1,989 results on '"Machine learning"'
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2. Análisis de modelos algorítmicos de aprendizaje automático para la predicción del estado vital a los seis meses tras fractura de cadera en pacientes mayores de 74 años
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Calvo Lorenzo, I., Uriarte Llano, I., Mateo Citores, M.R., Rojo Maza, Y., and Agirregoitia Enzunza, U.
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- 2025
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3. Una actualización sobre aspectos éticos en la investigación clínica: el abordaje de cuestiones sobre el desarrollo de nuevas herramientas de IA en radiología
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Gomes Lima Junior, A., Lucena Karbage, M.F., and Nascimento, P.A.
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- 2025
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4. Gemelos digitales pulmonares
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Fernández-Tena, Ana, Arnedo, Carlos, Houzeaux, Guillaume, and Eguzkitza, Beatriz
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- 2024
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5. Inteligencia artificial, vampirismo y la caja negra: aproximación especulativa en el marco del aprendizaje automático (AA)
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Serra-Navarro, David
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- 2024
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6. Inteligencia artificial en la industria de la hospitalidad latinoamericana: una revisión de alcance
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Ismael Castillo-Ortiz, Elizabeth Guevara Martínez, and Carmen Villar-Patiño
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turismo ,gastronomía ,machine learning ,latam ,predecir ,producir ,promocionar ,proporcionar ,Recreation. Leisure ,GV1-1860 - Abstract
Este trabajo tiene como objetivo determinar la aplicación de la inteligencia artificial en la industria de la hospitalidad en Latinoamérica. Para lograrlo, se optó por un enfoque de revisión de alcance, adecuado para casos como éste, en los que la literatura existente no ha sido revisada de manera exhaustiva o presenta una naturaleza compleja y heterogénea. En este estudio se seleccionaron 35 documentos que cumplían con los criterios: ser de autores latinoamericanos y con temática de inteligencia artificial aplicada en la región. Al realizar el análisis con la metodología de las 4 P’s (Predecir, Producir, Promocionar y Proporcionar) se obtuvo un total de 86 contribuciones, de éstas el primer lugar con 42% se concentra en proporcionar experiencias a clientes, 22% en predecir información de valor para la toma de decisiones, 19% en promocionar sus ofertas y el 17% en mejorar la producción de servicios y productos. Además, se encontró que las técnicas de inteligencia artificial más empleadas en el sector de la hospitalidad son aprendizaje de máquina y procesamiento de lenguaje natural. Sin embargo, se detectó que las investigaciones sobre esta temática en los países latinoamericanos representan menos del 3,5% de la producción global. Por tanto, con este trabajo se pretende contribuir a una mayor comprensión de la aplicación de la inteligencia artificial en la industria de la hospitalidad y turismo en América Latina, abarcando tanto la perspectiva del cliente como la empresarial. No obstante, resalta la urgencia de fortalecer la investigación en este ámbito para impulsar el crecimiento y la innovación de la industria de la hospitalidad y el turismo en la región, dado su papel fundamental en la economía de estos países.
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- 2025
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7. PRECIFICAÇÃO DO SEGURO AUTOMÓVEL COM MACHINE LEARNING E MODELOS LINEARES GENERALIZADOS
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Josemar C. Cabral and Eduardo Fraga Lima de Melo
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machine learning ,seguro automóvel ,tarifação ,glm ,Probabilities. Mathematical statistics ,QA273-280 - Abstract
Neste trabalho, aplicamos modelos de Machine Learning (árvores de regressão, random forest, boosting e XGBoost) para precificação ou tarifação de uma carteira de seguro automóvel e comparamos com modelos lineares generalizados (GLM) em dados de sinistros de seguro automóvel considerando as principais características do segurado e do veículo. Com base em critérios de avaliação de peformance fora-da-amostra, os resultados indicaram que o XGBoost é o melhor método preditivo tanto para frequência como para severidade, apresentando ganhos na predição quando comparado ao GLM comumente utilizado em tarifação de seguros.
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- 2024
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8. Algorithmic Management in the Work Environment: Responsible Interaction between the Employer, Technology Supplier, and Trade Union
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Paweł Nowik
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ethical ai ,machine learning ,algorithmic management ,human resources analytics ,hra ,Law - Abstract
This study aimed to investigate the barriers to responsible interaction between the global employer, technology provider, and trade union to realize the postulate of ethical artificial intelligence in the algorithmic management process. To overcome the barriers, this study offers a pedagogical explanation based on a universal schema for shaping a machine-learning model
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- 2024
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9. Arquitectura de IoT para el monitoreo de emisiones de gases contaminantes de vehículos y su validación a través de Machine Learning
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Torres Guin, Washington, Sánchez Aquino, José, Bustos Gaibor, Samuel, and Coronel Suárez, Marjorie
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- 2024
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10. La convergencia de la ciencia de datos y la medicina de laboratorio
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Nieto-Moragas Javier, Marull Arnall Anna, Calvo Boyero Fernando, Martin Pérez Salomón, Marqués García Fernando, Hernando Redondo Javier, Blanco Grau Albert, Cauqui Lende Cristian, Molina Borrás Ángel, Prieto Arribas Daniel, and de Rafael González Elena
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machine learning ,grupo de trabajo ,ciencia de datos ,formación ,Medical technology ,R855-855.5 - Published
- 2024
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11. Influence of high Andean grasslands on landslide reduction in Peru
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Albert Franco Cerna-Cueva, Katherin Lourdes Uriarte-Barraza, Grecia Isabel Lobatón-Tarazona, Wensty Saenz-Corrales, Casiano Aguirre-Escalante, Peter Coaguila-Rodriguez, and Manuel Reategui-Inga
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high andean grasslands ,landslide ,machine learning ,ecosystem services ,climate change ,Agriculture ,Agriculture (General) ,S1-972 - Abstract
Agricultural and urban expansion has caused considerable degradation of ecosystems. In the case of Peruvian high Andean grasslands, it was reported that between 2000 and 2009, this ecosystem was reduced by 7%. The limited or no protection they receive is partly due to the fact that the benefits of ecosystem services are not widely known. This research aims to establish and predict the influence of high Andean grasslands on the annual occurrence of landslides. To do so, we identified occurrences of landslides, falls, huaycos, avalanches, and alluviums in high Andean grasslands. We also examined urban areas and agricultural zones of Peru for the period from 2003 to 2016. Subsequently, we extracted data on precipitation, temperature, slopes, soil types, and geographical variables. This data was used to train a machine learning model. The results show that 96% of landslides occurred in human-intervened areas, and only 4% in high Andean grasslands. Precipitation and slope thresholds for landslide occurrence are higher in high Andean grasslands compared to agricultural and urban areas. The best-performing machine learning models were linear regression, Gaussian processes, random forest, and support vector machine. They had coefficients of determination of R² = 0.80, 0.80, 0.66, and 0.64, respectively. Predictions show that if agricultural or urban areas are established in wet or dry puna grasslands, the average number of occurrences multiplies. The multiplier factors are 2.1 and 7.08, the number of deaths by 2.8 and 10.49, the number of houses destroyed by 2.4 and 7.51, and the number of roads destroyed by 2.2 and 7.37, respectively. The study demonstrates that conserving high Andean grasslands significantly reduces landslides compared to urban or agricultural areas.
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- 2024
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12. Contribution of machine learning to the analysis of grade repetition in Spain: A study based on PISA data
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Alexander Constante Amores, Delia Arroyo Resino, María Sánchez Munilla, and Inmaculada Asensio Muñoz
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pisa ,grade repetition ,machine learning ,contextual variables ,multilevel logistic regression ,Education (General) ,L7-991 - Abstract
Introduction: The rate of grade repetition is excessively high in Spain despite being a controversial measure. In order to obtain evidence to contribute to reducing it in compulsory education, the present work is an in-depth study of the PISA 2018 context indices that are most closely linked to this phenomenon. Method: With the sample of Spanish students (n = 35 943), we used an automatic machine learning method to select and order the predictors, and multilevel logistic regression (students and centres) to quantify the contribution of each one. Results: For each educational stage we obtained the 30 most significant contextual variables, which explain 65.5% of the grade repetition variance in primary education and almost 55.7% in secondary education. Conclusions: The main indicators are principally at student level, which suggests the suitability of psychoeducational interventions based more on individual support than general policies. This gives rise to potentially more efficient and equitable measures than grade repetition, aimed at, for example, the management of learning time or academic/professional guidance, and predictors with specific differential significance at each stage. Methodologically, the study contributes to improving the specification of predictive models.
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- 2024
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13. Neurofeedback-driven Emotional Regulation Training in a virtual reality environment: a machine learning approach using OpenBCI
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Sandra Milena Camelo Roa and Belman Jahir Rodríguez
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Neurofeedback ,Emotional regulation ,Virtual reality ,Machine learning ,OpenBCI ,Training ,Psychology ,BF1-990 - Abstract
This paper addresses the design and development of an advanced neurofeedback system for training in emotional regulation skills and competencies; the system integrates a Virtual Reality (VR) platform with a 16-channel OpenBCI device for real-time capture of electroencephalographic (EEG) signals. The main objective of the research lies in the application of machine learning algorithms, specifically Random Forest and K-Nearest Neighbors (KNN), for the classification of emotional states in terms of valence and arousal. These algorithms achieve an accuracy of up to 83% for arousal classification and 90% for valence. EEG signals are processed and classified in real time and the results are integrated into a virtual reality environment created in Unity. This adaptive environment changes according to the detected emotional states, allowing for more precise regulation. In addition, a diaphragmatic breathing protocol has been developed within the virtual reality environment as an intervention strategy for emotional regulation. The system is in its final stage of piloting to establish the efficacy of the system.
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- 2025
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14. Inteligencia artificial y spots políticos: La campaña presidencial de Guillermo Lasso en YouTube y en TikTok (2021)
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Esteban García Andrade
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Inteligencia artificial ,machine learning ,spot político ,Guillermo Lasso ,YouTube ,TikTok ,Communication. Mass media ,P87-96 - Abstract
Este artículo fusiona los conceptos de dispositivo, ideología e imaginario para analizar el impacto del algoritmo machine learning, un tipo de IA, en la creación, la producción, la circulación y el consumo de los spots políticos de Guillermo Lasso difundidos en YouTube y en TikTok durante las elecciones presidenciales de 2021 en Ecuador. A nivel cualitativo, el spot político opera como una tecnología de poder y matriz ideológica (dimensión significante) que, potenciada por el algoritmo de machine learning (dimensión asignificante), prepara la actividad imaginaria cerebral del elector para impregnarla de sentido a través de la narrativa política. A nivel cuantitativo, el número de reproducciones/ visualizaciones de determinados spots políticos permite inferir que la atención del elector se transforma en un mecanismo de producción de plusvalía y de reproducción de relaciones de poder (economía de la atención). El artículo abarca los resultados de la contienda democrática; los resultados de la campaña de comunicación política en You-Tube y en TikTok; la función del spot político en tanto dispositivo ideológico-imaginario, explicada a partir de una pieza audiovisual, considerando la influencia del algoritmo machine learning; y la discusión de la imagen de Guillermo Lasso en TikTok.
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- 2025
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15. The Future of Gaming: How Artificial Intelligence is Revolutionizing the Industry
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Dariana Gomez-Alvarez, Michel Lopez-Franco, David Bonilla Carranza, Carlos Lopez-Franco, and Lilibet Lopez-Franco
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Artificial Intelligence ,Gaming ,Player Engagement ,Game Design ,Machine Learning ,Computer engineering. Computer hardware ,TK7885-7895 - Abstract
Artificial Intelligence (AI) is not just a tool for improving video games; it is revolutionizing the entire industry. This opinion paper explores how AI is transforming gaming experiences, enhancing player engagement, and reshaping game design. We argue that AI’s role in gaming is not just beneficial but essential for the future of interactive entertainment. Through an examination of current trends and future possibilities, we provide our perspective on the profound impacts of AI on the gaming world.
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- 2025
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16. Modelo basado en YOLOv8 para la detección automática de daños en tejados residenciales
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Alisson Souza Silva, Arthur Rios de Azevedo, Fernando Humberto de Almeida Moraes Neto, and Paulo Henrique Ferreira da Silva
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maintenance management ,machine learning ,You Only Look Once (YOLO) ,roof inspection ,rooftop assessment ,Building construction ,TH1-9745 - Abstract
This study developed an automated image recognition model for inspecting residential roofs using the YOLOv8 architecture to identify three types of damage. The methodology involved images from 167 buildings captured by drones and annotated in CVAT, which were used to train and test the model. YOLOv8 was applied for anomaly detection and classification, achieving 79% precision. The limitations were the small dataset and the limited variety of capture angles. The originality of the work lies in the innovative use of YOLOv8 for roof inspection. Future research will focus on developing the YOLOv9 and YOLOv10 architectures and expanding the dataset and damage classes.
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- 2025
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17. Inteligencia Artificial: Machine Learning, para Detección Temprana de Plagas y Enfermedades de Cultivos Básicos, Nicaragua
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Saira María Urbina Cienfuegos and Jazcar Josué Bravo Rivas
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machine learning ,plagas ,enfermedades ,cultivos ,Industrial engineering. Management engineering ,T55.4-60.8 ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Computer engineering. Computer hardware ,TK7885-7895 - Abstract
El presente artículo, muestra aspectos relevantes del proceso de desarrollo de la aplicación móvil que incorpora técnicas de Machine Learning para detectar de forma temprana plagas y enfermedades en cultivos de granos básicos como maíz, frijol y sorgo, estos son indispensables para el consumo humano en Nicaragua. Se utilizó metodología de desarrollo ágil Scrum, se adoptaron tecnologías como Android Studio, lenguaje de programación Java, Google Teachable Machine para entrenamiento del modelo de aprendizaje automático y TensorFlow Lite para incorporar modelo en la aplicación móvil. Los resultados muestran un Sprint con sus historias de usuarios, estas se convirtieron en funcionalidades que incluyen el modelo para el reconocimiento de imágenes con precisión de 95.8% utilizando un conjunto de datos de 252 imágenes de cultivos sanos y enfermos. La metodología indica organización de la programación según patrón Modelo – Vista – Controlador y métricas utilizadas por el modelo. Las conclusiones hacen énfasis en detalles de los resultados obtenidos en Sprint#1. Al final, también se mencionan retos a superar al aplicar aprendizaje automático en el sector agrícola.
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- 2025
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18. Talent scouting and standardizing fitness data in football club: systematic review
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Moch Yunus, Ronal Surya Aditya, Nanang Tri Wahyudi, Daifallah M. Al Razeeni, and Reem Iafi AlMutairi
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Football ,Data Science ,Big Data ,Digital Technology ,Athletic Performance ,Machine Learning ,Sports ,GV557-1198.995 - Abstract
Talent scouting and fitness data standardization in professional football clubs have become central topics in recent research. This review aims to consolidate advancements in technology, big data, and data analytics, examining their roles in optimizing talent identification and fitness evaluation within football clubs. A systematic search strategy was applied across academic databases, including PubMed, IEEE Xplore, and Scopus, using keywords like "football talent scouting," "fitness data standardization," "data analytics in sports," and "machine learning in football performance." Studies selected for review involved professional football players and interventions using digital technologies and data-driven methods within club settings, covering experimental, observational, and mixed-method designs in football environments. This review highlights the impact of integrating quantitative player statistics with advanced analytics to enhance recruitment precision and team performance, showing that data models—such as classification and regression—can predict performance scores with up to 94% accuracy for forward positions, underscoring the transformative role of data analytics in professional football.
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- 2024
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19. Landslide Susceptibility Modeling Using Artificial Neural Networks in the Municipality of Joinville, southern Brazil
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Renato Ribeiro Mendonça, Guilherme Garcia de Oliveira, and Carlos Gustavo Tornquist
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Disaster Prevention ,Machine Learning ,Landslide Susceptibility ,Geology ,QE1-996.5 - Abstract
Assessing landslide susceptibility in a municipality is crucial for disaster prevention, and Artificial Neural Networks (ANN´s) have proven effective in this analysis. This study aimed to model landslides susceptibility in the municipality of Joinville, Santa Catarina state, southern Brazil, using ANNs. The municipality has a significant history of such events, allowing for an inventory of occurrence areas (OC) through polygon mapping on satellite images. For non-occurrence areas (NO), a 1 km radius buffer was used, subtracting OC from it. Random points were generated at 10 m intervals, with a value of 1 for OC and 0 for NO. The explanatory variables were divided into three groups: (i) morphometric variables, (ii) horizontal distances to roads and structural lineaments, and (iii) geo-environmental cartographic databases. Five ANN´s configurations were tested. Validation employed metrics such as area under the ROC curve (AUC) and overall accuracy (ACC), with the best modeling yielding an AUC of 0.90 and ACC of 0.84. This result utilized all explanatory variables except land use and cover, which caused a slight bias in the ANN due to the predominance of landslides in forested areas in the inventory. Geology played a crucial role in determining susceptibility.
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- 2024
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20. Startup Success Prediction with PCA-Enhanced Machine Learning Models
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Youngkeun Choi
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Startup success prediction ,Machine learning ,Principal Component Analysis (PCA) ,Support Vector Classifier (SVC) ,Venture capital ,Investment decision-making ,Technology ,Technology (General) ,T1-995 - Abstract
This study evaluates the effectiveness of various machine learning algorithms in predicting startup success and explores the performance improvement achieved by applying Principal Component Analysis (PCA) to the models. By analyzing logistic regression, support vector classifier (SVC), XGBoost, and other supervised learning algorithms, the study demonstrates that PCA enhances the generalization performance of most models. Notably, Support Vector Classifier (SVC) showed an accuracy of 0.78, precision of 0.83, recall of 0.73, and F1 score of 0.74 without PCA, but performance significantly improved with PCA, recording an accuracy of 0.90, precision of 0.90, recall of 0.89, and F1 score of 0.89. Academically, this research contributes to the literature by examining how dimension reduction can boost the accuracy of machine learning models for startup success prediction, providing a valuable intersection of machine learning and venture capital studies. Practically, it offers investors AI-driven decision- making tools to enhance the precision of investment evaluations and better identify startups with high growth potential. Despite its contributions, this study is limited by the specific dataset used, suggesting that future research could explore various datasets and alternative dimension reduction techniques. Future studies could also assess real-time data application and incorporate deep learning models to improve predictive performance in startup success evaluation.
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- 2024
21. Machine Learning en la detección y predicción de enfermedades del ganado
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Marco Vieto-Vega
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Machine learning ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
La detección temprana y la predicción de enfermedades en el ganado son esenciales para garantizar la salud y el bienestar de los animales, mejorar la productividad y reducir las pérdidas económicas. En este contexto, el Machine Learning (ML), un avance prominente dentro de la inteligencia artificial emerge como una herramienta revolucionaria para transformar el proceso de identificación y manejo de enfermedades en los animales. Esta tecnología permite desarrollar algoritmos complejos capaces de analizar grandes volúmenes de datos clínicos y ambientales, identificando patrones de alerta temprana en síntomas y comportamientos asociados a enfermedades. A través de modelos predictivos, el ML evalúa factores de riesgo y estima la probabilidad de aparición de enfermedades, lo que mejora significativamente la precisión diagnóstica y la efectividad de los tratamientos. Este artículo revisa de manera exhaustiva el uso de ML en la producción ganadera, abordando aplicaciones, modelos y técnicas de vanguardia para la detección y manejo sanitario del ganado, y plantea oportunidades para una gestión ganadera más eficiente y ética, considerando además los desafíos éticos y de privacidad inherentes a la implementación de estas tecnologías
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- 2024
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22. Application of machine learning for brain tumor diagnosis using magnetic resonance images: a comparative analysis
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Patel Rahul kumar-Manilal and D. J. Shah
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magnetic resonance imaging ,support vector machine ,random forest ,convolutional neural network ,brain tumor ,machine learning ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
A brain tumor is an abnormal growth of cells that may lead to cancer. MRI scans are the conventional method of diagnosing brain tumors. This paper investigates the potential of machine learning (ML) in interpreting MRI images for brain tumors. The study described applies and evaluates three different methods. The study applied and evaluated three different methods for identifying brain tumors: a self-defined a support vector machine (SVM), a Random forest (RF), and a convolution neural network (CNN). The Bra-TS 2018 dataset is used in this study on MRI brain images containing images of glioma, meningioma, pituitary, and no tumors. Python 3.11 was used for interpreting MRI images for brain tumors. The accuracy of the proposed CNN, RF, and SVM were found to be 99.29%, 99.06%, and 98.36%, respectively. The CNN approach has higher accuracy than innovative techniques.
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- 2024
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23. A Simple Credit Rating Prediction Model for FinTech Companies Using SMOTE and MRMR Techniques
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Jesús Gopar Sánchez
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FinTech ,Credit Rating ,SMOTE ,MRMR ,Machine Learning ,Finance ,HG1-9999 ,Economics as a science ,HB71-74 - Abstract
FinTech companies have made the financial industry more efficient and have increased financial inclusion. However, it has also brought new risks to the financial system. Regulators, investors, and researchers are concerned that their financial difficulties could affect the financial system. Our study aims to delve deeper into the effectiveness of machine learning techniques in identifying early warnings of FinTech companies’ credit risk impairment. Using commonly employed accounting and market measures in the literature, we created various classifiers to predict FinTech credit ratings. Classification algorithms face a challenge when the number of observations between classes is not equivalent, affecting their performance. Due to the limited size of publicly traded FinTech stocks with an issuer-level credit rating, our database has few observations and is highly imbalanced. The results of our study show that the SMOTE oversampling technique improves the predictive power of machine learning algorithms and that feature selection algorithms such as MRMR allow the generation of less complex and easierto-understand models. Our results suggest that the KNN classification algorithm has higher accuracy in predicting FinTech’s credit ratings.
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- 2024
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24. Expanded Design
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Roberto Bottazzi
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Urban design ,Architecture ,Machine learning ,Paradigms ,Aesthetics ,Creativity ,Philosophy (General) ,B1-5802 ,Technology - Abstract
The introduction of automated algorithmic processes (e.g. machine learning) in creative disciplines such as architecture and urban design has expanded the design space available for creativity and speculation. Contrary to previous algorithmic processes, machine learning models must be trained before they are deployed. The two processes (training and deployment) are separate and, crucially for this paper, the outcome of the training process is not a spatial object directly implementable but rather code. This marks a novelty in the history of the spatial design techniques which has been characterised by design instruments with stable properties determining the bounds of their implementation. Machine Learning models, on the other hand, are design instruments resulting from the training they undertake. In short, training a machine learning model has become an act of design. Beside spatial representation traditionally comprising of drawings, physical or CAD models, Machine Learning introduces an additional representational space: the vast, abstract, stochastic, multi-dimensional space of data, and their statistical correlations. This latter domain – broadly referred to as latent space – has received little attention by architects both in terms of conceptualising its technical organisation and speculating on its impact on design. However, the statistical operations structuring data in latent space offer glimpses of new types of spatial representations that challenge the existing creative processes in architectural and urban design. Such spatial representation can include non-human actors, give agency to a range of concerns that are normally excluded from urban design, expand the scales and temporalities amenable to design manipulation, and offer an abstract representation of spatial features based on statistical correlations rather than spatial proximity. The combined effect of these novelties that can elicit new types of organisation, both formally and programmatically. In order to foreground their potential, the paper will discuss the impact of ml models in conjunction with larger historical and theoretical questions underpinning spatial design. In so doing, the aim is not to abdicate a specificity of urban design and uncritically absorb computational technologies; rather, the creative process in design will provide a filter through which critically evaluate machine learning techniques. The paper tasks to conceptualise the potential of latent space design by framing it through the figure of the paradigm. Paradigms are defined by Thomas Kuhn as special members of a set which they both give rise to and make intelligible. Their ability to relate parts to parts not only resonates with the technical operations of ml models, but they also provide a conceptual space for designers to speculate different spatial organisation aided by algorithmic processes. Paradigms are not only helpful to conceptualise the use of ml models in urban design, they also suggest an approach to design that privileges perception over structure and curation over process. The creative process that emerges is one in ml models are speculative technical elements that can foreground relations between diverse datasets and engender an urbanism of relations rather than objects. The application of such algorithmic models to design will be supported by the research developed by students part of Research Cluster 14 part of the Master in Urban Design at The Bartlett School of Architecture in London.
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- 2024
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25. Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens
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Bona Yun, Xinyu Liao, Jinsong Feng, and Tian Ding
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Foodborne pathogens ,food safety ,antimicrobial resistance ,machine learning ,Nutrition. Foods and food supply ,TX341-641 ,Food processing and manufacture ,TP368-456 - Abstract
The World Health Organization (WHO) has identified antimicrobial resistance (AMR) as one of the top three global dangers to public health. One of the most vital factors contributing to the high prevalence of AMR is the misuse/overuse of antibiotics for treatment and/or as a growth promoter in the food industry. AMR can be transmitted to humans via food, the environment, or other channels through horizontal gene transfer. Therefore, efficient methods are urgently needed to determine whether bacteria are resistant to antibiotics. This work provides a review of the advances in machine learning (ML) techniques for predicting and identifying AMR in foodborne pathogens. We also emphasize the groundbreaking potential of whole genome sequencing (WGS) and spectroscopy technologies combined with ML in the context of AMR detection. These offer enormous potential because of their unique characteristics, which can overcome inherent limits in existing detection approaches.
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- 2024
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26. Precision harvesting: comparative analysis of machine learning and generative AI-based classifiers for guava fruit maturity assessment using thermal imaging
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Zeeshan Ali Haq, Zainul Abdin Jaffery, and Shabana Mehfuz
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Fuzzy logic system ,machine learning ,generative AI ,thermal image ,quality assessment ,Nutrition. Foods and food supply ,TX341-641 ,Food processing and manufacture ,TP368-456 - Abstract
Guava is a highly nutritious fruit with abundance of health benefits and is medically recommended for daily consumption. Therefore, quality assessment of guava is of significance. Manual quality grading of fruits is being performed for a long time which suffers from subjective biases. Consequently, development of a computer vision-based automated system for quality grading of fruits is essential. In this paper, maturity assessment of guava is performed using various machine learning classifiers, including neural network, fuzzy logic system, and generative AI models. Thermal images of guava are used for maturity assessment. Performance of these models is evaluated by determining the confusion matrix and classification report, considering both class-wise and overall classification. After careful observation, FLS-based ANFIS is highly recommended for grading the guava on the basis of its maturity level. Furthermore, thermal imaging is also very significant for the development of a holistic computer vision-based fruit quality assessment model.
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- 2024
- Full Text
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27. A scoping review
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I Gusti Ayu Tirtayani, I Made Wardana, Putu Yudi Setiawan, and I Gst. Ngr. Jaya Agung Widagda K
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Scoping review ,Machine learning ,Social media marketing ,Data analysis ,Marketing strategy ,Accounting. Bookkeeping ,HF5601-5689 - Abstract
This research conducts a scoping review on the application of machine learning (ML) in social media marketing, an increasingly pivotal area in digital marketing strategies. Machine learning plays a crucial role in analyzing user data, identifying consumer behavior, personalizing content, and optimizing marketing campaigns. Through a systematic search of academic journals, conference proceedings, and other relevant sources, this review identifies and synthesizes studies that explore the use of ML in social media marketing. The selected studies are analyzed to provide a comprehensive overview of ML's impact and applications within this domain. Key findings highlight the significant role of ML in enhancing personalization, improving user engagement, and driving more effective marketing strategies. However, challenges such as data privacy concerns, algorithmic biases, and the need for greater transparency are also noted. The practical implications for marketers include the importance of ethical practices in data handling, algorithm development, and consumer trust-building. Additionally, this review identifies gaps in the current literature and suggests directions for future research, offering valuable insights for both researchers and practitioners aiming to leverage machine learning in social media marketing.
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- 2024
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28. GREEN CHEMISTRY: USING MORE SUSTAINABLE TECHNIQUES TO ESTIMATE THE WATER QUALITY OF THE UBERABA RIVER USING ORBITAL OPTICAL SENSORS
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Tatiane Carvalho Maeda, Franciele Morlin Carneiro, José Waldir de Sousa Filho, Luciano Shozo Shiratsuchi, and Geoffroy Roger Pointer Malpass
- Subjects
machine learning ,remote sensing ,sustainability ,water quality. ,Chemistry ,QD1-999 - Abstract
Green Chemistry aims to prevent pollution resulting from activities in the chemical sector. Generally, laboratory activities generate pollution because toxic substances are used to perform analyses, which produce waste once the analyses are completed. This study aimed to monitor the quality of the Uberaba River using more sustainable and non-destructive techniques through orbital remote sensing and to develop methods for estimating water quality at reduced costs using orbital sensors and machine learning. The values of water quality parameters, such as iron, nitrate, chloride, total phosphorus, and total nitrogen, were studied between 2018 and 2023. Orbital sensing data (spectral bands and vegetation indices) were taken from the exact geographic coordinates of the collection points. Data were analyzed using Pearson’s correlation coefficient and random forest regression analysis. The study concludes that it is possible to perform orbital remote sensing by estimating water quality through random forest regression, correlating the information obtained from Sentinel-2 images with the values of these parameters. This approach is a sustainable technique that does not generate waste and represents a 100% saving compared to conventional chemical analyses.
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- 2024
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29. Application of topic modelling and neural network analysis to analyze life satisfaction
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Young-Chool Choi
- Subjects
life satisfaction ,machine learning ,multi-layer perceptron ,neural network analysis ,quality of life ,topic modeling ,Museums. Collectors and collecting ,AM1-501 ,Bibliography. Library science. Information resources - Abstract
Abtract This study aims analyze the important influencing factors that affect the life satisfaction of Koreans, and to identify the relative importance of these factors. For this purpose, we utilize academic papers on what influences life satisfaction, and questionnaire data from the survey on social integration conducted annually by the Korean Government. A topic modelling analysis method was used to derive important influencing factors, and a neural network analysis method, one of the machine learning methods, was used to analyze the relative importance of influencing factors. The analysis showed that the factor that had the greatest impact on Koreans’ life satisfaction was satisfaction with work. Other factors included self-esteem, level of worry and anxiety, and level of satisfaction with health status. The study used methods such as topic modeling and neural network analysis to derive the main factors affecting life satisfaction and analyze the relative importance of the factor involved. The study results suggest that in recognition of the importance of job satisfaction, future research should be expanded, and that the Korean Government should introduce various policies to increase job satisfaction.
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- 2024
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30. Landslide susceptibility mapping using logistic regression, random forests, and artificial neural networks: a case study in Mariana/MG, Brazil
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Mateus Oliveira Xavier and César Falcão Barella
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Machine learning ,Predictive analysis ,Landslide conditioning factors ,Risk management ,Geology ,QE1-996.5 - Abstract
The landslide susceptibility mapping (LSM) plays an important role in risk management. This study evaluated the predictive capabilities of three machine learning (ML) approaches applied to LSM: logistic regression (LR), random forests (RF), and artificial neural networks (ANN). The study was conducted in a mountainous region of Mariana/MG, Brazil. Initially, a point inventory with 364 landslides and 364 stable regions was randomly partitioned in a 70% training and 30% testing ratio for the models. Nine landslide conditioning factors (LCF), ranked by information gain (IG), were considered: slope angle (IG=0.486), geomorphology (IG=0.235), topographic wetness index - TWI (IG=0.138), lithology (IG=0.077), slope orientation (IG=0.067), topographic position index - TPI (IG=0.052), distance from drainage (IG=0.032), slope curvature (IG=0.029) and the distance from roads (IG=0.024). The evaluation of the area under the curve (AUC-ROC) and the classification efficiency rates in high () and low () susceptibility were used to compare the results of the approaches. The results demonstrated that although RF (AUC-ROC=0,947, =6,808, =0,030) slightly outperformed LR (AUC-ROC=0,936, =5,695, =0,050) and ANN (AUC-ROC=0,934, =6,495, =0,060), all the approaches exhibited high predictive capability in identifying areas susceptible to landslides.
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- 2024
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31. Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review
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Henrique Henriques and Luis Nobre Pereirsa
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artificial intelligence ,hotel demand forecast ,revenue management ,machine learning ,artificial neural networks ,digital transformation ,Recreation leadership. Administration of recreation services ,GV181.35-181.6 - Abstract
This systematic literature review (SLR) explores current state-of-the-art artificial intelligence (AI) methods for forecasting hotel demand. Since revenue management (RM) is crucial for business success in the hotel industry, this study aims to identify state-of-the-art effective AI-based solutions for hotel demand forecasting, including machine learning (ML), deep learning (DP), and artificial neural networks (ANNs). The study conducted an SLR using the PRISMA model and identified 20 papers indexed in Scopus and the Web of Science. It addresses the gaps in the literature on AI-based demand forecasting, highlighting the need for clarity in model specification, understanding the impact of AI on pricing accuracy and financial performance, and the challenges of available data quality and computational expertise. The review concludes that AI technology can significantly improve forecasting accuracy and empower data-driven decisions in hotel management. Additionally, this study discusses the limitations of AI-based demand forecasting, such as the need for high-quality data. It also suggests future research directions for further enhancing AI forecasting techniques in the hospitality industry.
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- 2024
32. Dynamic Malware Analysis Using Machine Learning-Based Detection Algorithms
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Erly Galia Villarroel Enriquez and Juan Gutiérrez-Cárdenas
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malware ,machine learning ,detección ,Systems engineering ,TA168 - Abstract
Con la creciente popularidad del uso de teléfonos celulares, el riesgo de infecciones por malware en dichos dispositivos ha aumentado, lo que genera pérdidas financieras tanto para individuos como para organizaciones. Las investigaciones actuales se centran en la aplicación del aprendizaje automático para la detección y clasificación de estos programas malignos. Debido a esto el presente trabajo utiliza la frecuencia de llamadas al sistema para detectar y clasificar malware utilizando los algoritmos XGBoost, LightGBM y random forest. Los resultados más altos se obtuvieron con el algoritmo de LightGBM, logrando un 94.1% de precisión y 93.9% tanto para exactitud, recall y f1-score, lo que demuestra la efectividad tanto del uso del aprendizaje automático como del uso de comportamientos dinámicos del malware para la mitigación de amenazas de seguridad en dispositivos móviles.
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- 2024
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33. A study on the impact of data balance on rainfall prediction through artificial neural networks using surface microwave radiometers
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Lourenço José Cavalcante Neto and Alan James Peixoto Calheiros
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rainfall prediction ,data balancing ,machine learning ,amazon ,atto campina ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The National Institute for Space Research (INPE) has been a partner in significant projects that conduct atmospheric investigations impacting various sectors, such as the Amazon Tall Tower Observatory (ATTO) project. Since 2009, the project has conducted studies on the interactions between climate and the Amazon forest. ATTO has played an essential role in providing large volumes of data obtained by meteorological sensors, contributing to a deeper understanding of the atmospheric dynamics of the region. In a landscape where Artificial Intelligence-based rainfall forecast models gain prominence, this study explores the imbalance of data from the ATTO Campina field experiment and its influence on short-term rainfall forecasts using Artificial Neural Networks (ANNs). Metrics such as MAE, RMSE, and POD, as well as FAR indices, were applied in the assessment and revealed the connection between data balance and forecast results. More balanced data or data with greater weights for different rainfall ranges yield better results. The study emphasizes the importance of reliable data for training rain forecast models, aiming to improve the dexterity of these models. This approach is fundamental to increase the reliability of these models in real environments.
- Published
- 2024
34. Electricity Energy Demand Prediction Using Computational Intelligence Techniques
- Author
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Camila Martins Saporetti and Bruno da S. Macêdo
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electric energy ,machine learning ,meta-heuristic ,gray wolf optimization ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Energy is an important pillar for the economic development of a country. The demand for electricity is something that continues to grow, one of the contributing factors is the emergence of various technological equipment and the consequent use by the population. There are several resources that can be exploited to generate electricity, with hydroelectric power stations being one of the most used resources. As electrical energy cannot be stored, there is a need to estimate its consumption, looking for a way to meet this energy demand. In this context, this study seeks to apply machine learning techniques, using the Grey Wolf Optimization (GWO) meta-heuristic to optimize regression models, to predict the demand for electricity in Brazil, and it aims to estimate how much energy should be produced. For the predictions, the period between the years 2017 to 2022 was used, totaling around 2,190 samples. The methodology involves pre-processing, crossvalidation, parameters optimization and regression. The results show that Random Forest performed well in the experiments carried out, presenting a coefficient of determination (R2) of 0.8751, Root Mean Squared Error (RMSE) of 0.0554 and Mean Absolute Error (MAE) of 0.0348 in the best model.
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- 2024
35. Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care
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Sahar Borna, Cesar A. Gomez-Cabello, Sophia M. Pressman, Syed Ali Haider, Ajai Sehgal, Bradley C. Leibovich, Dave Cole, and Antonio Jorge Forte
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artificial intelligence ,natural language processing ,large language model ,machine learning ,ChatGPT ,Bard ,Public aspects of medicine ,RA1-1270 ,Psychology ,BF1-990 - Abstract
In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA’s responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.
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- 2024
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36. The Birth of the Third Author: Stylometric Analysis of the Stories of Honorio Bustos Domecq
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Boris V. Kovalev
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jorge luis borges ,adolfo bioy casares ,bustos domecq ,latin american literature ,stylometry ,delta ,svm ,rolling stylometry ,machine learning ,American literature ,PS1-3576 - Abstract
The article is devoted to the stylometric analysis of stories written by Jorge Luis Borges and Adolfo Bioy Casares under the common pseudonym Honorio Bustos Domecq. The work poses two questions: 1) Is Bustos Domecq’s style different from the writing style of Borges and Bioy Casares? 2) What is the share of influence of each of the co-authors on the formation of Bustos Domecq's style? To solve research problems, it is used Delta method, one of the most reliable stylometric tools to date; as well as the support vector machine, a common machine learning method that is used to solve classification problems. It turns out that Bustos Domecq's style differs from the style of the stories of Borges and Bioy Casares. However, in the texts of Bustos Domecq the authorial signal of Bioy Casares predominates, which is revealed on the basis of both stylometric and historical-literary analysis. The influence of Borges is more clearly manifested only in the first stories (1940s), when Bioy Caceres is a literary disciple of Borges, and in the second half of the 1960s, at a time of serious emotional upheavals for Borges, as well as his world recognition. Analysis of the topics of the stories, where Borges's authorial signal predominates, also confirms the results of stylometric experiments: the story “The Long Search for Tai An”, as well as the first texts of the collection The Chronicles of Bustos Domec “Tribute to Cesar Paladion”, “An Evening with Ramon Bonavena” and others really correspond to the poetics of Borges.
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- 2024
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37. Converge of data science and laboratory medicine
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Nieto-Moragas Javier, Marull Arnall Anna, Calvo Boyero Fernando, Martin Perez Salomón, Marqués García Fernando, Hernando Redondo Javier, Blanco Grau Albert, Cauqui Lende Cristian, Molina Borrás Ángel, Prieto Arribas Daniel, and de Rafael González Elena
- Subjects
machine learning ,working group ,data science ,training ,Medical technology ,R855-855.5 - Published
- 2024
- Full Text
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38. Performances of several machine learning algorithms and of logistic regression to predict Fasciola hepática in cattle
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Malik Ergin and Özgür Koçkan
- Subjects
Fasciola hepatica ,classification ,data mining ,fluke ,machine learning ,Agriculture (General) ,S1-972 - Abstract
Abstract The objective of this work was to compare the performances of logistic regression and machine learning algorithms to predict infection caused by Fasciola hepatica in cattle. A dataset on 30,151 bovines from Uruguay was used. Logistic regression (LR) and the algorithms k-nearest neighbor (KNN), classification and regression trees (CART), and random forest (RF) were compared. The interquartile range (IQR) and z-score were used to improve the classification and compared to each another. Sex, age, carcass conformation score, fat score, productive purpose, and carcass weight were used as independent variables for all algorithms. Infection by F. hepática was used as a binary dependent variable. The accuracies of LR, KNN, CART, and RF were 0.61, 0.57, 0.57, and 0.58, respectively. The variable importance of LR showed that adult cattle tended to be infected by F. hepatica. All models showed low accuracy, but LR successfully distinguished variables related to F. hepatica. Both the IQR and z-score show similar results in improving the classification metrics for the used dataset. In the dataset, data related to climate or factors such as body weight can improve the reliability of the model in future studies.
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- 2024
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39. Development of a machine learning model to estimate length of stay in coronary artery bypass grafting
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Renato Camargos Couto, Tania Pedrosa, Luciana Moreira Seara, Vitor Seara Couto, and Carolina Seara Couto
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Length of Stay ,Machine Learning ,Coronary Artery Bypass ,Public aspects of medicine ,RA1-1270 - Abstract
ABSTRACT OBJECTIVE: To develop and validate a predictive model utilizing machine-learning techniques for estimating the length of hospital stay among patients who underwent coronary artery bypass grafting. METHODS: Three machine learning models (random forest, extreme gradient boosting and neural networks) and three traditional regression models (Poisson regression, linear regression, negative binomial regression) were trained in a dataset of 9,584 patients who underwent coronary artery bypass grafting between January 2017 and December 2021. The data were collected from hospital discharges from 133 centers in Brazil. Algorithms were ranked by calculating the root mean squared logarithmic error (RMSLE). The top performing algorithm was validated in a never-before-seen database of 2,627 patients. We also developed a model with the top ten variables to improve usability. RESULTS: The random forest technique produced the model with the lowest error. The RMLSE was 0.412 (95%CI 0.405–0.419) on the training dataset and 0.454 (95%CI 0.441–0.468) on the validation dataset. Non-elective surgery, admission to a public hospital, heart failure, and age had the greatest impact on length of hospital stay. CONCLUSIONS: The predictive model can be used to generate length of hospital stay indices that could be used as markers of efficiency and identify patients with the potential for prolonged hospitalization, helping the institution in managing beds, scheduling surgeries, and allocating resources.
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- 2024
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40. SGDWOA: A Novel Approach in Whale Optimisation for Accurate Cell Classification in Oral Squamous Cell Carcinoma Using Machine Learning
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Anuradha Suresh-Pandit and Vaibhav V. Dixit
- Subjects
oral cancer ,oral squamous cell carcinoma ,histopathological images ,AI-based system ,machine learning ,data preprocessing ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Oral squamous cell carcinoma (OSCC), a common head and neck cancer, is often unnoticed but can be identified early. Diagnosing this heterogeneous tumour requires extensive human experience, and artificial intelligence can help to improve diagnosis. This study used novel methodologies based on feature selection and classification in an attempt to obtain good findings for the early detection of OSCC. By using cutting-edge hybrid strategies to extract features and improve classification, this work seeks to bridge the gap among deep learning and machine learning procedures. Initially, preprocessing is done to address artifacts in the OSCC dataset. The first method uses SMOTE oversampling and feature scaling in conjunction with Resnet 50 and Efficientnet B5 models for feature extraction. In the second method, the best feature set is chosen using the Statistic gain Dynamic Remodelled Whale Optimization Algorithm (SDRWOA), and the Random Forest Classifier is then employed to classify cancer types into poor, moderate, and well categories. The finding shows that the proposed model beats the other classifiers by attaining the maximum overall accuracy, recall and F1-score of 98% and precision of 97.6%. In conclusion, the suggested approach advances the development of extremely precise and effective OSCC diagnosis techniques.
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- 2024
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41. Inteligência artificial: Vigiexcelência, uma estratégia desenvolvida durante a pandemia de Covid-19
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Eliza Miranda Ramos and Alexandra Maria Almeida Carvalho
- Subjects
COVID-19 ,Machine Learning ,Vigilância ,Epidemiologia ,Redes Neurais ,Medicine (General) ,R5-920 - Abstract
Objetivo: Descrever o processo de desenvolvimento de uma IA com o uso de Machine Learning para a tomada de decisões relacionadas ao Covid-19. Metodologia: Este estudo quantitativo, descritivo e exploratório utilizou dados secundários de domínio público dos sistemas da rede SUS, coletados na plataforma OpenDataSUS e na rede municipal de saúde. O objetivo foi descrever o processo de desenvolvimento de uma IA chamada VIGIEXCELÊNCIA com o uso de Machine Learning para tomada de decisão rápida em resposta à Covid-19. Resultado: Pelo meio do uso do machine learning o algoritmo foi capaz de realizar avaliações e fornecer respostas rápidas com base em modelos preditivos. Conclusão: O uso de IA na vigilância epidemiológica melhora a prestação de serviço de saúde.
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- 2024
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42. Artificial intelligence in psychiatric diagnosis: challenges and opportunities in the era of machine learning
- Author
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Kirolos Eskandar
- Subjects
artificial intelligence ,psychiatric diagnosis ,machine learning ,mental health technology ,personalized psychiatry ,Psychiatry ,RC435-571 - Abstract
The integration of artificial intelligence (AI) into psychiatric diagnosis heralds a new era in mental health care, offering unprecedented opportunities to enhance diagnostic accuracy, personalize treatment, and streamline clinical workflows. A systematic approach was utilized, adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. This literature review explores the current state of AI in psychiatric diagnosis, highlighting key technologies such as machine learning, natural language processing, and deep learning. We discuss the application of these technologies across various psychiatric disorders, including depression, anxiety, and schizophrenia. While AI holds immense promise, significant challenges remain, including issues of data privacy, model bias, and the clinical validation of AI tools. Furthermore, ethical and regulatory considerations must be addressed to ensure responsible implementation. This review also examines the potential future directions of AI in psychiatry, emphasizing the importance of collaboration between AI systems and human clinicians. As the field evolves, AI has the potential to transform psychiatric practice, offering new avenues for early detection, personalized care, and therapeutic monitoring.
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- 2024
- Full Text
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43. Diseño y simulación de un modelo de predicción para la evaluación de la competencia digital docente usando técnicas de Machine Learning
- Author
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Wiston Forero-Corba and Francisca Negre Bennásar
- Subjects
competencia digital docente ,machine learning ,inteligencia artificial ,aprendizaje adaptativo ,tecnología emergente ,Theory and practice of education ,LB5-3640 - Abstract
Machine Learning (ML) es un campo de la inteligencia artificial que, a través de técnicas, elabora predicciones de datos masivos. La competencia digital docente (CDD) refiere comúnmente a las habilidades y destrezas de los docentes en sistemas digitales y su aplicación en los procesos de enseñanza-aprendizaje. La investigación sobre CDD es importante para las instituciones, ya que de su evaluación dependen el aprendizaje, trayectoria, dirección y comportamiento de los alumnos. La CDD en Colombia se basa en 5 elementos: Comunicativa, de gestión, investigativa, pedagógica y tecnológica, y cada uno de ellos se mide en tres niveles: Explorador, integrador e innovador. Las preguntas de investigación fueron: (1) ¿Qué tipo de resultados podemos esperar de la predicción de la CDD con técnicas de ML? (2) ¿Qué técnicas de ML son efectivas para predecir la CDD? (3) ¿Qué ventajas trae predecir la CDD con técnicas de ML? La metodología pretende diseñar un modelo de predicción de la CDD en Colombia aplicando 9 técnicas de ML usando el software Orange Data Mining. Los resultados muestran la alta efectividad que tienen las técnicas inteligentes para predecir la CDD. El modelo muestra que es retroalimentable, escalable y permite proponer itinerarios personalizados de aprendizaje.
- Published
- 2024
- Full Text
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44. IA aplicada a la identificación de características de ocupación de menores en hogares colombianos para detectar posible trabajo infantil
- Author
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Roxana-María Romero-Luna, Hugo-Armando Ordoñez-Erazo, and Carlos-Alberto Cobos-Lozada
- Subjects
Trabajo Infantil ,Machine Learning ,Inteligencia Artificial ,Technology ,Science - Abstract
El trabajo infantil es una antigua problemática mundial que tiende a ser normalizada en ciertos contextos culturales, sociales y económicos. Colombia, un país en vías de desarrollo que ha sido afectado por el conflicto armado y su complicada topografía, enfrenta también la falta de oportunidades laborales para madres o jefes de hogar. Estos factores, junto con los desafíos en el acceso a la educación, hacen que el país sea vulnerable al trabajo infantil. En este artículo se utilizan datos de la Encuesta de Calidad de Vida 2022 del Departamento Administrativo Nacional de Estadística (DANE), específicamente del módulo de trabajo infantil. Con estos datos, se propone un enfoque de inteligencia artificial (IA) basado en algoritmos de aprendizaje automático para clasificar la ocupación de menores de edad en los hogares colombianos. Se encontró que el modelo óptimo se obtiene al aplicar el algoritmo de clasificación DecisionTreeClassifier en datos debidamente procesados y utilizando validación cruzada estratificada. Al analizar los atributos seleccionados, que son los más importantes y mejor rankeados, se observa que la ocupación de los menores está fuertemente influenciada por las características económicas y la composición del hogar. Se concluye que las técnicas de IA son cruciales para identificar los factores que inciden en problemas sociales como el trabajo infantil, y que pueden servir de apoyo para los entes gubernamentales en la implementación de estrategias de mitigación y prevención.
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- 2024
- Full Text
- View/download PDF
45. Estado de Bienestar Digital en Ecuador: Datificación Ciudadana y Machine Learning en la construcción y gestión de la pobreza
- Author
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Alexandra Gualavisí
- Subjects
Estado de bienestar digital ,datificación ,machine learning ,política social ,Social sciences (General) ,H1-99 ,Anthropology ,GN1-890 - Abstract
En la era de la gobernanza digital, la utilización creciente de datos y tecnologías de Big Data e Inteligencia Artificial en los sistemas de protección social se ha convertido en un asunto relevante. Los esfuerzos del Estado por impulsar la datificación ciudadana y la implementación de la IA para la gestión y provisión de servicios y beneficios sociales están vehiculizados por las ideas de neutralidad, objetividad y eficiencia tecnológica. No obstante, estas tecnologías no solo pueden asignar beneficios sociales; sino que también tienen el potencial de controlar, monitorear, sancionar y excluir a los beneficiarios. Ante el tecno-optimismo que marca el diseño de la política social contemporánea, específicamente sus mecanismos de asignación, este trabajo apuesta por los Estudios de la Ciencia, Tecnología y Sociedad para dar luz sobre cómo los sistemas de datos y las tecnologías de IA transforman la noción de pobreza e impactan en la definición de beneficiarios y no beneficiarios. Este enfoque se centra en desentrañar la dimensión política –de la politics– de los sistemas de datos y las tecnologías de la IA que comienzan a mediar la relación entre el Estado y los ciudadanos. A través del estudio del caso ecuatoriano del Registro Social, el objetivo recae en nutrir el examen crítico de los Estados digitales de bienestar en la región, analizando sus implicaciones en la sociedad y su potencial para producir o amplificar desigualdad y exclusión; desafiando las visiones deterministas del cambio tecnológico y del diseño de políticas públicas.
- Published
- 2024
- Full Text
- View/download PDF
46. Prediction of the volume fraction of liquid-liquid two-phase flow in horizontal pipes using Long-Short Term Memory Networks
- Author
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Cristian A. Hernández-Salazar, Alejandro Carreño-Verdugo, and Octavio Andrés González-Estrada
- Subjects
Artificial neural network ,LSTM ,machine learning ,two-phase flow ,volume fraction ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
This paper presents the development of a Long Short-Term Memory neural network designed to predict the volume fraction of liquid-liquid two-phase flows flowing through horizontal pipes. For this purpose, a comprehensive database was compiled using information sourced from existing research, comprising 2156 experimental data points utilized for model construction. The input of the algorithm consists of a vector containing the superficial velocities of the substances (oil and water), the mixture velocity, internal pipe diameter, and oil viscosity, while the output is the volume fraction of oil. Training and validation procedures involved preparing and segmenting the data, using 80% of the total information for training and the remaining 20% for validation. Model selection, based on performance evaluation, was conducted through 216 experiments. The predictive model with the best performance had a Mean Squared Error (MSE) of 3.5651E-05, a Mean Absolute Error (MAE) of 0.0045, and a Mean Absolute Percentage Error (MAPE) of 3.0250%. This performance was obtained by structuring the model with a ReLu transfer function, 20 epochs, a learning rate of 0.1, a sigmoid transfer function, a batch size of 1, ADAM optimizer, and 150 neurons in the hidden layer.
- Published
- 2024
- Full Text
- View/download PDF
47. Community-Based Early Warning System Model for Stream Overflow In Barranquilla
- Author
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Iván Andrés Felipe Serna-Galeano, Ernesto Gómez-Vargas, and Julián Rolando Camargo-López
- Subjects
stream overflow ,social network ,machine learning ,natural language processing ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Context: This work aims to design and create a community-based early warning model as an alternative for the mitigation of disasters caused by stream overflow in Barranquilla (Colombia). This model is based on contributions from social networks, which are consulted through their API and filtered according to their location. Methods: With the information collected, cleaning and debugging are performed. Then, through natural language processing techniques, the texts are tokenized and vectorized, aiming to find the vector similarity between the processed texts and thus generating a classification. Results: The texts classified as dealing with stream overflow are processed again to obtain a location or assign a default one, in order to for them to be georeferenced in a map that allows associating the risk zone and visualizing it in a web application to monitor and reduce the potential damage to the population. Conclusions: Three classification algorithms were selected (random forest, extra trees, and k-neighbors) to determine the best classifier. These three algorithms exhibited the best performance and R2 regarding the data processed in the regressions. These algorithms were trained, with the k-neighbor algorithm exhibiting the best performance.
- Published
- 2024
- Full Text
- View/download PDF
48. metroSonus. Una alternativa incluyente para la formación y la dirección de músicos con discapacidad visual
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Zambrano, Juanita Reina and Gaitán Lozano, Diego Felipe
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- 2024
- Full Text
- View/download PDF
49. SATISFACCIÓN DEL TURISTA USANDO FACTORES MOTIVACIONALES: COMPARACIÓN DE MODELOS DE APRENDIZAJE ESTADÍSTICO
- Author
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Vanegas, Juan Gabriel and Muñetón Santa, Guberney
- Published
- 2024
- Full Text
- View/download PDF
50. Educators' Perspectives about Artificial Intelligence Integration as a Tool for Digital Language Teaching
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Metwally, Amal Abdelsattar and Hamad, Mona M.
- Published
- 2023
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