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 - Swayamjit Saha, Garga Chatterjee, Kuntal Ghosh Abstract - Visualizing data plays a pivotal role in portraying important scientific information. Hence, visualization techniques aid in displaying relevant graphical interpretations from the varied structures of data, which is found otherwise. In this paper, we explore the COVID-19 pandemic influence trends in the subcontinent of India in the context of how far the infection rate spiked in the year 2021 and how the public health division of the country India has helped to curb the spread of the novel virus by installing vaccination centers and administering vaccine doses to the population across the diaspora of the country. The paper contributes to the empirical study of understanding the impact caused by the novel virus to the country by doing extensive explanatory data analysis of the data collected from MoHFW, India. Our work contributes to the understanding that data visualization is prime in understanding public health problems and beyond and taking necessary measures to curb the existing pandemic.
Authors - Louay Al Nuaimy, Hazem Migdady, Mahammad Mastan Abstract - Accurate time series forecasting is vital in areas such as finance, weather prediction, and energy management. Traditional forecasting methods often struggle to effectively model the intricate patterns and nonlinearities present in real-world data. This study proposes the feedback-matching neural network (FMNN), a deep learning model that evolves from the feedback-matching algorithm (FMA). By embedding the core concepts of FMA into a neural network structure, the FMNN can recognize and match historical patterns in time series data, leading to more accurate predictions. Extensive experiments reveal that the FMNN outperforms several conventional statistical models and modern neural networks in terms of forecasting accuracy, as evaluated by the weighted absolute percentage error (WAPE). The FMNN enhances prediction accuracy by offering a sophisticated method for identifying and leveraging repeating trends within the data.
Authors - Alexandre Evain, Redouane Khemmar, Mathieu Orzalesi, Sofiane Ahmedali Abstract - This paper presents MYv7 (Mono-YOLOv7), an adaptation of the YOLOv7 architecture tailored specifically for 3D monocular object detection. Rather than competing with specialized 3D methods, we demonstrate the efficacy of enhancing 3D monocular detection using improved 2D object detection algorithms. We showcase how improvements in 2D algorithms can enhance 3D predictions, presenting MYv7’s twofold advantage over a YOLOv5-based method: increased speed and accuracy. These gains are crucial for efficient operation on embedded systems with limited computational resources. Our results highlight the potential of using advancements in 2D detection methods to significantly improve 3D monocular object recognition, opening new avenues for real-world applications.
Authors - Ali Belgacem, Abbas BRADAI Abstract - This summary research paper provides a comprehensive overview of Vehicle-to-Everything (V2X) communications, including various communication types and the roles of base stations. It covers resource allocation techniques and beamforming for high-quality connectivity and addresses energy efficiency optimization metrics. The paper also discusses artificial intelligence methods and their integration to optimize these systems and enhance performance. This research serves as a valuable guide for those aiming to contribute to advancements in 6G technologies for efficient vehicular communications.
Authors - Iaroslav Omelianenko Abstract - Neuroevolution algorithms need to evaluate at the end of each epoch the fitness scores of each organism in a population of solvers within the problem space where a solution is sought. This evaluation often involves running complex environmental simulations, which can significantly slow down the training speed if done sequentially. This work proposes a solution that utilizes the inherent capabilities of the Go programming language to run complex simulations in local parallel processes (routines). The efficiency of this proposed solution is compared to sequential evaluation using two classic reinforcement learning experiments, specifically single and double pole balancing. Direct comparisons indicate that the proposed solution is up to five times faster than the sequential approach when complex environmental simulations are required for objective function evaluation.
Authors - Kayode Oyetade, Anneke Harmse, Tranos Zuva Abstract - The introduction of AI in education has the potential to address educational inequalities and improve outcomes, but it also raises concerns about cultural responsiveness and biases in AI systems. To ensure equitable outcomes, strategies are needed to address these concerns. However, there is a limited understanding of effective approaches for promoting cultural sensitivity and equity in AI-powered educational content, highlighting a significant gap in existing literature. Using literature review methodology, this study aims to explore strategies to enhance cultural sensitivity and mitigate biases in AI-powered educational content, focusing on the intersection of technology and cultural diversity. By addressing concerns related to bias in AI algorithms, our findings highlight the importance of cultural inclusivity in AI-driven educational tools and advocates for proactive measures to embed cultural responsiveness into AI development processes. This review contributes to the discussion on responsibly integrating AI in education, promoting educational environments that value and reflect diverse cultural identities, and promoting a more inclusive educational experience globally.
