Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44\%, 85.47\%, 85.40\%, and 87.13\%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening. 

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Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches

COVID-19 has claimed several human lives to this date. People are dying not only because of physical infection of the virus but also because of mental illness, which is linked to people's sentiments and psychologies. People's written texts/posts scattered on the web could help understand their psychology and the state they are in during this pandemic. In this paper, we analyze people's sentiment based on the classification of tweets collected from the social media platform, Twitter, in Nepal. For this, we, first, propose to use three different feature extraction methods-fastText-based (ft), domain-specific (ds), and domain-agnostic (da)-for the representation of tweets. Among these three methods, two methods ("ds" and "da") are the novel methods used in this study. Second, we propose three different convolution neural networks (CNNs) to implement the proposed features. Last, we ensemble such three CNNs models using ensemble CNN, which works in an end-to-end manner, to achieve the end results. For the evaluation of the proposed feature extraction methods and CNN models, we prepare a Nepali Twitter sentiment dataset, called NepCOV19Tweets, with 3 classes (positive, neutral, and negative). The experimental results on such dataset show that our proposed feature extraction methods possess the discriminating characteristics for the sentiment classification. Moreover, the proposed CNN models impart robust and stable performance on the proposed features. Also, our dataset can be used as a benchmark to study the COVID-19-related sentiment analysis in the Nepali language.

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Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets.

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach-parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion.

https://doi.org/10.1016/j.compbiomed.2022.105350

COVID-19 image classification using deep learning: Advances, challenges and opportunities

Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a large number of training parameters. There is a necessity to explore lightweight deep learning models without compromising the classification accuracy. In this paper, we propose a lightweight deep learning model using the pre-trained MobileNetV2 model and attention module. First, the convolution features are extracted to capture the high-level object-based information. Second, an attention module is used to capture the interesting semantic information. The convolution and attention modules are then combined together to fuse both the high-level object-based information and the interesting semantic information, which is followed by the fully connected layers and the softmax layer. Evaluation of our proposed method, which leverages transfer learning approach, on three public fruit-related benchmark datasets shows that our proposed method outperforms the four latest deep learning methods with a smaller number of trainable parameters and a superior classification accuracy. Our model has a great potential to be adopted by industries closely related to the fruit growing and retailing or processing chain for automatic fruit identification and classifications in the future.

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Fruit classification using attention-based MobileNetV2 for industrial applications

Computer-aided diagnosis (CAD) methods such as Chest X-rays (CXR)-based method is one of the cheapest alternative options to diagnose the early stage of COVID-19 disease compared to other alternatives such as Polymerase Chain Reaction (PCR), Computed Tomography (CT) scan, and so on. To this end, there have been few works proposed to diagnose COVID-19 by using CXR-based methods. However, they have limited performance as they ignore the spatial relationships between the region of interests (ROIs) in CXR images, which could identify the likely regions of COVID-19’s effect in the human lungs. In this paper, we propose a novel attention-based deep learning model using the attention module with VGG-16. By using the attention module, we capture the spatial relationship between the ROIs in CXR images. In the meantime, by using an appropriate convolution layer (4th pooling layer) of the VGG-16 model in addition to the attention module, we design a novel deep learning model to perform fine-tuning in the classification process. To evaluate the performance of our method, we conduct extensive experiments by using three COVID-19 CXR image datasets. The experiment and analysis demonstrate the stable and promising performance of our proposed method compared to the state-of-the-art methods. The promising classification performance of our proposed method indicates that it is suitable for CXR image classification in COVID-19 diagnosis.

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Attention-based VGG-16 model for COVID-19 chest X-ray image classification

Due to the rapid growth of smart grids, use of smart meters (SMs) have increased in the recent days. The main problem with the use of SMs is that by observing the SMs reading, it is possible to infer the daily activities of the consumers. Therefore, protection of privacy is a major concern related to SMs. Using rechargeable batteries (RBs) is a popular method in protecting the privacy in SMs as these methods do not tamper with SM readings. The major problem in RB-based mechanism is that the energy management unit (EMU) cannot protect privacy, if the demand is lower or higher for a longer period. To overcome this problem, in this paper a heuristic method has been proposed by considering time varying target output load based on the three major properties of artificial fish swarm optimization algorithm. For the optimal choice of the time varying target output load, RB constraints as well as reduction of the average cost of energy have been considered in our proposed method. We have proposed two privacy preserving mechanisms for both offline and online scenarios. The proposed method preserves privacy while reducing the cost of energy. Simulation results show that the proposed method is able to provide privacy by overcoming the problem identified in the existing methods.

Enhanced Smart Meter Privacy Protection Using Rechargeable Batteries

Usage of smart meters (SMs) have significantly increased in the recent days due to the advantages they offer. However, it is possible for an adversary to extract private information about a consumer by observing the SM readings. Therefore, it is important to protect the privacy of consumers using SMs. Among the SM privacy protection mechanisms, rechargeable battery (RB)-based mechanisms are preferred as they do not alter SM readings. The existing mechanisms cannot protect the privacy when the consumer energy usage is either low or high for a longer period. Furthermore, these mechanisms do not perform well in online scenario where the energy management unit (EMU) only knows the current and past consumer energy demands. To solve this problem, in this article, we proposed a novel online privacy protection mechanism to protect the privacy of SMs using a progressive average-based algorithm (PABA). The proposed PABA uses two uniquely designed algorithms to ensure protection of privacy and energy cost reduction during peak and off-peak periods. Moreover, an adaptive output smoothing technique is used to further enhance the privacy. Compared with the privacy protection mechanisms designed for tackling SM privacy, the proposed mechanism achieves higher amount of privacy while reducing the energy cost. The validity of the proposed privacy enhancement mechanism is demonstrated by simulation results.

Progressive Average-Based Smart Meter Privacy Enhancement Using Rechargeable Batteries

In this work, the optimization of an off-grid micro-hybrid system is evaluated. This is conducted with the estimation of the proper sizing of each element and the steady-state voltage, frequency, and power responses of the microgrid. Kangaroo Island in South Australia is considered to be the test case location and the grid incorporates solar PV (photo-voltaic), diesel generator, battery storage, and wind turbine. Optimal sizing of the studied microgrid is carried out for four various power dispatch techniques: (i) cycle charging (CC), (ii) generator order (GO), (iii) load following (LF), and (iv) combined dispatch (CD). The proposed off-grid micro-hybrid is optimized for three performance indices; minimal Levelized Cost of Energy (LCOE), CO2 emission, and Net Present Cost (NPC). Using iHOGA (improved hybrid optimization by genetic algorithm), microgrid optimization software, all the above-mentioned dispatch strategies have been implemented and following this, MATLAB/Simulink platform has been used for the steady-state studies. The results show that the LF strategy is the utmost optimum dispatch technique in terms of the studied performance indices i.e. considering the optimal size and voltage and frequency responses. The results obtained from these studies provide a pathway for the estimation of the resource-generation-load combination for the islanded off-grid microgrid for its optimal operation with the various dispatch strategies.

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Assortment of Dispatch Strategies with the Optimization of an Islanded Hybrid Microgrid

Wireless charging vehicle to grid (V2G) system is not-so-futuristic. It can maintain the power supply balance and stabilize the grid through a wireless link between vehicles using batteries based on energy supply-demand statistics. Most of the existing privacy preservation methods are based on data tampering approaches (e.g., encryption, adding noise, aggregation), which are not practicable over V2G due to complexity in billing and inaccurate state estimation. In this paper, we develop a novel adaptive demand-side energy management framework by employing federated learning-based privacy preservation for the wireless charging V2G systems. Our framework learns the temporal evolution of energy consumption of dynamic charging electric vehicles in a distributed fashion and exploits the reinforcement learning model for cost-saving and reward maximization. The convergence and the privacy preservation properties of the framework are demonstrated with extensive evaluations.

Mohammad Belayet Hossain

Papers

Attention-based VGG-16 model for COVID-19 chest X-ray image classification

Enhanced Smart Meter Privacy Protection Using Rechargeable Batteries

Progressive Average-Based Smart Meter Privacy Enhancement Using Rechargeable Batteries

Assortment of Dispatch Strategies with the Optimization of an Islanded Hybrid Microgrid

Data Privacy of Wireless Charging Vehicle to Grid (V2G) Networks With Federated Learning