An Intrusion Detection System Using Machine Learning for Internet of Medical Things

The COVID-19 pandemic has impacted everyday life, the global economy, travel, and commerce. In many cases, the tight measures put in place to stop COVID-19 have caused depression and other diseases. As many medical systems over the world are unable to hospitalize all the patients, some of them may get home healthcare assistance, while the government and healthcare organizations have access to substantial sickness management data. It allows patients to routinely update their health status and have it sent to distant hospitals. In certain cases, the medical authorities may designate quarantine stations and provide supervision equipment and platforms (such as Internet of Medical Things (IoMT) devices) for performing an infection-free treatment, whereas IoMT devices often lack enough protection, making them vulnerable to many threats. In this paper, we present an intrusion detection system (IDS) for IoMTs based on the following gradient boosting machines approaches: XGBoost, LightGBM, and CatBoost. With more than 99% in many evaluation measures, these approaches had a high detection rate and could be an effective solution in preventing attacks on IoMT devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covid-19 Detection and Classification using Transfer learning with XGboost

The 2019 new corona virus (COVID-19), with a genesis phase in China, has dispersed apace amid individuals subsisting in distinct nations and is rising toward about twelve lakh cases in the balance as per the intuition of the European center for Health Security and Communicable diseases and ECDC. There is a foreordained figure of COVID-19 trial caskets attainable in medical centers because of the escalating cases in day-to-day life. In this way, it is important to execute a programmed location framework as a snappy elective conclusion alternative to forestall COVID-19 transmitting between peoples. In this examination, three disparate Convolutional neural system- based models (XGBOOST/LIGHTGBM, Inception-ResNetV2 and InceptionV3) have been put forward for the whereabouts of coronavirus and pneumonia contaminated convalescent by harnessing thoracic radiographic screening. Receiver Operating Characteristics (ROC) investigations and disordered networks by those tripartite models are bestowed and deteriorated by exploiting 5-superimpose traverse accredit. Contemplating the demonstration outcome obtained, it is perceived that the pre- prepared XGBOOST/LIGHTGBM model accouters the most upraised characterization execution with 98.6% exactness amongst the other two propounded models (96% correctness for InceptionV3 and 85% exactness for Inception-ResNetV2). © 2023 IEEE.

Clinical data-driven approach to identifying COVID-19 and influenza from a gradient-boosting model

Corona Virus Disease 2019 (COVID-19) and influenza are both caused by viruses, seriously affect human health, and are highly infectious. However, because the clinical manifestations of these two groups of diseases have almost identical symptoms, separate Polymerase Chain Reaction (PCR) tests must be used for patients in each disease group. This study proposes an automatic data-processing model based on artificial intelligence and gradient boosting to identifying COVID-19 and influenza. The model can learn directly from raw data without the need for human input to delete empty data. Methodology and techniques operate in two stages: first, it evaluates and processes data to reduce the dataset's complexity using the light gradient boosting machine (LightGBM);then, in the second stage, it builds a classification model for each disease group based on the extreme gradient boosting (XGBoost) method. The research tools showed that combining two gradient-boosting models both LightGBM and XGBoost to generate automatic COVID-19 and influenza classifiers from clinical data produced strong results and a superior performance versus one model alone, with an overall accuracy of over 99.96%. In the future, the developed model will enable patients to be diagnosed simply and accurately and thereby reduce countries' testing costs for COVID-19 and similar pandemics that may arise. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Tourist Trajectory Prediction Based on Improved LightGBM

Considering the influences of the COVID-19 disease, systemic risks with respect to the tourism industry and the erratic preferences of the tourists have fiercely affected the performance of machine learning models for tourist trajectory prediction. This paper introduces a noise-reduced and Bayesian optimized light gradient boosting machine(LightGBM) to forecast the likelihood of visitors entering the consequent scenic attraction, accommodating to the variability of tourism attributes. The empirical evidence of tourism data in Luoyang City Hall from March 2020 to November 2021 illustrates that our practice surpasses the baseline LightGBM mechanism as well as a random search-based technique regarding prediction loss by 5.39% and 4.42% correspondingly. The proposed research demonstrates a promising stride in the improvement of intelligent tourism in the experimental area by enhancing tourist experiences and allocating tourism resources efficiently, which can also be smoothly applied to other scenic spots. © 2023 SPIE.

A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA.

This prevalence of coronavirus disease 2019 (COVID-19) has become one of the most serious public health crises. Tree-based machine learning methods, with the advantages of high efficiency, and strong interpretability, have been widely used in predicting diseases. A data-driven interpretable ensemble framework based on tree models was designed to forecast daily new cases of COVID-19 in the USA and to determine the important factors related to COVID-19. Based on a hyperparametric optimization technique, we developed three machine learning algorithms based on decision trees, including random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), and three linear ensemble models were used to integrate these outcomes for better prediction accuracy. Finally, the SHapley Additive explanation (SHAP) value was used to obtain the feature importance ranking. Our outcomes demonstrated that, among the three basic machine learners, the prediction accuracy was the following in descending order: LightGBM, XGBoost, and RF. The optimized LAD ensemble was the most precise prediction model that reduced the prediction error of the best base learner (LightGBM) by approximately 3.111%, while vaccination, wearing masks, less mobility, and government interventions had positive effects on the control and prevention of COVID-19.

COVID-19: the impact of the pandemic fear on IPO underpricing

Purpose The purpose of this study is to assess how the information disclosed in prospectuses impacted the initial public offering (IPO) underpricing at a time of high government interference amid the ongoing pandemic. Design/methodology/approach The design of this study has several tracks, namely, a macro-level track, which is represented by the government measures to halt the pandemic;a micro-level track, which is followed by textual analysis of IPO prospectuses;and, finally, a machine learning track, in which the authors use state-of-the-art tools to improve their linear regression model. Findings The authors found that strict government anti-COVID-19 measures indeed contribute to the reduction of the IPO underpricing. Interestingly, the mere fact of such measures taking place is enough to take effect on financial markets, regardless of the resulting efficiency of such measures. At the micro-level, the authors show that prospectus sentiments and their significance differ across prospectus sections. Using linear regression and machine learning models, the authors find robust evidence that such sections as "Risk factors", "Prospectus summary", "Financial Information" and "Business" play a crucial role in explaining the underpricing. Their effect is different, namely, it turns out that the more negative "Risk factors" and "Financial Information" sentiment, the higher the resulting underpricing. Conversely, the more positive "Prospectus summary" and "Business" sentiments appear, the lower the resulting underpricing is. In addition, we used machine learning methods. Consisting of more than 580 IPO prospectuses, the study sample required modern and powerful machine learning tools like Isolation Forest for pre-processing or Random Forest Regressor and Light Gradient Boosting Model for modelling purposes, which enabled the authors to gain better results compared to the classic linear regression model. Originality/value At the micro level, this study is not confined to 2020, but also embraces 2021, the year of the record number of IPOs held. Moreover, in this paper, these were prospectuses that served as a source of management sentiment. In addition, the authors used a tailor-made government stringency index. At the micro level, basing the study on behavioural finance hypotheses, the authors conducted both separate and holistic analysis of prospectuses to assess investors' reaction to different aspects of IPO companies as well as to the characteristics of the IPOs themselves. Lastly, the authors introduced a few innovations to the research methodology. Textual analysis was conducted on a corpus of prospectuses included in a study sample. However, the authors did not use pre-trained dictionaries, but instead opted for FLAIR, a modern open-source framework for natural language processing.

Detection of COVID-19 using Wavelet Transform, Machine Learning and Deep Learning Models

Since December 2019, the world is fighting against the newly found virus named COVID-19 whose symptoms are closer to pneumonia. Being highly contagious, it has spread all over the world, and hence the World Health Organization has declared this as a global pandemic. Some patients infected with this virus have severe symptoms which are fatal. Hence the early discovery of COVID-19 infected patients is necessary to avoid further community spread. The available tests such as RTPCR and Rapid Antigen Tests are not 100% accurate and do not give quick results either. Therefore, it is the need of the hour to explore identification methodologies that are quick, accurate, and easily scalable. This work intends to do so using different machine learning and deep learning models. First, the step involves feature extraction using Gray Level Co-occurrence Matrix (GLCM) and classification with LightGBM classifier which gives an accuracy of 92.78%. This is then further improved to 95.79% using wavelets. Further, the CNN architectures with max-pooling and DWT layers are compared and it's found that CNN architecture with max-pooling layer gives better accuracy of 95.72%. Thus, this work presents a comparative analysis of Machine Learning Algorithms and CNN architectures for better accuracy and time. © 2022 IEEE.

Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM

The COVID-19 disease was first discovered in Wuhan, China, and spread quickly worldwide. After the COVID-19 pandemic, many researchers have begun to identify a way to diagnose the COVID-19 using chest X-ray images. The early diagnosis of this disease can significantly impact the treatment process. In this article, we propose a new technique that is faster and more accurate than the other methods reported in the literature. The proposed method uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images. Using the univariate feature selection algorithm, we refined the features for the most important ones. Then we applied the selected features as input to the LightGBM (Light Gradient Boosting Machine) algorithm for classification. To assess the effectiveness of the proposed method, the ChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest, was used. The proposed method achieved 98.54% and 91.11% accuracies in the two-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia) classification problems, respectively. It is worth mentioning that we have used Gradient-weighted Class Activation Mapping (Grad-CAM) for future analysis. © 2022 IEEE.

Prediction of Used Car Prices Using Machine Learning

As the Indian auto-industry entered BS-VI era from April 2020, the value proposition of used cars grew stronger, as the new cars became expensive due to additional technology costs. Moreover, the unavailability of public transport and fear of infection force people toward self-mobility during the outbreak of Covid-19 pandemic. But, the surge in demand for used cars made some car sellers to take advantage from customers by listing higher prices than normal. In order to help consumers aware of market trends and prices for used cars, there comes the need to create a model that can predict the cost of used cars by taking into consideration about different features and prices of other cars present in the country. In this paper, we have used different machine learning algorithms such as k-nearest neighbor (KNN), random forest regression, decision tree, and light gradient boosting machine (LightGBM) which is able to predict the price of used cars based on different features specific to Indian buyers, and we have implemented the best model by comparing with other models to serve our cause. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Machine Learning Approach for Sentiment Analysis of Book Reviews in Bangla Language

With the advent of technology, Sentiment polarity detection has recently piqued the interest of NLP researchers. Sentiment analysis determines the profound meaning of an article. Due to COVID-19 pandemic, online shopping is the safest way of shopping. Moreover, there are product quality and service issues. Our target is to analyze the book reviews which provide positive and negative reviews in Bangla language. For this, a total of 5500 user generated Bengali reviews are collected from various book review pages of social media. In order to get the best possible result, sentiment analysis is used. Thereafter, five different algorithms are applied to predict with almost high accuracy. Among them, the Random Forest provides us the maximum accuracy which is 98.39%. © 2022 IEEE.

Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia.

In this paper, the authors investigated changes in mass concentrations of particulate matter (PM) during the Coronavirus Disease of 2019 (COVID-19) lockdown. Daily samples of PM1, PM2.5 and PM10 fractions were measured at an urban background sampling site in Zagreb, Croatia from 2009 to late 2020. For the purpose of meteorological normalization, the mass concentrations were fed alongside meteorological and temporal data to Random Forest (RF) and LightGBM (LGB) models tuned by Bayesian optimization. The models' predictions were subsequently de-weathered by meteorological normalization using repeated random resampling of all predictive variables except the trend variable. Three pollution periods in 2020 were examined in detail: January and February, as pre-lockdown, the month of April as the lockdown period, as well as June and July as the "new normal". An evaluation using normalized mass concentrations of particulate matter and Analysis of variance (ANOVA) was conducted. The results showed that no significant differences were observed for PM1, PM2.5 and PM10 in April 2020-compared to the same period in 2018 and 2019. No significant changes were observed for the "new normal" as well. The results thus indicate that a reduction in mobility during COVID-19 lockdown in Zagreb, Croatia, did not significantly affect particulate matter concentration in the long-term..

An APT Malware Classification Method Based on Adaboost Feature Selection and LightGBM

Advanced Persistent Threat (APT) attack activities with the theme of COVID-19 and vaccine are also growing rapidly. The target of APT attack has gradually expanded from government agencies to vaccine manufacturers, medical industry and so on. What's more, APT groups have a strict organizational structure and professional division of labor and malware delivered by the same APT groups are similar. Classifying malware samples into known APT groups in time can minimize losses as soon as possible and keep relevant industries vigilant. In our paper, we proposed a multi-classification method of APT malware based on Adaboost and LightGBM. We collect real APT malware samples that have been delivered by 12 known APT groups. The API call sequence of each APT malware is obtained through the sandbox. For the relationship between adjacent APIs, we use TF-IDF algorithm combined with bi-gram. Then, Adaboost algorithm is used to select out the important API features, which form the target feature subset. Finally, we use the above subset combined with LightGBM ensemble algorithm to train multiple classifiers, named Ada-LightGBM. The experimental results show that our method is superior to the single Adaboost and LightGBM method. The classifier has good recognition performance for the test samples. © 2021 IEEE.

Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM

The Coronavirus was detected in Wuhan, China in late 2019 and then led to a pandemic with a rapid worldwide outbreak. The number of infected people has been swiftly increasing since then. Therefore, in this study, an attempt was made to propose a new and efficient method for automatic diagnosis of Corona disease from X-ray images using Deep Neural Networks (DNNs). In the proposed method, the DensNet169 was used to extract the features of the patients' Chest X-Ray (CXR) images. The extracted features were given to a feature selection algorithm (i.e., ANOVA) to select a number of them. Finally, the selected features were classified by LightGBM algorithm. The proposed approach was evaluated on the ChestX-ray8 dataset and reached 99.20% and 94.22% accuracies in the two-class (i.e., COVID-19 and No-findings) and multi-class (i.e., COVID-19, Pneumonia, and No-findings) classification problems, respectively. © 2022 IEEE.

Integrated model for COVID-19 diagnosis based on computed tomography artificial intelligence, and clinical features: a multicenter cohort study.

Background: We developed and validated a machine learning diagnostic model for the novel coronavirus (COVID-19) disease, integrating artificial-intelligence-based computed tomography (CT) imaging and clinical features. Methods: We conducted a retrospective cohort study in 11 Japanese tertiary care facilities that treated COVID-19 patients. Participants were tested using both real-time reverse transcription polymerase chain reaction (RT-PCR) and chest CTs between January 1 and May 30, 2020. We chronologically split the dataset in each hospital into training and test sets, containing patients in a 7:3 ratio. A Light Gradient Boosting Machine model was used for the analysis. Results: A total of 703 patients were included, and two models-the full model and the A-blood model-were developed for their diagnosis. The A-blood model included eight variables (the Ali-M3 confidence, along with seven clinical features of blood counts and biochemistry markers). The areas under the receiver-operator curve of both models [0.91, 95% confidence interval (CI): 0.86 to 0.95 for the full model and 0.90, 95% CI: 0.86 to 0.94 for the A-blood model] were better than that of the Ali-M3 confidence (0.78, 95% CI: 0.71 to 0.83) in the test set. Conclusions: The A-blood model, a COVID-19 diagnostic model developed in this study, combines machine-learning and CT evaluation with blood test data and performs better than the Ali-M3 framework existing for this purpose. This would significantly aid physicians in making a quicker diagnosis of COVID-19.

Forecast of the COVID-19 Epidemic Based on RF-BOA-LightGBM.

In this paper, we utilize the Internet big data tool, namely Baidu Index, to predict the development trend of the new coronavirus pneumonia epidemic to obtain further data. By selecting appropriate keywords, we can collect the data of COVID-19 cases in China between 1 January 2020 and 1 April 2020. After preprocessing the data set, the optimal sub-data set can be obtained by using random forest feature selection method. The optimization results of the seven hyperparameters of the LightGBM model by grid search, random search and Bayesian optimization algorithms are compared. The experimental results show that applying the data set obtained from the Baidu Index to the Bayesian-optimized LightGBM model can better predict the growth of the number of patients with new coronary pneumonias, and also help people to make accurate judgments to the development trend of the new coronary pneumonia.

Personalized Prediction of Hospital Mortality in COVID-19-Positive Patients.

OBJECTIVE: To develop predictive models for in-hospital mortality and length of stay (LOS) for coronavirus disease 2019 (COVID-19)-positive patients. PATIENTS AND METHODS: We performed a multicenter retrospective cohort study of hospitalized COVID-19-positive patients. A total of 764 patients admitted to 14 different hospitals within the Cleveland Clinic from March 9, 2020, to May 20, 2020, who had reverse transcriptase-polymerase chain reaction-proven coronavirus infection were included. We used LightGBM, a machine learning algorithm, to predict in-hospital mortality at different time points (after 7, 14, and 30 days of hospitalization) and in-hospital LOS. Our final cohort was composed of 764 patients admitted to 14 different hospitals within our system. RESULTS: The median LOS was 5 (range, 1-44) days for patients admitted to the regular nursing floor and 10 (range, 1-38) days for patients admitted to the intensive care unit. Patients who died during hospitalization were older, initially admitted to the intensive care unit, and more likely to be white and have worse organ dysfunction compared with patients who survived their hospitalization. Using the 10 most important variables only, the final model's area under the receiver operating characteristics curve was 0.86 for 7-day, 0.88 for 14-day, and 0.85 for 30-day mortality in the validation cohort. CONCLUSION: We developed a decision tool that can provide explainable and patient-specific prediction of in-hospital mortality and LOS for COVID-19-positive patients. The model can aid health care systems in bed allocation and distribution of vital resources.