Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

River discharge simulation using variable parameter McCarthy–Muskingum and wavelet-support vector machine methods

In this study, an extended version of variable parameter McCarthy–Muskingum (VPMM) method originally proposed by Perumal and Price (J Hydrol 502:89–102, 2013) was compared with the widely used data-based model, namely support vector machine (SVM) and hybrid wavelet-support vector machine (WASVM) to simulate the hourly discharge in Neckar River wherein significant lateral flow contribution by intermediate catchment rainfall prevails during flood wave movement. The discharge data from the year 1999 to 2002 have been used in this study. The extended VPMM method has been used to simulate 9 flood events of the year 2002, and later the results were compared with SVM and WASVM models. The analysis of statistical and graphical results suggests that the extended VPMM method was able to predict the flood wave movement better than the SVM and WASVM models. A model complexity analysis was also conducted which suggests that the two parameter-based extended VPMM method has less complexity than the three parameter-based SVM and WASVM model. Further, the model selection criteria also give the highest values for VPMM in 7 out of 9 flood events. The simulation of flood events suggested that both the approaches were able to capture the underlying physics and reproduced the target value close to the observed hydrograph. However, the VPMM models are slightly more efficient and accurate, than the SVM and WASVM model which are based only on the antecedent discharge data. The study captures the current trend in the flood forecasting studies and showed the importance of both the approaches (physical and data-based modeling). The analysis of the study suggested that these approaches complement each other and can be used in accurate yet less computational intensive flood forecasting

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

A prediction approach for multichannel EEG signals modeling using local wavelet SVM

Accurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal. © 2006 IEEE.published_or_final_versio

A Time-Series-Based Feature Extraction Approach for Prediction of Protein Structural Class

This paper presents a novel feature vector based on physicochemical property of amino acids for prediction protein structural classes. The proposed method is divided into three different stages. First, a discrete time series representation to protein sequences using physicochemical scale is provided. Later on, a wavelet-based time-series technique is proposed for extracting features from mapped amino acid sequence and a fixed length feature vector for classification is constructed. The proposed feature space summarizes the variance information of ten different biological properties of amino acids. Finally, an optimized support vector machine model is constructed for prediction of each protein structural class. The proposed approach is evaluated using leave-one-out cross-validation tests on two standard datasets. Comparison of our result with existing approaches shows that overall accuracy achieved by our approach is better than exiting methods