| Description |
1 online resource |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Summary |
Cognitive Systems and Signal Processing in Image Processing presents different frameworks and applications of cognitive signal processing methods in image processing. This book provides an overview of recent applications in image processing by cognitive signal processing methods in the context of Big Data and Cognitive AI. It presents the amalgamation of cognitive systems and signal processing in the context of image processing approaches in solving various real-word application domains. This book reports the latest progress in cognitive big data and sustainable computing. Various real-time case studies and implemented works are discussed for better understanding and more clarity to readers. The combined model of cognitive data intelligence with learning methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues for computer vision in real-time. |
| Contents |
Intro -- Cognitive Systems and Signal Processing in Image Processing -- Copyright -- Contents -- Contributors -- Chapter 1: A cognitive approach to digital health based on deep learning focused on classification and recognition of whi ... -- 1. Introduction -- 2. Literature review -- 2.1. Cognitive systems concepts -- 3. Cognitive systems in medical image processing -- 3.1. Cognitive systems in the context of predictive analytics -- 4. Neural networks concepts -- 4.1. Convolutional neural network -- 4.2. Deep learning -- 5. Metaheuristic algorithm proposal (experiment) -- 6. Results and discussion -- 7. Conclusions -- 8. Future research directions -- References -- Chapter 2: Assessment of land use land cover change detection in multitemporal satellite images using machine learning al ... -- 1. Introduction -- 2. Related works -- 2.1. Gaps identified in existing works -- 3. Proposed work -- 3.1. Study area -- 3.2. Data collection -- 4. Methodology -- 4.1. Maximum likelihood classification -- 5. Results and discussions -- 5.1. Maximum likelihood classification -- 5.1.1. Change detection based on MLC maps -- 5.2. Normalized difference vegetative index classification -- 5.2.1. Change detection based on NDVI classified maps -- 6. Accuracy assessment -- 7. Conclusion -- References -- Further reading -- Chapter 3: A web application for crowd counting by building parallel and direct connection-based CNN architectures -- 1. Introduction -- 2. Background -- 3. CNN algorithmic model -- 3.1. Data process -- 3.1.1. Gaussian blur algorithms -- 3.1.2. Binary space partitioning architecture -- 3.2. Core model structure -- 3.2.1. Transfer learning -- 3.2.2. Activation function -- 3.2.3. Batch normalization -- 3.3. ADCCNet model -- 3.4. Train model by learning data -- 3.4.1. Data enhancement -- 3.4.2. Criterion -- 3.4.3. Gradient optimization -- 3.5. Analyze error. |
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3.5.1. Underfitting and overfitting -- 3.5.2. Loss value -- 3.5.3. Training epochs -- 3.5.4. Learning rate -- 3.6. Verify web applications -- 3.6.1. Login and register module -- 3.6.2. Display module -- 3.6.3. Solve picture module -- 3.6.4. Take a question module -- 4. Experimental results -- 5. Future research directions -- 6. Conclusion -- Appendices -- A. An example of ShangHaiTech dataset .mat file -- B. Verify web applications feature showcase -- Acknowledgment -- References -- Chapter 4: A cognitive system for lip identification using convolution neural networks -- 1. Introduction -- 2. Survey of related work -- 2.1. Summary of existing approaches -- 2.2. Shortcomings of previous work -- 2.2.1. Motivation -- 3. Feature extraction and classification using CNN -- 3.1. Cognitive computing -- 3.1.1. Convolution network -- 3.2. Database -- 4. Results -- 5. Conclusion and future work -- References -- Chapter 5: An overview of the impact of PACS as health informatics and technology e-health in healthcare management -- 1. Introduction -- 2. Review literature on cognitive systems concepts -- 2.1. Cognitive systems in medical image processing -- 2.2. Cognitive systems in the context of predictive analytics -- 3. Review literature on implementation of PACS systems -- 4. PACS systems application -- 5. PACS environments and systems management -- 5.1. PACS extension in the healthcare management -- 6. Discussion -- 7. Future trends -- 8. Conclusions -- References -- Chapter 6: Change detection techniques for a remote sensing application: An overview -- 1. Introduction -- 2. Remote sensing data -- 3. Data preprocessing -- 4. Change detection technique -- 4.1. Algebra approach -- 4.1.1. Image differencing -- 4.1.2. Image ratioing -- 4.1.3. Image regression -- 4.1.4. Vegetation index differencing -- 4.1.5. Change vector analysis -- 4.2. Transformation approach. |
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4.2.1. Principal component analysis -- 4.2.2. Kauth-Thomas transformation/tasseled cap transformation -- 4.2.3. Chi-square transform -- 4.3. Classification approaches -- 4.3.1. Postclassification comparison -- 4.3.2. Expectation-maximization algorithm -- 4.3.3. Hybrid change detection -- 4.3.4. Artificial neural network -- 4.4. Geographical information system approach -- 4.5. Visual analysis -- 4.6. Other approaches -- 5. Conclusion -- References -- Chapter 7: Facial emotion recognition via stationary wavelet entropy and particle swarm optimization -- 1. Introduction -- 1.1. Related work of facial emotion recognition -- 1.2. Structure of this chapter -- 2. Dataset -- 3. Methodology -- 3.1. Stationary wavelet entropy -- 3.2. Single-hidden-layer feedforward neural network -- 3.3. Particle swarm optimization -- 3.4. Implementation -- 3.5. Measure -- 4. Experiment results and discussions -- 4.1. Confusion matrix of proposed method -- 4.2. Statistical results -- 4.3. Comparison to state-of-the-art approaches -- 5. Conclusions -- References -- Chapter 8: A research insight toward the significance in extraction of retinal blood vessels from fundus images and its v ... -- 1. Introduction -- 1.1. Organization of the chapter -- 2. Literature review -- 2.1. Role of retinal blood vessels in disease detection -- 2.1.1. Retinal pathologies -- 2.1.2. Cardiovascular diseases -- 2.1.3. Cerebrovascular diseases -- 2.1.4. Cancers -- 2.2. Different methods for segmentation -- 2.2.1. Supervised techniques -- 2.2.2. Unsupervised technique -- 3. Extraction of retinal blood vessels using supervised technique -- 3.1. Materials -- 3.2. Methodology -- 3.2.1. Preprocessing -- 3.2.2. Feature extraction -- Gabor filtering -- 3.2.3. Feature vector construction and principal component analysis -- 3.2.4. Supervised technique -- 3.2.5. Postprocessing -- 3.3. Result. |
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3.3.1. Qualitative analysis -- 3.3.2. Quantitative analysis -- 3.3.3. Performance comparison of our method with the state-of-the-art methods in terms of execution time -- 4. Extraction of retinal blood vessels using unsupervised technique -- 4.1. Materials -- 4.2. Proposed method -- 4.2.1. Preprocessing -- 4.2.2. Segmentation -- 4.2.3. Postprocessing -- 5. Result -- 5.1. Qualitative analysis -- 5.2. Quantitative analysis -- 5.3. Comparison of our method against existing methods -- 6. Conclusion -- 7. Future scope -- References -- Chapter 9: Hearing loss classification via stationary wavelet entropy and cat swarm optimization -- 1. Introduction -- 2. Dataset -- 3. Methodology -- 3.1. Stationary wavelet entropy -- 3.2. Single-hidden-layer feedforward neural network -- 3.3. Cat swarm optimization -- 3.4. Implementation -- 3.5. Measure -- 4. Experiment results and discussions -- 4.1. Confusion matrix of proposed method -- 4.2. Statistical results -- 4.3. Comparison to state-of-the-art approaches -- 5. Conclusions -- References -- Chapter 10: Early detection of breast cancer using efficient image processing algorithms and prediagnostic techniques: A ... -- 1. Introduction -- 2. Literature review -- 3. Breast cancer: A brief introduction -- 3.1. Overview of breast cancer -- 3.2. Symptoms of breast cancer -- 3.3. Categories of breast cancer -- 3.3.1. Inflammatory breast cancer -- 3.3.2. Triple-negative breast cancer -- 3.3.3. Metastatic breast cancer -- 3.4. Male breast cancer -- 3.5. Breast cancer stages -- 3.6. Diagnosis of breast cancer -- 3.7. Breast cancer treatment -- 3.7.1. Surgery -- 3.7.2. Radiation therapy -- 3.7.3. Chemotherapy -- 3.7.4. Hormone therapy -- 3.8. Medications -- 3.9. Risk factors for breast cancer -- 3.10. Breast cancer survival rate -- 3.11. Breast cancer prevention -- 3.11.1. Lifestyle factors -- 3.12. Breast cancer screening. |
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3.13. Preemptive treatment -- 3.14. Breast test -- 3.14.1. Self-test -- 3.14.2. Breast test by a doctor -- 3.15. Breast cancer awareness -- 4. Cognitive approaches in breast cancer techniques -- 4.1. Cognitive image processing -- 4.2. Knowledge-based vision systems -- 4.3. Integration of knowledge bases in vision systems -- 4.4. Image processing, annotation, and retrieval -- 4.5. Human activity recognition -- 4.6. Medical images analysis -- 5. Proposed methodology -- 5.1. Workflow -- 6. Algorithms used -- 7. Results and discussion -- 8. Conclusion -- References -- Chapter 11: Groundnut leaves and their disease, deficiency, and toxicity classification using a machine learning approach -- 1. Introduction -- 1.1. Groundnut crop -- 1.2. Major diseases -- 1.3. Major deficiencies -- 1.4. Disease, deficiency, and toxicity management -- 1.5. Lack of accurate detection -- 2. Literature review -- 3. Methodology -- 3.1. Image dataset -- 3.2. Image acquisition -- 3.3. Preprocessing of the acquired image -- 3.4. Image segmentation -- 3.5. Clustering technique -- 3.5.1. K-means clustering algorithm -- 3.6. Feature extraction -- 3.7. Classification -- 3.7.1. Support vector machine classifier -- 3.7.2. Random forest classifier -- 3.7.3. K-nearest neighbor classifier -- 3.7.4. Decision tree classifier -- 3.7.5. Neural network classifier -- 4. Results and discussion -- 4.1. Experimental results -- 4.2. Performance evaluation -- 4.2.1. Classification matrix -- 5. Conclusion -- Acknowledgment -- References -- Chapter 12: EEG-based computer-aided diagnosis of autism spectrum disorder -- 1. Introduction -- 2. Related work -- 3. Proposed work -- 4. Performance analysis -- 5. Conclusion -- References -- Chapter 13: Toward improving the accuracy in the diagnosis of schizophrenia using functional magnetic resonance imaging ( ... -- 1. Introduction -- 2. Literature review. |
| Subject |
Signal processing.
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Image processing.
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Artificial intelligence.
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Artificial Intelligence |
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Traitement du signal.
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Traitement d'images.
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Intelligence artificielle.
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image processing.
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artificial intelligence.
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Artificial intelligence
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Image processing
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Signal processing
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| Added Author |
Zhang, Yu-Dong, editor.
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Sangaiah, Arun Kumar, 1981- editor.
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| Other Form: |
Print version: 0128244100 9780128244104 (OCoLC)1258779868 |
| ISBN |
9780323860093 (electronic book) |
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0323860095 (electronic book) |
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9780128244104 (electronic book) |
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0128244100 (electronic book) |
| Standard No. |
AU@ 000070477258 |
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UKMGB 020434792 |
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