Future Smart Cities: Requirements, Emerging Technologies, Applications, Challenges, and Future Aspects

Javed, Abdul Rehman; Zikria, Yousaf Bin; Rehman, Saif ur; Shahzad, Faisal; Jalil, Zunera

doi:10.36227/techrxiv.14722854.v1

Cited by 21 publications

(18 citation statements)

References 77 publications

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“…The CBOW model is a predominant choice in various ML applications. In [33][34][35], a bidirectional gated recurrent unit (GRU) was implemented in association with attention mechanism. The CBOW model was modified in the approach defining the context window and use of a weighted module to extract the word vectors from the text.…”

Section: Analysis Modelmentioning

confidence: 99%

Emotion Recognition and Classification of Film Reviews Based on Deep Learning and Multimodal Fusion

Sun

2022

Wireless Communications and Mobile Computing

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In terms of cross-cultural exchanges, the film is not only an important embodiment of a country’s cultural soft power but also the most direct and favorable way of communication. The advent of the all-around well-off era has propelled people’s demand for spiritual, cultural, and entertainment which promotes the vigorous development of the film culture industry. The expansion and development of China’s film market, domestic films, are a reflection and extension of China’s culture and ideas. It plays an extremely important role in enhancing cultural self-confidence and cultural output. In order to better grasp the emotional tendency of the audience, understand the viewing needs, and put forward suggestions on domestic film production, it is very necessary to analyze the emotion of film reviews and dig deep into semantics. Since the evaluation of film works considers many factors that are complex and changeable, the choice of model plays a significant role in the process of emotion analysis. The deep learning model represented by a deep neural network has high tolerance to sentence noise, has strong information discrimination, and features self-learning ability. It also has great advantages in emotion classification tasks. This study conducts an in-depth study and research on the traditional emotion analysis methods and finally puts forward an effective emotion analysis framework that combines the traditional emotion analysis method and deep learning network. This framework enhances the text vectorization representation and emotion classification model by performing emotion analysis. The effectiveness is verified by corresponding experiments which justify the superiority of the approach.

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Section: Analysis Modelmentioning

confidence: 99%

Emotion Recognition and Classification of Film Reviews Based on Deep Learning and Multimodal Fusion

Sun

2022

Wireless Communications and Mobile Computing

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“…where η denotes the learning rate. Especially, we use the Adam optimizer [31][32][33] to dynamically adjust the learning rate. In Algorithm 2, the training algorithm is given in the parallel processing form.…”

Section: 42mentioning

confidence: 99%

An Improved Data-Driven Decision Feedback Receiver via Deep Unfolding

Liu

Zhao

Song

2022

Mathematical Problems in Engineering

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The proposed decision feedback receiver (DFR) is an end-to-end data-driven iterative receiver, and the performance gain is achieved from iterations. However, the mismatch problem between the training set and test set exists in the DFR training, and thus performance degradation, slow convergence speed, and oscillation are introduced. On the other hand, deep unfolding using parameter sharing is a practical method to reduce the model parameter number and improve the training efficiency, but the problem whether the parameter sharing will cause performance degradation is rarely considered. In this work, we generally discuss and analyze these two problems, and solution to solve the problem or conditions that the problem no longer exists is then introduced. We give the improvements to address the mismatch problem in the DFR, and thus we propose an improved-DFR via deep unfolding. The improved-DFRs with and without parameter sharing, namely, DFR-I and DFR-IS, are both developed with low computation complexity and model complexity and can be executed by parallel processing. Besides, the practical training tricks and performance analysis including computation complexity and model complexity are given. In the experiments, the improved-DFRs outperform the DFR in various scenarios, in terms of convergence speed and symbol error rate. The simulation results also show that the DFR-IS is easier to train, and the slight performance loss can be reduced by increasing model complexity, in comparison to DFR-I.

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“…These aspects can help maintain the social inclusion and satisfaction of the ordinary people in the country. Javed et al (2022) conducted significant research to discover and inspect the most recent technological developments, which would serve as the foundation for the approaching strong period. In their opinion, deep learning (DL), machine learning (ML), internet of things (IoT), mobile computing, big data, blockchain, sixth-generation (6G) networks, WiFi-7, industry 5.0, robotic systems, heating, ventilation, and air conditioning (HVAC), digital forensic, industrial control systems, connected and automated vehicles (CAVs), electric vehicles, product recycling, flying cars, pantry backup, calamity backup, and critical integration of cybersecurity to keep telecommunications secure are all examples of how deep learning, machine learning.…”

Section: Critical Aspects Of Future Smart Citiesmentioning

confidence: 99%

Research on Intelligent Management Solutions for Sustainable, Low-Carbon Future Smart Cities and Industrial Parks in China

Cheng

Wu²,

Karia³

2022

MJSSH

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China is a developed country promoting intelligent services, which can increase the country's sustainability appropriately. This is done by using innovative technologies to reduce the carbon footprints in the country. The technical innovations have helped maintain low-carbon for the country's future development. Intelligent technologies in the country help manage renewable energy by making environmental transformation easier (Darda et al., 2019). This research article will help find different management solutions for sustainable low-carbon for future smart cities and industrial parks in China. Moreover, this article will help find an in-depth review of China's policies to manage the future of the industrial parks.

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Future Smart Cities: Requirements, Emerging Technologies, Applications, Challenges, and Future Aspects

Abstract: Future Smart Cities

Cited by 21 publications

References 77 publications

Emotion Recognition and Classification of Film Reviews Based on Deep Learning and Multimodal Fusion

Emotion Recognition and Classification of Film Reviews Based on Deep Learning and Multimodal Fusion

An Improved Data-Driven Decision Feedback Receiver via Deep Unfolding

Research on Intelligent Management Solutions for Sustainable, Low-Carbon Future Smart Cities and Industrial Parks in China

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