FuzzyNet-Based Modelling Smart Traffic System in Smart Cities Using Deep Learning Models

Mall, Pawan Kumar; Narayan, Vipul; Pramanik, Sabyasachi; Srivastava, Swapnita; Faiz, Mohammad; Sriramulu, Srinivasan; Kumar, Mayank

doi:10.4018/978-1-6684-6408-3.ch005

Cited by 40 publications

(13 citation statements)

References 46 publications

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“…Mall, P. K., et al [16] concentrated on developing a foundational system for detecting traffic signs, informing drivers, and ensuring their safety using fuzzyNet modeling. However, the system is only intended to recognize a limited number of traffic signs automatically.For traffic flow forecasting, B. Vijayalakshmi et al [17] proposed an attention-based convolutional neural network long short-term memory (CNN-LSTM) model.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Ismaeel,

Mary,

Chelliah

et al. 2023

Sustainability

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Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets.

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Section: Related Workmentioning

confidence: 99%

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Ismaeel,

Mary,

Chelliah

et al. 2023

Sustainability

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“…Table VII tabulated a few research directions for the Smart Environment research problem using the Multi-Agent System. [116], traffic estimation, autonomous transport system, handling freight and logistics [117], material finding in Cargo, etc. Smart Mobility is the combination of ICT and mobility i.e.…”

Section: Smart Environmentmentioning

confidence: 99%

Next Generation of Multi-Agent Driven Smart City Applications and Research Paradigms

Arora,

Jain,

Yadav

et al. 2023

IEEE Open J. Commun. Soc.

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Smart City has seen a growing interest among government, researchers, and industrialists. Smart cities use digital technologies to enhance the quality of life for residents while promoting sustainability and efficient resource management. By integrating various technologies such as ICT, AI, and IoT, smart cities can improve the delivery of public services, optimize transportation systems, reduce energy consumption, and enhance public safety, among other benefits. The goal of smart cities is to create a more livable, efficient, and sustainable urban environment for residents, businesses, and visitors. The smart city focuses on automating different disciplines, including smart environment, smart home, smart economy, smart mobility, and, smart governance. Due to that Multi-Agent driven Smart city has received tremendous attention from the research community to get an intelligent solution to complex problems from different disciplines by subdividing responsibilities into multiple agents and empowering agents through AI. In this regard, it is vital to explore the usage of Multi-Agent in different critical application areas of smart cities to achieve an end-to-end intelligent urban area. In this paper, a detailed description of the Multi-Agent process for Smart city application areas is provided along with resources and future research directions. Four different application areas-Smart Home, Smart Governance, Smart Environment, and Smart Mobility are discussed in detail.

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“…Prior to 2011, only a solitary study had ventured into assessing the ramifications of data perturbation on data mining tools [15]. This study, conducted using the renowned IRIS and BUPA Liver datasets, yielded inconclusive results regarding its influence on classification accuracy.…”

Section: IIImentioning

confidence: 99%

Exploring Privacy-Preserving Methods via Perturbation Data Mining Employing Diverse Noise Strategies

Ranjeet Kumar Rai

2023

IJRITCC

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Knowledge discovery from data, commonly referred to as data mining. it involves the extraction of significant information, which may be previously unknown, concealed, or relevant, from extensive data sets or databases through the utilization of statistical methodologies. With the introduction of enhanced hardware technologies, there has been a proliferation in the storage and recording of personal data pertaining to individuals. Sophisticated organizations employ data mining algorithms to uncover hidden patterns or insights within data. Data mining techniques find application in diverse fields such as marketing, medical diagnosis, forecasting system, and national security. However, in scenarios where data privacy is paramount, mining certain types of data without violating the privacy of data owners presents a formidable challenge, sparking growing concerns among privacy advocates. To address these concerns, it is imperative to advance data mining procedures that are complex to individual privacy considerations. Perturbation of data plays a pivotal role in Privacy-Preserving Data Mining (PPDM). Additive data safeguard data privacy. In contrast, multiplicative data perturbation involves a series of transformations, including rotation, translation, and the addition of noise components to the perturbed data copy.

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FuzzyNet-Based Modelling Smart Traffic System in Smart Cities Using Deep Learning Models

Cited by 40 publications

References 46 publications

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Next Generation of Multi-Agent Driven Smart City Applications and Research Paradigms

Exploring Privacy-Preserving Methods via Perturbation Data Mining Employing Diverse Noise Strategies

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