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 - Ayoung YANG, Atsushi ISHIBASHI, Ryota IMAI, Tsuyoshi MIYASHITA, Tadasuke FURUYA Abstract - As interest in autonomous ship research grows and challenges from natural disasters increase, the accurate assessment of marine environments is becoming increasingly important. However, current marine environment assessments are primarily focused on evaluating marine resources and environmental conservation, with limited applicability to vessel navigation. This study proposes the creation of a 3D map that integrates both underwater and above-water data, specifically targeting key areas of vessel navigation. The above-water data were collected using LiDAR(Light Detection and Ranging), while the underwater data were mapped using multibeam sonar. This map offers a level of realism that is not achievable with traditional nautical charts, enhancing maritime safety and supporting the operation of autonomous ships through a new format of data.
Authors - Selwa ELFIRDOUSSI, Hind KABAILI, Ghita SEKKAT Abstract - The COVID-19 pandemic disrupted many sectors, including education. The confinement of administrative bodies, teachers, and students confronted us with an unavoidable reality: the need for distance learning. Once schools reopened, several countries and institutions began adopting blended learning models, combining both distance and face-to-face modes. This sudden shift revived research in the field of education, specifically what is known as "Educational Data Mining," a discipline aimed at developing new tools for extracting and utilizing educational data. This paper presents a Machine Learning Model aims to predict student performance in blended learning by understanding the impact of various social, economic, personal, and other factors on student performance, and to identify students at risk of failure.
Authors - Phuong Thao Nguyen Abstract - Artificial Intelligence (AI) plays a transformative role in modern auditing by revolutionizing traditional methodologies and enhancing the overall audit process. The integration of AI technologies in auditing allows for the analysis of vast amounts of financial data, enabling auditors to identify anomalies, trends, and potential errors with unprecedented speed and precision. The significance of AI in identifying financial errors is paramount, as it enhances the detection of discrepancies that may go unnoticed through conventional auditing practices. By leveraging advanced algorithms and machine learning techniques, AI can recognize patterns and flag unusual transactions, thereby significantly reducing the risk of financial misstatements. Moreover, AI enhances the accuracy, efficiency, and compliance of financial audits. Automated data processing and real-time analytics minimize manual intervention, allowing auditors to focus on higher-level analysis and judgment-based tasks. AI tools also facilitate continuous auditing, enabling organizations to maintain compliance with regulatory standards and improve overall financial reporting. This paper provides an overview of the innovative ways AI is reshaping the auditing landscape, emphasizing its potential to elevate the quality and reliability of financial audits while streamlining processes and reducing costs.
Authors - Asmaa Abdul-Razzaq Al-Qaisi, Maryam Yaseen Abdullah, Enas Muzaffer Jamel, Raghad K. Abdulhassan Abstract - New technologies, particularly in recent years, are revolutionising the way the world of cultural heritage, as well as museum and exhibition spaces, is understood. In this context, virtual reality (VR), in particular, is seen as a valuable tool to enrich and enhance traditional visits, using virtual elements to make visitors' experiences more engaging and interactive. Furthermore, as arousing emotions is a fundamental aspect in the creation of museum itineraries, VR techniques are flanked by physiological techniques such as electroencephalography (EEG) that allow for a comprehensive analysis of visitors' feelings. Using EEG-based indicators, this paper aims to analyse the emotional state of a sample of visitors engaged in a first physical, then virtual experience. Interaction, in this case, took place with five specially chosen objects belonging to the collection of the museum of handicrafts of Valle d’Aosta region in order to classify the different levels of involvement. The results suggest that EEG analysis contributed significantly to the understanding of emotional and cognitive processes in traditional and immersive experiences, highlighting the potential of VR technologies in enhancing participants' cognitive engagement.
Authors - Kannary Keth, Samia Ben Rajeb, Virak Han Abstract - This paper presents a comprehensive literature review of research articles on Building Information Modeling in the past decade in thirteen Asian countries, including Cambodia, Thailand, Vietnam, Lao, Indonesia, Malaysia, Philippines, Singapore, Brunei, and Myanmar. Based on a Scopus search using keywords such as Building Information Modeling /Modelling /Model /Management /BIM, barrier/challenge, and the names of the 13 countries, the review identified 81 journal articles. Thirty-two articles were selected to extract the barrier statements. Only literature from four countries, China, Vietnam, Indonesia, and Malaysia, was found and selected. The semantic analysis by NVivo software included word frequency based on the literature review. As a result, 45 main barriers with six classifications: Cost, Technology, People, Environment, Organization, and Education were identified. Furthermore, the classification with high potential factors to influence the adoption of BIM in those countries is the environment, which demonstrates the external concerns, including standards, legality, guidelines, and regulations. Moreover, the main concern in China is a need for more willingness and awareness of BIM; in Vietnam, there is a lack of national standards; in Indonesia and Malaysia, there is concern about high costs. However, the study’s limitations include limited literature sources, exclusion of non-English sources, exclusion of article citations, and absence of expert validation.
Authors - Ivan Ursul Abstract - This paper presents a comprehensive approach to real-time fall detection using advanced Transformer-based architectures tailored for deployment on resource-constrained devices. Our dataset, collected over four months using the WitMotion BWT61CL IMU and complemented by smartphone video recordings, provides a rich, multi-modal source for modelling fall and non-fall events in diverse environments. Our work focuses on the deployment and performance evaluation of three Transformer-based models—Standard Transformer, Performer, and Linformer— each optimized for latency and accuracy in processing timeseries accelerometer data. Rigorous data preprocessing, including noise filtering and feature extraction, was applied to enhance signal quality. We evaluate the models on a dataset comprising 403 samples, achieving a peak accuracy of 98% with the Standard Transformer, and competitive results of 96% with the Performer and Linformer. The Performer model emerges as the most efficient latency, achieving an average response time of 34ms, while the Standard Transformer and Linformer require 350ms and 110ms, respectively. This efficiency, combined with high sensitivity and specificity, underscores the Performer model’s suitability for real-time embedded systems. Our findings demonstrate that advanced Transformer models, with optimized hyperparameters and efficient architectures, can deliver accurate, low-latency fall detection solutions, paving the way for enhanced safety in applications requiring real-time monitoring on compact hardware.
