developed showing an improvement in the respiratory rate prediction accuracy. …”
Get full text

VP-SFDA: Visual Prompt Source-Free Domain Adaptation for Cross-Modal Medical Image by Yixin Chen, Yan Wang, Zhaoheng Xie

“…Similarly, the VP-LD and VP-DPL methods also showed significant improvements over their base algorithms in both abdominal and cardiac MRI to CT tasks. …”
Get full text

Research on machine vision online monitoring system for egg production and quality in cage environment by Zhenlong Wu, Hengyuan Zhang, Cheng Fang

“…The core of the system, an enhanced You Only Look Once Version 8-small (YOLOv8s) model, was augmented by substituting the Residual Network-18 backbone and integrating the Shuffle Attention mechanism, significantly improving egg detection precision. …”
Get full text

Machine learning discovery of the dielectric properties of strontium-containing condensed matter by Dongyang Huang, Jiaxing Fu, Chenghao Yu

“…This model serves as a valuable reference and guide, improving the efficiency of dielectric material selection and the discovery of novel high-performance dielectrics.…”
Get full text

Deriving and applying core competencies for intellectual property-based startup entrepreneurs: a data-driven approach to technology evaluation by Wongyu Lee, Jaeho Shin, Donghee Yoo

“…Abstract This study identifies the core competencies of managers of intellectual property (IP)-based startups and proposes an evaluation model to enhance managerial competency criteria during technology assessments. …”
Get full text

Digital Watermarking Technology for AI-Generated Images: A Survey by Huixin Luo, Li Li, Juncheng Li

“…This survey provides a comprehensive review of three core areas: (1) the evolution of image generation technologies, highlighting key milestones such as the transition from GANs to diffusion models; (2) traditional and state-of-the-art digital image watermarking algorithms, encompassing spatial domain, transform domain, and deep learning-based approaches; (3) watermarking methods specific to AIGC, including ownership authentication of AI model and diffusion model, and watermarking of AI-generated images. …”
Get full text