Samankaltaisia teoksia: A COMPARATIVE BEHAVIOURAL STUDY OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR NETWORK INTRUSION DETECTION ACROSS DATASETS OF DIFFERING STATISTICAL CHARACTER /
- AN ADVERSARIAL APPROACH FOR INTRUSION DETECTION USING DEEP LEARNING /
- DEEP LEARNING BASED MULTIPLE OBJECT DETECTION AND DISTANCE MANAGEMENT FROM CUSTOM-BUILT DATASET WITH HETEROGENIOUS TRAFFIC /
- Deep learning /
- ADAPTIVE AND AGGREGATED MODEL FUSION FOR FEDERATED INTRUSION DETECTION IN HETEROGENEOUS EDGE NETWORKS /
- A COMPREHENSIVE STUDY OF DEEP LEARNING, TEMPORAL ANALYSIS AND SEMANTIC-ANOMALY SUSION FOR IOT AND IIOT INTRUSION DETECTION /
- Deep learning with python /
Aihe: Electrical, Electronic and Communication Engineering.
- 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 /
- BASE TRANSIT OF A GRADED BANE ALyGa1-yAs HBT USING THE ANALYTICAL MODEL DEVELOPED FOR SIGE DEVICE WITH FOR GAUSSIAN DOPED BASE /
- THE EARLY VOLTAGE AND CURRENT GAIN OF IN GA AS DOUBLE HETERO JUNCTION BIPOLAR TRANSISTOR /
- EFFECT OF POLARIZATION MODE DISPERSION (PMD) IN DIFFERENT CORE SHAPES AND BIT ERROR RATE (BER) PERFORMANCE IN FIBER OPTIC COMMUNICATION /
Aihe: This Thesis Paper of EECE in BSc Program.
- ANALYSIS OF BER PERFORMANCE ON DIFFERENT MODULATION TECHNIQUES OVER AWGN CHANNEL /
- DESIGN OF A DUAL-BAND MICROSTRIP PATCH ANTENNA WITH ENHANCED BANDWIDTH AND GAIN FOR 5G APPLICATIONS /
- CLASSIFICATION OF AUTISM SPECTRUM DISORDER FROM RS-FMRI SCANS BY FEATURE REDUCTION ON ENSEMBLE MACHINE LEARNING AND CNN/
- INVESTIGATION OF THE TOPSIDE IONOSPHERE OVER BANGLADESH USING SWARM, COSMIC AND GNSS DATA INCLUDING IRI PREDICTIONS /
- INVESTIGATION OF IONOSPHERIC DIURNAL DOUBLE MAXIMA OVER BANGLADESH /
- FREQUENCY STABILITY ENHANCEMENT WITH VSG CONTROL FOR A PV DOMINATED GRID /
Tekijä: Shadman, Samin
Tekijä: Supervised by Maj Md Abdul Wahed
- 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 /
- A COMPUTATIONALLY EFFICIENT DEEP LEARNING FRAMEWORK FOR SLEEP APNEA DETECTION /
- MULTI-MODEL CLASSIFIER ENSEMBLE FOR URBAN TRAFFIC CONGESTION /