Missing data imputation: A comprehensive review
M Alwateer, ES Atlam, MM Abd El-Raouf… - Journal of Computer and …, 2024 - scirp.org
Missing data presents a significant challenge in statistical analysis and machine learning,
often resulting in biased outcomes and diminished efficiency. This comprehensive review …
often resulting in biased outcomes and diminished efficiency. This comprehensive review …
Predicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning Techniques
The volatile and non-linear nature of stock market data, particularly in the post-pandemic
era, poses significant challenges for accurate financial forecasting. To address these …
era, poses significant challenges for accurate financial forecasting. To address these …
[HTML][HTML] TMS: Ensemble Deep Learning Model for Accurate Classification of Monkeypox Lesions Based on Transformer Models with SVM
Background/Objectives: The emergence of monkeypox outside its endemic region in Africa
has raised significant concerns within the public health community due to its rapid global …
has raised significant concerns within the public health community due to its rapid global …
Monthly stream temperatures along the Danube River: Statistical analysis and predictive modelling with incremental climate change scenarios
The aim of the study is to analyse changes and predict the course of mean monthly water
temperatures of the Danube River at various locations for the future. The first part of the …
temperatures of the Danube River at various locations for the future. The first part of the …
[HTML][HTML] Explainable artificial intelligence systems for predicting mental health problems in autistics
The recognition of mental disorder symptoms is crucial for timely management and reduction
of recurring symptoms and disabilities. The ability to predict and explain mental health …
of recurring symptoms and disabilities. The ability to predict and explain mental health …
Two novel nonlinear multivariate grey models with kernel learning for small-sample time series prediction
L Wang, N Li, M **e, L Wu - Nonlinear Dynamics, 2023 - Springer
For many applications, small-sample time series prediction based on grey forecasting
models has become indispensable. Many algorithms have been developed recently to make …
models has become indispensable. Many algorithms have been developed recently to make …
Hybrid annotation and classification for predicting attitudes towards COVID-19 vaccines for Arabic tweets
EMG Younis, R Mohamed, AA Ali… - Social Network Analysis …, 2024 - Springer
In March 2020, the whole world suffered from the coronavirus pandemic. This virus is a sort
of virus that comes in many forms, some of which may kill. It mainly affects the human …
of virus that comes in many forms, some of which may kill. It mainly affects the human …
Examining Emotional Reactions to the COVID‐19 Crisis Through Twitter Data Analysis: A Comparative Study of Classification Techniques
COVID‐19 has significantly impacted peoples' mental health because of isolation and social
distancing measures. It practically impacts every segment of people's daily lives and causes …
distancing measures. It practically impacts every segment of people's daily lives and causes …
Does future tuna landing stock meet the target? Forecasting tuna landing in Malaysia using seasonal ARIMA model
A Nasir, YN Kamaruzzaman - International Journal of Social …, 2024 - emerald.com
Purpose This study was conducted to forecast the monthly number of tuna landings between
2023 and 2030 and determine whether the estimated number meets the government's …
2023 and 2030 and determine whether the estimated number meets the government's …
Research on Predicting Skilled Labor Availability to Enhance Sustainability Building Practices
The construction industry belongs to a very large industry within a country. Therefore, the
construction industry contributes greatly to the national economy and is consistent in …
construction industry contributes greatly to the national economy and is consistent in …