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19 pages, 5289 KiB

Open AccessArticle

A Novel Deep Learning Approach for Real-Time Critical Assessment in Smart Urban Infrastructure Systems

by Abdulaziz Almaleh

Electronics 2024, 13(16), 3286; https://doi.org/10.3390/electronics13163286 - 19 Aug 2024

Viewed by 612

Abstract

The swift advancement of communication and information technologies has transformed urban infrastructures into smart cities. Traditional assessment methods face challenges in capturing the complex interdependencies and temporal dynamics inherent in these systems, risking urban resilience. This study aims to enhance the criticality assessment [...] Read more.

The swift advancement of communication and information technologies has transformed urban infrastructures into smart cities. Traditional assessment methods face challenges in capturing the complex interdependencies and temporal dynamics inherent in these systems, risking urban resilience. This study aims to enhance the criticality assessment of geographic zones within smart cities by introducing a novel deep learning architecture. Utilizing Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependency modeling, the proposed framework processes inputs such as total electricity use, flooding levels, population, poverty rates, and energy consumption. The CNN component constructs hierarchical feature maps through successive convolution and pooling operations, while the LSTM captures sequence-based patterns. Fully connected layers integrate these features to generate final predictions. Implemented in Python using TensorFlow and Keras on an Intel Core i7 system with 32 GB RAM and an NVIDIA GTX 1080 Ti GPU, the model demonstrated a superior performance. It achieved a mean absolute error of 0.042, root mean square error of 0.067, and an R-squared value of 0.935, outperforming existing methodologies in real-time adaptability and resource efficiency. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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14 pages, 13628 KiB

Open AccessArticle

GAN-Based High-Quality Face-Swapping Composite Network

by Qiaoyue Man, Young-Im Cho, Seok-Jeong Gee, Woo-Je Kim and Kyoung-Ae Jang

Electronics 2024, 13(15), 3092; https://doi.org/10.3390/electronics13153092 - 5 Aug 2024

Viewed by 732

Abstract

Face swapping or face replacement is a challenging task that involves transferring a source face to a target face while maintaining the target’s facial motion and expression. Although many studies have made a lot of encouraging progress, we have noticed that most of [...] Read more.

Face swapping or face replacement is a challenging task that involves transferring a source face to a target face while maintaining the target’s facial motion and expression. Although many studies have made a lot of encouraging progress, we have noticed that most of the current solutions have the problem of blurred images, abnormal features, and unnatural pictures after face swapping. To solve these problems, in this paper, we proposed a composite face-swapping generation network, which includes a face extraction module and a feature fusion generation module. This model retains the original facial expression features, as well as the background and lighting of the image while performing face swapping, making the image more realistic and natural. Compared with other excellent models, our model is more robust in terms of face identity, posture verification, and image quality. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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21 pages, 9372 KiB

Open AccessArticle

An Autonomous Cooperative Navigation Approach for Multiple Unmanned Ground Vehicles in a Variable Communication Environment

by Xudong Lin and Mengxing Huang

Electronics 2024, 13(15), 3028; https://doi.org/10.3390/electronics13153028 - 1 Aug 2024

Viewed by 576

Abstract

Robots assist emergency responders by collecting critical information remotely. Deploying multiple cooperative unmanned ground vehicles (UGVs) for a response can reduce the response time, improve situational awareness, and minimize costs. Reliable communication is critical for multiple UGVs for environmental response because multiple robots [...] Read more.

Robots assist emergency responders by collecting critical information remotely. Deploying multiple cooperative unmanned ground vehicles (UGVs) for a response can reduce the response time, improve situational awareness, and minimize costs. Reliable communication is critical for multiple UGVs for environmental response because multiple robots need to share information for cooperative navigation and data collection. In this work, we investigate a control policy for optimal communication among multiple UGVs and base stations (BSs). A multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed to update the control policy for the maximum signal-to-interference ratio. The UGVs communicate with both the fixed BSs and a mobile BS. The proposed control policy can navigate the UGVs and mobile BS to optimize communication and signal strength. Finally, a genetic algorithm (GA) is proposed to optimize the hyperparameters of the MADDPG-based training. Simulation results demonstrate the computational efficiency and robustness of the GA-based MADDPG algorithm for the control of multiple UGVs. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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20 pages, 2965 KiB

Open AccessArticle

Predicting People’s Concentration and Movements in a Smart City

by Joao C. Ferreira, Bruno Francisco, Luis Elvas, Miguel Nunes and Jose A. Afonso

Electronics 2024, 13(1), 96; https://doi.org/10.3390/electronics13010096 - 25 Dec 2023

Cited by 1 | Viewed by 997

Abstract

With the rapid growth of urbanization and the proliferation of mobile phone usage, smart city initiatives have gained momentum in leveraging data-driven insights to enhance urban planning and resource allocation. This paper proposes a novel approach for predicting people’s concentration and movements within [...] Read more.

With the rapid growth of urbanization and the proliferation of mobile phone usage, smart city initiatives have gained momentum in leveraging data-driven insights to enhance urban planning and resource allocation. This paper proposes a novel approach for predicting people’s concentration and movements within a smart city environment using mobile phone data provided by telecommunication operators. By harnessing the vast amount of anonymized and aggregated mobile phone data, we present a predictive framework that offers valuable insights into urban dynamics. The methodology involves collecting and processing location-based data obtained from telecommunication operators. Using machine learning techniques, including clustering and spatiotemporal analysis, we developed models to identify patterns in people’s movements and concentration across various city regions. Our proposed approach considers factors such as time of day, day of the week, and special events to capture the intricate dynamics of urban activities. The predictive models presented in this paper demonstrate the ability to predict areas of high concentration of people, such as commercial districts during peak hours, as well as the people flow during the time. These insights have significant implications for urban planning, traffic management, and resource allocation. Our approach respects user privacy by working with aggregated and anonymized data, ensuring compliance with privacy regulations and ethical considerations. The proposed models were evaluated using real-world mobile phone data collected from a smart city environment in Lisbon, Portugal. The experimental results demonstrate the accuracy and effectiveness of our approach in predicting people’s movements and concentration. This paper contributes to the growing field of smart city research by providing a data-driven solution for enhancing urban planning and resource allocation strategies. As cities continue to evolve, leveraging mobile phone data from telecommunication operators can lead to more efficient and sustainable urban environments. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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24 pages, 10518 KiB

Open AccessArticle

Worker Abnormal Behavior Recognition Based on Spatio-Temporal Graph Convolution and Attention Model

by Zhiwei Li, Anyu Zhang, Fangfang Han, Junchao Zhu and Yawen Wang

Electronics 2023, 12(13), 2915; https://doi.org/10.3390/electronics12132915 - 3 Jul 2023

Cited by 1 | Viewed by 1386

Abstract

In response to the problem where many existing research models only consider acquiring the temporal information between sequences of continuous skeletons and in response to the lack of the ability to model spatial information, this study proposes a model for recognizing worker falls [...] Read more.

In response to the problem where many existing research models only consider acquiring the temporal information between sequences of continuous skeletons and in response to the lack of the ability to model spatial information, this study proposes a model for recognizing worker falls and lays out abnormal behaviors based on human skeletal key points and a spatio-temporal graph convolutional network (ST-GCN). Skeleton extraction of the human body in video sequences was performed using Alphapose. To resolve the problem of graph convolutional networks not being effective enough for skeletal key points feature aggregation, we propose an NAM-STGCN model that incorporates a normalized attention mechanism. By using the activation function PReLU to optimize the model structure, the improved ST-GCN model can more effectively extract skeletal key points action features in the spatio-temporal dimension for the purposes of abnormal behavior recognition. The experimental results show that our optimized model achieves a 96.72% accuracy for recognition on the self-built dataset, which is 4.92% better than the original model; the model loss value converges below 0.2. Tests were performed on the KTH and Le2i datasets, which are both better than typical classification recognition networks. The model can precisely identify abnormal human behaviors, facilitating the detection of abnormalities and rescue in a timely manner and offering novel ideas for smart site construction. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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13 pages, 4992 KiB

Open AccessArticle

Efficient Face Region Occlusion Repair Based on T-GANs

by Qiaoyue Man and Young-Im Cho

Electronics 2023, 12(10), 2162; https://doi.org/10.3390/electronics12102162 - 9 May 2023

Viewed by 1263

Abstract

In the image restoration task, the generative adversarial network (GAN) demonstrates excellent performance. However, there remain significant challenges concerning the task of generative face region inpainting. Traditional model approaches are ineffective in maintaining global consistency among facial components and recovering fine facial details. [...] Read more.

In the image restoration task, the generative adversarial network (GAN) demonstrates excellent performance. However, there remain significant challenges concerning the task of generative face region inpainting. Traditional model approaches are ineffective in maintaining global consistency among facial components and recovering fine facial details. To address this challenge, this study proposes a facial restoration generation network combined a transformer module and GAN to accurately detect the missing feature parts of the face and perform effective and fine-grained restoration generation. We validate the proposed model using different image quality evaluation methods and several open-source face datasets and experimentally demonstrate that our model outperforms other current state-of-the-art network models in terms of generated image quality and the coherent naturalness of facial features in face image restoration generation tasks. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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14 pages, 2300 KiB

Open AccessArticle

Energy-Constrained UAV Data Acquisition in Wireless Sensor Networks with the Age of Information

by Jinxuan Xiong, Zhimin Li, Hongzhi Li, Lin Tang and Shaohong Zhong

Electronics 2023, 12(7), 1739; https://doi.org/10.3390/electronics12071739 - 6 Apr 2023

Cited by 3 | Viewed by 1430

Abstract

This paper considers a wireless sensor network (WSN) assisted by the unmanned aerial vehicle (UAV) in the Internet of Things (IoT). The UAV departs from the data center to the ground node to collect sensor node data as a relay. Under the constraints [...] Read more.

This paper considers a wireless sensor network (WSN) assisted by the unmanned aerial vehicle (UAV) in the Internet of Things (IoT). The UAV departs from the data center to the ground node to collect sensor node data as a relay. Under the constraints of battery energy, the UAV will travel to and from the data center repeatedly and transmit the collected sensor node data. The freshness of the node data received by the data center is measured by the Age of Information (AoI) as a performance metric. A genetic algorithm is used to plan the flight trajectory of the UAV. To ensure the data’s integrity and accuracy in a single sensor node, the UAV continuously collects sensor node data when the distance from the sensor node is less than the minimum acquisition distance. Through simulation experiments, we analyzed the influence of changing acquisition distance, the initial battery capacity, acquisition success probability, and transmission power on the peak age of information and the average age of information. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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14 pages, 2422 KiB

Open AccessArticle

Transformer-Based User Alignment Model across Social Networks

by Tianliang Lei, Lixin Ji, Gengrun Wang, Shuxin Liu, Lan Wu and Fei Pan

Electronics 2023, 12(7), 1686; https://doi.org/10.3390/electronics12071686 - 3 Apr 2023

Cited by 3 | Viewed by 1908

Abstract

Cross-social network user identification refers to finding users with the same identity in multiple social networks, which is widely used in the cross-network recommendation, link prediction, personality recommendation, and data mining. At present, the traditional method is to obtain network structure information from [...] Read more.

Cross-social network user identification refers to finding users with the same identity in multiple social networks, which is widely used in the cross-network recommendation, link prediction, personality recommendation, and data mining. At present, the traditional method is to obtain network structure information from neighboring nodes through graph convolution, and embed social networks into the low-dimensional vector space. However, as the network depth increases, the effect of the model will decrease. Therefore, in order to better obtain the network embedding representation, a Transformer-based user alignment model (TUAM) across social networks is proposed. This model converts the node information and network structure information from the graph data form into sequence data through a specific encoding method. Then, it inputs the data to the proposed model to learn the low-dimensional vector representation of the user. Finally, it maps the two social networks to the same feature space for alignment. Experiments on real datasets show that compared with GAT, TUAM improved ACC@10 indicators by 11.61% and 16.53% on Facebook–Twitter and Weibo–Douban datasets, respectively. This illustrates that the proposed model has a better performance compared to other user alignment models. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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14 pages, 1932 KiB

Open AccessArticle

Deep Learning Recommendations of E-Education Based on Clustering and Sequence

by Furkat Safarov, Alpamis Kutlimuratov, Akmalbek Bobomirzaevich Abdusalomov, Rashid Nasimov and Young-Im Cho

Electronics 2023, 12(4), 809; https://doi.org/10.3390/electronics12040809 - 6 Feb 2023

Cited by 25 | Viewed by 3366

Abstract

Commercial e-learning platforms have to overcome the challenge of resource overload and find the most suitable material for educators using a recommendation system (RS) when an exponential increase occurs in the amount of available online educational resources. Therefore, we propose a novel DNN [...] Read more.

Commercial e-learning platforms have to overcome the challenge of resource overload and find the most suitable material for educators using a recommendation system (RS) when an exponential increase occurs in the amount of available online educational resources. Therefore, we propose a novel DNN method that combines synchronous sequences and heterogeneous features to more accurately generate candidates in e-learning platforms that face an exponential increase in the number of available online educational courses and learners. Mitigating the learners’ cold-start problem was also taken into consideration during the modeling. Grouping learners in the first phase, and combining sequence and heterogeneous data as embeddings into recommendations using deep neural networks, are the main concepts of the proposed approach. Empirical results confirmed the proposed solution’s potential. In particular, the precision rates were equal to 0.626 and 0.492 in the cases of Top-1 and Top-5 courses, respectively. Learners’ cold-start errors were 0.618 and 0.697 for 25 and 50 new learners. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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16 pages, 4442 KiB

Open AccessArticle

A Linear Quadratic Regression-Based Synchronised Health Monitoring System (SHMS) for IoT Applications

by Divya Upadhyay, Puneet Garg, Sultan Mesfer Aldossary, Jana Shafi and Sachin Kumar

Electronics 2023, 12(2), 309; https://doi.org/10.3390/electronics12020309 - 6 Jan 2023

Cited by 13 | Viewed by 2005

Abstract

In recent days, the IoT along with wireless sensor networks (WSNs), have been widely deployed for various healthcare applications. Nowadays, healthcare industries use electronic sensors to reduce human errors while analysing illness more accurately and effectively. This paper proposes an IoT-based health monitoring [...] Read more.

In recent days, the IoT along with wireless sensor networks (WSNs), have been widely deployed for various healthcare applications. Nowadays, healthcare industries use electronic sensors to reduce human errors while analysing illness more accurately and effectively. This paper proposes an IoT-based health monitoring system to investigate body weight, temperature, blood pressure, respiration and heart rate, room temperature, humidity, and ambient light along with the synchronised clock model. The system is divided into two phases. In the first phase, the system compares the observed parameters. It generates advisory to parents or guardians through SMS or e-mails. This cost-effective and easy-to-deploy system provides timely intimation to the associated medical practitioner about the patient’s health and reduces the effort of the medical practitioner. The data collected using the proposed system were accurate. In the second phase, the proposed system was also synchronised using a linear quadratic regression clock synchronisation technique to maintain a high synchronisation between sensors and an alarm system. The observation made in this paper is that the synchronised technology improved the performance of the proposed health monitoring system by reducing the root mean square error to 0.379% and the R-square error by 0.71%. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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14 pages, 2160 KiB

Open AccessArticle

Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders

by Fazliddin Makhmudov, Alpamis Kutlimuratov, Farkhod Akhmedov, Mohamed S. Abdallah and Young-Im Cho

Electronics 2022, 11(23), 4047; https://doi.org/10.3390/electronics11234047 - 6 Dec 2022

Cited by 18 | Viewed by 2801

Abstract

Meticulous learning of human emotions through speech is an indispensable function of modern speech emotion recognition (SER) models. Consequently, deriving and interpreting various crucial speech features from raw speech data are complicated responsibilities in terms of modeling to improve performance. Therefore, in this [...] Read more.

Meticulous learning of human emotions through speech is an indispensable function of modern speech emotion recognition (SER) models. Consequently, deriving and interpreting various crucial speech features from raw speech data are complicated responsibilities in terms of modeling to improve performance. Therefore, in this study, we developed a novel SER model via attention-oriented parallel convolutional neural network (CNN) encoders that parallelly acquire important features that are used for emotion classification. Particularly, MFCC, paralinguistic, and speech spectrogram features were derived and encoded by designing different CNN architectures individually for the features, and the encoded features were fed to attention mechanisms for further representation, and then classified. Empirical veracity executed on EMO-DB and IEMOCAP open datasets, and the results showed that the proposed model is more efficient than the baseline models. Especially, weighted accuracy (WA) and unweighted accuracy (UA) of the proposed model were equal to 71.8% and 70.9% in EMO-DB dataset scenario, respectively. Moreover, WA and UA rates were 72.4% and 71.1% with the IEMOCAP dataset. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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Review

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20 pages, 1988 KiB

Open AccessReview

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

by Abdussalam Elhanashi, Pierpaolo Dini, Sergio Saponara and Qinghe Zheng

Electronics 2023, 12(24), 4925; https://doi.org/10.3390/electronics12244925 - 7 Dec 2023

Cited by 14 | Viewed by 3644

Abstract

The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed [...] Read more.

The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep-belief networks (DBNs), have demonstrated significant efficacy in mitigating these challenges by furnishing robust tools for learning and extraction of insights from vast and diverse IoT-generated data. This survey article offers a comprehensive and meticulous examination of recent scholarly endeavors encompassing the amalgamation of deep-learning techniques within the IoT landscape. Our scrutiny encompasses an extensive exploration of diverse deep-learning models, expounding on their architectures and applications within IoT domains, including but not limited to smart cities, healthcare informatics, and surveillance applications. We proffer insights into prospective research trajectories, discerning the exigency for innovative solutions that surmount extant limitations and intricacies in deploying deep-learning methodologies effectively within IoT frameworks. Full article

(This article belongs to the Special Issue AI Technologies and Smart City)

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