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[null,"1.","Record Nr.",null,"UNINA9911020077403321"]
[null,null,"Autore",null,"Srivastava Sumit"]
[null,null,"Titolo",null,"Bio-Inspired Optimization for Medical Data Mining"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"9781394214211"]
[{"$nulls":4},"1394214219"]
[{"$nulls":4},"9781394214204"]
[{"$nulls":4},"1394214200"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (334 pages)"]
[null,null,"Disciplina",null,"610.285"]
[null,null,"Soggetti",null,"Medical informatics - Data processing"]
[{"$nulls":4},"Data mining"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Bioinspired Algorithms: Opportunities and Challenges -- 1.1 Introduction -- 1.1.1 Definition and Significance of Bioinspired Algorithms -- 1.1.2 Overview of the Chapter -- 1.2 Bioinspired Principles and Algorithms -- 1.2.1 Evolutionary Algorithms -- 1.2.2 Swarm Intelligence Algorithms -- 1.2.3 Artificial Neural Networks -- 1.2.4 Other Bioinspired Algorithms -- 1.3 Opportunities of Bioinspired Algorithms -- 1.3.1 Solving Complex Optimization Problems -- 1.3.2 Robustness in Dealing With Uncertainty and Noise -- 1.3.3 Parallel and Distributed Computing -- 1.3.4 Application Areas and Success Stories -- 1.4 Challenges of Bioinspired Algorithms -- 1.4.1 Parameter Tuning and Algorithm Configuration -- 1.4.2 Lack of Theoretical Analysis and Understanding -- 1.4.3 Risk of Premature Convergence -- 1.4.4 Computational Cost for Large-Scale Problems -- 1.4.5 Ethical Considerations and Limitations -- 1.5 Prominent Bioinspired Algorithms -- 1.5.1 Genetic Algorithms -- 1.5.2 Particle Swarm Optimization -- 1.5.3 Ant Colony Optimization -- 1.5.4 Artificial Neural Networks -- 1.6 Applications of Bioinspired Algorithms -- 1.6.1 Optimization Problems -- 1.6.2 Pattern Recognition and Machine Learning -- 1.6.3 Swarm Robotics -- 1.6.4 Other Domains -- 1.7 Future Research Directions -- 1.7.1 Improving Efficiency and Scalability -- 1.7.2 Enhancing Interpretability and Explainability -- "]
[{"$nulls":4},"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."]
[{"$nulls":4},"2.2 Methodology -- 2.2.1 Collection of Plant Material -- 2.2.2 Qualitative Analysis of Phytochemicals -- 2.2.3 Study of In Vitro Antiurolithiatic Activity Using Titrimetry Method -- 2.2.3.1 Preparation of Calcium Oxalate -- 2.2.3.2 Preparation of Semipermeable Membrane From Eggs -- 2.2.3.3 In Vitro Antiurolithiatic Test Using Titrimetry Method -- 2.3 Result and Discussion -- 2.3.1 In Vitro Antiurolithiatic Activity Test -- 2.3.2 Analysis of Dissolved Calcium Oxalate -- 2.4 Conclusion -- References -- Chapter 3 Parkinson's Disease Detection Using Voice and Speech- Systematic Literature Review -- 3.1 Introduction -- 3.2 Research Questions -- 3.3 Method -- 3.3.1 Search Strategy -- 3.3.2 Inclusion Criteria -- 3.3.3 Subprocesses Involved in PD Detection Process -- 3.3.4 Data Sets -- 3.3.4.1 Parkinson's Data Set-UCI Machine Learning Dataset -- 3.3.4.2 PC-GITA Dataset -- 3.3.4.3 mPower Dataset -- 3.3.4.4 Mobile Device Voice Recordings (MDVR-KCL) Dataset -- 3.3.4.5 Italian Parkinson's Voice and Speech (IPVS) Dataset -- 3.3.4.6 Parkinson Speech Dataset With Multiple Types of Sound Recordings Dataset -- 3.3.4.7 Parkinson's Telemonitoring Dataset -- 3.4 Algorithms -- 3.5 Features -- 3.5.1 Acoustic Features -- 3.5.1.1 Jitter (Local, Absolute) -- 3.5.1.2 Jitter (Local) -- 3.5.1.3 Jitter (rap) -- 3.5.1.4 Jitter (ppq5) -- 3.5.1.5 Shimmer (Local) -- 3.5.1.6 Shimmer (local, dB) -- 3.5.1.7 Shimmer (apq3) -- 3.5.1.8 Shimmer (apq5) -- 3.5.2 Spectogram-Based Methods -- 3.5.2.1 MFCC -- 3.6 Conclusion -- References -- Chapter 4 Tumor Detection and Classification -- 4.1 Introduction -- 4.2 Methods Used for Detection of Tumors -- 4.3 Methods Used for Classification of Tumours -- 4.3.1 Segmentation -- 4.3.2 Region Growing Method -- 4.3.3 Seeded Region Growing Method -- 4.3.4 Unseeded Region Growing Method -- 4.3.5 .-Connected Method -- 4.3.6 Threshold Based Method."]
[{"$nulls":4},"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 -- "]
[{"$nulls":4},"5.5 Tumor Classification -- 5.5.1 TNM Staging System -- 5.5.2 Histological Grading -- 5.5.3 Molecular Subtyping -- 5.6 Challenges and Future Directions -- References -- Chapter 6 Classification of Brain Tumor Using Machine Learning Techniques: A Comparative Study -- 6.1 Introduction."]
[{"$nulls":4},"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."]
[{"$nulls":4},"11.2 Literature Review -- 11.2.1 Neural Networks -- 11.2.2 Trees -- 11.2.3 K-Nearest Neighbors -- 11.3 Proposed Model -- 11.3.1 Predictive Models in Health -- 11.4 Experimental Results and Discussion -- 11.4.1 Prediction of Diabetes with Artificial Neural Networks Supervised Learning Algorithms -- 11.4.2 Improving the Prediction Ratio of Diabetes Diagnoses Using Fuzzy Logic and Neural Networks -- 11.4.3 ARIC: Type 2 Diabetes Risk Predictive Model -- 11.4.4 Evaluation of Neural Network Algorithms for Prediction Models of Type 2 Diabetes -- 11.4.5 Reliable and Objective Recommendation System for the Diagnosis of Chronic Diseases -- 11.5 Conclusion -- References -- Chapter 12 Diagnosis of Neurological Disease Using Bioinspired Algorithms -- 12.1 Introduction -- 12.1.1 Neurological Diseases -- 12.1.2 Introduction to Bioinspired Algorithms -- 12.1.3 Types of Bioinspired Algorithms Commonly Used in Healthcare -- 12.1.4 Advantages and Limitations of Bioinspired Algorithms -- 12.1.5 Limitations -- 12.1.6 Applications of Bioinspired Algorithms in Healthcare -- 12.1.7 Benefits of Bioinspired Algorithms in Healthcare Over Traditional Approaches -- 12.2 Neurological Disease Diagnosis -- 12.2.1 Bioinspired Algorithms for Neurological Disease Diagnosis -- 12.2.2 Neural Networks in Neurological Disease Diagnosis -- 12.2.2.1 How NNs Can Be Trained Using Bioinspired Optimization Techniques -- 12.2.3 Other Bioinspired Algorithms in Neurological Disease Diagnosis -- 12.3 Challenges and Future Directions -- 12.4 Conclusion -- References -- Chapter 13 Optimizing Artificial Neural-Network Using "]
[{"$nulls":4},"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."]
[{"$nulls":4},"13.4 Comparative Study on Different Implementations."]
[null,null,null,"Sommario/riassunto","This book is a comprehensive exploration of bio-inspired optimization techniques and their potential applications in healthcare. Bio-Inspired Optimization for Medical Data Mining is a groundbreaking book that delves into the convergence of nature's ingenious algorithms and cutting-edge healthcare technology. Through a comprehensive exploration of state-of-the-art algorithms and practical case studies, readers gain unparalleled insights into optimizing medical data processing, enabling more precise diagnosis, optimizing treatment plans, and ultimately advancing the field of healthcare. Organized into 15 chapters, readers learn about the theoretical foundation of pragmatic implementation strategies and actionable advice. In addition, it addresses current developments in molecular subtyping and how they can enhance clinical care. By bridging the gap between cutting-edge technology and critical healthcare challenges, this book is a pivotal contribution, providing a roadmap for leveraging nature-inspired algorithms. In this book, the reader will discover Cutting-edge bio-inspired algorithms designed to optimize medical data processing, providing efficient and accurate solutions for complex healthcare challenges; How bio-inspired optimization can fine-tune diagnostic accuracy, leading to better patient outcomes and improved medical decision-making; How bio-inspired optimization propels healthcare into a new era, unlocking transformative solutions for medical data analysis; Practical insights and actionable advice on implementing bio-inspired optimization techniques and equipping effective real-world medical data scenarios; Compelling case studies illustrating how bio-inspired optimization has made a significant impact in the medical field, inspiring similar success stories. Audience This book is designed for a wide-ranging audience, including medical professionals, healthcare researchers, data scientists, and technology enthusiasts."]