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 - A B Sagar, K Ramesh Babu, Syed Usman, Deepak Chenthati, E Kiran Kumar, Boppana Balaiah, PSD Praveen, G Allen Pramod Abstract - Agricultural disasters, mostly ones caused by biological threats, pose severe threats to global food security and economic stability. Early detection and effective management are essential for mitigating these risks. In this research paper we propose a comprehensive disaster prediction and management framework integrating any of the resources like social networks or Internet of Things (IoT) for data collection. The model combines real-time data collection, risk assessment, and decision-making processes to forecast agricultural disasters and suggest mitigation strategies. The mathematical foundation of this model defines relationship between key variables, such as plant species, infestation agent species, tolerance levels, and infestation rates. The system relies on IoT or mobile-based social network agents for data collection at the ground level, to get precise and consistent information from diverse geographic regions. The model further includes a hierarchical risk assessment process that identifies, evaluates, and assesses risks based on predefined criteria, enabling informed decision-making for disaster mitigation. Multiplant species and multi-infestation agent interactions are also considered to capture the complexities of agricultural systems. The proposed framework provides a scalable approach to predicting and managing agricultural disasters, particularly targeting biological threats. By incorporating real-time data and dynamic decision-making mechanisms, the model considerably improves the resilience of agricultural systems against both localized and large-scale threats.
Authors - Herrera Nelson, Paul Francisco Baldeon Egas, Gomez-Torres Estevan, Sancho Jaime Abstract - Quito, the capital of Ecuador, is the economic core of the country where commercial, administrative, and tourist activities are concentrated. With population growth, the city has undergone major transformations resulting in traffic congestion problems that affect health, cause delays in daily activities, and increase pollution levels among other inconveniences. Over time, important mobility initiatives have been implemented such as traffic control systems, monitoring, construction of peripheral roads, and the "peak and license plate" measure that restricts the use of vehicles during peak hours according to their license plate, a strategy also adopted in several Latin American countries. However, these actions have not been enough, and congestion continues to increase, causing discomfort to citizens. Given this situation, the implementation of a low-cost computer application has been proposed that allows identifying traffic situations in real time and making decisions to improve this problem using processed data from the social network Twitter and traffic records from the city of Quito.
Authors - Elissa Mollakuqe, Hasan Dag, Vesa Mollakuqe, Vesna Dimitrova Abstract - Groupoids are algebraic structures, which generalize groups by allowing partial symmetries, and are useful in various fields, including topology, category theory, and algebraic geometry. Understanding the variance explained by Principal Component Analysis (PCA) components and the correlations among variables within groupoids can provide valuable insights into their structures and relationships. This study aims to explore the use of PCA as a dimensionality reduction technique to understand the variance explained by different components in the context of groupoids. Additionally, we examine the interrelationships among variables through a color-coded correlation matrix, facilitating insights into the structure and dependencies within groupoid datasets. The findings contribute to the broader understanding of data representation and analysis in mathematical and computational frameworks.
Authors - Laurent BARTHELEMY Abstract - In 2024 [7], the author proposed a calculation of weather criteria for vessel boarding against the ladder of an offshore wind turbine, based on a regular wave. However international guidelines [2] prescribe that "95% waves pass with no slip above 300mm (or one ladder rung)". In order to meet such acceptability criteria, it becomes necessary to investigate boarding under a real state, which is an irregular wave. The findings meet the results from other publications [6] [7]. The outcome then is to propose boarding optimisation strategies, compared to present professional practises. The purpose is to achieve less gas emissions, by minimising fuel consumption.
Authors - Amro Saleh, Nailah Al-Madi Abstract - Machine learning (ML) enables valuable insights from data, but traditional ML approaches often require centralizing data, raising privacy and security concerns, especially in sensitive sectors like healthcare. Federated Learning (FL) offers a solution by allowing multiple clients to train models locally without sharing raw data, thus preserving privacy while enabling robust model training. This paper investigates using FL for classifying breast ultrasound images, a crucial task in breast cancer diagnosis. We apply a Convolutional Neural Network (CNN) classifier within an FL framework, evaluated through methods like FedAvg on platforms such as Flower and TensorFlow. The results show that FL achieves competitive accuracy compared to centralized models while ensuring data privacy, making it a promising approach for healthcare applications.
Authors - Ahmed D. Alharthi, Mohammed M. Tounsi Abstract - The Hajj pilgrimage represents one of the largest mass gatherings globally, posing substantial challenges in terms of health and safety management. Millions of pilgrims converge each year in Saudi Arabia to fulfil their religious obligations, underscoring the critical need to address the various health risks that may emerge during such a large-scale event. Health volunteering plays a pivotal role in delivering timely and high-quality medical services to pilgrims. This study introduces the Integrated Health Volunteering (IHV) framework, designed to enhance health and safety outcomes through an optimised, rapid response system. The IHV framework facilitates the coordinated deployment of healthcare professionals—including doctors, anaesthetists, pharmacists, and others—in critical medical emergencies such as cardiac arrest and severe haemorrhage. Central to this framework is the integration of advanced technologies, including Artificial Intelligence algorithms, to support health volunteers’ decision-making. The framework has been validated and subjected to accuracy assessments to ensure its efficacy in real-world situations, particularly in the context of mass gatherings like the Hajj.
