Míreanna comhchosúla: MACHINE LEARNING MODEL TO DETECT PNEUMONIA OF COVID-19 PATIENTS FROM CHEST X-RAY IMAGES /
- ENHANCING CHEST DISEASE DIAGNOSIS: ADVANCED DEEP LEARNING ARCHITECTURES FOR CHEST X-RAY ANALYSIS /
- SENTIMENT ANALYSIS OF COVID-19 TWEETS USING MACHINE LEARNING TECHNIQUES /
- A REVIEW ON COVID-19: WASTE MANAGEMENT IN BANGLADESH /
- AN EMPIRICAL STUDY OF EXPLORE THE HEALTH IMPACTS OF POST COVID-19 /
- SKIN DISEASE DETECTION FROM IMAGES USING MACHINE LEARNING ALGORITHMS /
- DETECTION OF BRAIN TUMORS FROM MRI IMAGES USING MACHINE LEARNING APPROACHES /
Topaic: Thesis Paper of CSE in M.Sc Prog.
- APPLICATION OF MACHINE LEARNING IN SORTING NANO PARTICLES CHARACTERIZED BY OPTICAL FORCE /
- SENTIMENT ANALYSIS OF COVID-19 TWEETS USING MACHINE LEARNING TECHNIQUES /
- PARETO-OPTIMAL PHYLOGENETIC TREE RECONCILIATION CONSIDERING DUPLICATIONS TRANSFERS, LOSSES AND INCOMPLETE LINEAGE SORTING /
- SIMILARITY ESTIMATION OF DNA SEQUENCES USING GRAYSCALE IMAGE ENCODING SCHEME ON 2D GRAPHMAP /
- MACHINE LEARNING MODEL TO DETECT PNEUMONIA OF COVID-19 PATIENTS FROM CHEST X-RAY IMAGES /
Topaic: Computer Science and Engineering.
- IMPLEMENTA OF GENOME TRANSPOSITION IN GRIMM /
- FUSION OF FINGERPRINT AND HEARTBEAT BIO METRICS FOR SECURITY ENHANCEMENT /
- ANALYSIS OF MULTIPLICATION BASED ALGORITHMS /
- PREDICTION OF URINARY TRACT INFECTIONS USING HMM
- MOBILE VIDEO TRANSMITTING ROBOT /
- PARAMETRIC STUDY OF STUDENT LEARNING IN IT USING DATA MINING
Údar: Anti, Maksuda Rahman
Údar: Supervised by Dr. Md. Shohrab Hossain
- MACHINE LEARNING MODEL TO DETECT PNEUMONIA OF COVID-19 PATIENTS FROM CHEST X-RAY IMAGES /
- IDENTIFICATION OF DDOS ATTACK THROUGH INTRUSION DETECTION MODEL USING ENSEMBLE MACHINE LEARNING /
- DEVELOPMENT OF A TASK MANAGEMENT SYSTEM FOR SMALL AND MEDIUM SIZED ENTERPRISES /
- DEVELOPMENT OF HUMAN RESOURCE MANAGEMENT SYSTEM FOR SMALL SCALE SOFTWARE COMPANY /
- STATIC ANALYSIS BASED MALWARE DETECTION FOR ZERO-DAY ATTACKS IN ANDROID APPLICATIONS /
- MACHINE LEARNING BASED SEPSIS PREDICTION FOR FOOT INFECTION IN DIABETIC PATIENTS /