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 - Mariam Esmat, Mohamed Elgemeie, Mohamed Sokar, Heba Ali, Sahar Selim Abstract - This paper explores the relationship between deep learning approaches and the intricate nature of EEG signals, focusing on the development of a P300 brain speller. The study uses an underutilized dataset to explore the classification of EEG signals and distinguishing features of "target" and "non-target" signals. The data processing adhered to current literature standards, and various deep learning methods, including Recurrent Neural Networks, Artificial Neural Networks, Transformers, and Linear Discriminant Analysis, were employed to classify processed EEG signals into target and non-target categories. The classification performance was evaluated using the area under the curve (AUC) score and accuracy. This research lays a foundation for future advancements in understanding and utilizing the human brain in neuroscience and technology.
Authors - Angel Peredo, Hector Lugo, Christian Narcia-Macias, Jose Espinoza, Daniel Masamba, Adan Gandarilla, Erik Enriquez, Dong-Chul Kim Abstract - This paper explores the under-examined potential of offline reinforcement learning algorithms in the context of Smart Grids. While online methods, such as Proximal Policy Optimization (PPO), have been extensively studied, offline methods, which inherently avoid real-time interactions, may offer practical safety benefits in scenarios like power grid management, where suboptimal policies could lead to severe consequences. To investigate this, we conducted experiments in Grid2Op environments with varying grid complexity, including differences in size and topology. Our results suggest that offline algorithms can achieve comparable or superior performance to online methods, particularly as grid complexity increases. Additionally, we observed that the diversity of training data plays a crucial role, with data collected through environment sampling yielding better results than data generated by trained models. These findings underscore the value of further exploring offline approaches in safety-critical applications.
Authors - Mohammed Sabiri, Bassou Aouijil Abstract - Let Rm = IFpr [v]= < vm - v >, where p is an odd prime, IFpr is a finite field with pr elements and vm = v. In this study, we investigate quantum codes over IFpr by using constacyclic codes over Rm, which are dual containing. Furthermore, by using cyclic codes over the ring Rm and their decomposition over the finite field IFpr into cyclic codes, a LCD codes are given as images of LCD codes over Rm.
Authors - Hector Lugo, Angel Peredo, Christian Narcia-Macias, Jose Espinoza, Daniel Masamba, Adan Gandarilla, Erik Enriquez, DongChul Kim Abstract - Cancer continues to be a major global health challenge, with high rates of morbidity and mortality. Traditional chemotherapy regimens often overlook individual patient variability, leading to suboptimal outcomes and significant side effects. This paper presents the application of Reinforcement Learning (RL) and Decision Transformers (DT) for developing personalized chemotherapy strategies. By leveraging offline data and simulated environments, our approach dynamically adjusts dosing strategies based on patient responses, optimizing therapeutic efficacy while minimizing toxicity. Experimental results show that DTs outperform both traditional Constant Dose Regimens (CDR) and online training methods like Proximal Policy Optimization (PPO), leading to improved survival times and reduced mortality. Our findings highlight the potential of RL and DTs to revolutionize cancer treatment by offering more effective and personalized therapeutic options.
Authors - Sharmila Rathod, Aryan Panchal, Krish Ramle, Ashlesha Padvi, Jash Panchal Abstract - Diabetes or Hyperglycemia, a condition where an individual is characterized by significantly elevated blood sugar levels, may pose a significant threat to the effective lifespan as well as may pose a significant risk for various cardiovascular diseases. Reliable and non-invasive monitoring of hyperglycemia and also hypoglycemia is important for timely intervention and prognosis. The paper presents an extensive and structured survey dealing with the non-invasive glucose monitoring and diabetes detection using machine learning and signal analysis techniques. The paper focuses on a comparative analysis approach which showcases the literature in tabular and diagrammatic form. Examination of 10 papers that deal with Photoplethysmography (PPG) and Electrocardiography (ECG) signals to detect glucose variations using machine learning techniques has been carried out. The review highlights the respective proposed system, unique findings, improvements, techniques, methods, future prospects, comparison with previous studies, feature importance and model evaluation as well as stated accuracy. This comprehensive analysis aims to provide insights into the methodologies in non-invasive glycemic conditions thereby contributing to the development of improved disease analysis.
Authors - Anastasia Vitvitskaya, Almaz Galimov Abstract - We are living in the age of digitalization, a time when the latest technologies are changing everything around us. Artificial intelligence and digitalization have affected all aspects of our life and society. It is important to realise that the Covid-19 pandemic accelerated the development of digital technologies. Technologies of augmented and virtual reality (AR/VR) are used in many fields, including education. Online platforms allowed people to work and study remotely from the comfort of their homes, which made the online format more popular. Now, informal online education and the use of generative artificial intelligence is actively developing, but it is crucial to understand the implications that the active use of artificial intelligence in education will have. The purpose of the study is to identify the tasks for which generative artificial intelligence is used. As a method of research, we used the collection and analysis of scientific literature, as well as the method of survey, in which 750 people answered for which purposes they use artificial intelligence. The article considers theoretical and practical aspects of generative artificial intelligence application, defines and classifies the tasks.
