Article
Computer Science, Information Systems
Jurgita Markeviciute, Jolita Bernataviciene, Ruta Levuliene, Viktor Medvedev, Povilas Treigys, Julius Venskus
Summary: The growing number of COVID-19 cases worldwide has put pressure on healthcare services and public institutions. Forecasting methods and modeling techniques are important tools for governments to manage pandemics and their impact on public health. This study aims to provide short-term forecasts of disease epidemiology for policymakers and public institutions. The effectiveness of an attention-based method was evaluated using data from Lithuania, which could be applied to any country and pandemic situation.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Majeed S. Jassim, Gulnur Coskuner, Nahid Sultana, S. M. Zakir Hossain
Summary: This study investigates the impact of successive COVID-19 lockdowns on domestic waste generation in the Kingdom of Bahrain. The researchers utilized the BiLSTM network model to predict daily domestic waste data in 2020 and compared its performance with the ARIMA model. The results show that BiLSTM outperformed ARIMA in short-term forecasts of domestic waste generation, suggesting it is a reliable tool for waste management policymakers.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Igor Ilic, Berk Gorgulu, Mucahit Cevik, Mustafa Gokce Baydogan
Summary: Time series forecasting involves developing a model based on past observations to predict future events. The explainable boosted linear regression (EBLR) algorithm enhances predictions by explaining errors and incorporating nonlinear features. It provides interpretable results and high predictive accuracy, making it a promising method for time series forecasting.
PATTERN RECOGNITION
(2021)
Article
Chemistry, Multidisciplinary
Diego Duarte, Chris Walshaw, Nadarajah Ramesh
Summary: This application is developed to analyze and predict pressure in resource management indicators, especially in the context of the healthcare system and the impact of COVID pandemic. The study compares statistical models like ARIMA, Prophet, and machine learning model GRNN for forecasting key performance indicators, showing that GRNN performed better in terms of accuracy and reliability, especially in extreme scenarios like the COVID pandemic.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Interdisciplinary Applications
Yanrui Ning, Hossein Kazemi, Pejman Tahmasebi
Summary: This paper proposes a machine learning-based time series forecasting method for predicting the production performance of unconventional reservoirs. Through the study and comparison of three algorithms, ARIMA and LSTM models are found to perform well and exhibit robustness in predicting oil and gas production in wells across the DJ Basin.
COMPUTERS & GEOSCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Afshin Ashofteh, Jorge M. Bravo, Mercedes Ayuso
Summary: Quantifying and analyzing excess mortality in crises is crucial, and this paper introduces a novel ensemble learning model selection strategy for seasonal time series forecasting, which significantly improves accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Multidisciplinary Sciences
Moiz Qureshi, Nawaz Ahmad, Saif Ullah, Ahmed Raza ul Mustafa
Summary: Forecasting is a popular topic in various disciplines due to the uncertainty of underlying phenomena, which can be estimated using mathematical functions. As technology advances, algorithms are updated to capture the ongoing phenomena. Machine learning algorithms, such as MLP, ELM, ARIMA, and ES models, are utilized to model and predict the real exchange rate data set. The study split the data into training and testing, and the model that best meets the KPI criteria is selected for predicting the behavior of the real exchange rate data set.
Article
Mathematics, Interdisciplinary Applications
Ahmed I. Shahin, Sultan Almotairi
Summary: This paper introduces a deep learning time-series prediction model to forecast COVID-19 confirmed, recovered, and death cases. The proposed model demonstrates high accuracy in experiments, outperforming other forecasting models with lower error values and a higher R-squared value of 0.99.
FRACTAL AND FRACTIONAL
(2021)
Article
Environmental Sciences
Mallory Lai, Yongtao Cao, Shaun S. Wulff, Timothy J. Robinson, Alexys McGuire, Bledar Bisha
Summary: Wastewater-based epidemiology (WBE) has emerged as a viable tool for monitoring COVID-19, and this study aims to incorporate WBE information into predicting new weekly cases using a time-series based machine learning strategy. The results show that feature engineering and machine learning can enhance the performance and interpretability of WBE for COVID-19 monitoring, and different features are recommended for short-term and long-term nowcasting and forecasting. The proposed methodology performs as well, if not better, than simple predictions based on extensive monitoring and testing. Overall, this research provides insights into the potential of machine learning based WBE for predicting and preparing for future waves of COVID-19 or pandemics.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Computer Science, Artificial Intelligence
Naresh Kumar, Seba Susan
Summary: The study optimizes the hyperparameters of fuzzy time series forecasting for the COVID-19 pandemic using Particle Swarm Optimization, proposing nested FTS-PSO and exhaustive search FTS-PSO techniques. The exhaustive search FTS-PSO outperformed all methods in forecasting coronavirus confirmed cases, demonstrating its effectiveness in achieving optimal hyperparameter values.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Dalton Borges, Maria C. Nascimento
Summary: This paper investigates a COVID-19 hospital ICU demand forecasting model based on the Prophet-LSTM method. Through comparison and testing with other models, the results show that this model has higher accuracy and significantly reduces the mean absolute error.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Electrical & Electronic
Rishi Raj Sharma, Mohit Kumar, Shishir Maheshwari, Kamla Prasan Ray
Summary: The combination of EVDHM and ARIMA is used to forecast nonstationary time series, with promising results in predicting COVID-19 daily new cases.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Artificial Intelligence
Christos Katris
Summary: This paper presents a data-driven procedure based on time series to track outbreaks, using various models to predict case evolution, considering model combinations for more accurate and robust results, utilizing statistical probability distributions for future scenarios, building an epidemiological model and calculating an epidemiological ratio for outbreak estimation.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Biomedical
Nasrin Talkhi, Narges Akhavan Fatemi, Zahra Ataei, Mehdi Jabbari Nooghabi
Summary: The study identified the MLP network model as suitable for predicting confirmed cases in Iran, while the Holt-Winter model is suitable for forecasting future death cases. According to the data trend and forecast results, the number of confirmed cases is almost constant, while death cases are decreasing.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Mathematics, Interdisciplinary Applications
Serkan Balli
Summary: The Covid-19 pandemic is the most important health disaster the world has faced in the past eight months, predicting its trend has become a challenge. A study analyzed COVID-19 data and proposed a time series prediction model, estimating the global pandemic will peak at the end of January 2021 with approximately 80 million people cumulatively infected.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Engineering, Biomedical
Phornpot Chainok, Karla de Jesus, Leandro Coelho, Helon Vicente Hultmann Ayala, Mateus Gheorghe de Castro Ribeiro, Ricardo J. Fernandes, Joao Paulo Vilas-Boas
Summary: The purpose of this study was to predict the performance determinant factors of 15m backstroke-to-breaststroke turning using machine-learning models and comparing linear and tree-based models. The collected data revealed that the best models showed similar performance in different turning techniques, with balanced contributions between turn-in and turn-out variables.
SPORTS BIOMECHANICS
(2023)
Review
Computer Science, Artificial Intelligence
Luiza Scapinello Aquino da Silva, Yan Lieven Souza Lucio, Leandro dos Santos Coelho, Viviana Cocco Mariani, Ravipudi Venkata Rao
Summary: The Jaya Algorithm, a population-based optimization method, has become a valuable tool in swarm intelligence. This paper provides a comprehensive review and bibliometric study of the algorithm's applicability and variants, emphasizing its versatility. The study aims to inspire new researchers to utilize this simple and efficient algorithm for problem-solving. Evaluation: 8/10.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Mathematics, Interdisciplinary Applications
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Gabriel Trierweiler Ribeiro, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: Efficient models for short-term load forecasting in electricity distribution and generation systems are crucial for companies' energetic planning. In this study, an ensemble learning model based on dual decomposition approach, machine learning models and hyperparameters optimization is proposed. The model successfully decomposes the time series and handles the non-linearities, and achieves accurate load forecasting results with reduced errors.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Energy & Fuels
Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: The cost of electricity and gas has a direct impact on people's everyday routines, but the value of electricity is closely related to spot market prices, which can increase in winter due to higher energy demand. Existing models for forecasting energy costs are not robust enough due to competition, seasonal changes, and other variables. This study proposes combining seasonal and trend decomposition using LOESS and Facebook Prophet methodologies to improve the accuracy of analyzing time series data on Italian electricity spot prices.
Article
Computer Science, Artificial Intelligence
Allan Christian Krainski Ferrari, Gideon Villar Leandro, Leandro dos Santos Coelho, Myriam Regattieri De Biase Silva Delgado
Summary: This work proposes a fuzzy mechanism to improve the convergence of the rat swarm optimizer algorithm. The proposed fuzzy model uses the normalized fitness and population diversity as input. The results show that the fuzzy mechanism improves convergence and is competitive with other metaheuristics.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Chemistry, Analytical
Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: Insulators installed outdoors are prone to accumulation of contaminants, causing increased conductivity and leakage current, eventually leading to flashover. To enhance power system reliability, it is possible to predict fault development and potential shutdown by evaluating the increase in leakage current. This paper proposes a method, optimized EWT-Seq2Seq-LSTM with attention, which combines empirical wavelet transform (EWT) to reduce non-representative variations and the attention mechanism with LSTM recurrent network for prediction. The model achieved a 10.17% lower mean square error (MSE) compared to standard LSTM and a 5.36% lower MSE compared to the model without optimization, demonstrating the effectiveness of the attention mechanism and hyperparameter optimization.
Article
Chemistry, Analytical
Andressa Borre, Laio Oriel Seman, Eduardo Camponogara, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The dataset is used to train a hybrid CNN-LSTM architecture, which employs quantile regression to manage uncertainties in the data. The results show that this approach outperforms traditional reference models, making it beneficial for companies to optimize maintenance schedules and improve the performance of their electric machines.
Article
Chemistry, Analytical
Guilherme Augusto Silva Surek, Laio Oriel Seman, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper aims to evaluate and map the current scenario of human actions in red, green, and blue videos using deep learning models. A semi-supervised learning approach is employed to evaluate a residual network (ResNet) and a vision transformer architecture (ViT). The results obtained using a bi-dimensional ViT structure demonstrated great performance in human action recognition, achieving an accuracy of 96.7% on the HMDB51 dataset.
Article
Chemistry, Analytical
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Jose Henrique Kleinubing Larcher, Andre Mendes, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper proposes a new hybrid framework combining STACK ensemble learning and a JADE algorithm for nonlinear system identification. The model performs well in decoding EEG signals, achieving an average explanation of 94.50% and 67.50% of data variability, and outperforms other methods in terms of accuracy.
Article
Chemistry, Analytical
Stefano Frizzo Stefenon, Laio Oriel Seman, Nemesio Fava Sopelsa Neto, Luiz Henrique Meyer, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper presents a novel hybrid method for fault prediction based on the time series of leakage current of contaminated insulators. The proposed CFRW-GMDH method, with a root-mean-squared error of 3.44x10(-12), outperformed other models in fault prediction. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply.
Article
Mathematics, Interdisciplinary Applications
Bo Li, Tian Huang
Summary: This paper proposes an approximate optimal strategy based on a piecewise parameterization and optimization (PPAO) method for solving optimization problems in stochastic control systems. The method obtains a piecewise parameter control by solving first-order differential equations, which simplifies the control form and ensures a small model error.
CHAOS SOLITONS & FRACTALS
(2024)
Article
Mathematics, Interdisciplinary Applications
Guram Mikaberidze, Sayantan Nag Chowdhury, Alan Hastings, Raissa M. D'Souza
Summary: This study explores the collective behavior of interacting entities, focusing on the co-evolution of diverse mobile agents in a heterogeneous environment network. Increasing agent density, introducing heterogeneity, and designing the network structure intelligently can promote agent cohesion.
CHAOS SOLITONS & FRACTALS
(2024)
Article
Mathematics, Interdisciplinary Applications
Gengxiang Wang, Yang Liu, Caishan Liu
Summary: This investigation studies the impact behavior of a contact body in a fluidic environment. A dissipated coefficient is introduced to describe the energy dissipation caused by hydrodynamic forces. A new fluid damping factor is derived to depict the coupling between liquid and solid, as well as the coupling between solid and solid. A new coefficient of restitution (CoR) is proposed to determine the actual physical impact. A new contact force model with a fluid damping factor tailored for immersed collision events is proposed.
CHAOS SOLITONS & FRACTALS
(2024)