Conference Agenda

This study analyzes advances in precision medicine for the management of chronic diseases, with a focus on the role of artificial intelligence (AI) and information and communication technologies (ICT). Chronic diseases, such as cancer, diabetes and cardiovascular diseases, present significant challenges due to their complexity and high impact on quality of life and healthcare systems. The integration of AI allows personalizing diagnoses and treatments by analyzing genomic and clinical data, improving clinical efficacy and reducing costs. A systematic review based on PRISMA criteria was used, evaluating 494 articles, of which 40 were selected for meeting rigorous methodological standards. The findings highlight the transformative potential of AI, although there are still ethical, economic and technological barriers that limit its implementation. This work provides a comprehensive view of the impact of AI on modern medicine and underscores the need for additional research to overcome these challenges and maximize its accessibility and effectiveness

This article describes the comparative analysis of the use of CPU and GPU in the training and validation of three convolutional neural network models, to diagnose lung diseases from central chest X-rays. Since tuberculosis and Covid-19 present similar symptoms, it is possible to confuse the diagnosis and provide incorrect treatment. For this reason, two convolutional network models, InceptionV3 and VGG16, and an arbitrary one consisting of ten hidden layers, were chosen to compare them and thus achieve a more accurate diagnosis. Likewise, the JupyterLab interface was used with the Python programming language, complemented by the TensorFlow and Keras libraries. Then, for the training stage, 2,535 images were used, and the transfer learning technique was applied using the computer's CPU and GPU, with the purpose of analyzing the effectiveness when comparing each of the presented cases; likewise, this group of images also included the diagnosis of healthy patients. Regarding the evaluation, the metrics Accuracy, Recall, F1-Score, and General Precision were used to identify the performance of the arbitrary network model when compared to the other mentioned models. In this way, the arbitrarily proposed convolutional neural network model achieved higher accuracy, equivalent to 92.70% when the CPU was used, while when the GPU was used, this accuracy increased to 94.28%.

Financial fraud remains a critical challenge globally, with estimated annual losses reaching $5.38 trillion. In Peru, the absence of advanced technological solutions has intensified this issue, leading to significant economic losses of up to $100,000 per incident. Methods like internal and external audits are considered traditional for fraud detection and have proven to be insufficient, identifying only 15% and 4% of fraud cases. To address these shortcomings, this study proposes a deep neural network (DNN) model to detect anomalies in financial statements, leveraging machine learning techniques to improve fraud detection capabilities and provide security in finances. The model analyzes structured financial data, detecting irregularities through feature engineering and anomaly detection techniques. A dataset of 356 financial records from a Peruvian company in the hydrocarbons sector for the years 2021 and 2022 is utilized. The model’s architecture consists of multiple densely connected layers optimized to capture nonlinear relationships within financial data. Furthermore, to compensate for the imbalance of classes, the Synthetic Minority Over-sampling Technique was used, enhancing the model’s ability to identify fraudulent patterns with greater accuracy. The proposed model demonstrates a substantial improvement over conventional machine learning techniques, achieving an accuracy of 80%, a recall of 80%, and an AUC of 86%, great performance. Additionally, the model efficiently processes financial data in a faster manner, making it suitable for real-time fraud detection applications in high-risk environments. This study underlines the prospect of deep learning to improve anomaly detection, strengthen financial transparency, and enhance risk management in Peruvian organizations and beyond.

Quantum computing, based on superposition principles and quantum methods, is revolutionizing industrial sectors by solving complex optimization and simulation problems, surpassing the capabilities of classical computers. The objective of this research was to analyze recent advances in reducing processing times for large volumes of data and its impact on industrial development. The PRISMA methodology was used to conduct a systematic review of recent literature (2018-2023), defining specific inclusion criteria and conducting exhaustive searches in scientific databases such as IEEE Xplore and SpringerLink. The selected articles were evaluated for their quality and relevance. The results show that advances in quantum circuits, error correction and qutrits have significantly improved the efficiency of quantum calculations, enabling advances in molecular simulations and materials design. However, challenges remain in the scalability and stability of quantum systems. The main conclusion is that, although technological barriers must be overcome, the implementation of these innovations could transform various industries, as long as interdisciplinary research and collaboration continue to solve current problems.

This systematic literature review (RSL) investigates Artificial Intelligence (AI) as a tool to enhance the development of skills in high school students. The effectiveness of AI-based teaching strategies compared to traditional methods and the importance of skills such as critical thinking, problem solving and creativity were analyzed. A comprehensive literature review was conducted in Scopus using the PRISMA methodology, identifying 45 relevant articles. The results indicate that AI improves the development of key competencies and allows personalization of learning adapted to individual needs. Therefore, AI can contribute significantly to the academic growth and comprehensive development of students, and the need to continue researching these approaches in diverse educational contexts is highlighted.

This systematic review aims to analyze the impact of Artificial Intelligence (AI) in Business-to-Customer (B2C) sales processes and its contribution to the optimization of commercial strategies, decision making and personalization of the customer experience. To this end, the PICO and PRISMA methodologies were applied, carrying out a search in indexed databases that resulted in the identification of 181 articles, of which 12 met the inclusion criteria and were analyzed in depth. The findings show that the implementation of AI has generated significant improvements in market segmentation, the management of large volumes of data and the optimization of marketing and sales strategies. Challenges associated with its adoption were also identified, such as the need for advanced technological infrastructure, organizational adaptation and ethical management of data use. The discussion compares different perspectives on these findings and highlights AI as a key strategic tool in the digital transformation of B2C commerce. However, its implementation requires a structured approach and careful planning to maximize its benefits and minimize risks. This review provides an updated framework on the application of AI in commerce and provides relevant information for companies seeking to strengthen their competitiveness in digital environments.