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 - Libero Nigro, Franco Cicirelli Abstract - This paper proposes the Evolutionary Random Swap (ERS) clustering algorithm that extends the basic behavior of Random Swap (RS) by a population of candidate solutions (centroid configurations), preliminarily established through a proper seeding procedure, which provides the swap data points that RS uses in the attempting step of improving the current clustering solution. The new centroid solution improves the previous solution in the case it reduces the Sum of Squared Errors (SSE) index. ERS, though, can also be used to optimize (maximize), in not large datasets, the Silhouette (SI) coefficient which measures the degree of separation of clusters. High-quality clustering is mirrored by clusters with high internal cohesion and a high external separation. The paper describes the design of ERS that is currently implemented in parallel Java. Different clustering experiments concerning the application of ERS to both benchmark and real-world datasets are reported. Clustering results can be compared, for accuracy and execution time performance, to the use of the basic RS algorithm. Clustering quality is also checked with the application of other known algorithms.
Authors - Amir Ince, Saurav Keshari Aryal, Howard Prioleau Abstract - With the rise of social media, vast amounts of text, including code-switching, are being generated, presenting unique linguistic challenges for sentiment analysis. This study explores how existing models perform without fine-tuning to understand the challenges of analyzing code-switched data. We propose a prompt tuning approach based on generated versus human-labeled code-switched dataset. Our results show that the Few-shot technique and the Prompt Optimization Framework with Dataset Examples offer the most consistent performance, highlighting the importance of real-world examples and language-specific data in improving multilingual sentiment analysis. However, the studied models and technique do no exhibit the ability to significant triage sentiments for Hindi and Dravidian languages.
Authors - Hector Rafael Morano Okuno Abstract - The use of large language models (LLMs) has spread to various areas of knowledge. However, it is necessary to continue exploring them to determine their scope. In this work, an LLM is investigated to generate G-code programs for machine parts in Computer numerical control (CNC) milling machines. Prompt Engineering is employed to communicate with LLM, and a series of prompts are used to inquire about its scope. Among the results are the manufacturing operations that an LLM can program and the problems that arise in the developed G-codes. Finally, a sequence of steps is proposed to create G-codes using LLMs, and the prompt structures are shown to help users understand how the LLMs work when generating G-codes.
Authors - Hanaa Mohsin Ahmed, Muna Ghazi Abdulsahib Abstract - Fuzzy deep learning, which combines fuzzy logic and deep learning techniques to handle uncertainty and imprecision in the data as a first task and learn hierarchical representations of the data as a second task, is a promising method for feature data classification method with many usefully and important applications that meagres with several disciplines of knowledge. This work uses a fuzzy logic deep learning model to classify feature data on transmission casing data in specific. For the first time as an approach, fuzzy logic deep learning has been used to use transmission casing data, a well-known benchmark dataset application for classification tasks in specific. The results of the experiments show that the proposed model outperforms the deep learning-based classification model, classifying the transmission casing data with a higher accuracy of 100% and more robustness. We also go over potential future research directions for Transmission-based fuzzy deep learning feature data classification.
Authors - Titi Andriani, Chairul Hudaya, Iwa Garniwa Abstract - The transition toward more sustainable renewable energy sources has driven advancements in energy storage technology, including the development of Battery Energy Storage Systems (BESS). To improve the reliability and efficiency of BESS, implementing an effective monitoring system is essential, especially for detecting and diagnosing battery faults. The most commonly utilized methodologies for the diagnosis of faults in battery systems encompass knowledge-based, model-based, and data-based approaches. Artificial Intelligence (AI) holds significant potential to enhance fault diagnosis systems through predictive models capable of analyzing large datasets, identifying patterns, and forecasting potential faults. This work offers a thorough investigation of AI applications for BESS fault diagnosis, supported by an in-depth review of reliable sources such as Science Direct, IEEE Xplore, and Scopus. A total of 723 papers from scientific publications over the last five years were initially considered in this research. Following a rigorous screening process, including duplicate removal and the application of exclusion and inclusion criteria, 28 studies were selected for quantitative analysis. This study not only examines the types of faults that can be diagnosed but also assesses the challenges associated with recent advancements in this technology. In this context, the research identifies several aspects that have been applied within the theory of AI-based fault diagnosis for BESS and offers recommendations for further research. The results of this study are intended to aid in the creation of fault diagnosis systems that are more dependable and effective, which in turn will support the transition to cleaner and more sustainable energy.
Authors - Vasyl Yurchyshyn, Yaroslav Yurchyshyn Abstract - A living organism can be seen as a tool designed to perform specific functions, while both living and non-living matter represent distinct manifestations of nature. This work proposes considering living and non-living matter as physical systems, integrating existing scientific and technological advancements in the fields of physics, biology, and computer science. It suggests that scientific and technological developments in physical systems can also be applied to biological systems. The work addresses issues related to coding within living organisms and physical systems, and explores potential models for their functioning. The use of the golden ratio in living organisms and the potential benefits of applying these codes to physical systems are examined. Additionally, the refinement of physical quantities using the approaches discussed is addressed. Key issues in the modelling of living matter are highlighted, and various approaches to addressing these challenges are explored. The binary encoding and encoding based on π, e, and the golden ratio are considered.
