Lignende værker: 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 /
Emne: Biomedical Engineering.
- Introduction to Biomedical Equipment Technology /
- Biomedical engineering fundamentals /
- Biomedical digital signal processing : C-language examples and laboratory experiments for the IBM PC /
- Biomedical ethics for engineers : ethics and decision making in biomedical and biosystem engineering /
- Biomedical engineering handbook /
- ADVANCED TECHNOLOGIES IN RADIOTHERAPY FOR INTRA-FRACTIONAL MOTION OF TUMOUR : IMAGE GUIDED RADIOTHERAPY (IGRT) /
Emne: 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 /
Forfatter: Shrestha, Kumar
Forfatter: 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 /