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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Cano Lengua, Miguel Angel
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Design of a chatbot in a mobile application for managing payments and controlling activities in a fast school organization Medina, Gustavo Teves; Cano Lengua, Miguel Angel; Medrano, Hugo Villaverde
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1271-1286

Abstract

The fast school (FS) educational organization, like many contemporary educational institutions, faces challenges in efficient payment management and rigorous control of activities. Technology, particularly through mobile applications, has shown to be a potential solution to these problems, allowing institutions to stay at the forefront and provide optimized services to their educational community. Therefore, this research work focuses on how a chatbot, integrated into a mobile application, can improve payment management and control of activities in the FS educational organization. Through a detailed study on current trends in educational technology, the design and development of a chatbot adapted to the specific needs of the organization is presented. This chatbot not only facilitates payment processes, offering immediate responses and managing transactions, but also allows for more efficient control of academic and extracurricular activities, improving the experience of its users. In conclusion, the integration of chatbots in mobile applications is presented as a viable and promising solution to face and overcome management challenges in modern educational environments, providing adaptive and user-centered tools that enhance the operational efficiency of institutions. This work is developed with the Scrum methodology and presents a security gateway validated by a digital token.
Efficiency search: application of nature-inspired algorithms in artificial intelligence forecasting models Neira Villar, José Rolando; Cano Lengua, Miguel Angel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3528-3541

Abstract

This study reviews how nature-inspired optimization algorithms (NIOAs) have been applied to artificial intelligence-based demand forecasting, using preferred reporting items for systematic reviews and meta-analyses (PRISMA) and clustering analysis to examine 36 selected articles. The findings reveal that NIOAs, particularly genetic algorithms and swarm intelligence methods, including their hybrids, have been frequently applied to long short-term memory (LSTM) and other backpropagation neural network models (BPNN). A key insight is the differentiated application of NIOAs depending on network depth: In shallow networks, they have been effectively used to optimize trainable parameters, whereas in deep networks, their role has focused primarily on hyperparameter optimization due to the prohibitive dimensionality of trainable weights. In all studies, NIOA-optimized models consistently outperform conventional baselines based on backpropagation. However, persistent challenges such as excessive execution times and slow convergence have led to the development of more efficient hybrid strategies and adaptive mechanisms for automated exploration-exploitation control. By mapping explored and unexplored pathways, summarizing key outcomes and techniques, and identifying promising methodologies, this review offers a practical foundation to guide future experiments and implementations involving NIOA-based optimization strategies in neural network models. As a conceptual contribution, it also proposes an innovative use of multispace optimization to address one of the most critical challenges identified: the optimization of trainable parameters in deep neural networks.
Novel framework and reference architecture for artificial intelligence models for stock markets Cancho-Rodriguez, Ernesto David; Cano Lengua, Miguel Angel
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11171

Abstract

This original research article proposes, as its contribution, a novel unified framework and reference architecture for stock market prediction in postCOVID financial markets, which exhibit unprecedented volatility and nonlinear dynamics, demanding more robust predictive approaches than traditional models can provide. This original framework integrates artificial intelligence (AI) and machine learning (ML) models, ranging from classical techniques support vector machine (SVM) to deep learning (DL) architectures such as long short-term memory (LSTM) neural networks and gated recurrent unit (GRU) models, within a modular system encompassing data ingestion, sentiment processing, predictive optimization, reinforcement learning (RL), and cloud-based portfolio management. Another key original contribution is the synthesis of standards (ISO 23053, ISO 38505, ISO 20546) with big data methodological frameworks (REBD and Biggy), forming a unified meta-framework that orchestrates predictive signals from sentiment analysis (SA) and macroeconomic indicators. Experimental realworld stock market validation on mining-sector stocks demonstrated, with a 100% success rate, consistent investment outperformance over passive Buy Hold baselines, yielding investment optimizations of up to +11.11 pp: the evaluated portfolios achieved 23.57% and 8.25% returns versus their 19.94% and 5.41% baselines, respectively. These results confirm the validity of the proposed novel framework as a reproducible reference architecture, an original contribution empirically grounded and experimentally validated for the development of future financial AI systems.
Co-Authors Cancho-Rodriguez, Ernesto David Medina, Gustavo Teves Medrano, Hugo Villaverde Neira Villar, José Rolando