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Research Article
Perception and Trust in Autonomous Vehicles Post Cyber Security Incidents
Adam Gorine*
,
Sana Abid Khan
Issue:
Volume 7, Issue 4, December 2024
Pages:
122-138
Received:
9 August 2024
Accepted:
2 September 2024
Published:
18 October 2024
Abstract: The integration of Autonomous Vehicles (AVs) into modern systems of transportation brings with it a new and transformative era. Central to the successful realisation of this transformation is the public’s trust in these vehicles and their safety, particularly in the aftermath of cyber security breaches. The following research therefore explores the various factors underpinning this trust in the context of cyber security incidents. A dual-methodological approach was used in the study. Quantitative data was gathered from structured questionnaires distributed to and completed by a cohort of 151 participants and qualitative data, from comprehensive semi-structured interviews with AV technology and cyber security experts. Rigorous Structural Equation Modelling of the quantitative data then allowed for the identification of the key factors influencing public trust from the standpoint of the research participants including the perceived safety of AV technology, the severity of cyber security incidents, the historic cyber security track record of companies and the frequency of successful cyber security breaches. The role of government regulations, though also influential, emerged as less so. The qualitative data, processed via thematic analysis, resonated with the findings from the quantitative data. This highlighted the importance of perceived safety, incident severity, regulatory frameworks and corporate legacy in shaping public trust. Whilst cyber incidents no doubt erode trust in AVs, a combination of technological perception, regulatory scaffolding and corporate history critically impacts this. These insights are instrumental for stakeholders, from policymakers to AV manufacturers, in charting the course of AV assimilation successfully in future.
Abstract: The integration of Autonomous Vehicles (AVs) into modern systems of transportation brings with it a new and transformative era. Central to the successful realisation of this transformation is the public’s trust in these vehicles and their safety, particularly in the aftermath of cyber security breaches. The following research therefore explores the...
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Research Article
Exploring AES Encryption Implementation Through Quantum Computing Techniques
Adam Gorine*,
Muhammad Suhaib
Issue:
Volume 7, Issue 4, December 2024
Pages:
139-155
Received:
30 August 2024
Accepted:
26 September 2024
Published:
18 October 2024
Abstract: A coming great revolution in technology is quantum computing, which opens new attacks on most of the developed cryptographic algorithms, including AES. These emerging quantum capabilities risk weakening cryptographic techniques, which safeguard a vast amount of data across the globe. This research uses Grover's algorithm to explore the vulnerabilities of the Advanced Encryption Standard to quantum attacks. By implementing quantum cryptographic algorithms and Quantum Error Correction on simulators and quantum hardware, the study evaluates the effectiveness of these techniques in mitigating noise and improving the reliability of quantum computations. The study shows that while AES is theoretically at risk due to Grover’s algorithm, which demonstrates a theoretical reduction in AES key search complexity, current hardware limitations and noise levels encountered in today’s quantum computers reduce the immediate threat and limit practical exploitation. The research also examines NTRU encryption, a quantum-resistant alternative, highlighting its robustness in quantum environments. The findings emphasize the need for further development in QEC and quantum-resistant cryptography to secure digital communications against future quantum threats. Future work will focus on advancing QEC techniques and refining quantum algorithms, addressing both hardware and theoretical advancements, including the potential use of high-capacity processors like Jiuzhang 3.0. These improvements will ensure the scalability of quantum-resistant systems to practical key sizes and usage scenarios.
Abstract: A coming great revolution in technology is quantum computing, which opens new attacks on most of the developed cryptographic algorithms, including AES. These emerging quantum capabilities risk weakening cryptographic techniques, which safeguard a vast amount of data across the globe. This research uses Grover's algorithm to explore the vulnerabilit...
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Research Article
Navigating the Digital Marketing Field: The Role of AI and Emotional Storytelling in Consumer Engagement
Issue:
Volume 7, Issue 4, December 2024
Pages:
156-169
Received:
6 September 2024
Accepted:
23 September 2024
Published:
18 October 2024
Abstract: This study explores the intersection of Artificial Intelligence (AI) and digital storytelling in marketing. By focusing on how AI-driven techniques can enhance emotional attachment and influence consumer behavior. With the rapid advancement of AI, its integration into marketing strategies has become crucial. Particularly for personalizing consumer experiences and enhancing brand narratives. This study investigated the role of AI in creating emotionally engaging narratives, a largely unexplored area in marketing and advertising. The study is motivated by the need to understand the dynamics between AI-driven techniques and emotional attachment in digital marketing. The paper hypothesizes a significant relationship between consumers’ emotional attachment to brands and purchasing behavior. A mixed-methods research approach was employed by combining a survey with interviews to test this hypothesis. This study assesses how emotional attachment, influenced by AI and storytelling, affects consumer purchasing decisions and brand loyalty in online shopping. It also evaluates the effectiveness of AI-driven storytelling techniques in digital marketing campaigns from the perspective of online consumers. Preliminary findings suggest more than 98% recall of story-based branding. More than 67% believe storytelling and emotional attachment may impact purchasing decisions. More than 89% of people recall a brand based on a particular story. While emotional attachment significantly influences consumer purchasing behavior, other factors also play a crucial role. This study reveals that AI’s role in marketing is valued, but the essence of storytelling should remain grounded in human experiences.
Abstract: This study explores the intersection of Artificial Intelligence (AI) and digital storytelling in marketing. By focusing on how AI-driven techniques can enhance emotional attachment and influence consumer behavior. With the rapid advancement of AI, its integration into marketing strategies has become crucial. Particularly for personalizing consumer ...
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Review Article
A Review Paper on IPv4 and IPv6: A Comprehensive Survey
Issue:
Volume 7, Issue 4, December 2024
Pages:
170-175
Received:
3 August 2024
Accepted:
4 October 2024
Published:
29 October 2024
Abstract: Even though more customers are regularly coming to the Internet, IPv4 addresses have been reduced by the Internet Assigned Numbers Authority (IANA) and have been deactivated in domain name registries (RIRs). IPv6, being the sole important next-generation Internet protocol, has yet to be fully developed and deployed, owing to the lack of a scheme that might address the transfer of IPv4 resources to IPv6 networks as well as collective communication between the two incompatible protocols. The Transmission Control Protocol/Internet protocol version 4 (TCP/IPv4) addresses have been reported as being on the verge of collapsing, while the next generation Internet Protocol version 6 (IPv6) is being identified on a regular basis. Among other advantages, IPv6 provides a significantly wider address space, better address design, and more security. IPv6 distribution necessitates a thorough and meticulous setup in order to avoid network disturbance and reap the benefits of IPv6. Because of the problems with IPv4, IPv6 is currently becoming increasingly popular among organizations, businesses, and Internet Service Providers (ISP). This paper we will explores the evolution of Internet Protocol version 4 (IPv4), its key features, challenges, and limitations, and examines how Internet Protocol version 6 (IPv6) addresses these issues. Additionally, we will highlight the key differences between the two protocols and discuss the transition process from IPv4 to IPv6.
Abstract: Even though more customers are regularly coming to the Internet, IPv4 addresses have been reduced by the Internet Assigned Numbers Authority (IANA) and have been deactivated in domain name registries (RIRs). IPv6, being the sole important next-generation Internet protocol, has yet to be fully developed and deployed, owing to the lack of a scheme th...
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Research Article
Development of a Semantic Web-Ontology E-Learning Platform
Issue:
Volume 7, Issue 4, December 2024
Pages:
176-182
Received:
31 August 2024
Accepted:
18 September 2024
Published:
31 October 2024
Abstract: This paper is focused on developing A Semantic Web-Ontology E-Learning Platform, which is a system that combines semantic web and ontology technology to guarantees a sophisticated learning environment that provides the learners with adaptable and customized learning resources based on learners’ knowledge requirement. With this system, learners can log in from their comfort zone anytime, to receive their online lesson as provided by their tutor. The system has an added advantage of providing a personalized learning to students through creation of intelligent search engine and ontology backbone consisting of learning data and their meta data. The learner, through this search engine, searches the ontology semantically for the learning materials that suits his/her profile. The system also has the capability of filtering the search results by matching them with the profile of a particular learner using inference engine, such that the result best suited for the user’s academic need is presented. This work will not only promote self-directed learning but will also facilitate quick search of learning materials, by narrowing the search based on specified learner’s interest. The methodology adopted for this work is Object-Oriented Analysis and Design Methodology (OOADM) and programing languages used are Php-Mysql and Java Script. The system will be of great benefit to schools, other learning institutions and organization seeking to educate their manpower.
Abstract: This paper is focused on developing A Semantic Web-Ontology E-Learning Platform, which is a system that combines semantic web and ontology technology to guarantees a sophisticated learning environment that provides the learners with adaptable and customized learning resources based on learners’ knowledge requirement. With this system, learners can ...
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Research Article
Novel Building Detection and Location Intelligence in Aerial Satellite Imagery
Issue:
Volume 7, Issue 4, December 2024
Pages:
183-194
Received:
30 August 2024
Accepted:
26 September 2024
Published:
18 December 2024
DOI:
10.11648/j.ajcst.20240704.16
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Abstract: The accurate detection and extraction of building information from aerial imagery is of paramount importance in urban planning, land use analysis, and disaster management. This study presents a comprehensive investigation into the development of a robust and efficient methodology for building detection in satellite imagery utilizing state-of-the-art deep learning techniques. We conducted a comparative analysis of three distinct semantic segmentation models based on the U-Net architecture: a baseline U-Net trained from scratch, a U-Net incorporating a pre-trained ResNet34 encoder, and a U-Net with custom architectural enhancements. Our methodological approach encompassed data augmentation strategies, transfer learning techniques, and ensemble methods to optimize model performance. The Inria Aerial Image Labelling Dataset served as the primary source for model training and validation. We explored the efficacy of various loss functions, including dice loss, focal loss, and weighted cross-entropy, to address class imbalance and enhance segmentation accuracy. Model performance was rigorously evaluated using a comprehensive set of metrics, including pixel-wise accuracy, Intersection over Union (IoU), and F1-score. Our highest-performing individual model achieved a dice score of 92 percent on the validation set, while the implementation of ensemble techniques further improved detection accuracy to 93 percent on the heldout test set. Post-processing algorithms, incorporating traditional computer vision methods, were applied to refine building polygon delineation. This research demonstrates the efficacy of deep learning-based segmentation approaches for building detection in aerial imagery and offers valuable insights into potential applications across various domains, including urban planning, construction monitoring, and disaster response. Future research directions may explore building classification, change detection analysis, and model optimization for real-time applications in dynamic urban environments.
Abstract: The accurate detection and extraction of building information from aerial imagery is of paramount importance in urban planning, land use analysis, and disaster management. This study presents a comprehensive investigation into the development of a robust and efficient methodology for building detection in satellite imagery utilizing state-of-the-ar...
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