Machine learning-assisted remote patient monitoring with data analytics

The health condition of the patients needs to be monitored with immense care. Healthcare promotes good health, helps in monitoring the patient's health status, disease diagnosis, and its management along with recovery. Monitoring the health condition postdischarge or postoperation is required to ensure a speedy recovery. Healthcare services can benefit from technological advancements to ensure better service. Healthcare assisted with machine learning techniques plays a significant role in the effective diagnosis of ailments, monitoring patient's health condition, and extend support in taking suitable measures during abnormality. In the proposed work, we collect the patient's data using sensors and upload them to the cloud. The collected data are subjected to preprocessing followed by analysis. The patient's health is remotely monitored, and machine learning techniques are applied to foretell abnormalities in the patient's health condition. Existing remote monitoring systems are not flexible and, hence, may result in an increased number of false positives. We try to reduce unnecessary alerts via machine learning methods and data analytics. Essential attributes like pulse rate, blood pressure, temperature, gender, and cholesterol levels of the patient are taken into consideration while predicting the results. In the time of pandemics, like COVID-19 with the scarce availability of medical personnel and treatment resources, this prediction may help in taking appropriate measures at the earliest. We train the model with the Kaggle Heart Disease UCI data set and test the model with real-time patient data. We apply our model to k nearest neighbor (KNN) and Naïve Bayes algorithm. The KNN has performed well over the Naïve Bayes algorithm. © 2022 Scrivener Publishing LLC.

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.

Prediction of Happiness Score of Countries by Considering Maximum Infection Rate of People by COVID-19 using Random Forest Algorithm

In this paper, the relationship between COVID-19 Maximum Infection Rate (MIR) and the happiness indicators has been investigated for the prediction of Happiness Score of Countries using Random Forest (RF) algorithm. The per-formance of the proposed algorithm is also compared against five other algorithms such as Linear Regression (LR), Ada Boost Classifier (ABC), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (NB) and Logistic Regression. The comparison of performance includes parameters like training accuracy, testing accuracy and computation time. It is clear from the observation that the proposed approach is superior to others. Then the parameters like MAE, MSE, RMSE, R2 Score, Adjusted R2 Score is calculated. This proposed algorithm can be used for other classification and regression work involving large amount of data with missing values like COVID- 19 datasets. © 2022 IEEE.

Risk Analysis and Prediction of Covid-19 Infection Using Machine Learning Techniques

Coronavirus (COVID-19), the lethal contagious virus which has caused a pandemic, has metastasized all over the world starting from China. The figures observed of the number of casualties, is in millions and billions. This new malicious virus has caused panic amongst pubic, implanted fear and number of doubts in people's minds. There is lack of information as scientists are working on eradicating this deadly virus, less information has instilled doubts and people are panicking being helpless about how to cope up with the virus. Ways to protect oneself from getting infected, how could and where could one seek medical help when needed, these kinds of queries should be sorted out and the public needs to be educated about the virus. This will help calm down the public. This would also aid in keeping tranquil environment and even help in health and government sector workers to carry on with their duties without any obstacles. © 2022 WCSE. All Rights Reserved.

A comprehensive tool for rapid and accurate prediction of disease using DNA sequence classifier.

In the current pandemic situation where the coronavirus is spreading very fast that can jump from one human to another. Along with this, there are millions of viruses for example Ebola, SARS, etc. that can spread as fast as the coronavirus due to the mobilization and globalization of the population and are equally deadly. Earlier identification of these viruses can prevent the outbreaks that we are facing currently as well as can help in the earlier designing of drugs. Identification of disease at a prior stage can be achieved through DNA sequence classification as DNA carries most of the genetic information about organisms. This is the reason why the classification of DNA sequences plays an important role in computational biology. This paper has presented a solution in which samples collected from NCBI are used for the classification of DNA sequences. DNA sequence classification will in turn gives the pattern of various diseases; these patterns are then compared with the samples of a newly infected person and can help in the earlier identification of disease. However, feature extraction always remains a big issue. In this paper, a machine learning-based classifier and a new technique for extracting features from DNA sequences based on a hot vector matrix have been proposed. In the hot vector representation of the DNA sequence, each pair of the word is represented using a binary matrix which represents the position of each nucleotide in the DNA sequence. The resultant matrix is then given as an input to the traditional CNN for feature extraction. The results of the proposed method have been compared with 5 well-known classifiers namely Convolution neural network (CNN), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) algorithm, Decision Trees, Recurrent Neural Networks (RNN) on several parameters including precision rate and accuracy and the result shows that the proposed method gives an accuracy of 93.9%, which is highest compared to other classifiers.

Detection of Lung Ultrasound Covid-19 Disease Patients based Convolution Multifacet Analytics using Deep Learning

Deep Learning techniques for ultrasound images, from the front end to the most advanced applications, are the potential effect of deep learning methods on many aspects of the analysis of the ultrasound images. The Covid-19 epidemic has exposed global health care vulnerabilities, especially in developing countries. Lung Ultra-Sound (LUS) imaging as a real-time analytic tool for lung injuries is superior to X-rays and similar to CT, enabling real-time diagnosis. Relying on operator training and experience is the main limitation of the range. COVID-19 lung ultrasonography mainly reflects the pattern of pneumonia, and pleural effusion is not common. The previous system does not provide image accuracy, clarity, it is cost-effective screening large-scale traditional tests are not possible. To overcome the issues, this work proposed the method Convolutional Multi -Facet Analytics (CMFA) algorithm for using the Lung Ultra-Sound (LUS) imaging. Initially start the Preprocessing step based on the Geometric Image Noise Filtering (GINT) for removed the image noises, and unwanted values from the images, second steps of the image processing for Feature selection using the K-Nearest Neighbor (KNN) and Adaptive Gradient Boosting Algorithm (AGBA) for optimizing the image feature od efficient to reduce the same information form he original dataset. And then bagging with K-Nearest Neighbor (KNN) and Adaptive Gradient Boosting Regression (AGBR) Algorithm estimate the images feature weights like (shape, size, etc.) to test, and verify the best combined classifier model splitting training and testing for feature selection and evaluating the results in Softmax activation function. Classified the train and test features using the Convolutional Multi-Facet Analytics (CMFA) algorithm for analyzing the variety of different important features from the dataset. The simulation results show that Sensitivity, specificity, accuracy, and Error rate score shows better results. © 2022 IEEE.

The Effect of Fake Reviews on e-Commerce During and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection

The outbreak of Covid-19 and the enforcement of lockdown, social distancing, and other precautionary measures lead to a global increase in online shopping. The increasing significance of online shopping and extensive use of e-commerce has increased competition between companies for online selling. Highlights that online reviews play a significant role in boosting a business or slandering it. Product review is an essential factor in customers' decision-making, leading to an intense topic known as fraudulent or fake reviews detection. Given these reviews' power over a business, the treacherous acts of giving false reviews for personal gains have increased with time. In our research, we proposed a fake review detection model by using Text Classification and techniques related to Machine Learning. We used classifiers such as Support Vector Machine, K-Nearest Neighbor, and logistic regression (SKL), using a bigram model that detects fraudulent reviews based on the number of pronouns, verbs, and sentiments. Our proposed methodology for detecting fake online reviews outperforms on the yelp dataset and the TripAdvisor dataset compared to other state-of-the-art techniques with 95% and 89.03% accuracy.