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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences
Mohd. Tahir Ismail
Universiti Sains Malaysia
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Hybrid model in machine learning–robust regression applied for sustainability agriculture and food security Mukhtar Mukhtar; Majid Khan Majahar Ali; Mohd. Tahir Ismail; Ferdinand Murni Hamundu; Alimuddin Alimuddin; Naseem Akhtar; Ahmad Fudholi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4457-4468

Abstract

A dataset containing 1924 observations used in this study to evaluate the effect of 435 different independent variables on one dependent variable. Big data has some issues such as irrelevant variables and outliers. Therefore, this study focused on analysing and comparing the impact of three different variable selection based on machine learning techniques, including random forest (RF), support vector machines (SVM), and Boosting. Further, the M robust regression was applied to address the outliers using M–bi square, M–Hampel, and M–Huber. Random forest and M-Hampel results revealed the significant comparing from the other methods such as mean absolute error (MAE) 175.33995, mean square error (MSE) 31.8608, mean average percentage error (MAPE) 9.16091, sum of square error (SSE) 89270.45, R–square 0.829511, and R–square adjusted 0.82670. Also, these techniques indicated that the 8 selection criteria were lower than the other techniques including Akaike information criterion (AIC) 47.25915, generalized cross validation (GCV) 47.27169, hannan-quinn (HQ) 47.60351, RICE (47.2845), SCHWARZ 51.7099, sigma square (SGMASQ) 46.50605, SHIBATA 47.23489, and final prediction error (FPE) 47.25929. Therefore, the study recommended that the best random forest and M-Hampel models are helpful to show the minimum issues and efficient validation for analysing and comparing big data.
Car following and lane changing behavior using NGSIM and China data Md Mijanoor Rahman; Mohd. Tahir Ismail; Majid Majahar Ali
International Journal of Advances in Applied Sciences Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1212.111 KB) | DOI: 10.11591/ijaas.v8.i1.pp14-25

Abstract

Road safety is imperative theme because increasing road fatalities deaths in world. Besides road fatalities, traffic jam is increasing, human is frustrated for uncomfortable journey. The roads safety and passengers comfortable of the roadway system are vastly depended on the Car following (CF) and Lane Changing (LC) features of drivers. CF and LC theory describe the driver behavior by following paths in a traffic stream. In this research, researchers have compared to US-101 Next-Generation-Simulation (NGSIM) data with Beijing forth ring road, China freeways real trajectory data by CF and LC models. The CF data has been calibrated with Genetic Algorithm (GA). Reproducing Kernel Hilbert Space (RKHS) is generated the LC beginning and finishing points. Findings revealed that the CF parameters as maximum acceleration, minimum deceleration, free speed, minimum headway and stopping distance percentages of Chinese data are 74.71%, 79.95%, 66.57%, 0.018% and 65.65% respectively of NGSIM data. After completing the comparison, researchers have been found out optimization safety and comfortable acceleration-deceleration and LC beginning-finishing points of driver behavior. Here this analysis generates the driver behavior at real traffic network on the express highways of specific two roads US-101 (NGSIM) data and Chinese freeways data. Since NGSIM data is well simulated so road traffic is more safety and comfortable for journey.
Co-Authors Ahmad Fudholi Majid Khan Majahar Ali Majid Majahar Ali Md Mijanoor Rahman Mukhtar Mukhtar Naseem Akhtar