Effective Implementation of GPU-based Revised Simplex algorithm applying new memory management and cycle avoidance strategies

Graphics Processing Units (GPUs) with high computational capabilities used as modern parallel platforms to deal with complex computational problems. We use this platform to solve large-scale linear programing problems by revised simplex algorithm. To implement this algorithm, we propose some new memory management strategies. In addition, to avoid cycling because of degeneracy conditions, we use a tabu rule for entering variable selection in the revised simplex algorithm. To evaluate this algorithm, we consider two sets of benchmark problems and compare the speedup factors for these problems. The comparisons demonstrate that the proposed method is highly effective and solve the problems with the maximum speedup factors 165.2 and 65.46 with respect to the sequential version and Matlab Linprog solver respectively.Comment: 27 pages, 6 Tables, 10 Figures, Extracted from a PhD research program in Department of Computer Science of Amirkabir University of Technology, Tehran, Ira

Auction-based approximate algorithm for Grid system scheduling under resource provider strategies

In this paper a new mathematical model is proposed for task scheduling and resource allocation in Grid systems. In this novel model, load balancing, starvation prevention and failing strategies are stated as the constraints and the solution is restricted with a predefined quality of service for users with different priorities. These strategies are defined by resource providers based on the amount of submitted jobs to Grid. To solve the proposed model, a modern approximate Auction-based algorithm is developed and it is implemented as a prototype of Grid simulator namely Multi-S-Grid. The results are illustrated on 18 different large-scale Grid systems with different random capabilities and different users. The outcomes reveal the reasonable performance of the proposed Auction-based algorithm to solve Grid system optimization models.Comment: 19 pages, 1 Table, 17 Figures, extracted from MSc project with Department of Computer Science, Amirkabir University of Technology, Tehran , Ira

Modified SMOTE Using Mutual Information and Different Sorts of Entropies

SMOTE is one of the oversampling techniques for balancing the datasets and it is considered as a pre-processing step in learning algorithms. In this paper, four new enhanced SMOTE are proposed that include an improved version of KNN in which the attribute weights are defined by mutual information firstly and then they are replaced by maximum entropy, Renyi entropy and Tsallis entropy. These four pre-processing methods are combined with 1NN and J48 classifiers and their performance are compared with the previous methods on 11 imbalanced datasets from KEEL repository. The results show that these pre-processing methods improves the accuracy compared with the previous stablished works. In addition, as a case study, the first pre-processing method is applied on transportation data of Tehran-Bazargan Highway in Iran with IR equal to 36.Comment: 10 Pages, 4 Tables, 8 Figures, Extracted from an MSc project with Department of Computer Science, Amirkabir University of Technology, Tehran, Ira

Driver Identification by Neural Network on Extracted Statistical Features from Smartphone Data

The future of transportation is driven by the use of artificial intelligence to improve living and transportation. This paper presents a neural network-based system for driver identification using data collected by a smartphone. This system identifies the driver automatically, reliably and in real-time without the need for facial recognition and also does not violate privacy. The system architecture consists of three modules data collection, preprocessing and identification. In the data collection module, the data of the accelerometer and gyroscope sensors are collected using a smartphone. The preprocessing module includes noise removal, data cleaning, and segmentation. In this module, lost values will be retrieved and data of stopped vehicle will be deleted. Finally, effective statistical properties are extracted from data-windows. In the identification module, machine learning algorithms are used to identify drivers' patterns. According to experiments, the best algorithm for driver identification is MLP with a maximum accuracy of 96%. This solution can be used in future transportation to develop driver-based insurance systems as well as the development of systems used to apply penalties and incentives.Comment: 13 pages, 2 Figures, 5 Tables, The 18th International Conference on Traffic and Transportation Engineering, 2020, Tehran, Ira

Smartphone based Driving Style Classification Using Features Made by Discrete Wavelet Transform

Smartphones consist of different sensors, which provide a platform for data acquisition in many scientific researches such as driving style identification systems. In the present paper, smartphone data are used to evaluate the driving styles based on maneuvers analysis. The data obtained for each maneuver is the speed of the vehicle steering and the vehicle's direct and lateral acceleration. To classify the drivers based on their driving style, machine-learning algorithms can be used on these data. However, these data usually contains more information than it is needed and cause a bad effect on the learning accuracy. In addition, they may transfer some wrong information to the learning algorithm. Thus, we used Haar discrete wavelet transformation to remove noise effects. Then, we get the discrete wavelet transformation with four levels from smartphone sensors data, which include low-to-high frequencies, respectively. The obtained features vector for each maneuver includes the raw signal variance as well as the variance of the wavelet transform components. On these vectors, we use the k-nearest neighbors algorithm for features selection. Then, we use SVM, RBF and MLP neural networks on these features to separate braking and dangerous speed maneuvers from the safe ones as well as dangerous turning, U-turn and lane-changing maneuvers. The results are very interesting.Comment: 9 Pages, 4 Tables, 1 Figure, Extracted from M.SC Project (2018), Department of Computer Science, Amirkabir University of Technology, Tehran, Ira

A real-time warning system for rear-end collision based on random forest classifier

Rear-end collision warning system has a great role to enhance the driving safety. In this system some measures are used to estimate the dangers and the system warns drivers to be more cautious. The real-time processes should be executed in such system, to remain enough time and distance to avoid collision with the front vehicle. To this end, in this paper a new system is developed by using random forest classifier. To evaluate the performance of the proposed system, vehicles trajectory data of 100 car's database from Virginia tech transportation institute are used and the methods are compared based on their accuracy and their processing time. By using TOPSIS multi-criteria selection method, we show that the results of the implemented classifier is better than the results of different classifiers including Bayesian network, naive Bayes, MLP neural network, support vector machine, nearest neighbor, rule-based methods and decision tree. The presented experiments reveals that the random forest is an acceptable algorithm for the proposed driver assistant system with 88.4% accuracy for detecting warning situations and 94.7% for detecting safe situations.Comment: 26 Pages, 7 Tables, 16 Figures, Extracted from an MSc project with Department of Computer Science, Amirkabir University of Technology, Tehran, Ira

Adaptive Low-Rank Factorization to regularize shallow and deep neural networks

The overfitting is one of the cursing subjects in the deep learning field. To solve this challenge, many approaches were proposed to regularize the learning models. They add some hyper-parameters to the model to extend the generalization; however, it is a hard task to determine these hyper-parameters and a bad setting diverges the training process. In addition, most of the regularization schemes decrease the learning speed. Recently, Tai et al. [1] proposed low-rank tensor decomposition as a constrained filter for removing the redundancy in the convolution kernels of CNN. With a different viewpoint, we use Low-Rank matrix Factorization (LRF) to drop out some parameters of the learning model along the training process. However, this scheme similar to [1] probably decreases the training accuracy when it tries to decrease the number of operations. Instead, we use this regularization scheme adaptively when the complexity of a layer is high. The complexity of any layer can be evaluated by the nonlinear condition numbers of its learning system. The resulted method entitled "AdaptiveLRF" neither decreases the training speed nor vanishes the accuracy of the layer. The behavior of AdaptiveLRF is visualized on a noisy dataset. Then, the improvements are presented on some small-size and large-scale datasets. The preference of AdaptiveLRF on famous dropout regularizers on shallow networks is demonstrated. Also, AdaptiveLRF competes with dropout and adaptive dropout on the various deep networks including MobileNet V2, ResNet V2, DenseNet, and Xception. The best results of AdaptiveLRF on SVHN and CIFAR-10 datasets are 98%, 94.1% F-measure, and 97.9%, 94% accuracy. Finally, we state the usage of the LRF-based loss function to improve the quality of the learning model.Comment: 11 pages, 5 figures, 3 Tables

Regularized Deep Networks in Intelligent Transportation Systems: A Taxonomy and a Case Study

Intelligent Transportation Systems (ITS) are much correlated with data science mechanisms. Among the different correlation branches, this paper focuses on the neural network learning models. Some of the considered models are shallow and they get some user-defined features and learn the relationship, while deep models extract the necessary features before learning by themselves. Both of these paradigms are utilized in the recent intelligent transportation systems (ITS) to support decision-making by the aid of different operations such as frequent patterns mining, regression, clustering, and classification. When these learners cannot generalize the results and just memorize the training samples, they fail to support the necessities. In these cases, the testing error is bigger than the training error. This phenomenon is addressed as overfitting in the literature. Because, this issue decreases the reliability of learning systems, in ITS applications, we cannot use such over-fitted machine learning models for different tasks such as traffic prediction, the signal controlling, safety applications, emergency responses, mode detection, driving evaluation, etc. Besides, deep learning models use a great number of hyper-parameters, the overfitting in deep models is more attention. To solve this problem, the regularized learning models can be followed. The aim of this paper is to review the approaches presented to regularize the overfitting in different categories of ITS studies. Then, we give a case study on driving safety that uses a regularized version of the convolutional neural network (CNN).Comment: A review paper with 8 pages, 2 figures, and 2 tables, submitted to 18th International Conference on Traffic & Transportation Engineering, Tehran, February 25-27, 2020. Artificial Intelligence Review (2021

Roadside acoustic sensors to support vulnerable pedestrians via their smartphone

We propose a new warning system based on smartphones that evaluates the risk of motor vehicle for vulnerable pedestrian (VP). The acoustic sensors are embedded in roadside to receive vehicles sounds and they are classified into heavy vehicle, light vehicle with low speed, light vehicle with high speed, and no vehicle classes. For this aim, we extract new features by Mel-frequency Cepstrum Coefficients (MFCC) and Linear Predictive Coefficients (LPC) algorithms. We use different classification algorithms and show that MLP neural network achieves at least 96.77% in accuracy criterion. To install this system, directional microphones are embedded on roadside and the risk is classified there. Then, for every microphone, a danger area is defined and the warning alarms have been sent to every VPs smartphones covered in this danger area.Comment: 7 Pages, 8 Figures, 4 Table

Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering

Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms. In recent years, a variety of efforts are investigated on feature selection problems based on unsupervised viewpoint due to the laborious labeling task on large datasets. In this paper, we propose a novel approach on unsupervised feature selection initiated from the subspace clustering to preserve the similarities by representation learning of low dimensional subspaces among the samples. A self-expressive model is employed to implicitly learn the cluster similarities in an adaptive manner. The proposed method not only maintains the sample similarities through subspace clustering, but it also captures the discriminative information based on a regularized regression model. In line with the convergence analysis of the proposed method, the experimental results on benchmark datasets demonstrate the effectiveness of our approach as compared with the state of the art methods