Prediction of Corona Virus Disease Outcome and Future Forecast the Trend of COVID-19 in India: Observational Study

This work is motivated by the disease caused by the novel corona virus Covid-19, rapid spread in India. An encyclopaedic search from India and worldwide social networking sites was performed between 1 March 2020 and 20 Jun 2020. Nowadays social network platform plays a vital role to track spreading behaviour of many diseases earlier then government agencies. Here we introduced the approach to predict and future forecast the disease outcome spread through corona virus in society to give earlier warning to save from life threats. We compiled daily data of Covid-19 incidence from all state regions in India. Five states (Maharashtra, Delhi, Gujarat, Rajasthan and Madhya-Pradesh) with higher incidence and other states considered for time series analysis to construct a predictive model based on daily incidence training data. In this study we have applied the predictive model building approaches like k-nearest neighbour technique, Random-Forest technique and stochastic gradient boosting technique in COVID-19 dataset and the simulated outcome compared with the observed outcome to validate model and measure the performance of model by accuracy (ACC) and Kappa measures. Further forecast the future trends in number of cases of corona virus deceased patients using the Holt Winters Method. Time series analysis is effective tool for predict the outcome of corona virus disease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

A Novel CNN Framework for the Detection of COVID-19 Using Manta Ray Optimization and KNN Classifier in LUS Images

Reverse transcription polymerase chain reaction (RT-PCR) is the gold standard for the diagnosis of COVID-19. Studies have proven that non-invasive techniques based on medical imaging can be used as an alternative to RT-PCR. The use of medical imag-ing technologies along with RT-PCR could improve the diagnosis and management of the disease. Even though several methods exist for diagnosing COVID-19 from X-ray images and CT scans, ultrasound image has not been explored much to diagnose the disease. In this study, we built a deep learning model using ultrasound images for a fast and efficient disease diagnosis. Pre-trained Convolutional Neural Networks (CNN), trained on the ImageNet database has been utilized for feature extraction. The nature-inspired Manta Ray Foraging Optimization (MRFO) algorithm is applied for dimensionality reduction and K-Nearest-Neighbour (KNN) for classification. Model training has been performed using a publicly available POCUS dataset consisting of 2944 ultrasound images sampled from more than 200 Lung Ultrasound (LUS) videos. Experimentations conducted in this study prove the efficiency of the model in the diagnosis of COVID-19. The model achieved an accuracy of 99.4337% using MobilenetV2 as the pre-trained network. © 2023, Ismail Saritas. All rights reserved.

Comparison of Predictive Machine Learning Models to Predict the Level of Adaptability of Students in Online Education

With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), K-Nearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as accuracy, specificity, sensitivity, F1 count and precision, have been used to evaluate the performance of each model. The results have shown that all five models can provide optimal results in terms of prediction. For example, the RF and XGB models presented the best performance with an accuracy rate of 92%, outperforming the other models. In consequence, it is suggested to use these two models RF and XGB for prediction of students' adaptability level in online education due to their higher prediction efficiency. Also, KNN, SVM and LR models, achieved a performance of 85%, 76%, 67%, respectively. In conclusion, the results show that the RF and XGB models have a clear advantage in achieving higher prediction accuracy. These results are in line with other similar works that used ML techniques to predict adaptability levels. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Artificial Intelligence based prediction system for General Medicine

COVID-19 is highly infectious and has been extensively spread worldwide, with approximately 651 million definite cases crosswise the globe including Pakistan. At that era of pandemic where patients are not able to approach a doctor for even the routine checkups, in such curial situation even normal disease checkups are ignored by many families due to pandemic situations, those diseases may lead to be a perilous disease are results of it. Human disorders portray scenarios that even disturb or permanently cutoff the essential functions of a body parts. Consequently, the aim is to transform raw health data potential into actionable insights to applying the promising outcomes of Body Sensor Network (BSN) and State-of-Art Artificial Intelligence (AI) techniques to get proper medicine allocation to the particular health state of patient. In this paper the different techniques of Deep Learning and Machine Learning introduced to predict the actual medicine for the specific health state of patient according to data from the BSN. Experiments have been conducted on large dataset which shepherd it into 16 states of patient's health which will allotted to AI model to predict the medicine accordingly to the health state of patient. Experimental results show the 87.46% by Random Forest, 92.74% by K-Nearest Neighbors, 74.57% by Naive Bayes, 94.41% by Extreme Gradient Boost, 84.88% by Multi-Layer Perceptron in terms of precision of model training in event of classification. © 2023 IEEE.

A Novel Deep Learning based Black Fungus Detection using the Bagging Ensemble with K-Nearest Neighbor

Machine learning is widely employed, and broadly speaking, scientists consider applying it everywhere. Around the same period, we can see that India has been devastated by the second corona wave. In a single day, more than 4 lakh instances arrive. Meanwhile, reports of the arrival of a new, fatal fungus called Mucormycosis emerged (Black fungus). Additionally, this fungus expanded quickly throughout numerous states, leading some of them to designate this illness an epidemic. People with weak immunity functions, including those who have had the corona virus and some of whom are still recovering, are more likely to get a black fungus infection since their bodies can't successfully fight it off. Bagging Ensemble with K-Nearest Neighbor is a modified machine learning approach that will be developed in this study (BKNN). The traditional methods, including K-Nearest Neighbor ensemble with bagging classification, are the basis for the suggested methodology. After the image processing techniques, including pre-processing and segmentation, were reviewed, the accuracy score for this classifier was 96.4 percent, which would have been the highest of all the findings. This paper described how machine learning was beneficial during the Corona era, much as it would be beneficial during epidemics like mucormycosis. The last section of this essay presents accurate, graphical evidence for all items addressed, along with explicit specifications. © 2022 IEEE.

EFFICIENT COVID-19 DISEASE DIAGNOSIS BASED ON COUGH SIGNAL PROCESSING AND SUPERVISED MACHINE LEARNING

The spread of the coronavirus has claimed the lives of millions worldwide, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of the coronavirus to limit its spread. In this work, we propose an automated system to detect COVID-19 based on the cough as one of the most important infection indicators. Several studies have shown that coughing accounts for 65% of the total symptoms of infection. The proposed system is mainly based on three main steps: first, cough signal detection and segmentation;second, cough signal extraction;and third, three techniques of supervised machine learning-based classification: Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Decision Tree (DT). Our proposed system showed high performance through good accuracy values, where the best accuracy for classifying female coughs was 99.6% using KNN and 88% for males using SVM. © 2022 by the Authors.

Benign and Malignant Skin Lesion Detection from Melanoma Skin Cancer Images

Skin cancer is the most dangerous and lethal cancer that affects millions of people each year. The accurate identification of skin cancers can not be accomplished without expert dermatologists. However, specific research studies of WHO in Canada, US and Australia, show that in the year 1960s to 1980s, the cases of skin cancer has noted more than two times increased in comparison with the previous years. The identification of skin cancer in its early stage is an expensive and difficult task because it doesn't cause too much bad in the initial phase. Whereas, the growth of skin cancer requires biopsy and many other treatments each time which is quite costly as per the statistics of India. This challenge makes it a necessary step to identify the existence of skin cancer in the early stages to increase immortality. With the evolution and progression in technology, there are various methods which have participated in and solved medical issues including covid19, pneumonia and many others. Similarly, machine learning(ML) and deep learning(DL) models are applicable to diagnosing skin cancer in its early stages. In this work, the support vector machine (SVM), naive bayes (NB), K-nearest neighbour (KNN) and neural networks(NN) have been used for classifying benign and malignant lesions. Furthermore, for the feature extraction from the dataset, a pre-trained SqueezeNet model has been used. The classification results of KNN, SVM, NB and NN have been shown in the accuracy, recall, F1-Measure, precision, AUC and ROC. The comparison of the models has resulted that the NN model outperforms all other models when applied with the SqueezeNet feature extractor with the highest accuracy, F1-Measure, recall, precision and AUC as 88.2%, 0.882, 0.882, 0.882 and 0.957, respectively. Lastly, the performance metrics analogies results of each model have been illustrated for the classification of benign and malignant lesions. © 2023 IEEE.

COVID-19 prediction based on GLCM features of radiography image using SVM, KNN, and backpropagation ANN classifiers

COVID-19 is a type of disease that transmits a new variant of virus known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) in the same novel coronavirus family as SARS-CoV and Middle East Respiratory Syndrome Coronovirus (MERS-COV). A fast method to detect the disease is essential to prevent larger transmission and to look after the infected patients. The Chest X-ray, one of the detection methods of COVID-19 can be used in the examination process of suspected cases. In this paper, a COVID-19 detection model through chest x-ray images is proposed by using Grey Level Co-occurrence Matrix (GLCM) with Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Backpropagation Artificial Neural Network (BP-ANN) classifiers. In this case, Principal Component Analysis (PCA) will be added as a mean to optimize features extraction process. The aim of this work is to find the best classifier for predicting chest x-ray images as normal, pneumonia, or COVID-19 suspect. The BP-ANN emerged as the best classifier with 85,5% accuracy, 85,8% precision, and 86,1% recall. © 2023 Author(s).

Predictive model to determine the growth of mobile money transactions in Zambia using data mining techniques

Mobile money has been known to be a successful venture around the world especially so, for African countries due to the many limitations that traditional banks have like operations, expensive transaction costs and cumbersome process to open account to mention but a few. The presence of mobile money has not only allowed the unbanked population to have accounts but has also alleviated poverty for many rural communities. Zambia has seen an increase of mobile money accounts and COVID-19 has exacerbated this increase. Therefore, this paper sought to determine data mining algorithm best predicts mobile money transaction growth. This paper was quantitative in nature and used aggregated monthly mobile money data (from Zambian mobile network operators) from 2013 to 2020 as its sample which was collected from Bank of Zambia and Zambia Information Communications and Technology Authority. The paper further used WEKA data mining tool for data analysis following the Cross-Industrial Standard Process for data mining guidelines. The performance from best to least is K-nearest neighbour, random forest, support vector machines, multilayer perceptron and linear regression. The predictions from data mining techniques can be deployed to predict growth of mobile money and hence be used in financial inclusion policy formulation and other strategies that can further improve service delivery by mobile network operators.

A Novel Approach for Predicting Covid 19 Recovered Cases using Nourishment by Comparing KNN over SVM

To predict the accuracy value of COVID19 recovered number of patients using Nourishment. Material and Methods: For forecasting accuracy percentage of COVID19 recovered patient health diet, Novel K Nearest Neighbour with test size (N=10) and Support Vector Machine with test size (N=10) were iterated 20 times to COVID19 recovered number of patients with g power as 80 %, threshold 0.014 and confidence interval as 95%. Sigmoid function is used in K Nearest Neighbour prediction to probability to help enhance accuracy. Results: In comparison to Support Vector Machine 66% percent Accuracy, Novel K Nearest Neighbour produced substantial results with 94 % Accuracy. Support Vector Machine and K Nearest Neighbour statistical significance is p=1.000(p<0.05) Independent sample T-test value states that the results in the study are significant. Conclusion: KNN is a straightforward and efficient algorithm for quickly building Models of machine learning. KNN predicting COVID19 Health Diet % with more accuracy. © 2022 IEEE.

Detection of COVID-19 Patients using Speech Recognition with Support Vector Machine' and Comparing with 'K Nearest Neighbour Algorithm'

This research endeavor is focused on identifying patients with the Covid-19 virus via the use of a novel voice recognition technique that makes use of a Support Vector Machine (abbreviated as 'SVM') and compares its accuracy with that of 'K-Nearest Neighbor' (abbreviated as 'KNN'). When it comes to speech recognition, the SVM method is regarded to be group 1, and the KNN method is considered to be group 2, and both groups have a total of 20 samples. The outcomes of these data were analyzed using statistical analysis using a'independent sample T-test,' which has a margin of error of 5% and a pretest power of 80%. At a significance of 0.042 (p 0.05), KNN obtains an accuracy of 87.5% whereas SVM achieves an accuracy of 96.5%. As compared to KNN, the prediction accuracy of Covid-19 employing SVM in novel voice recognition achieves much higher levels of accuracy. © 2023 IEEE.

Social Media Sentiment Analysis on Third Booster Dosage for COVID-19 Vaccination: A Holistic Machine Learning Approach

Over a period of more than two years the public health has been experiencing legitimate threat due to COVID-19 virus infection. This article represents a holistic machine learning approach to get an insight of social media sentiment analysis on third booster dosage for COVID-19 vaccination across the globe. Here in this work, researchers have considered Twitter responses of people to perform the sentiment analysis. Large number of tweets on social media require multiple terabyte sized database. The machine learned algorithm-based sentiment analysis can actually be performed by retrieving millions of twitter responses from users on daily basis. Comments regarding any news or any trending product launch may be ascertained well in twitter information. Our aim is to analyze the user tweet responses on third booster dosage for COVID-19 vaccination. In this sentiment analysis, the user sentiment responses are firstly categorized into positive sentiment, negative sentiment, and neutral sentiment. A performance study is performed to quickly locate the application and based on their sentiment score the application can distinguish the positive sentiment, negative sentiment and neutral sentiment-based tweet responses once clustered with various dictionaries and establish a powerful support on the prediction. This paper surveys the polarity activity exploitation using various machine learning algorithms viz. Naïve Bayes (NB), K- Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Valence Aware wordbook and sEntiment thinker (VADER) on the third booster dosage for COVID-19 vaccination. The VADER sentiment analysis predicts 97% accuracy, 92% precision, and 95% recall compared to other existing machine learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Gabor Filter-Based Caries Image Feature Analysis Using Machine Learning

Dental caries sufferers in Indonesia demonstrate a higher frequency than other dental diseases even before the Covid-19 pandemic. The high risk of spreading the virus during the pandemic hinders handling dental care patients. Teledentistry is suggested as the main alternative to reduce the risk of spreading the virus. This study aims to establish a system for classifying the level of dental caries based on texture applicable for clinical implementation. Dental caries images were extracted using the Gabor Filter method and classified using the Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). A downsampling technique was applied to this system to reduce the large number of features affecting the classification time. System testing revealed that the Cubic SVM model generated the best result: Accuracy of 90.5%, precision of 89.75%, recall of 89.25%, specificity of 91.75%, and f-score of 88.5%. © 2022 IEEE.

COVID-19 Detection in the Images of Chest CT Scan Using K-NN in Comparison with NB Classifier to Improve the Accuracy

This chapter is about the improvisation in the accuracy in COVID-19 detection using chest CT-scan images through K-Nearest Neighbour (K-NN) compared with Naive-Bayes (NB) classifier. The sample size considered for this detection is 20, for group 1 and 2, where G-power is 0.8. The value of alpha and beta was 0.05 and 0.2 along with a confidence interval at 95%. The K-NN classifier has achieved 95.297% of higher accuracy rate when compared with Naive Bayes classifier 92.087%. The results obtained were considered to be error-free since it was having the significance value of 0.036 (p < 0.05). Therefore, in this work K-Nearest Neighbor has performed significantly better than Naive Bayes algorithm in detection of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comparison of K-Nearest Neighbor and Logistic Regression Algorithms on Sentiment Analysis of Covid-19 Vaccination on Twitter with Vader And Textblob Labeling

In this analysis, the methods used are the K-Nearest Neighbor classification method and the Logistic Regression classification method with data taken on the twitter application. This study examines the level of accuracy in public sentiment regarding covid-19 vaccination with positive and negative labels. The AUC value in the KNN algorithm with TextBlob labeling is 0.765 with and 0.76S for VaderSentiment labeling are both included in the fair classification criteria. Meanwhile, the Logistic Regression algorithm produces an accuracy of 84.97% with a ratio of 90:10 for Labeling TextBlob, while for Labeling VaderSentiment with a ratio of 90:10 results in an accuracy of 85.22%. Both algorithms are validated using K-Fold Cross Validation with a fold count of 10. The comparison results obtained when conducting an evaluation with the confusion matrix showed that the Logistic Regression algorithm with VaderSentiment labeling had the highest accuracy value compared to the K-Nearest Neighbor algorithm with TextBlob and VaderSentiment labeling. © 2022 IEEE.

Computer-aided diagnosis of COVID-19 disease from chest X-ray images integrating deep feature extraction

The novel coronavirus (COVID-19) has an enormous impact on the daily lives and health of people residing in more than 200 nations. This article proposes a deep learning-based system for the rapid diagnosis of COVID-19. Chest x-ray radiograph images were used because recent findings revealed that these images contain salient features about COVID-19 disease. Transfer learning was performed using different pre-trained convolutional neural networks models for binary (normal and COVID-19) and triple (normal, COVID-19 and viral pneumonia) class problems. Deep features were extracted from a fully connected layer of the ResNET50v2 model and feature dimension was reduced through feature reduction methods. Feature fusion of feature sets reduced through analysis of variance (ANOVA) and mutual information feature selection (MIFS) was fed to Fine K-nearest neighbour to perform binary classification. Similarly, serial feature fusion of MIFS and chi-square features were utilized to train Medium Gaussian Support Vector Machines to distinguish normal, COVID-19 and viral pneumonia cases. The proposed framework yielded accuracies of 99.5% for binary and 95.5% for triple class experiments. The proposed model shows better performance than the existing methods, and this research has the potential to assist medical professionals to enhance the diagnostic ability to detect coronavirus disease. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Prediction of Unemployment using Machine Learning Approach

Unemployment is a circumstance which arises when people above a specific age are not engaged in any kind of activities which contribute to the economic welfare of the individual and country. Unemployment is becoming a rising concern which is making the daily life of people difficult. Unemployment causes poverty and depression among the citizens. Nowadays there are different opportunities in different sectors. But people are not aware of those opportunities. Different states are there where there is a lack of skilled labour whereas many states are there that have skilled labour but less opportunities. Another reason for unemployment since 2020 is the COVID-19 pandemic. We have selected this topic to spread awareness among the citizens. This work attempts to detect the states of India which are in serious need of increasing employment opportunities. We have applied the concept of Supervised Machine Learning algorithms to detect the states with the lowest employment rate. The data visualization gives a better picture of the trends in unemployment rate over years. There has been a use of different popular algorithms like Logistic Regression, Support Vector Machine, K-nearest neighbors (kNN) Algorithm and Decision Tree. In the end we have tried to find the algorithm which is going to give us more accuracy so that necessary steps can be taken for the employment of the eligible and deserving people. © 2022 IEEE.

Improved Accuracy in Speech Recognition System for Detection of Covid-19 Using K Nearest Neighbour and Comparing with Artificial Neural Network

The objective of this research is to recognize the speech signals for identifying the Covid-19 using K Nearest Neighbour (KNN) and comparing accuracy with an Artificial Neural Network (ANN). Speech recognition using KNN is considered as group 1 and Artificial Neural Network is considered as group 2, where each group has 20 samples. ANN is a machine learning program in which the input is processed by numerous elements and produces the output based on predefined functions. KNN is defined to find the relations between the query and pick the value closest to the query. These groups were analyzed by an independent sample T-test with 5% of alpha, and 80% of pretest power. ANN and KNN achieve an accuracy of 83.5% and 91.49% respectively (significance < 0.05). This analysis observed that KNN has significantly higher accuracy than ANN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Research on KNN-Based GNSS Coordinate Classification for Epidemic Management

As epidemics such as COVID-19 and monkeypox spread, tracing specific people with restricted activities (targets) within administrative areas (targeted areas) is an effective option to slow the spread. Global Navigation Satellite Systems (GNSS) that can provide autonomous geospatial positioning of targets can assist this issue. K-nearest neighbors (KNN) is one of the most widely used algorithms for various classifications or predictions. In this paper, we will use the technique of KNN to classify the areas of the targets and explore the relationship between the density of targets to a area and the accuracy of classifications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification

In many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One of the more modern Metaheuristic (MH) algorithms used to tackle numerous issues in various fields is the Beluga Whale Optimization (BWO) method. However, BWO has a lack of diversity, which could lead to being trapped in local optimaand premature convergence. This study presents two stages for enhancing the fundamental BWO algorithm. The initial stage of BWO's Opposition-Based Learning (OBL), also known as OBWO, helps to expedite the search process and enhance the learning methodology to choose a better generation of candidate solutions for the fundamental BWO. The second step, referred to as OBWOD, combines the Dynamic Candidate Solution (DCS) and OBWO based on the k-Nearest Neighbor (kNN) classifier to boost variety and improve the consistency of the selected solution by giving potential candidates a chance to solve the given problem with a high fitness value. A comparison study with present optimization algorithms for single-objective bound-constraint optimization problems was conducted to evaluate the performance of the OBWOD algorithm on issues from the 2022 IEEE Congress on Evolutionary Computation (CEC'22) benchmark test suite with a range of dimension sizes. The results of the statistical significance test confirmed that the proposed algorithm is competitive with the optimization algorithms. In addition, the OBWOD algorithm surpassed the performance of seven other algorithms with an overall classification accuracy of 85.17% for classifying 10 medical datasets with different dimension sizes according to the performance evaluation matrix.