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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Ovalle, Christian
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Application of artificial intelligence in music generation: a systematic review Gutiérrez Paitan, Frank Manuel; Acuña Meléndez, María; Ovalle, Christian
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3715-3726

Abstract

Our analysis explores the benefits of artificial intelligence (AI) in music generation, showcasing progress in electronic music, automatic music generation, evolution in music, contributions to music-related disciplines, specific studies, contributions to the renewal of western music, and hardware development and educational applications. The identified methods encompass neural networks, automation and simulation, neuroscience techniques, optimization algorithms, data analysis, and Bayesian models, computational algorithms, and music processing and audio analysis. These approaches signify the complexity and versatility of AI in music creation. The interdisciplinary impact is evident, extending into sound engineering, music therapy, and cognitive neuroscience. Robust frameworks for evaluation include Bayesian models, fractal metrics, and the statistical creator-evaluator. The global reach of this research underscores AI's transformative role in contemporary music, opening avenues for future interdisciplinary exploration and algorithmic enhancements.
Application of classification algorithms for smishing detection on mobile devices: literature review Calero Sinche, Dylan Faredh; Acuña Meléndez, María; Ovalle, Christian
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3750-3760

Abstract

Smishing is a form of phishing carried out via mobile devices to steal confidential information from victims. The number of smishing attacks has increased in recent years due to the large number of users acquiring these easy-to-use and functional devices. This literature review objective is to examine the techniques and methods used in smishing attacks using classification algorithms. To do so, we conducted a manual search process and selected 155 articles from Scopus and 29 articles from access to research for development and innovation (ARDI). Of these, 36 articles met the inclusion criteria. In addition, the algorithms most commonly used by the studies were random forest classification techniques, decision trees, and neural networks. These studies analyzed various machine learning models for detecting phishing and smishing messages. The attack simulation scenarios included generating web pages, sending fake links (URLs), and installing malicious applications. The analysis evaluated web pages and SMS messages using a database containing legitimate as well as smishing messages. Based on the results, it is suggested to combine these methods to improve detection performance, making it more robust and promising.
Methodology applied to computer audit with artificial intelligence: a systematic review Reymundez Suarez, Sheyla; Martínez Huamani, Bryan; Acuña Meléndez, María; Ovalle, Christian
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3727-3738

Abstract

This systematic review focused on evaluating the impact of the machine learning operations (MLOps) methodology on anomaly detection and the integration of artificial intelligence (AI) projects in computer auditing. Data collection was carried out by searching for articles in databases, such as Scopus and PubMed, covering the period from 2018 to 2024. The rigorous application of the preferred reporting items for systematic reviews and metaanalyses (PRISMA) methodology allowed 88 significant records to be selected from an initial set of 1,389, highlighting the completeness of the selection phase. Both quantitative and qualitative analysis of the data obtained revealed emerging trends in the research and provided key insights into the implementation of MLOps in AI projects, especially in response to increasing complexity, whereby the adoption of the MLOps methodology stands out as a crucial component to optimize anomaly detection and improve integration in the context of information technology auditing. This systematic approach not only consolidates current knowledge but also stands as an essential guide for researchers and practitioners, and the information derived from this systematic review provides valuable guidance for future practices and decisions at the intersection of AI and information technology auditing.
Systematic review of artificial intelligence with near-infrared in blueberries Cayhualla Amaro, Liset; Rau Reyes, Sebastian; Acuña Meléndez, María; Ovalle, Christian
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3761-3771

Abstract

The fruit quality has a direct impact on how the fruit looks and how tasty the fruit is. The correct use of tools to determine fruit quality is essential to offer the best product for the final consumer. This study has used the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The study objective was elaborate a systematic literature review (SLR) about research of the application of techniques based on artificial intelligence to analyze indicators obtained by near infrared spectroscopy (NIRS) and chemometrics to determine the quality of fruits, including blueberries. The most frequently addressed indicator is the soluble solids concentration (SSC) which was used in several studies with techniques such as support vector machines (SVM) and convolutional neural networks (CNN). According to the results obtained, it is possible to use these techniques to predict blueberry quality indicators. There was an acceptable performance and high accuracy of these models. However, future research could cover other techniques and help to provide better quality control of products in food industries.
Deep learning for the identification of autism traits in children through facial expressions: a systematic review Palomino, Daniella Romani; Ovalle, Christian; Cordova-Berona, Heli
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3279-3290

Abstract

This study employs a bibliometric analysis to examine research on the application of artificial intelligence, specifically deep learning, in the detection of autism traits through facial expressions. Using quantitative methodologies. The analysis revealed a notable growth in scientific output from 2019, with an emphasis on techniques such as convolutional neural networks and systems based on the facial action coding system (FACS- CNN). The results highlight improvements in diagnostic accuracy thanks to the use of deep learning, although challenges related to data quality and availability remain. This study underscores the importance of international collaboration and technological innovation to advance the diagnosis and treatment of autism, offering a comprehensive perspective on current and future trends in this interdisciplinary field.
Near-infrared spectroscopy and machine learning to detect olive oil type: a systematic review Ortecho, Leonardo Ledesma; José, Enrique Romero; Ovalle, Christian; Berona, Heli Alejandro Cordova
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4120-4132

Abstract

The present study evaluates the effectiveness of visible/near-infrared spectroscopy (VIS/NIR) combined with machine learning in olive oil type detection. A search strategy based on the population, intervention, comparison, and outcome (PICO) framework was employed to formulate specific equations used in Scopus, ScienceDirect, and PubMed databases. After applying exclusion criteria, 53 studies were included in the review following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The reviewed studies demonstrate that VIS/NIR spectroscopy coupled with machine learning allows rapid and accurate identification of different types of olive oil, highlighting the detection of fatty acids, polyphenols, and other vital compounds. However, variability in samples and processing conditions present significant challenges. Although the results are promising, further research is required to fully validate the efficacy and feasibility of this technology in industrial settings. This review provides a comprehensive overview of the advances, challenges, and opportunities in this field, highlighting the need to optimize machine learning models and standardize analysis procedures for practical application in the food industry.
Co-Authors Acuña Meléndez, María Berona, Heli Alejandro Cordova Calero Sinche, Dylan Faredh Cayhualla Amaro, Liset Cordova-Berona, Heli Gutiérrez Paitan, Frank Manuel José, Enrique Romero Martínez Huamani, Bryan Ortecho, Leonardo Ledesma Palomino, Daniella Romani Rau Reyes, Sebastian Reymundez Suarez, Sheyla