Kooperativer Bibliotheksverbund

UID:

Format: 1 DVD, PAL, Ländercode 2, 94 Min., farb., Dolby digital , 12 cm

ISBN: 3866153252 , 9783866153257

Series Statement: Süddeutsche Zeitung - Cinemathek 99

Uniform Title: Fa yeung nin wa

Note: Bildformat 1.78:1 (16:9) , Paralleltitel: Fa yeung nin wa , Orig.: Hongkong, Frankreich 2000 , Dt., chines. ; Untertitel dt.

Language: Chinese

Subjects: General works

Keywords: Film

Author information: Wong, Kar-wai 1958-

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 DVD, NTSC, Ländercode 1, 111 Min., farb., stereo , 12 cm

Uniform Title: Clean 〈engl., franz., chines.〉

Content: "Als sie nach dem Drogentod ihres Geliebten, einem Rock-Musiker am Ende seiner Karriere, im Gefängnis einsitzt, reift bei einer jungen Frau der Gedanke, die eigene Drogensucht zu überwinden. Ausschlaggebend ist die Hoffnung, dadurch das Sorgerecht für ihren kleinen Sohn zu bekomment. [...] Von zwei überzeugenden Hauptdarstellern getragenes Drama, das von dem Wunsch geleitet wird, dass jeder sein Leben ändern kann, wenn ihm Vertrauen und Liebe entgegengebracht wird." [film-dienst.de]

Note: Cannes 2004 Beste Darstellerin: Maggie Cheung , widescreen , Orig.: Kanada, Großbritannien, Frankreich 2004 , Enth. interviews with Maggie Cheung, Nick Nolte, Olivier Assayas, Tricky and Metric ; US theatrical trailer , Engl., franz., chines.

Language: English

Keywords: Interview ; DVD-Video ; Film ; Interview

Author information: Assayas, Olivier 1955-

Author information: Eno, Brian 1948-

UID:

Format: 1 Online-Ressource (xi, 244 Seiten).

ISBN: 978-1-4704-3538-7

Series Statement: Mathematical surveys and monographs volume 213

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-4704-3045-0

Language: English

Subjects: Mathematics

Keywords: Primzahl

DOI: 10.1090/surv/213

URL: Volltext  (URL des Erstveröffentlichers)

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Blu-ray Disc (ca. 110 Min.) , Tonformat: Dolby TrueHD/5.1 (cantonese) ; DD/5.1 (cantonese, mandarin) ; 2.0 (commentary)

Note: Ländercode: A, B, C , Orig.: Hong Kong, 2008 , Kantonese und Mandarin mit Mandarin (traditional), Mandarin (simplified), engl., und Malay Untertiteln

Language: Chinese

UID:

Format: 1 DVD Video (ca. 86 Min.) , Tonformat: DD/2.0 dt., cantonesisch) , Bildformat: 1:1,78 Widescreen (anamorph)

Note: Ländercode: 2 , Dt. und cantonesisch

Language: German

UID:

Format: 1 DVD, RC 2, 107 Min., farb., DTS 5.1 und 2.0 , 12 cm

Note: Orig.: CA, GB, F 2004. - Sprache: franz. und engl. - Untertitel in franz. Sprache

Language: English

Subjects: General works

Author information: Assayas, Olivier 1955-

Author information: Eno, Brian 1948-

UID:

Format: 1 DVD, PAL, Ländercode 2, 110 Min., farb., Dolby digital , 12 cm

Series Statement: Momentum Pictures world cinema collection

Uniform Title: Clean 〈chin., engl., franz.〉

Content: "Als sie nach dem Drogentod ihres Geliebten, einem Rock-Musiker am Ende seiner Karriere, im Gefängnis einsitzt, reift bei einer jungen Frau der Gedanke, die eigene Drogensucht zu überwinden. Ausschlaggebend ist die Hoffnung, dadurch das Sorgerecht für ihren kleinen Sohn zu bekomment. [...] Von zwei überzeugenden Hauptdarstellern getragenes Drama, das von dem Wunsch geleitet wird, dass jeder sein Leben ändern kann, wenn ihm Vertrauen und Liebe entgegengebracht wird." [film-dienst.de]

Note: Cannes 2004 Beste Darstellerin: Maggie Cheung , Bildformat 16:9 , Orig.: Frankreich, Kanada, Großbritannien 2004 , Enth. Interviews with writer/director Olivier Assayas and cast including Maggie Cheung and Nick Nolte ( 57 Min.) , Franz., engl., kantones. mit engl. Untertiteln ; engl. Untertitel für Hörgeschädigte

Language: English

Subjects: General works

Author information: Assayas, Olivier 1955-

Author information: Eno, Brian 1948-

UID:

Format: 1 DVD, PAL, Ländercode 2, 95 Min., farb. , 12 cm

Uniform Title: Wong gok ka moon

Note: Bildformat 1.85:1 (anamorph) , Orig.: Hongkong 1988 , Dt., kantones., optional mit dt. Untertiteln

Language: German

Subjects: Comparative Studies. Non-European Languages/Literatures

Author information: Wong, Kar-wai 1958-

UID:

Format: 1 DVD, PAL, Ländercode 2, 90 Min., farb. , 12 cm

Uniform Title: A Fei jing juen

Note: Bildformat 1.85:1 (anamorph) , Orig.: Hongkong 1990 , Dt., kantones., optional mit dt. Untertiteln

Language: German

Subjects: Comparative Studies. Non-European Languages/Literatures

Author information: Wong, Kar-wai 1958-