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 - Carlo Giuseppe Pirrone Abstract - This study explores urban accessibility for the elderly, focusing on the importance of integrating objective and subjective measures for a comprehensive assessment. Objective measures, such as cumulative opportunity measures (CUM) or measurable travel times, quantify spatial data while often neglecting personal experiences and user perceptions. Subjective measures, obtained through surveys, become crucial in defining the ease of access to services, including satisfaction levels derived from the journey, barriers due to individual factors (age, health, disability), as well as comfort and safety. A combined methodology would promote a new interpretation of urban accessibility. A case study conducted in Rende, Italy, illustrates a practical application by mapping healthcare services and public transport to assess pedestrian accessibility. A pilot survey gathered elderly residents' perceptions of distance, travel time, and service satisfaction. Preliminary results indicate a reluctance to walk, overestimated perceived distances and a strong reliance on private vehicles, highlighting the need for infrastructures and services to provide a better connection of the elderly to healthcare services. Ongoing research will further refine the study by adapting objective measures to local perceptions to develop a specific accessibility indicator for the area.
Authors - Dilshan De Silva, Dulaj Dawlagala Abstract - The research demonstrates a reliable and efficient IoT solution aimed good at improving the safety of campers and hikers in outdoor settings. It consists of one primary device and a set of other peripherals, which employ LoRa communication, GPS positioning, and environmental parameters for location-based services. The primary device system consists of an ESP32 microcontroller fitted with a LoRa 433 MHz module and NEO-7MGPS powered using SYN-ACK protocol that allows the device to constantly communicate with the subordinate devices. The subordinate devices which are also based on ESP32 has LoRa modules and OLED screen for the purposes of receiving and providing information about the geofences and locational alerts. A significant strength of this system is that it is able to function in remote areas by bringing all the devices together into a mesh network so that data and devices can be synchronized without relying on the internet for connection purposes. Both primary and subordinate devices have the ability to connect to the internet wherever possible, update messages, synchronize, and transfer messages efficiently. Failure is further minimized for effective communication and precise positioning which are important in the management of outdoor safety hazards. The first prototype tests have shown the ability of the system to solve problems such as real-time interaction, data integration, and functioning in difficult environments. To npm and deploy the research further developed IoT systems for outdoor applications, effective outdoor deployment strategies were developed. As the next step, the system will be tested on a larger scale to assess its scalability, and user-centered interfaces will be redesigned to accommodate real-world scenarios.
Authors - Jude Osakwe, Sinte Mutelo, Nelson Osakwe Abstract - This study aimed to investigate the interrelationship between data governance and compliance with privacy regulations. A systematic literature review was conducted to synthesise the existing research on data governance's impact on compliance with privacy regulations. The study found that data governance has a positive association with compliance, with integrated data governance methods and processes supporting decision-making, and stakeholders' involvement guaranteeing transparent processes. The findings also suggested that the impact of data governance on privacy regulations compliance needs a certain maturity level and top management support. Key recommendations for organisations are outlined to enhance their governance frameworks, promote transparency, and align resources effectively in order to bolster compliance with privacy regulations. The study concludes by addressing identified research gaps and offering directions for future studies aimed at exploring the evolving landscape of data governance and privacy compliance.
Authors - Nabeela Kausar, Ramiza Ashraf, Saeed Nawaz Khan Abstract - Skin cutaneous melanoma is one of the most aggressive forms of skin cancer, with various prognostic factors that can significantly impact patients’ survival outcomes. Survival analysis helps in identifying key factors influencing patient outcomes and guides in clinical decision-making. In literature, statistical methods have been used for the survival analysis of skin cancer patients but these methods have limitations. To address the limitations of traditional statistical methods in survival analysis, researchers have developed a range of machine learning (ML) based survival analysis techniques. These ML techniques offer advanced capabilities for modeling complex relationships and improving prediction accuracy. But "black box" nature of ML models poses a challenge, especially in fields like healthcare where understanding the rationale behind predictions is crucial. It this work, Explainable AI (XAI) based survival analysis has been carried out using XGboost model and clinical features of skin cutaneous melanoma patients. XAI models explain their prediction by showing the important features involved in the prediction to demonstrate their reliability to be used by the clinicians. To validate the performance of XAI model, in this work, multivariate regression based Cox Proportional Hazard (CPH) model has been developed which shows the relationship of patients’ clinical features and survival time. The pro-posed XAI based model has C-index value of 84.3% and shows that age, pathology T stage, and pathology N stage are key factors influencing the survival of skin cutaneous melanoma patients. The CPH model further validates the strong association between these features and patient survival.
Authors - Mulima Chibuye, Jackson Phiri Abstract - Agricultural systems have been modeled and prediction of yields used in the space since the beginning of agriculture ,improvements in the crop science and better tools made the task much easier through the ages, from using the position of the sun to determining that certain weeds signify a good harvest to actually determining what factors precede observable phenomena, the space has be-come so advanced such that we are able to build better prediction models and the promise of quantum computation that can model much more complex systems and interactions among the individual parameters within the system promise to make us predict yields of crops with much better accuracy than has ever been deemed feasible. With the technology that we have available now, we can apply properties of physical systems on classical computers such as mimicking chaos theory to add randomness to our predictions as that is the way nature works. That randomness is due to how initial conditions might potentially fluctuate and we would normally call it random because we are missing certain parameters that if we collect, would greatly improve how we predict physical chaotic systems. The aim of this work is to explore how we can incorporate chaos in agricultural systems by making use of a hybrid approach to known systems like dense neural networks and more recent methods such as Echo State Networks.
Authors - Cynthia Sherin B, Poovammal E Abstract - Vehicle detection and classification has a key role in evolution of intelligent transportation system. Accuracy in the detection enhances the efficiency of intelligent traffic monitoring systems. The paper shows a comparative study on the performance of YOLOv8 and YOLOv10 detection models on the novel Vehicle Identification aNd Classification (VINC) dataset introduced. They detect multi-class vehicles such as cars, trucks, buses, bicycles and bikes. The achievements of each model are assessed using precision, recall, F1 score and confusion matrices. The experimental results demonstrates the supremacy of YOLOv10 in detecting very small and more complex vehicle structures in the traffic scenario than YOLOv8. Alternatively, YOLOv8 also exhibited equivalent detection accuracy in detecting large vehicles like buses and trucks, by capturing the minute variations in the processed features. The detection models achieve precision of 97.2% and 93.6% for YOLOv10 and YOLOv8 respectively. YOLOv10 achieves high recall rate and F1 score of 92.4% and 81.4% respectively. Thus, the detection performance of these vehicles expresses the robust characteristics of both the YOLO versions. This research paper delineates the merits and drawbacks of these two versions on real-time circumstances, thereby creating faster and precise detection models.
