ASSMA-SLM: Autonomous System for Smart Motor-Vehicles integrating Artificial and Soft Learning Mechanisms

Saleem, Muhamamd; Khadim, Assma; Fatima, Mayraj; Khan, Muhammad Asghar; Nair, Harish K G; Asif, Muhammad

doi:10.1109/iccr56254.2022.9995824

Cited by 60 publications

(2 citation statements)

References 39 publications

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“…The empirical results show a significant improvement in detecting the majority of categorical attacks. M. Saleem et al [18] proposed an Autonomous System for Smart Motor-Vehicles (ASSMA-SLM) that integrates Artificial and Soft Learning Mechanisms. The system aims to effectively monitor, route, and predict smart city mobility.…”

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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“…Secure communication [35], mobile agent protocol [36], machine learning [37], artificial neural networks [38], blockchain technology [39,57], electronic health records [40], novel secret key generation techniques [41], secret sharing schemes [42], and secure information management [43] can be applied in home improvement to enhance security and privacy of personal and financial data [44]. Machine learning approaches can be used in home improvement to analyze user data, predict preferences, and provide personalized recommendations for design, renovation, and maintenance [53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

Applications in Home Improvement Retailer, Koctas

Mourad

Raza

Amjad

et al. 2023

2023 International Conference on Business Analytics for Technology and Security (ICBATS)

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It sounds like Koçtaş is a leader in the home improvement sector in Turkey, and they are focused on providing the best service and customer experience possible. They are also actively working to accelerate their digital investments and use their vast amount of customer data to innovate in the industry. One way they are using this data is by collecting and analyzing video camera images using AI. This allows them to detect humans and identify which products and shelves are most viewed in their stores. This information can then be used to optimize store layout and product placement for a better customer experience. Another way Koçtaş is innovating is through the implementation of kiosks that use Natural Language Processing (NLP) to interact with customers. These kiosks can understand and respond to questions asked by customers using AI, providing a more personalized and human-like experience. Finally, Koçtaş is using Dynamic Creative Optimization to create personalized advertisements for their customers. This method allows them to optimize the content and format of their ads based on the individual preferences and behavior of their customers, leading to more effective marketing. Overall, Koçtaş is using technology and data to drive innovation and provide a better customer experience in the home improvement industry.

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Artificial intelligence (AI) advancements for transportation security: in-depth insights into electric and aerial vehicle systems

Kumar,

Altalbe

2024

Environ Dev Sustain

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ASSMA-SLM: Autonomous System for Smart Motor-Vehicles integrating Artificial and Soft Learning Mechanisms

Cited by 60 publications

References 39 publications

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Applications in Home Improvement Retailer, Koctas

Artificial intelligence (AI) advancements for transportation security: in-depth insights into electric and aerial vehicle systems

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