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| Longitud fija campo de control |
05326ntdaa2200313 ab4500 |
| 003 - IDENTIFICADOR DEL NÚMERO DE CONTROL |
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| Identificador del número de control |
UnInEc |
| 005 - FECHA Y HORA DE LA ÚLTIMA TRANSACCIÓN |
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| Fecha y hora de la última transacción |
20231220091002.0 |
| 006 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - CARACTERÍSTICAS DEL MATERIAL ADICIONAL |
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| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA |
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140501s9999 mx ||||f |||| 00| 0 spa d |
| 020 ## - NÚMERO INTERNACIONAL NORMALIZADO PARA LIBROS |
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| Número Internacional Normalizado para Libros (ISBN) |
9781032081649 |
| 040 ## - FUENTE DE LA CATALOGACIÓN |
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| Centro catalogador de origen |
CIBESPAM MFL |
| 041 ## - CÓDIGO DE LENGUA |
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| Código de lengua del texto;banda sonora o título independiente |
eng |
| 082 ## - NÚMERO DE LA CLASIFICACIÓN DECIMAL DEWEY |
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| Número de clasificación |
006.31 |
| Cutter |
K96 |
| 100 ## - PUNTO DE ACCESO PRINCIPAL-NOMBRE DE PERSONA |
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| Nombre de persona |
Kumar Tyagi, Amit |
| 245 ## - MENCIÓN DE TÍTULO |
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| Título |
Recurrent Neural Networks. |
| Resto del título |
Concepts and Applications |
| 250 ## - MENCIÓN DE EDICIÓN |
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| Mención de edición |
First Edition |
| 260 ## - PUBLICACIÓN, DISTRIBUCIÓN, ETC. (PIE DE IMPRENTA) |
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| Lugar de publicación, distribución, etc. |
New York |
| Nombre del editor, distribuidor, etc. |
CRC Press Taylor & Francis Group |
| Fecha de publicación, distribución, etc. |
2023 |
| 300 ## - DESCRIPCIÓN FÍSICA |
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| Extensión |
xvi, 396 pages |
| Otras características físicas |
Figures, tables; |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
-- Section I: Introduction<br/>1. A Road Map to Artificial Neural Network<br/>- Arpana Sharma, Kanupriya Goswami, Vinita Jindal and Richa Gupta<br/>2. Applications of Recurrent Neural Network: Overview and Case Studies<br/>- Kusumika Krori Dutta, S. Poornima, Ramit Sharma, Deebul Nair and Paul G. Ploeger<br/>3. Image to Text Processing Using Convolution Neural Networks<br/>- V. Pattabiraman and R. Maheswari<br/>4. Fuzzy Orienteering Problem Using Genetic Search<br/>- Partha Sarathi Barma, Saibal Majumder and Bijoy Kumar Mandal<br/>5. A Comparative Analysis of Stock Value Prediction Using Machine Learning Technique<br/>- V. Ramchander and Richa<br/>-- Section II: Process and Methods<br/>6. Developing Hybrid Machine Learning Techniques to Forecast the Water Quality Index (DWM-Bat & DMARS)<br/>- Samaher Al-Janabi, Ayad Alkaim and Zuhra Al-Barmani<br/>7. Analysis of RNNs and Different ML and DL Classifiers on Speech- Based Emotion Recognition System Using Linear and Nonlinear Features<br/>- Shivesh Jha, Sanay Shah, Raj Ghamsani, Preet Sanghavi and Narendra M. Shekokar<br/>8. Web Service User Diagnostics with Deep Learning Architectures<br/>-S. Maheswari<br/>9. D-SegNet: A Modified Encoder-Decoder Approach for Pixel-Wise Classification of Brain Tumor from MRI Images<br/>- K. Aswani and D. Menaka<br/>10. Data Analytics for Intrusion Detection System Based on Recurrent Neural Network and Supervised Machine Learning Methods<br/>- Yakub Kayode Saheed<br/>-- Section III: Applications<br/>11. Triple Steps for Verifying Chemical Reaction Based on Deep Whale Optimization Algorithm (VCR-WOA)<br/>- Samaher Al-Janabi, Ayad Alkaim and G. Kadhum<br/>12. Structural Health Monitoring of Existing Building Structures for Creating Green Smart Cities Using Deep Learning<br/>- Nishant Raj Kapoor, Aman Kumar, Harish Chandra Arora and Ashok Kumar<br/>13 Artificial Intelligence-Based Mobile Bill Payment System Using Biometric Fingerprint<br/>- A. Kathirvel, Debashreet Das, Stewart Kirubakaran, M. Subramaniam and S. Naveneethan<br/>14. An Efficient Transfer Learning–Based CNN Multi-Label Classification and ResUNET Based Segmentation of Brain Tumor in MRI<br/>- V. Abinash, S. Meghanth, P. Rakesh, S. A. Sajidha, V. M. Nisha and A. Muralidhar Samaher Al-Janabi and Ayad Alkaim<br/>15. Deep Learning–Based Financial Forecasting of NSE Using Sentiment Analysis<br/>- Aditya Agarwal, Romit Ganjoo, Harsh Panchal and Suchitra Khoje<br/>16. An Efficient Convolutional Neural Network with Image Augmentation for Cassava Leaf Disease Detection<br/>- Ratnavel Rajalakshmi, Abhinav Basil Shinow, Aswin Murali, Kashinadh S. Nair and J. Bhuvana<br/>-- Section IV: Post–COVID-19 Futuristic Scenarios– Based Applications: Issues and Challenges<br/>17. AI-Based Classification and Detection of COVID-19 on Medical Images Using Deep Learning<br/>- V. Pattabiraman and R. Maheswari<br/>18. An Innovative Electronic Sterilization System (S-Vehicle, NaOCI.5H2O and CeO2NP)<br/>- Samaher Al-Janabi and Ayad Alkaim<br/>19. Comparative Forecasts of Confirmed COVID-19 Cases in Botswana Using Box-Jenkin’s ARIMA and Exponential Smoothing State-Space Models<br/>- Ofaletse Mphale and V. Lakshmi Narasimhan<br/>20. Recent Advancement in Deep Learning: Open Issues, Challenges, and a Way Forward<br/>- Sakshi Purwar and Amit Kumar Tyagi |
| 520 ## - NOTA DE SUMARIO |
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| Sumario, etc, |
The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding.<br/><br/>FEATURES<br/><br/>- Covers computational analysis and understanding of natural languages<br/>- Discusses applications of recurrent neural network in e-Healthcare<br/>- Provides case studies in every chapter with respect to real-world scenarios<br/>- Examines open issues with natural language, health care, multimedia (Audio/Video), transportation, stock market, and logistics<br/>The text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology. |
| 650 ## - PUNTO DE ACCESO ADICIONAL DE MATERIA - TÉRMINO DE MATERIA |
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| Término de materia o nombre geográfico como elemento inicial |
Networks |
| 650 ## - PUNTO DE ACCESO ADICIONAL DE MATERIA - TÉRMINO DE MATERIA |
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| Término de materia o nombre geográfico como elemento inicial |
Computer |
| 650 ## - PUNTO DE ACCESO ADICIONAL DE MATERIA - TÉRMINO DE MATERIA |
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| Término de materia o nombre geográfico como elemento inicial |
Engineering |
| 650 ## - PUNTO DE ACCESO ADICIONAL DE MATERIA - TÉRMINO DE MATERIA |
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| Término de materia o nombre geográfico como elemento inicial |
Electrical |
| 700 ## - PUNTO DE ACCESO ADICIONAL - NOMBRE DE PERSONA |
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| Nombre de persona |
Abraham, Ajith |
| 912 ## - DATOS OPENBIBLIO |
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| Fecha de última modificación |
2023-12-20 |
| Usuario que lo modifico por última vez |
Paúl Villacreses |
| 913 ## - ÁREA Y CARRERA |
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| Área de Conocimiento |
Información y Comunicación (TIC) |
| Carrera |
Carrera de Computación |
| Líneas de Investigación Institucionales |
Generación de tecnología para la soberanía alimentaria |
| 942 ## - ENTRADA DE ELEMENTOS AGREGADOS (KOHA) |
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| Fuente de clasificaión o esquema |
Dewey Decimal Classification |
| Koha [por defecto] tipo de item |
Libros |