| Autore |
Srivastava Sumit
|
| Edizione | [1st ed.] |
| Pubbl/distr/stampa |
Newark : , : John Wiley & Sons, Incorporated, , 2024
|
| Descrizione fisica |
1 online resource (334 pages)
|
| Disciplina |
610.285
|
| Altri autori (Persone) |
AnandAbhineet
KumarAbhishek
SainiBhavna
RathorePramod Singh
|
| Soggetto topico |
Medical informatics - Data processing
Data mining
|
| ISBN |
1-394-21421-9
1-394-21420-0
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Bioinspired Algorithms: Opportunities and Challenges -- 1.1 Introduction -- 1.1.1 Definition and Significance of Bioinspired Algorithms -- 1.1.2 Overview of the Chapter -- 1.2 Bioinspired Principles and Algorithms -- 1.2.1 Evolutionary Algorithms -- 1.2.2 Swarm Intelligence Algorithms -- 1.2.3 Artificial Neural Networks -- 1.2.4 Other Bioinspired Algorithms -- 1.3 Opportunities of Bioinspired Algorithms -- 1.3.1 Solving Complex Optimization Problems -- 1.3.2 Robustness in Dealing With Uncertainty and Noise -- 1.3.3 Parallel and Distributed Computing -- 1.3.4 Application Areas and Success Stories -- 1.4 Challenges of Bioinspired Algorithms -- 1.4.1 Parameter Tuning and Algorithm Configuration -- 1.4.2 Lack of Theoretical Analysis and Understanding -- 1.4.3 Risk of Premature Convergence -- 1.4.4 Computational Cost for Large-Scale Problems -- 1.4.5 Ethical Considerations and Limitations -- 1.5 Prominent Bioinspired Algorithms -- 1.5.1 Genetic Algorithms -- 1.5.2 Particle Swarm Optimization -- 1.5.3 Ant Colony Optimization -- 1.5.4 Artificial Neural Networks -- 1.6 Applications of Bioinspired Algorithms -- 1.6.1 Optimization Problems -- 1.6.2 Pattern Recognition and Machine Learning -- 1.6.3 Swarm Robotics -- 1.6.4 Other Domains -- 1.7 Future Research Directions -- 1.7.1 Improving Efficiency and Scalability -- 1.7.2 Enhancing Interpretability and Explainability -- 1.7.3 Integration With Other Computational Techniques -- 1.7.4 Addressing Ethical Concerns -- 1.8 Conclusion -- 1.8.1 Summary of Key Points -- 1.8.2 Implications and Future Prospects of Bioinspired Algorithms -- References -- Chapter 2 Evaluation of Phytochemical Screening and In Vitro Antiurolithiatic Activity of Myristica fragrans by Titrimetry Method Using Machine Learning -- 2.1 Introduction.
2.2 Methodology -- 2.2.1 Collection of Plant Material -- 2.2.2 Qualitative Analysis of Phytochemicals -- 2.2.3 Study of In Vitro Antiurolithiatic Activity Using Titrimetry Method -- 2.2.3.1 Preparation of Calcium Oxalate -- 2.2.3.2 Preparation of Semipermeable Membrane From Eggs -- 2.2.3.3 In Vitro Antiurolithiatic Test Using Titrimetry Method -- 2.3 Result and Discussion -- 2.3.1 In Vitro Antiurolithiatic Activity Test -- 2.3.2 Analysis of Dissolved Calcium Oxalate -- 2.4 Conclusion -- References -- Chapter 3 Parkinson's Disease Detection Using Voice and Speech- Systematic Literature Review -- 3.1 Introduction -- 3.2 Research Questions -- 3.3 Method -- 3.3.1 Search Strategy -- 3.3.2 Inclusion Criteria -- 3.3.3 Subprocesses Involved in PD Detection Process -- 3.3.4 Data Sets -- 3.3.4.1 Parkinson's Data Set-UCI Machine Learning Dataset -- 3.3.4.2 PC-GITA Dataset -- 3.3.4.3 mPower Dataset -- 3.3.4.4 Mobile Device Voice Recordings (MDVR-KCL) Dataset -- 3.3.4.5 Italian Parkinson's Voice and Speech (IPVS) Dataset -- 3.3.4.6 Parkinson Speech Dataset With Multiple Types of Sound Recordings Dataset -- 3.3.4.7 Parkinson's Telemonitoring Dataset -- 3.4 Algorithms -- 3.5 Features -- 3.5.1 Acoustic Features -- 3.5.1.1 Jitter (Local, Absolute) -- 3.5.1.2 Jitter (Local) -- 3.5.1.3 Jitter (rap) -- 3.5.1.4 Jitter (ppq5) -- 3.5.1.5 Shimmer (Local) -- 3.5.1.6 Shimmer (local, dB) -- 3.5.1.7 Shimmer (apq3) -- 3.5.1.8 Shimmer (apq5) -- 3.5.2 Spectogram-Based Methods -- 3.5.2.1 MFCC -- 3.6 Conclusion -- References -- Chapter 4 Tumor Detection and Classification -- 4.1 Introduction -- 4.2 Methods Used for Detection of Tumors -- 4.3 Methods Used for Classification of Tumours -- 4.3.1 Segmentation -- 4.3.2 Region Growing Method -- 4.3.3 Seeded Region Growing Method -- 4.3.4 Unseeded Region Growing Method -- 4.3.5 .-Connected Method -- 4.3.6 Threshold Based Method.
4.3.7 K-Means Method -- 4.3.8 Watershed Method -- 4.3.9 Comparison of Different Segmentation Techniques Based on the Advantages and Disadvantages -- 4.3.10 Comparison of Different Segmentation Techniques Based on Accuracy -- 4.3.11 Comparison of Region Based and Threshold Based Segmentation Techniques Based on Different Parameters -- 4.4 Machine Learning -- 4.4.1 Supervised Learning -- 4.4.2 Unsupervised Learning -- 4.4.3 Reinforcement Learning -- 4.4.4 K-Nearest Neighbour (KNN) -- 4.4.5 Support Vector Machine (SVM) -- 4.4.6 Random Forest -- 4.5 Deep Learning (DL) -- 4.5.1 Convolutional Neural Networks (CNN) -- 4.5.1.1 Convolution Layer -- 4.5.1.2 Pooling Layer -- 4.5.1.3 Architecture of CNN -- 4.5.1.4 Comparison of Different Variations of CNN Techniques -- 4.5.2 Long Short-Term Memory (LSTM) -- 4.5.3 Artificial Neural Network (ANN) -- 4.5.4 Accuracy of Different Models Discussed Above -- 4.5.5 Accuracy of Other Different Techniques Being Used -- 4.6 Performance Metrics -- 4.6.1 Accuracy -- 4.6.2 Precision -- 4.6.3 Recall -- 4.6.4 Specificity -- 4.6.5 F1-Measure -- 4.7 Method Wise Trend of Using Techniques for Detection of Brain Tumor -- 4.8 Conclusion -- References -- Chapter 5 Advancements in Tumor Detection and Classification -- 5.1 Introduction -- 5.2 Imaging Techniques Used in Tumor Detection and Classification -- 5.2.1 X-Ray -- 5.2.2 CT Scan -- 5.2.3 MRI -- 5.2.4 Ultrasound -- 5.3 Molecular Biology Techniques -- 5.3.1 PCR -- 5.3.2 FISH -- 5.3.3 Next-Generation Sequencing -- 5.3.4 Western Blotting -- 5.4 Machine Learning and Artificial Intelligence -- 5.5 Tumor Classification -- 5.5.1 TNM Staging System -- 5.5.2 Histological Grading -- 5.5.3 Molecular Subtyping -- 5.6 Challenges and Future Directions -- References -- Chapter 6 Classification of Brain Tumor Using Machine Learning Techniques: A Comparative Study -- 6.1 Introduction.
6.2 Related Work -- 6.3 Datasets -- 6.4 Experimental Setup -- 6.5 Results and Discussion -- 6.5.1 Evaluation Metrics -- 6.6 Conclusion -- 6.6.1 Significance of the Study -- References -- Chapter 7 Exploring the Potential of Dingo Optimizer: A Promising New Metaheuristic Approach -- 7.1 Introduction -- 7.2 Architecture of Dingo Optimizer -- 7.3 Initialization Process -- 7.3.1 Population Size -- 7.3.2 Dingo Population Initialization -- 7.3.3 Fitness Assessment -- 7.3.4 Best Dingo -- 7.3.5 Recordkeeping -- 7.4 Iteration Phase -- 7.6 Other Optimization Techniques -- 7.7 Conclusion -- References -- Chapter 8 Bioinspired Genetic Algorithm in Medical Applications -- 8.1 Introduction -- 8.2 The Genetic Algorithm -- 8.3 Radiology -- 8.4 Oncology -- 8.5 Endocrinology -- 8.6 Obstetrics and Gynecology -- 8.7 Pediatrics -- 8.8 Surgery -- 8.9 Infectious Diseases -- 8.10 Radiotherapy -- 8.11 Rehabilitation Medicine -- 8.12 Neurology -- 8.13 Health Care Management -- 8.14 Conclusion -- References -- Chapter 9 Artificial Immune System Algorithms for Optimizing Nanoparticle Design in Targeted Drug Delivery -- 9.1 Introduction -- 9.2 Artificial Immune Cells -- 9.3 The Artificial Immune System Architecture -- References -- Chapter 10 Diabetic Retinopathy Detection by Retinal Blood Vessel Segmentation and Classification Using Ensemble Model -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Proposed System -- 10.4 Conclusion and Future Scope -- References -- Chapter 11 Diabetes Prognosis Model Using Various Machine Learning Techniques -- 11.1 Introduction -- 11.1.1 Disease Identification -- 11.1.2 Data, Information, and Knowledge -- 11.1.3 Knowledge Discovery in Databases -- 11.1.4 Predictive Analytics -- 11.1.5 Supervised Learning and Machine Learning -- 11.1.6 Predictive Models -- 11.1.7 Data Validation and Cleaning -- 11.1.8 Discretization.
11.2 Literature Review -- 11.2.1 Neural Networks -- 11.2.2 Trees -- 11.2.3 K-Nearest Neighbors -- 11.3 Proposed Model -- 11.3.1 Predictive Models in Health -- 11.4 Experimental Results and Discussion -- 11.4.1 Prediction of Diabetes with Artificial Neural Networks Supervised Learning Algorithms -- 11.4.2 Improving the Prediction Ratio of Diabetes Diagnoses Using Fuzzy Logic and Neural Networks -- 11.4.3 ARIC: Type 2 Diabetes Risk Predictive Model -- 11.4.4 Evaluation of Neural Network Algorithms for Prediction Models of Type 2 Diabetes -- 11.4.5 Reliable and Objective Recommendation System for the Diagnosis of Chronic Diseases -- 11.5 Conclusion -- References -- Chapter 12 Diagnosis of Neurological Disease Using Bioinspired Algorithms -- 12.1 Introduction -- 12.1.1 Neurological Diseases -- 12.1.2 Introduction to Bioinspired Algorithms -- 12.1.3 Types of Bioinspired Algorithms Commonly Used in Healthcare -- 12.1.4 Advantages and Limitations of Bioinspired Algorithms -- 12.1.5 Limitations -- 12.1.6 Applications of Bioinspired Algorithms in Healthcare -- 12.1.7 Benefits of Bioinspired Algorithms in Healthcare Over Traditional Approaches -- 12.2 Neurological Disease Diagnosis -- 12.2.1 Bioinspired Algorithms for Neurological Disease Diagnosis -- 12.2.2 Neural Networks in Neurological Disease Diagnosis -- 12.2.2.1 How NNs Can Be Trained Using Bioinspired Optimization Techniques -- 12.2.3 Other Bioinspired Algorithms in Neurological Disease Diagnosis -- 12.3 Challenges and Future Directions -- 12.4 Conclusion -- References -- Chapter 13 Optimizing Artificial Neural-Network Using Genetic Algorithm -- 13.1 Introduction -- 13.1.1 ANN -- 13.1.2 Genetic Algorithm -- 13.2 Methodology -- 13.2.1 Mathematical Working -- 13.3 Brief Study on Existing Implementations -- 13.3.1 Using Different Types of ANNs -- 13.3.2 Using MLPs.
13.4 Comparative Study on Different Implementations.
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| Record Nr. | UNINA-9910877425703321 |
Info
Bio-Inspired Optimization for Medical Data Mining
