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: 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 resource

Edition: First edition.

ISBN: 9781119711629 , 1119711622 , 9781119711513 , 1119711517 , 9781119711612 , 1119711614

Content: "Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science, bringing with it a new era of tremendous opportunities and challenges due to plenty of easily available criminal data for further analysis. The aim of data analytics is to prevent threats before they happen using classical statistical issues, machine learning, artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods on various data sources, including social media, GPS devices, video feed from street cameras; and license plate readers, travel and credit card records and the news media, as well as government and proprietary systems. Intelligent data analytics ensures efficient data mining techniques to solve criminal investigations. Prediction of future terrorist attacks according to city, type of attack, target and weapon, claim mode, and motive for attack through classification techniques will facilitate the decision-making process of security organizations so as to learn from previously stored attack information; and then rate the targeted sectors/areas accordingly for security measures. By using intelligent data analytics models with multiple levels of representation, raw to higher abstract level representation can be learned at each level of the system. Algorithms based on intelligent data analytics have demonstrated great performance in a variety of areas, including data visualization, data pre-processing (fusion, editing, transformation, filtering, and sampling), data engineering, database mining techniques, tools and applications, etc"--

Note: Rumor Detection and Tracing its Source to Prevent Cyber-Crimes on Social Media / Ravi Kishore Devarapalli, Anupam Biswas -- Internet of Things (IoT) and Machine to Machine (M2M) Communication Techniques for Cyber Crime Prediction / Jaiprakash Narain Dwivedi -- Crime Predictive Model Using Big Data Analytics / Hemanta Kumar Bhuyan, Subhendu Kumar Pani -- The Role of Remote Sensing and GIS in Military Strategy to Prevent Terror Attacks / Sushobhan Majumdar -- Text Mining for Secure Cyber Space / Supriya Raheja, Geetika Munjal -- Analyses on Artificial Intelligence Framework to Detect Crime Pattern / R Arshath Raja, N Yuvaraj, NV Kousik -- A Biometric Technology-Based Framework for Tackling and Preventing Crimes / Ebrahim AM Alrahawe, Vikas T Humbe, GN Shinde -- Rule-Based Approach for Botnet Behavior Analysis / Supriya Raheja, Geetika Munjal, Jyoti Jangra, Rakesh Garg -- Securing Biometric Framework with Cryptanalysis / Abhishek Goel, Siddharth Gautam, Nitin Tyagi, Nikhil Sharma, Martin Sagayam -- The Role of Big Data Analysis in Increasing the Crime Prediction and Prevention Rates / Galal A AL-Rummana, Abdulrazzaq H A Al-Ahdal, GN Shinde -- Crime Pattern Detection Using Data Mining / Dipalika Das, Maya Nayak -- Attacks and Security Measures in Wireless Sensor Network / Nikhil Sharma, Ila Kaushik, Vikash Kumar Agarwal, Bharat Bhushan, Aditya Khamparia -- Large Sensing Data Flows Using Cryptic Techniques / Hemanta Kumar Bhuyan -- Cyber-Crime Prevention Methodology / Chandra Sekhar Biswal, Subhendu Kumar Pani.

Additional Edition: Print version: Intelligent data analytics for terror threat prediction Hoboken : Wiley, 2021. ISBN 9781119711094

Language: English

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

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119711629

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119711629

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119711629

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: 1 online resource (348 pages)

Edition: 1st ed.

ISBN: 9788770221818

Content: This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics.The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data Science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.

Additional Edition: Print version: Pani, Subhendu Kumar Applications of Machine Learning in Big-Data Analytics and Cloud Computing Aalborg : River Publishers,c2021

Language: English

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

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UID:

Format: 1 Online-Ressource(22 1 online resource (317 pages))

ISBN: 9781799866954 , 1799866955 , 9781799874799

Series Statement: Advances in data mining and database management (ADMDM) book series

Content: "With blockchain technology and artificial intelligence fueling the concept and growth of the Industrial Internet of Things, this book investigates the intersection of information science, computer science, and electronics engineering as it ushers in a new era for industrial and manufacturing companies"--

Note: "Premier Reference Source" -- taken from front cover. - Includes bibliographical references and index. - Description based on title screen (IGI Global, viewed 05/08/2021)

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781799866947

Language: English

DOI: 10.4018/978-1-7998-6694-7

URL: Volltext  (URL des Erstveröffentlichers)