10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
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Authors - Sulafa Badi, Salam Khoury, Kholoud Yasin, Khalid Al Marri Abstract - This study investigates consumer attitudes toward Mobility as a Service (MaaS) in the context of the UAE's diverse population, focusing on the factors influencing adoption intentions. A survey of 744 participants was conducted to assess public perceptions, employing hierarchical and non-hierarchical clustering methods to identify distinct consumer segments. The analysis reveals five clusters characterised by varying demographics, travel lifestyles, and attitudes towards MaaS, highlighting the influence of UTAUT2 variables, including performance expectancy, social influence, hedonic motivation, price value, and perceived risk. Among the clusters, ‘Enthusiastic Adopters’ and ‘Convenience-Driven Adopters’ emerge as key segments with a strong reliance on public transport and a willingness to adopt innovative transportation solutions. The findings indicate a shared recognition of the potential benefits of MaaS despite differing opinions on its implementation. This research contributes to the theoretical understanding of MaaS adoption by offering an analytical typology relevant to a developing economy while also providing practical insights for policymakers and transport providers. By tailoring services to meet the unique needs of various consumer segments, stakeholders can enhance the integration of MaaS technologies into the UAE's transportation system. Future research should explore the dynamic nature of public sentiment regarding MaaS to inform ongoing development and implementation efforts.
Authors - Rakhi Bharadwaj, Priyanshi Patle, Bhagyesh Pawar, Nikita Pawar, Kunal Pehere Abstract - The detection of forged signatures is a critical challenge in various fields, including banking, legal documentation, and identity verification. Traditional methods for signature verification rely on handcrafted features and machine learning models, which often struggle to generalize across varying handwriting styles and sophisticated forgeries. In recent years, deep learning techniques have emerged as powerful tools for tackling this problem, leveraging large datasets and automated feature extraction to enhance accuracy. In this literature survey paper, we have studied and analyzed various research papers on fake signature detection, focusing on the accuracy of different deep learning techniques. The primary models reviewed include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). We evaluated the performance of these methods based on their reported accuracy on benchmark datasets, highlighting the strengths and limitations of each approach. Additionally, we discussed challenges such as dataset scarcity and the difficulty of generalizing models to detect different types of forgeries. Our analysis provides insights into the effectiveness of these methods and suggests potential directions for future research in improving signature verification systems.
Authors - Shilpa Bhairanatti, Rubini P Abstract - While the rollout of 5G cellular networks will extend into the next decade, there is already significant interest in the technologies that will form the foundation of its successor, 6G. Although 5G is expected to revolutionize our lives and communication methods, it falls short of fully supporting the Internet-of-Everything (IoE). The IoE envisions a scenario where over a million devices per cubic kilometer, both on the ground and in the air, demand ubiquitous, reliable, and low-latency connectivity. 6G and future technologies aim to create a ubiquitous wireless connectivity for entire communication system. This development will accommodate the rapidly increasing number of intelligent devices and communication demand. These objectives can be achieved by incorporating THz band communication, wider spectrum resources with minimized communication error. However, this communication technology faces several challenges such as energy efficiency, resource allocation, latency etc., which needs to be addressed to improve the overall communication performance. To overcome these issues, we present a roadmap for Point to Point (P2P) and Point-to-Multipoint (P2MP) communication where channel coding mechanism is introduced by considering Turbo channel coding scheme as base approach. Furthermore, deep learning based training is provided to improve the error correcting performance of the system. The performance of proposed model is measured in terms of BER for varied SNR levels and additive white noise channel distribution scenarios, where experimental analysis shows that the proposed coding approach outperformed existing error correcting schemes.
Authors - Hiep. L. Thi Abstract - A brief summary of the paper, highlighting key points such as the increasing role of UAVs in various sectors, the challenges related to data storage on UAVs, and proposed solutions for improving both the efficiency and security of data management. Include a note on the scope of the study, methodologies, and key findings.
Authors - Gareth Gericke, Rangith B. Kuriakose, Herman J. Vermaak Abstract - Communication architectures are demonstrating their significance in the development landscape of the Fourth industrial revolution. Nonetheless, the progress of architectural development lags behind that of the Fourth industrial revolution itself, resulting in subpar implementations and research gaps. This paper examines the prerequisites of Smart Manufacturing and proposes the utilization of a novel communication architecture to delineate a pivotal element, information appropriateness, showcasing its efficient application in this domain. Information appropriateness, leverages pertinent information within the communication flow at a machine level facilitating real-time monitoring, decision-making, and control over production metrics. The metrics scrutinized herein include production efficiency, bottleneck mitigation, and network intelligence, while accommodating architectural scalability. These metrics are communicated and computed at a machine level to assess the efficacy of a communication architecture at this level, while also investigating its synergistic relationship across other manufacturing tiers. Results of this ongoing study shed insights into data computation and management at the machine level and demonstrate an effective approach for handling pertinent information at critical junctures. Furthermore, the adoption of a communication architecture helps minimize information redundancy and overhead in both transmission and storage for machine level communication.
Authors - Y. Abdelghafur, Y. Kaddoura, S. Shapsough, I. Zualkernan, E. Kochmar Abstract - Early reading comprehension is crucial for academic success, involving skills like making inferences and critical analysis, and the Early Grade Reading Assessment (EGRA) toolkit is a global standard for assessing these abilities. However, creating stories that meet EGRA's standards is time-consuming and labour-intensive and requires expertise to ensure readability, narrative coherence, and educational value. In addition, creating these stories in Arabic is challenging due to the limited availability of high-quality resources and the language's complex morphology, syntax, and diglossia. This research examines the use of large language models (LLMs), such as GPT-4 and Jais, to automate Arabic story generation, ensuring readability, narrative coherence, and cultural relevance. Evaluations using Arabic readability formulas (OSMAN and SAMER) show that LLMs, particularly Jais and GPT, can effectively produce high-quality, age-appropriate stories, offering a scalable solution to support educators and enhance the availability of Arabic reading materials for comprehension assessment.
