An Efficient COVID-19 Disease Outbreak Prediction Using BI-SSOA-TMLPNN and ARIMA

Globally, people's health and wealth are affected by the outbreak of the corona virus. It is a virus, which infects from common fever to severe acute respiratory syndrome. It has the potency to transmit from one person to another. It is established that this virus spread is augmenting speedily devoid of any symptoms. Therefore, the prediction of this outbreak situation with mathematical modelling is highly significant along with necessary. To produce informed decisions along with to adopt pertinent control measures, a number of outbreak prediction methodologies for COVID-19 are being utilized by officials worldwide. An effectual COVID-19 outbreaks' prediction by employing Squirrel Search Optimization Algorithm centric Tanh Multi-Layer Perceptron Neural Network (MLPNN) (SSOA-TMLPNN) along with Auto-Regressive Integrated Moving Average (ARIMA) methodologies is proposed here. Initially, from the openly accessible sources, the input time series COVID-19 data are amassed. Then, pre-processing is performed for better classification outcomes after collecting the data. Next, by utilizing Sine-centered Empirical Mode Decomposition (S-EMD) methodology, the data decomposition is executed. Subsequently, the data are input to the Brownian motion Intense (BI) - SSOA-TMLPNN classifier. In this, the diseased, recovered, and death cases in the country are classified. After that, regarding the time-series data, the corona-virus's future outbreak is predicted by employing ARIMA. Afterwards, data visualization is conducted. Lastly, to evaluate the proposed model's efficacy, its outcomes are analogized with certain prevailing methodologies. The obtained outcomes revealed that the proposed methodology surpassed the other existing methodologies.

Smart Health Systems Components, Challenges, and Opportunities

Smart health is a relatively new paradigm where information and communication technology is utilized to improve health care and medical services. In this article, we provide a literature-based overview of smart health systems, their components, architecture, technologies, benefits, applications, challenges, and opportunities. In addition, we discuss the potential benefits of big data, data analytics, artificial intelligence (AI), and machine learning (ML) in smart health systems. Moreover, we discuss the challenges as well as the open research issues that need further investigation to facilitate the implementation of smart health systems.

COVID-19: X-ray image classification using transfer learning DCNN approach

COVID-19 is a coronavirus that causes sickness in the human respiratory system. It is the most recent virus that is wreaking havoc on the entire world. It spreads mainly through contact with an infected person. There are some vaccinations available to prevent this condition now. The flu causes symptoms such as fever, coughing, and breathing difficulties in humans. COVID-19: Classification of X-Ray Images This paper suggests using a Deep Convolution Neural Network-based Transfer Learning methodology. Deep CNN learns picture patterns and classifies X-RAY pictures using transfer learning technology. A dataset is created using publicly available photos of COVID-19 X-Ray. All images have been resized and rotated by 2 to 20 degrees. The file contains 6677 COVID-19 pictures and 5753 stock pictures. DCNN predictability is 99.64 percent on a training set, while on a test set, it is 99.79 percent. After the transfer of learning, predictive accuracy on the training set is 99.19 percent, while predictive accuracy on the test set is 99.31 percent. © 2022 Author(s).

Patient Prediction Through Convolutional Neural Networks

This paper presents a methodology for predicting the lung diseases of patients through medical images using the Convolutional neural network (CNN). The importance of this work comes from the current SARS-CoV-2 pandemic simulation where with the presented method in this work, pneumonia infection from healthy situation can be diagnosed using the X-ray images. For validating the presented method, various X-ray images are employed in the Python coding environment where various libraries are used: TensorFlow for tensor operations, Scikit-learn for machine learning (ML), Keras for artificial neural network (ANN), matplotlib and seaborn libraries to perform exploratory data analysis on the data set and to evaluate the results visually. The practical simulation results reveal 91% accuracy, 90% precision, and 96% sensitivity making prediction between diseases. [ FROM AUTHOR]

A Meta-analysis of Machine Learning for the Diagnosis of Covid-19 Disease

Coronavirus 2019 has wreaked havoc on people's lives all across the globe. The number of positive cases is increasing, and the Asian country is now one of the most severely impacted. This article examines machine learning models that are more accurate at predicting covid. Based on the data from China, regression-based, decision tree-based, naive Bayes, and random forest-based models were developed and verified on a sample from India. A data-driven strategy with better precision, such as the one used here, is beneficial for the government and public to respond in a proactive manner. This study reveals that the suggested framework has superior capabilities in detecting COVID-19. © 2022 IEEE.

A Review of Covid-19 Detection and Diagnosis Methods Based on Deep Learning

(COVID-19) is an epidemic disease with symptoms similar to the flu. Since December 2019, the new Coronavirus has appeared in China and spread all around the world. As a result, the rapid and severe spread. For example, computed tomography (CT) and x-rays are utilized to find an available and accurate diagnostic tool. Artificial intelligence technologies make a significant contribution to the diagnosis and analysis of medical images. As a consequence, it was used to detect and diagnose Covid-19. Thus, the entire major medical scanning will be covered in this review paper, especially Focus mostly on last year's combination of Artificial Intelligence with CT and X-ray. Both are commonly used on front-line medical clinics to identify the recent results of fighting COVID-19. © 2022 American Institute of Physics Inc.. All rights reserved.

Forecasting pharmacy purchases orders

Inventory represents the largest asset in pharmacy products distribution. Forecasting pharmacy purchases is essential to keep an effective stock balancing supply and demand besides minimizing costs. In this work, we investigate how to forecast product purchases for a pharmaceutical distributor. The data contains inventory sale histories for more than 10 thousand active products during the past 15 years. We discuss challenges on data preprocessing of the pharmacy data including cleaning, feature constructions and selections, as well as processing data during the COVID period. We experimented on different machine learning and deep learning neural network models to predict future purchases for each product, including classical Seasonal Autoregressive Integrated Moving Average (SARIMA), Prophet from Facebook, linear regression, Random Forest, XGBoost and Long Short-Term Memory (LSTM). We demonstrate that a carefully designed SARIMA model outperformed the others on the task, and weekly prediction models perform better than daily predictions.

Symbiotic Relationship Between Machine Learning and Industry 4.0: A Review

Industry 4.0 though launched less than a decade ago, has revolutionized the way technologies are being used. It has found its application in almost every field of manufacturing, cybersecurity, health, banking, and other services. Industry 4.0 is heavily dependent on interconnectivity and data. Machine learning (ML) acts as a foundation for building industry 4.0 applications. In this paper, we have provided a broad view of how ML is necessary to accomplish the benefits of industry 4.0. The paper includes ML usage in companies and the limitations of ML, which need to be mitigated. There are also some instances of the failure of ML algorithms and their repercussions. Though industry 4.0 requires a lot more inputs and capital than normal processes, the long-run benefits outweigh the initial costs. ML is gaining popularity, and extensive research is happening to exploit its potential and develop full smart applications.

A Comparison of Machine Learning Methods for best Accuracy COVID-19 Diagnosis Using Chest X-Ray Images

Coronavirus (COVID-19) changed the view of people towards life in all the countries of the world in December 2019. The virus has made chaos that cannot be predicted. This problem requires using a variety of technologies to aid in the identification of COVID-19 patients and to control the disease spread. For suspected instances of COVID-19 disease, chest X-ray (CXR) imaging is a standard with fewer costs, but it does not need a COVID-19 examination approach without using technology to help for a suitable diagnosis. In response to this issue, a big dataset of CXR images was divided into four classes found on the website Kaggle. Dealing with large data of the images needs dataset reprocessing through choosing the optimal method for getting speed and best accuracy. Dataset reprocessing converts into gray level then adjust image intensity, resize and extract the best features then apply Machine Learning ML models. The use of different prediction models, ML algorithms, and their performances are calculated with evaluation on the dataset after reprocessing. Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and K-Nearest Neighbors (KNN) are models used to foretell the specialized who would be diagnosed with COVID-19 quickly by using CXR images classification. The KNN has revealed the best accuracy compared with the others such as GNB, DT, SGD, LR, and RF. Also, KNN has the best-weighted average for all parameters, which are precision, sensitivity, and F1-score compared with the other models. © 2022 IEEE.

Artificial intelligence

Artificial intelligence-based information and modeling methods give an insight into prevalent diseases and the reasons behind their spread. On the basis of such insights, preventive actions can be taken to suppress the effects of such contagious diseases. This is a prominent application of artificial intelligence that is being used to help humankind to reduce the effects of such communicable diseases. COVID-19 is not the first pandemic that has spread throughout the world. The world has witnessed and battled a large number of such pandemics in the past. Some of the prominent diseases that have affected the world in the past include SARS, Marburg, Ebola, and Nipah. This chapter reviews some of the very effective efforts made in this direction to address the above-mentioned points. Protection of the data and other ethical issues related to application of machine learning in regard to COVID-19 is also discussed. © 2022 Elsevier Inc. All rights reserved.

Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India

The novel coronavirus (nCOV) is a new strain that needs to be hindered from spreading by taking effective preventive measures as swiftly as possible. Timely forecasting of COVID-19 cases can ultimately support in making significant decisions and planning for implementing preventive measures. In this study, three common machine learning (ML) approaches via linear regression (LR), sequential minimal optimization (SMO) regression, and M5P techniques have been discussed and implemented for forecasting novel coronavirus disease-2019 (COVID-19) pandemic scenarios. To demonstrate the forecast accuracy of the aforementioned ML approaches, a preliminary sample-study has been conducted on the first wave of the COVID-19 pandemic scenario for three different countries including the United States of America (USA), Italy, and Australia. Furthermore, the contributions of this study are extended by conducting an in-depth forecast study on COVID-19 pandemic scenarios for the first, second, and third waves in India. An accurate forecasting model has been proposed, which has been constructed on the basis of the results of the aforementioned forecasting models of COVID-19 pandemic scenarios. The findings of the research highlight that LR is a potential approach that outperforms all other forecasting models tested herein in the present COVID-19 pandemic scenario. Finally, the LR approach has been used to forecast the likely onset of the fourth wave of COVID-19 in India.

Evaluation of Dietary Habits in Relation to Covid-19 Mortality Rate Using Machine Learning Techniques

Coronavirus attacks have affected countless countries. The death rates between most countries are increasing day by day, and we have attempted to propose many considerations about the principal problems that cause dangerous infections across the globe. In this work, the dietary patterns of 170 countries are considered to identify correlations between diet practices and death rates, confirmed and recovered cases caused by COVID-19. We have used data from food intake by countries and data associated with the spread of COVID-19 and other health issues that help get new insights into the importance of nutrition and eating habits to combat the spreading of infectious diseases. We have built a machine learning model (regressor) such as ridge regressor, support vector regression, random forest, and XGBoost regressor to predict the mortality rate based on food intake information and Obesity. Two approaches were considered: One with all food-related features taken as parameters and a simpler one, which reduced the dimensionality by using only two features: Animal products and vegetal products. Both have issues (mainly of spread and non-linearity), but we could use different models and metrics. Next, we have built a model to predict obesity rates based on eating habits in each country. The proposed model was far more effective, and the general inclination of the information was taken and anticipated. We have also used data visualization approaches to get better insights into the data considered. © 2022, Success Culture Press. All rights reserved.

News Classification using Natural Language Processing

The lives of people have been affected massively with the entrance of web and internet-based life. In addition general public life is affected by mass-media. These privileges are exploited by many people due to political influence, for luxuries, increasing their social status. Covid-19 pandemic is one example where the people aren't completely aware of daily cases due to the spread of fake feed. People use web-based social networking platforms to disseminate these counterfeit items on purpose. This affects the original functioning and intent of online news sites. So in order to curb such malicious news from being spread we need some tools to automate the process and find efficient ways to classify it. The major goal of this study is to develop a reliable and accurate model that uses ML algorithms and NLP techniques to classify a given news article as false or genuine, allowing only authentic news to be presented to the public. © 2022 IEEE.

Machine Learning - Based Diagnosis of Covid-19 using Clinical Data

Coronavirus (COVID-19) is a worldwide pandemic caused by SARS Coronavirus 2. (SARS-CoV-2). The COVID-19 epidemic has put global healthcare systems in jeopardy. This study's purpose is to develop and evaluate an automated COVID-19 infection detection system using machine learning and chest x-ray images. Early diagnosis and treatment may help avert major illness and even death. It is presently the most favoured and accurate approach for COVID-19 diagnosis. X-ray imaging of the chest may be used instead of the rRT-PCR test to look for early COVID-19 symptoms. A new machine learning (ML)-based analytical framework for automated COVID-19 diagnosis is created utilizing chest X-ray pictures of likely patients. The proposed framework for COVID-19 disease diagnosis using X-ray images has a 99 percent accuracy for Covid and a 92 percent accuracy for Non-covid in two-class categorization. The investigation suggests the COVID-19 detection framework is better. © 2022 IEEE.

Forecasting and classification of new cases of COVID 19 before vaccination using decision trees and Gaussian mixture model

Regarding the pandemic taking place in the world from the spread of the Coronavirus pandemic and viral mutations, the need has arisen to analyze the epidemic data in terms of numbers of infected and deaths, different geographical regions, and the dynamics of the spread of the virus. In China, the total number of reported infections is 224,659 on June 11, 2022. In this paper, the Gaussian Mixture Model and the decision tree method were used to classify and predict new cases of the virus. Although we focus mainly on the Chinese case, the model is general and adapted to any context without loss of validity of the qualitative results. The Chi-Squared (χ2) Automatic Interaction Detection (CHAID) was applied in creating the decision tree structure, the data has been classified into five classes, according to the BIC criterion. The best mixture model is the E (Equal variance) with five components. The considered data sets of the world health organization (WHO) were used from January 5, 2020, to 12, November 2021. We provide numerical results based on the Chinese case. © 2022 THE AUTHORS

FLOSSC: A Computer-Aided Tool for Detecting Corona Virus Infections through Chest X-Ray Images

Artificial intelligence has radically altered the world, and it continues to progress at an alarming rate as time passes. AI applications include healthcare and medical solutions, illness diagnostics, agriculture, constructing security infrastructures, autonomous cars, intelligent systems, industrial production, robotics, and much more. COVID19 is a deadly virus that first appeared in China in 2019 and soon spread over the world. By 2020, the globe had witnessed a tremendous epidemic, with countless lives lost as a result of this dreadful virus, which has now become a severe health danger. Furthermore, in 2021, several nations will be infected with new Covid19 forms that are more deadly and spread quicker. The research describes the proposed methodology for diagnosing covid-19 and pneumonia from human chest X-ray images using transfer learning with Resnet-18 and VGG-16 neural networks. The focal loss function was also used to homogenize the imbalanced dataset, which included X-ray images of normal, pneumonia, and Covid-19 patients. The purpose is to assess the performance and accuracy of fine-tuned neural networks after including Binary Cross-Entropy (BCE) and Focal Loss (FL) functions. However, when we used our Resnet-18 and VGG-16 neural networks with BCE and FL functions, the VGG-16 with FL function outperformed all other models, with training and validation accuracy of 98.37 percent and 97.37 percent, correspondingly.

Machine Learning Approaches in Deal with the COVID-19, Comprehensive Study

The novel Covid illness (COVID-19) has spread more than 219 nations on the globe as a pandemic, making disturbing impacts on medical care, financial conditions, and global connections. The primary goal of the review is to give the Artificial Intelligence's technological aspect and other applicable innovations and their suggestions for standing up to COVID-19 and prevention of the pandemic's frightful impacts. This article presents various approaches with AI moves toward that have huge contribution in the medical service fields, then, at that point, features and sorts their applications in facing Corona virus, like identification and finding, information examination and treatment methods, exploration and medication improvement, social control and benefits, and the expectation of outbreaks. The review tends to the connection between the innovations and the pandemics just as the expected effects of innovation in medical care with the presentation of AI and normal language processing devices. It is usual that this exhaustive review will uphold specialists in demonstrating medical services frameworks and drive further investigations in cutting edge innovations. At last, we conclude that enticing simulated artificial intelligence techniques, probabilistic models, as well as supervised learning are needed to handle future pandemic difficulties. © The Electrochemical Society

Applications of artificial intelligence to detect android botnets: A Survey

With the growing popularity of Android smart devices, and especially with the recent advances brought on by the COVID-19 pandemic on digital adoption and transformation, the importance of protecting these devices has grown, as they carry very sensitive data. Malicious attacks are targeting Android since it is open source and has the highest adoption rate among mobile platforms. Botnet attacks are one of the most often forgotten types of attacks. In addition, there is a lack of review papers that can clarify the state of knowledge and indicate research gaps in detecting android botnets. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of Android Botnet detection. This study attempts to provide a comprehensive overview of the deployed AI apps for future academics interested in performing Android Botnet Detection studies. We focused on the applications of artificial intelligence and its two prominent subdomains, machine learning (ML) and deep learning (DL) techniques. The study presents available Android Botnet datasets suitable for detection using ML and DL algorithms. Moreover, this study provides an overview of the methodologies and tools utilized in APK analysis. The paper also serves as a comprehensive taxonomy of Android Botnet detection methods and highlights a number of challenges encountered while analyzing Android Botnet detection techniques. The research gaps indicated an absence of hybrid analysis research in the area, as well as a lack of an up-to-date dataset and a time-series dataset. The findings of this paper show valuable prospective directions for future research and development opportunities. Author

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays.

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.

IoT Adoption and Application for Smart Healthcare: A Systematic Review.

In general, the adoption of IoT applications among end users in healthcare is very low. Healthcare professionals present major challenges to the successful implementation of IoT for providing healthcare services. Many studies have offered important insights into IoT adoption in healthcare. Nevertheless, there is still a need to thoroughly review the effective factors of IoT adoption in a systematic manner. The purpose of this study is to accumulate existing knowledge about the factors that influence medical professionals to adopt IoT applications in the healthcare sector. This study reviews, compiles, analyzes, and systematically synthesizes the relevant data. This review employs both automatic and manual search methods to collect relevant studies from 2015 to 2021. A systematic search of the articles was carried out on nine major scientific databases: Google Scholar, Science Direct, Emerald, Wiley, PubMed, Springer, MDPI, IEEE, and Scopus. A total of 22 articles were selected as per the inclusion criteria. The findings show that TAM, TPB, TRA, and UTAUT theories are the most widely used adoption theories in these studies. Furthermore, the main perceived adoption factors of IoT applications in healthcare at the individual level are: social influence, attitude, and personal inattentiveness. The IoT adoption factors at the technology level are perceived usefulness, perceived ease of use, performance expectancy, and effort expectations. In addition, the main factor at the security level is perceived privacy risk. Furthermore, at the health level, the main factors are perceived severity and perceived health risk, respectively. Moreover, financial cost, and facilitating conditions are considered as the main factors at the environmental level. Physicians, patients, and health workers were among the participants who were involved in the included publications. Various types of IoT applications in existing studies are as follows: a wearable device, monitoring devices, rehabilitation devices, telehealth, behavior modification, smart city, and smart home. Most of the studies about IoT adoption were conducted in France and Pakistan in the year 2020. This systematic review identifies the essential factors that enable an understanding of the barriers and possibilities for healthcare providers to implement IoT applications. Finally, the expected influence of COVID-19 on IoT adoption in healthcare was evaluated in this study.