Repositorio Universidad Internacional Iberoamericana
Not a member yet
907 research outputs found
Sort by
Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops
No full textPlant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses
Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models
No full textThyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significantly impact metabolism and overall health. Hypothyroidism can cause a slowdown in bodily processes, leading to symptoms such as fatigue, weight gain, depression, and cold sensitivity. Hyperthyroidism can lead to increased metabolism, causing symptoms like rapid weight loss, anxiety, irritability, and heart palpitations. Prompt diagnosis and appropriate treatment are crucial in managing thyroid disorders and improving patients’ quality of life. Thyroid illness affects millions worldwide and can significantly impact their quality of life if left untreated. This research aims to propose an effective artificial intelligence-based approach for the early diagnosis of thyroid illness. An open-access thyroid disease dataset based on 3,772 male and female patient observations is used for this research experiment. This study uses the nominal continuous synthetic minority oversampling technique (SMOTE-NC) for data balancing and a fine-tuned light gradient booster machine (LGBM) technique to diagnose thyroid illness and handle class imbalance problems. The proposed SNL (SMOTE-NC-LGBM) approach outperformed the state-of-the-art approach with high-accuracy performance scores of 0.96. We have also applied advanced machine learning and deep learning methods for comparison to evaluate performance. Hyperparameter optimizations are also conducted to enhance thyroid diagnosis performance. In addition, we have applied the explainable Artificial Intelligence (XAI) mechanism based on Shapley Additive exPlanations (SHAP) to enhance the transparency and interpretability of the proposed method by analyzing the decision-making processes. The proposed research revolutionizes the diagnosis of thyroid disorders efficiently and helps specialties overcome thyroid disorders early
Análisis crítico sobre el rol del Equipo de Gestión en el Desarrollo Curricular de los Centros Educativos modalidad Técnico-Profesional de la Regional 14, Nagua
No full textEl sistema educativo dominicano comprende desde el nivel inicial hasta el superior, siendo de gran interés en esta investigación el secundario, especialmente la modalidad Técnico-Profesional, que imparte asignaturas académicas y técnicas, estas últimas gestionadas a través de la Dirección Nacional de Educación Técnico-Profesional atendiendo a las demandas de la zona. Están llamados a ofrecer una sólida formación con innovación y amplia perspectiva para la actividad productiva, lo que implica un análisis crítico sobre el trabajo del equipo de gestión de acuerdo a lo establecido, el cual tiene un rol preponderante en el ámbito pedagógico, siendo la “columna principal” encargado de su funcionamiento. En las dieciocho regionales de educación que conforman los órganos de ejecución del Ministerio de Educación de la República Dominicana (MINERD) hay politécnicos; en la regional No. 14 de Nagua convergen nueve centros distribuidos en seis distritos. Son necesarios para estudiantes que estén interesados en salir con un bachillerato técnico acreditado. Los antecedentes encontrados son el sustentáculo de la investigación, cuyo tema ya ha sido abordado desde diversos contextos. En la justificación se detalla la importancia del trabajo para el sector educativo, desde los centros educativos hasta los encargados de crear políticas públicas. El mismo es interesante porque hoy se habla de una transformación educativa que nace de varias normativas legales y encierran compromisos de mejora relacionados a gestión, calidad educativa y formación Técnico-Profesional. El estudio se realiza bajo el enfoque Mixto, que permite, mediante la técnica Delphi, recoger datos cualitativos y cuantitativos de una muestra de 28 participantes. Se espera con él aportar un recurso que permita analizar y evaluar resultados, tanto de los centros como de los programas implementados, para responder a la sociedad, que espera ver los avances en materia de educación. El mismo también servirá de guía para futuras investigaciones sobre logros e impactos
Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence
No full textThe prevalence of sleep disorders, characterized by issues with quality, timing, and sleep duration is increasing globally. Among modifiable risk factors, diet quality has been suggested to influence sleep features. The Mediterranean diet is considered a landmark dietary pattern in terms of quality and effects on human health. However, dietary habits characterized by this cultural heritage should also be considered in the context of overall lifestyle behaviors, including sleep habits. This study aimed to systematically revise the literature relating to adherence to the Mediterranean diet and sleep features in observational studies. The systematic review comprised 23 reports describing the relation between adherence to the Mediterranean diet and different sleep features, including sleep quality, sleep duration, daytime sleepiness, and insomnia symptoms. The majority of the included studies were conducted in the Mediterranean basin and reported a significant association between a higher adherence to the Mediterranean diet and a lower likelihood of having poor sleep quality, inadequate sleep duration, excessive daytime sleepiness or symptoms of insomnia. Interestingly, additional studies conducted outside the Mediterranean basin showed a relationship between the adoption of a Mediterranean-type diet and sleep quality, suggesting that biological mechanisms sustaining such an association may exist. In conclusion, current evidence suggests a relationship between adhering to the Mediterranean diet and overall sleep quality and different sleep parameters. The plausible bidirectional association should be further investigated to understand whether the promotion of a healthy diet could be used as a tool to improve sleep quality
A deep learning approach for Named Entity Recognition in Urdu language
No full textNamed Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies
A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study
No full textAim: The development of predictive models for patients treated by emergency medical services (EMS) is on the rise in the emergency field. However, how these models evolve over time has not been studied. The objective of the present work is to compare the characteristics of patients who present mortality in the short, medium and long term, and to derive and validate a predictive model for each mortality time. Methods: A prospective multicenter study was conducted, which included adult patients with unselected acute illness who were treated by EMS. The primary outcome was noncumulative mortality from all causes by time windows including 30-day mortality, 31- to 180-day mortality, and 181- to 365-day mortality. Prehospital predictors included demographic variables, standard vital signs, prehospital laboratory tests, and comorbidities. Results: A total of 4830 patients were enrolled. The noncumulative mortalities at 30, 180, and 365 days were 10.8%, 6.6%, and 3.5%, respectively. The best predictive value was shown for 30-day mortality (AUC = 0.930; 95% CI: 0.919–0.940), followed by 180-day (AUC = 0.852; 95% CI: 0.832–0.871) and 365-day (AUC = 0.806; 95% CI: 0.778–0.833) mortality. Discussion: Rapid characterization of patients at risk of short-, medium-, or long-term mortality could help EMS to improve the treatment of patients suffering from acute illnesses
An enhanced approach for predicting air pollution using quantum support vector machine
No full textThe essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely recognized as one of the most effective classification machine learning techniques currently available. Since, in conventional systems, the SVM kernel technique tends to sluggish down and even fail as datasets become increasingly complex or jumbled. To compare the execution time and accuracy of conventional SVM classification to that of quantum SVM classification, the appropriate quantum features for mapping need to be selected. As the dataset grows complex, the importance of selecting an appropriate feature map that outperforms or performs as well as the classification grows. This paper utilizes conventional SVM to select an optimal feature map and benchmark dataset for predicting air quality. Experimental evidence demonstrates that the precision of quantum SVM surpasses that of classical SVM for air quality assessment. Using quantum labs from IBM’s quantum computer cloud, conventional and quantum computing have been compared. When applied to the same dataset, the conventional SVM achieved an accuracy of 91% and 87% respectively, whereas the quantum SVM demonstrated an accuracy of 97% and 94% respectively for air quality prediction. The study introduces the use of quantum Support Vector Machines (SVM) for predicting air quality. It emphasizes the novel method of choosing the best quantum feature maps. Through the utilization of quantum-enhanced feature mapping, our objective is to exceed the constraints of classical SVM and achieve unparalleled levels of precision and effectiveness. We conduct precise experiments utilizing IBM’s state-of-the-art quantum computer cloud to compare the performance of conventional and quantum SVM algorithms on a shared dataset
An improved deep convolutional neural network-based YouTube video classification using textual features
No full textVideo content on the web platform has increased explosively during the past decade, thanks to the open access to Facebook, YouTube, etc. YouTube is the second-largest social media platform nowadays containing more than 37 million YouTube channels. YouTube revealed at a recent press event that 30,000 new content videos per hour and 720,000 per day are posted. There is a need for an advanced deep learning-based approach to categorize the huge database of YouTube videos. This study aims to develop an artificial intelligence-based approach to categorize YouTube videos. This study analyzes the textual information related to videos like titles, descriptions, user tags, etc. using YouTube exploratory data analysis (YEDA) and shows that such information can be potentially used to categorize videos. A deep convolutional neural network (DCNN) is designed to categorize YouTube videos with efficiency and high accuracy. In addition, recurrent neural network (RNN), and gated recurrent unit (GRU) are also employed for performance comparison. Moreover, logistic regression, support vector machines, decision trees, and random forest models are also used. A large dataset with 9 classes is used for experiments. Experimental findings indicate that the proposed DCNN achieves the highest receiver operating characteristics (ROC) area under the curve (AUC) score of 99% in the context of YouTube video categorization and 96% accuracy which is better than existing approaches. The proposed approach can be used to help YouTube users suggest relevant videos and sort them by video category
Lifestyle Factors Associated with Children’s and Adolescents’ Adherence to the Mediterranean Diet Living in Mediterranean Countries: The DELICIOUS Project
No full textBackground/Objectives. Traditional dietary patterns are being abandoned in Mediterranean countries, especially among younger generations. This study aimed to investigate the potential lifestyle determinants that can increase adherence to the Mediterranean diet in children and adolescents. Methods. This study is a cross-sectional analysis of data from five Mediterranean countries (Italy, Spain, Portugal, Egypt, and Lebanon) within the context of the EU-funded project DELICIOUS (UnDErstanding consumer food choices & promotion of healthy and sustainable Mediterranean Diet and LIfestyle in Children and adolescents through behavIOUral change actionS). This study comprised information on 2011 children and adolescents aged 6–17 years old collected during 2023. The main background characteristics of both children and parents, including age, sex, education, and family situation, were collected. Children’s eating (i.e., breakfast, place of eating, etc.) and lifestyle habits (i.e., physical activity level, sleep, and screen time) were also investigated. The level of adherence to the Mediterranean diet was assessed using the KIDMED index. Logistic regression analyses were performed to test for likelihood of higher adherence to the Mediterranean diet. Results. Major determinants of higher adherence to the Mediterranean diet were younger age, higher physical activity level, adequate sleep duration, and, among dietary habits, having breakfast and eating with family members and at school. Parents’ younger age and higher education were also determinants of higher adherence. Multivariate adjusted analyses showed that an overall healthier lifestyle and parents’ education were the factors independently associated with higher adherence to the Mediterranean diet. Conclusions. Higher adherence to the Mediterranean diet in children and adolescents living in the Mediterranean area is part of an overall healthy lifestyle possibly depending on parents’ cultural background
A Lightweight Energy-Efficient Routing Scheme for Real-Time WSN-VANET-Based Applications
No full textAn extensive examination reveals that Wireless Sensor Networks (WSNs) offer a promising solution for essential sensing and event-driven data communication needs. WSNs hold significant potential for event-driven communication, primarily owing to their decentralized and infrastructure-free operational characteristics. However, the traditional WSN’s inherent static nature imposes limitations on its applicability, particularly in scenarios requiring generic operating characteristics or routing protocols for Vehicle-to-Vehicle (V2V) communication. This constraint arises from the immobility of sensor nodes within the network. Nonetheless, adopting a forward-looking perspective that incorporates mobility into WSNs opens up opportunities to create a mobile-WSN solution tailored for V2V communication. In response to the challenges posed by mobile-WSNs and the pursuit of a cost-effective V2V communication solution, a Lightweight Energy-Efficient Cross-layer Routing (LRECR) scheme has been proposed for WSN-VANET-based networks. The proposed routing model aims to enhance the timely delivery of Real-Time Data (RTD) with low latency, provide optimal resources for Non-Real Time (NRT) data delivery, optimize resource allocation, minimize delay, reduce energy consumption, and lower buffer and holding costs. These comprehensive parameters empower mobile-WSNs to fulfill the requirements of a Quality of Service (QoS)-oriented and energy-efficient V2V communication system