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).
Dimensionality reduction for SARS-CoV-2 antibodies prediction
The analysis of genomic data allows to comprehend biological processes at the molecular level. A challenging application is the classification of antibodies according to the antigens they bind. Antibodies, the heart of the immune system, are proteins that bind to specific antigens to inactivate pathogens. Antibody classification requires datasets with structural and functional information about antibodies. The Observed Antibody Space is a dataset collecting genomic sequences of antibodies from several species and OAS contains information on the structure of antibodies. This paper examines the impact of dimensionality reduction techniques on the classification of SARS-CoV-2 antibodies, utilizing genetic sequence data from the Observed Antibody Space database. Specifically, we focus on transforming amino acid sequences from the Complementarity Determining Region, CDR, into word embeddings for subsequent processing in machine learning models. This transformation enables the use of unlabeled, high-dimensional data but presents the challenge of the curse of dimensionality, which can affect the models’ accuracy and efficiency. To address this problem, two dimensionality reduction techniques are applied and evaluated: Principal Component Analysis and Uniform Manifold Approximation and Projection. We developed 36 classification models using Support Vector Machines, Random Forests, and K-Nearest Neighbors algorithms, testing each on original datasets and on reduced datasets. The objective is to determine whether dimensionality reduction improves model performance. The study provides insights into how these techniques can facilitate predictive analysis in bioinformatics and contribute to the development of efficient models for identifying relevant antibodies in immunology. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1142
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Using Data Sciences for Analyzing Residential Electricity Consumption in Marginalized Mexican Municipalities
In Mexico, the municipal marginalization index is used as a fundamental tool for planning and formulating public policies aimed at improving the socioeconomic conditions of the population. However, one of the limitations of this index is its exclusion of residential electricity consumption. In this regard, the present study explores the relationship between electricity consumption and the marginalization index. The research was conducted following the methodological framework of Data Science. As a result, geographic areas that cover municipalities with similar levels of residential electricity consumption were identified and characterized. It is noteworthy to mention that an inverse correlation was found between such consumption and marginalization index. Finally, these contributions could improve decision-making processes in public policies aimed at reducing economic and social inequalities, thus promoting more equitable development of the most lagging municipalities. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.942
Method for the Unification and Reduction of the Search Space of V Gene Segments in Sequence Alignments
The identification and characterisation of V genes poses a significant challenge due to the substantial number of alignments generated by diverse sequencing systems. This study proposes a method for the unification and reduction of the search space, with the objective of optimising the identification of V genes. The method integrates preprocessing, normalisation, and clustering using Gaussian Mixture Models. This approach facilitates data consolidation and reduces redundancy, thereby enhancing the efficiency and accuracy of the subsequent analysis. The elbow method was employed to determine the optimal number of groups, achieving a 98% reduction in the search space. The findings were validated through the use of metrics such as mean absolute error, mean squared error, and root mean squared error, thereby confirming the effectiveness of the method in improving the precision of gene identification. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1028
Application of Berlo's Communication Model to Agile Software Development with Scrum in Distributed Teams
This study examines communication challenges in distributed teams operating within the Scrum framework. To enhance communication effectiveness, Berlo’s model was adapted. An instrument was developed and validated by experts through surveys and interviews with professionals experienced in agile project management. The instrument was subsequently applied to 44 practitioners across seven countries, assessing its capacity to improve communication and optimise the flow of information between teams and stakeholders. The findings confirmed that the adaptation of Berlo’s model is effective in addressing communication challenges in distributed teams. In conclusion, the implementation of Berlo’s model in Scrum teams reinforces clarity and cohesion in communication, thereby contributing to the success of agile projects in global environments. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1001
An Experimental Study on Grouping Mutation Operators within the GGA-CGT Applied to the One-Dimensional Bin Packing Problem
The Grouping Genetic Algorithm with Controlled Gene Transmission (GGA-CGT) is among the most effective algorithms for solving the one-dimensional Bin Packing Problem (1D-BPP), a classical NP-hard combinatorial optimisation problem with numerous industrial and logistical applications. This study aims to identify the characteristics that enable a mutation operator to perform better within this algorithm by implementing five state-of-the-art mutation operators: Elimination, Merge & Split, Swap, Insertion, and Item Elimination. Performance was evaluated in terms of the number of optimal solutions obtained. Our findings indicate that the GGA-CGT performs best with the least disruptive operators and that both the gene selection strategy and the item selection strategy can enhance the performance of mutation operators. These findings were validated by redesigning and improving a state-of-the-art item-oriented operator, achieving a 26% improvement over the best baseline version of the same operator. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1004
Adaptive Traffic Congestion Prediction Using LSTM Networks and Physics-Based Models
Accurately modelling and predicting traffic congestion is essential for effective traffic management in dynamic urban environments. This study introduces a hybrid model that combines a deep neural network architecture, designed to capture long-term temporal dependencies, with physics-based approaches to traffic modelling. The deep learning component identifies complex temporal patterns and non-linear behaviours in traffic evolution, while the physics-based component incorporates dynamic constraints that enhance the model’s robustness and ability to generalise. This integration leverages the predictive power of machine learning while maintaining consistency with the fundamental principles governing vehicle flow. Experiments conducted using real-world traffic data demonstrate strong predictive performance, establishing a solid foundation for the development of intelligent transportation management systems and providing advanced tools to support adaptive mobility optimisation in congested urban settings. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1021
Using the Zipf Distribution to Mitigate the Matthew Effect and Improve Fairness in Bias-SVD Algorithm Recommendations
Recommendation Systems (RS) are useful tools to help users find items of interest within an universe of options in this era of Big Data 3.0. Models such as Bias-SVD, SVD++, and their variants became classic and widely used in the heart of RS in e-commerce. However, these models do not consider popularity biases due to the Matthew effect present in the data structure, which leads to unfair recommendations. To address this problem, some researchers have proposed strategies that compensate for long-tail elements to try to increase the probability of their being recommended, other approaches use techniques such as the Zipf distribution to generate the predictions without prior knowledge of the data. However, these proposals have not been widely accepted because they do not consider user-item interactions in the training process. This paper presents a strategy that makes use of the Zipf distribution in the Bias-SVD model to incorporate popularity biases and improve the fairness of recommendations. Three variants of said model were implemented to show the validity of the strategy. The loss function uses Mean Square Error (MSE) and the error minimization is done using the ADAM algorithm. For the Experimental work, two MovieLens data sets with different distributions were used. The results show that it is possible to improve the fairness of recommendations by reducing the Matthew effect in the Bias-SVD model. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.937
Classification of Soil Type in Buildings Using Pseudo Spectral Acceleration Readings from Earthquake Events
Soil type is a critical factor influencing the seismic performance of buildings, as it affects the level of damage sustained during earthquakes. This paper presents a novel approach to classifying building soil types using pseudo-spectral acceleration readings recorded during seismic events. By leveraging machine learning classifiers, the study develops a model that accurately identifies soil types from pseudo-spectral acceleration data, achieving an accuracy of 89.16%. The methodology involves preprocessing the seismic data, extracting key features, and applying various classifiers to determine the most effective model. Performance is evaluated using metrics such as accuracy, precision, and recall. The findings indicate that this approach significantly improves soil classification accuracy over traditional methods, providing a practical tool for seismic hazard assessment and building design. This research further advances earthquake engineering by offering a data-driven solution to enhance building resilience. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1006
Nonlinear Systems Modeling Using Polynomial Neural Networks: Comparison
Nonlinear systems have not been extensively utilized in engineering research. This is a consequence of the lack of effective analytical approaches analogous to those developed for linear systems, which are well understood and readily analyzable. This paper presents the application of two artificial neural networks (ANNs) for online identification of nonlinear systems as an alternative to linear systems theory. To illustrate this, the nonlinear model of a single inverted pendulum on a cart (SIPC) was identified from experimental data obtained from a laboratory prototype. The model structure employed in this study was based on the nonlinear autoregressive model with exogenous inputs (NARX) on Volterra polynomial basis function (VPBF) and Chebyshev polynomial basis function (CPBF) neural networks. The neural network structures were trained with experimental data from the SIPC prototype, which was recorded for a duration of 60 seconds. Subsequently, these models were validated using experimental data from a separate 15-second recording. The neural networks with Chebyshev polynomials demonstrated slightly superior performance. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.936
A Survey of Machine Learning Based Systems for Evaluating Expertise Classification in Medical Simulators
Medical simulators provide a safe environment for practising crucial procedures, particularly in virtual simulators where objective and quantitative data can be collected for developing machine learning algorithms for automatic expertise classification. This survey analyses 13 automatic evaluation systems used in medical simulators and identifies best practices for integrating ML algorithms. Among these systems, nine employed commercial simulators, particularly NeuroVR and the Da Vinci robotic systems, while four utilised custom simulators. The survey outlines the main steps in the integration of machine learning algorithms: data collection, metric generation and selection, training, and testing. Metric selection was identified as a crucial factor affecting both the accuracy of the algorithm and the comprehension of the evaluation. Typically, multiple machine learning algorithms were applied to the same dataset to compare results and identify the most effective model. Overall, this survey suggests that transparent algorithms are preferable, as they enhance physicians’ understanding. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.940
A Systematic Literature Review on Technical Debt in Software Development: Types, Tools, and Concerns
Software projects often accumulate technical debt, which undermines code quality, maintainability, and long-term viability. This article analyses twelve categories of technical debt and examines the capabilities of existing mitigation tools. To achieve this, we conducted a systematic literature review. Based on the results, we categorise and describe each debt type as it occurs in practice, and we assess prominent tools (such as AnaConDebt, CAST, DebtFlag, Visminer TD, and TD-Tracker) with respect to the debt types they target, the programming languages they support, and their adherence to established software quality models or methodologies. Our analysis shows that most current solutions focus on code and architectural debt, whereas documentation and process debt remain largely unaddressed. Moreover, most tools do not conform to software quality models or standards, which none of them explicitly incorporate. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1150
Exploring key factors for cohesion in software development teams
Team cohesion plays a significant role in software development, shaping performance and productivity. It fosters strong connections among members working towards a shared objective. Cohesion is a dynamic process influenced by personal, interpersonal and domain-related factors. To identify key drivers of cohesion in software teams, we posed four open-ended questions to 137 industry-experienced developers. Responses most frequently highlighted effective communication, shared goals and mutual support as enablers of better organisation, role clarity and synchronisation, thereby providing structural support for cohesion. Additionally, crisis management, handling external dependencies, conflict resolution and adaptability were cited as foundations for problem-solving. Leadership, recognition and synergy were reported to foster social affinity and receptiveness to peer feedback. Overall, respondents associated cohesion with outcomes such as working affinity, social affinity, dependability, receptiveness to feedback, team-based decision-making and efficiency. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.957
SensorApp: A Move Speaks Louder Than Words: Personality recognition using a dataset obtained from sensors and mobile device usage habits
Personality is a set of traits that describe people, such as emotions and behavioral patterns. Therefore, it is easy to believe that these patterns influence the way in which people interact with mobile devices. Since they are so widely used in everyday life and considering that most people have one of them, it becomes fundamental to create a dataset based on mobile device usage habits. The purpose of this is to recognize the personality of the users based on how they use their data. This paper presents the methodology in which the data was collected, as well as the neural networks used to perform preliminary personality recognition tests, using different metrics, getting promising personality prediction data with the use of ANN and MLP networks and the data obtained from mobile devices. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1018
A Bayesian Equilibria in the Game of Effort Estimation in Agile Global Software Development
This research examines leader–team interactions in agile software development within a global context, with a focus on effort estimation. Drawing on principal–agent theory, we analyse the interaction on the assumption that the Scrum Master guides the development team under imperfect information. We model the interaction as a sequential game with incomplete information. In the first stage, the Scrum Master allocates resources to the development team; in the second stage, the team exerts effort. Both parties are characterised by types that capture their knowledge and skills. As these types are private information, we derive the Bayesian Nash equilibrium to determine the equilibrium effort levels. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1020
Application of Retrieval-Augmented Generation (RAG) Systems in Software Engineering Education: An Approach Based on Generative AI and DevOps
This paper presents a systematic literature review of the application of retrieval-augmented generation (RAG) systems in educational settings, with a focus on teaching software engineering and related computing disciplines. Drawing on case studies, academic experiments, and surveys of teachers and students, it provides an overview of the current landscape, highlighting perceptions, reported effectiveness, and the technology’s impact in academia. Based on an analysis of 71 selected scientific papers, the review synthesises evidence on the extent to which RAG systems mitigate hallucinations and improve human–AI interaction. In addition, it suggests that many approaches discussed across studies could be strategically aligned with the integration of DevOps practices and RAG, enhancing their use through automation, continuous improvement, and the agile adoption of technologies within educational processes. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1003
Towards A Clinical Interface For Speaker Identification And Speech-To-Text Transcription For Recording Medical Consultations In Spanish
This paper presents the development of an advanced clinical interface built on the LattePanda Sigma, an embedded device designed for edge computing. The interface integrates OpenAI language models and Whisper for automated speech-to-text transcription, together with accurate speaker diarisation in clinical settings using the pyannote/speaker-diarization-3.1 model. A dataset of ten doctor–patient conversations in Spanish—translated and re-recorded to suit the local context—was used to evaluate the models. Automatic transcriptions generated by the models were compared with the reference transcripts using the ROUGE metric. Average ROUGE scores of 0.9028 for the Small model and 0.9260 for the Medium model indicate high transcription accuracy. The reference transcripts were also used to assess the segments identified by the pyannote model. Finally, the paper analyses the system’s usefulness and effectiveness in improving Spanish-language clinical records. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.900
A Review of the perspective on the implementation of evolutionary algorithms in cyber security on IoT infrastructure
The rapid growth of the Internet of Things (IoT) in industrial environments has increased efficiency but also heightened vulnerability to sophisticated cyber-attacks. Traditional cyber security approaches are insufficient to protect critical infrastructure, creating a need for dynamic, adaptive solutions. Evolutionary algorithms (EAs), owing to their ability to explore large search spaces and optimise parameters, offer a promising route to enhancing IoT security. This review highlights the integration of EAs with deep-learning techniques to improve intrusion detection and system resilience. Building on this background, we propose an adaptive cyber-security framework that leverages evolutionary optimisation and continual learning to detect, prevent and mitigate attacks in real time. The study emphasises the importance of validating hybrid models in real-world settings and of optimising computational efficiency. Future work should investigate autonomous response mechanisms and the scalability of solutions for large-scale Industrial IoT (IIoT) deployments, ensuring robust protection against emerging threats and aligning academic advances with industry needs. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1023
Missing data imputation with Harmony Search Algorithm
Incomplete data poses a significant obstacle in data science and machine learning, influencing model outcomes and occurring commonly across domains such as health, nutrition, electricity, agriculture, chemistry and water resources. Missing data refers to the absence of information for one or more variables in a dataset. Accurate imputation is therefore crucial to ensure the reliability and validity of analyses and predictive models. This study proposes a Harmony Search Algorithm (HSA) to address missing-data imputation, emphasising its flexibility and adaptability. The approach seeks the best imputations by minimising error metrics such as MAE, MSE and RMSE. Computational tests indicate that HSA is a promising method for imputing missing data in a range of contexts. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.999
Experimental Study of Optimization Algorithms for Resource Allocation in Cloud Computing
Cloud computing is essential for executing scientific workflows, offering scalable resources to process large volumes of data. However, efficient resource allocation remains a challenge due to fluctuations in demand and inadequate planning, which can increase makespan and thus energy consumption. In this context, infrastructure as a service is primarily used, allowing the rental of virtual machines with different characteristics. This study focuses on identifying the factors that influence the improvement of makespan for executing workflows from different disciplines and characteristics. The factors evaluated in this study are workfloF size, structure, and the number of virtual machines (VMs) required. The algorithms in this study consist of four heuristics and one metaheuristic, specifically a genetic algorithm (GA). Regarding the factors, the most relevant in CC is the number of VMs, where increasing the number of VMs reduces makespan to a certain extent, following the behavior described by the law of diminishing returns. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.983
A New Robust Model for Heart Disease Detection on PCG signals using Entropygrams and a CNN
Every year, 17.9 million people die from heart-failure–related conditions, making it the leading cause of mortality worldwide; early diagnosis could prevent many deaths. The most common way to detect cardiac abnormalities is through medical auscultation. Accurate diagnosis often depends on clinicians’ auscultation skills; however, they frequently have to listen in noisy environments. We propose a method for the automatic diagnosis of cardiovascular disease that is robust to noise. We extract entropy spectrograms from phonocardiograms to reliably convert audio signals into images, and then use a two-dimensional convolutional neural network (2D-CNN) to classify patients as healthy or unhealthy. To evaluate the method, we added white noise to the original recordings. The results show that entropy spectrograms are more robust than conventional feature-extraction techniques such as energy spectrograms or mel-frequency spectrograms. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1031
An exploratory analysis of hazardous substances registered in the Mexican Pollutant Releases and Transfers Register in the State of Tamaulipas
The North American Free Trade Agreement required Mexico, the United States and Canada to measure pollutants released and transferred to the environment. In Mexico, the Ministry of Environment and Natural Resources publishes an annual report of pollutants released or transferred by public and private facilities across industrial sectors. Each facility must report substances listed in Official Mexican Standard NOM-165-SEMARNAT-2013. In this study, we used the SINAT tool to obtain records of pollutant releases by municipalities in the state of Tamaulipas. We preprocessed and analysed the substances released in Tamaulipas and mapped them to their corresponding groups based on monographs from the International Agency for Research on Cancer (IARC). These groupings were then used to identify toxic substances—such as benzene, arsenic and asbestos—classified as carcinogenic to humans (Group 1) in Tamaulipas. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.948
Binary Classification of Corrosion in Images through the LibAUC Library
Corrosion is a critical problem that damages metal surfaces in different environments, with a significant economic impact and safety risks. It mainly affects industrial installations and people's physical integrity, which is why it is very important to detect it. In the present work, corrosion classification was carried out using image analysis with a library named LibAUC. In search of state-of-the-art, this library has been used in other areas, but not for corrosion. The methodology consisted of the following: collection of images, image preprocessing, modification of the library code for compatibility with updated libraries, adaptation of the deep model learning of melanoma classification for corrosion classification, execution of the model with training and validation images. The metric used for the performance of the model was the AUC (Area Under ROC), which achieved a value of 0.9973. It is concluded that the LibAUC library has a high performance for binary corrosion classification. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1037
The Pyramid of Consciousness: A Conceptual Model for Artificial Consciousness and Human– Artificial Intelligence Hybrid Integration
This paper examines the continuum from natural to artificial consciousness, highlighting the biological basis of subjective experience. It introduces synthetic consciousness as a hybrid of neural and algorithmic systems and examines ethical, legal, and ontological implications of human–AI integration. The Pyramid of Consciousness framework guides reflection on autonomy, identity, and the shifting boundary between organic cognition and intelligent machines. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1184
Design and Control of a Low-Cost Ball and Beam Platform for Control System Education
The Ball & Beam system is one of the most comprehensive case studies in the field of control, as it is naturally unstable. This paper proposes a control approach for the Ball & Beam system composed of a PID controller and a Kalman filter, which are used to estimate system states and mitigate problems caused by the sensor’s high sensitivity to noise. The control is implemented using the Arduino software. Finally, the results of the simulation and implementation show that the control is effective despite the noisy signal, resulting in a low-cost prototype for educational purposes. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1140
Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis
We propose a hybrid approach for multilingual sentiment analysis that combines extractive and abstractive summarization to address the limitations of standalone methods. The model integrates TF-IDF-based extraction with a fine-tuned XLM-R abstractive module, enhanced through dynamic thresholding and cultural adaptation. Experiments across 10 languages demonstrate significant improvements over baselines, achieving an accuracy of 0.90 for English and 0.84 for low-resource languages. The approach also achieves 22% greater computational efficiency compared to traditional methods. Practical applications include real-time brand monitoring and cross-cultural discourse analysis. Future work will focus on optimizing performance for low-resource languages through 8-bit quantization. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1185
Fuzzy Model for Power Transformer Condition Monitoring and Fault Detection
Power transformers are equipment of great importance, and their availability is crucial for the security and continuity of the electricity supply for domestic and industrial users. During their life cycle, transformers are exposed to various environmental and operational conditions that affect their performance, especially when these exceed the operational design limits. This paper describes the use of Fuzzy Logic models as supporting tools for the automatic classification of power transformer operating conditions. The proposed methodology involves a binary classification (failure or no failure), followed by a multi-classification into seven types of failures. For this purpose, a power transformer fault database was developed, compiling information from operational data curated by power transformer experts. The results show a high predictive capacity for transformer fault conditions, with 96% balanced accuracy, and acceptable effectiveness in detecting different faults. This approach may serve as useful guidance in power transformer condition monitoring, helping engineers to reduce the time required to detect and repair incipient faults. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1160
A Hybrid Optimization Algorithm for Enhanced Web services with QoS measures in Cloud Computing
In the environment of Big Data analytics worldwide, cloud web services were deployed across Internet and Intranet domains. Moreover, cloud computing, while possessing significant advantages and experiencing rapid development, faces trust complexities, privacy concerns, and security issues. These challenges necessitate the implementation of Quality of Service (QoS) measures in optimisation techniques for web service selection. This study focuses on the selection of component services and the use of an efficient algorithm with end-to-end quality measures. However, data diversification and service characteristics may reduce the accuracy of these measures. To address this, a novel QoS-based web service selection algorithm was developed, incorporating both weighted and subjective attributes. The proposed methodology employs a hybrid optimisation algorithm that integrates randomised attribute searches with the Invasive Weed Optimisation (IWO) algorithm. Furthermore, it calculates QoS measures based on the weighted attributes of web services. Many researchers have applied nature-inspired concepts to deal with optimisation complexities in Big Data, including the Eagle Perching Algorithm to improve the efficiency of cloud web services. In addition, the evolution of the Bald Eagle Search (BES) algorithm has been utilised as a nature-inspired approach, providing an efficient technique for optimisation problems by imitating the behaviour of bald eagles. The results demonstrate that the proposed methodology achieves improved performance metrics when compared with existing approaches, confirming its effectiveness in the evaluation of web service optimisation. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.307
Model of a Low-Cost Educational Prototype of the Furuta Pendulum
Didactic control systems are tools that facilitate the understanding of topics related to control and stabilisation. However, due to the high cost of these devices, their implementation in schools is limited. This work presents the design, construction, and testing of a low-cost didactic prototype of a Furuta-type pendulum for the analysis of topics related to this system. The construction included parts designed in SolidWorks and produced with 3D printing, threaded rods, a precision linear potentiometer, a DC motor, a BTS7960 H-bridge, and an Arduino Nano. The functionality of the project was verified through the implementation of two digital tools: a Kalman filter to improve the measured signal and a PID controller to regulate the angular speed of the motor. It was demonstrated that the prototype can be stabilised under the operational considerations of PID control without the use of an angular position sensor. Moreover, the Kalman filter must be kept small in order to avoid information loss. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1133
Design and Implementation of a Low-Cost Thermal Chamber with PID Control and Kalman Filter State Estimation
An automatic temperature control system is an important application used in almost all modern gadgets, smart homes and industries; however, there are almost no available prototypes to practise with, and the ones that do exist cost more than an average student in Mexico can afford. This project is based on the implementation of a digital PID controller, realised through widely studied mathematical concepts, applied to a fan that blows air into an enclosed area housing an incandescent bulb, thereby controlling the internal temperature. A Kalman filter was used to reduce the thermocouple noise. The performance of the controller was tested by observing its ability to maintain the desired temperature setpoint steadily. This test highlighted the PID controller’s ability to reach and maintain the desired temperature, validating its effectiveness as both an educational tool and a method of control for industrial applications. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1134
Multiclass Classification of Neurological and Psychiatric Conditions Using Synthetic Neuroinformatics Biomarkers and EEG Band Simulation
This study presents a machine learning framework for multiclass classification of neurological and psychiatric disorders using synthetic neuroinformatics biomarkers and EEG spectral simulations. Synthetic data modeled cognitive-motor features (visual delay, motor gain, expectancy weight, memory capacity, EEG bands), while real EEG data from 50 PhysioNet recordings validated performance. An XGBoost model optimized through grid search (24 combinations, five-fold validation) achieved 97.8% accuracy and 0.978 F1-score. Results showed excellent classification of neurotypical, ADHD, ASD, dementia, depression, GAD, Parkinson’s, psychosis, and Tourette’s, demonstrating strong potential for neuropsychiatric diagnosis support. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.1228
Relationship of Climatological Variables With Confirmed and Suspected Cases of the Dengue Vector
Dengue is endemic in Mexico, with epidemic cycles occurring approximately every three to five years, associated with the introduction of new viral serotypes into susceptible populations. This study analysed the relationship between climatological variables and dengue incidence in Morelos, Mexico, during the period 1999–2009, aiming to identify favourable conditions to inform territorially focused prevention and control measures, using spatial analysis methods. The analysis of the influence of climate on dengue transmission in Morelos demonstrated the importance of precipitation in developing early warning systems for potential epidemics. It is recommended that territorially focused preventive strategies be implemented, such as the elimination of breeding sites, fumigation, and social mobilisation in areas at risk due to environmental, social, and epidemiological factors. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.416
On the prediction of the labor welfare by using Markov chain approach
In this contribution, the Markov chain approach is used to predict the job satisfaction of employees in a telecommunications company in Mexico. Under certain assumptions, the Chapman–Kolmogorov equation is applied to estimate the probabilities that: employees remain satisfied if they are satisfied; if they are not satisfied, the probability that they remain unsatisfied; if they are satisfied, the probability that they will become unsatisfied; and finally, if they are unsatisfied, the probability that they will become satisfied, within a one-year prediction horizon. Our proposal could be applied to support decision-making in human capital management strategies. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.959
On tractable subclasses of the class 2mu-3MON
The SAT problem is important in the theory of computational complexity. It has been deeply studied because solutions for fragments of SAT can be transformed into solutions for several CSPs, including problems in areas such as Artificial Intelligence and Operations Research. Although SAT is an NP-complete problem, it is known that SAT is fixed-parameter tractable if we take any hypertree width as a parameter. In this work, we present several hypergraphs and countable classes of hypergraphs. For these classes of hypergraphs, we analyze their generalized hypertree width to prove that all the CSPs modeled with those hypergraphs are tractable. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.586
Fine-Tuning Large Language Models and Machine Learning for Extracting Entities and Relationships in Spanish Medical Texts
In this paper, we delve into the techniques used to extract information from medical texts written in Spanish. Our study focuses on fine-tuning Large Language Models by training them on Spanish medical texts and adjusting various hyperparameters. We also explore traditional machine learning algorithms like support vector machines, decision trees, and nearest neighbours. Our analysis aims to evaluate the tool's ability to identify entities and relationships between them. Our results show that the support vector machine algorithm outperformed Large Language Models in entity identification, achieving a 78.72% F1 score compared to 68.04%. However, Large Language Models demonstrated superior performance in relationship identification, achieving a 57.15% F1 score compared to 35.5% for machine learning algorithms. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.575
A metaheuristic proposal using Java for the quadratic assignment problem
One of the classic combinatorial optimization problems belonging to the NP-hard class is the quadratic assignment problem. The interest in solving the problem lies in its high computational complexity, as well as its applications in: logistics, electronic circuits, gate assignment in airports, among others. In this work, the Greedy Random Adaptive Search Procedure metaheuristic was implemented to find its solutions. The main contribution of this work is the adaptation of a neighborhood structure contained in k-exchanges in the post-processing phase. The tests were performed for 29 large-scale instances whose dimensions range from 64 to 254 taken from the QAPLIB. The approximate solutions were found through a metaheuristic that bases its search on neighborhoods and local search algorithms. Java was the programming language used for the implementation of metaheuristics; its execution allowed balancing the parameters to obtain competitive results with respect to the values ​​known in the literature. The results reported and presented in this work achieved the proposed objectives. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.563
Detection of Cyberbullying Using Bernoulli Restricted Boltzmann Machine and Multilayer Perceptron Neural Networks
This paper introduces a model that detects harassment cases across different social media platforms using natural language processing and deep learning. Therefore, this model forms part of a system that can identify this problem in various texts. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.589
Four Supervised Models for Identifying Suicide Indicators in Text Data
The increasing prevalence of mental health disorders like depression and anxiety often leads to suicide, with individuals frequently expressing such thoughts on social media. Utilizing machine learning techniques to analyze social media texts would work towards preventing these outcomes, even though predicting suicide risk remains a challenge. In this study machine learning classifiers were developed aiming to detect suicide indicators using the Kaggle Suicide and Depression Detection dataset (Komati, N. Suicide Watch). Four models—Multinomial Naive Bayes, Gradient Boost Machine (GBM), Random Forest and Support Vector Machines—were tested, yielding promising results: Among the four models presented here SVM with a 0.95 Precision and 0.94 F1 score showed the best results. Keywords: Suicide, Supervised Learning, Machine Learning, Naïve Bayes, Gradient Boosting Machine, Random Forest, Support Vector Machines. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.582
Measuring the Performance of Large Language Models with Hyperparameters Calibration and Machine Learning Approaches for Sentiment Analysis in Spanish Texts
Social networks have become an important source of information in recent years, offering a platform for visualizing people's opinions on various industries and research topics. These opinions may be expressed in Spanish, providing fresh and updated insights that can be analyzed using computational approaches. This research aims to understand the sentiment conveyed in ideas or opinions within Spanish text. As a result, various methods are evaluated to identify the most effective approach. Computational techniques, especially Natural Language Processing (NLP), have become essential for automating this analysis. While traditional machine learning algorithms have been employed for sentiment analysis, the rise of large language models since 2017 has introduced significant challenges in assessing their impact on various NLP tasks. This paper presents a comparison between machine learning approaches and calibrated large language models for sentiment analysis in Spanish texts, measuring their performance. The calibration process consists of two stages: a coarse calibration using exploratory methods and a fine calibration that involves an algorithm for searching hyperparameter values. The evaluation process showed that the most effective machine learning approach combines unigrams and bigrams with the Bayes algorithm, along with exploratory parameter tuning and feature selection, achieving an accuracy of 72.72% and an F1 score of 72.81%. Furthermore, by applying LoRA, a technique that optimizes the fine-tuning of pre-trained models, it was found a best model that we call and store as twitter-xlm-roberta-base/SentUAM, which reached an accuracy of 71.99% and an F1 score of 71.78% in the sentiment analysis of Spanish texts. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.574