1
|
Faisal Abbas Shah S, Mazhar T, Shloul TA, Shahzad T, Hu YC, Mallek F, Hamam H. Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain. PeerJ Comput Sci 2024; 10:e1776. [PMID: 38435609 PMCID: PMC10909218 DOI: 10.7717/peerj-cs.1776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/05/2023] [Indexed: 03/05/2024]
Abstract
Real-time data gathering, analysis, and reaction are made possible by this information and communication technology system. Data storage is also made possible by it. This is a good move since it enhances the administration and operation services essential to any city's efficient operation. The idea behind "smart cities" is that information and communication technology (ICTs) need to be included in a city's routine activities in order to gather, analyze, and store enormous amounts of data in real-time. This is helpful since it makes managing and governing urban areas easier. The "drone" or "uncrewed aerial vehicle" (UAV), which can carry out activities that ordinarily call for a human driver, serves as an example of this. UAVs could be used to integrate geospatial data, manage traffic, keep an eye on objects, and help in an emergency as part of a smart urban fabric. This study looks at the benefits and drawbacks of deploying UAVs in the conception, development, and management of smart cities. This article describes the importance and advantages of deploying UAVs in designing, developing, and maintaining in smart cities. This article overviews UAV uses types, applications, and challenges. Furthermore, we presented blockchain approaches for addressing the given problems for UAVs in smart research topics and recommendations for improving the security and privacy of UAVs in smart cities. Furthermore, we presented Blockchain approaches for addressing the given problems for UAVs in smart cities. Researcher and graduate students are audience of our article.
Collapse
Affiliation(s)
- Syed Faisal Abbas Shah
- Department of Computer Science & Information Technology, Virtual University of Pakistan, Lahore, Pakistan
| | - Tehseen Mazhar
- Department of Computer Science & Information Technology, Virtual University of Pakistan, Lahore, Pakistan
| | - Tamara Al Shloul
- Department of General Education, Liwa College of Technology, Abu Dhabi, United Arab Emirates
| | - Tariq Shahzad
- School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa
| | - Yu-Chen Hu
- Department of Computer Science & Information Management, Providence University, Taichung City, Taiwan
| | - Fatma Mallek
- Faculty of Engineering, University of Moncton, Moncton, Canada
| | - Habib Hamam
- Faculty of Engineering, University of Moncton, Moncton, Canada
- College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia
- International Institute of Technology and Management, Libreville, Commune d’Akanda, Gabon
- Spectrum of Knowledge Production & Skills Development, Sfax, Tunisia
| |
Collapse
|
2
|
Mazhar T, Talpur DB, Shloul TA, Ghadi YY, Haq I, Ullah I, Ouahada K, Hamam H. Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence. Brain Sci 2023; 13:brainsci13040683. [PMID: 37190648 DOI: 10.3390/brainsci13040683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 04/02/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website's content as a technical resource and reference.
Collapse
Affiliation(s)
- Tehseen Mazhar
- Department of Computer Science, Virtual University, Lahore 55150, Pakistan
| | - Dhani Bux Talpur
- Department of Information and Computing, University of Sufism and Modern Sciences, Bhit Shah 70140, Pakistan
| | - Tamara Al Shloul
- Department of General Education, Liwa College of Technology, Abu Dhabi 15222, United Arab Emirates
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Abu Dhabi 112612, United Arab Emirates
| | - Inayatul Haq
- School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Inam Ullah
- Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
| | - Khmaies Ouahada
- School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
| | - Habib Hamam
- College of Computer Science and Engineering, University of Ha'il, Ha'il 55476, Saudi Arabia
- International Institute of Technology and Management, Commune d'Akanda, Libreville BP 1989, Gabon
- Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada
- Spectrum of Knowledge Production & Skills Development, Sfax 3027, Tunisia
| |
Collapse
|
3
|
Rasheed Z, Ma YK, Ullah I, Al Shloul T, Tufail AB, Ghadi YY, Khan MZ, Mohamed HG. Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning. Brain Sci 2023; 13:brainsci13040602. [PMID: 37190567 DOI: 10.3390/brainsci13040602] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023] Open
Abstract
Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis.
Collapse
Affiliation(s)
- Zahid Rasheed
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
| | - Yong-Kui Ma
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
| | - Inam Ullah
- Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam 13120, Republic of Korea
| | - Tamara Al Shloul
- Department of General Education, Liwa College of Technology, Abu Dhabi P.O. Box 41009, United Arab Emirates
| | - Ahsan Bin Tufail
- Department of Computer Science, National University of Science and Technology, Balochistan Campus, Quetta 87300, Pakistan
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | | | - Heba G. Mohamed
- Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| |
Collapse
|