C-means clustering and deep-neuro-fuzzy classification for road weight measurement in traffic management system

Sumit, Sakhawat Hosain; Akhter, Shamim

doi:10.1007/s00500-018-3086-0

Cited by 28 publications

(23 citation statements)

References 24 publications

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“…All these attributes are combined with varying ranges, as presented in Table I. The selected attributes from GUI are placed to generate the weight value by C-means clustering and Deep-Neuro-Fuzzy software [8]. The weight output range is 0-10.…”

Section: B Integrating Road Weight Modulementioning

confidence: 99%

“…A new intelligence traffic management system (ITMS) [8] [11] [20] [21] [22] [30] was proposed and implemented to find an optimum route from source to destination. Each route is segmented and called as road segment.…”

Section: Introductionmentioning

confidence: 99%

“…Each route is segmented and called as road segment. We investigate environmental, vehicles and road related information for each road segment and generate a road weight for this particular road segment using Deep-Neuro-Fuzzy model [8][20] [21] [22]. However, ITMS needs some simulation or emulation to provide that the system works accurately.…”

Section: Introductionmentioning

confidence: 99%

“…However, ITMS needs some simulation or emulation to provide that the system works accurately. In this paper, we are proposing a traffic simulation environment by integrating Sumo Urban Mobility Simulation (SUMO) tool and a realtime Intelligent Traffic Management System (ITMS) [8] [11] [20] [21] [22]. Such simulations are expected to offer the prediction and on-the-fly feedback for better decision-making on complex traffic managerial issues and corporate them to the end-user applications.…”

Section: Introductionmentioning

confidence: 99%

“…User can set random values to the decision attributes, or IoT tool collects attributes values directly from the environment and vehicles. Deep-Neuro-Fuzzy framework [8] is used to classify those values and to formulate a weight for a mapped road segment. We expose a multi-agent-based optimum routefinding algorithm-ACO based System into SUMO simulator tool to make the ITMS more robust and self-adaptive.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modeling Ant Colony Optimization for Multi-Agent based Intelligent Transportation System

Akhter¹,

Ahsan²,

Quaderi³

2019

IJACSA

Self Cite

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This paper focuses on Sumo Urban Mobility Simulation (SUMO) and real-time Traffic Management System (TMS) simulation for evaluation, management, and design of Intelligent Transportation Systems (ITS). Such simulations are expected to offer the prediction and on-the-fly feedback for better decision-making. In these regards, a new Intelligent Traffic Management System (ITMS) was proposed and implemented-where a path from source to destination was selected by Dijkstra algorithm, and the road segment weights were calculated using real-time analyses (Deep-Neuro-Fuzzy framework) of data collected from infrastructure systems, mobile, distributed technologies, and socially-build systems. We aim to simulate the ITMS in pragmatic style with micro traffic, open-source traffic simulation model (SUMO), and discuss the challenges related to modeling and simulation for ITMS. Also, we expose a new model-Ant Colony Optimization (ACO) in SUMO tool to support a multi-agent-based collaborative decisionmaking environment for ITMS. Beside we evaluate ACO model performance with exiting built-in optimum route-finding SUMO models (Contraction Hierarchies Wrapper)-CHWrapper, A-star (A*), and Dijkstra) for optimum route choice. The results highlight that ACO performs better than other algorithms.

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Section: B Integrating Road Weight Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling Ant Colony Optimization for Multi-Agent based Intelligent Transportation System

Akhter¹,

Ahsan²,

Quaderi³

2019

IJACSA

Self Cite

View full text Add to dashboard Cite

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A survey of applied machine learning techniques for optical orthogonal frequency division multiplexing based networks

Mrabet

Dayoub

Belghith

2021

Trans Emerging Tel Tech

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In this survey, we analyze the newest machine learning (ML) techniques for optical orthogonal frequency division multiplexing (O-OFDM)-based optical communications. ML has been proposed to mitigate channel and transceiver imperfections. For instance, ML can improve the signal quality under low modulation extinction ratio or can tackle both determinist and stochastic-induced nonlinearities such as parametric noise amplification in long-haul transmission. The proposed ML algorithms for O-OFDM can in particularly tackle inter-subcarrier nonlinear effects such as four-wave mixing and cross-phase modulation. In essence, these ML techniques could be beneficial for any multi-carrier approach (e.g. filter bank modulation). Supervised and unsupervised ML techniques are analyzed in terms of both O-OFDM transmission performance and computational complexity for potential real-time implementation. We indicate the strict conditions under which a ML algorithm should perform classification, regression or clustering. The survey also discusses open research issues and future directions towards the ML implementation.

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A fuzzy data augmentation technique to improve regularisation

Dabare

Wong

Shiratuddin

et al. 2021

Int J of Intelligent Sys

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Deep learning (DL) has achieved superior classification in many applications due to its capability of extracting features from the data. However, the success of DL comes with the tradeoff of possible overfitting. The bias towards the data it has seen during the training process leads to poor generalisation. One way of solving this issue is by having enough training data so that the classifier is invariant to many data patterns. In the literature, data augmentation has been used as a type of regularisation method to reduce the chance for the model to overfit. However, most of the relevant works focus on image, sound or text data. There is not much work on numerical data augmentation, although many real-world problems deal with numerical data. In this paper, we propose using a technique based on Fuzzy C-Means clustering and fuzzy membership grades. Fuzzy-related techniques are used to address the variance problem by generating new data items based on fuzzy numbers and each data item's belongings to different fuzzy clusters. This data augmentation technique is used to improve the generalisation of a Deep Neural Network that is suitable for numerical data. By combining the proposed fuzzy data augmentation technique with the Dropout regularisation technique, we manage to balance the classification model's bias-variance tradeoff. Our proposed technique is evaluated using four

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C-means clustering and deep-neuro-fuzzy classification for road weight measurement in traffic management system

Cited by 28 publications

References 24 publications

Modeling Ant Colony Optimization for Multi-Agent based Intelligent Transportation System

Modeling Ant Colony Optimization for Multi-Agent based Intelligent Transportation System

A survey of applied machine learning techniques for optical orthogonal frequency division multiplexing based networks

A fuzzy data augmentation technique to improve regularisation

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