A novel and accurate deep learning-based Covid-19 diagnostic model for heart patients.

Using radiographic changes of COVID-19 in the medical images, artificial intelligence techniques such as deep learning are used to extract some graphical features of COVID-19 and present a Covid-19 diagnostic tool. Differently from previous works that focus on using deep learning to analyze CT scans or X-ray images, this paper uses deep learning to scan electro diagram (ECG) images to diagnose Covid-19. Covid-19 patients with heart disease are the most people exposed to violent symptoms of Covid-19 and death. This shows that there is a special, unclear relation (until now) and parameters between covid-19 and heart disease. So, as previous works, using a general diagnostic model to detect covid-19 from all patients, based on the same rules, is not accurate as we prove later in the practical section of our paper because the model faces dispersion in the data during the training process. So, this paper aims to propose a novel model that focuses on diagnosing accurately Covid-19 for heart patients only to increase the accuracy and to reduce the waiting time of a heart patient to perform a covid-19 diagnosis. Also, we handle the only one existed dataset that contains ECGs of Covid-19 patients and produce a new version, with the help of a heart diseases expert, which consists of two classes: ECGs of heart patients with positive Covid-19 and ECGs of heart patients with negative Covid-19 cases. This dataset will help medical experts and data scientists to study the relation between Covid-19 and heart patients. We achieve overall accuracy, sensitivity and specificity 99.1%, 99% and 100%, respectively. Supplementary Information: The online version contains supplementary material available at 10.1007/s11760-023-02561-8.

A new metaheuristic optimization model for financial crisis prediction: Towards sustainable development

Global crises such as the COVID-19 pandemic and other recent environmental, financial, and economic disasters have weakened economies around the world and marginalized efforts to build a sustainable economy and society. Financial crisis prediction (FCP) has a significant impact on the economy. The growth and strength of a country's economy can be gauged by accurately predicting how many companies will fail and how many will succeed. Traditionally, there have been a number of approaches to achieving a successful FCP. Despite this, there is a problem with the accuracy of classification and prediction and with the legality of the data that is being used. Earlier studies have focused on statistical, machine learning (ML), and deep learning (DL) models to predict the financial status of a company. One of the biggest limitations of most machine learning models is model training with hyper-parameter fine-tuning. With this motivation, this paper presents an outlier detection model for FCP using a political optimizer-based deep neural network (OD-PODNN). The OD-PODNN aims to determine the financial status of a firm or company by involving several processes, namely preprocessing, outlier detection, classification, and hyperparameter optimization. The OD-PODNN makes use of the isolation forest (iForest) based outlier detection approach. Moreover, the PODNN-based classification model is derived, and the DNN hyperparameters are fine-tuned to boost the overall classification accuracy. To evaluate the OD-PODNN model, three different datasets are used, and the outcomes are inspected under varying performance measures. The results confirmed the superiority of the proposed OD-PODNN methodology over recent approaches.

A novel and accurate deep learning based Covid-19 diagnostic model for heart patients only (preprint)

Using radiographic changes of COVID-19 in the medical images, artificial intelligence techniques such as deep learning are used to extract some graphical features of COVID-19 and present a Covid-19 diagnostic tool. Differently from previous works that focus on using deep learning to analyze CT scans or X-ray images, this paper uses deep learning to scan electro diagram (ECG) images to diagnose Covid-19. Covid-19 patients with heart disease are the most people exposed to violent symptoms of Covid-19 and death. This shows that there is a special, unclear relation (until now) and parameters between covid-19 and heart disease. So, as previous works, using a general diagnostic model to detect covid-19 from all patients, based on the same rules, is not accurate as we prove later in the practical section of our paper because the model faces dispersion in the data during the training process. So, this paper aims to propose a novel model that focuses on diagnosing accurately Covid-19 for heart patients only to increase the accuracy and to reduce the waiting time of a heart patient to perform a covid-19 diagnosis. Also, we handle the only one existed dataset that contains ECGs of Covid-19 patients and produce a new version, with the help of a heart diseases expert, which consists of two classes: ECGs of heart patients with positive Covid-19 and ECGs of heart patients with negative Covid-19 cases. This dataset will help medical experts and data scientists to study the relation between Covid-19 and heart patients. We achieve overall accuracy, sensitivity and specificity 99%, 98.67% and 100%, respectively.

Advanced Cognitive Algorithm for Biomedical Data Processing: COVID-19 Pattern Recognition as a Case Study.

Automated disease prediction has now become a key concern in medical research due to exponential population growth. The automated disease identification framework aids physicians in diagnosing disease, which delivers accurate disease prediction that provides rapid outcomes and decreases the mortality rate. The spread of Coronavirus disease 2019 (COVID-19) has a significant effect on public health and the everyday lives of individuals currently residing in more than 100 nations. Despite effective attempts to reach an appropriate trend to forecast COVID-19, the origin and mutation of the virus is a crucial obstacle in the diagnosis of the detected cases. Even so, the development of a model to forecast COVID-19 from chest X-ray (CXR) and computerized tomography (CT) images with the correct decision is critical to assist with intelligent detection. In this paper, a proposed hybrid model of the artificial neural network (ANN) with parameters optimization by the butterfly optimization algorithm has been introduced. The proposed model was compared with the pretrained AlexNet, GoogLeNet, and the SVM to identify the publicly accessible COVID-19 chest X-ray and CT images. There were six datasets for the examinations: three datasets with X-ray pictures and three with CT images. The experimental results approved the superiority of the proposed model for cognitive COVID-19 pattern recognition with average accuracy 90.48, 81.09, 86.76, and 84.97% for the proposed model, support vector machine (SVM), AlexNet, and GoogLeNet, respectively.

Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment.

In the current pandemic, smart technologies such as cognitive computing, artificial intelligence, pattern recognition, chatbot, wearables, and blockchain can sufficiently support the collection, analysis, and processing of medical data for decision making. Particularly, to aid medical professionals in the disease diagnosis process, cognitive computing is helpful by processing massive quantities of data rapidly and generating customized smart recommendations. On the other hand, the present world is facing a pandemic of COVID-19 and an earlier detection process is essential to reduce the mortality rate. Deep learning (DL) models are useful in assisting radiologists to investigate the large quantity of chest X-ray images. However, they require a large amount of training data and it needs to be centralized for processing. Therefore, federated learning (FL) concept can be used to generate a shared model with no use of local data for DL-based COVID-19 detection. In this view, this paper presents a federated deep learning-based COVID-19 (FDL-COVID) detection model on an IoT-enabled edge computing environment. Primarily, the IoT devices capture the patient data, and then the DL model is designed using the SqueezeNet model. The IoT devices upload the encrypted variables into the cloud server which then performs FL on major variables using the SqueezeNet model to produce a global cloud model. Moreover, the glowworm swarm optimization algorithm is utilized to optimally tune the hyperparameters involved in the SqueezeNet architecture. A wide range of experiments were conducted on benchmark CXR dataset, and the outcomes are assessed with respect to different measures . The experimental outcomes pointed out the enhanced performance of the FDL-COVID technique over the other methods.

DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing.

The rapid spread of novel coronavirus pneumonia (COVID-19) has led to a dramatically increased mortality rate worldwide. Despite many efforts, the rapid development of an effective vaccine for this novel virus will take considerable time and relies on the identification of drug-target (DT) interactions utilizing commercially available medication to identify potential inhibitors. Motivated by this, we propose a new framework, called DeepH-DTA, for predicting DT binding affinities for heterogeneous drugs. We propose a heterogeneous graph attention (HGAT) model to learn topological information of compound molecules and bidirectional ConvLSTM layers for modeling spatio-sequential information in simplified molecular-input line-entry system (SMILES) sequences of drug data. For protein sequences, we propose a squeezed-excited dense convolutional network for learning hidden representations within amino acid sequences; while utilizing advanced embedding techniques for encoding both kinds of input sequences. The performance of DeepH-DTA is evaluated through extensive experiments against cutting-edge approaches utilising two public datasets (Davis, and KIBA) which comprise eclectic samples of the kinase protein family and the pertinent inhibitors. DeepH-DTA attains the highest Concordance Index (CI) of 0.924 and 0.927 and also achieved a mean square error (MSE) of 0.195 and 0.111 on the Davis and KIBA datasets respectively. Moreover, a study using FDA-approved drugs from the Drug Bank database is performed using DeepH-DTA to predict the affinity scores of drugs against SARS-CoV-2 amino acid sequences, and the results show that that the model can predict some of the SARS-Cov-2 inhibitors that have been recently approved in many clinical studies.

Security and Privacy Issues in Medical Internet of Things: Overview, Countermeasures, Challenges and Future Directions

The rapid development and the expansion of Internet of Things (IoT)-powered technologies have strengthened the way we live and the quality of our lives in many ways by combining Internet and communication technologies through its ubiquitous nature. As a novel technological paradigm, this IoT is being served in many application domains including healthcare, surveillance, manufacturing, industrial automation, smart homes, the military, etc. Medical Internet of Things (MIoT), or the use of IoT in healthcare, is becoming a booming trend towards improving the health and wellbeing of billions of people by offering smooth and seamless medical facilities and by enhancing the services provided by medical practitioners, nurses, pharmaceutical companies, and other related government and non-government organizations. In recent times, this MIoT has gained higher attention for its potential to alleviate the massive burden on global healthcare, which has been caused by the rise of chronic diseases, the aging population, and emergency situations such as the recent COVID-19 global pandemic, where many government and non-government medical resources were challenged, owing to the rising demand for medical resources. It is evident that with this recent growing demand for MIoT, the associated technologies and its interconnected, heterogeneous nature adds new concerns as it becomes accessible to confidential patient data, often without patient or the medical staff consciousness, as the security and privacy of MIoT devices and technologies are often overlooked and undermined by relevant stakeholders. Hence, the growing security breaches that target the MIoT in healthcare are making the security and privacy of Medical IoT a crucial topic that is worth scrutinizing. In this study, we examined the current state of security and privacy of the MIoT, which has become of utmost concern among many security experts and researchers due to its rapid demand in recent times. Nevertheless, pertaining to the current state of security and privacy, we also examine and discuss a number of attack use cases, countermeasures and solutions, recent challenges, and anticipated future directions where further attention is required through this study.

Hybrid Computational Intelligence Algorithm for Autonomous Handling of COVID-19 Pandemic Emergency in Smart Cities.

New cities exploit the smartness of the IoT-based architecture to run their vital and organizational processes. The smart response of pandemic emergency response services needs optimizing methodologies of caring and limit infection without direct connection with patients. In this paper, a hybrid Computational Intelligence (CI) algorithm called Moth-Flame Optimization and Marine Predators Algorithms (MOMPA) is proposed for planning the COVID-19 pandemic medical robot's path without collisions. MOMPA is validated on several benchmarks and compared with many CI algorithms. The results of the Friedman Ranked Mean test indicate the proposed algorithm can find the shortest collision-free path in almost all test cases. In addition, the proposed algorithm reaches an almost %100 success ratio for solving all test cases without constraint violation of the regarded problem. After the validation experiment, the proposed algorithm is applied to smart medical emergency handling in Egypt's New Galala mountainous city. Both experimental and statistical results ensure the prosperity of the proposed algorithm. Also, it ensures that MOMPA can efficiently find the shortest path to the emergency location without any collisions.

A hybrid feature selection model based on butterfly optimization algorithm: COVID-19 as a case study.

The need to evolve a novel feature selection (FS) approach was motivated by the persistence necessary for a robust FS system, the time-consuming exhaustive search in traditional methods, and the favourable swarming manner in various optimization techniques. Most of the datasets have a high dimension in many issues since all features are not crucial to the problem, which reduces the algorithm's accuracy and efficiency. This article presents a hybrid feature selection approach to solve the low precision and tardy convergence of the butterfly optimization algorithm (BOA). The proposed method is dependent on combining the algorithm of BOA and the particle swarm optimization (PSO) as a search methodology using a wrapper framework. BOA is started with a one-dimensional cubic map in the proposed approach, and a non-linear parameter control technique is also implemented. To boost the basic BOA for global optimization, PSO algorithm is mixed with the butterfly optimization algorithm (BOAPSO). A 25 dataset evaluates the proposed BOAPSO to determine its efficiency with three metrics: classification precision, the selected features, and the computational time. A COVID-19 dataset has been used to evaluate the proposed approach. Compared to the previous approaches, the findings show the supremacy of BOAPSO for enhancing performance precision and minimizing the number of chosen features. Concerning the accuracy, the experimental outcomes demonstrate that the proposed model converges rapidly and performs better than with the PSO, BOA, and GWO with improvement percentages: 91.07%, 87.2%, 87.8%, 87.3%, respectively. Moreover, the proposed model's average selected features are 5.7 compared to the PSO, BOA, and GWO, with average features 22.5, 18.05, and 23.1, respectively.

A model for the effective COVID-19 identification in uncertainty environment using primary symptoms and CT scans.

The rapid spread of the COVID-19 virus around the world poses a real threat to public safety. Some COVID-19 symptoms are similar to other viral chest diseases, which makes it challenging to develop models for effective detection of COVID-19 infection. This article advocates a model to differentiate between COVID-19 and other four viral chest diseases under uncertainty environment using the viruses primary symptoms and CT scans. The proposed model is based on a plithogenic set, which provides higher accurate evaluation results in an uncertain environment. The proposed model employs the best-worst method (BWM) and the technique in order of preference by similarity to ideal solution (TOPSIS). Besides, this study discusses how smart Internet of Things technology can assist medical staff in monitoring the spread of COVID-19. Experimental evaluation of the proposed model was conducted on five different chest diseases. Evaluation results demonstrate that the proposed model effectiveness in detecting the COVID-19 in all five cases achieving detection accuracy of up to 98%.