Application of machine learning in the prediction of COVID-19 daily new cases: A sco** review
COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the
rate of COVID-19 spread and modeling of its course have critical impact on both health …
rate of COVID-19 spread and modeling of its course have critical impact on both health …
Satellite telemetry data anomaly detection using causal network and feature-attention-based LSTM
Z Zeng, G **, C Xu, S Chen, Z Zeng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Most of the data-driven satellite telemetry data anomaly detection methods suffer from high
false positive rate (FPR) and poor interpretability. To solve the above problems, we propose …
false positive rate (FPR) and poor interpretability. To solve the above problems, we propose …
Temporal deep learning architecture for prediction of COVID-19 cases in India
To combat the recent coronavirus disease 2019 (COVID-19), academician and clinician are
in search of new approaches to predict the COVID-19 outbreak dynamic trends that may …
in search of new approaches to predict the COVID-19 outbreak dynamic trends that may …
Predictive modeling of biomedical temporal data in healthcare applications: review and future directions
Predictive modeling of clinical time series data is challenging due to various factors. One
such difficulty is the existence of missing values, which leads to irregular data. Another …
such difficulty is the existence of missing values, which leads to irregular data. Another …
Carbon price forecasting based on news text mining considering investor attention
D Pan, C Zhang, D Zhu, S Hu - Environmental Science and Pollution …, 2023 - Springer
The carbon market relies on market-oriented financial means to solve the problem of carbon
emissions. An effective carbon pricing mechanism can improve market efficiency and better …
emissions. An effective carbon pricing mechanism can improve market efficiency and better …
A new CNN-based method for short-term forecasting of electrical energy consumption in the COVID-19 period: the case of Turkey
I Atik - IEEE Access, 2022 - ieeexplore.ieee.org
This study proposes a new convolutional neural network (CNN) method with an input-signal
decomposition algorithm. With the proposed CNN architecture, hourly electricity …
decomposition algorithm. With the proposed CNN architecture, hourly electricity …
Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters
Abstract The Coronavirus disease 2019 (COVID-19) pandemic has severely crippled the
economy on a global scale. Effective and accurate forecasting models are essential for …
economy on a global scale. Effective and accurate forecasting models are essential for …
[PDF][PDF] Implementasi Long Short Term Memory (LSTM) dan Bidirectional Long Short Term Memory (BiLSTM) Dalam Prediksi Harga Saham Syariah
DI Puteri - Euler: Jurnal Ilmiah Matematika, Sains dan …, 2023 - pdfs.semanticscholar.org
Perkembangan pasar saham di Indonesia pada saat ini berkembang cukup pesat. Hal ini
dapat dilihat berdasarkan jumlah investor yang mengalami peningkatan setiap tahunnya …
dapat dilihat berdasarkan jumlah investor yang mengalami peningkatan setiap tahunnya …
Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models
Deep learning methods have become the state of the art for spatio-temporal predictive
analysis in a wide range of fields, including environmental management, public health …
analysis in a wide range of fields, including environmental management, public health …
A novel multi-module approach to predict crime based on multivariate spatio-temporal data using attention and sequential fusion model
Forecasting crime is complex since several complicated aspects contribute to a crime.
Predicting crime becomes more challenging because of the enormous number of everyday …
Predicting crime becomes more challenging because of the enormous number of everyday …