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.

Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model

The SARS-CoV-2 coronavirus is the cause of the COVID-19 disease in humans. Like many coronaviruses, it can adapt to different hosts and evolve into different lineages. It is well-known that the major SARS-CoV-2 lineages are characterized by mutations that happen predominantly in the spike protein. Understanding the spike protein structure and how it can be perturbed is vital for understanding and determining if a lineage is of concern. These are crucial to identifying and controlling current outbreaks and preventing future pandemics. Machine learning (ML) methods are a viable solution to this effort, given the volume of available sequencing data, much of which is unaligned or even unassembled. However, such ML methods require fixed-length numerical feature vectors in Euclidean space to be applicable. Similarly, euclidean space is not considered the best choice when working with the classification and clustering tasks for biological sequences. For this purpose, we design a method that converts the protein (spike) sequences into the sequence similarity network (SSN). We can then use SSN as an input for the classical algorithms from the graph mining domain for the typical tasks such as classification and clustering to understand the data. We show that the proposed alignment-free method is able to outperform the current SOTA method in terms of clustering results. Similarly, we are able to achieve higher classification accuracy using well-known Node2Vec-based embedding compared to other baseline embedding approaches. © 2022 IEEE.

Investigations on machine learning models to envisage coronavirus in patients

The outbreak of the novel coronavirus pandemic (COVID-19) caused severe threats to humankind across the globe. The COVID-19 virus fits to the large family of virus that stimulate illness that may range from common flu to severe diseases like Middle East respiratory syndrome (MERS-CoV) and severe acute respiratory syndrome (SARS-CoV). Hence, the virus affects the mankind variably ranging from mild to moderate and sometimes very severe leading to mortality. The virus is contagious, and necessary prevention and protection mechanism protocols have been strictly adhered by the public to prevent the community spread which has not succeeded. Researchers have investigated that the spread of the severity of spread could be achieved through herd immunity. This pandemic outbreak has affected the globe to a great extent;hence, effective mechanisms are under investigation to diagnose the disease at the initial stages to prevent spread. Machine learning (ML), a subset of artificial intelligence (AI), provides models that have the ability to inevitably learn and evolve over experience. The ML algorithms are used in diverse applications, its contribution to medical management especially in preventive medicines, medicinal chemistry, imaging, and genetic medicines are inevitable. The sovereign intelligence and capability of ML algorithms make it manifest to use it in the COVID-19-based research. This chapter focusses on usage of ML algorithms to detect the severity of COVID-19 virus in human kind. The model investigates on predicting the severity of risk, risk of infection, and who is at risk of developing a severe case. The ML algorithms described in this chapter aims at identifying the presence of the disease in a patient. This work analyzes the foreseen of the diseased people from people with minor indications built on 111 impute relating to medical and the clinical examination facts. The diagnostic knowledge entailed attributes, such as age group, gender, body temperature, respiratory proportion, heart rate, and BP. The blood/urine examination data contain information related to various categories of blood examination values and urine examination values. Numerous ML models, such as Naive Bayes, SVM, artificial neural network, k-nearest neighbor (kNN), convolutional neural network (CNN), logistic regression, and decision tree were used in prediction and severity analysis. The experimentation may investigate the effectiveness of the above algorithms for detecting the patients infected with the virus as well as the severity level. Further, this research could be enhanced to provide treatment recommendations in the future. © 2022 Scrivener Publishing LLC.

Early Detection of Covid-19 Through Cough Sound Recognition using LPC and K-NN algorithm

COVID-19 (Coronavirus Disease 2019) is an infectious disease caused by the SARS-CoV-2 virus. This disease has spread worldwide since the beginning of 2020. Patients with this highly contagious disease generally experience only mild to moderate respiratory problems such as sore throat, cough, runny nose, fever, shortness of breath, and fatigue. However, some will become seriously ill and may cause severe respiratory distress or in severe cases multiple organ failure. Therefore, early identification of COVID-19 patients is very important. In this study, a disease detection system was created using an open dataset from COUGHVID which were contained the coughing sound of the Covid-19 disease. The implementation of the cough voice recognition system uses the K-Nearest Neighbor (K-NN) machine learning method and the Linear Predictive Coding (LPC) as method of extracting features from voice. The system was built using the Raspberry Pi 3 b+ microcontroller with microphone voice input and connected to a 3.5-inch LCD touchscreen display as the interface of the system device. The test uses a coughing sound as input through a microphone and processed by LPC feature extraction. At each running process, about 399 MB of memory is used from a total of 1 GB of memory. Meanwhile, the prediction of coughing sounds with the K-NN classification algorithm using 5 neighbors produces accuracy of 62% to predict disease. © 2022 ACM.

Face Mask Detection using an Automated Hybrid Deep Learning Method in the COVID Scenario

Currently, in light of the health catastrophe due to the COVID-19 which has been unfolding all over the world. Wearing a defensive mask has ended up a substitute normal. Face recognition technology is most commonly implemented for surveillance and other applications. Traditional machine learning classifiers as well as deep transfer learning classifiers have been used to accomplish the face mask detection mechanism. In this paper, two hybrid deep learning models MobileNetV2-SVM and MobilNetV2-KNN has been proposed for the task of face mask detection. The models involve two processes: feature extraction and classification. For initialization, the MobileNetV2 pre-trained weights from ImageNet were employed, and during training, data augmentation and resampling were applied. By integrating the model with an SVM classifier and a KNN classifier, the model is further refined, creating hybrid models that are effective in terms of processing. The Kaggle dataset of 45000 images (22582 images are masked and 23423 images that are unmasked) of the proposed model/system is trained using MobilenetV2 and classified using SVM and K-NN algorithm in different models. Various machine learning frameworks were used like pandas, TensorFlow, Keras and NumPy. The accuracy achieved by the SVM model is 98.17% and 95.22% accuracy are achieved by using the K-NN classifier. © 2022 IEEE.

Classifying the Students' Behavior on e-Learning System using Fine-Tuning K-NN Method

monitoring the student's behavior is challenging for teachers in online learning, which is crucial to solving. It is because, in this pandemic period, online learning is required to minimize the spreading of coronavirus. However, research in this domain is not much. This study provides an alternative to this problem by classifying students' behavior in the e-Learning system, where the k-NN is applied to mine the students' behavior. In addition, this paper also tests the proper parameters to improve the performance of k-NN: k and distance. The experimental result shows that the best performance on the cross-validation technique is reached by Euclidean distance and, on the percentage-split, is achieved by distance-Manhattan. These are indicated by the highest accuracy level obtained by neighbors of five and 20 fold, about 96.9% on the cross-validation technique. On the percentage split technique, the highest accuracy level, about 95.3%, is reached by neighbors of four and split 50%. In this best performance, four students are misclassified on the cross-validation and six on the percentage split. © 2022 IEEE.

Optimization of Infant Birth Predictions during the Covid-19 Pandemic Using the Particle Swarm Optimization Based K-Nn Algorithm Method

Every mother wants to give birth to a perfect and healthy child. many things cause newborns to die, some of which are malnutrition during the womb, fetuses that have abnormalities in the body, and factors of premature birth. Deaths due to exposure to the Covid-19 virus are certainly a serious problem. Several factors influence childbirth, such as placental and fetal factors, maternal factors, lifestyle factors, and what is happening now due to the covid-19 virus. Therefore, the author is interested and wants to review to find out the characteristics of mothers who give birth due to exposure to the covid virus and are normal. The results of tests carried out by optimizing the Particle Swarm Optimization-based K-NN Algorithm resulted in an accuracy value of 93%. The accuracy value can be said to be good enough to determine the characteristics of the mother who gave birth under normal or premature conditions. © 2022 IEEE.

Covid19 Vaccination and Lifestyle Counsel using Machine Learning algorithms

Machine Learning is a predominant area in Artificial Intelligence. It gets the ability to make predictions by learning the past observed values and information. This learning process is Machine Learning. A large amount of data is accessed and processed to gain more accurate results. Nowadays anyone around the world can use any Machine Learning algorithm to obtain competitive and accurate results. The main objective of this project is to recommend the Life style modification of the people after covid19 and to predict whether the particular person needs for the vaccination intake or not by accessing thousands of patient details. Hence the accuracy rate is very high compared to other predicting processes. These techniques are used to predict the current health conditions of the people. © 2022 IEEE.

Investigation of the Effect of Edge Detection Algorithms in the Detection of Covid-19 Patients with Convolutional Neural Network-Based Features on Chest X-Ray Images

Early diagnosis of COVID-19 is essential to ensure that treatment can be initiated early and to prevent the disease from spreading to other people. In this paper, a deep learning-based method that uses chest X-ray images from normal, COVID-19 and viral pneumonia patients is proposed to enable automatic detection of COVID-19 patients. In addition, Canny, Roberts, Sobel edge detection methods were applied to the images to determine the lesioned area or the perimeter of the area where they are restricted to examine the effect of deep learning on the classification performance. According to the obtained results, when the created deep learning-based model is used in the original data, the classification performance is 94.44% and the highest is 82.30% when edge detection algorithms are used. In addition, although the Sobel algorithm provides better results than other edge detection methods, it can be seen that the classification performance obtained with the original images is higher.

Accent Classification of Native and Non-Native Children using Harmonic Pitch

To combat the Covid-19 outbreak, the education system shifted away from the classroom to distinct e-learning on digital platforms, which made effective use of voice-based recognition systems, especially for preliterate children. Children’s speech recognition systems face multiple challenges owing to their immature vocal tracts, and they demand more intelligence due to the fact that children with diverse accents utter words differently. Accent refers to a unique style of pronouncing a language, particularly one associated with a specific nation, place, or socio-economic background. This paper aims to extract reliable acoustic and prosodic speech cues of accent for classification of native and non-native preschool children using harmonic pitch estimation along with Mel Frequency Cepstral Coefficients (MFCCs) to train the k-Nearest Neighbour (k-NN) classifier. The experimental results reveal that the proposed robust model outperforms various feature extractors in accent classification of native and non-native children in terms of accuracy & F-Measure and more discriminate against noisy environments. © 2022 IEEE.

Selecting a Better Classifier Using Machine Learning for COVID-19

This paper will elaborate that how timely available data and Machine learning algorithms can help in determining premature exposure of coronavirus (COVID-19) and aided the world in formulating to reduce the loss. We will investigate which machine learning algorithms are best fit to predict COVID-19 data sets. In this study our focus will be on the spread of COVID-19 internationally in different countries. This study will serve as a resource for the future research and development on COVID-19 by producing better research in this field. To achieve the outcomes and future forecasting of COVID-19, we analyze the records and datasets of COVID-19 through Machine Learning algorithms. For this purpose, we used six algorithms to construct classifiers such as K-Nearest Neighbor (K-NN), Decision Tree, Support Vector Machine (SVM), Naive Bayes, Logistic Regression and Random Forecast. These algorithms were applied on Python a machine learning software. The dataset is acquired by WHO data sets and data sets provided online at GitHub and compiled and organized by different communities to track the spread of the virus. The Performance of the best classifier will be measured using Accuracy. The model developed with Decision Tree is one of the most efficient classifier with the highest percentage of accuracy of 99.85 percent, and is followed by Random Forecast with 99.60 percent, Naive Bayes with 97.52 percent accuracy, Logistic Regression with 97.49 percent accuracy, Support Vector Machine with 98.85 percent accuracy and K-NN with 98.06 percent accuracy. In our research, we discussed two types of classification: Binary and Multinomial. Support Vector Machine and Decision Tree give us precise results. Other classifier models gave satisfactory outcomes. The outcomes may be helping to predict the future circumstances of COVID-19. From the past studies we have used Autoregressive integrated moving average (ARIMA) model for time series data. SIR models to check the spread of Nowcasting and forecasting the spread of 2019-nCoV in China and worldwide. © 2022 University of Bahrain. All rights reserved.

An Opinion Mining of Text in COVID-19 Issues Along with Comparative Study in ML, BERT & RNN

The global world is crossing a pandemic situation where this is a catastrophic outbreak of Respiratory Syndrome recognized as COVID-19. This is a global threat all over the 212 countries that people every day meet with mighty situations. On the contrary, thousands of infected people live rich in mountains. Mental health is also affected by this worldwide coronavirus situation. Due to this situation online sources made a communicative place that common people shares their opinion in any agenda. Such as affected news related positive and negative, financial issues, country and family crisis, lack of import and export earning system etc. different kinds of circumstances are recent trendy news in anywhere. Thus, vast amounts of text are produced within moments therefore, in subcontinent areas the same as situation in other countries and peoples opinion of text and situation also same but the language is different. This article has proposed some specific inputs along with Bangla text comments from individual sources which can assure the goal of illustration that machine learning outcome capable of building an assistive system. Opinion mining assistive system can be impactful in all language preferences possible. To the best of our knowledge, the article predicted the Bangla input text on COVID-19 issues proposed ML algorithms and deep learning models analysis also check the future reachability with a comparative analysis. Comparative analysis states a report on text prediction accuracy is 91% along with ML algorithms and 79% along with Deep Learning Models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comparative Performance Analysis of Machine Learning Classifier for COVID-19 Detection using Chest X-Ray Images

Machine learning is a data analysis method that has been used to solve several problems. Image classification is one of them. During this pandemic caused by the spread of Covid-19, the number of people infected was relatively high. The COVID-19 pandemic has had a severe impact on people's lives. One of the critical steps for overcoming this pandemic lies in the ability of medical personnel to identify patients who are infected with Covid-19 at an early age. Detecting Covid-19 from patient radiographs may be one of the quickest ways to identify Covid-19 patients. Many researchers have applied machine learning to predict Covid-19. The data are based on chest X-ray images of the patient. In this study, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Convolutional neural network (CNN) methods were used to find the best analytical performance in predicting COVID-19 based on chest X-ray images of the patients. The research results from the three methods used, the performance of the CNN method is the best compared to the other two methods, namely SVM and KNN. © 2022 IEEE.

Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia

The machine learning approach has been widely used in many areas of studies, including the tourism sector. It can offer powerful estimation for prediction. With a growing number of tourism activities, there is a need to predict tourists’ classification for monitoring, decision making, and planning formulation. This paper aims to predict visitors to totally protected areas in Sarawak using machine learning techniques. The prediction model developed would be able to identify significant factors affecting local and foreign visitors to these areas. Several machine learning techniques such as k-NN, Naive Bayes, and Decision Tree were used to predict whether local and foreign visitors’ arrival was high, medium, or low to these totally protected areas in Sarawak, Malaysia. The data of local and foreign visitors’ arrival to eighteen totally protected areas covering national parks, nature reserves, and wildlife centers in Sarawak, Malaysia, from 2015 to 2019 were used in this study. Variables such as the age of the park, distance from the nearest city, types of the park, recreation services availability, natural characteristics availability, and types of connectivity were used in the model. Based on the accuracy measure, precision, and recall, results show Decision Tree (Gain Ratio) exhibited the best prediction performance for both local visitors (accuracy = 80.65) and foreign visitors (accuracy = 84.35%). Distance to the nearest city and size of the park were found to be the most important predictors in predicting the local tourist visitors’ park classification, while for foreign visitors, age, type of park, and the natural characteristics availability were the significant predictors in predicting the foreign tourist visitors’ parks classification. This study exemplifies that machine learning has respectable potential for the prediction of visitors’ data. Future research should consider bagging and boosting algorithms to develop a visitors’ prediction model.

Enhanced Vaccine Recommender System to prevent COVID-19 based on Clustering and Classification

Due to the fact that countries are presently dealing with the third wave of COVID-19 pandemic and in present time, the data of vaccines for preventing COVID-19 has triggered massive information, it is vital to create a system that can assist decision-makers and health care practitioners in combating COVID-19 and to combat the problem of vaccine information overload is to provide patients with personalized vaccine recommendations. Because of the ability of recommender systems (RSs) that use Collaborative Filtering (CF) to interpret decision-maker expectations, methodologies, it widely used and direct them towards linked tools that are acceptable to recommend the suitable vaccine for the persons. In this paper, we adopted an Enhanced Vaccine RSs for preventing COVID-19, which is called EVRSs-19. EVRSs-19 face some problems such as sparsity and diversity of vaccines data. To overcome these problems, we adopted two proposals. First, use clustering of K-Means to cluster the persons in several groups according to vaccine types to cope with sparsity of vaccines data. Second, use the K-Nearest Neighbors classifier-depend model of CF to discover neighbors in each vaccine cluster to increase diversity. Evaluating the EVRSs-19 system implemented on vaccines data in two testing using some metrics and the findings of these metrics after running the clustering and classification show that the system of EVRSs-19 has a perfect structure. Such as recall (0.92), precision (0.89), diversity score (9). As the vaccines recommendation list progressed, NDCG and DCG for persons are decreased. © 2021 IEEE.

IEEE 802.11 as a Passive Sensor for Crowd Density Counting in Closed Areas

Crowd density counting obtained popularity in recent years with COVID-19 and the social separation constraints that have to be enforced in public areas. Many methods and techniques can be utilized for crowd density counting. However, these techniques depend on expensive equipment and massive deployment of different sensors in the targeted area. In this work, a simple crowd density counting framework based on measuring the received signal strength (RSS) of IEEE802.11, known as, WIFI in closed areas is leveraged. An access point (AP) and a Raspberry PI kit has been located in a closed area to harvest the RSS value when people pass through the area. K-NN machine learning algorithm has been trained with different features extracted from the RSS to predict the number of people in the area. Finally, an Android smartphone App has been written to monitor the counted number to enforce the counting constraint in the closed areas. The model has been deployed in the engineering faculty. Our results show that K-NN with RSS features for passively crowd density counting achieved 88% accuracy. However, this accuracy dropped to 75% with people running scenario © 2021, Periodicals of Engineering and Natural Sciences. All Rights Reserved.

Implementation of Text Mining for Sentiment Analysis of Online Lectures during the Covid-19 Pandemic

Strategy against the spread of the Covid-19 virus in Indonesia by enacting Large-Scale Social Restrictions. The implementation of the Scale Social Restrictions forced all universities in Indonesia to close their institutes and conduct lectures online. Online lectures are considered as a solution to continue the teaching process during a pandemic. However, the lack of adaptation and sudden changes caused various responses and public opinions on social media. For this reason, this study aims to conduct text mining on Twitter in order to analyze public sentiment on the topic of "online lectures"the data obtained are classified into 2 classes (positive and negative). The results of the accuracy of the nave Bayes test with the cross validation technique obtained a result of 81.57%. For class precision, positive predictions are 100%, while for negative predictions the results are 73.06% and recall from true positive is 63.13% for true negative is 100%. And for the accuracy of K-Nearest Neighbor 62.10%, for class precision positive prediction is 62.06% while for negative prediction results are 62.13% and recall from true positive is 62.24% for true negative is 61.95% © 2021 IEEE.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.