Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource (xxiii, 510 Seiten) : , 92 Illustrationen, 58 in Farbe.

ISBN: 978-3-031-22390-7

Series Statement: Lecture notes in computer science 13640

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-22389-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-22391-4

Language: English

Subjects: Computer Science

Keywords: Computersicherheit ; Konferenzschrift

DOI: 10.1007/978-3-031-22390-7

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (xix, 530 Seiten) : , 77 Illustrationen, 32 in Farbe.

ISBN: 978-3-031-22301-3

Series Statement: Lecture notes in computer science 13494

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-22300-6

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-22302-0

Language: English

Subjects: Computer Science

Keywords: Datensicherung ; Kryptologie ; Anwendungssoftware ; Konferenzschrift

DOI: 10.1007/978-3-031-22301-3

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource : , Illustrationen, Diagramme.

ISBN: 978-3-030-91356-4

Series Statement: Lecture notes in computer science 13118

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-91355-7

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-91357-1

Language: English

Subjects: Computer Science

Keywords: Computersicherheit ; Konferenzschrift ; Konferenzschrift

DOI: 10.1007/978-3-030-91356-4

URL: Volltext  (URL des Erstveröffentlichers)

URL: Volltext  (URL des Erstveröffentlichers)

Author information: Katsikas, Sōkratēs K. 1960-

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Format: 1 Online-Ressource (IX, 163 p. 41 illus., 24 illus. in color).

Edition: 1st ed. 2021

ISBN: 978-981-3367-26-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-3367-25-8

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-3367-27-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-3367-28-9

Language: English

Subjects: Computer Science

Keywords: Datensicherung ; Maschinelles Lernen

DOI: 10.1007/978-981-33-6726-5

URL: Volltext  (URL des Erstveröffentlichers)

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (xxiii, 510 Seiten) : , 92 Illustrationen, 58 in Farbe.

ISBN: 978-3-031-22390-7

Series Statement: Lecture notes in computer science 13640

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-22389-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-22391-4

Language: English

Subjects: Computer Science

Keywords: Computersicherheit ; Konferenzschrift

DOI: 10.1007/978-3-031-22390-7

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (xix, 530 Seiten) : , 77 Illustrationen, 32 in Farbe.

ISBN: 978-3-031-22301-3

Series Statement: Lecture notes in computer science 13494

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-22300-6

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-22302-0

Language: English

Subjects: Computer Science

Keywords: Datensicherung ; Kryptologie ; Anwendungssoftware ; Konferenzschrift

DOI: 10.1007/978-3-031-22301-3

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XIV, 464 p. 61 illus. , online resource.

ISBN: 9783319083445

Series Statement: Lecture Notes in Computer Science, 8544

Content: This book constitutes the refereed conference proceedings of the 19th Australasian Conference on Information Security and Privacy, ACISP 2014, held in Wollongong, NSW, Australia, in July 2014. The 26 revised full papers and 6 short papers presented in this volume were carefully selected from 91 submissions. The papers are organized in topical sections on cryptanalysis; cryptographic protocols; fine-grain cryptographic protocols; key exchange, fundamentals, lattices and homomorphic encryption, and applications.

Note: Cryptanalysis -- Cryptographic protocols -- Fine-grain cryptographic protocols -- Key exchange -- Fundamentals -- Lattices -- Homomorphic encryption -- Applications.

In: Springer eBooks

Additional Edition: Printed edition: ISBN 9783319083438

Language: English

Subjects: Computer Science

Keywords: Konferenzschrift ; Konferenzschrift

DOI: 10.1007/978-3-319-08344-5

URL: http://dx.doi.org/10.1007/978-3-319-08344-5

URL: Volltext

URL: Inhaltsverzeichnis

URL: Abstract

URL: Volltext  (lizenzpflichtig)

URL: Volltext  (lizenzpflichtig)

URL: Cover

URL: Inhaltstext

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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: X, 346 S. : , graph. Darst. ; , 235 mm x 155 mm.

ISBN: 3-642-41226-2 , 978-3-642-41226-4

Series Statement: Lecture notes in Computer science 8209

Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-3-642-41227-1

Language: English

Subjects: Computer Science

Keywords: Computersicherheit ; Datensicherung ; Kryptologie ; Chiffrierung ; Kryptosystem ; Schlüsselaustauschprotokoll ; Elektronische Unterschrift ; Beweisbarkeit ; Kryptoanalyse ; Computersicherheit ; Datensicherung ; Interaktives Beweissystem ; Hash-Algorithmus ; Computersicherheit ; Datensicherung ; Kryptologie ; Chiffrierung ; Kryptosystem ; Sicherheitsprotokoll ; Elektronische Unterschrift ; Interaktives Beweissystem ; Kryptosystem ; Elektronische Unterschrift ; Konferenzschrift ; Konferenzschrift ; Konferenzschrift ; Konferenzschrift

URL: Inhaltstext

URL: Inhaltsverzeichnis

URL: Inhaltstext

URL: Inhaltsverzeichnis

UID:

Format: 1 Online-Ressource (XV, 253 Seiten)

ISBN: 9783319930497

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-319-93048-0

Language: English

Subjects: Computer Science

Keywords: Public-Key-Kryptosystem

DOI: 10.1007/978-3-319-93049-7

URL: Volltext  (URL des Erstveröffentlichers)