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).
Machine Learning Approach to Multiclass Sentiment Analysis in Dravidian Language Code-Mixed Text
In this study, we focused on sentiment analysis of code-mixed text in Dravidian languages, specifically Tamil-English and Tulu-English, using advanced natural language processing (NLP) techniques. We address the challenges posed by linguistic diversity, the unique nature of code-mixing, and the limited availability of annotated datasets with the application of the Logistic Regression machine learning model enhanced by feature engineering. Our preprocessing approach involves normalizing input text, handling class imbalances, and applying sub word tokenization to accommodate the agglutinative and mixed nature of Dravidian languages. The model was trained for a multi-class sentiment classification task, distinguishing between the classes. Our experiment achieved an F1 score of 0.4409 on Tamil-English and 0.4509 on Tulu-English, this demonstrates the model's ability and limit to process Dravidian scripts and capture sentiments in code-mixed data on a multiclass dataset. Our methodology approach can be extended and improved to tackle other low-resource language challenges in future work. The code and dataset used are available at https://github.com/ABITCONSULT/Sentiment-Analysis-in-Dravidian-Language Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1280 Dimensions. Open Alex.
Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
Hope Speech Detection: A Comparative Study across Four Languages
This paper presents a multilingual approach to hope speech detection using both traditional machine learning (Ridge Classifier with TF-IDF) and transformer-based models (LaBSE). We evaluate performance across four languages English, Spanish, Urdu, and German using the PolyHope dataset. Experimental results demonstrate that LaBSE consistently outperforms the Ridge baseline, particularly in low-resource settings like Urdu. Our findings highlight the effectiveness of multilingual transformers in capturing nuanced expressions of hope across diverse linguistic and cultural contexts. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1279 Dimensions. Open Alex.
Artificial Intelligence for Sustainable Ocean Management Using Satellite Data
The integration of artificial intelligence and satellite remote sensing provides an innovative approach to sustainable ocean management. This study demonstrates how oceanographic sensors and AI‑driven predictive models enhance the monitoring and governance of Marine Protected Areas (MPAs) and sustainable fishing zones. Multivariate datasets are used to map areas of high primary productivity in the Gulf of Mexico, employing QGIS and ArcGIS for spatial analysis. Long Short‑Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks trained on historical time series forecast ecological risks, including hypoxic zones and harmful algal blooms. Unsupervised clustering and dimensionality reduction identify anomalies relative to natural oceanographic patterns, supporting more adaptive and precautionary ocean governance. The integration of artificial intelligence and satellite remote sensing provides an innovative approach to sustainable ocean management. This study demonstrates how oceanographic sensors and AI‑driven predictive models enhance the monitoring and governance of Marine Protected Areas (MPAs) and sustainable fishing zones. Multivariate datasets are used to map areas of high primary productivity in the Gulf of Mexico, employing QGIS and ArcGIS for spatial analysis. Long Short‑Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks trained on historical time series forecast ecological risks, including hypoxic zones and harmful algal blooms. Unsupervised clustering and dimensionality reduction identify anomalies relative to natural oceanographic patterns, supporting more adaptive and precautionary ocean governance. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1291 Dimensions. Open Alex.
Robust Webcam-Based Gaze Tracking Using Double-Eye Convolutional Networks with Kalman Filtering and Clustered Calibration
Webcam-based gaze tracking has emerged as a cost-effective alternative to infrared eye-tracking systems; however, its robustness under real-world conditions remains limited, especially when monocular (single-eye) models are employed. This study offers a systematic comparison between single-eye (SET) and double-eye (DET) convolutional neural network architectures for appearance-based gaze estimation utilizing standard webcams. A lightweight CNN processes normalized eye-region images to predict gaze coordinates on the screen and is evaluated through two complementary protocols: (i) a static 9-point calibration and (ii) a dynamic trajectory-following task (“blue-ball”) that traverses screen edges and corners. To enhance generalization, the dataset incorporates controlled data augmentation—pose variation, illumination changes, blur/noise, and partial occlusions—and employs a participant-stratified experimental design. Robustness and temporal consistency are further improved by integrating a Kalman filter for trajectory smoothing and a DBSCAN-based clustering calibration stage that suppresses outliers and stabilizes gaze estimates. Performance metrics include pixel error, angular error, and trajectory stability. Under identical training and evaluation conditions, the DET model consistently surpasses the SET model, attaining lower spatial error and smoother trajectories, particularly under asymmetric lighting and partial occlusions. Utilizing only a consumer-grade webcam and a lightweight CNN, the proposed methodology delivers real-time performance without infrared sensors or proprietary licenses, thereby providing an accessible, reproducible, and robust solution for gaze estimation in human–computer interaction applications. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1304 Dimensions. Open Alex.
Pretrained Generative Transformer (PGT) for the estimation of the response variable using causal relationships with fast convergence
The transformer architecture has been used very successfully in translating text into various languages. Additionally, transformer architecture has been successfully adapted for use in vision systems and, more recently, for estimating remaining useful life across various industrial fields. The training patterns of an artificial neural network have no relationship among the input variables. They are used to estimate response variables using a training algorithm, such as backpropagation or its variants. In the present research, an accurate estimation transformer structure is proposed to develop causal relationships between input variables and their respective training patterns and identify the importance and relevance of causal input-output relationships. The complete encoder-decoder structure is used. Input training patterns are entered into the encoder and decoder. In this way, the input data is processed by three self-attention blocks: one in the encoder and two in the decoder, allowing the model to capture dependencies between them and strengthening the base of training patterns. The system's output represents the required estimate. One of the main research contributions is to add two linear transformations at the end of the structure to identify critical and relevant input variables and training patterns. The results of a transformer structure using variable dependencies achieve fast convergence and excellent accuracy than those of a multilayer neural network trained on patterns that are unrelated to each other. Multiple simulations were carried out, and the proposed structure achieved 100% accuracy in estimates in only two epochs using the Levenberg-Marquardt algorithm, unlike the multilayer network. The architecture shown can be used across any production sector to estimate and identify the most significant variables. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1271 Dimensions. Open Alex.
Hybrid Combination of the Dragonfly and Firefly Algorithms with Parameter Adaptation using Type-2 Fuzzy Logic and its Comparison with Cuckoo Search
This paper explores a hybrid approach combining the Dragonfly Algorithm (DA) and Firefly Algorithm (FA) to balance exploration and exploitation, avoiding local optima and refining solutions in promising areas. The hybrid achieved higher-quality solutions, faster convergence to optimal results, and adaptability to diverse optimization problems through complementary strategies. Additionally, the Cuckoo Search Algorithm (CS), known for its effectiveness in global optimization via random search and solution space exploitation, was integrated. To further enhance performance, Type-2 Fuzzy Logic was applied for parameter adaptation in the algorithms. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.872 Dimensions. Open Alex.
Development of a Genetic Algorithm for the Formation of Sports Teams
This project addresses a significant challenge in the formation of sports teams within the university environment of the Reynosa Rodhe Multidisciplinary Academic Unit (UAMRR) of the Autonomous University of Tamaulipas (UAT). The project identifies the need to establish balanced and competitive teams that reflect the diversity of skills in sports activities. The project proposes the development of a genetic algorithm aimed at the automatic generation of optimal sports teams using techniques based on natural evolutionary processes, such as selection, crossing, and mutation. This algorithm is integrated into a web application with a database that stores detailed information about the students. It is developed in an intuitive design to ensure an optimal user experience and facilitate application navigation. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1192 Dimensions. Open Alex.
A New Hybrid Algorithm Proposed for the Analysis and Classification of Signals Obtained from Head Movements
In this paper, we present a new algorithm that combines Hu invariant moments, data preprocessing techniques, and fuzzy logic to analyze and classify signals obtained from the macro and micro head movements that a person makes. Firstly, we project different videos to elicit the disgust emotion in various participants. Following the stimulation, we recorded all the participants' reactions on video. The next step is to apply Hu moments to transform the different movements that the participant made to create a signal. This process yields polynomial regression and applies normalization methods to standardize the size and numerical scale of all signals. We implement a fuzzy inference system to classify the signals. The results presented by this algorithm show high performance in sorting movements to detect disgust emotion. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1120 Dimensions. Open Alex.
The Web platform for diabetes prediction using weighted machine learning techniques based on personal and clinical indicators
Diabetes is a chronic metabolic disease characterized by elevated levels of glucose in the blood (or blood sugar), which over time leads to severe damage to the heart, blood vessels, eyes, kidneys, and nerves. The most common type is type 2 diabetes, usually in adults, which occurs when the body becomes resistant to insulin or does not produce enough insulin. By using artificial intelligence (AI) techniques in complex problems such as disease diagnosis, a degree of certainty in the results has been achieved to identify a specific type of disease. These applications have been advantageous because large amounts of patient data can be analyzed to find patterns. This work proposes a platform for the prediction of type 2 diabetes based on clinical or personal indicators. To do this, two supervised classification models were constructed using the PIMA Indian Diabetes dataset and the Centers for Disease Control and Prevention (CDC) dataset, integrating both into a web platform for prediction with new data to support the decisions of doctors and healthcare professionals. By integrating different algorithms into the final predictive model through voting weighting, the accuracy percentage in prediction has been increased. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1190 Dimensions. Open Alex.
Prediction of Co, Co2, and Particulate Matter Using Random Forest: Implementation of a Smart Monitoring Prototype in Nuevo Leon, Mexico
This study introduces a predictive air quality monitoring system based on Random Forest machine learning models and low-cost embedded sensors. The system was designed and implemented in Guadalupe, Nuevo León, Mexico, to monitor carbon monoxide (CO), car-bon dioxide (CO2), and particulate matter (PM). Real-time data was collected using a Particle Photon 2 microcontroller with four different sensors. The data was processed using Python scripts, and the Random Forest model was trained to predict future pollutant values. Results demonstrated strong model performance, validated through statistical evaluation metrics and graphical comparisons. The proposed system shows promise for deployment in smart urban environments. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1234 Dimensions. Open Alex.
Comparative Analysis of Bitcoin Price Prediction Models: A Systematic literature review
This paper presents a systematic literature review of machine learning models used for predicting Bitcoin prices. We identified and analyzed 26 different machine learning models notable for their effectiveness in predictive tasks. Out of these, 6 models are discussed in detail, focusing on their advantages, disadvantages, and potential hybrid approaches. Additionally, we collected 21 evaluation metrics, identifying 8 as the most relevant for cryptocurrency price prediction. In terms of datasets, we found a reliance on public sources such as Kaggle and Yahoo Finance; however, challenges related to the inconsistency in data availability remain. Lastly, we noted a lack of standardized procedures for comparing models, highlighting the need for the development of systematic methodologies to standardize evaluations in this field. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1166 Dimensions. Open Alex.
Content Web Services Deliverability: A Systematic Review
Content Web Services are Web services whose main function is to deliver files or content. However, deliverability is difficult to measure if methods do not exist or if the attributes involved, such as those related to clients and servers, are not identified. To find the attributes involved, a systematic review was carried out with studies from 2015 to 2024 using the PRISMA method. In the first step, there were 2,660 studies from four digital libraries, after five inclusion and eight exclusion criteria, 79 studies were selected. Most of the works were found to be dealing with improving service architectures or computer networking. The 79 studies were fully read, four studies were finally selected, and only one deals with deliverability. There are platforms and attributes that influence content delivery; however, studies considering Web services for content delivery are very scarce. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1168 Dimensions. Open Alex.
A Comparative Study of BERT-Based Models for Sarcasm Detection in Social Media Texts
Social media has transformed communication, facilitating the rapid exchange of emotions and ideas between users. This shift has created the necessity for the development of sentiment analysis tools, enabling businesses to gain insights into audience reactions. However, detecting sentiment remains a challenging task due to the presence of informal language, abbreviations, and, notably, sarcasm, which can modify the intended message. Sarcasm, often conveyed through ironic or contrary statements, is particularly difficult to identify in text, as it lacks the non-verbal cues typically present in face-to-face communication. Recent advancements in deep learning, particularly the emergence of BERT (Bidirectional Encoder Representations from Transformers) based models, have significantly enhanced sarcasm detection by capturing nuanced contextual meanings of words. This paper compares several BERT-based models—BERT, RoBERTa, ALBERT, DistilBERT, and DeBERTa—to assess their effectiveness in sarcasm detection on iSarcasmEval and Sarcasm Corpus V2 datasets. Key performance metrics, including accuracy, computational efficiency, and the ability to capture complex contextual relationships, are analyzed to identify the most suitable model for sarcasm detection tasks. DeBERTa achieved the best performance on both datasets in this challenging task. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.853 Dimensions. Open Alex.
An Explainable Artificial Immune System (XAIS) for Classification in Biomedical Classification Tasks
Biomedical datasets often contain noise, missing values, imbalance, and heterogeneous feature structures, making them difficult to model reliably and complicating the extraction of discriminative patterns required for effective classification. Although modern machine-learning models can achieve strong performance on such data, many of these approaches operate as opaque systems, offering little insight into how decisions are produced—an essential requirement in biomedical applications. This work introduces the Explainable Artificial Immune System (XAIS), an immune-inspired classification model that delivers prototype-based explanations derived from similarity-driven antibody responses and complemented by performance-aware indicators, providing users with direct evidential insight into each decision. XAIS was evaluated on eight publicly available biomedical datasets using stratified 5-fold cross-validation and compared against standard machine-learning classifiers. The results show that XAIS attains competitive predictive performance while offering structured, instance-level evidential explanations, underscoring its potential as a transparent and trustworthy foundation for biomedical decision-support systems. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1278 Dimensions. Open Alex.
Quality and Randomness Assessment of Images Generated by a Convolutional Autoencoder
This paper presents an analysis of the quality and randomness of information in images generated with a convolutional autoencoder (CAE). The CAE convolved altered color images from CIFAR-10 dataset. The CIFAR-10 images were altered by randomly setting 30%, 60%, and 90% of pixel values to [0, 0, 0] or [255, 255, 255], respectively. In the validation stage, Mean Square Error (MSE) loss function reached 0.0115 and the accuracy metric 0.7461. Similarity Structural Index Measure (SSIM), Pearson Correlation Coefficient (PCC) and Peak Signal-to-Noise Ratio (PSNR) metrics, assessed quality of generated images. The assessment results ranged as follows: SSIM [0.3251, 0.6830], PCC [0.5034, 0.9358], and PSNR [18.63 dB, 26.04 dB]. The Shannon metric assessed randomness both locally and globally for each image, ranging from 1.25 to 2.49 bits, and from 6.61 to 9.71 bits, respectively. CAE implementations highlight their potential for applications in technological innovation. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.952 Dimensions. Open Alex.
Video-based detection of the parasite responsible for causing Chagas disease
This study introduces a technique for identifying the presence of the parasite Trypanosoma cruzi in video recordings, the primary cause of Chagas disease. Early diagnosis—especially during the acute phase—is vital to prevent serious complications. The primary contribution of this work is a computational algorithm capable of detecting the presence of Chagas parasites in capillary tubes containing blood samples. While prior algorithms relied on stained blood samples, this study employs unstained blood samples, utilizing the motility of living parasites for detection. The proposed approach combines optical flow estimation using Farnebäck’s algorithm with a classification stage where several machine learning models were evaluated. Among these, the best performance in terms of accuracy was achieved by a convolutional neural network based on the ResNet-18 architecture. The dataset consists of 24 videos totaling 32 minutes of recording. The results demonstrate optimal performance, achieving a F1-score of 0.9383. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1139 Dimensions. Open Alex.
Design and Evaluation of a Third-Order Regression Model for Estimating Glucose Levels from ECG Signal Features
Diabetes mellitus, also known simply as diabetes, is a chronic disease that affects the absorption of glucose from food, increasing levels in the blood. This disease mainly affects adults (type 2 diabetes) but can also occur in children (type 1 or childhood diabetes), as well as in pregnant women (gestational diabetes). Diabetes is one of the main diseases with the highest incidence and high mortality rate worldwide. Diabetes is a disease for which there is no cure; however, continuous monitoring and control of blood glucose levels reduce the risk of associated complications, such as gastrointestinal issues, vision loss, limb amputations (e.g., diabetic foot), and damage to vital organs such as the heart and kidneys. This article presents the results of the statistical and clinical evaluation for the estimation of blood glucose levels based on features derived from the ECG signal. The primary features used as estimating variables were heart rate, heart rate variability, amplitudes of the R, S, T waves, and the QT interval. For the electrocardiogram recording, lead VII was used. The ANOVA analysis showed a result of 0. 0.7236.The clinical validation using the Clarke error grid showed a validation data percentage of 58.54% in zone A, 39.04% in zone B, 1.21% in zone C, and 1.21% in zone D, with a correlation of 0. 8525. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1110 Dimensions. Open Alex.
Quantitative Analysis of Consumer Perception on E-commerce Platforms: The Modulating Role of Age and Perceived Ethnography
This study assesses the predictive capability of 27 Customer Experience (CX) factors on Age and the influence of Skin Tone (as an ethnographic proxy) on the valuation of these factors in e-commerce. A cross-sectional quantitative design was utilized with a sample of N=1425 experienced online consumers. The methodology employed Analysis of Variance (ANOVA) (Fisher, 1925) to validate sample independence by Age, Chi-square Test (χ2) (Snedecor & Cochran, 1989) to assess ethnographic dependence, and Multiple Linear Regression (MLR) (Cohen et al., 2003) to model Age prediction. MLR results indicated that the Age prediction model was significant (R2 Adjusted=0.154), confirming that User Experience (UX) and Live Chat Availability are inverse and direct predictors of Age, respectively. Ethnographic analysis using segregated heatmaps revealed a focalized and significant disparity based on Skin Tone in the Trust/Logistics and Brand/Support blocks. A trend of lower valuation was observed in darker skin tones (Tone 6) for factors like Price Transparency and Assisted Support (Live Chat Availability), while a higher demand was noted for Review and Rating Availability. It is concluded that CX personalization must transcend basic demographics, focusing on relational equity and risk mitigation for ethnographically vulnerable segments. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1275 Dimensions. Open Alex.
Conceptual synthesis of mechanisms based on qualitative reasoning
This paper presents an innovative method for qualitative reasoning in the conceptual synthesis of mechanisms, leveraging AI-derived knowledge-based principles. The methodology allows for discretizing a mechanism's overall behavior without computational implementation. Relative motion between components, represented as qualitative states, is captured in a qualitative motion vector. These vectors form a general movement matrix that characterizes the mechanism's behavior, providing insights into component functions, movements, and transitions. By using ratchets as restriction functions, underlying behaviors are isolated from the matrix. These behaviors are used to generate conceptual designs for new mechanisms fulfilling specific kinematic functions. A case study is presented: synthesizing a mechanism that converts oscillatory rotation into unidirectional rotation using a differential gear train. The qualitative behavior of the resulting design is visualized in a vector diagram and compared with a CAD simulation. This method provides a knowledge base for training AI models in conceptual synthesis without needing specialized software. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1208 Dimensions. Open Alex.
A Heuristic computation using hybrid PMP-Greedy and AHP model for categorization and measurement of a framework aligned to ISO/IEC 90003:2014
The ISO/IEC 90003:2014 standard oriented to the application of quality management in software development processes, requires a structured evaluation that considers technical and organizational criteria. Its purpose is to support organizations that develop or maintain software to implement an effective quality management system using the structure and requirements of ISO 9001:2008, specifically adapted to software-related activities. This article proposes a hybrid heuristic computational model based on the PMP-Greedy (Positional Major Mean Weight) and AHP (Analytic Hierarchy Process) methods to assign weights to the categories that make up a framework aligned to ISO/IEC 90003:2014 that allows companies engaged in software development to measure the degree of compliance with the standard, as well as, to assess conformance and non-conformance to achieve certification to the standard. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1155 Dimensions. Open Alex.
Evaluation of Machine Learning Algorithms for Estimating NDVI in Time Series of Multispectral Images for Precision Agriculture
Accurate estimation of the Normalized Difference Vegetation Index (NDVI) is crucial for precision agriculture and environmental monitoring. This study compares five machine learning algorithms LSTM, CNN-LSTM, XGBoost, Random Forest, and Gradient Boosting to predict NDVI using time series of multispectral Sentinel-2 data in a maize crop in Guasave, Sinaloa, Mexico. After data preprocessing, the models were evaluated using cross-validation and metrics such as MSE, MAE, RMSE, MAPE, and R2. The results showed that the LSTM model achieved the best performance in accuracy, while tree-based models struggled to predict extreme values. Recurrent neural networks demonstrated a greater capacity to capture complex temporal dependencies, although they still require improvements to optimize their robustness and precision. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.597 Dimensions. Open Alex.
Ranking of ICT providers for an HEI through TOPSIS method: a guide to Decision Making
This document is focused on guiding Decision Makers (DM) regarding the Information and Communications Technology (ICT) infrastructure that should be selected for proper administrative and academic operation within Higher Education Institutions (HEI). These technologies include the Internet, hardware infrastructure, institutional email, and educational platforms. This selection problem arises due to the diversity of providers offering ICT services in terms of speed, storage, number of users, and costs for each of the technologies. This decision involves a thorough evaluation of the different selection alternatives that converge in a real life scenario increasingly influenced by the use and application of technologies. The TOPSIS method is applied due to its ease of use in evaluating the various characteristics of each of the technologies and because it offers an ordering based on the values of proximity to an ideal solution, thus offering a ranking as a guide to support the decision of the DM according to his preferences. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1164 Dimensions. Open Alex.
An Overview of Artificial Intelligence, Ethic and Neuromodulation
Based on advances in computing and devices as an extension of the human body, new technologies focused on neuromodulation and with the help of artificial intelligence, can generate lucid dreams and even improve concentration. With noise cancellation through devices such as smart hearing aids or with the generation of devices that detect and modify brain waves, the human body has been empowered towards mental conditions and, in a sense, towards improving the quality of life through sleep. of life through sleep. However, we would be at a point where the ethics and philosophy of technology ethics and the philosophy of technology converge to identify the use of such devices to improve human devices to improve human conditions without overstepping the legal and ethical realm in their use. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1135 Dimensions. Open Alex.
Copyright (c) 2025 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
Generative AI and the scientific landscape: a bibliometric exploration of its global impact
The present comparative bibliometric study (2020-2025) of the Scopus and WoS databases on Generative Artificial Intelligence (GenAI) reveals accelerated growth, concentrating more than 95% of the production and reaching its peak impact in 2025. Thematically, the intersection of communication and technology/education dominates. Geographically, the United States leads production, but Asia-Pacific institutions (Hong Kong) are key. The field of GenAI is a massive trend driven by concentrated collaboration between North America and Asia. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1281 Dimensions. Open Alex.
Systematic Literature Review on the Use of Cluster Analysis in Software Design Activities
Cluster analysis is an unsupervised machine learning approach that groups data into homogeneous categories without the need for predefined labels. Although it was not originally developed for software engineering, this technique has increasingly been applied to support various activities in the software design phase. However, information about its use remains scattered across different studies. To address this gap, this work presents a systematic literature review synthesizing the state of the art on the application of cluster analysis in software design. Following a rigorous selection process, 14 primary studies published between 2019 and 2025 were identified from four digital libraries: IEEE Xplore, ACM Digital Library, Springer Link, and ScienceDirect. This review highlights the contexts in which clustering has been applied, emphasizing its predominant role in class decomposition tasks and the frequent adoption of the K-means algorithm, while also documenting the algorithms and tools used during design activities. Furthermore, the analysis discusses the benefits and challenges of adopting cluster analysis in this stage of development. The findings provide software engineering researchers and practitioners with a consolidated overview of the role of cluster analysis in software design, offering insights into its potential, limitations, and directions for future research and practice. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1262 Dimensions. Open Alex.
Evaluating the Impact of Domain Adaptation on Transformer-based Models for Low-Resource Purépecha-Spanish Translation
This work evaluates how domain adaptation affects Transformer-based neural machine translation (NMT) for the low-resource Purépecha–Spanish pair. Building on a system fine-tuned on a verse-aligned Bible corpus, we introduce an out-of-domain grammar-book dataset (1,626 sentence pairs: 1,297 used for adaptation, 329 held out for testing) to quantify (A) zero-shot transfer (Bible→G-test) versus (B) adaptation (Bible+G-train→G-test). Using BLEU and ROUGE, zero-shot performance is weak for Marian (BLEU=0.2272) and mBART-50 (BLEU=1.9992), revealing substantial domain mismatch. After adaptation, scores rise sharply: Marian reaches BLEU=21.2699, mBART-50 achieves BLEU=28.8776, with parallel gains in ROUGE (e.g., mBART-50 ROUGE-L=0.5791). Qualitatively, adaptation reduces repetitive/degenerate outputs and improves handling of metalinguistic terminology and everyday constructions. These results show that multilingual pretrained Transformers + lightweight in-domain data provide strong improvements for low-resource NMT under domain shift and highlight the value of diverse domains and speaker-informed evaluation. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1265 Dimensions. Open Alex.
Fine tuning Transformers models for converting handwritten scientific texts into LaTeX format
This study introduces a methodology for optical character recognition (OCR) that leverages transformer-based architectures to enhance the detection and recognition of textual content within images. The approach integrates state-of-the-art models, employing DETR (Detection Transformer) for the generation of bounding boxes corresponding to candidate text sequences, and TrOCR for transcribing the text contained within these regions. Both models were fine-tuned on a proprietary dataset comprising handwritten and digitized notes from mathematics-related subjects, including differential equations, calculus, linear algebra, programming, etc. The dataset predominantly consists of mathematical expressions represented in LaTeX format, thereby allowing the proposed method to effectively address the recognition of complex symbolic content in mathematical texts. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1266 Dimensions. Open Alex.
Evaluation of GRU with Attention and DistilBERT in Text Classification Tasks
In this paper compares two deep learning models for multiclass text classification in the cyberbullying domain. The corpus is written in English and includes six classes representing different types of harassment. The trained models are a GRU network with an attention mechanism and the DistilBERT transformer. The aim applies to a 5-fold cross-validation scheme. And uses accuracy, precision, recall, and F1 as evaluation metrics. The GRU model achieves significantly higher performance (accuracy = 0.83, F1 = 0.45) than DistilBERT (accuracy = 0.018, F1 = 0.004). The results obtained indicate that the GRU architecture with word embedding performs more effectively on this task. This is due to the dataset’s linguistic characteristics, as well as the limited fine-tuning capacity of the lightweight transformer. The findings emphasize the need to select model architectures that align with the corpus properties and the classification task’s complexity. Future work will involve fine-tuning strategies and evaluating additional transformer-based models. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1268 Dimensions. Open Alex.
Incremental Development of a Multilayer Neural Network for CAN bus Attack Detection
The growing integration of electronic systems in vehicles has transformed the automotive industry, enabling improved performance, reduced costs, and enhanced functionality. However, the increased complexity of vehicular networks and the lack of built-in cybersecurity mechanisms in communication protocols such as the Controller Area Network (CAN) bus have left modern vehicles vulnerable to cyberattacks. This paper presents a comprehensive investigation on intrusion detection in the CAN bus using multilayer neural networks (MLPs). A process is presented to find a neural network capable of detecting three types of attacks. This consisted of a series of experiments to find the minimal structure of a neural network that can detect malicious traffic on the CAN bus, allowing the identification of attack-free data and data with three types of attacks, denial-of-service (DoS), impersonation, and fuzzy attacks. Additionally, this work contextualizes CAN security issues, discusses related contributions, and presents future research opportunities. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1263 Dimensions. Open Alex.
OWL Consistency Models in Smart Contracts Design
Smart Contracts are stored and executed on a Blockchain network, thereby automatically enforcing the predefined rules once the execution conditions are satisfied. Hence, if the contract incorporates contradictory design rules, it may result in unforeseen outcomes within the blockchain environment. Accordingly, this proposal models the rules embedded in a Smart Contract through the Web Ontology Language (OWL), by applying the formal definition of consistency within a verification framework grounded in Description Logics. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1258 Dimensions. Open Alex.
An improved CNN-based methodology to prevent risky situations in people by recognizing emotions from facial features
This proposal shows a methodology designed for the process of detecting emotions through facial features. The process of facial features detection involves several stages among which the most important ones are: Acquisition of the training set of images, detection and segmentation of the face using face location techniques in images using Viola & Jones algorithm. We also make use of neural networks that are part of Deep Learning techniques. In this way we propose to recognize people’s faces and also perceive their emotions through gestures that are captured by means of a camera, allowing us to obtain these in soft real time. The processes of this methodology were developed and programmed in the MATLAB and Python code. There was a significant improvement in the recognition results throughout the CNN. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1261 Dimensions. Open Alex.
A Low-Cost IoT Wearable Device with XGBoost, CNN and SVM for Early Detection of Fever, Tachycardia and Hypoxia
Chronic and cardiovascular diseases present a significant global health threat, underscoring the need for remote monitoring technologies capable of ensuring continuous and accessible care. Vital signs such as body temperature, heart rate, and blood oxygen saturation are critical indicators for early detection of health alterations. This study proposes the design of a low-cost wearable device with non-invasive sensors for real-time acquisition and processing of these variables, integrating machine learning algorithms including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and XGBoost. A dataset of 2,480 samples (2,130 experimental, 350 public) was used for training and validation. The models achieved high predictive performance, with XGBoost obtaining an R² of 0.9765, accuracy of 95.8%, and F1-score of 0.96, surpassing SVM and CNN. These results highlight the potential of combining affordable wearable devices with advanced ML to enable early detection, preventive monitoring, and scalable healthcare solutions.
Optimizing Brain Signal Classification through Computational Neuroscience with EMOTIV and Free Software
This study presents a method for the acquisition, preprocessing, and classification of brain signals aimed at brain mapping and the development of a brain-computer interface (BCI) using non-invasive electroencephalography (EEG). The proposed system is based on the hypothesis that imagined motor actions can be translated into control commands to displace a robotic prototype in four basic directions: right, left, push, and pull. For this purpose, raw biosignals were collected with EMOTIV headsets (EPOC X and Insight), exported in .csv and .edf formats, and subjected to a preprocessing pipeline including artifact removal, digital filtering (1–40 Hz), and statistical outlier detection to ensure data quality. The signals were then analyzed with different classification methods (J48 Decision Tree, RandomForest, NaiveBayes, and Support Vector Machines) to identify the most suitable model for BCI-based neurocontrol. Results show that RandomForest achieved the highest accuracy, reaching 100% with external test data, followed closely by J48 with 99.01%, while NaiveBayes and SVM demonstrated limited reliability under cross-validation. Finally, the methodology was validated through the neurocontrol of a robotic prototype using Arduino hardware, where participants successfully executed directional commands via mental activity. These findings demonstrate the feasibility of integrating consumer-grade EEG devices, free software, and machine learning techniques to develop robust and accessible BCI systems for assistive robotics. The contribution of this work lies in optimizing mental command recognition and highlighting the potential of EEG-based neurocontrol technologies to enhance mobility and autonomy in individuals with motor disabilities. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1264 Dimensions. Open Alex.
The effect of inflation and the economy during the Pandemic years: A Methodological Proposal for Sentiment Analysis in Python
In this work, NLP Natural Language Processing has been used, specifically sentiment analysis for the problem "The effect of inflation and the economy during the pandemic years" using as a database a tweet written between the dates 01 - 01-2020 to 10-11-2022. The objective is to classify tweets as positive, negative and neutral and establish the impact of some factors related to the economy after the Covid-19 pandemic. A prediction was made based on historical data, on how inflation could be modified in the period 2025-2028. This analysis provides a method to track public inflation expectations by tracking opinions from rich network data. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1257 Dimensions. Open Alex.
A Comparative Study of Fusion Methods for Firearm Detection
In a previous study, we compared the performance of two well-known models, YOLOv8 and RT-DETR, for firearm detection. The results showed that YOLOv8 achieved superior performance, while RT-DETR also produced significant results. These findings suggested the potential to further improve detection by exploring result fusion methods. Unlike the original comparative analysis, this work investigates how integrating the outputs of multiple detectors can enhance both accuracy and robustness in firearm identification. The existing literature on result fusion, as explicitly applied to this field is scarce, leaving a promising line of research open. In this context, strategies such as averaging results, selecting the best detector, and time-weighted as well as real-time weighted methods are analyzed. The objective is to demonstrate that result fusion represents an effective method for enhancing the performance of object detection systems in complex scenarios. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1269 Dimensions. Open Alex.
Comparative Analysis of Tail Tip and Hip Based Features for Dog Tail Wag Classification with Depth Cameras and Support Vector Machines
Understanding dogs’ emotional patterns through their tail movement is key to strengthening the human-canine bond and improving their well-being. This study presents an innovative approach to identify the direction of tail movement (either left or right), using the dog’s hip as the primary reference point. Through a preprocessing and feature extraction process, a Support Vector Machine (SVM) classifier was trained with spatial data from the hip and compared to a classifier from a previous study. The results indicate that the classifier trained with hip data achieved a 99% score in accuracy, precision, and F1-score metrics. Additionally, the Friedman test was performed to verify whether there are statistically significant differences between the two classifiers. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1252 Dimensions. Open Alex.
Intelligent Automatic Feeder for Pregnant Sows
Pork consumption has increased considerably in recent years, making pig reproduction of vital importance. Feeding sows during the gestational period involves several factors that impact their health, well-being, and postpartum outcomes. Automated feeding systems for sows, unlike feed delivery by human feeders, can measure and adjust the quantity and quality of feed provided at scheduled times with diets established by human experts, allowing for the establishment of a nutritional program for each stage of pregnancy. They also allow for the calculation, using probabilistic and statistical tools, of future diets for the gestational period, which promotes the health and well-being of the sow. This research paper describes, and presents the results obtained from experiments carried out with intelligent automatic feeders for pregnant sows, implemented with Internet of Things (IoT) devices and operated with a predictive computing algorithm, which uses decision trees to predict feeding for the gestation stages of sows, using the weight of the sows, as well as the amount and type of feed provided to the sow. The experiments were carried out with a typical feeding process performed by a human operator, against a feeding process provided with automatic feeders to two different sets of pregnant sows. The results obtained with the automatic feeders show weights closer to the standard weights established for the gestational stages of the sows. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1260 Dimensions. Open Alex.
Conversational agent with integrated tracking based on generative AI considering the effect of document order and structure
This article describes the development of a conversational agent for managing and tracking academic procedures. Based on requirements analysis, the essential components were defined to provide real-time updates on processes and improve the user experience. The development phases included dialogue flow design, training with Generative Artificial Intelligence (GAI), implementation of a data warehouse, generators, database integration, a web interface, and a module. Follow-up. A crucial aspect was the importance of data order and structure. Standardization and organization of training content are fundamental to ensuring the accuracy and relevance of responses. The solution coherently articulates the components, ensuring robust interconnections. The follow-up module expands the agent's capabilities, offering comprehensive, efficient, and personalized interaction. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1267 Dimensions. Open Alex.