APPLICATION OF THE GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) METHOD IN FORECASTING THE CONSUMER PRICE INDEX IN FIVE CITIES OF SOUTH SULAWESI PROVINCE by Ahmad Zaki, Lutfiah Shafruddin, Irwan Thaha

“…The study focuses on five cities within South Sulawesi, where direct relationships between cities are possible, allowing the spatial model to be limited to the first-order. The data used in this study consists of monthly CPI data from January 2014 to March 2023. …”
Get full text

Development of an Augmented Reality Surgical Trainer for Minimally Invasive Pancreatic Surgery by Doina Pisla, Nadim Al Hajjar, Gabriela Rus, Bogdan Gherman, Andra Ciocan, Corina Radu, Calin Vaida, Damien Chablat

“…A convolutional neural network (CNN) model predicts forces without physical sensors, achieving a mean absolute error of 0.0244 N. …”
Get full text

A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning by Shahab Aldin Shojaeezadeh, Abdelrazek Elnashar, Tobias Karl David Weber

“…The spatio-temporal analysis of the model predictions demonstrates its transferability across different spatial and temporal context of Germany. …”
Get full text

Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks by Fengrui Jing, Zhenlong Li, Shan Qiao, M. Naser Lessani, Huan Ning, Wenjun Ma, Jinjing Hu, Pan Yang, Xiaoming Li

“…However, existing time series forecasting models that incorporate human mobility data have faced challenges in making localized predictions on a large scale across the country due to data costs and constraints. …”
Get full text

The radial spreading of volcanic umbrella clouds deduced from satellite measurements by Fred Prata, Andrew T. Prata, Rebecca Tanner, Roy G. Grainger, Michael Borgas, Thomas J. Aubry

“…This model also predicts the observed radial velocities. …”
Get full text

An Ensemble of Convolutional Neural Networks for Sound Event Detection by Abdinabi Mukhamadiyev, Ilyos Khujayarov, Dilorom Nabieva, Jinsoo Cho

“…An ensemble approach combines predictions from three models, achieving F1 scores of 71.5% for segment-based metrics and 46% for event-based metrics. …”
Get full text

Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition by Pawel Powroznik, Maria Skublewska-Paszkowska, Krzysztof Dziedzic, Marcin Barszcz

“…The proposed model demonstrated excellent tennis movement prediction ability.…”
Get full text

Early detection of Zymoseptoria tritici infection on wheat leaves using hyperspectral imaging dataData INRAE by Lorraine Latchoumane, Martin Ecarnot, Ryad Bendoula, Jean-Michel Roger, Silvia Mas-Garcia, Heloïse Villesseche, Flora Tavernier, Maxime Ryckewaert, Nathalie Gorretta, Pierre Roumet, Elsa Ballini

“…These data are valuable since they can be used as a basis to monitor disease's development over time, to build leaf classification models according to their infection status per genotype per day, to develop prediction models related to symptoms' appearance, or to test imaging and spectral analysis methods.…”
Get full text

Novel transfer learning based bone fracture detection using radiographic images by Aneeza Alam, Ahmad Sami Al-Shamayleh, Nisrean Thalji, Ali Raza, Edgar Anibal Morales Barajas, Ernesto Bautista Thompson, Isabel de la Torre Diez, Imran Ashraf

“…In this study, we propose a novel transfer learning-based approach called MobLG-Net for feature engineering purposes. Initially, the spatial features are extracted from bone X-ray images using a transfer model, MobileNet, and then input into a tree-based light gradient boosting machine (LGBM) model for the generation of class probability features. …”
Get full text

Observation and Numerical Simulation of Cross-Mountain Airflow at the Hong Kong International Airport from Range Height Indicator Scans of Radar and LIDAR by Ying Wa Chan, Kai Wai Lo, Ping Cheung, Pak Wai Chan, Kai Kwong Lai

“…In order to study the feasibility of predicting such disturbed airflow, a mesoscale meteorological model and a computational fluid dynamics model with high spatial resolution are used in this paper. …”
Get full text

Real-Time Fire Risk Classification Using Sensor Data and Digital-Twin-Enabled Deep Learning by In-Seop Na, Vani Rajasekar, Velliangiri Sarveshwaran

“…A key innovation is the use of digital twin technology, which dynamically integrates real-time data from IoT sensors and simulation models to predict fire disaster scenarios accurately. …”
Get full text

Estimating corn leaf chlorophyll content using airborne multispectral imagery and machine learning by Fengkai Tian, Jianfeng Zhou, Curtis J. Ransom, Noel Aloysius, Kenneth A. Sudduth

“…A UAV-based multispectral camera collected imagery at the same time as manual readings. Machine learning models developed based on image features derived from UAV images were used to predict leaf chlorophyll content. …”
Get full text

Canopy height mapping in French Guiana using multi-source satellite data and environmental information in a U-Net architecture by Kamel Lahssini, Nicolas Baghdadi, Guerric le Maire, Guerric le Maire, Ibrahim Fayad, Ibrahim Fayad, Ludovic Villard

“…The potential of a U-Net architecture trained on sparse and unevenly distributed GEDI data to generate a continuous canopy height map at a regional scale was assessed. The developed model, named CHNET, successfully produced a canopy height map of French Guiana at a 10-m spatial resolution, achieving relatively good accuracy compared to a validation airborne LiDAR scanning (ALS) dataset. …”
Get full text

Research on channel estimation based on joint perception and deep enhancement learning in complex communication scenarios by Xin Liu, Shanghong Zhao, Yanxia Liang, Shahid Karim

“…The framework initially acquires the received signal by converting the guide-frequency symbols at the transmitter into time-domain sequences to be transmitted, and after propagating through the direct channel and the IRS reflection channel, processes the data at the receiver. Subsequently, the spatial and temporal features in the received signal are extracted using the CRPG-Net model, with the adaptive optimization capability of the model enhanced by deep reinforcement learning. …”
Get full text

ENHANCING WEIGHTED FUZZY TIME SERIES FORECASTING THROUGH PARTICLE SWARM OPTIMIZATION by Armando Jacquis Federal Zamelina, Suci Astutik, Rahma Fitriani, Adji Achmad Rinaldo Fernandes, Lucius Ramifidisoa

“…Furthermore, the length of the interval and the extent to which previous values (Order length) are utilized in predicting the subsequent value are pivotal factors in WFTS modelization and its forecasting accuracy. …”
Get full text

Public health perspectives on green efficiency through smart cities, artificial intelligence for healthcare and low carbon building materials by Jingjing Sun, Xin Guan, Siqi Yuan, Yalin Guo, Yepei Tan, Yajuan Gao

“…The model utilized convolutional and pooling layers to capture local features and spatial information from image datasets, with tasks including image classification and segmentation.ResultsThe survey results indicate high awareness of smart cities (80%), with 60% associating them with environmental protection and green living. …”
Get full text

The theoretical cognition and research paradigm of land system science by LONG Hualou, CHEN Kunqiu, ZHENG Yuhan, ZHANG Yingnan

“…The research paradigm of land system science has gradually transformed from knowledge and phenomenon description to the simulation and prediction of complex land systems, which emphasizes the integration of multidisciplinary theories and methods as well as understanding the local-distant coupled interactions of land systems based on the integrated multiscale analysis of expanding boundaries, and focuses on the construction of large-scale quantitative land system models to provide comprehensive and dynamic integrated analysis, prediction and simulation of its dynamic mechanisms, integrated effects and future scenarios and risks, and appeals for an opening scientific collaboration in the era of big data.…”
Get full text

Rate-induced transitions and noise-driven resilience in vegetation pattern dynamics by L. Vanderveken, M. Crucifix

“…<p>Understanding the resilience and stability of vegetation patterns under changing environmental conditions is crucial for predicting ecosystem responses to climate change. This study investigates the dynamics of vegetation patterns in response to a spatially homogeneous decrease in rainfall across the entire domain. …”
Get full text

Neurophysiological predictors of deep learning based unilateral upper limb motor imagery classification by Justin Sonntag, Lin Yu, Xilu Wang, Thomas Schack

“…To understand whether neurophysiological features, which are directly related to neural mechanisms of motor imagery, might influence classification accuracy, most studies have largely leveraged traditional machine learning frameworks, leaving deep learning-based techniques underexplored.MethodsIn this work, three different deep learning models from the literature (EEGNet, FBCNet, NFEEG) and two common spatial pattern-based machine learning classifiers (SVM, LDA) were used to classify imagined right elbow flexion and extension from participants using electroencephalography data. …”
Get full text

CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: A potential imaging biomarker for treatment response and prognosis. by Choong Guen Chee, Young Hoon Kim, Kyoung Ho Lee, Yoon Jin Lee, Ji Hoon Park, Hye Seung Lee, Soyeon Ahn, Bohyoung Kim

“…Cox proportional hazards models were constructed to predict prognosis based on stage, age, and each texture feature. …”
Get full text