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.
Sign up or log in to bookmark your favorites and sync them to your phone or calendar.
Authors - Jorge Lituma, Anthony Moya, Remigio Hurtado Abstract - Dementia, a critical global health challenge recognized by the World Health Organization (WHO), affects millions of lives, with more than 50 million cases reported in 2019, a figure projected to double by 2050. Among its forms, Alzheimer’s disease is the most prevalent, underscoring the urgent need for early detection to improve patient outcomes and mitigate societal impact. Leveraging recent advancements in artificial intelligence, this study introduces an innovative deep learning framework aimed at revolutionizing the diagnostic process, providing valuable insights for the scientific community and practical tools for medical professionals. The proposed approach is structured into five key phases: data collection, preprocessing, model training using transfer learning, quality metrics validation including Accuracy, Precision, Recall, and F1-Score—and result interpretation through integrated gradients. A robust dataset of over 40,000 MRI images was utilized, achieving an exceptional accuracy of 99.86% in classifying the stages of Alzheimer’s disease. To ensure interpretability, integrated gradients were employed to highlight critical neuroanatomical markers, such as cortical atrophy and enlarged ventricles, distinguishing patients with dementia from healthy individuals. These findings validate the model’s reliability and demonstrate its potential as an innovative tool for advancing Alzheimer’s diagnosis and care.
Authors - Douglas Amobi Amoke, Yichun Li, Syed Mohsen Naqvi Abstract - Adopting machine learning solutions for monitoring vessel behaviour and surveillance in the maritime domain shows excellent promise. However, significant challenges arise due to the lack of publicly available vessel trajectory data labelled with Automatic Identification System (AIS) information. A new automated system has been proposed to preprocess and label vessel trajectory data collected from AIS at the Port of New York (NY), Blyth Port in Newcastle (NCL), United Kingdom, and a combined dataset called NYCL to address the labelling problem. This automated labelling system functions in three key stages. The first stage involves data collection and processing. The second stage transforms raw AIS data into meaningful vessel trajectory information. The third stage annotates and labels these trajectories, concluding with classification. The processed AIS data create vessel trajectories, with labels automatically generated. Finally, this work explores the classification models to demonstrate the effectiveness of labelled vessel trajectories in various maritime tasks.
Authors - MS Hasibuan, R Rizal Isnanto, Suryatiningsih, Chae Min A, Lee Kyung Min, Park So Hyeong Abstract - This study aims to design and implement a waste bank application to improve waste management efficiency through digital solutions. The application provides a dashboard to track waste collection activities in real-time, displaying data on waste amounts, schedules, and user contributions, enhancing transparency and efficiency. Test results show the system improves waste bank operations by 25% and simplifies waste management reporting.
Authors - Levyta Farah, Nurul Sukma Lestari, Dendy Rosman, Dewi Andriani Abstract - MSMEs (Micro, Small, and Medium Enterprises) and tourism have a very close relationship and support each other. The collaboration between the two has great potential in improving the economy and regional development. Therefore, active collaboration is needed between tourist destinations and MSMEs in the regions to support each other and enhance the quality of tourism in Indonesia. This research investigates the influence of digital innovation and sustainable strategies on MSME performance with the Penta helix as a moderating variable. The population of this research is MSMEs in Tangerang City, with a sample size of 303 respondents. The results of this research are that digital innovation does not affect MSME performance, while sustainability strategy and Penta Helix have a positive effect on MSME performance. This research also shows that Penta Helix can moderate digital innovation and sustainability strategies on performance. This research clarifies the contribution of variables to the growth and sustainability of MSMEs, strengthens their position in the global market, and enables the development of more robust policies and business practices, potentially significantly contributing to overall economic growth and supporting tourism in the Tangerang area.
Authors - Anamika Dhawan, Pankaj Mudholkar Abstract - Precision Agriculture has put in a lot of enhancement in improving agriculture in the last two decades. Plant monitoring is one of the essential applications of Precision Agriculture. In this study, an IoT-based system for rice leaf disease detection that runs on solar power and makes use of integrated machine learning on a Raspberry Pi 4 Model B is presented. In the classification of two important rice diseases, bacterial leaf blight and rice blast, the built custom Convolutional Neural Network (CNN) model, which was translated to TensorFlow Lite (TFLite) format for edge deployment, obtained a remarkable 94.28% accuracy. For scalable, effective disease detection in rice farming, this solar-powered, cost-effective device integrates edge AI and IoT.
Authors - Luka Jovanovic, Aleksandar Petrovic, Milan Tuba, Miodrag Zivkovic, Eva Tuba, Nebojsa Bacanin Abstract - Strong security measures are required due to the growing use of IoT devices and constantly growing network sizes. In order to tackle some of the most important issues in IoT security, this paper investigates the use of optimization metaheuristics in XGBoost hyperparameter tuning. In particular, we suggest a brand-new modified metaheuristic algorithm that is intended to improve diversity throughout the search process and is modeled after the firefly algorithm (FA). Experiments with simulations on a newly released IoT security dataset show how well the proposed optimizer works to enhance model performance. While tackling important issues related to hyperparameter optimization, such as striking a balance between exploration and exploitation, the method achieves a noteworthy accuracy of 0.996853. These findings demonstrate how the suggested approach may strengthen network security by using more accurate predictive modeling, opening the door for scalable and effective IoT systems in progressively complex settings.
