International Journal of Combinatorial Optimization Problems and Informatics
IJCOPI aim is to provide a forum for the dissemination and discussion of combinatorial optimization problems and informatics. It also carries a prestigious selection of top quality, refereed articles with the emphasis on new problems, methods, techniques and algorithms to solve the combinatorial optimization problems. IJCOPI is an open access and green journal on subjects related to the Combinatorial Optimization Problems and the Informatics. IJCOPI is part of the eco-friendly community with the publications by means e-publication mode. A peer review process ensures the content´s validity and relevance. IJCOPI has a quarterly frequency and IJCOPI publishes in the following periods of time: 1.-January-April 2.-May-August 3.-September-December This journal actively encourages contributions that align with and promote the advancement of the United Nations' Sustainable Development Goals (SDGs), particularly those related to quality education (SDG 4), clean water and sanitation (SDG 6), affordable and clean energy (SDG 7), industry and innovation (SDG 9), sustainable cities and communities (SDG 11), and climate action (SDG 13).
Mitigating Gender-Based Risks in Last-Mile Logistics through GPS Tracking and Safety Intervention for Female Delivery Workers
The perception of insecurity among women represents an obstacle to their participation in the last-mile delivery sector. A survey of approximately 100 women was conducted to establish a diagnosis, with 85.4% identifying the risk of experiencing harassment or violence during delivery activities as a determining factor when considering this type of employment. Various commercially available GPS devices were analysed, leading to the selection of one with low operating costs and a combination of technical and functional characteristics considered appropriate for delivery activities. In addition, a protocol for assisting female delivery workers was proposed. The proposal includes indicators intended to assess social and economic impact, with the aim of supporting increased female participation. The results suggest that the implementation of tracking technology could mitigate the perception of insecurity and may contribute to the development of safer and more inclusive work environments. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1183 Dimensions. Open Alex.
Copyright (c) 2025 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
A Neural Network-Based Predictive Model for Forest Fire Management in Michoacán
This study presents a predictive model based on neural networks to estimate the area affected by forest fires in Michoacán, with the aim of optimising resource management. A total of 929 historical fire records (2015–2024) from 18 localities were analysed, including six within the Monarch Butterfly Biosphere Reserve. Using variables such as operational cost, fire duration, impact, and firefighting personnel, several machine learning models were evaluated, including linear regression, decision trees, random forest, and a neural network. The neural network achieved the highest performance (R² = 0.84) and identified firefighting personnel, impact, and duration as the most influential factors. Although the dataset lacked key environmental variables, the neural network demonstrated a comparatively strong predictive capacity, suggesting potential applicability in future studies employing more comprehensive datasets. Overall, the findings provide a methodological basis that may support improvements in planning and decision-making for wildfire prevention and response when operating under real-world data constraints. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1156 Dimensions. Open Alex.
Remote Laboratory Based on FPGA Devices for Education 4.0
This paper reports a remote laboratory architecture for Education 4.0 applied to Digital Logic Design courses using FPGA devices. The development of the proposed architecture is based on embedded systems, in which the V-model methodology and an Internet of Things (IoT) technological environment are applied. Nine laboratory exercises were designed for the proposed remote laboratory. To evaluate learners’ acceptance of the remote laboratory, the Technology Acceptance Model was used. An evaluation instrument comprising six variables and 21 pre-coded items on a Likert scale was designed and administered to 28 learners via a survey. The Cronbach’s alpha coefficients calculated for the six variables ranged from 0.72 to 0.91, suggesting adequate reliability of the evaluation instrument. The Wilcoxon signed-rank test was applied, yielding a p-value below the significance threshold of 0.001, which indicates statistical support for learners’ acceptance of the remote laboratory. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1173 Dimensions. Open Alex.
The Technological Development Model for Environmental Monitoring Aimed at the Conservation of the Santa María del Lago Wetland Using IoT Sensors, Satellite Imagery, and Drones
In Bogotá, the absence of integrated data for wetland monitoring persists; therefore, a technological application structured in three phases was developed. In the first phase, information acquisition was conducted through flights using DJI Mini 2 and Phantom 4 Pro drones, with orthomosaics processed in Pix4D and WebODM, together with Landsat and Sentinel satellite imagery obtained from Google Earth Engine. In the second phase, the SIBIA platform was designed. This platform integrates Google Earth Engine panels, machine learning models implemented in Teachable Machine (a low-code environment), and their scaling in Python with OpenCV to classify vegetation and bodies of water. In the third phase, technical and community validation was carried out using IoT sensors, accuracy metrics (RMSE, MAE), and surveys with high reliability (α = 0.894). The results indicate high model accuracy (~0.98–1.00) and the generation of quality orthomosaics. In conclusion, the tool is shown to enhance environmental management and support sustainable decision-making for urban wetlands. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1189 Dimensions. Open Alex.
User-centered innovation approach: agave mead extractor
The maguey and pulque agroindustry in Mexico has remained underdeveloped, affecting quality and safety and limiting its competitiveness. This study outlines a sequential and iterative process for designing a device to extract aguamiel with added value for pulque producers in the northern State of Mexico over the period 2019–2024. The prototype was designed using a longitudinal, non-experimental research design framed within a qualitative frugal approach. The results indicate that the prototype reduces physical effort, enhances safety, and is positively received by producers. Areas for improvement related to efficiency and time satisfaction were also identified. The findings suggest that user-centred innovation contributes to stronger technological adoption and improvements in production processes. The participatory and iterative approach may serve as a replicable model for other small-scale agroindustrial technologies. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1161 Dimensions. Open Alex.
Design of a Predictive Model Using AI and GIS for the Management and Optimization of Resources in Forest Fires in Eastern Michoacán
This study describes a design and validation proposal for an artificial intelligence–based predictive model to estimate the area affected by forest fires and support the optimisation of operational resource use in the eastern region of Michoacán, Mexico. Using a dataset comprising 930 historical records (2015–2024), three supervised learning algorithms were evaluated: Multilayer Perceptron (MLP), Random Forest (RF), and XGBoost. The MLP model, optimised using L2 regularisation, Dropout, and cross-validation, achieved the highest performance, with a coefficient of determination (R²) of 0.76, compared with RF (0.57), XGBoost (0.51), and a weighted average ensemble model (0.60). The results were integrated into interactive cartographic platforms using QGIS and Leaflet JS, enabling the visualisation of areas with higher fire susceptibility and the prioritisation of interventions. A replicable tool with low computational requirements is put forward as suitable for institutional contexts with limited resources. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1181 Dimensions. Open Alex.
Design of a Reactor for Hydrometallurgical Recycling of Lead
This article details the design and operation of a 500 L stirred hydrometallurgical reactor for sustainable lead recycling from used lead–acid batteries. Mechanical design followed ASME Section VIII and API 650 standards, supported by finite element analysis and simulation conducted in SolidWorks. Sodium citrate was selected as the leaching reagent due to its effectiveness and comparatively low environmental impact. The agitation system, operated at 400 rpm with axial impellers and vertical baffles, demonstrated high mass transfer efficiency, achieving recoveries above 90%. Critical structural parameters were dynamically assessed, and solutions were proposed to reduce vibration and wear. This technology is presented as an eco-friendly alternative to traditional casting processes, with the potential to reduce occupational hazards and toxic emissions while promoting more sustainable recycling practices within the industry. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1165 Dimensions. Open Alex.
Frugal mechatronic design of a mobile self-balancing robot
Mobile robots are commonly used for tasks such as monitoring, mapping, and transportation. Among them, the self-balancing robot stands out, as it is capable of maintaining balance and adapting to different slopes, which makes it particularly suitable for material distribution. However, commercial robots are often costly, which limits their practical implementation. Rapid Manufacturing (RM), through additive manufacturing (AM), offers a potential approach by enabling low-cost and reproducible designs. This approach is characteristic of frugal innovation, allowing the robot’s structure and component selection to be scalable and low-cost, thereby supporting more efficient use of available resources. This manuscript describes a frugal mechatronic design for a self-balancing mobile robot with a 5 kg payload, based on a dynamic mechatronic model. A feasible solution was obtained through CAD development, appropriate component selection, and instrumentation for the synthesis of the physical configuration, while dynamic model simulations were used to parameterise inertial parameters. The resulting design is presented as a reproducible and affordable proposal. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1159 Dimensions. Open Alex.
Toward innovative smart agroalimentary solutions: A pilot optimization algorithm for selecting Oaxaca cheese with nutritional constraints
This work presents a combined analysis of the physicochemical characterisation of traditional and commercial Oaxaca cheeses, together with a pilot combinatorial optimisation study using a multi-objective optimisation algorithm. Cheeses that best meet nutritional content standards and regulatory restrictions were selected. Six samples were analysed for nutritional composition, including moisture, protein, and fat, using methods established by the Association of Official Analytical Chemists (AOAC). The field data were used to formulate a linear programming model with constraints derived from current regulations (COFOCALEC 733-2013), which concurrently maximises protein and fat content while minimising moisture. The results indicate that traditional cheeses contain average protein and fat contents of 21.7% and 25.4%, respectively, exceeding those of commercial cheeses (17.14% and 21.2%). The proposed algorithm was tested in a real case study, where it selected traditional cheeses on the basis of their higher nutritional value, as defined by the objective function. This optimal cheese selection methodology could be applied to problems involving extensive databases (Big Data) to support the definition of physicochemical characteristics relevant to the quality assessment of Oaxaca cheeses. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1182 Dimensions. Open Alex.
A Computational System for Real-Time Muscle Fatigue Monitoring Using Synthetic EMG Signals from the Gastrocnemius
Early detection of muscle fatigue is crucial in fields such as athletic performance, physical rehabilitation, and occupational health. This study describes an advanced synthetic electromyographic (EMG) signal generator that simulates the progressive recruitment of motor units for real-time muscle fatigue monitoring, with specific focus on the gastrocnemius muscle. The system implements a controlled simulation that typically initiates with 500 active motor units, allowing dynamic adjustment according to force and fatigue levels, which is intended to reflect neuromuscular adaptation during sustained contractions. To detect fatigue, dynamic thresholds based on the root mean square (RMS) and median frequency (MDF) of synthetic EMG signals were applied. These thresholds are continuously updated by considering a historical baseline and simulated physiological conditions. A progressive decrease in median frequency and a corresponding increase in RMS amplitude were observed, consistent with established neuromuscular responses under fatigue conditions. EMG signal visualisation facilitated the interpretation of the fatigue process and the compensatory recruitment of additional motor units. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1199 Dimensions. Open Alex.
The Synthetic Happiness Can Be the Future of Human Well-Being?
Ruiz-Vanoye et al. (2025) present, from an artificial intelligence perspective, an overview of human well-being derived from the use of synthetic data related to physical, mental, and personal healthcare. This proposal has led to the articulation of the concept of synthetic happiness, which, within an AI-based framework, suggests that human well-being could be enhanced through artificial processes that, in turn, may contribute positively to the development of natural processes. At the same time, this position draws attention to a range of ethical and legal issues that arise from the incorporation of synthetic data into domains associated with human well-being. This line of inquiry raises the question of whether synthetic happiness could represent a future pathway for human well-being. The article examines the ethical role of artificial intelligence alongside its potential involvement in the emergence of cyberaddictions and broader cybersocial challenges. Issues such as cyberbullying, social disintegration, health-related concerns, and mental health implications, among others, are identified as prospective directions for future research. These areas address complementary dimensions of synthetic happiness and are intended to inform efforts aimed at mitigating the negative consequences associated with the deployment of artificial intelligence technologies. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1212 Dimensions.. Open Alex.
Expert Opinion Fuzzy Model to Assess Energy Systems Resilience in Mexico
Existing energy resilience metrics have primarily focused on the technical performance of systems, with evaluations typically conducted using mathematical algorithms. However, as several authors have noted, energy resilience involves more than the ability to recover from a blackout, as it also encompasses social and economic contexts. In response to this limitation, the present study proposes a metric for assessing energy resilience that incorporates not only technical factors, but also environmental, social, economic, and institutional dimensions. The metric was validated through the judgements of experts with more than 15 years of professional experience in the field. Given the qualitative nature of the information associated with these dimensions, fuzzy logic is employed as the analytical approach. Two fuzzy logic models were applied: a simplified model comprising five variables and a more detailed model comprising fifteen variables organised into sub-principles. The results indicate a national energy resilience value of 1.55 for the simplified model and 2.0 for the detailed model, suggesting that the inclusion of a broader set of variables yields a more representative assessment. These findings support the view that fuzzy logic is an appropriate tool for this type of evaluation, as it allows expert knowledge to be systematically incorporated into the assessment process. Furthermore, the proposed approach may be adaptable to energy resilience evaluations conducted at both national and local scales. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1080 Dimensions. Open Alex.
Electronics and Programming for Bioprocess Control in Biotechnology Engineering: Accessible Solutions for Industry 4.0
This article examines the importance of teaching electronics and process control within biotechnology engineering programmes, particularly in the context of Industry 4.0. With the increasing adoption of automation and the Internet of Things (IoT), it has become important to equip students with practical and applied skills. However, economic constraints and limited access to specialised software in many developing countries present significant challenges. This study illustrates how the use of affordable microcontrollers, such as the ESP32, together with open-source software, can provide an effective alternative that enables hands-on learning without compromising educational quality. In particular, the ESP32 incorporates Wi-Fi connectivity, which allows online control and monitoring, as well as data storage in external databases. Practical projects involving data acquisition, signal conditioning, and actuator control are intended to offer students experience with real-world applications. In addition, project-based assessment supported by detailed rubrics may enhance students’ understanding and performance, thereby preparing them more effectively for the demands of the contemporary workforce. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1200 Dimensions. Open Alex.
Closed-Loop Control and Sensor Noise Reduction in a Low-Cost Aerodynamic Pendulum Using Digital PID and Kalman Filtering on Arduino Mega
This work presents the design, construction, and validation of a cost-effective aerodynamic pendulum as an alternative to commercial systems. The system employs a digital PID controller for real-time regulation of angular position, together with a Kalman filter to attenuate sensor noise. Its modular architecture is intended to facilitate the integration of hardware and software components, thereby supporting replication and experimentation without compromising measurement accuracy. The prototype incorporates low-cost components, including an Arduino Mega 2560, a DC motor, a precision potentiometer, and an L298N H-bridge. A closed-loop configuration based on PID control is used to reduce the error with respect to a 90° reference angle, while the Kalman filter contributes to improved reliability of angular measurements. The mechanical structure, constructed from plywood and plastic materials, provides adequate stability for experimental operation. Motor voltage is regulated through a pulse-width modulation (PWM) signal. The results indicate a rapid dynamic response and a degree of robustness to external disturbances. Overall, the project supports the feasibility of developing effective low-cost alternatives for control systems, particularly in educational and experimental contexts. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1137 Dimensions. Open Alex.
A Predictive Study of the 2024 Presidential Elections
A predictive study was conducted to examine user opinions expressed on the social network YouTube in relation to the 2024 presidential elections in Mexico. The methodology applied natural language processing techniques together with supervised classification algorithms for the purpose of electoral estimation. The procedure began with the systematic extraction of YouTube comments through the analysis of hashtags associated with the presidential candidates Claudia and Xóchitl. To this end, a download schedule with varied time slots was designed in order to obtain a stochastic and representative sample. A team of six researchers participated in the data collection process to help ensure heterogeneity and randomness in the dataset. The collected data were modelled using the Support Vector Machine algorithm, Naive Bayes, and linear regression to estimate trends. The results suggested that Claudia Sheinbaum would be the election winner, a prediction that was found to be consistent with the official election results. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1196
Detecting Hope in Social Media Discourse Using Machine and Deep Learning Classifiers
Hope speech refers to messages that convey optimism, support, or expectations of a better future. With the increasing use of social media as a medium for self-expression, analysing such messages may provide meaningful insights into the emotional well-being of individuals. However, hope speech detection has received comparatively limited attention in social media discourse analysis when contrasted with tasks such as hate speech detection. This study addresses this gap by conducting both binary and multiclass classification of hope speech in two languages: (i) English and (ii) Spanish. In the binary classification task, the objective is to distinguish between hopeful and non-hopeful tweets, whereas the multiclass task further categorises content into five classes: (i) Generalised Hope, (ii) Realistic Hope, (iii) Unrealistic Hope, (iv) Sarcasm, and (v) No Hope. Six traditional machine learning algorithms and three deep learning and transformer-based architectures were evaluated. The experimental results indicate that transformer-based models outperform traditional approaches in both languages. For English, RoBERTa achieved the highest performance (binary: 82.25% weighted F1; multiclass: 72.49% weighted F1), while for Spanish, XLM-RoBERTa attained the best results (binary: 86.32% weighted F1; multiclass: 77.01% weighted F1). These findings suggest the effectiveness of transformer-based models for multilingual hope speech detection. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1239
Toward Robust Offensive Language Detection in Urdu YouTube Discourse
This study addresses the task of detecting offensive language in Urdu text on social media, where harmful and offensive comments can lead to emotional distress. Using a dataset of Urdu comments collected from YouTube news channels, both deep learning (DL) and machine learning (ML) models were employed for offensive language detection. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF) and the Count Vectorizer approach to capture unigrams, bigrams, and trigrams. Four ML models—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Gaussian Naive Bayes (NB)—were evaluated, with SVM achieving comparatively stronger performance among the ML approaches. For the deep learning framework, Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) architectures were implemented using custom Word2Vec and FastText embeddings. The experimental results indicated that the CNN model outperformed the other evaluated approaches, suggesting its effectiveness for detecting offensive language in Urdu text. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1240 Dimensions. Open Alex.
A Comparative Study of Deep Learning and Transformer Models for Twitter Sentiment Analysis
In this work, we investigate sentiment classification on Twitter using the Sentiment140 dataset and compare traditional deep learning approaches, including CNN, BiLSTM, and GRU, with a lightweight transfer learning model, DistilBERT. The results indicate that the CNN, BiLSTM, and GRU models achieve accuracy levels ranging from 71% to 79%, whereas the fine-tuned DistilBERT model attains an accuracy of 86% with an F1-score of 0.86. These findings suggest that, while conventional neural models tend to focus on more local patterns, lightweight transformer-based architectures are better able to capture[HEDGING-END] broader linguistic representations, while still maintaining relatively efficient computational performance. Overall, this study offers a systematic and practical comparison of sentiment classification models, supported by a clearly defined methodology, empirical results, and corresponding tables and figures. The analysis may provide useful guidance for researchers and practitioners interested in sentiment analysis applications for social media data. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1241 Dimensions. Open Alex.
The Computational Theory of Mind: Ethical and Philosophical Implications in the Age of Artificial Intelligence
The Computational Theory of Mind (CTM) posits that the human mind operates in ways analogous to a computer, processing information through symbolic representations and formal rules. With the advent of Artificial Intelligence (AI) and Large Language Models (LLMs), the scope and implications of CTM have broadened considerably. This paper examines the ethical and philosophical dilemmas associated with research on and applications of CTM, with particular attention to human enhancement, privacy, consent, moral responsibility, and free will. It also considers how CTM intersects with longstanding philosophical debates on consciousness and personal identity, while addressing challenges raised by alternative perspectives, such as embodied cognition. Rather than advancing a prescriptive stance, the paper argues for a balanced approach that seeks to leverage technological developments while safeguarding human values and identity in an increasingly AI-driven context. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1245 Dimensions. Open Alex.
Learning in the Digital Age: The Educational Revolution with Artificial Intelligence and LKT
This study explores the impact of Artificial Intelligence (AI) and Learning and Knowledge Technologies (LKT) on contemporary education, with particular emphasis on how digitalisation reshapes teaching and learning processes. Within a rapidly evolving social context, AI has emerged both as a catalyst for innovation and as a source of educational challenges, especially in relation to the roles of teachers and students and to prevailing conceptions of learning. Empirical findings indicate that students experienced improvements in academic performance, attention, and motivation, with reported gains averaging 85% across core subjects such as mathematics, language, history, and science. The research adopts a positivist paradigm, employing a quantitative, hypothetical–deductive approach and a correlational design that combines documentary analysis with fieldwork. Data collection involved surveys, interviews, and observational techniques applied to both teachers and students. The results suggest that the integration of AI and LKT can foster active, personalised, and meaningful learning experiences. These practices appear to contribute to the development of key competencies, including digital literacy, critical thinking, and collaborative problem-solving, which are widely regarded as essential for participation in an increasingly global and digitally mediated society. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1249 Dimensions. Open Alex.
Towards Interpretable Inverse Model Control: The Role of Grey-Box Models
This work presents and compares two approaches for inverse modelling and control of a biotechnological system: a fuzzy rule-based model and a Kolmogorov–Arnold network (KAN) model. Both approaches aim to derive a control law that enables the system to reach a desired substrate concentration through model inversion. Whereas the fuzzy model offers interpretability via linguistic rules, the KAN model provides an explicit functional representation that allows for the analysis and visualisation of individual input contributions through univariate functions. It is shown how both models can be inverted online to determine the required dilution rate, thereby generating controlled trajectories that remain close to the reference signal. The results indicate that the use of interpretable models is viable for control applications and, in addition, may provide advantages in terms of transparency, pruning capability, and the automatic generation of symbolic expressions. These features can assist in determining or adapting implementations in critical control systems. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1244 Dimensions. Open Alex.
Identification and control of a Tail-Sitter Unmanned Aerial Vehicle
Unmanned aerial vehicles (UAVs) are increasingly employed in domains such as military operations, search-and-rescue missions, and surveillance. Recent research efforts have sought to integrate the advantages of fixed-wing and rotor-wing aircraft. Tail-sitter UAVs represent a class of hybrid platforms that combine rotor-wing vertical take-off and landing with fixed-wing forward-flight capability. These vehicles operate in three flight modes: vertical, horizontal, and transition. Depending on the transition mechanism, tail-sitter UAVs can be classified as mono-thrust transitioning (MTT), collective-thrust transitioning (CTT), or differential-thrust transitioning (DTT). Despite their potential, tail-sitter UAVs present substantial challenges in aerodynamic design and flight control, particularly with respect to instability and sensitivity to external disturbances. To address these issues, this work proposes an approach grounded in artificial intelligence and adaptive control principles. In particular, neural networks with tunable parameters, including inputs, weights, and transfer functions, are employed to enhance system robustness across varying operating conditions. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1230 Dimensions. Open Alex.
Financial education as a driver of innovative business in higher education students
This article presents a sample-based analysis of higher education students’ skills in financial knowledge, marketing, and entrepreneurship at the Cuautla Technological Institute. The analysis reveals relevant findings regarding students’ levels of preparation and confidence in these areas, with the aim of encouraging the development of innovative business initiatives through creative thinking. In Mexico, efforts to promote financial education have evolved over time in response to changing economic and social conditions. More broadly, the relevance of financial education extends beyond national contexts and affects individuals, families, and organisations, playing a role in overall economic well-being. Addressing potential gaps in financial education is therefore considered important, as such gaps can have direct implications for economic performance. In this regard, providing financial education from an early stage is often viewed as necessary, given that young people will later become members of university communities and, subsequently, participants in professional or entrepreneurial activities. From a financial perspective, a population lacking knowledge of income and expenditure planning, without a culture of saving, and without an understanding of responsible debt management may face difficulties in improving social well-being and, as a result, may contribute less effectively to national economic development. Consequently, further examination of this issue appears warranted, alongside initiatives that encourage individuals to adopt a more active and proactive stance with respect to financial decision-making and related competencies. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1219 Dimensions. Open Alex.
Innovation in Biometric Security and Digital Transformation: Redesigning Security Models in Society
This research aimed to generate applied knowledge to support the design, development, and implementation of an intelligent biometric system based on facial recognition, oriented towards strengthening security models in both public and private institutions. A technological solution was developed that integrates hardware, software, and artificial intelligence algorithms to provide access that is precise, rapid, secure, and hygienic, without requiring physical contact or the use of passwords or identification cards. The system supports multiple concurrent functions, including body temperature measurement and real-time logging of entries and exits, which may contribute to reducing risks associated with fraud and human intervention. The system was implemented at the Instituto Tecnológico Superior de Ciudad Hidalgo, where it was used to automate campus access for administrative personnel, academic staff, and students, in accordance with the principles associated with Society 5.0 and the development of intelligent and sustainable environments. Facial recognition is described as offering high levels of accuracy and adaptability, maintaining functionality under varying conditions such as mask usage and changes in lighting. The study adopted a qualitative research approach and employed an evolutionary prototyping development model, using TensorFlow and Python as the primary implementation tools. As a result, an access management system was developed that can be regarded as an innovative contribution to institutional security, while also supporting the integration of disruptive technologies within educational settings. In this context, automation and advanced authentication mechanisms are positioned as relevant components of contemporary infrastructural development. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1270 Dimensions. Open Alex.
Personalization of the tourism experience through machine learning techniques
This research focuses on analysing the experiences of visitors to Alameda Central during the Easter period in order to examine the degree of personalisation of the tourist experience. To this end, the study adopts a quantitative research paradigm and a non-experimental, cross-sectional design, and applies machine learning (ML) clustering algorithms. The main conclusions suggest that ML can function as a clustering tool through which visitors are grouped according to behavioural patterns, thereby potentially informing decision-making processes. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.803 Dimensions. Open Alex.
Preprocessing of amino acid chains of antibody structure for machine learning analysis
Antibody classification represents a task of growing importance in bioinformatics. In recent years, the identification of antibodies capable of recognising and neutralising SARS-CoV-2 has become a central focus in immunological research and bioinformatics. Antibody representation presents several challenges, as antibody structure and function are highly variable, which complicates the development of a universal classification framework. Antibodies are composed of heavy and light chains that contain hypervariable complementarity-determining regions, which define their specificity. These structural variations create substantial challenges for sequence alignment, feature extraction, and classification. In this research, three methods for representing amino acid sequences were compared: TF–IDF, Atchley Factors, and ProtVec. These representations were evaluated using decision trees, logistic regression, and support vector machines. A separate dataset was generated for each representation. The results suggest that the representation based on Atchley Factors achieved comparatively stronger performance in the task of antibody classification. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1030 Dimensions. Open Alex.
Systematic Review of Code Smell Patterns and Their Impact on Software Technical Debt
The accumulation of technical debt in legacy software is primarily manifested through the recurring need to modify code in order to correct defects, adapt it to new requirements, or alter existing functionalities. This situation reduces the useful lifespan of software systems by increasing their fragility, as a consequence of the continuous interventions required for maintenance and correction. Code smells constitute one of the principal contributors to this problem in software development. Consequently, it is essential to address this issue through appropriate design and coding practices in order to enhance code quality, maintainability, and readability. The objective of the present research is to identify the relative frequency of practices employed by developers during the design and coding phases that lead to the generation of malformed code, thereby contributing to entropy and increased technical debt in legacy systems. The study focuses on evaluating factors related to structural design and programming practices, while also conducting statistical analyses to identify recurring patterns associated with code smells. The results provide a detailed understanding of the most common practices and their impact on software quality. Furthermore, they highlight relevant gaps in current software engineering research and development, suggesting potential directions for future investigations aimed at addressing these shortcomings. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i1.947 Dimensions. Open Alex.
Selection of Electric Vehicle Battery using multi attribute decision making method
An electric vehicle (EV) battery provides the energy required to power vehicles, ranging from passenger cars to buses. Among EV components, the battery is widely regarded as one of the most critical, as it directly supports vehicle operation. In parallel, the adoption of electric vehicles has increased, largely due to their potential to reduce carbon emissions and contribute to environmental protection initiatives. Consequently, selecting an appropriate battery for electric vehicles represents a significant decision-making challenge. To identify a suitable battery option, a multi-criteria decision-making (MCDM) approach is employed, taking into account several fundamental performance specifications. These criteria include battery lifespan, efficiency, durability, recharge time, temperature dependence, cost, and weight. The present work applies a structured framework for EV battery selection through the use of multiple decision-making techniques, namely the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the Complex Proportional Assessment (COPRAS) method, and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA). Collectively, these methods are used to evaluate and rank battery alternatives, thereby providing a systematic and comparative basis for informed decision-making. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.383 Dimensions. Open Alex.
Design and Implementation of an Automated CFDI Analysis Algorithm for Accounting and Tax Optimization in SMEs
This study aimed to develop and evaluate an algorithm designed to reduce the time required for the analysis of Digital Tax Receipts (CFDI), with application to small and medium-sized enterprises (SMEs). Using a mixed and experimental approach, the performance of the algorithm was compared with manual review conducted by 170 Financial Engineering students. The results indicated that the algorithm was significantly more efficient, achieving an average reduction of 220.14 seconds and a very large effect size (Cohen’s d = 3.77). Furthermore, the algorithm achieved an effectiveness rate of 88% in determining the validity of CFDIs with respect to their deductibility for tax purposes, thereby highlighting its potential to optimise processes, improve productivity, and support decision-making in accounting and organisational contexts. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1214 Dimensions. Open Alex.
Repair and Initialization Functions in the Generation of School Schedules with Genetic Algorithms
The elaboration of school timetables is a complex and laborious task when performed manually, due to the large number of requirements and constraints that must be considered for its correct construction. In this work, the problem is addressed by using a simple Genetic Algorithms (GA) and compared with an improved approach that incorporates both an initialization function and a repair function. The study uses real data from the Computer Engineering course at the Centro Universitario UAEM Valle de México. The results obtained show that the initialization function significantly reduces errors from the first generations, while the repair function further accelerates the reduction of class or teacher splices. Thus, the effectiveness of the proposed approach to solve the scheduling problem in the university center is demonstrated. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1144 Dimensions. Open Alex.
Identification of Cardiac Arrhythmia by Selection of Relevant Variables Using Genetic Algorithms
This article presents the computational identification of cardiac arrhythmia from electrocardiogram (ECG) signal recordings to facilitate timely diagnosis and clinical management. The cardiac arrhythmia dataset from the public UCI repository was used, comprising 279 features and 452 classified cases. Several variable selection algorithms were applied, including filter methods such as OneR, Chi-square, information gain, symmetric uncertainty, gain ratio, CFS, and consistency, as well as a metaheuristic approach based on the genetic algorithm. The variables identified through the filter methods were subsequently used as inputs for the OneR, PART, Rpart, JRip, C4.5, SVMlin, KNN, and random forest classifiers. The results indicate two subsets of particular interest: 37 relevant variables achieving an average balanced accuracy of 82.93% using the Random Forest classifier in combination with the CFS filter method, and 12 relevant variables yielding an average balanced accuracy of 82.56% when the Random Forest classifier is combined with the genetic algorithm. These outcomes were obtained without the application of any data balancing method. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1145 Dimensions. Open Alex.
Ordered sequencing of Refactorings based on Preconditions and Postconditions
Refactoring is an essential practice in software development, aimed at improving the internal quality of code without modifying its external behaviour. This work introduces a modification to the Greedy algorithm through the integration of preconditions and postconditions to seek to optimise the ordering of refactoring operations. Unlike the traditional approach, which selects transformations without considering their cumulative impact, the proposed algorithm evaluates each step to help ensure progressive improvement in key metrics such as abstraction, modular protection, and implementation inheritance. To validate the effectiveness of the algorithm, it was applied to 30 applications selected from the GitHub repository, with the condition that each project remained executable both before and after refactoring. Metrics were evaluated at each stage of the process to assess the actual impact of the generated sequences. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1025 Dimensions. Open Alex.
Design of a recursive algorithm for DFA implementation: computational complexity analysis
Computational complexity, in its practical application, quantifies the time and memory space required for an algorithm’s execution, thereby allowing the assessment of its efficiency and limitations with respect to input size. In this work, the design of a recursive algorithm for implementing a deterministic finite automaton (DFA) to recognise regular languages is presented. The divide-and-conquer technique was employed to select appropriate data structures and to define a recursive function for resolving the transition functions between states. The recursive algorithm was implemented in the C++ language and exhibits linear temporal and spatial computational complexity, which is intended to support different configurations for the evaluation of multiple input strings. The proposed algorithm is consistent with the theoretical definitions, and the computational complexity analysis provides support for its classification based on the input parameters. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1172 Dimensions. Open Alex.
Recommending Computational Thinking Learning Paths using Fuzzy Logic
Nowadays, computational thinking plays a very important role in everyday life, which is why schools have sought to incorporate programming into their curricula to support its development. In this research, we focused on generating learning paths for children. To achieve this, we developed a tool that allowed us to capture data, which was then processed and analyzed using fuzzy logic. As a result, we obtained the next exercise each user should solve, which helped us create personalized learning paths for every user on our platform. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1170 Dimensions. Open Alex.