Computer and Information Science
ISSN / EISSN : 1913-8989 / 1913-8997
Published by: Canadian Center of Science and Education (10.5539)
Total articles ≅ 877
Latest articles in this journal
Published: 3 September 2021
Computer and Information Science, Volume 14; https://doi.org/10.5539/cis.v14n4p20
A Vehicular Ad hoc Network (VANET) is a distinctive situation of wireless ad hoc networks. The designing of the routing protocol considers a critical role in communication in VANET. VANET has specific features compared to other types of wireless ad hoc networks that impose special characteristics for designing of efficient routing protocols.The challenging factor in designing efficient routing protocols for VANET is the high movement of vehicles that incurs a rapid change in the network topology that causes frequent link breakage. This paper presents and evaluates different position-based routing protocols associated with VANETs. The evaluation aiming to determine appropriate specifications for optimal routing protocols’ features achieving best performance within different environmental conditions. The performance comparison is carried out in terms of Packet Delivery Rate (PDR), Void Problem Occurrence Rate (VPOR), and Average Hops Count (AHC).
Published: 24 August 2021
Computer and Information Science, Volume 14; https://doi.org/10.5539/cis.v14n4p1
One of e-Government implementation’s most notable advantages is the enhancement of transparency amongst public sector organizations that it brings, possibly caused by the availability of information, the newfound accountability, and the ability to track and monitor transactions within the public sector. With this in mind, the research at hand will centre on the issue of unemployment amongst postgraduates in Saudi Arabia, focusing on reaping the benefits of the centralised, highly efficient e-Government system to control the process of employment within Saudi universities and research centres. In this vein, this research proposes that e-Government (as well as other ICTs) should be tasked with fighting corruption by detailing the specifics and qualifications of the job-seeker in question within the relevant centralised websites. Indeed, such a process would allow for the filtering and selecting process by the employer to be both supervised and audited.
Published: 24 August 2021
Computer and Information Science, Volume 14; https://doi.org/10.5539/cis.v14n4p11
License plate detection and recognition are critical components of the development of a connected Intelligent transportation system, but are underused in developing countries because to the associated costs. Existing license plate detection and recognition systems with high accuracy require the usage of Graphical Processing Units (GPU), which may be difficult to come by in developing nations. Single stage detectors and commercial optical character recognition engines, on the other hand, are less computationally expensive and can achieve acceptable detection and recognition accuracy without the use of a GPU. In this work, a pretrained SSD model and a tesseract tessdata-fast traineddata were fine-tuned on a dataset of more than 2,000 images of vehicles with license plate. These models were combined with a unique image preprocessing algorithm for character segmentation and tested using a general-purpose personal computer on a new collection of 200 automobiles with license plate photos. On this testing set, the plate detection system achieved a detection accuracy of 99.5 % at an IOU threshold of 0.45 while the OCR engine successfully recognized all characters on 150 license plates, one character incorrectly on 24 license plates, and two or more incorrect characters on 26 license plates. The detection procedure took an average of 80 milliseconds, while the character segmentation and identification stages took an average of 95 milliseconds, resulting in an average processing time of 175 milliseconds per image, or 6 photos per second. The obtained results are suitable for real-time traffic applications.
Published: 28 July 2021
Computer and Information Science, Volume 14; https://doi.org/10.5539/cis.v14n3p78
We propose a new approach to text semantic analysis and general corpus analysis using, as termed in this article, a "bi-gram graph" representation of a corpus. The different attributes derived from graph theory are measured and analyzed as unique insights or against other corpus graphs, attributes such as the graph chromatic number and the graph coloring, graph density and graph K-core. We observe a vast domain of tools and algorithms that can be developed on top of the graph representation; creating such a graph proves to be computationally cheap, and much of the heavy lifting is achieved via basic graph calculations. Furthermore, we showcase the different use-cases for the bi-gram graphs and how scalable it proves to be when dealing with large datasets.
Published: 28 July 2021
Computer and Information Science, Volume 14; https://doi.org/10.5539/cis.v14n3p87
Reviewer Acknowledgements for Computer and Information Science, Vol. 14, No. 3, 2021
Published: 28 July 2021
Computer and Information Science, Volume 14; https://doi.org/10.5539/cis.v14n3p63
A substantial portion of critical information infrastructures in advanced economies comprises former public utilities, which in the 1980s/90s were fully or partially privatized, a change justified mainly on economic efficiency grounds. This entailed that these utility companies had to compete in the free market, thus being exposed to the same risks/opportunities as private companies. Much like businesses in other industrial sectors, utility companies have increasingly joined social media over the last decade, as ‘digital’ visibility through social networking platforms, such as Facebook, Twitter, and Instagram has become fundamental. The new (privatized) utilities have relied on marketing and ad campaigns to promote their business and generate revenues. Trust and reputation for companies are primary resources to attract new customers and/or keep old ones, especially for companies with a wide customer base. Trust and reputation are difficult assets to preserve on social media, as they can be subject to negative attacks, including fake campaigns. This paper is a probe that explores a potential attack vector to critical infrastructures via weakening customer and investor trust in (the now private) utilities by blemishing CII-utilities’ reputation on social media. More specifically, the paper considers the possibility of attacks that have the potential to undermine the stability and reliability of critical infrastructures and advances a preliminary justification of why that may happen. We do this by looking at cases in which negative social media campaigns with fake content have been successfully implemented via digital tools.
Published: 5 July 2021
Computer and Information Science, Volume 14; https://doi.org/10.5539/cis.v14n3p49
During the Coronavirus Disease 2019 (COVID-19) pandemic and the national lockdowns implemented in countries around the world, many universities worldwide made the transition from face-to-face delivery to online learning using e-learning systems. However, the successful transition from traditional class-based learning to online learning depends greatly on understanding the challenges related to the implementation and use of e-learning systems, as well as the technical and management factors that need to be enhanced. This study aimed to investigate the challenges related to the use of e-learning systems in Jordanian universities and to explore the technical and management aspects that impacted the successful implementation and use of e-learning systems during COVID-19. To achieve the study objectives, a questionnaire was developed by the researcher and distributed online to lecturers working at Jordanian universities. A total of 184 lecturers participated in the study. Based on the findings, the study provides recommendations which will help higher education policy makers, university management teams, and software developers build strategies to ensure the successful implementation and use of e-learning systems during the COVID-19 pandemic.
Published: 5 July 2021
Computer and Information Science, Volume 14; https://doi.org/10.5539/cis.v14n3p38
Perceptual image hashing system generates a short signature called perceptual hash attached to an image before transmission and acts as side information for analyzing the trustworthiness of the received image. In this paper, we propose a novel approach to improve robustness for perceptual image hashing scheme for generating a perceptual hash that should be resistant to content-preserving manipulations, such as JPEG compression and Additive white Gaussian noise (AWGN) also should differentiate the maliciously tampered image and its original version. Our algorithm first constructs a robust image, derived from the original input by analyzing the stability of the extracted features and improving their robustness. From the robust image, which does perceptually resemble the original input, we further extract the final robust features. Next, robust features are suitably quantized allowing the generation of the final perceptual hash using the cryptographic hash function SHA1. The main idea of this paper is to transform the original image into a more robust one that allows the extraction of robust features. Generation of the robust image turns out be quite important since it introduces further robustness to the perceptual image hashing system. The paper can be seen as an attempt to propose a general methodology for more robust perceptual image hashing. The experimental results presented in this paper reveal that the proposed scheme offers good robustness against JPEG compression and Additive white Gaussian noise.
Published: 5 July 2021
Computer and Information Science, Volume 14; https://doi.org/10.5539/cis.v14n3p25
Audio forensics is a field in forensics that is used to authenticate, enhance, and analyze audio files to aid in solving different crime investigations. Audio as a forensic evidence must be enhanced and analyzed to be admissible in courts of law. But more importantly, it must be authenticated in order to prove that it is authentic and no manipulations were done to it. In this paper, an overview on audio forensics is presented, previous related work to this topic is shown, and methodologies for audio enhancement and authentication are explained along with audio tampering ways and signatures presentation.
Published: 10 June 2021
Computer and Information Science, Volume 14; https://doi.org/10.5539/cis.v14n3p1
This study sought to explore factors that determine the public’s acceptance of and adoption behavior toward e-government health applications launched in Saudi Arabia (SA) by the Ministry of Health (MOH) during the COVID-19 pandemic. The research relied on several theories: the technology acceptance model (TAM), information system success model (ISSM), mobile services acceptance model (MSAM), and unified theory of acceptance and use of technology (UTAUT). The constructs of perceived ease of use (PEOU), perceived usefulness (PU), attitude (ATT), trust (TR), information quality (IQ), facilitating condition (FC), and social influence (SI) were utilized to investigate the user’s intention toward using e-government health applications. The proposed model and its seven hypotheses were tested by conducting a survey across social media among citizens and residents in SA. A total of 785 valid responses were analyzed by SmartPLS and a structural equation modeling technique. After analysis, the results showed that PEOU, PU, ATT, TR, IQ, FC, and SI have positive effects on behavioral intentions. As for contributions, this paper is the first research paper to investigate the adoption of e-government health applications launched by MOH in SA during the COVID-19 pandemic and to provide a theoretical framework for pursuing future research work in a similar scope.