Automated COVID-19 screening framework via Deep Convolutional Neural Network with Chest X-ray Medical Images

COVID-19 screening using chest X-rays plays a significant role in the early diagnosis of COVID-19 illness during the ongoing pandemic. Manually identifying this infection from chest X-ray films is a challenging and time-consuming technique due to time restrictions and the competence of radiologists. Also, the manual Covid-19 identification technique is made much more difficult and opaquer by the feature similarity between positive and negative chest X-ray images. Therefore, we propose an automated COVID-19 screening framework that utilizes artificial intelligence techniques with a transfer learning approach for COVID-19 diagnosis using chest X-ray images. Specifically, we employ the transfer learning concept for feature extraction before further processing with modified deep neural networks. Also, Grad-CAM visualization is used for our case study to support the predicted diagnosis. The results of the experiments on the publicly accessible dataset show that the convolutional neural network model, which is simple yet effective, performs significantly better than other deep learning techniques across all metrics, including accuracy, precision, recall, and F-measure. © 2022 IEEE.

A New Convolution Neural Layer Based on Weights Constraints

With the rapid development of artificial intelligence techniques, emerging deep neural networks (DNN) is one of the most effective ways to solve many challenges. Convolution neural networks (CNNs) are considered one of the most popular AI techniques used to extract and analyze meaningful features for image datasets, especially in the medical diagnosis field. In this paper, a proposed constrained convolution layer (COCL) for the CNN model is proposed. The new layer uses a constrained number of weights in each kernel trained in the phase of learning and excludes the others weights with zero values. The proposed method is introduced to extract a special type of feature considering the local shape of a sub-image (window) and topological relations between group pixels. The features extract according to a random distribution of weights in kernels that are determined considering a particular desired percentage. Furthermore, this paper proposed a CNN model architecture that uses COCL rather than the traditional CNN layer (TCL). The efficiency of the method is evaluated using three types of medical image datasets compared with the traditional convolution layer, pre-trained deep neural networks (pre-DNNs), and state-of-art methods. The proposed model outperforms other methods in terms of accuracy and F1 score metrics and exceeds more than 98%, 89%, and 93% for the three datasets used in the evaluation, respectively. © 2022 IEEE.

Text Based Diagnosis of COVID-19 Using Data Mining Techniques: A Comparative Study

In the course of the recent pandemic, we have witnessed non-clinical approaches such as data mining and artificial intelligence techniques being exceedingly utilized to restrain and combat the increase of COVID-19 across the globe. The emergence of artificial intelligence in the medical field has helped in reducing the immense burden on medical systems by providing the best means for diagnosis and prognosis of COVID-19. This work attempts to analyze & evaluate superlative models on robust data resources on symptoms of COVID-19, consisting of age, gender, demographic information, pre-existing medical conditions, and symptoms experienced by patients. This study establishes paradigmatic pipeline of supervised learning algorithms coupled with feature extraction techniques and surpassing the current state-of-the-art results by achieving an accuracy of 93.360. The optimal score was found by performing feature extraction on the data using principal component analysis (PCA) followed by binary classification using the AdaBoost classifier. In addition, the present study also establishes the contribution of various symptoms in the diagnosis of the malady. © 2022 IEEE.

CNN-Based Deep Features with Ensemble Learning for COVID-19 Classification

In this paper, we propose a hybrid system that can automatically detect coronavirus disease and speed up medical image analysis processes by using artificial intelligence technique. Our system consists of two parts: First, to perform feature extraction, we used a deep convolutional network that is based on the transfer learning technique, in this step, we include eight well-known convolutional neural networks for comparison purposes. In the second part, a voting classifier is considered, combining three classifiers, including random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN), to classify radiological images into three classes: COVID-19, normal, and pneumonia, collected from two public medical repositories. The results show that deep learning and radiological images are able to retrieve relevant COVID-19 features with an accuracy of 96.87%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Diagnosis of COVID-19 Using Artificial Intelligence Techniques

Artificial intelligence is being used in a variety of ways by those trying to address variants and for data management. AI, on the other hand, not only uses historical data, it makes assumptions about the data without applying a defined set of rules. This allows the software to learn and adapt to information patterns in more real time. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed. Chest CT is an emergency diagnostic tool to identify lung disease. Artificial intelligence (AI) gives big guidance in the rapid analysis of CT scans to differentiate variants of COVID-19 findings. This work focuses on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of Covid-19 Virus using Supervised Machine Learning Algorithms

Due to the continuous increase of Covid-19 infections as a global pandemic, it became necessary to detect it to avoid the damage caused by the spread of the infection. Artificial Intelligence (AI) techniques such as machine learning and deep learning have an important and effective role in the medical field applications like the classification of medical images and the detection of many diseases. In this article, we propose the use of several supervised machine learning classifiers for Covid-19 virus detection using chest x-ray (CXR) images. Five supervised classifiers are used: Support Vector Machines (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR) and Artificial Neural Network (ANN). A standard dataset of 1824 CXR images are used for training and testing;70% for training and 30% for testing. Four image embedders including Vgg16, Vgg19, SqueezeNet, and Inception-v3 are used in the experiments. Experiment results showed that most of these models achieved promising accuracy, precision, recall, and F1-scores. KNN, ANN, and LR classifiers have achieved highest classification accuracies using SqueezeNet image embedder. © 2022 IEEE.

Policy Making Based on Real-Scale Social Simulations

In this paper, we discuss decision-making processes for our communities using social simulation tools using some machine learning or artificial intelligence techniques. We take an example of considering preventive measures based on simulation results during COVID-19 pandemic. To avoid explosive infection in Japan, several preventive measures were considered. Among them, short-time business for restaurants, tourism support policies and vaccination schedules are included. I am involved in Covid-19 AI & Simulation Project Team (AISP) of Cabinet Secretariat, Japanese government. My contribution is to provide synthetic population data for real-scale social simulations for specific areas. In those simulations, we do not aim to predict a precise number of infected or severe patients by COVID-19 but to show several simulation results under various scenarios with different simulation parameters. After their simulation results are compared with each other, common outcomes are extracted from their results, and finally they are provided to the government. In that decision making process, their simulation results under several scenarios are shown to government officers, and final decision makings are left for politicians to decide. Since experts who are not elected are not able to take political responsibilities for their decision making, the AISP team shows several scenarios using their simulation models to support government officers and politicians to make their decisions. © 2022 IEEE.

Policy Making Based on Real-Scale Social Simulations

In this paper, we discuss decision-making processes for our communities using social simulation tools using some machine learning or artificial intelligence techniques. We take an example of considering preventive measures based on simulation results during COVID-19 pandemic. To avoid explosive infection in Japan, several preventive measures were considered. Among them, short-time business for restaurants, tourism support policies and vaccination schedules are included. I am involved in Covid-19 AI & Simulation Project Team (AISP) of Cabinet Secretariat, Japanese government. My contribution is to provide synthetic population data for real-scale social simulations for specific areas. In those simulations, we do not aim to predict a precise number of infected or severe patients by COVID-19 but to show several simulation results under various scenarios with different simulation parameters. After their simulation results are compared with each other, common outcomes are extracted from their results, and finally they are provided to the government. In that decision making process, their simulation results under several scenarios are shown to government officers, and final decision makings are left for politicians to decide. Since experts who are not elected are not able to take political responsibilities for their decision making, the AISP team shows several scenarios using their simulation models to support government officers and politicians to make their decisions. © 2022 IEEE.

A Novel Snore Detection and Suppression Method for a Flexible Patch With MEMS Microphone and Accelerometer

Sleep apnea impacts more and more people all over the world, and obstructive sleep apnea of which is the most frequent. Hence, research on snoring detection and related suppression methods is extremely urgent. In this article, a novel low-cost flexible patch with MEMS microphone and accelerometer is developed to detect snore event and sleeping posture, and a small vibration motor embedded in the patch is designed to suppress snoring. Theoretical analyses of short-time energy, piecewise average filtering (PAF), and Mel-frequency cepstral coefficients (MFCCs) processing are described in detail, and the improved MFCCs are put forward and used as the input of the convolutional neural network (CNN). Furthermore, the snore recognition method based on the combination of similarity analysis and CNN analysis is presented, followed by the snoring suppression method. Experimental results demonstrate that the main features of the sound signals can be extracted effectively by PAF and MFCCs processing, and the data compression ratio is about 99.41%. Besides, the locations of the eigenvectors can be found accurately based on short-time energy analysis. The numbers of high similarity of snoring signals within 30 s are larger than 3, while those of non-snoring signals are often less than 3. If the preliminary screening with similarity analysis is passed, CNN analysis will be conducted to judge whether there are snoring events. The accuracy of snore recognition with CNN analysis is calculated to be as high as 99.25%. Finally, the average snoring time measured by the smart patch with snoring suppression is reduced to 15 from 135 min, which indicates that the proposed snore recognition and suppression methods are effective.

Analytical Approach Toward Face Mask Detection Using Artificial Intelligence Techniques

By continuous hike of the deadly COVID-19 pandemic, the lifestyle of an individual has switched and changed all over the globe. Every individual has found it necessary to use a face mask in these situations. Identifying individual is wearing a face mask is very challenging due to wave of the deadly COVID-19 pandemic. The author proposed an approach in this study review work that would limit the evolution of the COVID-19 virus by personal identification who is not covering up any face mask. Many pieces of research have showed that wearing a mask reduces the possible chance of viral transmission of this life-threatening coronavirus and provides a sense of protection. The research during this zone has hiked over the past years. A typical review of the literature is studied to evaluate whether or not human beings are wearing masks, and based on these reviews, a modified analysis is done to detect which approach is feasible. This review included various search methodologies, too many research papers were recognized out of which seventeen are relevant papers. This paper will assess the research progresses related to the facial masks of an individual. It also helps the author to review out the ongoing and the forthcoming scenario of this research which have been working on facial mask detection using artificial intelligence. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

AI Techniques: Combating COVID-19 Pandemic

With COVID-19, more than millions of people from all over the world got infected due to this pandemic disease, has wrought havoc. Due to delay in detection of presence of COVID-19 in human body, it infected large number of people all around the globe. Besides all the available manual methods, Artificial Intelligence (AI) and Machine Learning (ML) can help in detecting, treating and monitoring the sternness of COVID-19. This paper intends to provide a complete overview of the role of AI and ML as one important tool for COVID-19 and associated epidemic screening, prediction, forecasting, contact tracing, and therapeutic development. AI is a game-changer in terms of disease diagnosis speed and accuracy. It's a promising technique for a fully transparent and autonomous monitoring system that can follow and cure patients remotely without transmitting the infection to others. AI Application areas in the field of health care are also identified. This paper examines the role of AI in combating the COVID-19 epidemic. We attempt to present a medical network architecture based on AI. The architecture employs artificial intelligence (AI) to efficiently and effectively carry out patient monitoring, diagnosis, and their cure. © 2022 IEEE.

The Concept of Using Artificial Intelligence Methods in Debt Financing of Business Entities

The COVID-19 crisis has speeded up the economy’s digitalization, including artificial intelligence techniques. Artificial intelligence methods are increasingly being implemented into finance from year to year. The research reveals the essence and concept of using artificial intelligence methods in general and in debt financing in particular. It is proposed to distinguish four criteria (context, data, model and tasks) in the concept of using artificial intelligence methods and to consider such usage through the prism of the life cycle of the artificial intelligence system. The list of tasks of artificial intelligence systems in debt financing is formed, and the main problem situations on debt financing management in which it is expedient to use artificial intelligence methods are identified. Since bonds are the primary tool for attracting debt financing in the stock market, and their scope requires the active implementation of digital technologies, the research clarified the algorithm for pricing bonds using artificial intelligence methods, which improves the interaction between lenders and borrowers. Particular attention is paid to identifying the benefits and risks of using artificial intelligence methods in debt financing and applying artificial intelligence methods in debt financing of business entities at different management levels. It is proved that in the conditions of total digitalization, the necessity of using modern information technologies, particularly methods of artificial intelligence, is necessary. © 2022 Copyright for this paper by its authors.

An Artificial Intelligence Technique for Covid-19 Detection with eXplainability using Lungs X-Ray Images

According to the World Health Organization, the coronavirus outbreak poses a daily threat to the global health system. Almost all countries' health resources are insufficient or unequally distributed. There are several issues, such as a lack of health care workers, beds, and intensive care units, to name a few. The key to the country's health systems overcoming this epidemic is to use limited resources at optimal levels. Disease detection is critical to averting an epidemic. The greater the success, the more tightly the covid viral spread may be managed. PCR (Polymerase chain reaction) testing is commonly used to determine whether or not a person has a virus. Deep learning approaches can be used to classify chest X-RAY images in addition to the PCR method. By analyzing multi-layered pictures in one go and establishing manually entered parameters in machine learning, deep learning approaches have become prominent in academic research. This popularity has a favorable impact on the available health datasets. The goal of this study was to detect disease in persons who had x-rays done for suspected COVID-19 (Coronavirus Disease-2019). A bi-nary categorization has been used in most COVID-19 investigations. Chest x-rays of COVID-19 patients, viral pneumonia patients, and healthy patients were obtained from IEEE [17] (Institute of Electrical and Electronics Engineers) and Kaggle [18]. Before the classification procedure, the data set was subjected to a data augmentation approach. These three groups have been classified through multiclassclassification deep learning models. We are also debating a taxonomy of recent contributions on the eXplainability of Artificial Intelligence (XAI). © 2022 IEEE.

An Overview of Augmenting AI Application in Healthcare

Artificial intelligence (AI) is showing a paradigm shift in all spheres of the world by mimicking human cognitive behavior. The application of AI in healthcare is noteworthy because of availability of voluminous data and mushrooming analytics techniques. The various applications of AI, especially, machine learning and neural networks are used across different areas in the healthcare industry. Healthcare disruptors are leveraging this opportunity and are innovating in various fields such as drug discovery, robotic surgery, medical imaging, and the like. The authors have discussed the application of AI techniques in a few areas like diagnosis, prediction, personal care, and surgeries. Usage of AI is noteworthy in this COVID-19 pandemic situation too where it assists physicians in resource allocation, predicting death rate, patient tracing, and life expectancy of patients. The other side of the coin is the ethical issues faced while using this technology like data transparency, bias, security, and privacy of data becomes unanswered. This can be handled better if strict policy measures are imposed for safe handling of data and educating the public about how treatment can be improved by using this technology which will tend to build trust factor in near future. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Comparison and Evaluation of Studies on Precision Medicine using AI

Precision medicine is a therapeutic idea that advocates tailoring treatment to a specific group of individuals rather than a one-size-fits-all approach. Artificial intelligence provides insights through advanced processing and reasoning, allowing the device to reason and learn while also assisting physicians in their outcomes. Precision medicine helps to cure critical diseases like Cancer, Cardiovascular diseases, Covid-19, and etc. This review paper aims on discussing how Artificial intelligence techniques in diagnosing critical diseases and how AI is helpful to patients and clinicians by using different methodologies. © 2022 IEEE.

AI-based diagnosis techniques for cardiac disease analysis and predictions

Artificial intelligence (AI) has developed speedily since the late 1980s. Enhancement of medical datasets and outcomes in the last twenty years has resulted in unprecedented improvement in AI-based journals. In addition, with the introduction of unparalleled computational efficiency, the accessibility of AI tools has improved. There are two fundamental tools in AI. The first is machine learning (ML), where organized information like electrophysiology (EP), images, and genetic information are broken down and examined. The second is natural language processing (NLP), where unorganized information is scrutinized. These two AI tools have enhanced strategies, calculations, and applications. Different endeavors and new techniques of AI have been utilized for ailments like cardiovascular disease (CVD), neural disorders, and cancer, among others. Presently, a sophisticated deep learning (DL) technique has instigated exceptional growth of AI in clinical imaging diagnostic frameworks. Thus, this chapter presents pivotal and specialized information about AI-based techniques for predicting, diagnosing, and analyzing cardiac diseases. © 2021 Elsevier Inc. All rights reserved.

Deep Reinforcement Learning-based Vaccine Distribution Strategies

The rapid development of artificial intelligence techniques is significantly promoting the resolution of various important decision-making issues such as material distribution, generation line optimization scheduling, and path planning. Currently, SARS-CoV-2 is raging over the world, and it is valuable to propose a vaccine distribution strategy to utilize limited vaccine resources rationally. In this paper, we aim to propose an optimal vaccine distribution strategy based on deep reinforcement learning(DRL) approaches. An End-to-End vaccine distribution model is proposed by combining the Deep Reinforcement Learning model and LinUCB algorithm to get an optimistic strategy of allocation. Experiment results demonstrated that vaccine distribution strategies based on this model show a strong capacity to control the epidemic and ensure stable government revenue compared with baseline strategies. © 2021 IEEE.

AI Techniques for Detecting and Diagnosing COVID-19

The new Coronavirus infection (COVID-19) which has been declared by the World Health Organization (WHO) has now spread all over the world. As per the few examinations, the COVID-19 has side effects like dry cough, sore throat, fever, gentle to direct respiratory disease and muscle throbs. The COVID-19 causes an adverse consequence on the lungs, it very well may be analyzed utilizing X-rays and CT scans of the lungs. As COVID-19 diagnosing is the lengthy process for testing and the vast majority of the strategies that are proposed might give false-positive and false-negative result. This paper predominantly points on the investigation and survey of the execution of Machine Learning (ML) and Artificial Intelligence (AI) techniques that are utilized to forestall and control the spread of coronavirus all across the world. © 2022 IEEE.

A Proposal for the Deep Unsupervised Identification of Relevant Areas in X-Rays for Covid Detection

Since the beginning of 2020, the diagnosis of the COVID-19 virus has been a major problem that has affected the lives of millions of people around the world. The detection time for COVID-19 with a standard detection method ranges from approximately 1 to 5 days. An efficient and fast way to detect the presence of both the COVID-19 virus is through the use of artificial intelligence (AI) techniques applied to images obtained by lung radiography. Typically, AI algorithms to detect COVID-19 consider the whole picture. However, there may be parts that affect the performance of the classifier. Furthermore, these algorithms do not indicate which is the most relevant area of this disease. In this work, we propose a deep learning approach to detect the presence of COVID-19 in lung images by recognizing the most relevant areas affected by the virus without considering human supervision. In the experiment, we considered different proposals, where the best one obtained an 88% reduction of the logit loss with respect to the baseline based on random regions near the center of the image. © 2021 IEEE.

COVID-19 Detection Through Smartphone-recorded Coughs Using Artificial Intelligence: An Analysis of Applicability for Pre-screening COVID-19 Patients in Vietnam

The Covid-19 pandemic is one of the most serious global health epidemics in recent decades. Its consequences have affected hundreds of millions of people in countries around the world because of the high contagiousness and mortality rate of the virus. Since the fourth wave of Covid-19 infections broke out and spread to many cities and provinces in Vietnam, there were over 10, 000 infected cases in the community within two months by the Delta coronavirus variants. Therefore, it is very necessary to have a faster and more effective method to prescreen and isolate infected patients as soon as possible. That is why the paper proposes a method using artificial intelligence techniques to detect covid-19 infected patients based on smartphone-recorded cough sounds. The learning models are built using the publicly available data as COUGHVID and Coswara. An analysis of the applicability of the learning models for prescreening Covid-19 patients in Vietnam is also mentioned in the paper. © 2021 IEEE.