A New Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Türkiye by Ulubey Çeken, Fadime Sertçelik, Abdullah İçen

“…The recent expansion of the strong-motion observation network, along with the increase in data obtained during major earthquakes and efforts to create consistent metadata for source, path, and site effects for both old and new records, has significantly improved the quality of data and the level of modeling in Türkiye. …”
Get full text

Domain adaptation of deep neural networks for tree part segmentation using synthetic forest trees by Mitch Bryson, Ahalya Ravendran, Celine Mercier, Tancred Frickey, Sadeepa Jayathunga, Grant Pearse, Robin J.L. Hartley

“…Supervised deep learning algorithms have recently achieved state-of-the-art performance in the classification, segmentation and analysis of 3D LiDAR point cloud data in a wide-range of applications and environments. …”
Get full text

Integrative machine learning model of RNA modifications predict prognosis and treatment response in patients with breast cancer by Tao Wang, Shu Wang, Zhuolin Li, Jie Xie, Qi Jia, Jing Hou

“…In addition, immune infiltration levels were evaluated via six different algorithms, and immune checkpoint inhibitor responsiveness was predicted. …”
Get full text

Network pharmacology and AI in cancer research uncovering biomarkers and therapeutic targets for RALGDS mutations by S. Mohammed Zaidh, Hariharan Thirumalai Vengateswaran, Mohammad Habeeb, Kiran Balasaheb Aher, Girija Balasaheb Bhavar, N. Irfan, K N V Chenchu Lakshmi

“…Structured E-pharmacophore model aided in capturing the binding cavity using eraser algorithms and fabricating a new selective lead compound for the KRSA. …”
Get full text

Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…Following model fusion, the AUC improved to 0.783 (95% CI: 0.724–0.841). The difference in performance between the fusion model and the traditional machine learning model was statistically significant (p = 0.036). …”
Get full text

Drought Detection in Satellite Imagery: A Layered Ensemble Machine Learning Approach by Muhammad Owais Raza, Naeem Ahmed Mahoto, Mana Saleh Al Reshan, Ali Alqazzaz, Adel Rajab, Asadullah Shaikh

“…The novelty of the study lies in its layered ensemble architecture that integrates multiple conventional classifiers with ensemble techniques for improved drought detection accuracy using satellite imagery. …”
Get full text

Treatment of infections in young infants in low- and middle-income countries: a systematic review and meta-analysis of frontline health worker diagnosis and antibiotic access. by Anne C C Lee, Aruna Chandran, Hadley K Herbert, Naoko Kozuki, Perry Markell, Rashed Shah, Harry Campbell, Igor Rudan, Abdullah H Baqui

“…Availability of first-line injectable antibiotics appears low in many health facilities in Africa and Asia. Improved data and advocacy are needed to increase the availability and appropriate utilization of antibiotics for young infant infections in LMICs.…”
Get full text

Exploring the capabilities of hyperspectral remote sensing for soil texture evaluation by Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker, Reza Alimardani, Pouya Bohlol

“…Furthermore, integrating preprocessed hyperspectral data with DSM covariates resulted in improved predictive performance for sand and clay content, yielding R2 values of 0.64 and 0.51, respectively. …”
Get full text

Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet by Aaron E. Maxwell, Sarah Farhadpour, Muhammad Ali

“…The geomorphon-based transfer learning resulted in the poorest performance while the ImageNet-based parameters generally improved performance in comparison to a random parameter initialization, even when the encoder was frozen or not trained. …”
Get full text

A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases by Veerayuth Kittichai, Morakot Kaewthamasorn, Apinya Arnuphaprasert, Rangsan Jomtarak, Kaung Myat Naing, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang

“…The model’s performance was also improved by a fine-tuned procedure between k-nearest neighbor and its normalized distance of each data point, including precision of 0.833 ± 0.134, specificity of 0.930 ± 0.054, recall of 0.838 ± 0.118, and accuracy of 0.915 ± 0.025, respectively. …”
Get full text

Assessment of sensor driven automatic smart soil and paddy seed metering mechanisms using artificial intelligence for paddy nurseries by Vinod Choudhary, Rajendra Machavaram, Prakhar Patidar, Gajendra Singh, Naseeb Singh, Lokesh Kumawat

“…Hence, the optimized conditions improved soil and seed distribution precision, ensuring uniform seedling emergence and sustainable paddy production.…”
Get full text

Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain by Jay Gopal, Jonathan Bao, Tessa Harland, Julie G. Pilitsis, Steven Paniccioli, Rachael Grey, Michael Briotte, Kevin McCarthy, Ilknur Telkes

“…Machine and deep learning hold enormous potential to predict patient responses to SCS therapy resulting in refined patient selection and improved patient outcomes.…”
Get full text

USE OF ARTIFICIAL INTELLIGENCE TO IDENTIFY AND CORRECT MISCONCEPTIONS ABOUT RADIATION by Oleksandr Tymoshchuk

“…These findings indicate that while AI has significant potential to improve basic radiation literacy and provide automated feedback, its current capabilities are limited in addressing more multidimensional and complex misconceptions. …”
Get full text

Impact of stain variation and color normalization for prognostic predictions in pathology by Siyu Lin, Haowen Zhou, Mark Watson, Ramaswamy Govindan, Richard J. Cote, Changhuei Yang

“…The failure to generalize did not improve even when the tinctorial difference corrections were made through either traditional color-tuning or stain normalization with the help of a Cycle Generative Adversarial Network (CycleGAN) process. …”
Get full text

Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery by Xiaoyu Hu, Xiuyuan Zhao, Wenhe Liu

“…Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. …”
Get full text

Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree by Giulia Di Teodoro, Marta Monaci, Laura Palagi

“…The interpretability of models has become a crucial issue in Machine Learning because of algorithmic decisions' growing impact on real-world applications. …”
Get full text

Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction by Qinhe Zhang, Jiachen Zhang, Tie Feng, Jialang Xue, Xinxin Zhu, Ningyang Zhu, Zhiheng Li

“…In this paper, an SDP approach using deep Q-learning network (DQN)-based feature extraction is proposed to eliminate irrelevant, redundant, and noisy features and improve the classification performance. In the data preprocessing phase, the undersampling method of BalanceCascade is applied to divide the original datasets. …”
Get full text

A Review of Quantitative Characterization of Phase Interface Dynamics and Optimization of Heat Transfer Modeling in Direct Contact Heat Transfer by Mingjian Wang, Jianxin Xu, Shibo Wang, Hua Wang

“…Many scholars have focused their research on optimizing the working conditions and structure of direct contact heat transfer in order to improve heat transfer efficiency. The non-equilibrium phenomenon between the two phases of direct contact heat transfer has not been thoroughly studied. …”
Get full text

A Survey on Video Compression Optimization Techniques for Accuracy Enhancement in Video Analytics Applications (VAPs) by Kholidiyah Masykuroh, Hendrawan, Eueung Mulyana, Farhan Krishna

“…Our findings highlight that adaptive compression techniques improve the trade-off between detection accuracy and efficiency. …”
Get full text

Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder by Shubham Chaudhry, Azzedine Abdedou, Azzeddine Soulaïmani

“…This method leverages deep learning techniques to handle the complexity of the data and improve prediction accuracy. The accuracy and efficiency of both models are evaluated through thermo-mechanical analysis of an AM-built part. …”
Get full text