This document presents a new artificial intelligence algorithm called the artificial corona-inspired optimization algorithm. The algorithm is inspired by how the corona virus spreads through a population and infects individuals. It simulates the stages of virus transmission and treatment to guide the selection of starting points and parameters for optimization problems. The algorithm does not require derivative information and has few parameters to set. Numerical examples are provided to demonstrate the algorithm's performance in finding optimal solutions to linear and nonlinear optimization problems within a reasonable time frame. The algorithm shows potential as a new approach for solving mathematical programming problems.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Covid-19 Detection using Chest X-Ray ImagesIRJET Journal
1) The document discusses using deep learning and machine learning models to detect Covid-19 from chest x-ray images with a high accuracy.
2) Specifically, it evaluates using convolutional neural networks (CNNs) which are well-suited for medical image classification tasks since they can learn spatial relationships within images.
3) Previous studies that developed CNN and other models for Covid detection from chest x-rays are reviewed, finding classification accuracies from 87-99% depending on the dataset and model used.
Pneumonia Classification using Transfer LearningTushar Dalvi
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
Pneumonia Detection Using Convolutional Neural Network WritersIRJET Journal
This document describes a study that developed a convolutional neural network (CNN) model to detect and classify pneumonia from chest x-ray images without any training. The model was able to extract relevant features from chest x-ray images and determine if a patient has pneumonia. The CNN model achieved high accuracy for pneumonia classification compared to other advanced methods that rely on transfer learning or manual feature engineering. The study used a dataset of 5,500 chest x-ray images and performed image processing techniques like background removal and cropping before extracting features with the CNN and classifying images.
Deep Learning Approach for Unprecedented Lung Disease PrognosisIRJET Journal
This document summarizes a research project that developed a deep learning model using convolutional neural networks to classify and predict various lung diseases from chest x-ray images. The model was able to achieve a high test accuracy of 91% in distinguishing between normal, tuberculosis, pneumonia, and COVID-19 cases. The research involved collecting chest x-ray image datasets from public sources, preprocessing the data, designing and training a CNN model using TensorFlow, and evaluating the model's performance on test data. The study demonstrated the effectiveness of machine learning and deep learning techniques for automated lung disease detection and prognosis to help improve medical diagnoses and patient outcomes.
COVID-19 knowledge-based system for diagnosis in Iraq using IoT environmentnooriasukmaningtyas
The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
MARIE: VALIDATION OF THE ARTIFICIAL INTELLIGENCE MODEL FOR COVID-19 DETECTIONijma
Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. In partnership with the municipality of Itapeva, Minas Gerais, we carried out patient analysis and, at the same time, we evolved the algorithms to meet the needs of health professionals and also expand support in tracking COVID-19 in the municipality. In this project we will describe cases and even signs and symptoms that were similar to the clinical performed by the doctor. The average recognition accuracy of COVID-19 was 0.97,4 ± 0.043.
AN AUTOMATED FRAMEWORK FOR DIAGNOSING LUNGS RELATED ISSUES USING ML AND DATA ...IRJET Journal
This document presents research on developing machine learning models to diagnose lung diseases using medical images. The researchers built and compared two pre-trained convolutional neural network models (MobileNet and VGG16) using transfer learning on chest X-ray and CT scan images. Supervised machine learning algorithms like random forest, decision trees, support vector machines, and logistic regression were also used. The models were trained on datasets of pneumonia, normal lungs, and other lung conditions. Evaluation showed the deep learning models achieved over 98% accuracy while the supervised learning algorithms had lower testing accuracies between 67-84%. An integrated desktop application was developed that can classify lung diseases in X-rays and CT scans to help diagnose conditions like pneumonia, lung cancer, COVID-
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Covid-19 Detection using Chest X-Ray ImagesIRJET Journal
1) The document discusses using deep learning and machine learning models to detect Covid-19 from chest x-ray images with a high accuracy.
2) Specifically, it evaluates using convolutional neural networks (CNNs) which are well-suited for medical image classification tasks since they can learn spatial relationships within images.
3) Previous studies that developed CNN and other models for Covid detection from chest x-rays are reviewed, finding classification accuracies from 87-99% depending on the dataset and model used.
Pneumonia Classification using Transfer LearningTushar Dalvi
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
Pneumonia Detection Using Convolutional Neural Network WritersIRJET Journal
This document describes a study that developed a convolutional neural network (CNN) model to detect and classify pneumonia from chest x-ray images without any training. The model was able to extract relevant features from chest x-ray images and determine if a patient has pneumonia. The CNN model achieved high accuracy for pneumonia classification compared to other advanced methods that rely on transfer learning or manual feature engineering. The study used a dataset of 5,500 chest x-ray images and performed image processing techniques like background removal and cropping before extracting features with the CNN and classifying images.
Deep Learning Approach for Unprecedented Lung Disease PrognosisIRJET Journal
This document summarizes a research project that developed a deep learning model using convolutional neural networks to classify and predict various lung diseases from chest x-ray images. The model was able to achieve a high test accuracy of 91% in distinguishing between normal, tuberculosis, pneumonia, and COVID-19 cases. The research involved collecting chest x-ray image datasets from public sources, preprocessing the data, designing and training a CNN model using TensorFlow, and evaluating the model's performance on test data. The study demonstrated the effectiveness of machine learning and deep learning techniques for automated lung disease detection and prognosis to help improve medical diagnoses and patient outcomes.
COVID-19 knowledge-based system for diagnosis in Iraq using IoT environmentnooriasukmaningtyas
The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
MARIE: VALIDATION OF THE ARTIFICIAL INTELLIGENCE MODEL FOR COVID-19 DETECTIONijma
Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. In partnership with the municipality of Itapeva, Minas Gerais, we carried out patient analysis and, at the same time, we evolved the algorithms to meet the needs of health professionals and also expand support in tracking COVID-19 in the municipality. In this project we will describe cases and even signs and symptoms that were similar to the clinical performed by the doctor. The average recognition accuracy of COVID-19 was 0.97,4 ± 0.043.
AN AUTOMATED FRAMEWORK FOR DIAGNOSING LUNGS RELATED ISSUES USING ML AND DATA ...IRJET Journal
This document presents research on developing machine learning models to diagnose lung diseases using medical images. The researchers built and compared two pre-trained convolutional neural network models (MobileNet and VGG16) using transfer learning on chest X-ray and CT scan images. Supervised machine learning algorithms like random forest, decision trees, support vector machines, and logistic regression were also used. The models were trained on datasets of pneumonia, normal lungs, and other lung conditions. Evaluation showed the deep learning models achieved over 98% accuracy while the supervised learning algorithms had lower testing accuracies between 67-84%. An integrated desktop application was developed that can classify lung diseases in X-rays and CT scans to help diagnose conditions like pneumonia, lung cancer, COVID-
A deep learning approach for COVID-19 and pneumonia detection from chest X-r...IJECEIAES
There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life.
Comprehensive study: machine learning approaches for COVID-19 diagnosis IJECEIAES
Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods.
Pneumonia Detection Using Deep Learning and Transfer LearningIRJET Journal
This document presents research on using deep learning and transfer learning techniques to detect pneumonia from chest x-ray images. The researchers trained several models, including CNNs, DenseNet, VGG-16, ResNet, and InceptionNet on a dataset of chest x-rays labeled as normal or pneumonia. The models achieved accuracy in detecting pneumonia of 89.6-97%, depending on the specific model. The researchers found that deep learning approaches like these have significant potential to improve the accuracy and efficiency of pneumonia diagnosis compared to traditional methods. Overall, the study demonstrated promising results for using machine and deep learning to classify medical images and detect health conditions like pneumonia.
An efficient convolutional neural network-extreme gradient boosting hybrid de...IJECEIAES
In this paper, we present an efficient deep-learning hybrid model comprising an extreme gradient boosting (XGBoost) supervised learning algorithm and convolutional neural networks (CNN) for the automated detection of diseases. The proposed model is implemented and tested to detect type-2 diabetes by measuring the acetone concentration in the exhaled breath. Acetone will be present in much higher concentrations in type-2 diabetic patients compared to non-diabetic people. A novel sensing module is designed and implemented in our study to measure the acetone concentration in exhaled breath. The proposed approach delivered good results, with a classification accuracy of 97.14%. The findings of this study show how effectively the proposed detection module functions in disease diagnosis applications. As the detection process is simple and non-invasive, people can undergo routine checks for diabetes with the proposed detection module.
Lung Cancer Detection using Convolutional Neural NetworkIRJET Journal
The document presents a study on detecting lung cancer using convolutional neural networks. Specifically, it uses the YOLO framework to accurately detect lung tumors and their locations in CT images. The proposed system first collects CT images and pre-processes them before training a YOLO object detection model. The trained model is then used to detect and localize tumors in test images and provide classification. Evaluation shows the model can successfully pinpoint tumors attached to blood vessels and distinguish between different types of lung cancer. The authors aim to improve the model through expanding the dataset and exploring updated deep learning techniques.
Enhancing COVID-19 forecasting through deep learning techniques and fine-tuningIJECEIAES
In this study, a comprehensive analysis of classical linear regression forecasting models and deep learning techniques for predicting coronavirus disease of 2019 (COVID-19) pandemic data was presented. Among the deep learning models, the long short-term memory (LSTM) neural network demonstrated superior performance, delivering accurate predictions with minimal errors. The neural network effectively addressed overfitting and underfitting issues through rigorous tuning. However, the diversity of countries and dataset attributes posed challenges in achieving universally optimal predictions. The current study explored the application of the LSTM in predicting healthcare resource demand and optimizing hospital management to provide potential solutions for overcrowding and cost reduction. The results showed the importance of leveraging advanced deep learning techniques for improved COVID-19 forecasting and extending the application of the models to address broader healthcare challenges beyond the pandemic. To further enhance the model performance, future work needed to incorporate additional attributes, such as vaccination rates and immune percentages.
This document presents a convolutional neural network model to detect pneumonia from chest x-ray images. The model is trained from scratch on a dataset of over 5,800 chest x-ray images categorized into pneumonia and normal images. The model uses preprocessing like resizing and normalization, data augmentation, and a custom sequential CNN model with convolutional and pooling layers to extract features and classify images. Evaluation metrics like precision, recall, accuracy and F1 score are used to analyze the trained model's performance at detecting pneumonia from chest x-rays. The proposed system aims to help diagnose pneumonia early and assist medical professionals, especially in remote areas.
An intelligent approach for detection of covid by analysing Xray and CT scan ...IRJET Journal
This document presents an intelligent approach to detect COVID-19 using X-ray and CT scan images. It discusses developing a deep learning model using a convolutional neural network (CNN) to analyze medical images and classify them as coming from COVID-19 positive or negative cases. The model would be integrated into a web application using Flask that allows users to upload images for rapid diagnosis. The goal is to address issues with traditional testing methods that can take days to get results, and to help reduce the spread of COVID-19 through faster detection. The document reviews several related studies applying deep learning to COVID-19 detection from medical images and discusses the materials and methodology used to develop and evaluate the proposed intelligent detection system.
A comparative analysis of chronic obstructive pulmonary disease using machin...IJECEIAES
Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.
New methodology to detect the effects of emotions on different biometrics in...IJECEIAES
This document presents a new methodology to detect the effects of emotions on different biometrics in real time. Two designs were implemented based on a microcontroller and National Instruments myRIO to measure four vital parameters (temperature, heartbeat, blood pressure, body resistance) in real-time while recording the effects of different emotions on those parameters. Over 400 people were tested while exposed to videos and music representing different emotions. The results showed that the design using NI myRIO achieved more accurate results and faster response time compared to the microcontroller-based design, qualifying it for use in intensive care units. The methodology contributes to early diagnosis of diseases by analyzing the impact of emotions on vital readings.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
1. Researchers developed an X-ray disease identifier using a deep learning model to analyze chest X-ray images and diagnose diseases.
2. They used the VGG19 classification model to process X-ray images from the NIH dataset and diagnose diseases, achieving over 60% accuracy for most diseases.
3. The system aims to assist radiologists by providing automated disease diagnoses from X-ray images to reduce their workload and enable diagnoses in remote areas.
Computer-aided diagnostic system kinds and pulmonary nodule detection efficacyIJECEIAES
This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.
1) A novel face mask detection framework called FMD-Yolo is proposed to detect whether people are wearing masks correctly in public settings.
2) FMD-Yolo uses an improved feature extractor called Im-Res2Net-101 and an enhanced feature fusion method called En-PAN to thoroughly extract and merge multi-scale information from input images.
3) Experimental results on two public datasets show that FMD-Yolo achieves state-of-the-art precision of 92.0% and 88.4%, outperforming other detection methods. FMD-Yolo demonstrates superior performance for face mask detection.
Gaussian Multi-Scale Feature Disassociation Screening in Tuberculosiseijceronline
This summary provides the high level information from the document in 3 sentences:
Tuberculosis is a major infectious disease that if left untreated can have high mortality rates, and while treatments exist diagnosis remains a challenge. The document discusses several methods for diagnosing tuberculosis including sputum smear microscopy, skin tests, and newer molecular diagnostic tests, as well as developing an automated method for detecting tuberculosis manifestations in chest radiographs. It proposes extracting the lung region from chest x-rays and then computing texture and shape features to classify the x-rays as normal or abnormal using a binary classifier in order to enable mass screening of large populations.
IRJET - Detecting Pneumonia from Chest X-Ray Images using Committee MachineIRJET Journal
This document discusses detecting pneumonia from chest X-ray images using a committee machine. It aims to build a web application that can accurately diagnose pneumonia by analyzing chest X-ray images. The system will use a dataset of over 5,800 chest X-ray images to predict if an image shows pneumonia. It will apply techniques like image processing, machine learning algorithms, and a committee machine to increase the accuracy of pneumonia detection compared to other methods.
Professor Aboul Ella hassanien publications related to COVID-19 and Emerging Technologies such as AI, Machine Learning, Drones, Blockchain, IoT, Big Data
Design and development of a fuzzy explainable expert system for a diagnostic ...IJECEIAES
Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability.
A Predictive Model for Novel Corona Virus Detection with Support Vector MachineBRNSSPublicationHubI
This document summarizes a research article that developed a predictive model using support vector machines to detect novel coronavirus. The model was trained on a dataset of 2000 coronavirus test cases from an online repository. The model achieved 96.68% accuracy in detecting coronavirus. Key features like headache, sore throat and fever showed strong relationships to detecting coronavirus positive cases. The study aims to help medical experts detect coronavirus early through automated machine learning models.
Development of Computational Tool for Lung Cancer Prediction Using Data MiningEditor IJCATR
The requirement for computerization of detection of lung cancer disease arises ever since recent-techniques which involve
manual-examination of the blood smear as the first step toward diagnosis. This is quite time-consuming, and their accurateness depends
upon the ability of operator's. So, prevention of lung cancer is very essential. This paper has surveyed various techniques used by previous
authors like ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.
A deep learning approach for COVID-19 and pneumonia detection from chest X-r...IJECEIAES
There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life.
Comprehensive study: machine learning approaches for COVID-19 diagnosis IJECEIAES
Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods.
Pneumonia Detection Using Deep Learning and Transfer LearningIRJET Journal
This document presents research on using deep learning and transfer learning techniques to detect pneumonia from chest x-ray images. The researchers trained several models, including CNNs, DenseNet, VGG-16, ResNet, and InceptionNet on a dataset of chest x-rays labeled as normal or pneumonia. The models achieved accuracy in detecting pneumonia of 89.6-97%, depending on the specific model. The researchers found that deep learning approaches like these have significant potential to improve the accuracy and efficiency of pneumonia diagnosis compared to traditional methods. Overall, the study demonstrated promising results for using machine and deep learning to classify medical images and detect health conditions like pneumonia.
An efficient convolutional neural network-extreme gradient boosting hybrid de...IJECEIAES
In this paper, we present an efficient deep-learning hybrid model comprising an extreme gradient boosting (XGBoost) supervised learning algorithm and convolutional neural networks (CNN) for the automated detection of diseases. The proposed model is implemented and tested to detect type-2 diabetes by measuring the acetone concentration in the exhaled breath. Acetone will be present in much higher concentrations in type-2 diabetic patients compared to non-diabetic people. A novel sensing module is designed and implemented in our study to measure the acetone concentration in exhaled breath. The proposed approach delivered good results, with a classification accuracy of 97.14%. The findings of this study show how effectively the proposed detection module functions in disease diagnosis applications. As the detection process is simple and non-invasive, people can undergo routine checks for diabetes with the proposed detection module.
Lung Cancer Detection using Convolutional Neural NetworkIRJET Journal
The document presents a study on detecting lung cancer using convolutional neural networks. Specifically, it uses the YOLO framework to accurately detect lung tumors and their locations in CT images. The proposed system first collects CT images and pre-processes them before training a YOLO object detection model. The trained model is then used to detect and localize tumors in test images and provide classification. Evaluation shows the model can successfully pinpoint tumors attached to blood vessels and distinguish between different types of lung cancer. The authors aim to improve the model through expanding the dataset and exploring updated deep learning techniques.
Enhancing COVID-19 forecasting through deep learning techniques and fine-tuningIJECEIAES
In this study, a comprehensive analysis of classical linear regression forecasting models and deep learning techniques for predicting coronavirus disease of 2019 (COVID-19) pandemic data was presented. Among the deep learning models, the long short-term memory (LSTM) neural network demonstrated superior performance, delivering accurate predictions with minimal errors. The neural network effectively addressed overfitting and underfitting issues through rigorous tuning. However, the diversity of countries and dataset attributes posed challenges in achieving universally optimal predictions. The current study explored the application of the LSTM in predicting healthcare resource demand and optimizing hospital management to provide potential solutions for overcrowding and cost reduction. The results showed the importance of leveraging advanced deep learning techniques for improved COVID-19 forecasting and extending the application of the models to address broader healthcare challenges beyond the pandemic. To further enhance the model performance, future work needed to incorporate additional attributes, such as vaccination rates and immune percentages.
This document presents a convolutional neural network model to detect pneumonia from chest x-ray images. The model is trained from scratch on a dataset of over 5,800 chest x-ray images categorized into pneumonia and normal images. The model uses preprocessing like resizing and normalization, data augmentation, and a custom sequential CNN model with convolutional and pooling layers to extract features and classify images. Evaluation metrics like precision, recall, accuracy and F1 score are used to analyze the trained model's performance at detecting pneumonia from chest x-rays. The proposed system aims to help diagnose pneumonia early and assist medical professionals, especially in remote areas.
An intelligent approach for detection of covid by analysing Xray and CT scan ...IRJET Journal
This document presents an intelligent approach to detect COVID-19 using X-ray and CT scan images. It discusses developing a deep learning model using a convolutional neural network (CNN) to analyze medical images and classify them as coming from COVID-19 positive or negative cases. The model would be integrated into a web application using Flask that allows users to upload images for rapid diagnosis. The goal is to address issues with traditional testing methods that can take days to get results, and to help reduce the spread of COVID-19 through faster detection. The document reviews several related studies applying deep learning to COVID-19 detection from medical images and discusses the materials and methodology used to develop and evaluate the proposed intelligent detection system.
A comparative analysis of chronic obstructive pulmonary disease using machin...IJECEIAES
Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.
New methodology to detect the effects of emotions on different biometrics in...IJECEIAES
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Artificial Corona-Inspired Optimization Algorithm: Theoretical Foundations,
Analysis, and Applications. American Journal of Artificial Intelligence
Article · September 2021
DOI: 10.11648/j.ajai.20210502.12
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2. American Journal of Artificial Intelligence
2021; 5(2): 56-65
http://www.sciencepublishinggroup.com/j/ajai
doi: 10.11648/j.ajai.20210502.12
ISSN: 2639-9717 (Print); ISSN: 2639-9733 (Online)
Artificial Corona-Inspired Optimization Algorithm:
Theoretical Foundations, Analysis, and Applications
Alia Youssef Gebreel
Operations Research, Faculty of Graduate Studies for Statistical Research, Cairo University, Cairo, Egypt
Email address:
To cite this article:
Alia Youssef Gebreel. Artificial Corona-Inspired Optimization Algorithm: Theoretical Foundations, Analysis, and Applications. American
Journal of Artificial Intelligence. Vol. 5, No. 2, 2021, pp. 56-65. doi: 10.11648/j.ajai.20210502.12
Received: July 28, 2021; Accepted: August 7, 2021; Published: August 27, 2021
Abstract: One of the important parts of computer science is Artificial Intelligence (AI). It deals with the development of
machines that can take decisions like humans on their own. Currently, AI can solve many difficult real-world problems because
it works much better and faster than humans. Researchers of operations research also are turning their heads towards AI instead
of traditional systems. Meanwhile, there are several AI models to solve mathematical optimization problems. They depend
heavily on a random search, but many of their solutions have been efficient at finding absolute optimum. This means that it is
necessary to choose another optimization model to get quite the optimum value. This paper introduces an artificially intelligent
algorithm in order to find the optimal solution for a given computational problem that minimizes or maximizes a particular
function. It is inspired by the corona virus that spreads throughout the world and infects healthy people. Its structure simulates
the stages of virus transmission and treatment. Because the starting point is so important for converging to the global optimum,
corona virus approach has guided researchers to select the starting point and parameters. Actually, this point depends on three
real numbers as the corona virus affects three main parts of the human body (nose, throat, respiratory). The proposed algorithm
has been found to be an optimal key to different applications. It doesn't require any derivative information and it is simple in
implementation with few parameters setting. Finally, some numerical examples are presented to illustrate the algorithm studied
here. The computational results show that it has high performance in finding an optimal solution within reasonable time.
Keywords: Artificial Intelligent Algorithms, Corona Virus (CV), Optimal Solution
1. Introduction
In December 2019, everyone was interested in corona
virus that has spread all over the world. It is an infectious
acute respiratory disease and its complications may lead to
death. This current disease is first reported in Wuhan, China,
and transmitted from bats to humans. Its name is derived
from a relation to the proteins that give it its coronary shape
as shown in Figure 1.
The corona virus is also named COVID-19 that is an
emerging contagious disease caused by the SARS-CoV-2
virus. It infects people of all ages, especially elder people and
persons with chronic diseases [1-5].
It could be transmitted from human to human by
inhalation or contact with infected droplets and the
incubation period ranges from 2 to 14 days. In addition,
there are opinions say that individuals who remain
asymptomatic could transmit the virus [3, 5].
Figure 1. A graphical representation of the ultra-structural morphology of
corona virus.
The stages of corona virus (COVID 19) and its treatment
are sketched basically as shown in the following Figure 2.
3. 57 Alia Youssef Gebreel: Artificial Corona-Inspired Optimization Algorithm: Theoretical
Foundations, Analysis, and Applications
Figure 2. A diagram representation of the stages of corona virus (Covid 19)
and its treatment.
Generally, hospitalized patients are classified into two
categories, the general COVID-19 which has been defined
according to the following criteria: obvious relief of
respiratory symptoms (for example, cough, chest distress, and
shortness of breath) after treatment, maintaining normal body
temperature for more than three days without the use of
corticosteroids or antipyretics, improving radiological
abnormalities in the chest scanner or X-rays after treatment, a
hospital stay of fewer than 10 days. Otherwise, it was
classified as COVID-19 refractory [3].
Hence, health education on knowledge for disease
prevention and control is also important to control and reduce
the corona virus infection rate. Treatment is essentially
supportive; the role of antiviral agents is yet to be established.
Prevention entails home isolation of suspected cases and those
with mild illnesses and strict infection control measures at
hospitals that include contact and droplet precautions [3, 5].
To simulate this disease for solving mathematical
programming problems, it is known that traditional
methods have been replaced by AI algorithms to solve such
problems. They are the fastest-growing field of computer
science and technology. These modern optimization
algorithms have been successfully employed in different
applications. Also, they are called heuristic or
meta-heuristic algorithms. Where, heuristic search refers to
a search strategy that attempts to optimize a problem by
iteratively improving the solution based on a given
heuristic function or a cost measure. Most of the existing
meta-heuristic algorithms imitate natural or scientific
phenomena, e.g. evolution in evolutionary algorithms,
functions of the brain in neural networks, physical
annealing of metal plates in simulated annealing, human
memory in tabu search, human's food in bacterial foraging,
and the music improvisation process in harmony search.
Following a similar trend, a new artificial intelligent
algorithm can be conceptualized from the corona virus
activity. Where, a doctor always desires to provide the best
adequate treatment to his patients, which can be obtained by
numerous practices [6-9].
Some studies have been published for applications of
corona virus in operations research such as:
The authors of the paper [2] introduced a brief overview of
the meta-heuristic COVID-19 method concerning its
applications for electricity demand time series forecasting and
hybridize deep learning models. It found that corona virus
optimization algorithm is easily transformed into a multi-virus
meta-heuristic, in which different corona virus strains search
for the best solution in a collaborative way.
But, Ahmed et al. in paper [1] presented an improved hybrid
classification approach for COVID-19 images by combining
the strengths of convolutional neural networks to extract
features and a swarm-based feature selection algorithm for
selecting the most relevant features. The results showed that
this approach has better performances in both classification
accuracy and the number of extracted features that have a
positive effect on resource consumption and storage
efficiency.
The natural-inspired human-based meta-heuristic
optimization algorithm is proposed by Mohammed Al-Betar et
al. [10], which is inspired by the herd immunity strategy as a
way to tackle the spreading of corona virus pandemics
(COVID-19).
Furthermore, G Dhiman et al. in the article [11]
illustrated the multi-objective optimization and a
deep-learning methodology for the detection of infected
corona virus patients with X-rays. They concluded that the
performance of their study is better than the other ten
competitor models based on the statistical research to select
the best classification pattern.
In general, all intelligent optimization algorithms are started
with random point(s) and their result shows time-consuming
process. For this reason, the research proposes an algorithm to
find the optimal solution for mathematical programming
problems. It relies on the deterministic value for each decision
variable that is selected from three real numbers; rather than
random numbers.
The remaining sections are organized as follows. Section 2
briefly outlines some types of intelligent optimization
algorithms. Followed by, section 3 describes in detail the
4. American Journal of Artificial Intelligence 2021; 5(2): 56-65 58
proposed algorithm for optimization problems. Section 4
presents some numerical examples to solve linear and
nonlinear optimization problems with their obtained results.
Finally, section 5 concludes the paper and provides future
work.
2. Some of Intelligent Optimization
Algorithms
There are several intelligent optimization techniques to
generate acceptable solutions, even though they cannot
guarantee optimality. They can incorporate problem-specific
knowledge to improve the quality of the solutions. This
section reviews some of them. These models include genetic
algorithm, hill-climbing, simulated annealing, tabu search,
particle swarm, ant colony, neural network, bacterial foraging,
and harmony search algorithm.
2.1. Genetic Algorithm
Genetic Algorithm (GA) imitates the mechanics of natural
selection and evaluation. It involves the following stages to
solve any optimization problem:
1) Representation of feasible solution to a problem as a
population of randomly generated individuals and
happens in generations.
2) Evaluation of the population using fitness function.
3) Generation of new population using the genetic
operators (crossover and mutation).
4) Selection of new population from the current population
[12, 14].
2.2. Hill-climbing
Hill-climbing is a mathematical optimization technique
which belongs to the family of local search. It is an iterative
algorithm that starts with an arbitrary solution to a problem,
then attempts to find a better solution by making an
incremental change to the solution [15-16].
2.3. Simulated Annealing
Simulated Annealing (SA) is a local search based on
meta-heuristic technique that proposed by Kirkpatrick et al. in
1983. It simulates the process of heating and cooling metals.
SA technique differs from other search methods in accepting
inferior solutions with certain probability. This is calculated
using the Boltzmann probability as:
PB=e− φ/ T
(1)
Where, T is the temperature that is decreasing according to
some cooling schedule during the search process, and φ is the
difference between the best solution’s fitness value and the
generated trial's fitness value. In this way, it attempts to avoid
getting stuck in the local optimal solutions [13, 17, 18].
2.4. Tabu Search
Tabu Search (TS) is a local heuristic search procedure. It is
guided by using an adaptive or flexible memory structure.
The information is stored in a Tabu list that contains all
moves. The quality of each solution is measured by an
objective function. In order to find the best admissible move
in a neighborhood, the move that has the shortest tabu tenure
is selected. If the TS is not converging, the search is reset
randomly [14, 19].
2.5. Particle Swarm Optimization
Particle Swarm Optimization (PSO) is inspired by the
behavior of bird flocking and fish schooling. It is one of the
evolutionary computation techniques. So, it likes GA in
starting with an initial population of random solutions. But,
PSO is unlike other algorithms in which each potential
solution (called a particle) is also assigned a randomized
velocity and then flown through the problem hyperspace
[19].
2.6. Ant Colony
Ant Colony (AC) is inspired by the behavior of ant
colonies. A number of ants cooperate with each other by
sharing their experiences in finding a solution. In real ant
colonies, this shared memory is represented by the
deposition of pheromones on the trail as ants forage for
food. If the trail is a "successful" one (in terms of increasing
the food supply), its usage will be increased, otherwise
"evaporation" takes place and the trail is eventually
abandoned. In artificial systems, this effect is represented
by a modification to the probability of choosing the next
element of the solution [6].
2.7. Neural Network
Neural Network (NN) is modeled on the mechanism of the
human brain. This network has the capacity of learning from
examples, memorizes, and creates relationships amongst data.
It works based on three main stages: the designing stage,
training stage, and testing stage [20].
2.8. Bacterial Foraging Algorithm
The bacterial Foraging Algorithm (BFA) is proposed by
Kevin M. Passino in 2000. It simulates the life cycles and the
foraging behaviors of Escherichia coli bacteria that live in the
human intestine to solve the optimization problems. There are
three processes on a population of simulated cells: chemotaxis,
reproduction, and elimination-dispersal. It is unfortunate that
BFO gives poor performance for multimodal and high
dimensional functions as compared to other optimization
techniques like GA and PSO [12, 21].
2.9. Harmony Search Algorithm
Harmony Search Algorithm (HSA) is developed by Zong
Woo Geem et al. in 2001. It is inspired by the natural musical
performance process that occurs when a musician searches for
a better state of harmony. Consequently, HSA starts by
initializing the problem and algorithm parameters. Then,
5. 59 Alia Youssef Gebreel: Artificial Corona-Inspired Optimization Algorithm: Theoretical
Foundations, Analysis, and Applications
initializing the harmony memory (HM), improvising a new
harmony from the HM, and updating the HM until the
termination criterion is satisfied.
It is clear that HS searches for the optimal solution through
multiple solution vectors as in GA. But, its reproduction
process is different from GA. While GA generates a new
offspring from two parents in the population, HS generates it
from all of the existing vectors stored in HM [12, 22].
These heuristic search algorithms have exponential time
and space complexities as they store complete information of
the path including the explored intermediate nodes [23].
To overcome the difficulties in the above optimization
models, the corona virus approach is suggested.
3. The Proposed Algorithm
This section introduces a corona-based idea to optimization
problems. Basically, just as corona virus infects three parts of
the human body (nose, throat, respiratory), so decision
variables can be assigned with any one of the three real
values (0.5, √0.5, 1) based on computational intelligence in
the optimization process. It should be noted that there is a
relation of these three values. Where, the second value is the
square root of the first, and the third value is double the first
value. Accordingly, the value "0.5" is considered as the
lifeblood of this algorithm. Additionally, just as the corona
virus infects the person(s) based on infection spread or
treatment or death, decision variables in computer memory
can be improved based on the objective function.
3.1. Procedure of Corona Algorithm
In the optimization process, the solution is estimated by
putting values of decision variables to objective or fitness
function and evaluating the function value with respect to
several aspects such as cost, profit, efficiency, and/or error.
Similarly in an intelligent corona algorithm, the solution of an
optimization problem is provided based on the following four
steps as shown in Figure 4:
Step 1. Initialize the optimization problem:
In this step, the initial population consists of one patient to
each decision variable. Essentially, there are two ways in
which a mathematical problem can be optimized:
1) The objective function is to minimize the infection
spread of corona virus.
2) The objective function is to maximize the treatment of
corona virus.
So, the optimization problem is specified as follows:
Optimize (minimize or maximize): f(x),
Subject to: x ∈ S={gm(x) ≤ 0, m=1, 2,..., M,
hl(x)=0, l=1, 2,..., L } (2)
Where:
f(x) is an objective function,
x=(x1, x2, …, xn)T
is a set of decision variables, which
optimizes (minimizes or maximizes) the objective function,
n is a number of decision variables,
T is the transpose operator,
S is a feasible set, defined as
S={x | x ∈ Rn
, gm(x) ≤ 0, m=1, 2, …, M, hl(x)=0, l=1, 2,…,
L},
The inequality gm(x) and equality hl(x) are real valued
functions defined on S,
M and L are the numbers of inequality and equality
constraints, respectively.
In addition, the termination criterion (maximum number of
searches) is determined (arbitrary).
Step 2. Prepare a memory vector:
The corona search (CS) algorithm has memory storage as a
vector, named corona memory (CM). Where, a group of
decision variables (that contains a population) is stored in this
vector. So, it can call an infected population. The problem
constraints (g1(x), g2(x), …, gM(x), h1(x), h2(x), …, hL(x)) and
the objective function value are also stored next to these
decision variables. In this step, the CS vector is initially filled
with any one of the three values ( 0.5, √0.5, 1 ). The
corresponding objective function value and constraints are
calculated and stored in CM as follows:
CM=[x1, x2, …, xn, g1(x), g2(x), …, gM(x), h1(x), h2(x), …,
hL(x), f(x)]T
. (3)
CM is similar to one vector of the harmony memory in the
harmony algorithm. It is a memory location that stores the
solution vector (set of decision variables). Interestingly, CM is
the most important part of corona search; it considers the heart
of this algorithm.
Step 3. Update the corona memory (CM):
In this step, a new corona vector (x=x1, x2, …, xn)T
is
generated based on the following formula:
xi+1=a1 xi + a2, (4)
Where:
a1 and a2 are real numbers. These arbitrary values are the
spreading rates of corona virus whether internal or external
factors, respectively. In most cases, the value of a2 is zero that
is expressed as a traveling rate or any external factor.
The possible range of values for each decision variable is
determined as xi
L
≤ xi ≤ xi
U
, i=1, 2, …, n. Where, xi
L
and xi
U
are the lower and upper bounds for each decision variable,
respectively. The lower bound xi
L
is any one of the initial
values (0.5, √0.5, 1) for each decision variable xi. But, the
upper bound (xi
U
) of xi is calculated by putting all variables
at zero values in the set of constraints except xi. Then the
upper bound has a maximum value of xi. Obviously, when
a patient dies or he (she) is treated, the corresponding
value of xi is zero. Thus, this operation is introduced as
follows:
0 If a patient is died or treated or social isolation
≤ ≤ , = 1, 2, … #. $%ℎ'() *'
(5)
The value of each decision variable obtained as already
mentioned is examined to determine whether it should be
feasible and improve the solution. If the new value is better,
then it is included in the corona vector. Else, it is excluded
6. American Journal of Artificial Intelligence 2021; 5(2): 56-65 60
from this vector.
The formula (5) considers the mastermind of the
algorithm, and it enables the algorithm to be more
efficient than other artificial intelligent systems. This
algorithm requires only two parameters: a1, and a2 that
are used to improve the solution. So, the good
information captured in the current iteration can be well
utilized to generate the global solution easily within
reasonable time. Moreover, this step likes the process of
search direction or step length of each bacterium in the
bacterial foraging algorithm (BFA).
Step 4. Checking the termination criterion:
After each iteration of the algorithm, two tasks are carried
out:
1) Check whether the current solution is optimal (if yes, it is
obtained),
2) If not, repeat Steps 2 and Steps 3 until the termination
criterion (or a maximum number of iterations) is
satisfied.
There is no doubt that this step simulates the following
events of COVID-19: Death, isolation, traveling to any place,
and the recovery or spreading of the corona virus. It is clear
that the end program means reaching the optimum value;
which simulates the death or life of the patient.
All of these steps are illustrated using pseudo-code as in the
following Figure 3:
Figure 3. Pseudo-code of CV algorithm.
Theorem 1:
If the value of each decision variable follows a linear
equation, then the intersection of them is considered as a
convex set that has an optimal solution.
Figure 4. Optimization procedure of the corona search algorithm.
Proof:
The proof of this theorem is a simple consequence of linear
programming optimization theory and the definition of
convex set [24-25]. Since every decision variable follows a
7. 61 Alia Youssef Gebreel: Artificial Corona-Inspired Optimization Algorithm: Theoretical
Foundations, Analysis, and Applications
linear equation, then the intersection of them forms a convex
shape. Additionally, from the characteristics of a convex set
that has an optimal solution, the proof is given.
3.2. The Analogy Between Corona Virus Activity and
Problem Optimization
There is an analogy between corona virus activity and
problem optimization as follows:
1) The corona virus activity ↔ Decision variable.
2) The infection spread ↔ Value range. ({One of the three
real numbers: (0.5, √0.5, 1), and the upper bound of each
variable}).
3) Eliminate the corona virus ↔ Solution vector. (The
population of infected people who will die or treatment
or social isolation).
4) The corona virus ↔ Fitness function.
5) Infection and treatment step ↔ Iteration.
6) Infected population ↔ Memory vector.
7) Corona operators:
The spreading rates of corona virus are:
a1: The corona virus infection rate or internal rate or direct
influencer or the degree of corona virus (in minimization
case) or the improvement rate (in maximization case).
a2: The traveling rate or external rate or indirect influencer.
Remarks:
1) Corona algorithm is governed by the three real
numbers (0.5, √0.5, 1), the upper bound of each variable,
and the corona formula (xi+1=a1 xi + a2).
2) The formula above makes the corona algorithm's work
is easy and accurate.
3.3. The Major Advantages of the Corona Virus
Optimization Algorithm
The proposed optimization algorithm has some advantages
such as:
1) The arbitrary values are already set according to the corona
virus activity easily.
2) The algorithm can execute by Excel (or MATLAB or any
other software) program to find an optimal solution. It has
the ability to end after a few or several iterations because
it finishes after the optimal solution is obtained.
3) The corona virus optimization algorithm has the ability to
finish its task in a fewer number of iterations with
reasonable time (comparing with others such as genetic
algorithm, neural network, bacteria foraging algorithm).
4) This algorithm is suitable for discrete and continuous
variables. Also, it has a better chance to find the global
optimal solution.
5) The stochastic random searches and derivative information
are unnecessary in the mathematical of corona search.
Whereas, it requires two parameters (a1 and a2) only, which
have deterministic values to guide the search process for
finding the optimal solution easily.
6) The actual values (with low or high degree) for the
spreading rates are reported due to the ability of corona
virus to cause infection.
4. Numerical Examples
The following examples are taken from the optimization
literature, which are used to show the validity and
effectiveness of the artificial corona algorithm.
Example 1:
Consider the following linear-programming problem with
four variables, four equality constraints, and four bounds:
Min: f(x)=2x1+ 6x2+ x3+ x4,
Subject to: x1+ 2 x2 + x4=6,
x1+ 2 x2+ x3+ x4=7,
x1+ 3 x2+ x3+ 2x4=7,
x1+ x2+ x3=5,
x1 ≥ 0, x2 ≥ 0, x3 ≥ 0, x4 ≥ 0.
This problem is presented by David and Yinyu [26]. Its
optimal solution using LINDO software is +
∗
=4, -
∗
=0, .
∗
=1,
/
∗
=2, and the objective function value is 11.
To solve this problem by the proposed algorithm, the steps
are as follows:
1) Initial corona memory (CM): It seems that the objective
function has a greater value multiplying by the second
variable "x2", and followed by the first variable "x1".
Therefore, it can be starting by initial solution as (0.5, 0.5,
0.7, 1)T
to find the optimal solution. The corresponding
value of the objective function "f(x)" is 5.7, but the four
constraints are not satisfied.
2) Update the corona memory: To update the memory of
corona vector, it can be seen firstly the last constraint or
the second constraint. The upper bound of x1, x2, x3, x4
are calculated as 7, 5, 7, 7, respectively. Thus, it is
necessary to set the variable "x2" at zero value, "x1"=0.5
× 8=4.0, "x3"=0.7 × 1 + 0.3=1.0, "x4"=1 × 2=2.0. In this
case, all constraints (C1, C2, C3, C4) are satisfied and the
corresponding objective function f(x) has a minimum
value of 11.0.
Table 1. The optimal results of example (1) by CA.
Data Initial solution Update solution
x1 0.5 0.5 × 8 + 0.0=4.0
x2 0.5 0.5 × 0 + 0.0=0.0
x3 0.7 0.7 × 1 + 0.3=1.0
x4 1.0 1.0 × 2 + 0.0=2.0
C1 2.5 6
C2 3.2 7
C3 3.3 7
C4 1.7 5
f(x) 5.7 11
Table 1 shows these operations for obtaining the optimal
solution. It is obvious that the optimal solution obtained using
the artificial corona algorithm is the same solution previously
presented.
Example 2:
This example has been previously solved by Zong Geem [27]
and Quan [28]. It consists of minimizing two variables as shown
in Figure 5. The six-hump camel-back function is defined as:
Min: f(x)=(4 x1
2
– 2.1 x1
4
+
+
.
x1
6
) + x1 x2 + (- 4 x2
2
+ 4 x2
4
),
Subject to: -5 ≤ xi ≤ 5, i=1, 2.
8. American Journal of Artificial Intelligence 2021; 5(2): 56-65 62
Figure 5. 2D Six-hump camel-back function.
The comparison of results is shown in Table 2. It can be
seen that the global solution of this problem obtained using the
presented algorithm is better than those reported previously in
the literature.
Example 3:
Consider the following nonlinear problem:
Min: (2x – 3y),
Subject to: x 2
+ y2
≤ 1,
x + y ≤ 1.
The optimal solution of this problem by Kuhn-Tucker
algorithm is x*
=0, y*
=1, and f*
=–3. For solution by CA, the
initial values for each decision variable are x=1.0, and y=0.5.
But, the upper bound of x and y is one.
As shown in Table 3, the optimal solution by the proposed
method is x*
=–0.5547003, y*
=0.832050225, C1=1.0,
C2=0.277349925, and f*
=2x*
– 3y*
=–3.605551275.
Figure 6. The feasible region of example 3.
Table 2. The optimal results for minimization of the six-hump camel-back function.
Memory vector Proposed method Other methods
x1 0.5 0.5 × -1 - 0.5 × 0.17967 - 0.08983 - 0.08975
x2 √0.5 √0.5 √0.5 + 0.0055 0.7126 0.7127
f(x) 0.227512 - 0.4796 - 1.0316284335 - 1.0316284276 - 1.031628401
Explanation Initial solution update solution Optimal solution by CA Optimal Exact solution Optimal solution by HA
Table 3. The optimal results of example (3) by CA.
Data Initial solution Update solution
x 1.0 1.0 – 0.446=– 0.554 1.0 – 1.5547003=– 0.5547003
y 0.5 0.5 0.5 + 0.332050225=0.832050225
C1 1.25 0.556916 1.0
C2 1.5 – 0.054 0.277349925
f(2x – 3y) 0.5 – 2.608 –3.605551275
It is clear that the optimal solution by algorithm corona is
more accurate than others.
Example 4:
This problem has been solved before by M. Mahdavi, M.
Fesanghary, and E. Damangir [29] using an improved
harmony search algorithm. It is stated as follows:
Min: f(x)=(x1
2
+ x2 - 11)2
+ (x1 + x2
2
- 7)2
,
Subject to: 4.84 - (x1 - 0.05)2
- (x2 - 2.5)2
≥ 0,
x1
2
+ (x2 - 2.5)2
- 4.84 ≥ 0,
0 ≤ x1 ≤ 6, 0 ≤ x2 ≤ 6.
The problem has two decision variables (x1, x2), two
nonlinear inequality constraints, and four boundary conditions.
The unconstrained objective function has a minimum solution
at (3, 2) with a function value of f(x)=0. But, this solution is no
more feasible. Where, the feasible region is a narrow
crescent-shaped region with the optimum value lying on the
second constraint as shown in the Figure 7.
The previous best solution reported by the authors of the
paper [29] was obtained at x*
=(2.2468258, 2.381863) with the
corresponding function value=13.5908421.
Figure 7. The constrained function of example 4.
The optimal solution by corona algorithm is provided at
9. 63 Alia Youssef Gebreel: Artificial Corona-Inspired Optimization Algorithm: Theoretical
Foundations, Analysis, and Applications
x*
=(2.2468256, 2.381859053734) with the corresponding
function value f*
(x*
)=13.59084169, C1=0.0, and
C2=0.2221826. Of course, corona's solution is better than the
previous paper.
Example 5:
The following problem has been presented by Valdimir [30]
as a multi-objective:
Min: f1=- (25 (x1 - 2)2
+ (x2 - 2)2
+ (x3 - 1)2
+ (x4 - 4)2
+(x5 - 1)2
),
Min: f2=(x1
2
+ x2
2
+ x3
2
+ x4
2
+ x5
2
+ x6
2
),
Subject to:
x1 + x2 - 2 ≥ 0,
- x1 - x2 + 6 ≥ 0,
x1 - x2 + 2 ≥ 0,
-x1 +3x2 + 2 ≥ 0,
- (x3 - 3)2
- x4 + 4 ≥ 0,
(x5 - 3)2
+ x6 - 4 ≥ 0,
x1, x2, x6 ∈ [0: 10], x4 ∈ [0: 6], x3, x5 ∈ [1: 5].
To get the ideal (utopia) point, the optimal solution is
obtained for each objective function of this problem. Firstly,
the individual optimal for objectives by LINGO software are
as follows:
The optimal solution for the first objective is f1
*
(x1=0, x2=2,
x3=5, x4=0, x5=5, x6=0.3513184)=- 148. The second objective
optimal is f2
*
(x1=x2=x3=x5=1, x4=x6=0)=4.
Secondly, the optimal solution for each objective is
obtained by corona algorithm as follows:
1) The initial solution: x1=0.5, x2=1, x3=1, x4=1, x5=1,
x6=√0.5, and the corresponding objective function is
-46.25.
2) The upper bound for each variable is as follows: x1, x2,
x6=10, x4=6, and x3, x5=5. But as seen from this problem,
the lower bound for two variables x3 and x5 is one.
3) The obtained optimal solution of the first objective is: f1
*
(x1=5, x2=1, x3=5, x4=0.00, x5=5, x6=0.00)=- 274. The
second objective optimal is the same as solution of
LINGO software: f2
*
(x1=x2=x3=x5=1, x4=x6=0)=4. The
following Table 4 presents the optimal solutions of this
example obtained using the proposed algorithm and
comparing these results with solutions solved by LINGO
software.
Table 4. Optimal results for each objective of example (5).
Data
The first objective function
The second objective
function by two approaches
Proposed method LINGO software's
solution
The initial solution The final solution
x1 0.50 0.5 × 10 + 0.00=5.00 0.00 1
x2 1.00 1.00 2.00 1
x3 1.00 1 × 5=5.00 5.00 1
x4 1.00 1 × 0=0.00 0.00 0
x5 1.00 1 × 5=5.00 5.00 1
x6 √0.5 √0.5 × 0.0=0.00 0.3513184 0
C1 - 0.50 4.00 0.00 0
C2 4.50 0.00 4.00 4
C3 1.50 6.00 0.00 2
C4 4.50 0.00 8.00 4
C5 -1.00 0.00 0.00 0
C6 √0.5 0.00 0.3513184 0
f1
*
- 46.25 -274 -148.00 - 42
f2
*
4.75 76 54.1234246 4
f1
*
+ f2
*
-41.50 -198 -93.8765754 -38
5. Conclusion
This study proposes a new artificial intelligent
methodology by simulating the corona virus to get the optimal
solution.
Firstly, the values of the decision variables are selected
from the three real numbers (0.5, √0.5, 1). Secondly, the
upper bound of each variable is calculated by putting other
variables at zero values. After that, the upper bound of each
variable has the highest value of each variable. These
bounds of each variable in the corona search algorithm
control the generation of a new solution based on the linear
formula. This formula is useful to get the optimal solution
easily. The corona algorithm doesn't need to use any
derivatives or randomness.
Moreover, it is compared with other artificial algorithms
such as GA, BA, and HA on solving the same mathematical
optimization problems. The results show that CA yields the
same or better solutions than the previous solutions by these
algorithms. Also, it presents the optimum value better than
those solved by LINGO software.
Besides, this artificial tool offers the optimal solution for
difficult or impossible problems reported previously in the
literature.
Some features of the proposed algorithm are simplicity in
concept, ease of implementation, and generating the optimal
solution with high accuracy and speed. These features make
this approach more efficient, robust, and effective to apply in
many real-world optimization problems.
Finally, this work shows that the proposed approach is
different from others for solving mathematical optimization
problems.
For future research, this algorithm is expected to solve the
multi-objective optimization programming problems to get
the best efficient solution.
10. American Journal of Artificial Intelligence 2021; 5(2): 56-65 64
Acknowledgements
Thanks are to ALLAH for his guidance and supports in
showing us the path.
References
[1] Ahmed T. Sahlol, DaliaYousri, Ahmed A. Ewees, Mohammed
A. A. Al-qaness, Robertas Damasevicius, & Mohamed Abd
Elaziz (2020). COVID-19 image classification using deep
features and fractional-order marine predators algorithm.
Scientific Reports, Nature research, 10: 15364,
https://DOI.org/10.1038/s41598 -020-71294-2, PP. 1-15.
[2] F. Martı´nez-A´ lvarez, G. Asencio-Corte´ s, J. F. Torres, D.
Gutie´rrez-Avile´ s, L. Melgar-Garcı´a, R. Pe´rez-Chaco´ n, C.
Rubio-Escudero, J. C. Riquelme, & A. Troncoso (2020).
Coronavirus optimization algorithm: A bioinspired meta
heuristic based on the COVID-19 propagation model, Big Data,
Mary Ann Liebert, Inc., DOI: 10.1089/big.2020.0051, 8 (4),
(Research gate).
[3] Hayat Ouassou, Loubna Kharchoufa, Mohamed Bouhrim,
Nour Elhouda Daoudi, Hamada Imtara, Noureddine
Bencheikh, Amine ELbouzidi, & Mohamed Bnouham
(2020). The pathogenesis of coronavirus disease 2019
(COVID-19): Evaluation and prevention. Journal of
Immunology Research, Hindawi, https://doi.org/10.
1155/2020/1357983, PP. 1-7.
[4] Mohammed A. A. Al-qaness, Ahmed A. Ewees, Hong Fan, &
Mohamed Abd El Aziz (2020). Optimization method for
forecasting confirmed cases of COVID-19 in China. Journal of
Clinical. Medicine, DOI: 10.3390/jcm9030674, 9 (674), PP.
1-15.
[5] Tanu Singhal (2020). A Review of Coronavirus Disease-2019
(COVID-19), The Indian Journal of Pediatrics, 87 (4), PP.
281–286.
[6] Colin R. Reeves (1996). Heuristic search methods: A review,
Conference Paper,
https://www.Researchgate.net/publication/263587732.
[7] Krishna Nand Patel, Scahin Raina, & Saurabh Gupta (2020).
Artificial intelligence and its models, Journal of Applied
Science and Computations (JASC), 7 (2), PP. 95-97.
[8] Prithwish Chakraborty, Gourab Ghosh Roy, Swagatam Das,
Dhaval Jain, & Ajith Abraham (2009). An improved harmony
search algorithm with differential mutation operator,
Fundamenta Informaticae, 95, PP. 1–26.
[9] Reza Sirjani, Azah Mohamed, & Hussain Shareef (2012).
Heuristic optimization techniques to determine optimal
capacitor placement and sizing in radial distribution networks:
A comprehensive review, Przeglad Elektrotechniczny, ISSN
0033-2097, R. 88 NR 7a.
[10] Mohammed Azmi Al-Betar, Zaid Abdi Alkareem Alyasseri,
Mohammed A. Awadallah, & Iyad Abu Doush, (2020).
Coronavirus herd immunity optimizer (CHIO), Neural
Computing and Applications, Springer-Verlag London Ltd.,
https://doi.org/10.1007/s00521-020-05296-6, PP. 1-32.
[11] Gaurav Dhiman, V. Vinoth Kumar, A Kaur, & A. Sharma
(2021). DON: Deep learning and optimization-based
framework for detection of novel coronavirus disease using
X-ray images. Interdisciplinary Sciences: Computational Life
Sciences, https://doi. org/10.1007/s12539- 021- 00418- 7, PP.
1-13.
[12] Alia Youssef Gebreel (2018). An overview of genetic
algorithm, bacterial foraging algorithm, and harmony search
algorithm, Global Scientific Journals, 6 (9), PP. 165-189.
[13] P. D. D. Dominic, Ahmad Kamil, P. Parthiban, & M.
Punniyamoorthy (2009). A comparative representation
approach to modern heuristic search methods in a job shop. The
International Journal of Applied Management and Technology,
6 (2), PP. 39-60.
[14] Timo Pukkala, & Mikko Kurttila (2004). Examining the
performance of six heuristic optimisation techniques in
different forest planning problems, Silva Fennica, research
articles, 39 (1), PP. 67-80.
[15] Julia Borghoff, Lars R. Knudsen, & Krystian Matusiewicz,
(2011). Hill climbing algorithms and trivium, Springer-Verlag
Berlin Heidelberg, PP. 57-73.
[16] Sheldon H. Jacobson, & Enver Yücesan, (2004). Analyzing the
performance of generalized hill climbing algorithms, Journal
of Heuristics, Kluwer Academic Publishers, Manufactured in
the Netherlands, 10, PP. 387-405.
[17] Andreas Fink, & Stefan Voss (1998). Applications of modern
heuristic search methods to pattern sequencing problems,
Computers & Operations Research, PP. 1-20.
[18] Baran Hekimoğlu, & Serdar Ekinci (2020). Optimally
designed PID controller for a DC-DC buck converter via a
hybrid whale optimization algorithm with simulated annealing,
https://www.researchgate.net/publication/339782631, DOI:
10.5152/Electrica.19034, 20 (1), PP. 19-27.
[19] K. Y. Lee, & M. A. El-Sharkawi (2008). Modern heuristic
optimization techniques with applications to power systems,
Power System Analysis, Computing, and Economics
Committee, IEEE Power Engineering Society.
[20] Alia Youssef Gebreel (2016). An adaptive interactive
multi-objective optimization approach based on decision
neural network, International Journal of Scientific &
Engineering Research, 7 (8), PP. 1178-1185.
[21] K. M. Bakwad, S. S. Pattnaik, B. S. Sohi, S. Devi, B. K.
Panigrahi, & Sastry V. R. Gollapudi (2010). Multimodal
function optimization using synchronous bacterial foraging
optimization technique, IETE Journal of Research, 56 (2), PP.
80-87.
[22] M. Fesanghary, E. Damangir, and I. Soleimani (2009). Design
optimization of shell, & tube heat exchangers using global
sensitivity analysis and harmony search algorithm, Applied
Thermal Engineering, 29, PP. 1026–1031.
[23] Girish P Potdar, & R C Thool (2014). Comparison of various
heuristic search techniques for finding shortest path,
International Journal of Artificial Intelligence & Applications
(IJAIA), 5 (4), PP. 63-74.
[24] Dimitri P. Bertseka (2009). Convex optimization theory,
Massachusetts Institute of Technology.
[25] Mokhtar S. Bazaraa, John J. Jarvis, & Hanif D. Sherali (2010).
Linear programming and network flows, Fourth edition, A
John Wiley & Sons, Inc., Publication.
11. 65 Alia Youssef Gebreel: Artificial Corona-Inspired Optimization Algorithm: Theoretical
Foundations, Analysis, and Applications
[26] David G. Luenberger, & Yinyu Ye (2008). Linear and
nonlinear programming, (Chapter 3-The simplex method),
PDF, Third edition, PP. 73.
[27] Zong Woo Geem (2008). Novel derivative of harmony search
algorithm for discrete design variables, Applied Mathematics
and Computation, 199, PP. 223–230.
[28] Quan-Ke Pan, P. N. Suganthan, M. Fatih Tasgetiren, & J. J.
Liang (2010). A self-adaptive global best harmony search
algorithm for continuous optimization problems. Applied
Mathematics and Computation, 216, PP. 830–848.
[29] M. Mahdavi, M. Fesanghary, & E. Damangir (2007). An
improved harmony search algorithm for solving optimization
problems", Science Direct, Applied Mathematics and
Computation, 188, PP. 1567–1579.
[30] Valdimir Sevasty Anov (2013). Hybrid multi-gradient explorer
algorithm for global multi-objective optimization, American
Institute of Aeronautics and Astronautics, 10 (94-99), PP. 1-15,
eartius, Inc.
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