Machine Learning Models and Architectures for Biomedical Signal Processing

Section 1: Introduction to bioinformatics 1.1 Recent trends of bioinformatics 1.2 Biomedical signal processing technique 1.3 Transfer Learning based Arrhythmia classification using Electrocardiogram Section 2: Machine learning models for biomedical signal processing 2.1 Exploring Machine Learning Models for Biomedical Signal Processing: A Comprehensive Review 2.2 Machine Learning for Audio Processing: From Feature Extraction to Model Selection 2.3 Pre-processing of MRI images suitable for Artificial Intelligence-based Alzheimer's Disease classification 2.4 Machine Learning Models for Text and Image Processing 2.5 Assistive Technology for Neuro-rehabilitation Applications Using Machine Learning Techniques 2.6 Deep Learning Architectures in Computer Vision based Medical Imaging Applications with Emerging Challenges 2.7 Relevance of Artificial Intelligence, Machine Learning, and Biomedical Devices to Healthcare Quality and patient Outcomes 2.8 AI-Based ECG Signal processing applications 2.9 Deep Learning Approach for the Prediction of Skin Diseases Section 3: Brain computer interfaces (BCI) 3.1 Brain-Computer Interface 3.2 Analysis on Types of Brain-Computer Interfaces for Disabled Person 3.3 Brain Computer Interfaces for elderly and disabled person Section 4: Real time architecture design for biomedical signals 4.1 Machine learning model implementation with FPGA'S 4.2 Smart Biomedical Devices for Smart Healthcare 4.3 FPGA implementation for explainable machine learning and deep learning models to real time problems Section 5: Software and Hardware-based Applications for biomedical Informatics 5.1 Software Applications for Biometric Informatics 5.2 Smart Medical Devices: Making Health Care More Intelligent 5.3 Security modules for biomedical signal processing 5.4 Artificial intelligence-based diagnostic tool for cardiovascular risk prediction 5.5 Machine Learning Algorithm approach in risk prediction of Liver Cancer

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• Aprendizagem mecânica
• Modelagem computacional e simulação
• Engenharia biomédica
• Biologia, ciências da vida
• Processamento de sinais

AI; ANN; ASIC; Alzheimer's disease; Arrhythmia; Artificial intelligence; Assistive technology (AT); Audio data augmentation; Audio processing; Audio segmentation; Audio signal processing; Bias correction; Biomedical devices; Biomedical signal processing; Biometrics; Body function; Brain computer interfaces (BCIs); CNN; CPU; Cardiology; Cardiovascular disease; Cardiovascular diseases (CVDs); Cardiovascular system; Clinical applications; Clustering; Communication; Computational intelligence; Computational pattern recognition; Computer in society; Computer science; Computer vision; Convolutional neural network; DL; Data analytics; Data processing; Data security; Deep learning; Delivery of healthcare; Diagnostic procedure; Digital signal processing; Disabilities; Disease diagnosis; ECG; EEG; EEG classification; EMG; Elderly; Electrocardiogram (ECG) signal; Electrocardiography; Electroencephalography; Electromyography; Electronic health records; Ensemble learning; Experimental approach in biochemistry; FPGA; Fault injection attack; Feature extraction; Filtering; GPU; Genomics; Good health and well-being; Hardware architecture; Hardware attacks; Health system; Healthcare industries; Human-centered computing; Human-computer interaction; Identification; Image processing; Internet of things; IoT; Liver cancer; Long-short term memory; ML; ML algorithm; Machine learning; Machine learning models; Magnetic resonance; Medical care; Medical imaging; Medical informatics; Mental disorder; Mobility; Model selection; Parkinson disease; Performance evaluation; Preprocessing pipeline; Privacy; Procedure by method; Recognition; Recurrent neural network; Registration; Rehabilitation; Rehabilitation robot; Reorientation; Risk assessment; Risk factor; Safety assessment; Scalogram; Security