A synergetic intuitionistic fuzzy model combining AHP, entropy, and ELECTRE for data fabric solution selection

Artificial Intelligence Review, Год журнала: 2025, Номер 58(5)

Опубликована: Фев. 19, 2025

Язык: Английский

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Tendencias Actuales en la aplicación del Bigdata y Agricultura Inteligente un Enfoque Bibliométrico

Estudios y Perspectivas Revista Científica y Académica, Год журнала: 2025, Номер 5(1), С. 310 - 332

Опубликована: Янв. 29, 2025

La necesidad por alimentar a la población mundial se ha convertido en un desafío nuestra sociedad. producción agrícola requiere de tecnificación que le permita cumplir con esta población. En este sentido Big Data convierte una las herramientas relevantes permiten gestionar y optimizar los recursos naturales e insumos agrícolas convirtiendo actividades el campo agricultura inteligente innova mejora resultados producción. El presente trabajo busca responder pregunta ¿Cuáles son tendencias actuales aplicación bigdata inteligente?. A través análisis bibliométrico buscamos interrogante determinar brecha investigación. Los alcanzados nos muestran 7 brechas investigación: bigdata, blockchain, smart farming, security, artificial intelligence internet of things, estos determinantes áreas investigación crecimiento requieren ser exploradas sus permitirán mejorar producción, alto nivel control su desarrollo sostenible sustentable.

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Data’s big three: how learning health systems, artificial intelligence and predictive analytics are transforming healthcare

BMJ Health & Care Informatics, Год журнала: 2025, Номер 32(1), С. e101414 - e101414

Опубликована: Март 1, 2025

Язык: Английский

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Does AI Prediction Scale to Decision Making?

Communications of the ACM, Год журнала: 2025, Номер unknown

Опубликована: Март 21, 2025

Looking beyond data-driven prediction.

Язык: Английский

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Construction of Physical Education Teaching Evaluation System in Colleges and Universities Under the Background of Big Data Application

International Journal of Web-Based Learning and Teaching Technologies, Год журнала: 2025, Номер 20(1), С. 1 - 16

Опубликована: Апрель 18, 2025

The traditional system focuses excessively on physical skills and fitness assessment, with problems such as single indicator, static approach, subject limitation inefficient data utilization, making it difficult to assess students in a comprehensive fair manner. rise of big technology has brought about turnaround, from classroom extracurricular multi-dimensional collection; real-time feedback dynamic evaluation, helping teachers tailor their teaching the needs students; accurate assessment progress, providing support for personalized growth planning. In digital era, innovation college sports evaluation is imperative. Although application disadvantages technical risk, security risks, high cost possible bias, through empirical research analysis, generally opens up new paths education colleges universities, vigorously promotes improvement quality development students.

Язык: Английский

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Balancing accuracy and interpretability: AI-driven predictive modeling of construction schedule performance in India

Asian Journal of Civil Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 19, 2025

Язык: Английский

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Role of AI-Driven Business Intelligence in Strengthening Software as a Service (SaaS) in the United States Economy and Job Market

Опубликована: Май 22, 2025

As the Software as a Service (SaaS) industry rapidly expands, convergence of AI and Business Intelligence (BI) technologies has triggered an important shift within industry, particularly in United States. The integration optimizes business processes strategic decision-making, reshaping employment dynamics, prompting urgent enquiries into broader economic labour market implications. This review investigates influence AI- driven intelligence on States SaaS economy market. findings reveal that AI-driven BI increases productivity innovation organizations, allowing for swift decision-making predictive strategies. These innovations return, increased revenue adjusted established corporate structures, thereby causing visible alterations dynamics. In conclusion, is transformative force economy, driving operational creating long-term opportunities technology sector; however, its benefits are associated with responsibility to invest human capital, ensuring workers equipped meet new demands through continuous learning skill development.

Язык: Английский

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Does AI Prediction Scale to Decision Making?

SSRN Electronic Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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The Role of Complex Systems in Predictive Analytics for E-Commerce Innovations in Business Management

Systems, Год журнала: 2024, Номер 12(10), С. 415 - 415

Опубликована: Окт. 5, 2024

This review explores the incorporation of complex systems theory into predictive analytics in e-commerce sector, particularly emphasizing recent advancements business management. By analyzing intersection these two domains, emphasizes potential models—including agent-based modeling and network theory—to improve precision efficacy analytics. It will provide a comprehensive overview applications emergent techniques tools, including real-time data analysis machine learning, inventory optimization, dynamic pricing, personalization customer experiences. In addition, this suggest future research directions to advance discipline address technical, ethical, practical challenges encountered during integration phase.

Язык: Английский

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AI-Enhanced Multi-Algorithm R Shiny App for Predictive Modeling and Analytics- A Case study of Alzheimer’s Disease Diagnostics (Preprint)

Опубликована: Дек. 18, 2024

Recent studies have demonstrated that AI can surpass medical practitioners in diagnostic accuracy, underscoring the increasing importance of AI-assisted diagnosis healthcare. This research introduces SMART-Pred (Shiny Multi-Algorithm R Tool for Predictive Modeling), an innovative AI-based application Alzheimer's disease (AD) prediction utilizing handwriting analysis Our objective is to develop and evaluate a non-invasive, cost-effective, efficient tool early AD detection, addressing need accessible accurate screening methods. methodology employs comprehensive approach AI-driven prediction. We begin with Principal Component Analysis dimensionality reduction, ensuring processing complex data. followed by training evaluation ten diverse, highly optimized models, including logistic regression, Naïve Bayes, random forest, AdaBoost, Support Vector Machine, neural networks. multi-model allows robust comparison different machine learning techniques To rigorously assess model performance, we utilize range metrics sensitivity, specificity, F1-score, ROC-AUC. These provide holistic view each model's predictive capabilities. For validation, leveraged DARWIN dataset, which comprises samples from 174 participants (89 patients 85 healthy controls). balanced dataset ensures fair our models' ability distinguish between individuals based on characteristics. The forest strong achieving accuracy 88.68% test set during analysis. Meanwhile, AdaBoost algorithm exhibited even higher reaching 92.00% after leveraging models identify most significant variables predicting disease. results current clinical tools, typically achieve around 81.00% accuracy. SMART-Pred's performance aligns recent advancements prediction, such as Cambridge scientists' 82.00% identifying progression within three years using cognitive tests MRI scans. Furthermore, revealed consistent pattern across all employed. "air_time" "paper_time" consistently stood out critical predictors (AD). two factors were repeatedly identified influential assessing probability onset, their potential detection risk assessment Even though some limitations exist SMART-Pred, it offers several advantages, being efficient, customizable datasets diagnostics. study demonstrates transformative healthcare, particularly may contribute improved patient outcomes through intervention. Clinical validation necessary confirm whether key this are sufficient accurately real-world settings. step crucial ensure practical applicability reliability these findings practice.

Язык: Английский

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