In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers’ vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Naïve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art.
The Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online learning, especially in under-resourced developing countries. Therefore, the construction of traditional courses (face to face), e-learning, or blended learning in limited conditions that still meet the needs of students is a problem faced by many universities today. To solve this problem, we propose a method of evaluating the influence of these factors on the e-learning system. From there, it is a matter of clarifying the importance and prioritizing construction investment for each factor based on the K-means clustering algorithm, using the data of students who have been participating in the system. At the same time, we propose a model to support students to choose one of the learning methods, such as traditional, e-learning or blended learning, which is suitable for their skills and abilities. The data classification method with the algorithms multilayer perceptron (MP), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and naïve bayes (NB) is applied to find the model fit. The experiment was conducted on 679 data samples collected from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam. With our proposed method, the results are obtained from experimentation for the different effects of infrastructure, teachers, and courses, also as features of these factors. At the same time, the accuracy of the prediction results which help students to choose an appropriate learning method is up to 81.52%.
In this work, we present a novel method, namely dynamic basic activity sequence matching (DAS), a combination of machine learning methods and flexible threshold based methods for distinguishing normal and abnormal driving patterns. Indeed, DAS relies on the activity detection module (ADM) presented in our previous work to analyze each driving pattern as a sequence of basic activities-stopping (S), going straight (G), turning left (L), and turning right (R). In fact, the threshold value and other parameters like the duration of long and short activities are iteratively induced from the collected dataset. Hence, DAS is flexible and independent of driving contexts such as vehicle modes and road conditions. Experimental results, on the dataset collected from numerous motorcyclists, show the outperformance of our proposed method against dynamic time warping and the two popular machine learning methods-random forest and neural network-in distinguishing the normal and abnormal driving patterns. Moreover, we propose an efficient framework composing of two phases: in the first phase, the normal and abnormal driving patterns are distinguished by relying on DAS. In the second phase, the detected abnormal patterns are further classified into various specific abnormal driving patterns-weaving, sudden braking, etc. This fusion framework again achieves the highest overall accuracy of 97.94%.
TThis paper presents a hybrid method that combines the genetic algorithm (GA) and the ant colony system algorithm (ACS), namely GACS, to solve the traffic routing problem. In the proposed framework, we use the genetic algorithm to optimize the ACS parameters in order to attain the best trips and travelling time through several novel functions to help ants to update the global and local pheromones. The GACS framework is implemented using the VANETsim package and the real city maps from the open street map project. The experimental results show that our framework achieves a considerably higher performance than A-Star and the classical ACS algorithms in terms of the length of the global best path and the time for trips. Moreover, the GACS framework is also efficient in solving the congestion problem by online monitoring the conditions of traffic light systems. KeywordsTraffic routing; Ant colony system; Genetic algorithm; VANET simulator. References [1] M. Dorigo, Ant colony optimization, Scholarpedia 2(3) (2007) 1461. https://doi.org/10/4249/scholarpedia.1461.[2] M.V. Dorigo, Maniezzo, A. Colorni, Ant system: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 26(1) (1996) 29-41.[3] M. Dorigo, L.M. Gambardella, Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Transactions on evolutionary computation 1(1) (1997) 53-66.[4] M. Dorigo, T. St¨utzle, Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.[5] D. Favaretto, E. Moretti, P. Pellegrini, On the explorative behavior of MAX-MIN Ant. System, In: St¨utzle T, Birattari M, Hoos HH (eds) Engineering Stochastic Local Search Algorithms, Designing, Implementing and Analyzing Effective Heuristics. SLS 2009, LNCS, Springer, Heidelberg, Germany 5752 (2009) 115-119.[6] F. Lobo, C.F. Lima, Z. Michalewicz (eds), Parameter Setting in Evolutionary Algorithms, Springer, Berlin, Germany, 2007.[7] T. Stützle, Manuel López-Ibáñez, Paola Pellegrini, Michael Maur, Marco Montes de Oca, Mauro Birattari, Marco Dorigo, Parameter adaptation in ant colony optimization in Autonomous search, Springer, 2011, pp. 191-215.[8] Z. Cai, H. Huang, Ant colony optimization algorithm based on adaptive weight and volatility parameters in Intelligent Information Technology Application, 2008. IITA'08, Second International Symposium IEEE, 2008.[9] J. Liu, Shenghua Xu, Fuhao Zhang, Liang Wang, A hybrid genetic-ant colony optimization algorithm for the optimal path selection, Intelligent Automation & Soft Computing, 2016, pp. 1-8.[10] D.Gaertner K.L. Clark, On Optimal Parameters for Ant Colony Optimization Algorithms, In IC-AI, 2005.[11] X.Wei, Parameters Analysis for Basic Ant Colony Optimization Algorithm in TSP, Reason 7(4) (2014) 159-170.[12] J.H. Holland, Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence, U Michigan Press, 1975.[13] K.D.E. Sastry, Goldberg, G. Kendall, Genetic algorithms, In Search methodologies, Springer, 2014, pp. 93-117.[14] S.M. Odeh, Management of an intelligent traffic light system by using genetic algorithm, Journal of Image and Graphics 1(2) (2013) 90-93.[15] A.M. Turky, M.S. Ahmad, M.Z.M. Yusoff, B.T. Hammad, Using Genetic Algorithm for Traffic Light Control System with a Pedestrian Crossing, In: Wen P., Li Y., Polkowski L., Yao Y., Tsumoto S., Wang G. (eds) Rough Sets and Knowledge Technology, RSKT 2009, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg 5589 (2009) 512-519.[16] Y.R.B. Al-Mayouf, Mahamod Ismail1, Nor Fadzilah Abdullah, Salih M. Al-Qaraawi, Omar Adil Mahdi, Survey On Vanet Technologies And Simulation Models, 2006.[17] S.A. Ben Mussa, M. Manaf, K.Z. Ghafoor, Z. Doukha, "Simulation tools for vehicular ad hoc networks: A comparison study and future perspectives", 2015 International Conference on Wireless Networks and Mobile Communications (WINCOM), Marrakech, 2015, pp. 1-8.[18] V. Cristea, Victor Gradinescu, Cristian Gorgorin, Raluca Diaconescu, Liviu Iftode, Simulation of vanet applications, Automotive Informatics and Communicative Systems, 2009.[19] L. Liang, J. Ye, D. Wei, Application of improved ant colony system algorithm in optimization of irregular parts nesting, In 2008 Fourth International Conference on Natural Computation, IEEE, 2008.[20] X. Yan, Research on the Hybrid ant Colony Algorithm based on Genetic Algorithm. International Journal of Signal Processing, Image Processing and Pattern Recognition 9(3) (2016) 155-166.
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