Samankaltaisia teoksia: QUANTUM-CLASSICAL NETWORK INTRUSION DETECTION USING QKD-INSPIRED SECURITY FEATURES /
- HYBRID SEMANTIC-QUANTUM-INSPIRED CLASSICAL STEGANOGRAPHY FOR HIGH-SECURITY REGION-OF-INTEREST HIDING /
- BATCH-WISE ENSEMBLE MODEL USING CLASSICAL AND QUANTUM SVM FOR EMAIL PHISHING DETECTION /
- AN ADVERSARIAL APPROACH FOR INTRUSION DETECTION USING DEEP LEARNING /
- ADAPTIVE AND AGGREGATED MODEL FUSION FOR FEDERATED INTRUSION DETECTION IN HETEROGENEOUS EDGE NETWORKS /
- ENHANCING CYBERSECURITY IN AUTOMOTIVE SYSTEMS: INTRUSION DETECTION WITH BOOSTING ALGORITHMS FOR CAN BUS NETWORKS /
- IDENTIFICATION OF DDOS ATTACK THROUGH INTRUSION DETECTION MODEL USING ENSEMBLE MACHINE LEARNING /
Aihe: 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 /
Aihe: 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 /