Smart Short-Term Load Forecasting through Coordination of LSTM-Based Models and Feature Engineering Methods during the COVID-19 Pandemic

Short-term load forecasting is essential for power companies because it is necessary to ensure sufficient capacity. This article proposes a smart load forecasting scheme to forecast the short-term load for an actual sample network in the presence of uncertainties such as weather and the COVID-19 epidemic. The studied electric load data with hourly resolution from the beginning of 2020 to the first seven days of 2021 for the New York Independent Operator is the basis for the modeling. The new components used in this article include the coordination of stacked long short-term memory-based models and feature engineering methods. Also, more accurate and realistic modeling of the problem has been implemented according to the existing conditions through COVID-19 epidemic data. The influential variables for short-term load forecasting through various feature engineering methods have contributed to the problem. The achievements of this research include increasing the accuracy and speed of short-term electric load forecasting, reducing the probability of overfitting during model training, and providing an analytical comparison between different feature engineering methods. Through an analytical comparison between different feature engineering methods, the findings of this article show an increase in the accuracy and speed of short-term load forecasting. The results indicate that combining the stacked long short-term memory model and feature engineering methods based on extra-trees and principal component analysis performs well. The RMSE index for day-ahead load forecasting in the best engineering method for the proposed stacked long short-term memory model is 0.1071. © 2023 Taylor & Francis Group, LLC.

Using Machine Learning to Predict Business Survival in COVID-19

The global recession due to the pandemic has knocked the business landscape and brought the world to its knees. There were a number of renowned companies that made the headlines for being the top industry hard hits. Nonetheless, there were businesses that survived this pandemic and navigated the COVID complexities so effectively that it tipped the scales in their favor. We attempt to study the factors that helped these businesses masterfully work their way through the conundrums of coronavirus pandemic. We first build a dataset that entailed information pertinent to businesses and relevant COVID-related information that was sourced from Yelp and other platforms. We used a variety of classifiers to make predictions about the survival of these businesses followed by that after assessing their performance through varied methods. The model efficiency was classified based on several rating techniques to evaluate both underperforming and profitable businesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Feature Selection Mechanism for Attention Classification using Gaze Tracking Data

The Covid-19 outbreak has caused disruptions in the education sector, making remote education the dominant mode for lecture delivery. The lack of visual feedback and physical interaction makes it very hard for teachers to measure the engagement level of students during lectures. This paper proposes a time-bounded window operation to extract statistical features from raw gaze data, captured in a remote teaching experiment and link them with the student's attention level. Feature selection or dimensionality reduction is performed to reduce the convergence time and overcome the problem of over-fitting. Recursive feature elimination (RFE) and SelectFromModel (SFM) are used with different machine learning (ML) algorithms, and a subset of optimal feature space is obtained based on the feature scores. The model trained using the optimal feature subset showed significant improvement in accuracy and computational complexity. For instance, a support vector classifier (SVC) led 2.39% improvement in accuracy along with approximately 66% reduction in convergence time. © 2022 IEEE.

Using Machine Learning to Predict Business Survival in COVID-19

The global recession due to the pandemic has knocked the business landscape and brought the world to its knees. There were a number of renowned companies that made the headlines for being the top industry hard hits. Nonetheless, there were businesses that survived this pandemic and navigated the COVID complexities so effectively that it tipped the scales in their favor. We attempt to study the factors that helped these businesses masterfully work their way through the conundrums of coronavirus pandemic. We first build a dataset that entailed information pertinent to businesses and relevant COVID-related information that was sourced from Yelp and other platforms. We used a variety of classifiers to make predictions about the survival of these businesses followed by that after assessing their performance through varied methods. The model efficiency was classified based on several rating techniques to evaluate both underperforming and profitable businesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Automatic Exploration of Domain Knowledge in Healthcare

Throughout the years, healthcare has been one of the privileged areas to apply the information discovery process, empowering and supporting medical staff on their daily activities. One of the main reasons for its success is the availability of medical expertise, which can be incorporated in training models to reach higher levels of performance. While this has been done painfully and manually, during the preparation step, it has become hindered with the advent of AutoML. In this paper, we present the automation of data preparation and feature engineering, while exploring domain knowledge represented through extended entity-relationship (EER) diagrams. A COVID-19 case study shows that our automation outperforms existing AutoML tools, such as auto-sklearn [4], both in quality of the models and processing times. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Exploring Bioinspired Feature Engineering Technique for Online Hate Speech Detection

The spreading of hate speech and toxicity on social media and other online platforms has increased severely in the past decade. In the current scenario also, when the whole world is suffering with outspread of COVID-19 online hate speech spreading more than before. The spread of such hate can jeopardize the mental and physical health of many people and is thus necessary to stop its spread on online social media. This paper aims to explore bioinspired algorithms like PSO and GA to detect online hate speech on social media and other online platforms. We explore the hybrid feature selection approach to select valuable and meaningful features from the hate speech dataset to classify between hate and not hate posts efficiently. Our experiments indicate the random behavior of Particle Swarm Optimization and Genetic Algorithm and the decrease in accuracy when applied individually to the experiments. The proposed hybrid approach gives the comparative results as TF-IDF when applied with the baseline machine learning models. © 2022 IEEE.

Towards predicting COVID-19 trends: Feature engineering on social media responses

During the course of this pandemic, the use of social media and virtual networks have been at an all-time high. Individuals have used social media to express their thoughts on matters related to the pandemic. It is difficult to predict current trends based on historic case data because trends are more connected to social activities which can lead to the spread of coronavirus. So, it's important for us to derive meaningful information from social media as it is widely used. Therefore, we grouped tweets by common keywords, found correlations between keywords and daily COVID-19 statistics and built predictive modeling. The features correlation analysis was very effective, so trends were predicted very well. A RMSE score of 0.0425504, MAE of 0.03295105 and RSQ of 0.5237014 in relation to daily cases. In addition, we found a RMSE score of0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily deaths. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.