Research on the Application of Generative Artificial Intelligence in Human-machine Cooperative Teaching DOI

Zejia Mi,

Kangkang Li

Опубликована: Ноя. 8, 2024

Язык: Английский

Applications, Challenges, and Future Directions of Human-in-the-Loop Learning DOI Creative Commons
Sushant Kumar, Sumit Datta, Vishakha Singh

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 75735 - 75760

Опубликована: Янв. 1, 2024

Machine learning (ML) has become a popular technique for various automation tasks in the era of Industry 4.0, such as analysis and synthesis visual data images videos, natural language speech, financial data, biomedical applications. However, ML-based techniques are facing difficulties like decision-making, thus incorporating user expertise into system might be advantageous. The goal adding human domain with is to provide more accurate prediction models. Human-in-the-loop (HITL) systems that integrate ML algorithms becoming common industries. there number methodological, technical, ethical development application HITL systems. This paper aims explore methodologies, challenges, opportunities associated implementations.We also discuss issues must resolved effective, including quality, bias, engagement. Besides, we explored several approaches can utilized enhance performance systems, active (AL), iterative ML, reinforcement learning, well current state art systems.We selectively highlighted advantages their potential increase decision-making process accountability transparency by utilizing experience improve capability. will very useful researchers, practitioners, policymakers.

Язык: Английский

Процитировано

14
Advancements in enzyme-based wearable sensors for health monitoring DOI
Srishti Mehrotra,

Pawankumar Rai,

Apoorva Saxena

и другие.

Microchemical Journal, Год журнала: 2024, Номер 200, С. 110250 - 110250

Опубликована: Фев. 27, 2024

Язык: Английский

Процитировано

7
Efficacy assessment for multi-vehicle formations based on data augmentation considering reliability DOI
Haoran Zhang, Ruohan Yang, Wei He

и другие.

Advanced Engineering Informatics, Год журнала: 2024, Номер 61, С. 102504 - 102504

Опубликована: Март 26, 2024

Язык: Английский

Процитировано

4
AI Safety and Security DOI
Mosiur Rahaman, Princy Pappachan,

Sheila Mae Orozco

и другие.

Advances in computational intelligence and robotics book series, Год журнала: 2024, Номер unknown, С. 354 - 383

Опубликована: Авг. 15, 2024

The chapter “AI Safety and Security” presents a comprehensive multi-dimensional exploration, addressing the critical aspects of safety security in context large language models. begins by identifying risks threats posed LLMs, delving into vulnerabilities such as bias, misinformation, unintended AI interactions, impacts like privacy concerns. Building on these identified risks, it then explores strategies methodologies for ensuring safety, focusing principles robustness, transparency, accountability discussing challenges implementing measures. It concludes with an insight long-term research, highlighting ongoing efforts future directions to sustain system amidst rapid technological advancements encouraging collaborative approach among various stakeholders. By integrating perspectives from computer science, ethics, law, social sciences, provides insightful analysis current security.

Язык: Английский

Процитировано

3
Architectural tactics to achieve quality attributes of machine-learning-enabled systems: A systematic literature review DOI Creative Commons
Vladislav Indykov, Daniel Strüber, Rebekka Wohlrab

и другие.

Journal of Systems and Software, Год журнала: 2025, Номер 223, С. 112373 - 112373

Опубликована: Фев. 13, 2025

Язык: Английский

Процитировано

0
Do we trust ourselves? Is the human the weak link? DOI Creative Commons
Kate Mercer, Kari D. Weaver, Ashley Rose Mehlenbacher

и другие.

IFLA Journal, Год журнала: 2025, Номер unknown

Опубликована: Фев. 13, 2025

Generative artificial intelligence tools are becoming ubiquitous in applications across personal, professional and educational contexts. Similar to the rise of social media technologies, this means they an embedded part people's lives, individuals using these for a variety benign purposes. This article examines how existing information literacy understandings will not work literacy, provides example searching, demonstrating its shortcomings. Present approaches may fall short answer required navigate new tools, begs question what comes next. The current scope technology necessitates multidisciplinary approach solving ‘what do with intelligence’ arguably most impactfully requires one acknowledge that has worked no longer suffice.

Язык: Английский

Процитировано

0
A comprehensive survey of Network Digital Twin architecture, capabilities, challenges, and requirements for Edge-Cloud Continuum DOI
Syed Mohsan Raza, Roberto Minerva, Noël Crespi

и другие.

Computer Communications, Год журнала: 2025, Номер unknown, С. 108144 - 108144

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Enhancing skin lesion classification: a CNN approach with human baseline comparison DOI Creative Commons
Deep Ajabani, Zaffar Ahmed Shaikh, Amr Yousef

и другие.

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2795 - e2795

Опубликована: Апрель 15, 2025

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high-confidence CNN while replacing low-confidence outputs expert assessments to enhance diagnostic accuracy. A model utilizing EfficientNetB3 backbone is trained datasets from ISIC-2019 and ISIC-2020 SIIM-ISIC melanoma classification challenges evaluated a 150-image test set. model’s are compared against 69 experienced medical professionals. Performance assessed using receiver operating characteristic (ROC) curves area under curve (AUC) metrics, alongside analysis resource costs. baseline achieves AUC 0.822, slightly below performance experts. However, improves true positive rate 0.782 reduces false 0.182, delivering better minimal involvement. offers scalable, resource-efficient solution address variability in image analysis, effectively harnessing complementary strengths humans CNNs.

Язык: Английский

Процитировано

0
Collaboration of AI, big data, and blockchain in Internet of Things (IoT): Emerging trends and perspectives DOI
Yunchuan Sun,

Yu Bai,

Zhangbing Zhou

и другие.

Internet of Things, Год журнала: 2024, Номер 27, С. 101234 - 101234

Опубликована: Май 31, 2024

Язык: Английский

Процитировано

1
Looping In: Exploring Feedback Strategies to Motivate Human Engagement in Interactive Machine Learning DOI
Hyorim Shin, Jeongeun Park, J. Yu

и другие.

International Journal of Human-Computer Interaction, Год журнала: 2024, Номер unknown, С. 1 - 18

Опубликована: Окт. 14, 2024

This study investigates effective feedback mechanisms to maintain human engagement in interactive machine learning (IML) systems, focusing on social media platforms. We developed "Loop," an IML system based human-in-the-loop (HITL) principles that recommends content while encouraging users report inaccuracies for model refinement. Loop implements three types of artificial intelligence (AI) user reports: (a) (ML)-centric, (b) personal-centric, and (c) community-centric feedback. In addition, we evaluated the relative effectiveness these under two different task criticality scenarios: high low. A with 30 participants was conducted evaluate through questionnaires interviews. Results showed preferred algorithmic improvements personal benefit over altruistic contributions community, especially low-criticality tasks. Furthermore, personal-centric had a significant impact satisfaction. Our findings provide insights into HITL-ML contributing design more engaging interfaces. discuss implications strategies proactive HITL-ML-based emphasizing importance tailored mechanisms.

Язык: Английский

Процитировано

1