Những quyển sách tương tự: ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK /
- ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK
- A COMBINED APPROACH OF DIGITAL SIGNAL PROCESSING AND MACHINE LEARNING ALGORITHM FOR FAST AND ACCURATE GENOME CLASSIFICATION /
- A MULTI-CNN FEATURE FUSION FRAMEWORK WITH CUSTOM CLASSIFICATION NETWORK FOR EFFICIENT SATELLITE IMAGE CLASSIFICATION /
- Umbilical cord blood banking and transplantation /
- Neuromechanics of Human Movement /
- QUANTIFICATION OF PULMONARY EDEMA FROM CHEST RADIOGRAPHS USING DEEP CNN /
Đề tài: Biomedical Engineering.
- DEVELOPMENT OF MULTICHANNEL BASED PROSTHETIC HAND FOR AMPUTEE /
- GENOME WIDE ASSOCIATION STUDIES (GWAS) IN THE CONTEXT OF HEALTHCARE AND AGRICULTURE /
- DEVELOPMENT OF A SMART FALL DETECTION SYSTEM FOR ELDERLY PEOPLE /
- DEVELOPMENT OF HEAL THCARE COMMUNICATION & ONLINE REPORT DELIVERY MANAGEMENT SYSTEM /
- DEVELOPMENT OF BLOOD GLUCOSE MONITORING SYSTEM /
- DEVELOPMENT OF MEDICAL IMAGE ENCRYPTION SYSTEM FOR SECURE IMAGE DATA TRANSFER /
Đề tài: This Thesis Paper of BME in M.Sc. Program.
- DEVELOPMENT OF CHICKEN EGG WHITE AND MUPIROCIN LOADED HYDROGEL DRESSING MATERIAL FOR INHIBITION OF BACTERIAL GROWTH /
- DEVELOPMENT OF A CLINICAL DIAGNOSIS AND DECISION SUPPORT SYSTEM FOR CHEST RADIOGRAPHY USING CNN /
- ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK /
- DEVELOPMENT OF CERAMIDE AND HONEY BASED BIODEGRADABLE DRESSING MATERIALS FOR THE APPLICATION TO BURN SKIN /
- PREDICTING THE DEPTH OF ANESTHESIA FOR OPERATING PATIENT USING MUSIC-BASED SPECTRAL FEATURES OF EEG SIGNALS /
Tác giả: Shrestha, Kumar
Tác giả: Supervised by Asst. Prof. Dr. Md. Asadur Rahman
- DEVELOPMENT OF A CLINICAL DIAGNOSIS AND DECISION SUPPORT SYSTEM FOR CHEST RADIOGRAPHY USING CNN /
- ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK /
- PREDICTING THE DEPTH OF ANESTHESIA FOR OPERATING PATIENT USING MUSIC-BASED SPECTRAL FEATURES OF EEG SIGNALS /