Documentos donde el Autor es "Elkamchouchi, Dalia H."

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Número de documentos: 7.

2022

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Rivers are dynamic geological agents on the earth which transport the weathered materials of the continent to the sea. Estimation of suspended sediment yield (SSY) is essential for management, planning, and designing in any river basin system. Estimation of SSY is critical due to its complex nonlinear processes, which are not captured by conventional regression methods. Rainfall, temperature, water discharge, SSY, rock type, relief, and catchment area data of 11 gauging stations were utilized to develop robust artificial intelligence (AI), similar to an artificial-neural-network (ANN)-based model for SSY prediction. The developed highly generalized global single ANN model using a large amount of data was applied at individual gauging stations for SSY prediction in the Mahanadi River basin, which is one of India’s largest peninsular rivers. It appeared that the proposed ANN model had the lowest root-mean-squared error (0.0089) and mean absolute error (0.0029) along with the highest coefficient of correlation (0.867) values among all comparative models (sediment rating curve and multiple linear regression). The ANN provided the best accuracy at Tikarapara among all stations. The ANN model was the most suitable substitute over other comparative models for SSY prediction. It was also noticed that the developed ANN model using the combined data of eleven stations performed better at Tikarapara than the other ANN which was developed using data from Tikarapara only. These approaches are suggested for SSY prediction in river basin systems due to their ease of implementation and better performance. metadata Yadav, Arvind; Chithaluru, Premkumar; Singh, Aman; Joshi, Devendra; Elkamchouchi, Dalia H.; Mazas Pérez-Oleaga, Cristina y Anand, Divya mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, cristina.mazas@uneatlantico.es, divya.anand@uneatlantico.es (2022) An Enhanced Feed-Forward Back Propagation Levenberg–Marquardt Algorithm for Suspended Sediment Yield Modeling. Water, 14 (22). p. 3714. ISSN 2073-4441

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The world population is on the rise, which demands higher food production. The reduction in the amount of land under cultivation due to urbanization makes this more challenging. The solution to this problem lies in the artificial cultivation of crops. IoT and sensors play an important role in optimizing the artificial cultivation of crops. The selection of sensors is important in order to ensure a better quality and yield in an automated artificial environment. There are many challenges involved in selecting sensors due to the highly competitive market. This paper provides a novel approach to sensor selection for saffron cultivation in an IoT-based environment. The crop used in this study is saffron due to the reason that much less research has been conducted on its hydroponic cultivation using sensors and its huge economic impact. A detailed hardware-based framework, the growth cycle of the crop, along with all the sensors, and the block layout used for saffron cultivation in a hydroponic medium are provided. The important parameters for a hydroponic medium, such as the concentration of nutrients and flow rate required, are discussed in detail. This paper is the first of its kind to explain the sensor configurations, performance metrics, and sensor-based saffron cultivation model. The paper discusses different metrics related to the selection, use and role of sensors in different IoT-based saffron cultivation practices. A smart hydroponic setup for saffron cultivation is proposed. The results of the model are evaluated using the AquaCrop simulator. The simulator is used to evaluate the value of performance metrics such as the yield, harvest index, water productivity, and biomass. The values obtained provide better results as compared to natural cultivation. metadata Kour, Kanwalpreet; Gupta, Deepali; Gupta, Kamali; Anand, Divya; Elkamchouchi, Dalia H.; Mazas Pérez-Oleaga, Cristina; Ibrahim, Muhammad y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, cristina.mazas@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation. Sensors, 22 (22). p. 8905. 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 This study involves a working limestone mine that supplies limestone to the cement factory. The two main goals of this paper are to (a) determine how long an operating mine can continue to provide the cement plant with the quality and quantity of materials it needs, and (b) explore the viability of combining some limestone from a nearby mine with the study mine limestone to meet the cement plant’s quality and quantity goals. These objectives are accomplished by figuring out the maximum net profit for the ultimate pit limit and production sequencing of the mining blocks. The issues were resolved using a branch-and-cut based sequential integer and mixed integer programming problem. The study mine can exclusively feed the cement plant for up to 15 years, according to the data. However, it was also noted that the addition of the limestone from the neighboring mine substantially increased the mine’s life (85 years). The findings also showed that, when compared with the production planning formulation that the company is now using, the proposed approach creates 10% more profit. The suggested method also aids in determining the desired desirable quality of the limestone that will be transported from the nearby mine throughout each production stage. metadata Joshi, Devendra; Chithaluru, Premkumar; Singh, Aman; Yadav, Arvind; Elkamchouchi, Dalia H.; Breñosa, Jose y Anand, Divya mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, josemanuel.brenosa@uneatlantico.es, divya.anand@uneatlantico.es (2022) An Optimized Open Pit Mine Application for Limestone Quarry Production Scheduling to Maximize Net Present Value. Mathematics, 10 (21). p. 4140. ISSN 2227-7390

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Traditional optimization of open pit mine design is a crucial component of mining endeavors and is influenced by many variables. The critical factor in optimization is the geological uncertainty, which relates to the ore grade. To deal with uncertainties related to the block economic values of mining blocks and the general problem of mine design optimization, under unknown conditions, the best ultimate pit limits and pushback designs are produced by a minimum cut algorithm. The push–relabel minimal cut algorithm provides a framework for computationally efficient representation and processing of the economic values of mining blocks under multiple scenarios. A sequential Gaussian simulation-based smoothing spline technique was created. To produce pushbacks, an efficient parameterized minimum cut algorithm is suggested. An analysis of Indian iron ore mining was performed. The developed mine scheduling algorithm was compared with the conventional algorithm, and the results show that when uncertainty is considered, the cumulative metal production is higher and there is an additional increase of about 5% in net present value. The results of this work help the mining industry to plan mines in such a way that can generate maximum profit from the deposits. metadata Joshi, Devendra; Chithaluru, Premkumar; Singh, Aman; Yadav, Arvind; Elkamchouchi, Dalia H.; Mazas Pérez-Oleaga, Cristina y Anand, Divya mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, cristina.mazas@uneatlantico.es, divya.anand@uneatlantico.es (2022) A Novel Large-Scale Stochastic Pushback Design Merged with a Minimum Cut Algorithm for Open Pit Mine Production Scheduling. Systems, 10 (5). p. 159. ISSN 2079-8954

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Large-scale distributed systems have the advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data within a time constraint becomes tricky, due to the complexity of data parallel task scheduling in a time constrained environment. This paper proposes data parallel task scheduling in cloud to address the minimization of cost and time constraints. By running concurrent executions of tasks on multi-core cloud resources, the number of parallel executions could be increased correspondingly, thereby, finishing the task within the deadline is possible. A mathematical model is developed here to minimize the operational cost of data parallel tasks by feasibly assigning a load to each virtual machine in the cloud data center. This work experiments with a machine learning model that is replicated on the multi-core cloud heterogeneous resources to execute different input data concurrently to accomplish distributive learning. The outcome of concurrent execution of data-intensive tasks on different parts of the input dataset gives better solutions in terms of processing the task by the deadline at optimized cost. metadata Rajalakshmi, N. R.; Dumka, Ankur; Kumar, Manoj; Singh, Rajesh; Gehlot, Anita; Akram, Shaik Vaseem; Anand, Divya; Elkamchouchi, Dalia H. y Delgado Noya, Irene mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, irene.delgado@uneatlantico.es (2022) A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud. Electronics, 11 (13). p. 2022. ISSN 2079-9292

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Remarkable progress in the Internet of Things (IoT) and the requirements in the Industrial era have raised new constraints of industrial data where huge data are gathered by heterogeneous devices. Recently, Industry 4.0 has attracted attention in various fields of industries such as medicines, automobiles, logistics, etc. However, every field is suffering from some threats and vulnerabilities. In this paper, a new model is proposed for detecting different types of attacks and it is analyzed with a deep learning technique, i.e., classifier-Convolution Neural Network and Long Short-Term Memory. The UNSW NB 15 dataset is used for the classification of various attacks in the field of Industry 4.0 for providing security and protection to the different types of sensors used for heterogeneous data. The proposed model achieves the results using Cortex processors, a 1.2 GHz processor, and four gigabytes of RAM. The attack detection model is written in Python 3.8.8 and Keras. Keras constructs the model using layers of Convolutional, Max Pooling, and Dense Layers. The model is trained using 250 batch size, 60 epochs, 10 classes. For this model, the activation functions are Relu and softmax pooling. metadata Anand, Ankita; Rani, Shalli; Singh, Aman; Elkamchouchi, Dalia H. y Delgado Noya, Irene mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, irene.delgado@uneatlantico.es (2022) Lightweight Hybrid Deep Learning Architecture and Model for Security in IIOT. Applied Sciences, 12 (13). p. 6442. ISSN 2076-3417

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Electroporation is a next generation bioelectronics device. The emerging application of electroporation requires high voltage pulses having a pulse-width in the nanosecond range. The essential use of a capacitor results in an increase in the size of the electroporator circuit. This paper discusses the modification of a conventional Marx generator circuit to achieve the high voltage electroporation pulses with a minimal chip size of the circuit. The reduced capacitors are attributed to a reduction in the number of stages used to achieve the required voltage boost. The paper proposes the improved isolation between two capacitors with the usage of optocouplers. Parametric analysis is presented to define the tuneable range of the electroporator circuit. The output voltage of 49.4 V is achieved using the proposed 5-stage MOSFET circuit with an input voltage of 12 V. metadata Ganesan, Selvakumar; Ghosh, Debarshi; Taneja, Ashu; Saluja, Nitin; Rani, Shalli; Singh, Aman; Elkamchouchi, Dalia H. y Delgado Noya, Irene mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, irene.delgado@uneatlantico.es (2022) A Modified Marx Generator Circuit with Enhanced Tradeoff between Voltage and Pulse Width for Electroporation Applications. Electronics, 11 (13). p. 2013. ISSN 2079-9292

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Association between socioeconomic and health variables and community-acquired pneumonia mortality rates in Chile from 1990 to 2021

Objectives To describe long-term trends in mortality attributed to community-acquired pneumonia (CAP) in Chile from 1990 to 2021, stratified by age group, and to evaluate associations with selected socioeconomic and demographic indicators. Study design Ecological, observational, longitudinal study using national secondary data. Methods CAP mortality rates were analyzed for the total population and by age group. Associations with the Human Development Index (HDI), poverty rate, aging index, and life expectancy at birth were examined using a hierarchical analytical approach. This included Spearman's rank correlation for initial exploration, multivariable linear regression to assess adjusted associations, and Prais–Winsten generalized least squares regression to account for first-order autocorrelation and shared temporal trends. Stationarity was evaluated using augmented Dickey–Fuller tests, with supplementary analyses using first-differenced variables. Missing data were imputed using time-based regression or interpolation, with sensitivity analyses performed. Results CAP mortality declined substantially across all age groups over the study period. Strong bivariate correlations were observed between mortality and all socioeconomic indicators; however, these associations were attenuated after adjustment for confounding and temporal autocorrelation. In multivariable and time-series models, HDI and the aging index remained significantly associated with CAP mortality in children (0–9 years) and older adults (≥65 years), whereas associations in intermediate age groups were not robust after accounting for shared secular trends. Poverty and life expectancy did not demonstrate independent associations in adjusted models. Conclusions CAP mortality in Chile has decreased markedly over the past three decades. Associations with socioeconomic indicators are strongest at the extremes of age and persist after accounting for temporal structure, although the ecological design precludes causal inference. These findings highlight the importance of considering demographic and socioeconomic context in population-level analyses of infectious disease outcomes.

Artículos y libros

Italo Salvador López Muñoz mail italo.lopez@doctorado.unini.edu.mx, Maria Loreto Romero Ladrón de Guevara mail , Christian R. Mejia mail , Shyla Del-Aguila-Arcentales mail , Aldo Alvarez-Risco mail , Neal M. Davies mail , Jaime A. Yáñez mail ,

López Muñoz

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An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer

This study presents a novel integrative approach for the analysis of high-dimensional gene expression data, leveraging the complementary strengths of unsupervised clustering and supervised classification. Using K-means clustering, the dataset is stratified into three distinct clusters, revealing intrinsic biological patterns and relationships. The resulting cluster assignments are subsequently employed as pseudo-labels to train machine learning models, including support vector machines, random forest, and a stacking ensemble classifier. To validate and enhance the robustness of clustering, complementary methodologies such as hierarchical clustering and DBSCAN are employed, with results visualized through PCA-driven dimensionality reduction. The high predictive accuracy achieved by the classifiers underscores the separability and reliability of the identified clusters. Furthermore, feature importance analysis highlighted key genetic determinants within each cluster, offering actionable insights into potential biomarkers and critical genomic features. This framework bridges the gap between exploratory unsupervised learning and predictive supervised modeling, providing a scalable and interpretable methodology for analyzing complex genomic datasets. Its applicability extends to biomarker discovery, patient stratification, and other precision medicine applications, emphasizing its utility in advancing genomic research and clinical practice.

Producción Científica

Eshmal Iman mail , Sohail Jabbar mail , Shabana Ramzan mail , Ali Raza mail , Farwa Raoof mail , Stefanía Carvajal-Altamiranda mail stefania.carvajal@uneatlantico.es, Vivian Lipari mail vivian.lipari@uneatlantico.es, Imran Ashraf mail ,

Iman

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A novel approach for disease and pests detection in potato production system based on deep learning

Vulnerability of potato crops to diseases and pest infestation can affect its quality and lead to significant yield losses. Timely detection of such diseases can help take effective decisions. For this purpose, a deep learning-based object detection framework is designed in this study to identify and classify major potato diseases and pests under real-world field conditions. A total of 2,688 field images were collected from two research farms in Punjab, Pakistan, across multiple growth stages in various seasonal conditions. Excluding 285 symptoms-free images from the earliest collection led to 2,403 images which were annotated into four biotic-stress classes: blight disease (n = 630), leaf spot disease (n = 370), leafroll virus (viral symptom complex; n = 888), and Colorado potato beetle (larvae/adults; n = 515), indicating class imbalance. Several state-of-the-art models were used including YOLOv8 variants (n/s/m), YOLOv7, YOLOv5, and Faster R-CNN, and the results are discussed in relation to recent potato disease classification studies involving cropped leaf images. Stratified splitting (70% training, 20% validation, 10% testing) was applied to preserve class distribution across all subsets. YOLOv8-medium achieve the best performance with mean average precision (mAP)@0.5 of 98% on the held-out test images. Results for stable 5-fold cross-validation show a mean mAP@0.5 of 97.8%, which offers a balance between accuracy and inference time. Model robustness was evaluated using 5-fold cross-validation and repeated training with different random seeds, showing a low variance of ±0.4% mAP. Results demonstrate promising outcomes under the real-world field conditions, while, broader cross-region and cross-season validation is intended for the future.

Producción Científica

Ahmed Abbas mail , Saif Ur Rehman mail , Khalid Mahmood mail , Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Aseel Smerat mail , Imran Ashraf mail ,

Abbas

<a class="ep_document_link" href="/28320/1/1-s2.0-S1876034126000912-main.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Concern for mpox infection in Latin America

Background Mpox arrived in Latin America and quickly began to replicate, so it is important to measure the concern it generates among residents. The study aims to assess whether country or other factors are associated with concern about mpox infection in Latin America. Methods The study uses a cross-sectional, multicenter design. Sampling was conducted using non-random snowball sampling. From August to September 2022, concern about being infected with mpox was assessed using a previously validated questionnaire (Cronbach's Alpha: 0.85); it was divided into nine countries and other social variables. Results From 1404 respondents, the majority of respondents were female (60.3%) and young (median age 25 years); also, a few reported that it was a significant problem (6% almost all the time and 11% often) and were concerned (6% almost all the time and 11% often) about the possibility of mpox infection. In multivariate analysis, men (aPR: 0.85; 95% CI: 0.73–0.99; p-value=0.046), younger (aPR: 0.98; 95% CI: 0.97–0.99; p-value<0.001), single (aPR: 0.78; 95% CI: 0.62–0.99; p-value=0.042) and, compared to Peru, those living in Colombia (aPR: 0.75; 95% CI. 0.58–0.97; p-value=0.027) and Costa Rica (aPR: 0.65; 95% CI: 0.44–0.96; p-value=0.032) reported the lowest concern; also, Bolivia (aPR: 1.16; 95% CI: 0.94–1.43; p-value=0.176) and Honduras (aPR: 1.01; 95% CI: 0.80–1.27; p-value=0.943) reported that their concerns tend to be higher. Conclusions There were evident differences across respondents' countries; these baseline results show that the first report was made in many countries that were also significantly affected by mpox and now face a new epidemic threatening public health.

Artículos y libros

Christian R. Mejia mail , Aldo Alvarez-Risco mail , Luciana Daniela Garlisi-Torales mail , Telmo Raúl Aveiro mail , Jamil Cedillo-Balcázar mail , Néstor Valentin Rocha-Saravia mail , Andrea Retana-González mail , Medally C. Paucar mail , Beatriz Mejia Raudales mail , Jose Armada mail , Shyla Del-Aguila-Arcentales mail , Neal M. Davies mail , Jaime A. Yáñez mail jaime.yanez@unini.edu.mx,

Mejia

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Fish consumption and brain structure: a comprehensive systematic review of observational studies

Background Age-related structural changes in the human brain, including cortical atrophy, reductions in grey and white matter volumes, and the accumulation of small vessel–related lesions such as white matter hyperintensities (WMH) and cerebral microbleeds, represent critical biological substrates underlying cognitive decline and dementia. Fish consumption has been associated with slower cognitive decline and reduced risk of dementia, but a comprehensive evaluation of its relation with brain structures is lacking. Aims The aim of this study was to systematically review current scientific literature providing evidence of relation between fish intake and brain structures in human studies. Methods Studies indexed in two major electronic databases have been screened based on a combination of keywords and MeSH terms. Studies were eligible whether they assessed fish consumption in relation to brain structures in the adult populations. Results A total of 24 studies conducted predominantly on older adults met inclusion criteria. Most brain volume measures were obtained via magnetic resonance imaging (MRI) procedures. Higher fish consumption was associated with reduced severity of white matter hyperintensities (a biomarker of cerebral small vessel disease and white matter damage) and cerebral micro-bleed, preservation of certain brain areas volumes (i.e., hippocampus, temporal lobe and periventricle white matter) and cortical thickness of specific areas (i.e., precuneus, parietal, and cingulate grey matter), among others, compared to lower intake. Some analyses found no association and isolated findings suggested possible adverse associations that were not consistently replicated. Studies reporting null findings may underline the possible relevance of the overall diet (i.e., adherence to the Mediterranean diet). Conclusions Inclusion of fish in a healthy and balanced diet is associated with better white matter grades on MRI and slower progression of white matter hyperintensities and reduction of vascular-related lesions of the aging brain, suggesting a potential role in preventing neurocognitive deterioration. Heterogeneity across studies underscores the need for additional studies.

Producción Científica

Justyna Godos mail , Giuseppe Caruso mail , Agnieszka Micek mail , Alberto Dolci mail , Zoltan Ungvari mail , Andrea Lehoczki mail , Lisandra León Brizuela mail , Evelyn Frias-Toral mail , Andrea Di Mauro mail , Mario Siervo mail , Michelino Di Rosa mail , Giuseppe Grosso mail ,

Godos