Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource (XXVI, 405 p. 164 illus).

Edition: 1st ed. 2022

ISBN: 978-3-030-79753-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79752-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79754-6

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79755-3

Language: English

DOI: 10.1007/978-3-030-79753-9

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XXV, 434 p. 258 illus., 192 illus. in color).

Edition: 1st ed. 2021

ISBN: 978-3-030-75657-4

Series Statement: Studies in Big Data 89

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-75656-7

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-75658-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-75659-8

Language: English

DOI: 10.1007/978-3-030-75657-4

URL: Volltext  (URL des Erstveröffentlichers)

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XXIX, 317 p. 84 illus., 57 illus. in color. , online resource.

Edition: 1st ed. 2023.

ISBN: 9783031228353

Series Statement: Intelligent Systems Reference Library, 237

Content: This book provides insights on blockchain technology and its applications in real-world business, supply chain, health care, education, HRM, retail, logistics and transport industries. This book grants a comprehensive understanding of how this technology is functioning within modern real-world applications and how it can influence the future of the real-world applications in industry. The chapters cover the case study, applications of blockchain, benefits and challenges, disruptive innovations in real-world applications, privacy and security concerns, and the recent trends of blockchain in real-world applications. It is ideally intended for marketers, advertisers, brand managers, executives, managers, IT specialists and consultants, researchers, businesses, practitioners, stakeholders, academicians, and students interested in blockchain technology and its role in supply chain, health care, education, HRM, retail, logistics and transport industries.

Note: An Introduction to Blockchain Technology: Recent Trends -- Bitcoin: Beginning of the Cryptocurrency Era -- HR Digital Transformation: Blockchain for Business -- Securing Electronic Health Record System in Cloud Environment Using Blockchain Technology -- Blockchain: The foundation of trust in Metaverse -- Survey on Blockchain Technology and Security Facilities in Online Education -- A Govern Chain - Integration of Government Function with Blockchain Technology -- Blockchain in Healthcare: A Review -- Blockchain: A Study of New Business Model -- Understanding the Blockchain Technology Adoption in Transportation Management: Application in Trucking Industry.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031228346

Additional Edition: Printed edition: ISBN 9783031228360

Additional Edition: Printed edition: ISBN 9783031228377

Language: English

DOI: 10.1007/978-3-031-22835-3

URL: https://doi.org/10.1007/978-3-031-22835-3

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (xxv, 383 Seiten)

ISBN: 9783030339661

Series Statement: Studies in Big Data 68

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-33965-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-33967-8

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-33968-5

Language: English

Keywords: Aufsatzsammlung

DOI: 10.1007/978-3-030-33966-1

URL: Volltext  (URL des Erstveröffentlichers)

Author information: Abraham, Ajith 1968-

UID:

Format: 1 Online-Ressource (XXVI, 405 p. 164 illus).

Edition: 1st ed. 2022

ISBN: 978-3-030-79753-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79752-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79754-6

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79755-3

Language: English

DOI: 10.1007/978-3-030-79753-9

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (450 pages)

Edition: 1st ed.

ISBN: 9780323956932

Note: Front Cover -- INTERNET OF THINGS AND MACHINE LEARNING FOR TYPE I AND TYPE II DIABETES -- INTERNET OF THINGS AND MACHINE LEARNING FOR TYPE I AND TYPE II DIABETES: USE CASES -- Copyright -- Contents -- Contributors -- Preface -- I - Diagnosis -- 1 - A systematic review on intelligent diagnosis of diabetes using rule-based machine learning techniques -- 1. Introduction -- 2. Literature search strategy -- 3. System overview -- 4. Dataset -- 5. Preprocessing methods -- 6. Algorithms for classification -- 6.1 Fuzzy system -- 6.2 SVM -- 6.3 Other algorithms -- 7. Application scenarios -- 8. Limitations and future directions -- 9. Conclusions -- References -- 2 - Ensemble sparse intelligent mining techniques for diabetes diagnosis -- 1. Introduction -- 2. Literature survey -- 3. Methodology -- 3.1 Data extraction -- 3.1.1 Dataset -- 3.2 Data preprocessing -- 3.2.1 Data cleaning and encoding -- 3.2.2 Data splitting -- 3.3 Model building -- 3.3.1 Libraries and modules used -- 3.4 Algorithms used -- 3.4.1 Existing models -- 3.4.1.1 Multilayer Perceptron (MLP) -- 3.4.1.2 Extra trees classifier -- 3.4.1.3 Linear Discriminant Analysis -- 3.4.1.4 Proposed model -- 4. Result analysis -- 4.1 Evaluation measures -- 4.1.1 Matthew's correlation coefficient -- 4.1.2 Log loss -- 4.1.3 Receiver Operator Characteristics -- 4.1.4 F1 score -- 4.2 Results obtained -- 4.2.1 Multilayer Perceptron -- 4.2.2 MCC mathematical calculations -- 4.2.3 Extra trees classifier -- 4.2.4 Mathematical calculations of ROC -- 4.2.5 True positive rate (TPR) -- 4.2.6 False Positive Rate (FPR) -- 4.2.7 Linear Discriminant Analysis -- 4.2.8 Stacking classifier -- 4.2.9 Mathematical calculation of F1 - score -- 4.3 Comparing algorithms -- 5. Conclusion and Future work -- References -- 3 - Detection of diabetic retinopathy using Deep Neural networks -- 1. Introduction -- 1.1 Diabetic retinopathy. , 1.2 Prevalence of diabetic retinopathy in India -- 1.3 Traditional diagnosis of diabetic retinopathy -- 1.4 Machine learning -- 2. System analysis -- 2.1 Supervised learning -- 2.2 Deep learning -- 2.3 Convolutional neural networks -- 2.3.1 Image classification and recognition -- 2.4 Classifiers -- 2.4.1 Linear classifiers -- 2.5 Previous automation techniques for diagnosis of diabetic retinopathy -- 2.5.1 Comparison with present techniques used in the project -- 3. Modules -- 3.1 TensorFlow installation -- 3.2 Downloading the training data set -- 3.3 Training the CNN model -- 3.3.1 Bottlenecks -- 3.3.2 Training -- 3.4 Testing -- 4. System design -- 5. Implementation -- 5.1 Graph file and label file -- 5.2 Testing the trained model -- 6. Results -- 7. Conclusion and future scope -- References -- 4 - An intelligent remote diagnostic approach for diabetes using machine learning techniques -- 1. Introduction -- 1.1 Remote healthcare monitoring system -- 1.2 ECG and diabetes -- 1.3 Motivation -- 2. Intelligent remote diagnostic approach for diabetes -- 2.1 ECG signal acquisition and processing -- 2.1.1 Adaptive filtering -- 2.1.2 Wavelet denoising -- 2.1.3 Event detection and segmentation -- 2.2 Feature extraction -- 2.2.1 Time domain features -- 2.2.2 Frequency domain features -- 2.2.3 Time-frequency domain features -- 2.2.3.1 Short-time Fourier transform -- 2.2.4 Continuous wavelet transform -- 2.3 Classification and evaluation -- 3. Conclusion -- References -- 5 - Diagnosis of diabetic retinopathy in retinal fundus images using machine learning and deep learning models -- 5Data availability -- 1. Introduction -- 2. Dataset -- 3. Texture feature extraction -- 3.1 GLCM features -- 3.2 GLRLM features -- 3.3 Laws texture feature -- 4. Transform based feature extraction -- 4.1 Gabor transform -- 4.2 Radon transform -- 5. Dimensionality reduction. , 6. Classification -- 6.1 k-NN classifier -- 6.2 SVM classifier -- 7. CNN-based deep learning algorithm for DR classification -- 7.1 Data augmentation -- 7.2 Proposed CNN architecture I -- 7.2.1 Convolutional layer -- 7.2.2 Activation function -- 7.2.3 Pooling layer -- 7.2.4 Dense and dropout layers -- 7.2.5 Output classification layer -- 7.3 Batch normalization -- 7.4 Optimization -- 7.5 Feature visualization -- 7.6 Performance analysis -- 8. Summary -- References -- 6 - Diagnosis of diabetes mellitus using deep learning techniques and big data -- 1. Introduction -- 2. Literature review -- 2.1 Motivation -- 3. Materials and methods -- 3.1 Proposed methodology -- 3.2 Data description -- 3.3 Data cleaning -- 3.4 Various challenges in the dataset -- 3.5 Inclusion and exclusion criteria of the patients -- 3.6 Preprocessing of data -- 3.6.1 Missing value handling technique -- 3.6.1.1 k-nearest neighbor (k-NN) imputation -- 3.6.2 Balancing of data -- 3.6.2.1 Synthetic minority oversampling technique -- 3.6.3 Feature extraction technique -- 3.6.3.1 Discrete wavelet transform (DWT) -- 3.6.4 Data normalization -- 3.7 Model development -- 3.7.1 Deep neural network (DNN) -- 3.7.2 Deep long-short-term memory (DLSTM) -- 4. Results and discussions -- 4.1 Simulation study -- 4.2 Performance measures -- 4.3 Results -- 4.4 Comparison with the winner list of WiDS Datathon 2021 -- 5. Conclusion and future work -- References -- II - Glucose monitoring -- 7 - IoT and machine learning for management of diabetes mellitus -- 1. Introduction -- 2. IoT and machine learning in general -- 3. Rationale of integrating IoT and machine learning in management of diabetes -- 4. IoT and machine learning in diabetes -- 4.1 IoT in diabetes management -- 4.1.1 Hand-held IoT-Based tablets -- 4.1.2 Wearable IoT-Based devices -- 4.1.3 Nanochips and sensors -- 4.1.4 Implants. , 4.2 Machine learning tools for management of diabetes -- 4.2.1 Categories of ML learning processes -- 4.2.1.1 Supervised learning -- 4.2.1.2 Unsupervised learning -- 4.2.1.3 Reinforcement learning -- 4.2.1.4 Feature selection -- 4.2.2 Classification algorithms -- 4.2.2.1 SVM classifier -- 4.2.2.2 KNN classifier -- 4.2.2.3 Random Forest -- 4.2.2.4 ANN classifier -- 4.2.2.5 Naive Bayes classifier -- 5. Proposed framework and methodology -- 6. Future of IoT and machine learning -- 7. Conclusions -- References -- 8 - Prediction of glucose concentration in type 1 diabetes patients based on machine learning techniques -- 1. Introduction -- 2. Glucose management in type 1 diabetes -- 3. Machine learning in healthcare -- 4. Predicting glucose concentrations using machine learning -- 5. Linear regression -- 6. Support vector machines -- 7. Random forest models -- 8. Deep learning models -- 9. Conclusion -- References -- 9 - ML-based PCA methods to diagnose statistical distribution of blood glucose levels of diabetic patients -- 1. Introduction -- 2. Related algorithms -- 2.1 Principal component analysis -- 2.2 Kernel principal component analysis (KPCA) -- 2.3 Least squares vector machine -- 3. Prediction of fasting blood glucose level based on KPCA-LSSVM -- 4. Experimental methods -- 4.1 Original data source and preprocessing -- 4.2 Kernel principal component analysis -- 4.3 Least squares support vector machine modeling -- 4.4 Methodological analysis -- 5. Conclusions -- References -- Further reading -- III - Prediction of complications and risk stratification -- 10 - Overview of new trends on deep learning models for diabetes risk prediction -- 1. Introduction -- 1.1 Abstraction in multiple layers -- 1.2 Larger datasets are beneficial for training -- 1.3 Feature extraction using automated means -- 1.4 Managing data from diverse sources -- 2. Overview of DL. , 3. The identification of diabetes mellitus -- 4. Management of blood sugar -- 5. Complications and their diagnosis -- 6. An overview of DL methods in a Nutshell -- 7. Discussion -- 7.1 Constraints and obstacles in the way -- 7.2 Possibilities and work in the future -- 8. Conclusion -- References -- 11 - Clinical applications of deep learning in diabetes and its enhancements with future predictions -- 1. Introduction -- 2. Artificial intelligence -- 3. Diagnosis of diabetes mellitus -- 4. Diabetes-related complications -- 4.1 Retinopathy -- 4.2 Diabetic foot ulcer -- 4.3 Diabetic neuropathy -- 5. Glucose measurement and prediction -- 5.1 Continuous glucose monitoring -- 5.2 Hypoglycemic episodes -- 5.3 Glucose prediction -- 6. Conclusion/future aspect -- References -- Further reading -- 12 - Exploring machine learning techniques for feature extraction and classification of diabetes related medical da ... -- 1. Introduction -- 2. Literature review -- 2.1 Diabetes classification on clinical datasets -- 2.2 Diabetes detection and classification on retinal image data -- 3. Diabetes datasets -- 3.1 PIMA Indians dataset -- 3.2 Asia Pacific tele-ophthalmology society (APTOS) dataset -- 4. Preprocessing -- 4.1 Handling missing values -- 4.2 Filtering outliers -- 4.3 Feature extraction -- 5. Classification techniques -- 5.1 Methods used with numerical datasets -- 5.1.1 Support vector machine -- 5.1.2 Logistic regression -- 5.1.3 Decision tree -- 5.1.4 Random forest -- 5.1.5 AdaBoost -- 5.1.6 XGBoost -- 5.1.7 K-nearest neighbors -- 5.1.8 Artificial neural network -- 5.1.9 Deep neural network -- 5.1.10 Recurrent neural network -- 5.1.11 Long short-term memory -- 5.2 Methods used with diabetes image datasets -- 5.2.1 Convolutional neural networks -- 5.2.2 Deep convolutional neural network -- 5.2.3 A residual network (ResNet) -- 5.2.4 Distributed deep learning. , 6. Comparative analysis and discussion.

Additional Edition: Print version: Dash, Sujata Internet of Things and Machine Learning for Type I and Type II Diabetes San Diego : Elsevier,c2024 ISBN 9780323956864

Language: English

UID:

Format: 1 Online-Ressource (XXVI, 405 p. 164 illus)

Edition: 1st ed. 2022

ISBN: 9783030797539

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79752-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79754-6

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79755-3

Language: English

DOI: 10.1007/978-3-030-79753-9

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XXVI, 405 p. 164 illus).

Edition: 1st ed. 2022

ISBN: 978-3-030-79753-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79752-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79754-6

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-79755-3

Language: English

DOI: 10.1007/978-3-030-79753-9

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (448 pages)

ISBN: 9781119711278 , 1119711274 , 9781119711254 , 1119711258 , 9781119711261 , 1119711266

Series Statement: Artificial Intelligence and Soft Computing for Industrial Transformation

Note: Mortality Prediction of ICU Patients Using Machine Learning Techniques / Babita Majhi, Aarti Kashyap, Ritanjali Majhi -- Artificial Intelligence in Bioinformatics / VSamuel Raj, Anjali Priyadarshini, Manoj Kumar Yadav, Ramendra Pati Pandey, Archana Gupta, Arpana Vibhuti -- Predictive Analysis in Healthcare Using Feature Selection / Aneri Acharya, Jitali Patel, Jigna Patel -- Healthcare 4.0: An Insight of Architecture, Security Requirements, Pillars and Applications / Deepanshu Bajaj, Bharat Bhushan, Divya Yadav -- Improved Social Media Data Mining for Analyzing Medical Trends / Minakshi Sharma, Sunil Sharma -- Bioinformatics: An Important Tool in Oncology / Gaganpreet Kaur, Saurabh Gupta, Gagandeep Kaur, Manju Verma, Pawandeep Kaur -- Biomedical Big Data Analytics Using IoT in Health Informatics / Pawan Singh Gangwar, Yasha Hasija -- Statistical Image Analysis of Drying Bovine Serum Albumin Droplets in Phosphate Buffered Saline / Anusuya Pal, Amalesh Gope, Germano S Iannacchione -- Introduction to Deep Learning in Health Informatics / Monika Jyotiyana, Nishtha Kesswani -- Data Mining Techniques and Algorithms in Psychiatric Health: A Systematic Review / Shikha Gupta, Nitish Mehndiratta, Swarnim Sinha, Sangana Chaturvedi, Mehak Singla -- Deep Learning Applications in Medical Image Analysis / Ananya Singha, Rini Smita Thakur, Tushar Patel -- Role of Medical Image Analysis in Oncology / Kaur Gaganpreet, Garg Hardik, Kumari Heena, Lakhvir Singh, Navroz Kaur, Shubham Kumar, Shadab Alam -- A Comparative Analysis of Classifiers Using Particle Swarm Optimization-Based Feature Selection / Chandra Sekhar Biswal, Subhendu Kumar Pani, Sujata Dash.

Additional Edition: Print version: Dash, Sujata. Biomedical Data Mining for Information Retrieval. Newark : John Wiley & Sons, Incorporated, ©2021 ISBN 9781119711247

Language: English

Keywords: Electronic books. ; Electronic books. ; Electronic books. ; Electronic books.

DOI: 10.1002/9781119711278

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119711278

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URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119711278

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119711278

UID:

Format: 30 PDFs (xxix, 538 pages)

ISBN: 9781522528586

Content: This book provides the reader with a broad coverage of the concepts, themes and instrumentalities of this important and evolving area of optimization. In doing so, the editors hope to encourage an even wider adoption of metaheuristic methods for assisting in problem solving, and to stimulate research that may lead to additional innovations in metaheuristic procedures.

Note: Chapter 1. Metaheuristic-based hybrid feature selection models -- Chapter 2. Swarm-based nature-inspired metaheuristics for neural network optimization -- Chapter 3. A novel hybrid model using RBF and PSO for net asset value prediction -- Chapter 4. Memetic algorithms and their applications in computer science -- Chapter 5. A new data hiding scheme combining genetic algorithm and artificial neural network -- Chapter 6. A statistical scrutiny of three prominent machine-learning techniques to forecast machining performance parameters of inconel 690 -- Chapter 7. Insights into simulated annealing -- Chapter 8. Automatic test data generation using bio-inspired algorithms: a travelogue -- Chapter 9. Development of an efficient prediction model based on a nature-inspired technique for new products: a case of industries from the manufacturing sector -- Chapter 10. Applications of hybrid intelligent systems in adaptive communication -- Chapter 11. DE-based RBFNs for classification with special attention to noise removal and irrelevant features -- Chapter 12. Competency mapping in academic environment: a swarm intelligence approach -- Chapter 13. An overview of the last advances and applications of greedy randomized adaptive search procedure -- Chapter 14. Defect detection of fabrics by grey-level co-occurrence matrix and artificial neural network -- Chapter 15. A holistic-based multi-criterion decision-making approach for solving engineering sciences problem under imprecise environment -- Chapter 16. A comprehensive review of nature-inspired algorithms for feature selection -- Chapter 17. Nature-inspired-algorithms-based cellular location management: scope and applications -- Chapter 18. Fuzziness in ant colony optimization and their applications -- Chapter 19. Application of natured-inspired technique to Odia handwritten numeral recognition -- Chapter 20. Intelligent technique to identify epilepsy using fuzzy firefly system for brain signal processing -- Chapter 21. Analysis and implementation of artificial bee colony optimization in constrained optimization problems -- Chapter 22. Escalation of prediction accuracy with virtual data: a case study on financial time series -- Chapter 23. Determination of spatial variability of rock depth of Chennai. , Also available in print. , Mode of access: World Wide Web.

Additional Edition: Print version: ISBN 1522528571

Additional Edition: ISBN 9781522528579

Language: English

Subjects: Computer Science

DOI: 10.4018/978-1-5225-2857-9

URL: Chapter PDFs via platform:

URL: Volltext  (URL des Erstveröffentlichers)