Deep learning application in smart cities: recent development, taxonomy, challenges and research prospects

Muhammad, Amina Nuhu; Aseere, Ali M.; Chiroma, Haruna; Shah, Habib; Gital, Abdulsalam Ya’u; Hashem, Mohamed

doi:10.1007/s00521-020-05151-8

Cited by 62 publications

(25 citation statements)

References 116 publications

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“…The findings in Table 2 shows that the MCNN outperformed the CNN on average performance for all the five classes of the VARK LS model. This is in line with a recent study in [46] that a shallow network works best for small datasets. For the performance of the CNN, Table 2 shows that the accuracy increases as the sample size used in the experiment increases.…”

Section: Discussionsupporting

confidence: 93%

Performance Comparison of Convolutional and Multiclass Neural Network for Learning Style Detection from Facial Images

Gambo¹,

Wajiga²,

Shuib³

et al. 2018

ICST Transactions on Scalable Information Systems

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Improving the accuracy of learning style detection models is a primary concern in the area of automatic detection of learning style, which can be achieved either through, attribute/feature selection or classification algorithm. However, the role of facial expression in improving accuracy has not been fully explored in the research domain. On the other hand, deep learning solutions have become a new approach for solving complex problems using Deep Neural networks (DNNs); these DNNs have deep architectures that are capable of decomposing problems into multiple processing layers, enabling and devising multiple mapping of complex problems functions. In this paper, we investigate and compare the performance of Convolutional Neural Network (CNN) and MultiClass Neural Network (MCNN) for classification of learners into VARK learning-style dimensions (i.e Visual, Aural, Reading Kinaesthetic, including Neutral class) based on facial images. The performances of the two networks were evaluated and compared using square mean error MSE for training and accuracy metric for testing. The results show that MCNN offers better and robust classification performance of VARK learning style based on facial images. Finally, this paper has demonstrated a potential of a new method for automatic classification of VARK LS based on Facial Expressions (FEs). Based on the experimental results of the models, this approach can benefit both researchers and users of adaptive e-learning systems to uncover the potential of using FEs as identifier learning styles for recommendations and personalization of learning environments.

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Section: Discussionsupporting

confidence: 93%

Performance Comparison of Convolutional and Multiclass Neural Network for Learning Style Detection from Facial Images

Gambo¹,

Wajiga²,

Shuib³

et al. 2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

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“…Deep learning approaches provide deeper insights into the system and its components making it easier to take better and informed data-driven decisions. The unparalleled data analyzing capabilities of deep learning technology aid in the pre-identification of any errors or future conditions of the systems and their components to provide predictive analysis and maintenance of the faulty components [171][172][173].…”

Section: Deep Learningmentioning

confidence: 99%

“…These issues comprise a lack of infrastructure, scarcity of technical knowhow, privacy, and security issues [4,[77][78][79][80][81]133,134,144,157,164,165,[172][173][174][175][176][177][178][181][182][183][184][185][186][187][188].…”

Section: Technical Issuesmentioning

confidence: 99%

Cyber-Physical Systems and Smart Cities in India: Opportunities, Issues, and Challenges

Ahmad

Ahad

Alam

et al. 2021

Sensors

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A large section of the population around the globe is migrating towards urban settlements. Nations are working toward smart city projects to provide a better wellbeing for the inhabitants. Cyber-physical systems are at the core of the smart city setups. They are used in almost every system component within a smart city ecosystem. This paper attempts to discuss the key components and issues involved in transforming conventional cities into smart cities with a special focus on cyber-physical systems in the Indian context. The paper primarily focuses on the infrastructural facilities and technical knowhow to smartly convert classical cities that were built haphazardly due to overpopulation and ill planning into smart cities. It further discusses cyber-physical systems as a core component of smart city setups, highlighting the related security issues. The opportunities for businesses, governments, inhabitants, and other stakeholders in a smart city ecosystem in the Indian context are also discussed. Finally, it highlights the issues and challenges concerning technical, financial, and other social and infrastructural bottlenecks in the way of realizing smart city concepts along with future research directions.

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“…The Internet of Things is a technology mode for quickly acquiring remote information through modern wireless communication technology [15], which aims to "connect all things", that is, in an environment, all people and things, things and things are connected through the network. This technology is based on the information collected by ubiquitous devices such as sensors, software platforms, and mobile phones to achieve communication and collaboration between objects [16]. Due to the above characteristics, the Internet of Things can be applied to medicine, that is, IoMT, which monitors the health of users by tracking the health records of patients [17], captures abnormal situations, and reminds users of their own health risks.…”

Section: Introductionmentioning

confidence: 99%

Research on Disease Prediction Based on Improved DeepFM and IoMT

Amin

Alhussein

et al. 2021

IEEE Access

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In recent years, with the increase of computer computing power, Deep Learning has begun to be favored. Its learning of non-linear feature combinations has played a role that traditional machine learning cannot reach in almost every field. The application of Deep Learning has also driven the advancement of Factorization Machine (FM) in the field of recommendation systems, because Deep Learning and FM can learn high-order and low-order features combinations respectively, and FM's hidden vector system enables it to learn information from sparse data. The integration of them has attracted the attention of many scholars. They have researched many classic models such as Factorization-supported Neural Network (FNN), Product-based Neural Networks (PNN), Inner PNN (IPNN), Wide&Deep, Deep&Cross, DeepFM, etc. for the Click-Through-Rate (CTR) problem, and their performance is getting better and better. This kind of model is also suitable for agriculture, meteorology, disease prediction and other fields due to the above advantages. Based on the DeepFM model, we predicts the incidence of hepatitis in each sample in the structured disease prediction data of the 2020 Artificial Intelligence Challenge Preliminary Competition, and make minor improvements and parameter adjustments to DeepFM. Compared with other models, the improved DeepFM has excellent performance in AUC. This research can be applied to electronic medical records to reduce the workload of doctors and make doctors focus on the samples with higher predicted incidence rates. For some changing data, such as blood pressure, height, weight, cholesterol, etc., we can introduce the Internet of Medical Things (IoMT). IoMT's sensors can be used to conduct transmission to ensure that the disease can be predicted in time, just in case. After joining IoMT, a healthcare system is formed, which is superior in forecasting and time performance.

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Deep learning application in smart cities: recent development, taxonomy, challenges and research prospects

Cited by 62 publications

References 116 publications

Performance Comparison of Convolutional and Multiclass Neural Network for Learning Style Detection from Facial Images

Performance Comparison of Convolutional and Multiclass Neural Network for Learning Style Detection from Facial Images

Cyber-Physical Systems and Smart Cities in India: Opportunities, Issues, and Challenges

Research on Disease Prediction Based on Improved DeepFM and IoMT

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