In-Vehicle Cognitive Route Decision Using Fuzzy Modeling and Artificial Neural Network

Saeed, Yousaf; Ahmed, Khalil; Zareei, Mahdi; Zeb, Asim; Vargas-Rosales, César; Awan, Khalid Mahmood

doi:10.1109/access.2019.2895832

Cited by 20 publications

(19 citation statements)

References 41 publications

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“…ese factors help law enforcement agencies to create strategies for counterterrorism. e deep learning algorithm 6 Complexity was used to learn the pattern of this big data available by GTD using most recent optimization techniques and make reasonable predictions and classifications. Even though many researchers have worked in the domain of using AI solutions for counterterrorism, no one has studied an effective mechanism of understanding factors of terrorism using deep learning, which is becoming very popular recently with the increased data and increased computational [53,54] power.…”

Section: Dealing With Unbalanced Classesmentioning

confidence: 99%

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Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.

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Section: Dealing With Unbalanced Classesmentioning

confidence: 99%

“…Machine learning algorithms have been used recently to study the different factors of terrorism [5,6]. NN and particularly DNN are getting popularity mainly because of the fact that a huge amount of labelled data is available recently.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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“…To efficiently deal with large-scale instances, heuristic algorithms are often applied to obtain "sub-optimal solutions" or "satisfactory solutions". At present, the most commonly adopted heuristic algorithms are Simulated Annealing (SA) [33], Genetic Algorithm (GA) [34], Tabu Search (TS) [35], Ant colony optimization (ACO) [36], Artificial Neural Network (ANN) [37], Particle Swarm Optimization (PSO) [38], etc.…”

Section: Related Workmentioning

confidence: 99%

Goods Consumed During Transit in Split Delivery Vehicle Routing Problems: Modeling and Solution

Yang¹,

Wang²,

Pang³

et al. 2020

IEEE Access

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This article presents the modeling and solution of an extended type of split delivery vehicle routing problem (SDVRP). In SDVRP, the demands of customers need to be met by efficiently routing a given number of capacitated vehicles, wherein each customer may be served multiple times by more than one vehicle. Furthermore, in many real-world scenarios, consumption of vehicles en route is the same as the goods being delivered to customers, such as food, water and fuel in rescue or replenishment missions in harsh environments. Moreover, the consumption may also be in virtual forms, such as time spent in constrained tasks. We name such a real-world SDVRP as Split Delivery Vehicle Routing Problem with Goods Consumed during Transit (SDVRP-GCT). In this paper, we give mathematical formulas to model SDVRP-GCT and provide solutions by extending three ant colony algorithms. To the best of our knowledge, this is the first research work specifically focussing on the SDVRP-GCT problem and its solutions. To assess the effectiveness of our proposed ant colony algorithms, we first apply them on widely adopted SDVRP benchmarking instances on different scales and their correspondingly extended SDVRP-GCT instances. Then, we formulate a real-world SDVRP-GCT instance for further assessment. Based on the extensive experimental results, we discuss the pros and cons of our proposed solutions and subsequently suggest their preferable application scenarios. In summary, our proposed solutions are shown as highly efficient in solving SDVRP-GCT instances.

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“…In order to improve the robustness of the single-link flexible manipulator control, an adaptive neural approximator is used to compensate for the system uncertainty, and a sliding mode control method is designed to rapidly move the system joint to a predetermined position and suppress the vibration on the manipulator [11]. Saeed et al [12] discusses the process of vehicle route decision making by using cognitive memory to store the route experience. It utilized the artificial neural networks to minimize the learning error rate and achieve the cognitive route decision.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Fractional Order Sliding Mode Control Method of Micro Gyroscope Using Double Feedback Fuzzy Neural Network

Fei

Chen

2020

IEEE Access

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In this paper, a dynamic fractional order sliding mode control method based on a double feedback fuzzy neural network controller is proposed to deal with the unknown parameters and upper bound of uncertainty. Firstly, the switching function of the dynamic fractional order sliding mode control is designed, which not only fixes switching function of the ordinary sliding mode control, but also increases the fractional order, so that the switching function has a higher degree of freedom. In addition, the expert experience of fuzzy logic and the self-learning ability of neural network are used to improve the control accuracy and estimate the upper bound of uncertainty. Meanwhile, by using Lyapunov stability theory, the adaptive laws of unknown parameters in the system are derived to realize online adjustment, which increases the robustness of the system. Finally, the simulation results show that the proposed control method is more effective than the ordinary adaptive sliding mode control method in terms of convergence speed, parameter fitting effect, output signal tracking speed and tracking error. INDEX TERMS Fractional order; Dynamical sliding mode control; Fuzzy system; Neural network; Micro gyroscope.

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In-Vehicle Cognitive Route Decision Using Fuzzy Modeling and Artificial Neural Network

Cited by 20 publications

References 41 publications

Prediction of Future Terrorist Activities Using Deep Neural Networks

Prediction of Future Terrorist Activities Using Deep Neural Networks

Goods Consumed During Transit in Split Delivery Vehicle Routing Problems: Modeling and Solution

Dynamic Fractional Order Sliding Mode Control Method of Micro Gyroscope Using Double Feedback Fuzzy Neural Network

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