-22b9b34417bc6092a74-1821 20211204035657002 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 11 30 2021 11 1 10.35940/ijitee.11.1 https://www.ijitee.org/download/volume-11-issue-1/ Department of Bachelor of Science, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam. Hung Thuan Nguyen Chi Quynh Nguyen Department of Computer Science, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam. The global air pollution is constantly increasing and causing negative effects on human health such as respiratory, cardiovascular diseases and cancers. Recently, pollution in Hanoi has become increasingly worse, especially when PM2.5 concentration is always at high level. Thus, PM2.5 prediction is of more urgency to issue early forecasts. Depending on air data including meteorological indicators and air pollution indicators collected in Hanoi, we have proposed a new characteristic extraction method that gave better results when uing the same algorithm compared to those of old methods. XGBoost algorithm was applied to predict the concentration of PM2.5 and the test showed that the accuracy of this algorithm is higher than that of other data mining algorithms while the training time is significantly lower. 11 30 2021 64 69 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.B8297.1111121 https://www.ijitee.org/wp-content/uploads/papers/v11i1/B82971210220.pdf National Institute of Applied Science and Technology INSAT, Tunis, Tunisia. Hela Lassoued Raouf Ketata National Institute of Applied Science and Technology INSAT, Tunis, Tunisia. Hajer Ben Mahmoud National Institute of Applied Science and Technology INSAT, Tunis, Tunisia. This paper presents a data driven system used for cardiac arrhythmia classification. It applies the Neuro-Fuzzy Inference System (ANFIS) to classify MIT-BIH arrhythmia database electrocardiogram (ECG) recordings into five (5) heartbeat types. In fact, in order to obtain the input feature vector from recordings, a time scale method based on a Discrete Wavelet Transform (DWT) was investigated. Then, the time scale features are selected by applying the Principal Component Analysis (PCA). Therefore, the selected input feature vectors are classified by the Neuro-Fuzzy method. However, the ANFIS configuration needs mainly the choice of an initial Fuzzy Inference System (FIS) and the training algorithm. Indeed, two clustering algorithms which are the fuzzy c-means (FCM) and the subtractive ( SUBCLUST) algorithms, are applied to generate the initial FIS. Besides, for tuning the ANFIS membership function and rule base parameters, Gradient descent and evolutionary training algorithms are also evaluated. Gradient descent consists of the backpropagation (BP) method and its hybridization with the least square algorithm (Hybrid). However, the evolutionary training methods involve the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA). Therefore, eight (8) ANFIS are configured and assessed. Accordingly, a comparison study between their obtained Root Mean Square Error (RMSE) is analyzed. At the end, we have selected an optimal ANFIS which uses the SUBTRUCT algorithm to generate the initial FIS and the GA to tune its parameters. Moreover, to guarantee the effectiveness of this work, a comparison study with related works is done. 11 30 2021 70 80 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.A9628.1111121 https://www.ijitee.org/wp-content/uploads/papers/v11i1/A96281111121.pdf