IoMT-Assisted Medical Vehicle Routing Based on UAV-Borne Human Crowd Sensing and Deep Learning in Smart Cities

Rezaee, Khosro; Khosravi, Mohammad R.; Attar, Hani; Menon, Varun G.; Khan, Mohammad Ayoub; Issa, Haitham; Qi, Lianyong

doi:10.1109/jiot.2023.3284056

Cited by 15 publications

(3 citation statements)

References 43 publications

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“…A strategic study was also directed to the KSA in crowd organization utilizing a SWOT study. Rezaee et al [14] inspect UAV excess and irregular population action designs. Furthermore, the aim is to examine accepted video frames from UAVs.…”

Section: Related Workmentioning

confidence: 99%

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Alsubai,

Dutta,

Alghayadh

et al. 2024

IEEE Access

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Smart Cities refer to urban areas which exploits recent technologies for improving the performance, sustainability, and livability of their infrastructure and services. Crowd Density Analysis (CDA), a vital component of Smart Cities, involves the use of sensors, cameras, and data analytics to monitor and analyze the density and movement of people in public spaces. CDA utilizing DL harnesses the control of neural networks to mechanically and exactly evaluate the density of crowds in numerous settings, mainly in smart cities. DL techniques like Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), are trained on vast datasets of crowd videos or images to learn complex designs and features. These models can forecast crowd density levels, recognize crowd anomalies, and offer real-time visions into crowd behavior. This study designs an Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities (AICDA-SSC) technique. The aim of the AICDA-SSC method is to analyze the crowd density and classify it into multiple classes by the use of hyperparameter-tuned DL models. To accomplish this, the AICDA-SSC technique applies contrast enhancement using the CLAHE approach. Besides, the complex and intrinsic features can be derived by the use of the Inception v3 model and its hyperparameters can be chosen by the use of the marine predator's algorithm (MPA). For crowd density detection and classification, the AICDA-SSC technique applies a gated recurrent unit (GRU) model. Finally, a chaotic sooty tern optimizer algorithm (CSTOA) based hyperparameter selection procedure takes place to increase the effectiveness of the GRU system. The experimental evaluation of the AICDA-SSC technique takes place on a crowd-density image dataset. The experimentation values showcase the superior performance of the AICDA-SSC method to the recently developed DL models.

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Section: Related Workmentioning

confidence: 99%

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Alsubai,

Dutta,

Alghayadh

et al. 2024

IEEE Access

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“…All these methods will help identify optimal features for intrusion detection systems and improve the performance of intrusion detection systems, especially in a dynamic and unpredictable environment. Rezaee et al [33] discussed the IoMT-Assisted Medical Vehicle Routing Based on UAV-Borne Human Crowd Sensing and Deep Learning in Smart Cities is a type of intelligent optimization approach which combines Internet of Things (IoT) technology, UAV-borne human crowd sensing, and deep learning technology to help guide medical vehicles to their destinations more efficiently. It utilizes IoT sensors installed on medical vehicles to collect road data such as traffic conditions and congestion to dynamically generate optimized routes.…”

Section: Related Workmentioning

confidence: 99%

Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

Ismaeel,

Janardhanan,

Sankar

et al. 2023

Sustainability

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This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns’ dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods.

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“…Methods sometimes involve adapting models from existing video classification tasks related to the specific challenge, such as action identification or theme recognition. Several studies have employed shot sample methods to reduce computational costs [12][13][14][15]. The approaches included selecting sample images from whole films.…”

Section: Introductionmentioning

confidence: 99%

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

Shao,

Guo

2024

J Cloud Comp

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It's evident that streaming services increasingly seek to automate the generation of film genres, a factor profoundly shaping a film's structure and target audience. Integrating a hybrid convolutional network into service management emerges as a valuable technique for discerning various video formats. This innovative approach not only categorizes video content but also facilitates personalized recommendations, content filtering, and targeted advertising. Given the tendency of films to blend elements from multiple genres, there is a growing demand for a real-time video classification system integrated with social media networks. Leveraging deep learning, we introduce a novel architecture for identifying and categorizing video film genres. Our approach utilizes an ensemble gated recurrent unit (ensGRU) neural network, effectively analyzing motion, spatial information, and temporal relationships. Additionally,w we present a sophisticated deep neural network incorporating the recommended GRU for video genre classification. The adoption of a dual-model strategy allows the network to capture robust video representations, leading to exceptional performance in multi-class movie classification. Evaluations conducted on well-known datasets, such as the LMTD dataset, consistently demonstrate the high performance of the proposed GRU model. This integrated model effectively extracts and learns features related to motion, spatial location, and temporal dynamics. Furthermore, the effectiveness of the proposed technique is validated using an engine block assembly dataset. Following the implementation of the enhanced architecture, the movie genre categorization system exhibits substantial improvements on the LMTD dataset, outperforming advanced models while requiring less computing power. With an impressive F1 score of 0.9102 and an accuracy rate of 94.4%, the recommended model consistently delivers outstanding results. Comparative evaluations underscore the accuracy and effectiveness of our proposed model in accurately identifying and classifying video genres, effectively extracting contextual information from video descriptors. Additionally, by integrating edge processing capabilities, our system achieves optimal real-time video processing and analysis, further enhancing its performance and relevance in dynamic media environments.

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IoMT-Assisted Medical Vehicle Routing Based on UAV-Borne Human Crowd Sensing and Deep Learning in Smart Cities

Cited by 15 publications

References 43 publications

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

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