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 - Eduardo Puraivan, Patricio Tapia, Miguel Rodriguez, Steffanie Kloss, Connie Cofre-Morales, Pablo Ormeno-Arriagada, Karina Huencho-Iturra Abstract - This study provides empirical evidence on the effectiveness of large language models (LLMs), particularly ChatGPT, for automating the identification and analysis of cognitive demand levels in reading comprehension assessment tasks, using Barret’s Taxonomy. The manual classification of these tasks, even for experienced teachers, poses challenges due to their complexity and the time required. To address this issue, a three-step methodology was developed: selection of reading comprehension activities, automatic classification by ChatGPT, and comparison with the classifications from a group of experts. The experiment included 25 questions based on four readings extracted from a fourth-grade teacher’s guide for primary education. The results showed variability in the agreement between ChatGPT’s classifications and those of the experts: 77% in Activity 1, 50% in Activity 2, 52% in Activity 3, and 67% in Activity 4. At the question level, agreements ranged from 0% to 100%, highlighting discrepancies even among the evaluators, which underscores the inherent subjectivity of the task. Despite these divergences, the results emphasize the potential of LLMs to streamline the classification of educational activities on a large scale and the need to continue refining these models to enhance their performance in more complex pedagogical tasks.
Authors - Larissa de Lima, Priscila Capriles, Nathan Oliveira Abstract - This paper explores the use of machine learning (ML) with various physical, chemical, and biological parameter combinations to predict water quality, focusing on the Water Quality Index (WQI). We assess the performance of several regression algorithms across five different data combinations and examine the impact of inference and class balancing techniques on model outcomes. Our analysis reveals that LightGBM achieved the highest accuracy in WQI regression at 93%. This research introduces a novel approach to calculatingWQI by automating the traditional manual and complex parameter collection and calculation process. By streamlining water quality monitoring, our ML-based method offers a more efficient and innovative solution. Additionally, the study provides practical insights into handling data scarcity and using statistical inference for skewed sampling distributions.
Authors - Atiqur Rehman, Karim Elia Fraoua, Amos David Abstract - Blockchain technology has the potential to revolutionize traditional financial systems by offering decentralized, secure, and transparent transaction processing. This research focuses on developing a blockchain-based investment platform that integrates the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. The platform addresses critical issues faced by traditional investment systems, such as security vulnerabilities, inefficiencies, and the lack of transparency. By incorporating smart contracts, the platform automates key investment processes such as order placement and settlement, significantly reducing reliance on intermediaries. The system is designed to process transactions in real-time, offering high throughput and low latency, ensuring a smooth user experience. Extensive testing, including unit testing, integration testing, and security testing, has been conducted to verify the platform’s performance, scalability, and robustness. Security measures such as end-to-end encryption and multi-factor authentication (MFA) further enhance the platform's reliability. While PBFT ensures fast and secure consensus, the scalability of the algorithm may present challenges as the platform grows. Future work will focus on optimizing the PBFT system, exploring hybrid blockchain models, and integrating the platform with external financial systems to extend its applicability. The research demonstrates that blockchain, when combined with PBFT, can create a secure, efficient, and scalable solution for managing investment transactions.
Authors - Amira Jemaa, Adnan Rashid, Sofiene Tahar Abstract - Explainable Artificial Intelligence (XAI) plays an important role in improving the transparency and reliability of complex machine learning models, especially in critical domains such as cybersecurity. Despite the prevalence of heuristic interpretation methods such as SHAP and LIME, these techniques often lack formal guarantees and may produce inconsistent local explanations. To fulfill this need, few tools have emerged that use formal methods to provide formal explanations. Among these, XReason uses a SAT solver to generate formal instance-level explanation for XGBoost models. In this paper, we extend the XReason tool to support LightGBM models as well as class-level explanations. Additionally, we implement a mechanism to generate and detect adversarial examples in XReason. We evaluate the efficiency and accuracy of our approach on the CICIDS-2017 dataset, a widely used benchmark for detecting network attacks.
Authors - Ayush Verma, Krisha Patel, Hardikkumar Jayswal, Nilesh Dubey, Dipika Damodar Abstract - Insurance fraud significantly undermines the financial stability of the insurance industry, resulting in billions of dollars lost annually due to fraudulent claims across sectors like healthcare, auto, and property insurance. This paper proposes a robust methodology for detecting insurance fraud through the strategic implementation of ensemble machine learning algorithms, specifically XGBoost and Random Forest. By analyzing extensive datasets that include policyholder demographics, claim histories, and risk factors, we develop predictive models that accurately identify fraudulent activities while minimizing false positives. The effectiveness of our approach is supported by a comprehensive literature review highlighting the performance of various machine learning models in fraud detection, as well as our application of preprocessing techniques and feature selection to enhance model accuracy. Our findings indicate that the integration of advanced AI and ML technologies can revolutionize fraud detection in the insurance sector, offering a more secure and efficient environment for both insurers and policyholders.
Authors - Honorato Ccalli Pacco Abstract - Mushrooms are important in human nutrition due to their nutritional value in terms of protein, vitamin and mineral content. The volume of mushroom cultivation is currently increasing. This research focuses in the modeling and simulation of temperature, humidity and irrigation time controlling in mushroom cultivation in a protected environment. Using fuzzy logic in an intelligent system that allows process control and the LabVIEW software that facilitates graphic programming by means of virtual instruments, the irrigation time program was obtained as an output variable or an input variable-dependent response (input variables were temperature and humidity) in the intelligent system. The result was a program that shows how to act in different situations of temperature and humidity in mushroom cultivation in a protected environment. The fuzzy logic program in LabVIEW allowed the simulation of the system in terms of irrigation time in mushroom cultivation in a protected environment to achieve the expected results. In experimental results it can be observed that at low temperatures (15 °C) and low humidity (35%) the irrigation time is an average value (44.03). With the high temperature (35°C) and high humidity (95%) in the protected environment, the irrigation time will be with a low value (22.32). And it could be simulated by varying the input variables.
