Navigating fairness: introducing the multidimensional AIM-FAIR scale for evaluating AI decision-making DOI Creative Commons

Nico Ehrhardt,

Manuela Renn,

Sonja Utz

и другие.

AI & Society, Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

Язык: Английский

Current Status of Emerging Contaminant Models and Their Applications Concerning the Aquatic Environment: A Review DOI Open Access
Zhuang Liu, Yonghai Gan, Jun Luo

и другие.

Water, Год журнала: 2025, Номер 17(1), С. 85 - 85

Опубликована: Янв. 1, 2025

Increasing numbers of emerging contaminants (ECs) detected in water environments require a detailed understanding these chemicals’ fate, distribution, transport, and risk aquatic ecosystems. Modeling is useful approach for determining ECs’ characteristics their behaviors environments. This article proposes systematic taxonomy EC models addresses gaps the comprehensive analysis applications. The reviewed include conventional quality models, multimedia fugacity machine learning (ML) models. Conventional have higher prediction accuracy spatial resolution; nevertheless, they are limited functionality can only be used to predict contaminant concentrations Fugacity excellent at depicting how travel between different environmental media, but cannot directly analyze variations parts same media because model assumes that constant within compartment. Compared other ML applied more scenarios, such as identification assessments, rather than being confined concentrations. In recent years, with rapid development artificial intelligence, surpassed becoming one newest hotspots study ECs. primary challenge faced by outcomes difficult interpret understand, this influences practical value an some extent.

Язык: Английский

Процитировано

1

Developing a Smart Learning System for Large Enterprises Based on Intelligent Augmented Reality DOI Open Access
Hsin‐Te Wu

Journal of Organizational and End User Computing, Год журнала: 2025, Номер 37(1), С. 1 - 14

Опубликована: Янв. 23, 2025

This paper proposes a smart learning system built on deep and augmented reality (AR) to support employees with practical IoT experimentation, from components circuit board pin connections programming control. For instance, can use their mobile phones capture images of electronic access AR-enhanced instructional materials for component properties. AR-assisted offers guidance at each experimental stage hands-on practice troubleshooting. The also incorporates the pair teaching method enhance quality confidence, enabling collaborate teammates throughout process. is further equipped an online whiteboard Q&A in-depth theoretical exploration experiment. Additionally, blockchain platform records analyzes employee's progress status, providing comprehensive view development.

Язык: Английский

Процитировано

0

Advances in bioinformatic methods for the acceleration of the drug discovery from nature DOI
Magdalena Maciejewska‐Turska, Milen I. Georgiev, Guoyin Kai

и другие.

Phytomedicine, Год журнала: 2025, Номер 139, С. 156518 - 156518

Опубликована: Фев. 14, 2025

Язык: Английский

Процитировано

0

Deepfake Media Forensics: Status and Future Challenges DOI Creative Commons
Irene Amerini, Mauro Barni, Sebastiano Battiato

и другие.

Journal of Imaging, Год журнала: 2025, Номер 11(3), С. 73 - 73

Опубликована: Фев. 28, 2025

The rise of AI-generated synthetic media, or deepfakes, has introduced unprecedented opportunities and challenges across various fields, including entertainment, cybersecurity, digital communication. Using advanced frameworks such as Generative Adversarial Networks (GANs) Diffusion Models (DMs), deepfakes are capable producing highly realistic yet fabricated content, while these advancements enable creative innovative applications, they also pose severe ethical, social, security risks due to their potential misuse. proliferation triggered phenomena like “Impostor Bias”, a growing skepticism toward the authenticity multimedia further complicating trust in interactions. This paper is mainly based on description research project called FF4ALL (FF4ALL-Detection Deep Fake Media Life-Long Authentication) for detection authentication focusing areas forensic attribution, passive active authentication, real-world scenarios. By exploring both strengths limitations current methodologies, we highlight critical gaps propose directions future ensure media integrity trustworthiness an era increasingly dominated by media.

Язык: Английский

Процитировано

0

Deep learning in nuclear medicine: from imaging to therapy DOI

Meng-Xin Zhang,

Pengfei Liu, Mengdi Zhang

и другие.

Annals of Nuclear Medicine, Год журнала: 2025, Номер unknown

Опубликована: Март 13, 2025

Язык: Английский

Процитировано

0

Homomorphic encryption-based fault diagnosis in IoT-enabled industrial systems DOI
Hoki Kim, Youngdoo Son, Junyoung Byun

и другие.

International Journal of Information Security, Год журнала: 2025, Номер 24(3)

Опубликована: Март 24, 2025

Язык: Английский

Процитировано

0

Fusion of quantum computing and explainable AI: A comprehensive survey on transformative healthcare solutions DOI
Shashank Sheshar Singh, Sumit Kumar, Rohit Ahuja

и другие.

Information Fusion, Год журнала: 2025, Номер unknown, С. 103217 - 103217

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges DOI Creative Commons

Jongseon Kim,

Hyungjoon Kim, HyunGi Kim

и другие.

Artificial Intelligence Review, Год журнала: 2025, Номер 58(7)

Опубликована: Апрель 23, 2025

Язык: Английский

Процитировано

0

Beyond Missing Data: A Multimodal Approach Using VR-EEG-MRI (VEEM) Biomarkers for Detecting MCI DOI
Yuwon Kim

Опубликована: Апрель 23, 2025

Язык: Английский

Процитировано

0

Navigating fairness: introducing the multidimensional AIM-FAIR scale for evaluating AI decision-making DOI Creative Commons

Nico Ehrhardt,

Manuela Renn,

Sonja Utz

и другие.

AI & Society, Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

Язык: Английский

Процитировано

0