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e-Learning

Welcome to our e-Learning page, your gateway to acquiring specialized knowledge and skills. Our comprehensive courses are designed to equip you with the expertise necessary to excel in these dynamic fields. Our training programs offer a rich learning experience tailored to your needs. Join us on this transformative educational journey and unlock new horizons in these cutting-edge disciplines.

Training & Classes

Training

Welcome to Computational Visua TEK! We offer specialized training in three main areas: Monte Carlo Simulation, Bioinformatics and Artificial Intelligence, and Machine Learning. Our courses are designed to provide participants with the knowledge and skills required to excel in these cutting-edge fields. Welcome to Computational Visua TEK!. Whether you are interested in advancing your skills in Monte Carlo techniques in radiation physics, exploring the wide world of biological data analysis, or delving into the applications of artificial intelligence and machine learning in medical settings, our expert-led training will provide you with the tools you need to succeed..View more

Classes

Step into the world of Medical Physics and Bioinformatics at Computational Visua TEK’s student classes! Discover the forefront of science and technology with our specialized training. Don’t miss this opportunity to shape the future of healthcare and life sciences. Join us now and embark on a journey of knowledge, innovation, and limitless possibilities!..View more

Cources

Monte Carlo simulation

  1. Introduction to Radiation Physics and Monte Carlo Simulations
    • Fundamentals of Radiation Interactions
    • Overview of Monte Carlo Simulation in Radiation Transport
  2. Particle Interactions and Cross-sections
    • Modeling Particle Interactions (e.g., photons, electrons, neutrons)
    • Data Sources for Interaction Cross-sections
  3. Monte Carlo Geometry and Material Modeling
    • Geometrical Representation of Radiation Sources and Detectors
    • Material Composition and Definitions for Simulation
  4. Random Number Generation and Sampling Techniques
    • Random Number Generation for Monte Carlo Simulations
    • Importance of Sampling in Radiation Transport
  5. Particle Tracking and Scattering Models
    • Tracking Charged Particles in Media
    • Multiple Scattering Models
  6. Scoring and Tallying Radiation Quantities
    • Measurement and Scoring of Radiation Doses
    • Dosimetry in Monte Carlo Simulations
  7. Variance Reduction Techniques in Radiation Physics
    • Importance Sampling for Efficient Simulations
    • Biasing Techniques in Medical Physics
  8. Monte Carlo in Radiation Therapy
    • Dose Calculation for Treatment Planning
    • Applications in External Beam Therapy
  9. Monte Carlo in Diagnostic Imaging
    • Simulation of X-ray and CT Imaging
    • Dosimetry in Diagnostic Radiology
  10. Radiation Shielding and Protection
    • Monte Carlo Simulations for Shielding Design
    • Assessment of Radiation Protection Measures
  11. Monte Carlo in Nuclear Medicine
    • Simulation of Radionuclide Imaging
    • Internal Dose Assessment
  12. Validation and Verification of Monte Carlo Simulations
    • Benchmarking against Experimental Data
    • Uncertainty Analysis in Monte Carlo Results

Medical Physics

  1. Introduction to Medical Physics
    • Overview of Medical Physics and its Applications
    • Historical Developments in Medical Physics
  2. Radiation Physics in Medicine
    • Fundamentals of Ionizing and Non-Ionizing Radiation
  3. Interaction of Radiation with Matter
  4. Radiation Therapy Physics
    • External Beam Radiotherapy
    • Brachytherapy
  5. Medical Imaging Physics
    • X-ray Radiography and Fluoroscopy
    • Computed Tomography (CT) Imaging
  6. Magnetic Resonance Imaging (MRI) Physics
    • Principles of MRI
    • MRI Sequences and Image Contrast
  7. Nuclear Medicine Physics
    • Radionuclide Production and Decay
    • Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET)
  8. Ultrasound Physics and Imaging
    • Principles of Ultrasound Generation and Propagation
    • Ultrasound Imaging Techniques
  9. Radiation Protection and Safety
    • Radiation Safety in Medical Settings
    • Regulatory Guidelines and Dose Limits
  10. Quality Assurance and Quality Control in Medical Physics
    • Equipment Testing and Calibration
    • Treatment Planning and Delivery Verification
  11. Medical Dosimetry
    • Measurement and Calculation of Radiation Dose
    • Treatment Planning Dosimetry
  12. Advanced Topics in Medical Physics
    • Image-Guided Radiation Therapy (IGRT)
    • Particle Therapy (Proton and Carbon Ion Therapy)
  13. Emerging Technologies in Medical Physics
    • Artificial Intelligence and Machine Learning in Medical Imaging
    • Advanced Imaging and Treatment Techniques

Bioinformatics

  1. Introduction to Bioinformatics
    • Overview of Bioinformatics and its Interdisciplinary Nature
    • Historical Developments and Milestones
  2. Biological Databases and Data Retrieval
    • Types of Biological Databases (e.g., Genomic, Proteomic)
    • Data Retrieval and Querying Techniques
  3. Sequence Analysis and Alignment
    • Sequence Alignment Algorithms (e.g., Needleman-Wunsch, Smith-Waterman)
    • Sequence Similarity Searching
  4. Structural Bioinformatics
    • Protein Structure Prediction Methods (e.g., Homology Modeling, Ab Initio Prediction)
    • Protein Structure Analysis and Visualization
  5. Functional Bioinformatics
    • Gene Expression Analysis (e.g., Microarrays, RNA-Seq)
    • Regulatory Networks and Pathway Analysis
  6. Comparative Genomics
    • Genome Assembly and Annotation
    • Phylogenetic Analysis and Evolutionary Relationships
  7. Proteomics and Metabolomics
    • Mass Spectrometry in Proteomics
    • Metabolite Profiling and Analysis
  8. Systems Biology and Network Analysis
    • Integration of Omics Data
    • Biological Network Analysis
  9. Bioinformatics in Drug Discovery and Development
    • In Silico Drug Design and Virtual Screening
    • Pharmacogenomics and Personalized Medicine
  10. Next-Generation Sequencing (NGS) and Applications
    • NGS Technologies and Data Analysis
    • NGS Applications in Genomics and Transcriptomics
  11. Structural Bioinformatics
    • Protein Structure Prediction
    • Protein Structure Analysis and Visualization
  12. Bioinformatics in Biomedical Research
    • Disease Genomics and Biomarker Discovery
    • Bioinformatics for Clinical Decision-Making

AI & ML

  1. Introduction to Artificial Intelligence and Machine Learning
    • Overview of AI and ML
    • Historical Background and Milestones
  2. Foundations of Machine Learning
    • Supervised, Unsupervised, and Reinforcement Learning
    • Training and Testing Data
  3. Data Preprocessing and Feature Engineering
    • Data Cleaning and Transformation
    • Feature Selection and Extraction
  4. Regression Algorithms
    • Linear Regression
    • Polynomial Regression
    • Regularization Techniques
  5. Classification Algorithms
    • Logistic Regression
    • Decision Trees and Random Forests
    • Support Vector Machines
  6. Clustering Algorithms
    • K-Means Clustering
    • Hierarchical Clustering
    • Density-Based Clustering
  7. Neural Networks and Deep Learning
    • Perceptrons and Multilayer Perceptrons
    • Convolutional Neural Networks (CNNs)
    • Recurrent Neural Networks (RNNs)
  8. Model Evaluation and Performance Metrics
    • Cross-Validation Techniques
    • Accuracy, Precision, Recall, F1 Score, ROC, etc.
  9. Hyperparameter Tuning and Model Selection
    • Grid Search and Random Search
    • Model Selection Strategies
  10. Natural Language Processing (NLP)
    • Text Preprocessing and Tokenization
    • Word Embeddings and Text Classification
  11. AI Ethics and Bias in Machine Learning
    • Ethical Considerations in AI Development
    • Addressing Bias and Fairness in ML Models
  12. Reinforcement Learning and AI Applications
    • Markov Decision Processes (MDPs)
    • Applications of AI and ML in Real-World Scenarios.

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