Intra- and inter-instance Location Correlation Network for human–object interaction detection

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 142, P. 109942 - 109942

Published: Jan. 5, 2025

Language: Английский

---

Revolutionizing solar energy resources: The central role of generative AI in elevating system sustainability and efficiency

Applied Energy, Journal Year: 2025, Volume and Issue: 382, P. 125296 - 125296

Published: Jan. 13, 2025

Language: Английский

---

Unleashing the Power of Generative AI in Agriculture 4.0 for Smart and Sustainable Farming

Cognitive Computation, Journal Year: 2025, Volume and Issue: 17(1)

Published: Feb. 1, 2025

Language: Английский

---

Determinants of Generative AI in Promoting Green Purchasing Behavior: A Hybrid Partial Least Squares–Artificial Neural Network Approach

Business Strategy and the Environment, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

ABSTRACT In the era of rapid technological advancement, generative artificial intelligence (AI) has emerged as a transformative force in various sectors, including environmental sustainability. This research investigates factors and consequences using AI to access information influence green purchasing behavior. It integrates theories such adoption model, value–belief–norm theory, elaboration likelihood cognitive dissonance theory pinpoint prioritize determinants usage for Data from 467 participants were analyzed hybrid methodology that blends partial least squares (PLS) with neural networks (ANN). The PLS outcomes indicate interactivity, responsiveness, knowledge acquisition application, concern, ascription responsibility are key predictors use information. Furthermore, concerns, values, personal norms, responsibility, individual impact, emerge ANN analysis offers unique perspective discloses variations hierarchy these predictors. provides valuable insights stakeholders on harnessing promote sustainable consumer behaviors

Language: Английский

---

Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(2), P. 59 - 59

Published: Feb. 15, 2025

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer and immunology, with object detection, feature extraction, classification, segmentation applications. Advancements in deep learning (DL) research have been a critical factor advancing computer techniques for mining. A significant improvement the accuracy of detection algorithms has achieved result emergence open-source software innovative neural network architectures. Automated now enables extraction quantifiable cellular spatial features from microscope images cells tissues, providing insights into organization various diseases. This review aims to examine latest AI DL mining microscopy images, aid biologists who less background knowledge machine (ML), incorporate ML models focus images.

Language: Английский

---

Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection

Journal of Cybersecurity and Privacy, Journal Year: 2025, Volume and Issue: 5(1), P. 9 - 9

Published: March 17, 2025

The increasing sophistication of fraud tactics necessitates advanced detection methods to protect financial assets and maintain system integrity. Various approaches based on artificial intelligence have been proposed identify fraudulent activities, leveraging techniques such as machine learning deep learning. However, class imbalance remains a significant challenge. We propose several solutions generative modeling address the challenges posed by in detection. Class often hinders performance models limiting their ability learn from minority classes, transactions. Generative offer promising approach mitigate this issue creating realistic synthetic samples, thereby enhancing model’s detect rare cases. In study, we introduce evaluate multiple models, including Variational Autoencoders (VAEs), standard (AEs), Adversarial Networks (GANs), hybrid Autoencoder–GAN model (AE-GAN). These aim generate samples balance dataset improve capacity. Our primary objective is compare these against traditional oversampling techniques, SMOTE ADASYN, context conducted extensive experiments using real-world credit card effectiveness our solutions. results, measured BEFS metrics, demonstrate that not only problem more effectively but also outperform conventional identifying

Language: Английский

---

Bearing fault diagnosis using multiple feature selection algorithms with SVM

Progress in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 13(2), P. 119 - 133

Published: June 1, 2024

Language: Английский

---

MEXFIC: A meta ensemble eXplainable approach for AI-synthesized fake image classification

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 116, P. 351 - 363

Published: Jan. 1, 2025

Language: Английский

---

Applications and perspectives of Generative Artificial Intelligence in agriculture

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 230, P. 109919 - 109919

Published: Jan. 10, 2025

Language: Английский

---

The Role of Generative Artificial Intelligence in Digital Agri-Food

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: unknown, P. 101787 - 101787

Published: March 1, 2025

Language: Английский

---