Dian Tjondronegoro
Datos Biográficos
| ID | 3470548 |
|---|---|
| NOMBRE | Dian Tjondronegoro |
| NOMBRES | Dian |
| APELLIDO | Tjondronegoro |
| FIRMA | TJONDRONEGORO D |
| AFILIACIONES | Griffith University |
| ORCID | 0000-0001-7446-2839 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 3 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 3 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2015 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2024 |
| ÍNDICE H | 0 |
Privacy-preserving AI-enabled video surveillance for social distancing
Purpose The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces. Design/methodology/approach The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce…
Collaborative federated learning framework to minimize data transmission for AI-enabled video surveillance
Purpose This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces. Design/methodology/approach This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framewor…
Mobile App Rating Scale
BACKGROUND: The use of mobile apps for health and well being promotion has grown exponentially in recent years. Yet, there is currently no app-quality assessment tool beyond "star"-ratings. OBJECTIVE: The objective of this study was to develop a reliable, multidimensional measure for trialling, classifying, and rating the quality of mobile health apps. METHODS: A literature search was conducted to identify articles containing explicit Web or app …
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Mobile App Rating Scale
BACKGROUND: The use of mobile apps for health and well being promotion has grown exponentially in recent years. Yet, there is currently no app-quality assessment tool beyond "star"-ratings. OBJECTIVE: The objective of this study was to develop a reliable, multidimensional measure for trialling, classifying, and rating the quality of mobile health apps. METHODS: A literature search was conducted to identify articles containing explicit Web or app …
Privacy-preserving AI-enabled video surveillance for social distancing
Purpose The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces. Design/methodology/approach The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce…
Collaborative federated learning framework to minimize data transmission for AI-enabled video surveillance
Purpose This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces. Design/methodology/approach This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framewor…
Computer Science (3 obras) · Video Surveillance and Tracking Methods (2 obras) · App store (1 obras) · Artificial Intelligence (1 obras) · Checklist (1 obras) · Clinical Psychology (1 obras) · Cloud computing (1 obras) · Computer security (1 obras) · Data science (1 obras) · Digital Mental Health Interventions (1 obras)