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Prof. Dr. Gwanggil Jeon
Department of Embedded Systems Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Korea

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0 Artificial Intelligence
0 Machine Learning
0 Remote Sensing
0 IoT
0 wireless communication

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IoT
Artificial Intelligence
Machine Learning
Remote Sensing
5g
wireless communication

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Journal article
Published: 06 August 2021 in Interdisciplinary Sciences, Computational Life Sciences
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Recent pandemic of COVID-19 (Coronavirus) caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) has been growing lethally with unusual speed. It has infected millions of people and continues a mortifying influence on the global population’s health and well-being. In this situation, genome sequence analysis and advanced artificial intelligence techniques may help researchers and medical experts to understand the genetic variants of COVID-19 or SARS-CoV-2. Genome sequence analysis of COVID-19 is crucial to understand the virus’s origin, behavior, and structure, which might help produce/develop vaccines, antiviral drugs, and efficient preventive strategies. This paper introduces an artificial intelligence based system to perform genome sequence analysis of COVID-19 and alike viruses, e.g., SARS, middle east respiratory syndrome, and Ebola. The system helps to get important information from the genome sequences of different viruses. We perform comparative data analysis by extracting basic information of COVID-19 and other genome sequences, including information of nucleotides composition and their frequency, tri-nucleotide compositions, count of amino acids, alignment between genome sequences, and their DNA similarity information. We use different visualization methods to analyze these viruses’ genome sequences and, finally, apply machine learning based classifier support vector machine to classify different genome sequences. The data set of different virus genome sequences are obtained from an online publicly accessible data center repository. The system achieves good classification results with an accuracy of 97% for COVID-19, 96%, SARS, and 95% for MERS and Ebola genome sequences, respectively.

ACS Style

Imran Ahmed; Gwanggil Jeon. Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses. Interdisciplinary Sciences, Computational Life Sciences 2021, 1 -16.

AMA Style

Imran Ahmed, Gwanggil Jeon. Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses. Interdisciplinary Sciences, Computational Life Sciences. 2021; ():1-16.

Chicago/Turabian Style

Imran Ahmed; Gwanggil Jeon. 2021. "Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses." Interdisciplinary Sciences, Computational Life Sciences , no. : 1-16.

Journal article
Published: 03 August 2021 in Multimedia Tools and Applications
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Recently, using deep learning(DL) in super-resolution(SR) has ac- hieved great success. These methods combine the convolutional neural network(CNN) to learn a general matrix function for an end-to-end mapping. However, as the width and depth of the network increase, there are two essential problems in the SR tasks. On the one hand, a wider and deeper network will bring better performance but increase the calculating complexity and memory consumption. On the other hand, the expanded architecture will miss the intermediate feature details in the information transmitting process. Hence, a SISR(Single Image Super-Resolution) network with wider feature information blocks(WFIB) is proposed to address these issues by making a balance between the network complexity and performance. Cascade residual block(CRB) helps the network make full use of contextual feature information. Extensive experiments verify that our network achieves better performance and with fewer parameters than the state-of-the-art methods.

ACS Style

Haoran Yang; Jiahui Tong; Qingyu Dou; Long Xiao; Gwanggil Jeon; Xiaomin Yang. Wide receptive field networks for single image super-resolution. Multimedia Tools and Applications 2021, 1 -18.

AMA Style

Haoran Yang, Jiahui Tong, Qingyu Dou, Long Xiao, Gwanggil Jeon, Xiaomin Yang. Wide receptive field networks for single image super-resolution. Multimedia Tools and Applications. 2021; ():1-18.

Chicago/Turabian Style

Haoran Yang; Jiahui Tong; Qingyu Dou; Long Xiao; Gwanggil Jeon; Xiaomin Yang. 2021. "Wide receptive field networks for single image super-resolution." Multimedia Tools and Applications , no. : 1-18.

Research article
Published: 01 August 2021 in Big Data
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The new and integrated area called Internet of Things (IoT) has gained popularity due to its smart, objects, services and affordability. These networks are based on data communication, augmented reality (AR), and wired and wireless infrastructures. The basic objective of these network is data communication, environment monitoring, tracking, and sensing by using smart devices and sensor nodes. The dAR is one of the attractive and advanced areas that is integrated in IoT networks in smart homes and smart industries to convert the objects into 3D to visualize information and provide interactive reality-based control. With attraction, this idea has suffered with complex and heavy processes, computation complexities, network communication degradation, and network delay. This article presents a detailed overview of these technologies and proposes a more convenient and fast data communication model by using edge computing and Fifth-Generation platforms. The article also introduces a Visualization Augmented Reality framework for IoT (VAR-IoT) networks fully integrated by communication, sensing, and actuating features with a better interface to control the objects. The proposed network model is evaluated in simulation in terms of applications response time and network delay and it observes the better performance of the proposed framework.

ACS Style

Kashif Naseer Qureshi; Adi Alhudhaif; Raja Waseem Anwar; Shahid Nazir Bhati; Gwanggil Jeon. Fully Integrated Data Communication Framework by Using Visualization Augmented Reality for Internet of Things Networks. Big Data 2021, 9, 253 -264.

AMA Style

Kashif Naseer Qureshi, Adi Alhudhaif, Raja Waseem Anwar, Shahid Nazir Bhati, Gwanggil Jeon. Fully Integrated Data Communication Framework by Using Visualization Augmented Reality for Internet of Things Networks. Big Data. 2021; 9 (4):253-264.

Chicago/Turabian Style

Kashif Naseer Qureshi; Adi Alhudhaif; Raja Waseem Anwar; Shahid Nazir Bhati; Gwanggil Jeon. 2021. "Fully Integrated Data Communication Framework by Using Visualization Augmented Reality for Internet of Things Networks." Big Data 9, no. 4: 253-264.

Special issue paper
Published: 28 July 2021 in Journal of Real-Time Image Processing
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Real-time video surveillance systems are widely deployed in various environments, including public areas, commercial buildings, and public infrastructures. Person detection is a key and crucial task in different video surveillance applications, such as person detection, segmentation, and tracking. Researchers presented different image processing and artificial intelligence-based approaches (including machine and deep learning) for person detection and tracking, but mainly comprised of frontal view camera perspective. A real-time person tracking and segmentation system is introduced in this work, using an overhead camera perspective. The system applied a deep learning-based algorithm, i.e., SiamMask, a simple, versatile, fast, and surpassing other real-time tracking algorithms. The algorithm also performs segmentation of the target person by combining a mask branch to the fully convolutional twin neural network for target or person tracking. First, the person video sequences are obtained from an overhead perspective, and then additional training is performed with the help of transfer learning. Finally, a comparison is performed with other tracking algorithms. The SiamMask algorithm delivers good results, with a tracking accuracy of 95%.

ACS Style

Imran Ahmed; Gwanggil Jeon. A real-time person tracking system based on SiamMask network for intelligent video surveillance. Journal of Real-Time Image Processing 2021, 1 -12.

AMA Style

Imran Ahmed, Gwanggil Jeon. A real-time person tracking system based on SiamMask network for intelligent video surveillance. Journal of Real-Time Image Processing. 2021; ():1-12.

Chicago/Turabian Style

Imran Ahmed; Gwanggil Jeon. 2021. "A real-time person tracking system based on SiamMask network for intelligent video surveillance." Journal of Real-Time Image Processing , no. : 1-12.

Editorial
Published: 27 July 2021 in Computers & Electrical Engineering
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ACS Style

Gwanggil Jeon; Abdellah Chehri. Introduction to the special section on Security and Privacy Issues in Smart Grid by Applying Deep Learning Techniques (VSI-gridl). Computers & Electrical Engineering 2021, 93, 107331 .

AMA Style

Gwanggil Jeon, Abdellah Chehri. Introduction to the special section on Security and Privacy Issues in Smart Grid by Applying Deep Learning Techniques (VSI-gridl). Computers & Electrical Engineering. 2021; 93 ():107331.

Chicago/Turabian Style

Gwanggil Jeon; Abdellah Chehri. 2021. "Introduction to the special section on Security and Privacy Issues in Smart Grid by Applying Deep Learning Techniques (VSI-gridl)." Computers & Electrical Engineering 93, no. : 107331.

Guest editorial
Published: 26 July 2021 in Journal of Real-Time Image Processing
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ACS Style

Gwanggil Jeon; Abdellah Chehri. Special issue on deep learning for emerging embedded real-time image and video processing systems. Journal of Real-Time Image Processing 2021, 18, 1167 -1171.

AMA Style

Gwanggil Jeon, Abdellah Chehri. Special issue on deep learning for emerging embedded real-time image and video processing systems. Journal of Real-Time Image Processing. 2021; 18 (4):1167-1171.

Chicago/Turabian Style

Gwanggil Jeon; Abdellah Chehri. 2021. "Special issue on deep learning for emerging embedded real-time image and video processing systems." Journal of Real-Time Image Processing 18, no. 4: 1167-1171.

Editorial
Published: 23 July 2021 in Remote Sensing
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Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications

ACS Style

Gwanggil Jeon. Editorial for the Special Issue “Advanced Artificial Intelligence and Deep Learning for Remote Sensing”. Remote Sensing 2021, 13, 2883 .

AMA Style

Gwanggil Jeon. Editorial for the Special Issue “Advanced Artificial Intelligence and Deep Learning for Remote Sensing”. Remote Sensing. 2021; 13 (15):2883.

Chicago/Turabian Style

Gwanggil Jeon. 2021. "Editorial for the Special Issue “Advanced Artificial Intelligence and Deep Learning for Remote Sensing”." Remote Sensing 13, no. 15: 2883.

Editorial
Published: 21 July 2021 in Entropy
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Information entropy is a basic concept in information theory associated with any random variable

ACS Style

Gwanggil Jeon. Information Entropy Algorithms for Image, Video, and Signal Processing. Entropy 2021, 23, 926 .

AMA Style

Gwanggil Jeon. Information Entropy Algorithms for Image, Video, and Signal Processing. Entropy. 2021; 23 (8):926.

Chicago/Turabian Style

Gwanggil Jeon. 2021. "Information Entropy Algorithms for Image, Video, and Signal Processing." Entropy 23, no. 8: 926.

Journal article
Published: 06 July 2021 in International Journal of System Assurance Engineering and Management
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ACS Style

Muhammad Junaid Nazar; Adi Alhudhaif; Kashif Naseer Qureshi; Saleem Iqbal; Gwanggil Jeon. Signature and flow statistics based anomaly detection system in software-defined networking for 6G internet of things network. International Journal of System Assurance Engineering and Management 2021, 1 .

AMA Style

Muhammad Junaid Nazar, Adi Alhudhaif, Kashif Naseer Qureshi, Saleem Iqbal, Gwanggil Jeon. Signature and flow statistics based anomaly detection system in software-defined networking for 6G internet of things network. International Journal of System Assurance Engineering and Management. 2021; ():1.

Chicago/Turabian Style

Muhammad Junaid Nazar; Adi Alhudhaif; Kashif Naseer Qureshi; Saleem Iqbal; Gwanggil Jeon. 2021. "Signature and flow statistics based anomaly detection system in software-defined networking for 6G internet of things network." International Journal of System Assurance Engineering and Management , no. : 1.

Journal article
Published: 29 June 2021 in Computers & Electrical Engineering
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The education system is one of the mechanisms and aspiration to build the society and contribute to human capital, well-being, and wealth. Security and privacy concerns in the educational organization is always significant due to various violent and terrorist activities. Technologies have been adopted for smart learning systems to improve the learning experience whereas security has been neglected inside or outside the institutions. The new integrated technologies have been adopted by using smart monitoring and sensing devices. The main objective of this paper is to analyze the Internet of Things (IoT) solutions specially designed for schools to provide smart and secure systems for educational settings. This paper also proposes a Secure system for the Internet of Schools Things (S-IoST) for smart schools based on a new advanced communication system integrated with 5 G cellular systems, sensing technologies, intelligent transportation systems, and IoT networks. The proposed system provides a more secure alert mechanism and facilitates the users at school and during mobility to the school or home. The proposed system evaluates in terms of data delivery, time, and response alert parameters .

ACS Style

Kashif Naseer Qureshi; Ayesha Naveed; Yamna Kashif; Gwanggil Jeon. Internet of Things for education: A smart and secure system for schools monitoring and alerting. Computers & Electrical Engineering 2021, 93, 107275 .

AMA Style

Kashif Naseer Qureshi, Ayesha Naveed, Yamna Kashif, Gwanggil Jeon. Internet of Things for education: A smart and secure system for schools monitoring and alerting. Computers & Electrical Engineering. 2021; 93 ():107275.

Chicago/Turabian Style

Kashif Naseer Qureshi; Ayesha Naveed; Yamna Kashif; Gwanggil Jeon. 2021. "Internet of Things for education: A smart and secure system for schools monitoring and alerting." Computers & Electrical Engineering 93, no. : 107275.

Original article
Published: 23 June 2021 in Complex & Intelligent Systems
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Vehicular networks as the key enablers in Intelligent Transportation Systems (ITS) and the Internet of Things (IoT) are key components of smart sustainable cities. Vehicles as a significant component of smart cities have emerging in-vehicle applications that can assist in good governance for sustainable smart cities. Most of these applications are delay sensitive and demand high computational capabilities that are provided by emerging technologies. Utilizing the distributed computational resources of vehicles with the help of volunteer computing is an efficient method to fulfill the high computational requirements of vehicles itself and the other components of smart cities. Vehicle as a resource is an emerging concept that must be considered to address the future challenges of sustainable smart cities. In this paper, an infrastructure-assisted job scheduling and task coordination mechanism in volunteer computing-based VANET called RSU-based VCBV is proposed, which enhances the architecture of VANET to utilize the surplus resources of vehicles for task execution. We propose job scheduling and task coordination algorithms for different volunteer models. Further, we design and implement an adaptive task replication method to seek fault tolerance by avoiding task failures due to locations of vehicles. We propose a task replication algorithm called location-based task replication algorithm. Extensive simulations validate the performance of our proposed volunteer models while comparing average task execution time and weight ratios with existing work.

ACS Style

Abdul Waheed; Munam Ali Shah; Abid Khan; Gwanggil Jeon. An infrastructure-assisted job scheduling and task coordination in volunteer computing-based VANET. Complex & Intelligent Systems 2021, 1 -21.

AMA Style

Abdul Waheed, Munam Ali Shah, Abid Khan, Gwanggil Jeon. An infrastructure-assisted job scheduling and task coordination in volunteer computing-based VANET. Complex & Intelligent Systems. 2021; ():1-21.

Chicago/Turabian Style

Abdul Waheed; Munam Ali Shah; Abid Khan; Gwanggil Jeon. 2021. "An infrastructure-assisted job scheduling and task coordination in volunteer computing-based VANET." Complex & Intelligent Systems , no. : 1-21.

Special issue paper
Published: 18 June 2021 in Concurrency and Computation: Practice and Experience
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Deep learning based neural networks and their variants have gained popularity due to their inherent flexibility to handle unforeseen especially when a chaotic time series big data are required to be dealt with. There are innumerable applications that are beneficiary of vast interest in computational intelligent approaches that include but not limited to robotics, healthcare, transport, industrial, decision making, and gaming. This paper attempts to investigate the effectiveness of using a neural nonlinear autoregressive with exogenous inputs (NARX) controller in an emerging application field of balancing systems like inverted pendulum (IP) using big data. This paper's aim has been to control an IP cart system by designing a neural NARX controller, and the focus is primarily on real-time processing in industrial applications grounded on big data ecosystems. In the proposed work, an IP system is mathematically modeled and first controlled utilizing a combination of classical proportional-integral-derivative (PID) controllers for cart and pendulum. Second, a chaotic time series input–output data are obtained and are used to train two NARX controllers for cart and pendulum, respectively. Both the controllers are designed as single-input single-output systems with one layer each at input and output with suitable number of hidden layers and neurons. Performance comparison of NARX system behavior with PID controller indicates that the NARX controllers successfully adapt to two different kinds of unknown inputs and effectively stabilize the plant. Simulation results confirm that NARX controllers follow the training parameters and exhibit superior performance and overall system stability than PID control. Experimental results demonstrate the effectiveness of the approach.

ACS Style

Imran Shafi; Zeeshan Malik; Sadia Din; Gwanggil Jeon; Jamil Ahmad. A computationally intelligent neural network‐based nonlinear autoregressive exogenous balancing approach for real‐time processing in industrial applications using big data. Concurrency and Computation: Practice and Experience 2021, e6382 .

AMA Style

Imran Shafi, Zeeshan Malik, Sadia Din, Gwanggil Jeon, Jamil Ahmad. A computationally intelligent neural network‐based nonlinear autoregressive exogenous balancing approach for real‐time processing in industrial applications using big data. Concurrency and Computation: Practice and Experience. 2021; ():e6382.

Chicago/Turabian Style

Imran Shafi; Zeeshan Malik; Sadia Din; Gwanggil Jeon; Jamil Ahmad. 2021. "A computationally intelligent neural network‐based nonlinear autoregressive exogenous balancing approach for real‐time processing in industrial applications using big data." Concurrency and Computation: Practice and Experience , no. : e6382.

Research article
Published: 11 June 2021 in International Journal of Intelligent Systems
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Smart health-care is the innovation that leads to enhanced diagnostic tools, improved patient treatment, and gadgets that ease the quality of life for majority of people. Textual clinical documents about an individual contain sensitive and semantically corelated terms. Most privacy-preserving approaches are not designed to prevent confidentiality threats. Although, recent approaches improved the utility of published output with generalized terms retrieved from several medical and general-purpose knowledge bases like SNOMED-CT and MASH. However, these models work on predefined sensitive terms using Wikipedia articles instead of authentic benchmarks. These Information Content-based methods are not capable to achieve the best balance between privacy and utility. The existing approaches guarantee syntactic privacy by sanitization but lack semantic privacy for textual clinical data. Therefore, it is imperative to design a confidentiality-aware framework to overcome these problems. Our proposed Confidentiality aware Textual Clinical Data Framework use preprocessed combinations of the terms instead of all combinations and perform automatic detection and sanitization of the sensitive and semantically correlated terms. The probabilistic sampling-based method guarantees the semantic privacy. We use high-level Petri nets to perform formal modeling of our proposed approach. Furthermore, we have also performed a detailed complexity analysis of the proposed framework.

ACS Style

Tehsin Kanwal; Syed A. Moqurrab; Adeel Anjum; Abid Khan; Joel J. P. C. Rodrigues; Gwanggil Jeon. Formal verification and complexity analysis of confidentiality aware textual clinical documents framework. International Journal of Intelligent Systems 2021, 1 .

AMA Style

Tehsin Kanwal, Syed A. Moqurrab, Adeel Anjum, Abid Khan, Joel J. P. C. Rodrigues, Gwanggil Jeon. Formal verification and complexity analysis of confidentiality aware textual clinical documents framework. International Journal of Intelligent Systems. 2021; ():1.

Chicago/Turabian Style

Tehsin Kanwal; Syed A. Moqurrab; Adeel Anjum; Abid Khan; Joel J. P. C. Rodrigues; Gwanggil Jeon. 2021. "Formal verification and complexity analysis of confidentiality aware textual clinical documents framework." International Journal of Intelligent Systems , no. : 1.

Conference paper
Published: 06 June 2021 in Blockchain Technology and Innovations in Business Processes
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Cloud computing is an embryonic and vast field. It is cost effective, flexible and customized solution provided to customers according to their needs. Due to cloud computing customers need of having their own data centers storages and services became truncated. Cloud service providers serves customers with infrastructure, storages, security, computation resources and fault rectifications. Due to the customized and flexible in nature cloud computing created many security concerns meanwhile security is the first requirement of customer. Storages, infrastructure, software and network need security measures. In this paper we deliver different defense techniques by using which cloud computing infrastructure could be made secure. It includes different protocols, encryption techniques, and hardware and software solutions for different level of security.

ACS Style

Gwanggil Jeon; Abdellah Chehri. Security Analysis Using Deep Learning in IoT and Intelligent Transport System. Blockchain Technology and Innovations in Business Processes 2021, 9 -19.

AMA Style

Gwanggil Jeon, Abdellah Chehri. Security Analysis Using Deep Learning in IoT and Intelligent Transport System. Blockchain Technology and Innovations in Business Processes. 2021; ():9-19.

Chicago/Turabian Style

Gwanggil Jeon; Abdellah Chehri. 2021. "Security Analysis Using Deep Learning in IoT and Intelligent Transport System." Blockchain Technology and Innovations in Business Processes , no. : 9-19.

Journal article
Published: 31 May 2021 in Sustainable Cities and Society
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Road traffic flow forecasting is among the most important use case associated with smart cities. Traffic forecasting allows drivers to select the fastest route towards their target destinations. A prerequisite for traffic flow management is accurate traffic forecasting. In this study, we introduce a framework for traffic forecasting that uses data on air pollution. The reason to select that data is air pollution rates are often associated with traffic congestion, and there is intensive research that exists to forecast air pollution by road traffic. To the best of our knowledge, any effort to enhance road traffic prediction through air quality and ensemble regression model techniques is not yet been proposed. In this research, our contribution is twofold. Firstly, we have performed a comparative analysis of 7 different regression models to find out which model gives better accuracy. Secondly, we propose a framework using regression models in which the first regression model's result is boosted using boosting ensemble method and is passed to the next regression model which shows that the proposed framework gives more satisfying results than the above 7 regression models. The experimental findings show the effectiveness of the proposed framework which decreases the error rate by 2.47 %.

ACS Style

Nimra Shahid; Munam Ali Shah; Abid Khan; Carsten Maple; Gwanggil Jeon. Towards greener smart cities and road traffic forecasting using air pollution data. Sustainable Cities and Society 2021, 72, 103062 .

AMA Style

Nimra Shahid, Munam Ali Shah, Abid Khan, Carsten Maple, Gwanggil Jeon. Towards greener smart cities and road traffic forecasting using air pollution data. Sustainable Cities and Society. 2021; 72 ():103062.

Chicago/Turabian Style

Nimra Shahid; Munam Ali Shah; Abid Khan; Carsten Maple; Gwanggil Jeon. 2021. "Towards greener smart cities and road traffic forecasting using air pollution data." Sustainable Cities and Society 72, no. : 103062.

Editorial
Published: 27 May 2021 in Multimedia Systems
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ACS Style

Valerio Bellandi; Abdellah Chehri; Salvatore Cuomo; Gwanggil Jeon. Special issue on deep learning for emerging big multimedia super-resolution. Multimedia Systems 2021, 27, 581 -587.

AMA Style

Valerio Bellandi, Abdellah Chehri, Salvatore Cuomo, Gwanggil Jeon. Special issue on deep learning for emerging big multimedia super-resolution. Multimedia Systems. 2021; 27 (4):581-587.

Chicago/Turabian Style

Valerio Bellandi; Abdellah Chehri; Salvatore Cuomo; Gwanggil Jeon. 2021. "Special issue on deep learning for emerging big multimedia super-resolution." Multimedia Systems 27, no. 4: 581-587.

Journal article
Published: 27 May 2021 in Computers & Electrical Engineering
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The constantly developing urbanization and the emergence of smart cities require better security surveillance and crowd monitoring systems. The growing availability of the Internet of Things (IoT) devices in public and private organizations also provide intelligent and secure surveillance solutions for real-time monitoring in public spaces. This article introduces an IoT-based crowd surveillance system that uses a deep learning model to detect and count people using an overhead view perspective. The Single Shot Multibox Detector (SSD) model with Mobilenetv2 as the basic network is used for the detection of people. The detection model’s accuracy is enhanced with a transfer learning approach. Two virtual lines are defined to count how many people are leaving and entering the scene. In order to assess performance, experiments are performed using different video clips. Results indicate that transfer learning increases the overall detection performance of the system with an accuracy of 95%.

ACS Style

Imran Ahmed; Misbah Ahmad; Awais Ahmad; Gwanggil Jeon. IoT-based crowd monitoring system: Using SSD with transfer learning. Computers & Electrical Engineering 2021, 93, 107226 .

AMA Style

Imran Ahmed, Misbah Ahmad, Awais Ahmad, Gwanggil Jeon. IoT-based crowd monitoring system: Using SSD with transfer learning. Computers & Electrical Engineering. 2021; 93 ():107226.

Chicago/Turabian Style

Imran Ahmed; Misbah Ahmad; Awais Ahmad; Gwanggil Jeon. 2021. "IoT-based crowd monitoring system: Using SSD with transfer learning." Computers & Electrical Engineering 93, no. : 107226.

Journal article
Published: 24 May 2021 in International Journal of Intelligent Systems
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ACS Style

Imran Ahmed; Marco Anisetti; Gwanggil Jeon. An IoT‐based human detection system for complex industrial environment with deep learning architectures and transfer learning. International Journal of Intelligent Systems 2021, 1 .

AMA Style

Imran Ahmed, Marco Anisetti, Gwanggil Jeon. An IoT‐based human detection system for complex industrial environment with deep learning architectures and transfer learning. International Journal of Intelligent Systems. 2021; ():1.

Chicago/Turabian Style

Imran Ahmed; Marco Anisetti; Gwanggil Jeon. 2021. "An IoT‐based human detection system for complex industrial environment with deep learning architectures and transfer learning." International Journal of Intelligent Systems , no. : 1.

Journal article
Published: 18 May 2021 in Applied Soft Computing
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Edge computing significantly expands the range of information technology in smart video surveillance applications in the era of intelligent and connected cities. Edge devices, including Internet of Things-based cameras and sensors, produce a large amount of data and have frequently become prominent components for various public surveillance and monitoring applications. The data generated by these smart devices are in the form of videos and images that need to be processed and analyzed in real-time with substantial computation resources. These developed techniques still require large computation resources for real-time surveillance applications. In this regard, Edge computing plays a promising role in order to provide high computation and low-latency requirements. With these motivations, in this work, a real-time top view-based person detection system is presented. We utilize a one-stage deep learning-based object detection algorithm, i.e., CenterNet, for person detection. The model detects the human as a single point, also referred to as its bounding box’s center point. The model does a key-point calculation to obtain the center point and regresses all other information regarding the target object’s features, size, location, and orientation. Training and testing of the model are performed on a top view data set. The detection results are also compared with conventional detection methods using the same data set. The overall detection accuracy of the model is 95%.

ACS Style

Imran Ahmed; Misbah Ahmad; Joel J.P.C. Rodrigues; Gwanggil Jeon. Edge computing-based person detection system for top view surveillance: Using CenterNet with transfer learning. Applied Soft Computing 2021, 107, 107489 .

AMA Style

Imran Ahmed, Misbah Ahmad, Joel J.P.C. Rodrigues, Gwanggil Jeon. Edge computing-based person detection system for top view surveillance: Using CenterNet with transfer learning. Applied Soft Computing. 2021; 107 ():107489.

Chicago/Turabian Style

Imran Ahmed; Misbah Ahmad; Joel J.P.C. Rodrigues; Gwanggil Jeon. 2021. "Edge computing-based person detection system for top view surveillance: Using CenterNet with transfer learning." Applied Soft Computing 107, no. : 107489.

Journal article
Published: 17 May 2021 in Journal of Information Security and Applications
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Drone-assisted solutions are a demanding and popular area of research where drones can send or collect data from ground networks. The recent Internet of Vehicles (IoV) networks has suffered from congestion issue due to rapid increase and a new architecture for data transmission. Data communication in this domain is possible using vehicle nodes. Due to unique features and high mobility of nodes, dynamic topologies, unpredictable network patterns, and other obstacles degraded these networks’ performance and leads to disconnection, delay, and packet dropping issues. Security is another significant concern especially in drones and vehicle nodes where malicious entry exists to disturb the network traffic. In this paper, we propose a Trust and Priority-based Drone Assisted Internet of Vehicles (TPDA-IoV) solution for data routing among vehicle nodes and drones for IoV networks. The proposed solution is based on three modules including Drone-to-drone (D2D) and Drone-to-vehicle (D2V) data communication and trust evaluation. This solution is designed especially when the IoV networks are suffered from congestion issues and the ground base station is not able to handle all communication received from vehicle nodes due to congested traffic patterns and populated urban roads and limited infrastructure setup. The messages need urgent decision services to avoid any serious issues in the network. The proposed solution provides drone-assisted resources to collect or send the data by using traffic density information received from the base station. This solution is the best option for congested networks where the channels are not able to transmit the data due to density and load on their devices. The proposed solution also covers the limited battery issues of drones by selecting the preferable energy level among drones. This solution is tested in terms of packet delivery ratio, network overhead, and end-to-end delay with state of eth art solutions. The proposed solution achieved a high performance as compared to existing solutions.

ACS Style

Kashif Naseer Qureshi; Adi Alhudhaif; Adeel Abass Shah; Saqib Majeed; Gwanggil Jeon. Trust and priority-based drone assisted routing and mobility and service-oriented solution for the internet of vehicles networks. Journal of Information Security and Applications 2021, 59, 102864 .

AMA Style

Kashif Naseer Qureshi, Adi Alhudhaif, Adeel Abass Shah, Saqib Majeed, Gwanggil Jeon. Trust and priority-based drone assisted routing and mobility and service-oriented solution for the internet of vehicles networks. Journal of Information Security and Applications. 2021; 59 ():102864.

Chicago/Turabian Style

Kashif Naseer Qureshi; Adi Alhudhaif; Adeel Abass Shah; Saqib Majeed; Gwanggil Jeon. 2021. "Trust and priority-based drone assisted routing and mobility and service-oriented solution for the internet of vehicles networks." Journal of Information Security and Applications 59, no. : 102864.