A High-Granularity, Machine Learning Informed Spatial Predictive Model for Epidemic Monitoring: The Case of COVID-19 in Lombardy Region, Italy by Lorenzo Gianquintieri, Andrea Pagliosa, Rodolfo Bonora, Enrico Gianluca Caiani

Get full text

Forecast of COVID-19 progress considering the seasonal fluctuations by Mariya Kascha, Maksym Palienko, Roman Marchenko

“…The urgency of the research rests on the negative influence of the SARS-CoV-2 virus on all spheres that deepen the global economic crisis. The forecast of the COVID-19 progress in Ukraine was carried out in the following logical sequence: 1) collection and analysis of statistical data; 2) testing stationarity and periodicity of the time series, using software Statistica (portable); 3) constructing the trend component 4) detecting the seasonal component by the fast Fourier transformation under excluded trend; 5) building the general model, checking its quality and adequacy; 6) forecast and elaboration on the recommendations. …”
Get full text

Advances in AI-based rainfall forecasting: a comprehensive review of past, present, and future directions with intelligent data fusion and climate change models by Farhan Amir Fardush Sham, Ahmed El-Shafie, Wan Zurina Wan Jaafar, Adarsh S, Mohsen Sherif, Ali Najah Ahmed

“…Artificial Intelligent (AI) model are well-suited for detecting complex temporal pattern in rainfall data, enabling improved short-, medium- and long-term performance. …”
Get full text

A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction by Xuemin Huang, Xiaoliang Zhuang, Fangyuan Tian, Zheng Niu, Yujie Chen, Qian Zhou, Chao Yuan

Subjects: Get full text

Evaluating LSTM Performance on Multivariate Time Series with One-Class SVM Outlier Detection by Ragita Anillya Putri, Sugiyarto Surono, Aris Thobirin

“…Weekly sales forecasting plays a crucial role in retail business planning and inventory management.This study evaluates the prediction performance of a Long Short-Term Memory (LSTM) model for weekly sales forecasting after data preprocessing using standardization and outlier detection with One-Class Support Vector Machine (OCSVM) method. …”
Get full text

Intelligent digital twin utilization for real-time forecasting and optimization of the ship's power system by Mykola Bulgakov, Oleksiy Melnyk

“…The proposed solution integrates dynamic energy balance modeling, telemetry signal processing using a Kalman filter, load forecasting with long short-term memory (LSTM) neural networks, anomaly detection mechanisms, and optimization modules. …”
Get full text

System analysis and information processing to solve the problem of detecting breakdowns of computer information storage by N. M. Kodatsky, E. A. Revyakina, A. R. Gazizov

Get full text

A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data by Qingmiao Wang, Jinghe Feng, Shuguang Li

Subjects: “…traffic information detection method…”
Get full text

Monitoring and forecasting of land use/land cover (LULC) in Al-Hassa Oasis, Saudi Arabia based on the integration of the Cellular Automata (CA) and the Cellular Automata-Markov Mod... by Ashraf Abdelkarim

“…This study sought to integrate the Cellular Automata-Markov Model (CA-Markov) and the Cellular Automata (CA) using sensing data for land cover maps for the years: 1988, 2000, 2013 and 2020 to monitor, detect, and predict the spatial and temporal of Land Use/Land Cover (LULC) change in Al-Hassa Oasis, Saudi Arabia. …”
Get full text

Application of the Delphi methodology to forecast competencies oriented towards AI-driven disinformation detection in an expanded DigComp framework by Cristina M. Arribas, Rubén Arcos, Manuel Gertrudix

Get full text

Structural time series modelling for weekly forecasting of enterovirus outpatient, inpatient, and emergency department visits. by Cathy W S Chen, Leon L Hsieh, Betty X Y Chu

“…The objectives are to understand infection patterns through model fitting, forecast future visits for proactive epidemic management, and improve forecast accuracy by incorporating holiday effects. …”
Get full text

A Performance Forecasting Model for Optimizing CDF-Funded Construction Projects in the Copperbelt Province, Zambia by Peter Kakoma, Penjani Hopkins Nyimbili, Moffat Tembo, Erastus Misheng’u Mwanaumo

“…This study addresses a critical gap in the literature and practice by developing a novel performance forecasting model tailored to the unique governance and technical context of CDF-funded projects. …”
Get full text

Short and Medium-Term Power Load Anomaly Detection Method Based on Convolutional Neural Network and EL-DCC by Zhipeng Li, Shaobo Liu, Yang Zhang, Zhaowei Wang, Lu Wang, Lu Huang

“…The accuracy of the power load forecasting detection model proposed by the study was maximum about 0.97 when the quantity was 1000. …”
Get full text

Internet of Things Smart Beehive Network: Homogeneous Data, Modeling, and Forecasting the Honey Robbing Phenomenon by Igor Kurdin, Aleksandra Kurdina

Get full text

Power distribution and forecasting using a probabilistic and systematic data processing model for renewable resources by Hammad Alnuman, Ghulam Abbas, Amr Yousef

“…To better anticipate short-term demand, optimize the balance between generation and distribution states, and dynamically detect and differentiate inappropriate surges in power distribution, this article proposes the Probabilistic Systematic Processing Method (PSPM), which utilizes reward-based state model learning. …”
Get full text

Energy-Efficient Management of Urban Water Distribution Networks Under Hydraulic Anomalies: A Review of Technologies and Challenges by Bowen Duan, Jinliang Gao, Huizhe Cao, Shiyuan Hu

Subjects: Get full text

The global, regional, and national alcohol-related colorectal cancer burden and forecasted trends: results from the global burden of disease study 2021 by Jinfeng Yao, Guo Chen

Get full text

Development of PM_2.5 Forecast Model Combining ConvLSTM and DNN in Seoul by Ji-Seok Koo, Kyung-Hui Wang, Hui-Young Yun, Hee-Yong Kwon, Youn-Seo Koo

“…Although its performance decreases over extended forecast periods, the ConvLSTM-DNN model demonstrates its utility as a robust forecasting tool. …”
Get full text

Mathematical Evaluation of Classical and Quantum Predictive Models Applied to PM2.5 Forecasting in Urban Environments by Jesús Cáceres-Tello, José Javier Galán-Hernández

Get full text

A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment by Ioannis Stergiou, Nektaria Traka, Dimitrios Melas, Efthimios Tagaris, Rafaella-Eleni P. Sotiropoulou

“…Addressing these limitations, this study introduces a hybrid deep learning model that integrates convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) for ozone forecast bias correction. …”
Get full text