Documentos donde el Autor es "Kuc Castilla, Ángel Gabriel"

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés The Polyphagous Shot Hole Borer (PSHB) is a highly invasive beetle that has been spreading like an epidemic across agricultural and forestry landscapes in recent years. Its rapid and destructive spread has turned it into a major global threat, causing widespread damage that continues to grow with time. Countries like South Africa, the United States, and Australia have implemented extensive measures to control the spread of PSHB, including the establishment of specialized agricultural support centers for early detection. However, there is still a strong need to make PSHB detection more accessible, allowing even non-experts to easily identify infections at an early stage. Artificial Intelligence (AI) has shown great promise in plant disease detection, but a major challenge in the case of PSHB was the lack of a suitable dataset for training AI models. In the proposed work, we first created a dedicated dataset by collecting images of trees infected with PSHB. We applied a range of preprocessing techniques to refine the dataset and prepare it for AI applications. Building on this, we developed a novel AI-based method, where we trained a deep learning model using a multi-convolutional layer network combined with a Fourier transformation layer. Additionally, an attention mechanism and advanced feature extraction techniques were incorporated to further boost model performance. As a result, the proposed approach achieved an impressive top accuracy of 92.3% in detecting PSHB infections, showing the potential of AI to offer a simple, efficient, and highly accurate solution for early disease detection. metadata Younas, Rabbiya; ur Rehman, Hafiz Muhammad Raza; Choi, Gyu Sang; Kuc Castilla, Ángel Gabriel; Uc Ríos, Carlos Eduardo y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, angel.kuc@uneatlantico.es, carlos.uc@unini.edu.mx, SIN ESPECIFICAR (2026) An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants. BMC Plant Biology. ISSN 1471-2229

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Ensuring safe and independent mobility for visually impaired individuals requires efficient obstacle detection systems. This study introduces an innovative smart knee glove, integrating machine learning technologies for real-time obstacle detection and alerting. The system is equipped with ultrasonic sensor, PIR sensor and a buzzer, with data processing managed by an Arduino Uno microcontroller. To enhance detection accuracy, multiple machine learning algorithms including Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF) and Gaussian Naïve Bayes (GNB) are utilized. A novel Voting Classifier ensemble method is proposed, effectively combining the strengths of these classifiers to maximize performance. Rigorous cross-fold validation ensures robust evaluation under varying conditions. Experimental results demonstrates that the system achieves an impressive 98.34% detection accuracy within a 4-meter range, with high precision, recall and F1 scores. These findings underscore the system’s reliability and potential to empower visually impaired users with safer, more autonomous navigation, marking a significant advancement in obstacle detection technologies. metadata Ikram, Sunnia; Bajwa, Imran Sarwar; Ikram, Amna; Díez, Isabel de la Torre; Uc Ríos, Carlos Eduardo y Kuc Castilla, Ángel Gabriel mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carlos.uc@unini.edu.mx, SIN ESPECIFICAR (2025) Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors. IEEE Access, 13. pp. 35309-35321. ISSN 2169-3536

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%. metadata Siddiqui, Hafeez Ur Rehman; Akmal, Ambreen; Iqbal, Muhammad; Saleem, Adil Ali; Raza, Muhammad Amjad; Zafar, Kainat; Zaib, Aqsa; Dudley, Sandra; Arambarri, Jon; Kuc Castilla, Ángel Gabriel y Rustam, Furqan mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, jon.arambarri@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence. Sensors, 24 (12). p. 3754. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Abierto Inglés Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM. metadata Ramzan, Mahrukh; Shoaib, Muhammad; Altaf, Ayesha; Arshad, Shazia; Iqbal, Faiza; Kuc Castilla, Ángel Gabriel y Ashraf, Imran mail SIN ESPECIFICAR (2023) Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm. Sensors, 23 (20). p. 8642. ISSN 1424-8220

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Introduction: Co-prevalence of long-COVID-19, cardiovascular diseases and diabetes is one of the major health challenges of the pandemic worldwide. Studies on long-COVID-19 and associated health outcomes are absent in Bangladesh. The main aim of this study was to determine the prevalence and impact of long-COVID-19 on preexisting diabetes and cardiovascular diseases (CVD) on health outcomes among patients in Bangladesh. Methods: We collected data from 3,250 participants in Bangladesh, retrospectively. Multivariable logistic regression model was used to determine the odds ratio between independent and dependent variables. Kaplan-Meier survival curve was used to determine the cumulative survival. Results: COVID-19 was detected among 73.4% (2,385 of 3,250) participants. Acute long-COVID-19 was detected among 28.4% (678 of 2,385) and chronic long-COVID-19 among 71.6% (1,707 of 2,385) patients. CVD and diabetes were found among 32%, and 24% patients, respectively. Mortality rate was 18% (585 of 3,250) among the participants. Co-prevalence of CVD, diabetes and COVID-19 was involved in majority of fatality (95%). Fever (97%), dry cough (87%) and loss of taste and smell (85%) were the most prevalent symptoms. Patients with co-prevalence of CVD, diabetes and COVID-19 had higher risk of fatality (OR: 3.65, 95% CI, 2.79–4.24). Co-prevalence of CVD, diabetes and chronic long-COVID-19 were detected among 11.9% patients. Discussion: Risk of hospitalization and fatality reduced significantly among the vaccinated. This is one of the early studies on long-COVID-19 in Bangladesh. metadata Sharif, Nadim; Sharif, Nazmul; Khan, Afsana; Halawani, Ibrahim F.; Alzahrani, Fuad M.; Alzahrani, Khalid J.; Díez, Isabel De la Torre; Ramírez-Vargas, Debora L.; Kuc Castilla, Ángel Gabriel; Parvez, Anowar Khasru y Dey, Shuvra Kanti mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, debora.ramirez@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Prevalence and impact of long COVID-19 among patients with diabetes and cardiovascular diseases in Bangladesh. Frontiers in Public Health, 11. ISSN 2296-2565

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Non-word and real-word errors are generally two types of spelling errors. Non-word errors are misspelled words that are nonexistent in the lexicon while real-word errors are misspelled words that exist in the lexicon but are used out of context in a sentence. Lexicon-based lookup approach is widely used for non-word errors but it is incapable of handling real-word errors as they require contextual information. Contrary to the English language, real-word error detection and correction for low-resourced languages like Urdu is an unexplored area. This paper presents a real-word spelling error detection and correction approach for the Urdu language. We develop an extensive lexicon of 593,738 words and use this lexicon to develop a dataset for real-word errors comprising 125562 sentences and 2,552,735 words. Based on the developed lexicon and dataset, we then develop a contextual spell checker that detects and corrects real-word errors. For the real-word error detection phase, word-gram features are used along with five machine learning classifiers, achieving a precision, recall, and F1-score of 0.84,0.79, and 0.81 respectively. We also test the proposed approach with a 40% error density. For real-word error correction, the Damerau-Levenshtein distance is used along with the n-gram model for further ranking of the suggested candidate words, achieving an accuracy of up to 83.67%. metadata Aziz, Romila; Anwar, Muhammad Waqas; Jamal, Muhammad Hasan; Bajwa, Usama Ijaz; Kuc Castilla, Ángel Gabriel; Uc-Rios, Carlos; Bautista Thompson, Ernesto y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carlos.uc@unini.edu.mx, ernesto.bautista@unini.edu.mx, SIN ESPECIFICAR (2023) Real Word Spelling Error Detection and Correction for Urdu Language. IEEE Access. p. 1. ISSN 2169-3536

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Antimicrobial and multidrug resistance (MDR) pathogens are becoming one of the major health threats among children. Integrated studies on the molecular epidemiology and prevalence of AMR and MDR diarrheal pathogens are lacking. A total of 404 fecal specimens were collected from children with diarrhea in Bangladesh from January 2019 to December 2021. We used conventional bacteriologic and molecular sequence analysis methods. Phenotypic and genotypic resistance were determined by disk diffusion and molecular sequencing methods. Fisher’s exact tests with 95% confidence intervals (CIs) was performed. Prevalence of bacterial infection was 63% (251 of 404) among children with diarrhea. E. coli (29%) was the most prevalent. E. coli, Shigella spp., V. cholerae, and Salmonella spp., showed the highest frequency of resistance against ceftriaxone (75–85%), and erythromycin (70–75%%). About 10–20% isolates of E. coli, V. cholerae and Shigella spp. showed MDR against cephem, macrolides, and quinolones. Significant association (p value < 0.05) was found between the phenotypic and genotypic resistance. The risk of diarrhea was the highest among the patients co-infected with E. coli and rotavirus [OR 3.6 (95% CI 1.1–5.4) (p = 0.001)] followed by Shigella spp. and rotavirus [OR 3.5 (95% CI 0.5–5.3) (p = 0.001)]. This study will provide an integrated insight of molecular epidemiology and antimicrobial resistance profiling of bacterial pathogens among children with diarrhea in Bangladesh. metadata Sharif, Nadim; Ahmed, Shamsun Nahar; Khandaker, Shamim; Monifa, Nuzhat Haque; Abusharha, Ali; Ramírez-Vargas, Debora L.; Díez, Isabel De la Torre; Kuc Castilla, Ángel Gabriel; Talukder, Ali Azam; Parvez, Anowar Khasru y Dey, Shuvra Kanti mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, debora.ramirez@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Multidrug resistance pattern and molecular epidemiology of pathogens among children with diarrhea in Bangladesh, 2019–2021. Scientific Reports, 13 (1). ISSN 2045-2322

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99.0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24.03%, 5.0%, and 24.10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99.0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41.72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol. metadata Benifa, J. V. Bibal; Chola, Channabasava; Muaad, Abdullah Y.; Hayat, Mohd Ammar Bin; Bin Heyat, Md Belal; Mehrotra, Rajat; Akhtar, Faijan; Hussein, Hany S.; Ramírez-Vargas, Debora L.; Kuc Castilla, Ángel Gabriel; Díez, Isabel de la Torre y Khan, Salabat mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, debora.ramirez@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas. Sensors, 23 (13). p. 6090. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Classification is a commonly used technique in data mining and is applied in various fields such as sentiment analysis, fraud detection, and fault diagnosis. Multiclass classification, which involves more than two classes, is more complex than binary classification. There are mainly two ways to approach multiclass classification, one is to expand the binary classifier into a multiclass classifier through various strategies and the other is to divide the multiclass classification problem into multiple binary problems (binarization). Two popular approaches for binarization are One vs One (OvO) and One vs All (OvA). It is simpler to aggregate the outputs of all binary classifiers as the number of classifiers decreases. However, it causes an imbalance of positive and negative sample numbers, which affects the classification effect of each binary classifier. In this article, we contribute to the field of ensemble learning and multi-class classification by proposing a new method called Ensemble Partition Sampling (EPS). This article presents a new approach to multiclass classification using an "Ensemble Partition Sampling" method within the "one-vs-all" (OvA) framework. The primary goal of this method is to tackle the problem of data imbalance by incorporating ensemble learning and preprocessing techniques into each binary dataset. The study found that Ensemble Partition Sampling (EPS) is the most effective method for imbalanced and multiclass imbalanced classification, outperforming other methods including OvA, SMOTE, k-means-SMOTE, Bagging-RB, DES-MI, OvO-EASY, and OvO-SMB. The study used CART, Random Forest, and SVM as classifiers, and the results consistently showed that EPS outperformed all other algorithms. The findings suggest that EPS is a highly effective method for improving classification performance in imbalanced and multiclass imbalanced datasets. metadata Jabir, Brahim; Díez, Isabel De la Torre; Bautista Thompson, Ernesto; Ramírez-Vargas, Debora L. y Kuc Castilla, Ángel Gabriel mail SIN ESPECIFICAR (2023) Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification. IEEE Access. p. 1. ISSN 2169-3536

Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés The economic valuation of coastal ecosystem services is a critical step for the design of sound public policies that support the preservation of the services that nature provides to society in the context of climate change. Using the value transfer method, we obtained the economic valuation that represents the loss of coastal ecosystem services caused by sea level rise in Mexico. Using the Bathtub method, digital elevation models and sea level data, we identified the areas in the country prone to flooding and the associated ecosystem impacts. In Mexico, the annual economic loss caused by the disappearance of coastal ecosystem services is estimated at $6,476,402,405 USD, where wetlands represent the greatest economic losses, since they represent the largest affected ecosystem by area. However, beaches and dunes are the most valued ecosystem due to the economic activities that occur in these areas. In the mangroves, the service as habitat, refuge and nursery is the most valued for its positive relationship with fisheries. The states with the most economic losses are Baja California Sur, Sinaloa and Campeche. The protection of the coastal zone in Mexico should be a priority in the development strategies in the country because its loss and/or rehabilitation imply high economic costs and compromises the wellbeing of society. metadata Fernández-Díaz, Violeta Z.; Canul Turriza, Román A.; Kuc Castilla, Ángel Gabriel y Hinojosa-Huerta, Osvel mail SIN ESPECIFICAR (2022) Loss of coastal ecosystem services in Mexico: An approach to economic valuation in the face of sea level rise. Frontiers in Marine Science, 9. ISSN 2296-7745

Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Cerrado Inglés Global climate change has generated several adverse effects, such as loss of sea ice, earlier breakup of ice on rivers and lakes, more intense heat waves and accelerated sea level rise. Of all the aforementioned effects, sea level rise is an unequivocal and forthcoming effect that has generated great interest. metadata Fernández-Díaz, Violeta Zetzangari; Canul Turriza, Roman; Kuc Castilla, Ángel Gabriel; Arreguín-Rodríguez, Gabriela J. y Mejía-Piña, Karla Gabriela mail SIN ESPECIFICAR (2022) Impact of Sea Level Rise and Flooding in Two Key Mexican Coastal Cities. Ocean Yearbook Online, 36 (1). pp. 139-157. ISSN 2211-6001

Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Español El análisis y comprensión de la variabilidad de la línea de costa, así como las tendencias de erosión-acreción son importantes para orientar los procedimientos de gestión costera. Este estudio se enfoca en la detección y evaluación de los cambios en la línea costera a fin de proponer la medida de protección más adecuada contemplando a los ecosistemas presentes en la playa de Sabancuy, en Campeche, México. metadata Kuc Castilla, Ángel Gabriel mail SIN ESPECIFICAR (2021) Propuesta de estrategias para el control de la erosión en la playa de Sabancuy, Campeche. Jóvenes Mexicanos Explorando. p. 64.