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 - Mithil Mistry, Hasti Vakani, Hardikkumar Jayswal, Nilesh Dubey, Mann Patel, Jai Mehtani, Dipika Damodar, Ayush kariya Abstract - This research explores the integration of advanced deep learning techniques for automated pothole and speed bump detection, highlighting various methodologies from recent literature. A systematic review reveals that models utilizing Convolutional Neural Networks (CNNs), including YOLOv3, VGG16, and EfficientNetB0, have achieved impressive accuracy rates in real-time road surface monitoring. In particular, the EfficientNetB0 model was finetuned using a comprehensive dataset comprising 400 annotated images of potholes and speed bumps, collected under diverse environmental conditions. The model achieved a validation accuracy of 91.91%, demonstrating robust performance in identifying road anomalies. Notably, the implementation of advanced data augmentation and regularization techniques, such as dropout and L2 regularization, contributed to preventing overfitting and enhancing generalization across varying contexts. This study underscores the potential of deep learning frameworks in improving road safety and maintenance efficiency, paving the way for future enhancements, including dataset expansion and real-time application integration.
Authors - Ishaan Bhattacharjee, Pranav H P, Harish Satheesh, Disha Jain, Bhaskarjyoti Das Abstract - Satirical content is notoriously difficult to detect, even humans often struggle to discern satire from genuine news. While significant strides have been made in computationally modeling textual satire using supervised learning, the challenge of detecting satire in multimodal content—combining both text and images—remains largely unexplored. In our research, we aim to address this gap by leveraging existing frameworks and tools to detect and differentiate multimodal satire from true news content. Satire builds on two key factors, i.e., knowledge and incongruity. Knowledge has two parts, i.e., local knowledge that is resident in image and text and global contextual knowledge that is not part of the content. Incongruity typically occurs between the first and second parts of the text. In this work, we present a three-step framework. First, we investigate multimodal frameworks such as BLIP, relying on its global knowledge without explicitly modeling the incongruity. Second, we attempt to model incongruity by focusing on the semantic gap between two parts of the text content while using a large language model in knowledge enhancement and next-sentence prediction. Finally, we combine the above two models utilizing local knowledge, global knowledge, and incongruity to offer class-leading performance. The investigations described in this work offer novel insights into the detection of satire in complex, multimodal content.
Authors - James Uys, Gunther Drevin, Lynette Drevin Abstract - Serious games have seen a rise in popularity as an alternative method to deliver information to learners. A problem that is often faced is maintaining learner engagement during the educational process. To address this, it is important to identify the elements which are essential to keeping a learner motivated during the learning experience. This research focused on motivational drivers behind learning as well as prevalent characteristics in serious games. These elements were then integrated into the development life cycle of a serious game. The developed artefact was evaluated using the RETAIN model.
Authors - Lloyd L.K. Modimogale, Jan H. Kroeze, Corne J. van Staden Abstract - The paper presents the results of a quantitative study examining the impact of the Fourth Industrial Revolution (4IR) on the South African coal mining sector, specifically focusing on reskilling. As mechanization and digital technologies increasingly permeate the industry, this study investigates the implications for employment and skill requirements among coal miners. Data on job displacement rates, skill sets, and reskilling initiatives within the coal mining workforce were collected using statistical analysis. The findings indicate a significant decline in demand for traditional low-skilled job roles, highlighting the urgent need for reskilling programs to facilitate workforce adaptation to new technological demands. The study aims to understand how coal miners view the impact of digitalization on the coal mine and the management of the reskilling process. The research highlights the need for proactive measures to mitigate job insecurity and ensure that workers remain relevant in a rapidly changing economic environment, thus contributing to the broader discourse on sustainable labor practices within the mining sector.
Authors - Mafalda Reis, Lidia Oliveira, Catarina Feio Abstract - The main objective of this research is to study the role of online influencers in the information consumption of young people in Portugal. It studies the information consumption habits of young people aged between 18 and 30, as well as their views on the role of influencers in transmitting information on social media and their relationship with the concept of opinion leadership. A literature review was conducted since 2018 to generate a view of the state of the art on the issue under study. The data collection instrument was a questionnaire survey, obtained using an interpretative methodology, through a quantitative analysis of the data. From the analysis of the 322 respondents to the questionnaire, it was concluded that: young people consume more information on social networks; women and students are the ones who follow influencers the most; the relationship between influencers and followers is not one of friendship; young people are mainly interested in health issues and national news; young people have already learned about a current issue because an influencer talked about it; young people consider influencers to be active members of a given online community.
Authors - Kornprom Pikulkaew, Apinantn Sumthumpruek Abstract - Investigating the emotional lives of animals is inherently complicated although the findings are rewarding in terms of conservation, psychology, and sociopsychology. In this paper, we consider the problem of animal emotion detection with the aid of deep learning using dogs' emotions in this study. In this case, four categories of emotions, angrily, joyfully, relaxed, and sadly were classified based on the best CNNs such as ResNet-50, EfficientNet, and MobileNet. Furthermore, by employing data preprocessing, data augmentation techniques, and structural explainability techniques like the Grad-CAM, we could improve how the model made critical decisions. Results confirmed that the model per-formed satisfactorily in detecting the subjects' emotions even though joyful and relaxed states were more pronounced with high levels of accuracy compared to others with emotions like sadness and anger lauded as a notable challenge, especially in discriminating attitudes that seemed too close to one another. The application of Grad-CAM was able to elaborate on the regions of interest incorporated by the model thus enhancing the explainability of the model. This paper is focused on the emergent developments in emotion detection in animals and further recommends for advancement of three-dimensional deep learning techniques, settlement of the dataset, and the introduction of more complicated explainable AI techniques such as Local Interpretable Model Explanations (LIME).
