Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models

Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a country.Consequently,a significant problem arises where not every region receives the same level of air quality information.This disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available options.To address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was proposed.Specifically,continuous location features in the form of latitude and longitude values were incorporated into our models.By utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were trained.Our experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly basis.Furthermore,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was discovered.Additionally,the implications and contributions of our research were discussed.By implementing our approach,the cost associated with relying solely on existing observatories can be substantially reduced.This reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions.

M-IDM: A Multi-Classication Based Intrusion Detection Model in Healthcare IoT

In recent years,the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected.Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset,such as the NSL-KDD dataset.However,such approaches do not reect the features that exist in real medical scenarios,leading to failure in potential threat detection.To address this problem,we proposed a novel intrusion classication architecture known as a Multi-class Classication based Intrusion Detection Model(M-IDM),which typically relies on data collected by real devices and the use of convolutional neural networks(i.e.,it exhibits better performance compared with conventional machine learning algorithms,such as naïve Bayes,support vector machine(SVM)).Unlike existing studies,the proposed architecture employs the actual healthcare IoT environment of National Cancer Center in South Korea and actual network data from real medical devices,such as a patient’s monitors(i.e.,electrocardiogram and thermometers).The proposed architecture classies the data into multiple classes:Critical,informal,major,and minor,for intrusion detection.Further,we experimentally evaluated and compared its performance with those of other conventional machine learning algorithms,including naïve Bayes,SVM,and logistic regression,using neural networks.