Monitoring the Trend of Energy Consumption during the Pandemic

The COVID-19 pandemic had a tremendous impact on socioeconomics and directly impacted the electrical system. In Malaysia, Grid System Operators (GSOs) were found to lack detailed information to differentiate the total energy demand before and during a pandemic. Working from home during the pandemic has changed the way of life and daily energy management methods for the domestic sector. This paper aims to study the national energy demand during the pandemic and then look into domestic energy management. The study included 3 phases. Phase 1 involved the analysis of data from the GSO to identify differences in energy demand before and during the pandemic. Next, in phase 2, a survey will be conducted on the energy management of the domestic sector. Finally, phase 3 involves household energy-saving proposals through examples of structural improvements. During the 2020 Movement Control Order (MCO) in Malaysia, the average total decrease in energy demand compared to 2019 was 15.82%. This high percentage is due to the closure of several economic sectors, such as trade and industry. From the survey, 88 110 respondents reported that domestic electricity bills increased during the MCO. Statistical analysis using ANOVA indicated no significant link between age range and behavior, knowledge, and total bills paid by respondents. Furthermore, this study also suggested structural upgrades incorporating 5-star air conditioning that can save RM389.47 per year, which will take 4.78 years to repay. This study concluded with suggestions on changes that can be implemented to aid homeowners with energy savings. © 2022 IEEE.

ECG- Based Emotion Detection via Parallel- Extraction of Temporal and Spatial Features Using Convolutional Neural Network

Emotion detection from an ECG signal allows the direct assessment of the inner state of a human. Because ECG signals contain nerve endings from the autonomic nervous system that controls the behavior of each emotion. Besides, emotion detection plays a vital role in the daily activities of human life, where we lately witnessed the outbreak of the (COVID-19) pandemic that has a bad influence on the affective states of humans. Therefore, it has become indispensable to build an intelligent system capable of predicting and classifying emotions in their early stages. Accordingly, in this study, the Parallel-Extraction of Temporal and Spatial Features using Convolutional Neural Network (PETSFCNN) is established. So, in-depth features of the ECG signals are extracted and captured from the suggested parallel 2-channel structure of 1-dimensional CNN network and 2-dimensional CNN network and then combined by feature fusion technique for more dependable classification results. Besides, Grid Search Optimized-Deep Neural Network (GSO-DNN) is adopted for higher classification accuracy. To verify the performance of the proposed method, our experiment was implemented on two different datasets. The maximum classification accuracy of 97.56% and 96.34% on both valence and arousal were gained, respectively using the internationally approved DREAMER dataset. While the same model on the private dataset achieved 76.19% for valence and 80.95% for arousal respectively. The classification results of the PETSFCNN-GSO-DNN model are compared with state-of-the-art methods. The empirical findings reveal that the proposed method can detect emotions from ECG signals more accurately and better than state-of-the-art methods and has the potential to be implemented as an intelligent system for affect detection.

An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification.

Recently, the COVID-19 epidemic has had a major impact on day-to-day life of people all over the globe, and it demands various kinds of screening tests to detect the coronavirus. Conversely, the development of deep learning (DL) models combined with radiological images is useful for accurate detection and classification. DL models are full of hyperparameters, and identifying the optimal parameter configuration in such a high dimensional space is not a trivial challenge. Since the procedure of setting the hyperparameters requires expertise and extensive trial and error, metaheuristic algorithms can be employed. With this motivation, this paper presents an automated glowworm swarm optimization (GSO) with an inception-based deep convolutional neural network (IDCNN) for COVID-19 diagnosis and classification, called the GSO-IDCNN model. The presented model involves a Gaussian smoothening filter (GSF) to eradicate the noise that exists from the radiological images. Additionally, the IDCNN-based feature extractor is utilized, which makes use of the Inception v4 model. To further enhance the performance of the IDCNN technique, the hyperparameters are optimally tuned using the GSO algorithm. Lastly, an adaptive neuro-fuzzy classifier (ANFC) is used for classifying the existence of COVID-19. The design of the GSO algorithm with the ANFC model for COVID-19 diagnosis shows the novelty of the work. For experimental validation, a series of simulations were performed on benchmark radiological imaging databases to highlight the superior outcome of the GSO-IDCNN technique. The experimental values pointed out that the GSO-IDCNN methodology has demonstrated a proficient outcome by offering a maximal sensy of 0.9422, specy of 0.9466, precn of 0.9494, accy of 0.9429, and F1score of 0.9394.