Using radial basis artificial neural networks to predict radiation hazard indices in geological materials
ENVIRONMENTAL MONITORING AND ASSESSMENT AN INTERNATIONAL JOURNAL DEVOTED TO PROGRESS IN THE USE OF MONITORING DATA IN ASSESSING ENVIRONMENTAL RISKS TO MAN AND THE ENVIRONMENT, cilt.196, sa.3, ss.315-328, 2024 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 196 Sayı: 3
- Basım Tarihi: 2024
- Doi Numarası: 10.1007/s10661-024-12459-8
- Dergi Adı: ENVIRONMENTAL MONITORING AND ASSESSMENT AN INTERNATIONAL JOURNAL DEVOTED TO PROGRESS IN THE USE OF MONITORING DATA IN ASSESSING ENVIRONMENTAL RISKS TO MAN AND THE ENVIRONMENT
- Derginin Tarandığı İndeksler: Food Science & Technology Abstracts, Scopus, Agricultural & Environmental Science Database, Science Citation Index Expanded (SCI-EXPANDED), ABI/INFORM, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Compendex, EMBASE, Environment Index, Geobase, Greenfile, CAB Abstracts, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
- Sayfa Sayıları: ss.315-328
- Anahtar Kelimeler: Radiation hazard indices, Radial basis function, Artificial neural networks, Gamma spectrometry measurements
- Dokuz Eylül Üniversitesi Adresli: Evet