Integrating Drone-Based LiDAR and Multispectral Data for Tree Monitoring by Beatrice Savinelli, Giulia Tagliabue, Luigi Vignali, Roberto Garzonio, Rodolfo Gentili, Cinzia Panigada, Micol Rossini

“…Key forest traits were then retrieved from the multispectral data using machine learning regression algorithms, which showed satisfactory performance in estimating the LAI (R² = 0.83, RMSE = 0.44 m² m⁻²) and CCC (R² = 0.80, RMSE = 0.33 g m⁻²). …”
Get full text

Introducing a Deep Neural Network Model with Practical Implementation for Polyp Detection in Colonoscopy Videos by Hajar Keshavarz, Zohreh Ansari, Hossein Abootalebian, Babak Sabet, Mohammadreza Momenzadeh

“…Method: This study investigates a simple and accurate deep-learning model for polyp detection. We address the challenge of limited labeled data through transfer learning and employ multi-task learning to achieve both polyp classification and bounding box detection tasks. …”
Get full text

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning. by Jiayi Wu, Yong-Bei Ma, Charles Congdon, Bevin Brett, Shuobing Chen, Yaofang Xu, Qi Ouyang, Youdong Mao

“…We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. …”
Get full text

From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The proposed study is founded on two principal pillars: the transformation of process tabular data (generated using the Conditional Tabular Generative Adversarial Network (CTGAN)), involving the mapping of features onto a fixed grid in a heatmap structure, and the configuration of the CNN algorithm to extract complex patterns in the data that are not readily apparent in the original tabular format. …”
Get full text

Method for Preprocessing Video Data for Training Deep-Learning Models for Identifying Behavioral Events in Bio-Objects by Marina Barulina, Alexander Andreev, Ilya Kovalenko, Ilya Barmin, Eduard Titov, Danil Kirillov

“…Monitoring moving bio-objects is currently of great interest for both fundamental and practical research. The advent of deep-learning algorithms has made it possible to automate the qualitative and quantitative analysis of the behavior of bio-objects recorded in video format. …”
Get full text

Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data by Ailing Zhang, Haobo Zhang

“…This study aimed to develop predictive models for depression in young adults using machine learning (ML) techniques and longitudinal data from the Beck Depression Inventory, structural MRI (sMRI), and resting-state functional MRI (rs-fMRI). …”
Get full text

Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review by Dhekre Saber Saleh, Mohd Shahizan Othman

“…By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. …”
Get full text

Global air quality index prediction using integrated spatial observation data and geographics machine learning by Tania Septi Anggraini, Hitoshi Irie, Anjar Dimara Sakti, Ketut Wikantika

“…This study utilizes 425 air pollution stations and the driving factors data globally from 2013 to 2024. The GML considers geographical characteristics in the analysis by calculating the optimal bandwidth area in its algorithm. …”
Get full text

Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms by Bryan Gonzalez, Gonzalo Garcia, Sergio A. Velastin, Hamid GholamHosseini, Lino Tejeda, Gonzalo Farias

“…The approach utilized the YOLO architecture, a widely recognized deep learning model for object detection and computer vision. …”
Get full text

Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions by H. S. Gowri Yaamini, Swathi K J, Manohar N, Ajay Kumar G

“…Through rigorous evaluations of diverseness in the datasets, the proposed YOLOv8n transfer learning models exhibits remarkable performance with a mean Average Precision (mAP) of 90.6 % and inference speed of 117 frames per second (fps) for lane detection whereas, a notable mAP of 81.3 % for traffic sign detection model with a processing speed of 56 fps. • YOLOv8n Transfer Learning approach by adjusting architecture for lane and traffic sign detection in Indian diverse Urban, Suburban, and Highway scenarios. • Dataset with 22,400 images of normal and complex Indian scenarios include crude weathering of roads, traffic conditions, diverse tropical weather conditions, partially occluded and partially erased lanes, and traffic signs. • The model performance with notable precision and frame wise inference.…”
Get full text

Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics by Tian Zhang, Ying Deng, Wentao Wang, Zhe Zhao, Yiling Wu, Haoqian Wang, Shutao Xia, Weifang Liao, Weijie Liao

“…Leveraging these characteristic genes, we constructed classification sub-models employing 13 types of machine learning algorithms, and we further integrated these sub-models into stacking-based ensemble models with Lasso regression, resulting in diagnostic models that required only a small set of gene expression inputs. …”
Get full text

Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel by Fariha Ahmed Nishat, M. F. Mridha, Istiak Mahmud, Meshal Alfarhood, Mejdl Safran, Dunren Che

“…This study aims to develop a lightweight machine learning-based diagnostic tool for the early and efficient detection of typhoid fever using clinical data. …”
Get full text

Federated learning and GWO-enabled consumer-centric healthcare internet of things for pancreatic tumour by Nuha Alruwais, Ghada Moh. Samir Elhessewi, Muhammad Kashif Saeed, Menwa Alshammeri, Othman Alrusaini, Abdulwhab Alkharashi, Samah Al Zanin, Yahia Said

“…This study aims to address this problem by introducing a unified framework that integrates the inherent capabilities of Federated Learning (FL) with the unique characteristics of the Grey Wolf Optimisation algorithm. …”
Get full text

A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
Get full text

Diagnostic accuracy of artificial intelligence for obstructive sleep apnea detection: a systematic review by Sara Haghighat, Muhammed Joghatayi, Julien Issa, Sarina Azimian, Janet Brinz, Ali Ashkan, Akhilanand Chaurasia, Zahra Rahimian, Linda Sangalli

“…Artificial intelligence (AI) algorithms can facilitate diagnosis by detecting patients’ signs and symptoms. …”
Get full text

Automatic Detection and Classification of Aurora in THEMIS All‐Sky Images by Jeremiah W. Johnson, Doğacan Su Öztürk, Donald Hampton, Hyunju K. Connor, Matthew Blandin, Amy Keesee

“…Abstract We report a novel machine‐learning algorithm for automatically detecting and classifying aurora in all–sky images (ASI) that is largely trained without requiring ground–truth labels. …”
Get full text

Enhancing anemia detection through multimodal data fusion: a non-invasive approach using EHRs and conjunctiva images by Muhammad Ramzan, Muhammad Usman Saeed, Ghulam Ali

“…Abstract Anemia detection using multimodal approaches leverages the integration of multiple data sources, such as imaging, clinical records, and hematological parameters, to improve diagnostic accuracy. …”
Get full text

Hyperbolic geometry enhanced feature filtering network for industrial anomaly detection by Yanjun Feng, Jun Liu, Yonggang Gai

“…Abstract In recent years, Cutting-edge machine learning algorithms and systems in Industry 4.0 enhance quality control and increase production efficiency. …”
Get full text

Rice Plant Disease Detection using Convolutional Neural Networks by A. Bala Ayyappan, T. Gobinath, M. Kumar, A. Sivaramakrishnan

Get full text

Pixel-Based Change Detection in Moving-Camera Videos Using Twin Convolutional Features on a Data-Constrained Scenario by Luiz G. C. Tavares, Allan F. Da Silva, Rafael Padilla, Lucas A. Thomaz, Sergio L. Netto, Eduardo A. B. Da Silva

“…This work proposes the Pixel-Based Change Detection using a Moving Camera (PBCD-MC) algorithm for detecting anomalies in a cluttered industrial site. …”
Get full text