Samankaltaisia teoksia: MULTI-MODEL CLASSIFIER ENSEMBLE FOR URBAN TRAFFIC CONGESTION /
- ENHANCED DETECTION OF ELECTRICITY THEFT IN SMART GRIDS USING A MACHINE LEARNING BOOSTING CLASSIFIERS BASED VOTING ENSEMBLE MODEL /
- AI-POWERED DERMATOLOGICAL SCREENING IN BANGLADESH: ENSEMBLE LEARNING APPROACH WITH MOBILE DEPLOYMENT AND USABILITY VALIDATION /
- CLASSIFICATION OF AUTISM SPECTRUM DISORDER FROM RS-FMRI SCANS BY FEATURE REDUCTION ON ENSEMBLE MACHINE LEARNING AND CNN/
- DEVELOPING MULTI-SCALE 3D ATTENTION NETWORKS FOR FINE-GRAINED EM SEGMENTATION /
- PERFORMANCE ASSESSMENT OF METAHEURISTIC OPTIMIZED PID CONTROLLERS FOR MULTI-AREA LOAD FREQUENCY CONTROL /
- ATTENTION-ENHANCED DEEP 3DU-NET FRAMEWORKS FOR MULTI-MODAL BRAIN TUMOR SEGMENTATION /
Aihe: Electrical, Electronic and Communication Engineering
- DESIGN AND ANALYSIS OF METAMATERIAL BASED BIOSENSOR TO DETERMINE BLOOD GLUCOSE CONCENTRATION /
- A COMPUTATIONALLY EFFICIENT DEEP LEARNING FRAMEWORK FOR SLEEP APNEA DETECTION /
- TEMPERATURE AND STATE OF CHARGE AWARE FAST CHARGING FOR LITHIUM-ION BATTERY SYSTEM /
- INTERPOLATION BASED ADAPTIVE REVERSIBLE DATA HIDING FOR DIGITAL IMAGE APPLICATIONS /
- ENERGY EFFICIENT OPERATIONS OF COMP BASED LTE-A CELLULAR NETWORKS WITH HYBERID POWER SUPPLIES /
- DESIGN AND CHARACTERIZATION OF BULK AND SOL FINFETS /
Aihe: This Thesis Paper of EECE in BSc Program.
- A COMPUTATIONALLY EFFICIENT DEEP LEARNING FRAMEWORK FOR SLEEP APNEA DETECTION /
- TEMPERATURE AND STATE OF CHARGE AWARE FAST CHARGING FOR LITHIUM-ION BATTERY SYSTEM /
- A COMPARATIVE STUDY OF INORGANIC LEAD HALIDE PEROVSKITES BY ANALYZING THEIR ELECTRICAL, OPTICAL AND MECHANICAL PROPERTIES /
- A COMPREHENSIVE INVESTIGATION OF GaSb/GaAs0.5Sb0.5/GaAs BASED DG-HJ-SP-HD VERTICAL TUNNEL FET FOR OPTIMIZED LOW POWER APPLICATIONS: EXPLORING THE IMPACT OF DEVICE PARAMETER VARIATIONS /
- PERFORMANCE ANALYSIS OF CONTEMPORARY POWER DISTRIBUTION NETWORKS USING IEEE 13 BUS SYSTEM /
- AN IOT BASED HEALTHCARE SOLUTION AND MEDICAL SPECIALTY CLASSIFICATION FROM BANGLA TEXT USING MACHINE AND DEEP LEARNING MODELS /
Tekijä: Sizan, Mayan Uddin
Tekijä: Supervised by Maj Md Abdul Wahed
- A COMPUTATIONALLY EFFICIENT DEEP LEARNING FRAMEWORK FOR SLEEP APNEA DETECTION /
- A COMPARATIVE BEHAVIOURAL STUDY OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR NETWORK INTRUSION DETECTION ACROSS DATASETS OF DIFFERING STATISTICAL CHARACTER /
- ENHANCED FEATURE EXTRACTION FOR SEIZURE FORECASTING: A COMPARATIVE IMPLEMENTATION OF PCA AND AUTOCODERS /
- MULTI-MODEL CLASSIFIER ENSEMBLE FOR URBAN TRAFFIC CONGESTION /