Antzeko izenburuak: AN ENSEMBLE DEEP LEARNING APPROACH FOR ENHANCED LUNG CANCER DETECTION FROM CT IMAGES /
- MACHINE LEARNING APPROACHES FOR THE DETECTION OF LUNG CANCER USING MRI IMAGES /
- FLOOD DETECTION AND FLOODED AREA SEGMENTATION USING ENSEMBLE MACHINE LEARNING AND DEEP LEARNING APPROACHES /
- MULTI-CLASS KIDNEY DISEASE CLASSIFICATION FROM CT IMAGES USING EXPLAINABLE DEEP LEARNING /
- DIABETIC RETINOPATHY STAGE DETECTION USING RETINAL FUNDUS IMAGES THROUGH ENSEMBLE LEARNING APPROACH/
- OVARIAN ULTRASOUND IMAGE ANALYSIS USING HYBRID DEEP LEARNING FRAMEWORK FOR CANCER DIAGNOSIS /
- AN ASSESSMENT AND COMPARISON OF RADIATION DOSE IN PET CT SCANS FOR CANCER PATIENTS /
Gaia: Computer Science and Engineering.
- IMPLEMENTATION OF AES128: A BEGINNING IN THE PRODUCTION OF IFF DEVICE FOR BANGLADESH DEFENSE SERVICES /
- CLOUD COMPUTING-FEASIBILITY AND PROSPECTS FOR IMPLEMENTATION IN BANGLADESH ARMY /
- DEEP REINFORCEMENT LEARNING BASED OPTIMAL COMPUTATION OFFLOADING IN FOG COMPUTING ENVIRONMENT /
- A MULTIMODAL FEDERATED-LEARNING BASED FRAMEWORK FOR PEDESTRIAN BEHAVIOR UNDERSTANDING AND ROAD-BLOCKAGE ASSESSMENT UNDER ADVERSE CONDITIONS /
- A DUAL-STAGE CNN FRAMEWORK FOR RICE LEAF DISEASE DETECTION AND CLASSIFICATION USING COLOR INTENSITY ENHANCED FEATURE EXTRACTION /
- EXPLORING COGNITIVE LOAD IN STORYTELLING ASPECTS ACROSS THE ONBOARDING PHASE OF A VIDEO GAME USING NASA-TLX /
Gaia: This Thesis Paper of CSE in B.Sc Program.
- A MULTIMODAL FEDERATED-LEARNING BASED FRAMEWORK FOR PEDESTRIAN BEHAVIOR UNDERSTANDING AND ROAD-BLOCKAGE ASSESSMENT UNDER ADVERSE CONDITIONS /
- A DUAL-STAGE CNN FRAMEWORK FOR RICE LEAF DISEASE DETECTION AND CLASSIFICATION USING COLOR INTENSITY ENHANCED FEATURE EXTRACTION /
- EXPLORING COGNITIVE LOAD IN STORYTELLING ASPECTS ACROSS THE ONBOARDING PHASE OF A VIDEO GAME USING NASA-TLX /
- EXPLAINABLE AI-BASED COMPARATIVE ANALYSIS OF TRANSFORMER MODELS FOR MISINFORMATION AND DISINFORMATION DETECTION /
- AI-ENHANCED COMPLAINT MANAGEMENT SYSTEM FOR GOVERNMENT MARKETPLACES /
- REAL VS FAKE IMAGE DETECTION USING A HYBRID CNN-TRANSFORMER ARCHITECTURE /