Mehdi Ghatee

Amirkabir University of Technology | TUS · Department of Mathematics and Computer Science

This paper deals with fuzzy multi-depot bus scheduling (FMDBS) problem in which the objective function and constraints are defined with fuzzy attributes. Credibility relation is used to formulate the problem as an integer multicommodity flow problem. A novel combination of branch-and-price and heuristic algorithms, is proposed to efficiently solve...

Driver identification refers to the task of identifying the driver behind the wheel among a set of drivers. It is applicable in intelligent insurance, public transportation control systems, and the rental car business. An critical issue of these systems is the level of privacy, which encourages a lot of research using non-visual data. This paper pr...

هنر مدلسازی و تبدیل مساله واقعی به مدل‌های قابل تحلیل و از آن پس حل مدل و تعمیم نتایج روی مساله واقعی یک سیر اساسی است که که کتاب حاضر سعی در آموزش آن دارد. بر اساس این نگاه به طبیعت، مباحث برنامه‌ریزی خطی و بهینه‌سازی ترکیبیاتی مورد توجه قرار می‌گیرند. این مسایل با توجه به نقشه جامع علمی کشور و آرمانهای ایران عزیز برای تبدیل به ده اقتصاد برتر جهان...

This paper considers the accident images and develops a deep learning method for feature extraction together with a mixture of experts for classifcation. For the frst task, the outputs of the last max-pooling layer of a Convolution Neural Network (CNN) are used to extract the hidden features automatically. For the second task, a mixture of advanced...

Background: The development of public transit network can enhance the efficiency of the system as well as raise interest to use the system. Feeder bus service fills the area that is far from the railway system; therefore, designing a feeder network in a gap area causes the expansion of the main transit network. Purpose: To present a modified potent...

Driver identification is a central research area in intelligent transportation systems, with applications in commercial freight transport and usage-based insurance. One way to perform the identification is to use smartphones as the main sensor devices. After extracting features from smartphone-embedded sensors, various machine learning methods can...

There are many high-dimensional data in real-world applications such as biology, computer vision, and social networks. Feature selection approaches are devised to confront high-dimensional data challenges with the aim of efficient learning technologies as well as reduction of models complexity. Due to the hardship of labeling on these datasets, the...

With great advances in vision and natural language processing, the generation of image captions becomes a need. In a recent paper, Mathews, Xie and He [1], extended a new model to generate styled captions by separating semantics and style. In continuation of this work, here a new captioning model is developed including an image encoder to extract t...

Influence maximization (IM) is a challenge in social networks, which depends on the spreader selection. We propose a quadratic programming model to identify a fixed number of initial spreaders to affect the maximum nodes within the minimum diffusion time. We solve this model using a new Distance Aware Spreader Finding (DASF) algorithm independent o...

During the COVID-19 pandemic, social media platforms were ideal for communicating due to social isolation and quarantine. Also, it was the primary source of misinformation dissemination on a large scale, referred to as the infodemic. Therefore, automatic debunking misinformation is a crucial problem. To tackle this problem, we present two COVID-19...

Driver identification is an important research area in intelligent transportation systems, with applications in commercial freight transport and usage-based insurance. One way to perform the identification is to use smartphones as sensor devices. By extracting features from smartphone-embedded sensors, various machine learning methods can identify...

Driver identification is an important research area in intelligent transportation systems, with applications in commercial freight transport and usage-based insurance. One way to perform the identification is to use smartphones as sensor devices. By extracting features from smartphone-embedded sensors, various machine learning methods can identify...

p>With the advent of intelligent systems, we are still facing a high number of fatal traffic accidents. Driver assistance systems can significantly reduce this rate. For example, when a driver uses a turn signal, driver assistance systems alert the object's presence in blind spot areas. Camera-based driver assistance systems for blind spots usually...

p>With the advent of intelligent systems, we are still facing a high number of fatal traffic accidents. Driver assistance systems can significantly reduce this rate. For example, when a driver uses a turn signal, driver assistance systems alert the object's presence in blind spot areas. Camera-based driver assistance systems for blind spots usually...

There exist many high-dimensional data in real-world applications such as biology, computer vision, and social networks. Feature selection approaches are devised to confront with high-dimensional data challenges with the aim of efficient learning technologies as well as reduction of models complexity. Due to the hardship of labeling on these datase...

Overfitting is one of the critical problems in deep neural networks. Many regularization schemes try to prevent overfitting blindly. However, they decrease the convergence speed of training algorithms. Adaptive regularization schemes can solve overfitting more intelligently. They usually do not affect the entire network weights. This paper detects...

Shallow neural networks process the features directly, while deep networks extract features automatically along with the training. Both models suffer from overfitting or poor generalization in many cases. Deep networks include more hyper-parameters than shallow ones that increase the overfitting probability. This paper states a systematic review of...

The aim of this paper is to develop an activity-based travel demand model by receiving cellular network data. Our contribution is to model the uncertainty of human behaviors and also the ambiguity in features affecting users’ activities. We used probabilities to model the first aspect and fuzzy theory to treat with the second; therefore, a hybrid m...

Model selection is a challenge, and a popular Convolutional Neural Networks (CNN) usually takes extra-need parameters. It causes overfitting in real applications. Besides, the extracted hidden features would be lost when the number of convolution layers increases. We use the least auxiliary loss-functions to solve both of these problems. To this en...

My last Book (in Persian), published by Amirkabir University of Technology Press Decision Support and Expert Systems in Intelligent Transportation کتاب آخر من که توسط انتشارات دانشگاه صنعتی امیرکبیر به چاپ رسیده است: سامانه های تصمیم یار و خبره در حمل و نقل هوشمند Abstract: Intelligent transportation systems are important parts of our life and...

Intelligent Transportation Systems (ITS) refer to a range of transportation applications based on communication and information technology. These systems by the aid of modern ideas, provide comfortable, efficient and safe services for transportation users. They are located in the linkage of information technology, computer science, electrical engin...

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 u...

Nowadays, we face a huge number of high-dimensional data in different applications and technologies. To tackle the challenge, various feature selection methods have been recently proposed for reducing the computational complexity of the learning algorithms as well as simplifying the learning models. Maintaining the geometric structures and consider...

This paper considers the accident images and develops a deep learning method for feature extraction together with a mixture of experts for classification. For the first task, the outputs of the last max-pooling layer of a Convolution Neural Network (CNN) are used to extract the hidden features automatically. For the second task, a mixture of advanc...

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....

Deep networks can learn complex problems, however, they suffer from overfitting. To solve this problem, regularization methods have been proposed that are not adaptable to the dynamic changes in the training process. With a different approach, this paper presents a regularization method based on the Singular Value Decomposition (SVD) that adjusts t...

ارائه اقدامات پيش‌گيري، کنترل و کاهش ميزان تصادفات نيازمند بررسي و شناخت ريشه‌ها و عوامل مهم و موثر در بروز تصادفات و ميزان شدت آنها است. اقدامات و تحقيقات بسياري براي شناخت خصوصيات عوامل موثر، روش‌هاي سنجش، مدل‌سازي و معرفي شاخص‌هاي ارزيابي ايمني، صورت گرفته است. بنابر اهميت و فراواني تصادفات و موقعيت‌هاي خطرناک جلوبه‌عقب در اين مقاله سيستم‌ فازي...

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 al...

Classification of high dimensional data suffers from curse of dimensionality and over-fitting. Neural tree is a powerful method which combines a local feature selection and recursive partitioning to solve these problems, but it leads to high depth trees in classifying high dimensional data. On the other hand, if less depth trees are used, the class...

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 u...

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...

This paper treats with a decision tree consisting of RBF neural networks in the nodes to decrease the classification time and to improve accuracy as well as generalization. We propose two knowledge transferring mechanisms between nodes to reduce the duplicate computations in training process. The resulted classifier is titled as ensemble of RBF neu...

Intelligent Transportation Systems (ITS) refer to a range of transportation applications based on communication and information technology. These systems by the aid of modern ideas, provide comfortable, efficient and safe services for transportation users. They are located in the linkage of information technology, computer science, electrical engin...

Smartphones play important roles in intelligent transportation systems and driving behavior evaluation contexts. By data mining of smartphone data, the driving style can be recognized efficiently. Then any incentive or punitive mechanisms can be applied to encourage the drivers to perform better. The insurance and enforcement departments can easily...

Dear ITS contributor It is my pleasure to announce that the special issue entitled as " Smartphone-Based Intelligent Transportation Systems " has been uploaded to the American Journal of Traffic and Transportation Engineering (http://www.ajtte.org). See the following: http://www.sciencepublishinggroup.com/specialissue/specialissueinfo?journalid=187...

Classification of the high-dimensional data by a new expert system is followed in the current paper. The proposed system defines some non-disjoint clusters of highly relevant features with the least inner-redundancy. For each cluster, a neural tree is implemented exploiting an Extreme Learning Machine (ELM) together an inference engine in any node....

Smartphones have a great ability to develop different types of smart city applications, due to the wide range of sensors, high processing power, storage capacity, connectivity and collaboration with others. Intelligent transportation systems also use these tools to collect important transport data, classification of multi-modal transportation data,...

Driving style evaluation by smartphones depends on the quality of the features extracted from sensors data. Typically, these features are extracted based on experiments, expertness, or heuristics. In more modern approaches, some automatic methods such as convolutional neural network (CNN) are used to extract features including obvious and hidden on...

This lecture was presented by Dr. Mehdi Ghatee in "The 1st National Conference on Mathematical Modeling in Science, Technology and Intelligent Systems ", University of Science and Technology of Mazandaran, Behshahr, Iran, 2019. The language of this lecture Persian.

Drivers' behavior evaluation is one of the most important problems in intelligent transportation systems and driver assistant systems. It has a great influence on driving safety and fuel consumption. One of the challenges in this regard is the modeling perspective to treat with uncertainty in judgments about driving behaviors. Really, assessing a s...

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 extra...

Classification of the high-dimensional data is challenging due to the curse of dimensionality, heavy computational burden and decreasing precision of algorithms. In order to mitigate these effects, feature selection approaches that can determine an efficient subset of features are utilized in the processing. However, most of these techniques attain...

Forest roads are constructed to facilitate forest protection, reforestation, logging operations and maximizing the value of forest products. Therefore forest roads are key infrastructures in the development of the region. This study aims to plan forest road network using artificial neural network and GIS regarding forest road technical principles....

Monitoring and evaluating of driving behavior is the main goal of this paper that encourage us to develop a new system based on Inertial Measurement Unit (IMU) sensors of smartphones. In this system, a hybrid of Discrete Wavelet Transformation (DWT) and Adaptive Neuro Fuzzy Inference System (ANFIS) is used to recognize overall driving behaviors. Th...

Prevention and control measures need to identify and examine the important factors affecting the occurrence and severity of accidents. Many research efforts to understand the characteristics of assessment methods, modeling and safety assessment based on the different measures. This paper proposes a collision-warning fuzzy system based on time to co...

This paper treats with an unstructured optimization problem with three kinds of data: some feasible solutions, some infeasible solutions and an objective function. Then a decision support system (DSS) is developed to recognize the feasible region by using radial basis function (RBF) neural network, feed forward neural network, support vector machin...

There are many systems to evaluate driving style based on smartphone sensors without enough awareness from the context. To cover this gap, we propose a new system namely CADSE system to consider the effects of traffic levels and car types on driving evaluation. CADSE system includes three subsystems to calibrate smartphone, to classify the maneuver...

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 entrop...

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 thi...

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...

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 c...

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 th...

Under dynamic conditions on bridges, we need a real-time management. To this end, this paper presents a rule-based decision support system in which the necessary rules are extracted from simulation results made by Aimsun traffic micro-simulation software. Then, these rules are generalized by the aid of fuzzy rule generation algorithms. Then, they a...

To protect vulnerable road users (VRU) like children and elderly, this paper presents a new warning system on smartphones. This system has three phases. First, we propose a new geometric approach to activate the system for necessary risky situations. Second, we extract some important features for VRUs and drivers based on their smartphones sensors...

In this paper, some methods are developed to find the lower bound for dynamic parking prices (LPP) to manage the central business district (CBD) demand. Based on these prices, the private traffic flows of user equilibrium model or stochastic user equilibrium model converge to the predicted flow derived by system optimum model. To obtain LPP, a bi-l...

The application of Benders decomposition method to a problem might result in a subproblem including integer variables. In this case, it is not able to apply the classical Benders algorithm. In this study we present a Branch-and-Cut algorithm, which introduces the notion of “Local Cuts” as well as “Global Cuts”. The integrality constraints of the su...

این سخنرانی درباره شهر هوشمند و نحوه اجرای ان در ایران است. به خصوص مطالب مربوط به سیستمهای حمل و نقل هوشمند و معماری سیستمهای حمل و نقل هوشمند در ایران مورد بررسی قرار گرفته است. برای دریافت اطلاعات بیشتر در این خصوص می توان به ادرس its.aut.ac.ir مراجعه نمود. فیلم سخنرانی نیز در ادرس http://www.aparat.com/v/C1cyr بارگذاری شده است.

A smartphone can be utilized as a cost-effective device for the purposes of intelligent transportation system. To detect the movement and the stationary statuses in the motorized and non-motorized modes, this study develops a new inference engine, including two sets of rules. The first sets of rules are defined by the related thresholds on the feat...

Mixture of experts is a neural network based ensemble learning approach consisting of several experts and a gating network. In this paper, we introduce regularized root-quartic mixture of experts (R-RTQRT-ME) by incorporating a regularization term into the error function to control the complexity of model and to increase robustness in confronting w...

Vehicular communication systems are developed not only to increase safety but also for mobility of road transportation. Roadside units (RSU) are the prominent elements of this technology. This equipment is installed on roadsides and at intersections to gather traffic information from vehicles and send messages and alarms to vehicles. Due to the cos...

In this paper, a new variable partitioning strategy in Benders decomposition method is applied that enables us to deal with a wide class of mixed-integer nonlinear programming problems including fixed-charge multicommodity network design (FMND) problems under congestion effects. It is proved that the proposed technique for an FMND problem leads to...

Mixture of experts (ME) as an ensemble method consists of several experts and a gating network to decompose the input space into some subspaces regarding to the experts specialties. To increase the diversity between experts in ME, this paper incorporates a correlation penalty function into the error function of ME. The significant of this modificat...

This paper tries to minimize the sum of a linear and a linear fractional function over a closed convex set defined by some linear and conic quadratic constraints. At first, we represent some necessary and sufficient conditions for the pseudoconvexity of the problem. For each of the conditions, under some reasonable assumptions, an appropriate secon...

This paper develops a new rule-based decision support system (RB-DSS) to find the safest solutions for routing, scheduling, and assignment in Hazmat transportation management. To define the safe program in RB-DSS, the accident frequency and severity are estimated for different scenarios of transportation, and they are used to classify the scenarios...

This paper deals with fuzzy integer linear programming problems with block angular structure in which the fuzzy constraints are simplified by using possibility and necessity relations. This main fuzzy problem is efficiently decomposed and is solved by a branch-and-price algorithm. In the nodes of the branch-and-price tree, the linear relaxation of...

A fuzzy emergency electronic brake light for chain-reaction crash Emergency electronic brake light is a type of the collision warning systems based on connected vehicle, which is used in chains rear-end accidents. In this technology, slowing down suddenly for the front vehicle, which is not in direct view of behind one, can inform through wireless...

Artificial intelligence’s objective is to replace human decision makers, while decision support system (DSS) has the aim of supporting rather than replacing. In a group DSS (GDSS) a number of managers need to be involved in the decision process. Roadway lane management can be implemented by different strategies whichever is appropriate for particul...

In this paper, a multi-perspective decision support system (MP-DSS)‎ ‎to design hierarchical public transportation network is developed‎. ‎Since this problem depends on different perspectives‎, ‎MP-DSS consists of two sub-systems with macro and micro sub-systems based on travel information‎, ‎land use and expert knowledge‎. ‎In the micro sub-system...

This paper deals with a new decision support system (DSS) for intelligent tunnel. This DSS includes two subsystems. In the first, the rules are extracted from incident severity database and micro-simulation results. Then simple fuzzy grid technique is applied to generate the rules. The accuracy degree of this subsystem is 63% in the presented exper...

This paper deals with multi-commodity flow problem with fractional objective function. The optimality conditions and the duality concepts of this problem are given. For this aim, the fractional linear programming formulation of this problem is considered and the weak duality, the strong direct duality and the weak complementary slackness theorems a...

This paper deals with linear programming problem with interval numbers as coefficients to exhibit with uncertainty. Since, the set of common intervals is not a field, we define generalized interval numbers to produce an algebraic interval field and on this field, we propose principle of uncertainty traverse instead of extension principle which perm...

Over the course of the century, many real-world applications of imbalanced data are emerged. One of its implication which is first considered in this context, is imbalanced accident data. In this paper, the data of transportation and accidents in Tehran-Bazargan highway between 2010 and 2015 is considered. In the pre-processing step, SMOTE is consi...

This paper proposes a new hybrid method namely SA-IP including simulated annealing and interior point algorithms to find the optimal congestion prices based on level of service (LOS) in order to maximize the mobility in urban network. By considering six fuzzy LOS for flows, the congestion prices of links can be derived by a bi-level fuzzy programmi...

This paper deals with preemptive priority based multi-objective network design problems in which construction times together with travel costs are taken into account. These cost and time objective functions are ordered lexicographically with respect to manager’s strategies in order to decrease total cost and total construction time of the network....

In this paper, an intelligent hyper framework is proposed to recognize protein folds from its amino acid sequence which is a fundamental problem in bioinformatics. This framework includes some statistical and intelligent algorithms for proteins classification. The main components of the proposed framework are the Fuzzy Resource-Allocating Network (...

This paper presents a multi-objectives optimization algorithm called NSGA2PI for designing the Radial Basis Function Networks (RBFNs) based on the non-dominated sorting genetic algorithms (NSGA-II) and pseudo inverse method. The main considered objectives are: higher classification ability and simpler structure network. NSGA2PI adjusts the RBF laye...

This paper deals with maximum cut problem on a graph with fuzzy edges. This problem is studied to cluster data under imprecise dependency. Applying the credibility measure, this fuzzy problem is transformed into a nonlinear mixed-integer programming problem. To solve the problem, an adaptive Hopfield neural network is proposed, with modern simulate...

This paper proposes a new hybrid method namely SA-IP including simulated annealing and interior point algorithms to find the optimal toll prices based on level of service (LOS) in order to maximize the mobility in urban network. By considering six fuzzy LOS for flows, the tolls of congested links can be derived by a bi-level fuzzy programming probl...