Электронный каталог

DDC 616.8/0475
E 11

    Early detection of neurological disorders using machine learning systems / / Sudip Paul, Pallab Bhattacharya, and Arindam Bit, editors. - Hershey, PA : : Medical Information Science Reference,, ©2019. - 1 online resource : : il. - (Advances in medical technologies and clinical practice book series). - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/4FBD56BA-9995-46DF-B872-B20D6B52B82F. - ISBN 9781522585688 (electronic bk.). - ISBN 1522585680 (electronic bk.)
Print version record.
Параллельные издания: Print version: : Early detection of neurological disorders using machine learning systems. - Hershey, PA : Medical Information Science Reference, [2020]. - ISBN 9781522585671
    Содержание:
Epileptic seizure detection and classification using machine learning -- Rekh Janghel, Yogesh Rathore, Gautam Tiparti -- A study on basal ganglia circuit and its relation with movement disorders / Ankita Tiwari, Raghuvendra Tripathi, Dinesh Bhatia -- Social media analytics to predict depression level in the users / Mohammad Shahid Husain -- Tremor identification using machine learning in Parkinson's disease -- Angana Saikia, Vinayak Majhi, Masaraf Hussain, Sudip Paul, Amitava Datta -- Soft computing based early detection of Parkinson's disease using non-invasive method based on speech analysis / Chandrasekar Ravi -- Neurofeedback -retrain the brain / Meena Gupta, Dinesh Bhatia -- Neurocognitive mechanisms for detecting early phase of depressive disorder analysis of event related potentials in human brain / Shashikanta Tarai -- Intelligent big data analytics in health : big data analytics in health / Ebru Bayrak, Pinar Kirci -- Motor imagery classification using EEG signals for brain computer interface applications / Subrota Mazumdar, Rohit Chaudharya, Suruchi Suruchi, Suman Mohanty, Divya Kumari, Aleena Swetapadma -- Mapping the intellectual structure of the field neurological disorders : a bibliometric analysis / S. Ravikmar -- Medical image segmentation an advanced approach / Ramgopal Kashyapdia.

Early detection of neurological disorders using machine learning systems / [Электронный ресурс] / Sudip Paul, Pallab Bhattacharya, and Arindam Bit, editors., ©2019. - 1 online resource : с. (Введено оглавление)

DDC 616.07/54
D 30

    Deep learning applications in medical imaging / / [edited by] Sanjay Saxena, Sudip Paul. - 4018/978-1-7998-5071-7. - Hershey, PA : : IGI Global, Medical Information Science Reference,, [2021]. - 1 online resource : : il ( час. мин.), 4018/978-1-7998-5071-7. - (Advances in medical technologies and clinical practice (AMTCP) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/209E768D-3FF2-4EEE-B580-5BD84B52C7B0. - ISBN 1799850722 (electronic book). - ISBN 9781799850724 (electronic bk.)
"Premier Reference Source" -- Cover. Description based on online resource; title from digital title page (viewed on February 24, 2021).
Параллельные издания: Print version: : Deep learning applications in medical imaging. - Hershey, PA : Medical Information Science Reference, [2021]. - ISBN 9781799850717
    Содержание:
Relevance of Machine Learning to Cardiovascular Imaging / Sumesh Sasidharan, Mohammad Salmasi, Selene Pirola, Omar Jarral -- Deep Learning Applications in Medical Imaging : Artificial Intelligence, Machine Learning and Deep Learning / S. Sasikala, S.J. Subhashini, P. Alli, J. Jane Rubel Angelina -- A Survey on Prematurity Detection of Diabetic Retinopathy Based on Fundus Images using Deep Learning Techniques / Amiya Dash, Puspanjali Mohapatra -- Malaria Parasites Detection Using Deep Neural Network / Biswajit Jena, Pulkit Thakar, Vedanta Nayak, Gopal Nayak, Sanjay Saxena -- Deep Learning for Medical Image Segmentation / Kanchan Sarkar, Bohang Li -- Current Trends in Integrating the Concept of Deep Learning in Medical Imaging / Kavitha S. Velammal, Anchitaalagammai J.V., S Murali, Grace Shalini T. -- A CONVblock For Convolutional Neural Networks / Hmidi Alaeddine, Malek Jihene -- Machine Learning for Prediction of Lung Cancer / Nikita Banerjee, Subhalaxmi Das -- Conventional and Non Conventional ANN's in Medical Diagnostics A Tutorial Survey of Architectures, Algorithms and Application / Devika G., Asha Karegowda.

Deep learning applications in medical imaging / [Электронный ресурс] / [edited by] Sanjay Saxena, Sudip Paul., [2021]. - 1 online resource : с. (Введено оглавление)

DDC 616.1/207547
M 78
Moein, Sara, (1983-).
    Electrocardiogram signal classification and machine learning : : emerging research and opportunities / / by Sara Moein. - Hershey PA : : IGI Global, Medical Information Science Reference,, [2018]. - 1 online resource. - (Advances in medical technologies and clinical practice (AMTCP) book series). - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/43275C94-15A7-4836-AA42-EE805ADAF09B. - ISBN 9781522555810 (electronic bk.). - ISBN 1522555811 (electronic bk.)
Print version record.
Параллельные издания: Print version: : Moein, Sara, 1983- Electrocardiogram signal classification and machine learning. - Hershey PA : Medical Information Science Reference, [2018]. - ISBN 9781522555803
    Содержание:
Medical diagnosis -- Introduction on heart -- Background -- Methodology -- Kinetic gas molecule optimisation (KGMO) -- Classification and feature extraction -- Conclusion.

Moein, Sara,. Electrocardiogram signal classification and machine learning : [Электронный ресурс] : emerging research and opportunities / / by Sara Moein., [2018]. - 1 online resource. с. (Введено оглавление)

DDC 006.3/5
R 33
Reese, Richard Martin, (1953-).
    Natural language processing with Java : : techniques for building machine learning and neural network models for NLP / / Richard M. Reese, AshishSingh Bhatia. - Second edition. - Birmingham, UK : : Packt Publishing,, 2018. - 1 online resource (1 volume) : : il. - (Community experience distilled). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/2DE14F98-BF8D-4707-911D-0A2DA67FE1F9. - ISBN 9781788993067 (electronic bk.). - ISBN 1788993063 (electronic bk.)
Description based on online resource; title from title page (Safari, viewed August 27, 2018).

Reese, Richard Martin,. Natural language processing with Java : [Электронный ресурс] : techniques for building machine learning and neural network models for NLP / / Richard M. Reese, AshishSingh Bhatia., 2018. - 1 online resource (1 volume) : с.

DDC 006.31
B 76
Bonaccorso, Giuseppe.
    Machine Learning Algorithms : : Popular Algorithms for Data Science and Machine Learning, 2nd Edition. / Giuseppe. Bonaccorso. - 2nd ed. - Birmingham : : Packt Publishing Ltd,, 2018. - 1 online resource (514 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/8603E122-AC9F-424A-B0D5-7128C268A9AB. - ISBN 9781789345483. - ISBN 1789345480
Introducing semi-supervised Support Vector Machines (S3VM). Print version record.
Параллельные издания: Print version: : Bonaccorso, Giuseppe. Machine Learning Algorithms : Popular Algorithms for Data Science and Machine Learning, 2nd Edition. - Birmingham : Packt Publishing Ltd, ©2018. - ISBN 9781789347999
    Содержание:
Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: A Gentle Introduction to Machine Learning; Introduction -- classic and adaptive machines; Descriptive analysis; Predictive analysis; Only learning matters; Supervised learning; Unsupervised learning; Semi-supervised learning; Reinforcement learning; Computational neuroscience; Beyond machine learning -- deep learning and bio-inspired adaptive systems; Machine learning and big data; Summary; Chapter 2: Important Elements in Machine Learning; Data formats; Multiclass strategies.
One-vs-allOne-vs-one; Learnability; Underfitting and overfitting; Error measures and cost functions; PAC learning; Introduction to statistical learning concepts; MAP learning; Maximum likelihood learning; Class balancing; Resampling with replacement; SMOTE resampling; Elements of information theory; Entropy; Cross-entropy and mutual information ; Divergence measures between two probability distributions; Summary; Chapter 3: Feature Selection and Feature Engineering; scikit-learn toy datasets; Creating training and test sets; Managing categorical data; Managing missing features.
Data scaling and normalizationWhitening; Feature selection and filtering; Principal Component Analysis; Non-Negative Matrix Factorization; Sparse PCA; Kernel PCA; Independent Component Analysis; Atom extraction and dictionary learning; Visualizing high-dimensional datasets using t-SNE; Summary; Chapter 4: Regression Algorithms; Linear models for regression; A bidimensional example; Linear regression with scikit-learn and higher dimensionality; R2 score; Explained variance; Regressor analytic expression; Ridge, Lasso, and ElasticNet; Ridge; Lasso; ElasticNet; Robust regression; RANSAC.
Huber regressionBayesian regression; Polynomial regression; Isotonic regression; Summary; Chapter 5: Linear Classification Algorithms; Linear classification; Logistic regression; Implementation and optimizations; Stochastic gradient descent algorithms; Passive-aggressive algorithms; Passive-aggressive regression; Finding the optimal hyperparameters through a grid search; Classification metrics; Confusion matrix; Precision; Recall; F-Beta; Cohen's Kappa; Global classification report; Learning curve; ROC curve; Summary; Chapter 6: Naive Bayes and Discriminant Analysis; Bayes' theorem.

Bonaccorso, Giuseppe. Machine Learning Algorithms : [Электронный ресурс] : Popular Algorithms for Data Science and Machine Learning, 2nd Edition. / Giuseppe. Bonaccorso, 2018. - 1 online resource (514 pages) с. (Введено оглавление)

DDC 005.133
S 17
Saleh, Hyatt,.
    Machine learning fundamentals : : Use Python and scikit-learn to get up and running with the hottest developments in machine learning / / Hyatt Saleh. - Birmingham : : Packt Publishing,, ©2018. - 1 online resource (240 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/CE373E47-94E7-44E9-B874-2FCA503EFF44. - ISBN 1789801761 (electronic bk.). - ISBN 9781789801767 (electronic bk.)
Description based upon print version of record. Supervised Learning Algorithms: Predict Annual Income
Параллельные издания: Print version: : Saleh, Hyatt Machine Learning Fundamentals : Use Python and Scikit-Learn to Get up and Running with the Hottest Developments in Machine Learning. - Birmingham : Packt Publishing Ltd,c2018. - ISBN 9781789803556
    Содержание:
Intro; Preface; Introduction to Scikit-Learn; Introduction; Scikit-Learn; Advantages of Scikit-Learn; Disadvantages of Scikit-Learn; Data Representation; Tables of Data; Features and Target Matrices; Exercise 1: Loading a Sample Dataset and Creating the Features and Target Matrices; Activity 1: Selecting a Target Feature and Creating a Target Matrix; Data Preprocessing; Messy Data; Exercise 2: Dealing with Messy Data; Dealing with Categorical Features; Exercise 3: Applying Feature Engineering over Text Data; Rescaling Data; Exercise 4: Normalizing and Standardizing Data
Activity 2: Preprocessing an Entire DatasetScikit-Learn API; How Does It Work?; Supervised and Unsupervised Learning; Supervised Learning; Unsupervised Learning; Summary; Unsupervised Learning: Real-Life Applications; Introduction; Clustering; Clustering Types; Applications of Clustering; Exploring a Dataset: Wholesale Customers Dataset; Understanding the Dataset; Data Visualization; Loading the Dataset Using Pandas; Visualization Tools; Exercise 5: Plotting a Histogram of One Feature from the Noisy Circles Dataset; Activity 3: Using Data Visualization to Aid the Preprocessing Process
K-means AlgorithmUnderstanding the Algorithm; Exercise 6: Importing and Training the k-means Algorithm over a Dataset; Activity 4: Applying the k-means Algorithm to a Dataset; Mean-Shift Algorithm; Understanding the Algorithm; Exercise 7: Importing and Training the Mean-Shift Algorithm over a Dataset; Activity 5: Applying the Mean-Shift Algorithm to a Dataset; DBSCAN Algorithm; Understanding the Algorithm; Exercise 8: Importing and Training the DBSCAN Algorithm over a Dataset; Activity 6: Applying the DBSCAN Algorithm to the Dataset; Evaluating the Performance of Clusters
Available Metrics in Scikit-LearnExercise 9: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index; Activity 7: Measuring and Comparing the Performance of the Algorithms; Summary; Supervised Learning: Key Steps; Introduction; Model Validation and Testing; Data Partition; Split Ratio; Exercise 10: Performing Data Partition over a Sample Dataset; Cross Validation; Exercise 11: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set; Activity 8: Data Partition over a Handwritten Digit Dataset; Evaluation Metrics
Evaluation Metrics for Classification TasksExercise 12: Calculating Different Evaluation Metrics over a Classification Task; Choosing an Evaluation Metric; Evaluation Metrics for Regression Tasks; Exercise 13: Calculating Evaluation Metrics over a Regression Task; Activity 9: Evaluating the Performance of the Model Trained over a Handwritten Dataset; Error Analysis; Bias, Variance, and Data Mismatch; Exercise 14: Calculating the Error Rate over Different Sets of Data; Activity 10: Performing Error Analysis over a Model Trained to Recognize Handwritten Digits; Summary

Saleh, Hyatt,. Machine learning fundamentals : [Электронный ресурс] : Use Python and scikit-learn to get up and running with the hottest developments in machine learning / / Hyatt Saleh., ©2018. - 1 online resource (240 p.) с. (Введено оглавление)

DDC 621.367
D 50
Dey, Sandipan,.
    Hands-on image processing with Python : : expert techniques for advanced image analysis and effective interpretation of image data / / Sandipan Dey. - Birmingham, UK : : Packt Publishing,, 2018. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/72DBFB5B-5E81-4D50-AD8B-BA1D2CDF0E77. - ISBN 178934185X. - ISBN 9781789341850 (electronic bk.)
Description based on online resource; title from title page (Safari, viewed February 1, 2019).
Параллельные издания: Print version: :

Dey, Sandipan,. Hands-on image processing with Python : [Электронный ресурс] : expert techniques for advanced image analysis and effective interpretation of image data / / Sandipan Dey., 2018. - 1 online resource (1 volume) : с.

DDC 005.133
L 82
Liu, Yuxi (Hayden),.
    Python machine learning by example : : easy-to-follow examples that get you up and running with machine learning / / Yuxi (Hayden) Liu. - Second edition. - Birmingham, UK : : Packt Publishing,, 2019. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/12AC60A3-C325-4FFA-96EE-2BEFDAB954BA. - ISBN 9781789617559 (electronic bk.). - ISBN 1789617553 (electronic bk.)
Online resource; title from PDF title page (EBSCO, viewed April 8, 2019)

Liu, Yuxi (Hayden),. Python machine learning by example : [Электронный ресурс] : easy-to-follow examples that get you up and running with machine learning / / Yuxi (Hayden) Liu., 2019. - 1 online resource с.

DDC 005.7
M 30
Marin, Ivan.
    Big data analysis with Python : : combine Spark and Python to unlock the powers of parallel computing and machine learning / / Ivan Marin, Ankit Shukla and Sarang VK. - Birmingham, UK : : Packt Publishing,, ©2019. - 1 online resource (276 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/C3233AC3-74EA-4C2D-A64D-76AE7AE62C11. - ISBN 1789950732 (electronic book). - ISBN 9781789950731 (electronic book)
Description based on online resource; title from digital title page (viewed on January 06, 2020).
Параллельные издания: Print version: : Marin, Ivan. Big Data Analysis with Python : Combine Spark and Python to Unlock the Powers of Parallel Computing and Machine Learning. - Birmingham : Packt Publishing Ltd, ©2019. - ISBN 9781789955286
    Содержание:
Chapter 1: The Python Data Science Stack -- Chapter 2: Statistical Visualizations -- Chapter 3: Working with Big Data Frameworks -- Chapter 4: Diving Deeper with Spark -- Chapter 5: Handling Missing Values and Correlation Analysis -- Chapter 6: Exploratory Data Analysis -- Chapter 7: Reproducibility in Big Data Analysis -- Chapter 8: Creating a Full Analysis Report

Marin, Ivan. Big data analysis with Python : [Электронный ресурс] : combine Spark and Python to unlock the powers of parallel computing and machine learning / / Ivan Marin, Ankit Shukla and Sarang VK., ©2019. - 1 online resource (276 pages) с. (Введено оглавление)

DDC 006.3/1
H 22

    Handbook of research on machine and deep learning applications for cyber security / / Padmavathi Ganapathi and D. Shanmugapriya, editors. - 4018/978-1-5225-9611-0. - Hershey, PA : : IGI Global,, [2020]. - 1 online resource (482 pages) ( час. мин.), 4018/978-1-5225-9611-0. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/FE21F3A1-7CC2-45CD-A366-C606E832BF62. - ISBN 1522596135 (electronic book). - ISBN 9781522596141 (electronic book). - ISBN 1522596143 (electronic book). - ISBN 9781522596134 (electronic bk.)
Description based on online resource; title from digital title page (viewed on September 03, 2019).
Параллельные издания: Print version: :
    Содержание:
Chapter 1. Review on intelligent algorithms for cyber security -- Chapter 2. A review on cyber security mechanisms using machine and deep learning algorithms -- Chapter 3. Review on machine and deep learning applications for cyber security -- Chapter 4. Applications of machine learning in cyber security domain -- Chapter 5. Applications of machine learning in cyber security -- Chapter 6. Malware and anomaly detection using machine learning and deep learning methods -- Chapter 7. Cyber threats detection and mitigation using machine learning -- Chapter 8. Hybridization of machine learning algorithm in intrusion detection system -- Chapter 9. A hybrid approach to detect the malicious applications in android-based smartphones using deep learning -- Chapter 10. Anomaly-based intrusion detection: adapting to present and forthcoming communication environments -- Chapter 11. Traffic analysis of UAV networks using enhanced deep feed forward neural networks (EDFFNN) -- Chapter 12. A novel biometric image enhancement approach with the hybridization of undecimated wavelet transform and deep autoencoder -- Chapter 13. A 3D-cellular automata-based publicly-verifiable threshold secret sharing -- Chapter 14. Big data analytics for intrusion detection: an overview -- Chapter 15. Big data analytics with machine learning and deep learning methods for detection of anomalies in network traffic -- Chapter 16. A secure protocol for high-dimensional big data providing data privacy -- Chapter 17. A review of machine learning methods applied for handling zero-day attacks in the cloud environment -- Chapter 18. Adoption of machine learning with adaptive approach for securing CPS -- Chapter 19. Variable selection method for regression models using computational intelligence techniques.

Handbook of research on machine and deep learning applications for cyber security / [Электронный ресурс] / Padmavathi Ganapathi and D. Shanmugapriya, editors., [2020]. - 1 online resource (482 pages) с. (Введено оглавление)