Klaim Artikel Anda
Verifikasi kepemilikan artikel akademik
Apakah artikel-artikel ini milik Anda?
Daftarkan diri Anda sebagai author untuk mengklaim artikel dan dapatkan profil akademik terverifikasi dengan fitur lengkap.
Badge Verifikasi
Profil terverifikasi resmi
Statistik Lengkap
H-index, sitasi, dan metrik
Visibilitas Tinggi
Tampil di direktori author
Kelola Publikasi
Dashboard artikel terpadu
Langkah-langkah Klaim Artikel:
- 1. Daftar akun author dengan email akademik Anda
- 2. Verifikasi email dan lengkapi profil
- 3. Login dan buka menu "Klaim Artikel"
- 4. Cari dan klaim artikel Anda
- 5. Tunggu verifikasi dari admin (1-3 hari kerja)
Menampilkan 1–2 dari 2 artikel
Reinforcement Learning for Personalised Critical Care Treatment using Scalable Parallel Computing
Utomo, Chandra Prasetyo
; Ichikawa, Kohei
; Insani, Nashuha
; Thonglek, Kundjanasith
; Xingyuan, Kang
; Maulani, Chaerita
; Rachmawati, Ummi Azizah
Sepsis is one of the leading causes of death in intensive care units. Many patients do not receive timely or effective treatment, which lowers their chances of survival. We developed a reinforcement learning–based framework to provide personalised treatment recommendations for sepsis patients. The model creates simple patient representations from treatment responses, groups patients with similar patterns, and learns the best treatment policy for each group. To reduce long training time, we use p...
Google Scholar
DOI
Optimizing Image Preprocessing for AI-Driven Cervical Cancer Diagnosis
Chandra Prasetyo Utomo
; Neng Suhaeni
; Nashuha Insani
; Elan Suherlan
; Nunung Ainur Rahmah
; Ahmad Rusdan Utomo
; Indra Kusuma
; Muhamad Fathurachman
; Dewa Nyoman Murti Adyaksa
Advance Sustainable Science, Engineering and Technology (ASSET)
Vol 7
, No 1
(2025)
Cervical cancer ranks among the top causes of cancer-related deaths in women globally. Early detection is vital for improving patient survival rates. The multiclass classification of cervical cell images presents challenges primarily due to the notable variations in cell sizes across different classes. Conventional AI methods for diagnosing cervical cancer often rely on image-resizing techniques that overlook crucial features like relative cell dimensions, which impairs the models' ability to di...
Google Scholar
DOI