Industry 4.0 technologies and their applications in fighting COVID-19 pandemic using deep learning techniques

Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network

Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis for timely and effective treatment. The present diagnostic method, which involves a battery of tests and medical experts, is costly and time-consuming. For this reason, it is crucial to establish an automated diagnostic system for accurate predictions. A particular field of focus in medical research is the use of machine learning and leukemia microarray gene data for blood cancer diagnosis. Even with a great deal of research, more improvements are needed to reach the appropriate levels of accuracy and efficacy. This work presents a supervised machine-learning algorithm for blood cancer prediction. This work makes use of the 22,283-gene leukemia microarray gene data. Chi-squared (Chi2) feature selection methods and the synthetic minority oversampling technique (SMOTE)-Tomek resampling is used to overcome issues with imbalanced and high-dimensional datasets. To balance the dataset for each target class, SMOTE-Tomek creates synthetic data, and Chi2 chooses the most important features to train the learning models from 22,283 genes. A novel weighted convolutional neural network (CNN) model is proposed for classification, utilizing the support of three separate CNN models. To determine the importance of the proposed approach, extensive experiments are carried out on the datasets, including a performance comparison with the most advanced techniques. Weighted CNN demonstrates superior performance over other models when coupled with SMOTE-Tomek and Chi2 techniques, achieving a remarkable 99.9% accuracy. Results from k-fold cross-validation further affirm the supremacy of the proposed model.

Contribution to pulmonary diseases diagnostic from X-ray images using innovative deep learning models

Pulmonary disease identification and characterization are among the most intriguing research topics of recent years since they require an accurate and prompt diagnosis. Although pulmonary radiography has helped in lung disease diagnosis, the interpretation of the radiographic image has always been a major concern for doctors and radiologists to reduce diagnosis errors. Due to their success in image classification and segmentation tasks, cutting-edge artificial intelligence techniques like machine learning (ML) and deep learning (DL) are widely encouraged to be applied in the field of diagnosing lung disorders and identifying them using medical images, particularly radiographic ones. For this end, the researchers are concurring to build systems based on these techniques in particular deep learning ones. In this paper, we proposed three deep-learning models that were trained to identify the presence of certain lung diseases using thoracic radiography. The first model, named "CovCXR-Net", identifies the COVID-19 disease (two cases: COVID-19 or normal). The second model, named "MDCXR3-Net", identifies the COVID-19 and pneumonia diseases (three cases: COVID-19, pneumonia, or normal), and the last model, named "MDCXR4-Net", is destined to identify the COVID-19, pneumonia and the pulmonary opacity diseases (4 cases: COVID-19, pneumonia, pulmonary opacity or normal). These models have proven their superiority in comparison with the state-of-the-art models and reached an accuracy of 99,09 %, 97.74 %, and 90,37 % respectively with three benchmarks.

HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning

Industrial automation systems are undergoing a revolutionary change with the use of Internet-connected operating equipment and the adoption of cutting-edge advanced technology such as AI, IoT, cloud computing, and deep learning within business organizations. These innovative and additional solutions are facilitating Industry 4.0. However, the emergence of these technological advances and the quality solutions that they enable will also introduce unique security challenges whose consequence needs to be identified. This research presents a hybrid intrusion detection model (HIDM) that uses OCNN-LSTM and transfer learning (TL) for Industry 4.0. The proposed model utilizes an optimized CNN by using enhanced parameters of the CNN via the grey wolf optimizer (GWO) method, which fine-tunes the CNN parameters and helps to improve the model's prediction accuracy. The transfer learning model helps to train the model, and it transfers the knowledge to the OCNN-LSTM model. The TL method enhances the training process, acquiring the necessary knowledge from the OCNN-LSTM model and utilizing it in each next cycle, which helps to improve detection accuracy. To measure the performance of the proposed model, we conducted a multi-class classification analysis on various online industrial IDS datasets, i.e., ToN-IoT and UNW-NB15. We have conducted two experiments for these two datasets, and various performance-measuring parameters, i.e., precision, F-measure, recall, accuracy, and detection rate, were calculated for the OCNN-LSTM model with and without TL and also for the CNN and LSTM models. For the ToN-IoT dataset, the OCNN-LSTM with TL model achieved a precision of 92.7%; for the UNW-NB15 dataset, the precision was 94.25%, which is higher than OCNN-LSTM without TL.

Telemedicine in Primary Practice in the Age of the COVID-19 Pandemic-Review

Background and Objectives: In the era of the COVID-19 pandemic, telemedicine, so far underestimated, has gained in value. Currently, telemedicine is not only a telephone or chat consultation, but also the possibility of the remote recording of signals (such as ECG, saturation, and heart rate) or even remote auscultation of the lungs. The objective of this review article is to present a potential role for, and disseminate knowledge of, telemedicine during the COVID-19 pandemic. Material and Methods: In order to analyze the research material in accordance with PRISMA guidelines, a systematic search of the ScienceDirect, Web of Science, and PubMed databases was conducted. Out of the total number of 363 papers identified, 22 original articles were subjected to analysis. Results: This article presents the possibilities of remote patient registration, which contributes to an improvement in remote diagnostics and diagnoses. Conclusions: Telemedicine is, although not always and not by everyone, an accepted form of providing medical services. It cannot replace direct patient-doctor contact, but it can undoubtedly contribute to accelerating diagnoses and improving their quality at a distance.

Towards intelligent supervision of operating rooms using stencil-based character recognition

The development of intelligent operating rooms is an example of a cyber-physical system resulting from the symbiosis of Industry 4.0 and medicine. A problem with this type of systems is that it requires demanding solutions that allow the real time acquisition of heterogeneous data in an efficient way. The aim of the presented work is the development of a data acquisition system, based on a real-time artificial vision algorithm which can capture information from different clinical monitors. The system was designed for the registration, pre-processing, and communication of clinical data recorded in an operating room. The methods for this proposal are based on a mobile device running a Unity application, which extracts information from clinical monitors and transmits the data to a supervision system through a wireless Bluetooth connection. The software implements a character detection algorithm and allows online correction of identified outliers. The results validate the system with real data obtained during surgical interventions, where only 0.42% values were missed and 0.89% were misread. The outlier detection algorithm was able to correct all the reading errors. In conclusion, the development of a low-cost compact solution to supervise operating rooms in real-time, collecting visual information non-intrusively and communicating data wirelessly, can be a very useful tool to overcome the lack of expensive data recording and processing technology in many clinical situations. The acquisition and pre-processing method presented in this article constitutes a key element towards the development of a cyber-physical system for the development of intelligent operating rooms.

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

According to the World Health Organization, millions of infections and a lot of deaths have been recorded worldwide since the emergence of the coronavirus disease (COVID-19). Since 2020, a lot of computer science researchers have used convolutional neural networks (CNNs) to develop interesting frameworks to detect this disease. However, poor feature extraction from the chest X-ray images and the high computational cost of the available models introduce difficulties for an accurate and fast COVID-19 detection framework. Moreover, poor feature extraction has caused the issue of 'the curse of dimensionality', which will negatively affect the performance of the model. Feature selection is typically considered as a preprocessing mechanism to find an optimal subset of features from a given set of all features in the data mining process. Thus, the major purpose of this study is to offer an accurate and efficient approach for extracting COVID-19 features from chest X-rays that is also less computationally expensive than earlier approaches. To achieve the specified goal, we design a mechanism for feature extraction based on shallow conventional neural network (SCNN) and used an effective method for selecting features by utilizing the newly developed optimization algorithm, Q-Learning Embedded Sine Cosine Algorithm (QLESCA). Support vector machines (SVMs) are used as a classifier. Five publicly available chest X-ray image datasets, consisting of 4848 COVID-19 images and 8669 non-COVID-19 images, are used to train and evaluate the proposed model. The performance of the QLESCA is evaluated against nine recent optimization algorithms. The proposed method is able to achieve the highest accuracy of 97.8086% while reducing the number of features from 100 to 38. Experiments prove that the accuracy of the model improves with the usage of the QLESCA as the dimensionality reduction technique by selecting relevant features.

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Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic

Understanding imaging research experiences, challenges, and strategies for academic radiology departments during and after COVID-19 is critical to prepare for future disruptive events. We summarize key insights and programmatic initiatives at major academic hospitals across the world, based on literature review and meetings of the Radiological Society of North America Vice Chairs of Research (RSNA VCR) group. Through expert discussion and case studies, we provide suggested guidelines to maintain and grow radiology research in the postpandemic era.

LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet.

ETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification

Pandemics influence people negatively and people experience fear and disappointment. With the global outspread of COVID-19, the sentiments of the general public are substantially influenced, and analyzing their sentiments could help to devise corresponding policies to alleviate negative sentiments. Often the data collected from social media platforms is unstructured leading to low classification accuracy. This study brings forward an ensemble model where the benefits of handcrafted features and automatic feature extraction are combined by machine learning and deep learning models. Unstructured data is obtained, preprocessed, and annotated using TextBlob and VADER before training machine learning models. Similarly, the efficiency of Word2Vec, TF, and TF-IDF features is also analyzed. Results reveal the better performance of the extra tree classifier when trained with TF-IDF features from TextBlob annotated data. Overall, machine learning models perform better with TF-IDF and TextBlob. The proposed model obtains superior performance using both annotation techniques with 0.97 and 0.95 scores of accuracy using TextBlob and VADER respectively with Word2Vec features. Results reveal that use of machine learning and deep learning models together with a voting criterion tends to yield better results than other machine learning models. Analysis of sentiments indicates that predominantly people possess negative sentiments regarding COVID-19.

A novel approach for COVID-19 Infection forecasting based on multi-source deep transfer learning

COVID-19 is a contagious disease; so, predicting its future infections in a provincial region requires the consideration of the related data (i.e., rates of infection, mortality and recovery, etc.) over a period of time. Clearly, the COVID-19 data of a particular provincial region can be easily modelled as a time-series. However, predicting the future COVID-19 infections in a particular region is quite challenging when the availability of COVID-19 dataset of the province is of little quantity. Accordingly, ML models when deployed for such tasks usually results in low infection prediction accuracy. To overcome such issues of low variance and high bias in a model due to data scarcity, multi-source transfer learning (MSTL) along with deep learning may be quite useful and effective. Therefore, this paper proposes a novel technique based on multi-source deep transfer learning (MSDTL) to efficiently forecast the future COVID-19 infections in the provinces with insufficient COVID-19 data. The proposed approach is a novel contribution as it considers the fact that future COVID-19 transmission in a region also depends on its population density and economic conditions (GDP) for accurate forecasting of the infections to tackle the pandemic efficiently. The importance of this feature selection is experimentally proved in this paper. Our proposed approach employs the well-known recurrent neural network architecture, the Long-short term memory (LSTM), a popular deep-learning model for history-dependent tasks. A comparative analysis has been performed with existing state-of-art algorithms to portray the efficiency of LSTM. Thus, formation of MSDTL approach enhances the predictive precision capability of the LSTM. We evaluate the proposed methodology over the COVID-19 dataset from sixty-two provinces belonging to different nations. We then empirically evaluate the performance of the proposed approach using two different evaluation metrics, viz. The mean absolute percentage error and the coefficient of determination. We show that our proposed MSDTL based approach is better in terms of the accuracy of the future infection prediction, and produces improvements up to 96% over its without-TL counterpart.

The changing role of innovation for crisis management in times of COVID-19: An integrative literature review

Since the outbreak of the COVID-19 pandemic, countries have concentrated on developing policies that encourage the creation of more innovative products and services in response to the global health emergency. Effective collaboration, communication, and Open Innovation (OI) among government entities, education and research institutions, and the private sector have been critical to each country's overall effectiveness during the economic crisis. The objective of this paper is to examine the relationship between innovation and COVID-19 critically to have a better understanding of future research and practice developments. A systematic evaluation was conducted, analyzing papers on innovation and the COVID-19 pandemic. A total of 218 studies were analyzed to determine the essential research directions in this domain. Our suggested framework is made of aggregate components, which include technology adaption, sustainable development, healthcare, and sustainable economic performance. These components form the basis for the identification of emerging research hotspots in the field of COVID and innovation, as well as frame the COVID-19 issue as an opportunity to raise awareness about the crucial role of innovation in business and society at large.