Results 1 to 10 of about 60,311 (308)

Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning

Machine Learning and Knowledge Extraction
Accurate forecasting of inbound visitor numbers is crucial for effective planning and resource allocation in the tourism industry. Preceding forecasting algorithms primarily focused on time series analysis, often overlooking influential factors such as ...
Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang   +2 more
doaj   +1 more source

Federated Learning-Based Service Caching in Multi-Access Edge Computing System

Applied Sciences
Multi-access edge computing (MEC) brings computations closer to mobile users, thereby decreasing service latency and providing location-aware services.
Tuan Phong Tran, Anh Hung Ngoc Tran, Thuan Minh Nguyen, Myungsik Yoo   +3 more
doaj   +1 more source

Representation Learning of Multi-Spectral Earth Observation Time Series and Evaluation for Crop Type Classification

Remote Sensing
Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest
Andrea González-Ramírez, Clement Atzberger, Deni Torres-Roman, Josué López   +3 more
doaj   +1 more source

A Hybrid Framework for Red Blood Cell Labeling Using Elliptical Fitting, Autoencoding, and Data Augmentation

Journal of Imaging
This study aimed to develop a local dataset of abnormal RBC morphology from confirmed cases of anemia and thalassemia in Thailand, providing a foundation for medical image analysis and future AI-assisted diagnostics.
Bundasak Angmanee, Surasak Wanram, Amorn Thedsakhulwong   +2 more
doaj   +1 more source

Personal Identification Using 3D Topographic Cubes Extracted from EEG Signals by Means of Automated Feature Representation

Signals
Electroencephalogram (EEG)-based identification offers a promising biometric solution by leveraging the uniqueness of individual brain activity patterns.
Muhammed Esad Oztemel, Ömer Muhammet Soysal   +1 more
doaj   +1 more source

Supervised and unsupervised learning of -dimensional even-offspring branching annihilating random walks

Machine Learning: Science and Technology
Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems.
Yanyang Wang, Wei Li, Feiyi Liu, Jianmin Shen   +3 more
doaj   +1 more source

Early Detection of Forest Disturbances in Homogeneous Stands – Deep Learning-Based Monitoring of Picea Abies

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Climate change has increased the vulnerability of forests to insect-related damage, resulting in widespread forest loss in Central Europe and highlighting the need for effective, continuous monitoring systems.
Maximilian Kirsch, Jakob Wernicke, Pawanjeet Singh Datta, Christine Preisach   +3 more
doaj   +1 more source

Enhancing GNSS Localization in Urban Canyons With a Hybrid CNN-Autoencoder Approach to LOS/NLOS Classification

IEEE Access
Non-Line-of-Sight (NLOS) reception is acknowledged as a primary source of positioning error in Global Navigation Satellite System (GNSS) applications, particularly within dense urban settings. When the direct satellite path is obstructed by buildings and
S. Titouni, I. Messaoudene, Y. Himeur, D. W. Dawoud, M. Belazzoug, B. Hammache, F. Chetouah, S. Atalla, W. Mansoor   +8 more
doaj   +1 more source

Reliable Task-Constrained Band Selection Method for Hyperspectral Anomaly Detection

Remote Sensing
Hyperspectral band selection utilizes a crucial band subset to represent original data. In hyperspectral anomaly detection tailored for specific tasks, detection performance can be enhanced by pre-selecting a subset of bands that are more representative.
Genrui Zhang, Wenzheng Wang, Yuqi Han, Chenwei Deng, Xingshi Luo   +4 more
doaj   +1 more source

The trade-off between maximizing reconstruction and physiological interpretation of muscle synergies with autoencoders

Frontiers in Human Neuroscience
IntroductionIn neuroscience, the muscle synergy method is a widely known computational approach for studying motor control from electromyographic (EMG) recordings.
Cristina Brambilla, Nicol Moscatelli, Valentina Lanzani, Lorenzo Molinari Tosatti, Alessandro Brusaferri, Alessandro Scano   +5 more
doaj   +1 more source