Research output per year
Research output per year
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. Our work contributes towards the following SDG(s):
-
SDG 2 Zero Hunger -
SDG 3 Good Health and Well-being -
SDG 7 Affordable and Clean Energy -
SDG 9 Industry, Innovation, and Infrastructure -
SDG 11 Sustainable Cities and Communities -
SDG 13 Climate Action -
SDG 14 Life Below Water -
SDG 15 Life on Land -
SDG 16 Peace, Justice and Strong Institutions
Fingerprint
Dive into the research topics where Laboratory of Intelligent Computing and Vision is active. These topic labels come from the works of this organisation's members. Together they form a unique fingerprint.
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Profiles
Research output
-
A comprehensive raw dataset of Ziehl-Neelsen-stained sputum smear microscopy images for Mycobacterium tuberculosis detection
Yuniarti, H., Fatichah, C., Sigit, R., Arifin, S. & da Costa, E., Aug 2026, In: Data in Brief. 67, 112908.Research output: Contribution to journal › Article › peer-review
Open Access -
Adaptive Feature Concatenation in YOLOv8n for Construction Site Safety Detection
Azhar, D. M., Bunyamin, R. F., Jannah, A. V. R., Suciati, N. & ‘Arif Faizin, M., 2026, In: International Journal of Robotics and Control Systems. 6, 1, p. 528-539 12 p.Research output: Contribution to journal › Article › peer-review
Open Access -
A distributive and attentive generative model for multi-party data synthesis in highly imbalanced data
Mustafa Kamal, I. & Fatichah, C., Mar 2026, In: Future Generation Computer Systems. 176, 108166.Research output: Contribution to journal › Article › peer-review
2 Citations (Scopus)
Press/Media
-
Researchers from Institut Teknologi Sepuluh Nopember Report on Findings in Computer Software [DiLLeMa: An extensible and scalable framework for distributed large language models (LLMs) inference on multi-GPU clusters]
Shiddiqi, A. M., Navastara, D. A., Akbar, R. J. & Ijtihadie, R. M.
16/02/26
1 item of Media coverage
Press/Media