Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method.

https://www.mdpi.com/2227-7080/9/3/52/pdf

Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance

Estimating PM2.5 concentration of the conterminous United States via interpretable convolutional neural networks.

A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology.

Intelligent modeling strategies for forecasting air quality time series: A review

This article devises a photograph-based monitoring model to estimate the real-time PM2.5 concentrations, overcoming currently popular electrochemical sensor-based PM2.5 monitoring methods’ shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM2.5 concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM2.5 monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM2.5 concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM2.5 concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.

PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning

Air pollution may cause many severe diseases. An efficient air quality monitoring system is of great benefit for human health and air pollution control. In this paper, we study image-based air quality analysis, in particular, the concentration estimation of particulate matter with diameters less than 2.5 micrometers (PM 2.5 ). The proposed method uses a deep Convolutional Neural Network (CNN) to classify natural images into different categories based on their PM2.5 concentrations. In order to evaluate the proposed method, we created a dataset that contains total 591 images taken in Beijing with corresponding PM2.5 concentrations. The experimental results demonstrate that our method are valid for image-based PM2.5 concentration estimation.

Image-based air quality analysis using deep convolutional neural network

Particulate matter with diameters less than 2.5 micrometers (PM2.5) is one of the most common air pollutants and may cause many severe diseases. An efficient PM2.5 monitoring system is of great benefit for human health and air pollution control. In this paper, we estimate PM2.5 concentrations using outdoor images by a proposed ensemble of deep neural networks-based regression, which uses a feedforward neural network to combine the PM2.5 predictions yielded by three convolutional neural networks, VGG-16, Inception-v3, and ResNet50, and calculate the final PM2.5 prediction of the image. A PM2.5 image dataset with 1460 photos was used for performance evaluation. The experimental results demonstrated that the proposed ensemble can provide more accurate PM 2.5 estimation than all three individual deep learning networks used and, therefore, can be used for image-based PM 2.5 monitoring.

Ensemble of Deep Neural Networks for Estimating Particulate Matter from Images

Airborne particulate matter with a diameter less than 25 micrometers $(\mathrm{PM}_{25})$ is one of the most harmful air pollutants, because $\mathrm{PM}_{25}$ can be inhaled into human body and cause serious health problems by transmitting hazardous chemicals deeply into lung and bloodstream A reliable, easily accessible, and low-cost $\mathrm{PM}_{25}$ monitoring system can greatly help people raise public awareness of $\mathrm{PM}_{25}$ and reduce health hazards of air pollution In this paper, we combine image and weather information to estimate $\mathrm{PM}_{25}$ indices of outdoor images using deep learning and support vector regression (SVR) techniques The proposed method first uses a convolutional neural network (CNN) to predict the $\mathrm{PM}_{25}$ index based on image information, and then the $\mathrm{PM}_{25}$ predicted by CNN and two weather features, humidity and wind speed, are combined to yield final estimated $\mathrm{PM}_{25}$ index using a created SVR model We assessed our method using two datasets collected from Shanghai City and Beijing City in China and experimental results demonstrated the effectiveness of the proposed method for $\mathrm{PM}_{25}$ estimation

Particle Pollution Estimation from Images Using Convolutional Neural Network and Weather Features

Different gestures have different action speeds, directions, and trajectories that can cause distinctive effects on the propagation of WiFi signals. In this paper, we present a new approach that uses deep transfer learning techniques to recognize gestures based on the channel state information (CSI) extracted from WiFi signals. Firstly, the CSI streams of gestures are captured and the gesture segments are extracted based on the CSI amplitude changes, and then the WiFi-based gesture recognition problem is innovatively converted to an image classification problem by expressing CSI streams as an image matrix. After that, two deep transfer learning methods are applied to recognize gestures using high-level features extracted by deep convolutional neural network (CNN) and fine-tuned CNN models. We evaluated our method using a collected dataset with 12 gestures in two environments, and the experimental results demonstrated that the proposed method outperformed other state-of-the-art WiFi-based gesture recognition methods.

Deep transfer learning for gesture recognition with WiFi signals

Facial recognition (FR) in unconstrained weather is still challenging and surprisingly ignored by many researchers and practitioners over the past few decades. Therefore, this paper aims to evaluate the performance of three existing popular facial recognition methods considering different weather conditions. As a result, a new face dataset (Lamar University database (LUDB)) was developed that contains face images captured under various weather conditions such as foggy, cloudy, rainy, and sunny. Three very popular FR methods—Eigenface (EF), Fisherface (FF), and Local binary pattern histogram (LBPH)—were evaluated considering two other face datasets, AT&T and 5_Celebrity, along with LUDB in term of accuracy, precision, recall, and F1 score with 95% confidence interval (CI). Computational results show a significant difference among the three FR techniques in terms of overall time complexity and accuracy. LBPH outperforms the other two FR algorithms on both LUDB and 5_Celebrity datasets by achieving 40% and 95% accuracy, respectively. On the other hand, with minimum execution time of 1.37, 1.37, and 1.44 s per image on AT&T,5_Celebrity, and LUDB, respectively, Fisherface achieved the best result.

Evaluating the Performance of Eigenface, Fisherface, and Local Binary Pattern Histogram-Based Facial Recognition Methods under Various Weather Conditions

Jing Zhang

Papers

Image-based air quality analysis using deep convolutional neural network

Ensemble of Deep Neural Networks for Estimating Particulate Matter from Images

Particle Pollution Estimation from Images Using Convolutional Neural Network and Weather Features

Deep transfer learning for gesture recognition with WiFi signals

Evaluating the Performance of Eigenface, Fisherface, and Local Binary Pattern Histogram-Based Facial Recognition Methods under Various Weather Conditions