top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
AI Solutions for the United Nations Sustainable Development Goals (UN SDGs) : A Practical Approach Using JavaScript / / by Tulsi Pawan Fowdur, Lavesh Babooram
AI Solutions for the United Nations Sustainable Development Goals (UN SDGs) : A Practical Approach Using JavaScript / / by Tulsi Pawan Fowdur, Lavesh Babooram
Autore Fowdur Tulsi Pawan
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024
Descrizione fisica 1 online resource (355 pages)
Disciplina 006.3
Altri autori (Persone) BabooramLavesh
LaliteshDobee
AshvenSanghan
GyaneetaLuchmunparsad
AdnaanKurmally Mohammad
AvishayeDomah
DheerajRadjoo
VandanaHanumunthadu
MaadhaveeMohadeb Sai
Soggetto topico Artificial intelligence
Java (Computer program language)
Programming languages (Electronic computers)
Artificial Intelligence
Java
Programming Language
ISBN 9798868805363
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: Introduction to Machine Learning Applications Development and the UN SDGs -- Chapter 2: Utilizing Machine Learning Algorithms for Power generation prediction and classification in Wind Farms -- Chapter 3: Crop Recommendation System Using Machine Learning Algorithms for achieving SDGs 2, 9, and 12 -- Chapter 4: Aligning Manufacturing Emissions with SDGs 9 and 13 Using Machine Learning Algorithms -- Chapter 5: Water Potability Testing Using Machine Learning -- Applying Machine Learning for Air Quality Monitoring Targeting SDG 3 and 13 -- Chapter 7: Clustering the Development of Worldwide Internet Connectivity with Unsupervised Learning for SDGs 7, 9, and 11.
Record Nr. UNINA-9910917787303321
Fowdur Tulsi Pawan  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Machine Learning For Network Traffic and Video Quality Analysis : Develop and Deploy Applications Using JavaScript and Node.js / / by Tulsi Pawan Fowdur, Lavesh Babooram
Machine Learning For Network Traffic and Video Quality Analysis : Develop and Deploy Applications Using JavaScript and Node.js / / by Tulsi Pawan Fowdur, Lavesh Babooram
Autore Fowdur Tulsi Pawan
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024
Descrizione fisica 1 online resource (475 pages)
Disciplina 006.31
Altri autori (Persone) BabooramLavesh
Soggetto topico Machine learning
Artificial intelligence
Programming languages (Electronic computers)
Machine Learning
Artificial Intelligence
Programming Language
ISBN 9798868803543
9798868803536
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: Introduction to NTMA and VQA -- Chapter 2: Network Traffic Monitoring and Analysis -- Chapter 3: Video Quality Assessment -- Chapter 4: Machine Learning Techniques for NTMA and VQA -- Chapter 5: NTMA Application with JavaScript -- Chapter 6: Video Quality Assessment Application Development with JavaScript -- Chapter 7: NTMA and VQA Integration.
Record Nr. UNINA-9910866580003321
Fowdur Tulsi Pawan  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Real-Time Cloud Computing and Machine Learning Applications
Real-Time Cloud Computing and Machine Learning Applications
Autore Fowdur Tulsi Pawan
Pubbl/distr/stampa New York : , : Nova Science Publishers, Incorporated, , 2021
Descrizione fisica 1 online resource (810 pages)
Collana Computer Science, Technology and Applications
Soggetto non controllato Expert Systems (Computer Science)
Computers
ISBN 1-5361-9813-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Contents -- Preface -- Chapter 1 -- Introduction -- 1.1. Overview of Cloud Computing, Its Benefits and Applications -- 1.1.1. Benefits of Cloud Computing -- 1.1.2. Applications of Cloud Computing -- 1.1.2.1. Cloud Application Development -- 1.1.2.2. Cloud as an Enabler for Industry 4.0 -- 1.1.2.3. Cloud Radio Access Networks (C-RAN) -- 1.1.2.4. Big Data Analytics -- Cloud Private Branch Exchange (PBX) -- 1.2. Overview of Machine Learning and AI -- 1.2.1. Benefits of Machine Learning and AI -- 1.2.2. Applications of Machine Learning and AI -- 1.3. Combining AI with Cloud Computing for Real-Time Applications -- 1.4. Book Overview -- References -- Chapter 2 -- Cloud Computing Fundamentals -- 2.1. Definitions of Cloud Computing -- 2.2. Computing Paradigms -- 2.3. Cloud Computing Architecture and Enabling Technologies -- 2.4. Cloud Computing Deployment Models and Service Classes -- 2.4.1. Deployment Models -- 2.4.2. Service Classes -- 2.5. Introduction to the Firebase Cloud Platform -- 2.5.1. Firebase Cloud Database Configurations -- 2.5.2. Creating a Firebase Project and Real-time Database -- 2.5.3. Sending and Reading Data from the Database with an Android Application -- 2.6. Application Hosting on Firebase Using Node.js -- 2.6.1. Node.js Overview and Installation -- 2.6.2. Hosting a Web Application Using Node.js on Firebase for Reading and Writing Data to the Firebase Real-Time Database -- 2.7. Introduction to IBM Cloud Platform -- 2.7.1. IBM Cloud Database Configurations -- 2.7.2. Desktop Application to Send and Receive Data to the IBM Cloudant Database -- 2.7.3. Mobile Application to Send and Receive Data to the IBM Cloudant Database -- 2.8. Application Hosting on IBM Bluemix via the Eclipse IDE -- 2.8.1. Creating a Desktop Application to Send a Request to the CalculateArea servlet.
2.8.2. Creating a Mobile Application to Send a Request to the CalculateArea Servlet -- References -- Chapter 3 -- Machine Learning Algorithms -- 3.1. Definition of AI, Machine Learning and Deep Learning -- 3.1.1. Artificial Narrow Intelligence (ANI) -- 3.1.2. Artificial General Intelligence (AGI) -- 3.1.3. Artificial Super Intelligence (ASI) -- 3.2. Overview of Machine Learning Algorithms -- 3.3. Unsupervised Learning Algorithms -- 3.3.1. Unsupervised Shallow Learning Models -- 3.3.1.1. K-Means Clustering -- 3.3.1.2. Hierarchical Clustering -- 3.3.1.3. Gaussian Mixture Models -- 3.3.2. Unsupervised Deep Learning Models -- 3.3.2.1. Restricted Boltzmann Machine (RBM) -- 3.4. Supervised Learning Algorithms -- 3.4.1. Supervised Shallow Learning Models -- 3.4.1.1. Simple Linear Regression -- 3.4.1.2. Multiple Linear Regression -- 3.4.1.3. Polynomial Regression -- 3.4.1.4. Naïve Bayes -- 3.4.1.5. K-Nearest Neighbour -- 3.4.2. Supervised Deep Learning Models -- 3.4.2.1. Multi-Layered Perceptrons -- 3.4.2.2. Convolutional Neural Network -- 3.5. Reinforcement Learning Algorithms -- 3.5.1. Q-Learning -- 3.5.2. SARSA -- 3.6. Ensemble Learning Algorithms -- 3.6.1. Random Forest -- 3.7. Deploying Javascript Machine Learning Algorithms on Firebase -- 3.7.1. Main Layout of the Application -- 3.7.2. Incorporating the KNN Flower Classification Link -- 3.7.3. Incorporating the Regression Algorithm Link -- 3.7.4. Incorporating the Clustering Algorithm Link -- References -- Chapter 4 -- Data Capture and Client Architecture for a Cloud-Based Real-Time Network Analytics System -- 4.1. Overview of Machine Learning Algorithms for Network Analytics -- 4.1.1. Classification of Network Data -- 4.1.2. Regression Analysis for Network Data -- 4.2. Complete System Model of the Network Analytics System -- 4.3. Mobile Application for Network Data Capture and Analytics.
4.3.1. Creating the Android project -- 4.3.2. Adding Libraries -- 4.3.3. Android Application Layout -- 4.3.3.1. Visual Outlook on Final Application -- 4.3.3.2. Building the Visuals of activity_main.xml -- 4.3.3.3. Building the Visuals of Prediction.xml -- 4.3.3.4. Building the Visuals for csvdownload.xml -- 4.3.4. Declaring Global Variables in Main Activity -- 4.3.5. Retrieving the Last Index from the Cloud -- 4.3.6. Traffic Monitoring in the onCreate() Method -- 4.3.6.1. Initialising Components -- 4.3.6.2. Getting Initial Readings -- 4.3.6.3. Monitor Button -- 4.3.6.4. Stop Button -- 4.3.6.5. Go to Analysis Button -- 4.3.6.6. Go to Download Button -- 4.3.7. Getting Network Parameters -- 4.3.7.1. Getting Speed Data -- 4.3.7.2. Getting Packet Data -- 4.3.7.3. Getting Wi-Fi Data -- 4.3.8. Building the Main Thread -- 4.3.8.1. Creating a Thread with Runnable Class -- 4.3.8.2. Fetching Network Data -- 4.3.8.3. Updating UI and Live Monitor -- 4.3.8.4. Pushing Values to the Local Server -- 4.3.8.5. Pushing Values Directly to the Cloud -- 4.3.9. Live Graph Plotting -- 4.3.9.1. Initialising the Chart -- 4.3.9.2. Creating and Populating the Spinner -- 4.3.9.3. Applying the Adapter to the Spinner -- 4.3.9.4. Getting Spinner Value as a String -- 4.3.9.5. Programmatically Adding Data onto the Live Graph -- 4.3.10. Performing Analytics Using Cloud Servlet or Local Server -- 4.3.10.1. The "Predict" Button -- 4.3.10.2. The "Classify" Button -- 4.3.11. Downloading to .csv Files -- 4.3.11.1. Declaring Global Variables -- 4.3.11.2. Initialising Components in onCreate() -- 4.3.11.3. Browse Button -- 4.3.11.4. Download Last N Samples Button -- 4.3.11.5. Download by a Specific Date -- 4.3.11.6. Issuing the Directory Picker -- 4.3.11.7. Verifying Read and Write Permissions -- 4.3.11.8. Handling Permissions Request -- 4.3.11.9. Fetching Values from Cloudant Database.
4.3.12. Setting Permissions in Manifest -- 4.3.13. Testing the Mobile application -- 4.3.13.1. Live Monitor -- 4.3.13.2. Downloading Functionalities -- 4.3.13.3. Performing Analytics -- 4.4. Desktop Application for Network Data Capture and Analytics -- 4.4.1. Creating the NetBeans project -- 4.4.2. Desktop Application Layout -- 4.4.2.1. Visual Outlook on Final Application -- 4.4.2.2. Adding a JFrame Form to the Project -- 4.4.2.3. Adding Components to JFrame -- 4.4.3. Renaming Components -- 4.4.4. Adding Libraries -- 4.4.5. Declaring Global Variables in Netmonitor.Java -- 4.4.6. Creating Live Monitor Layout -- 4.4.7. Retrieving the Last Index from the Cloud -- 4.4.8. Start Button -- 4.4.8.1. Retrieving the Number of Items -- 4.4.8.2. Finding Network Interface in Use -- 4.4.8.3. Getting Initial Parameters -- 4.4.8.4. Updating GUI and Live Monitor -- 4.8.4.5. Pushing Values to the Local Server -- 4.8.4.6. Pushing Values to the Cloud -- 4.4.9. Stop Button -- 4.4.10. Clearing Graph -- 4.4.11. Performing Analytics Using Servlet or Local Server -- 4.4.11.1. Predict Button -- 4.4.11.2. Classify Button -- 4.4.12. Downloading to .csv Files -- 4.4.12.1. Browse Button -- 4.4.12.2. Download Last N Samples Button -- 4.4.12.3. Download by Specific Date Button -- 4.4.12.4. Fetching Values from Cloudant Database -- 4.4.13. Testing the Desktop Application -- 4.4.13.1. Live Monitor -- 4.4.13.2. Downloading Functionalities -- 4.4.13.3. Performing Analytics -- 4.5. Cloud Database Configurations for Network Analytics -- 4.5.1. Creating a Cloudant Database to Store Network Data -- 4.5.2. Adding an Index to the Database -- References -- Chapter 5 -- Server and Servlet Architectures for a Cloud-Based Real-Time Network Analytics System -- 5.1. Local Server Implementation for Network Data Capture and Forecasting -- 5.1.1. Creating the NetBeans Project.
5.1.2. Local Server Layout -- 5.1.2.1. Visual Outlook on Final Application -- 5.1.2.2. Adding Components to JFrame -- 5.1.3. Renaming Components -- 5.1.4. Adding Libraries -- 5.1.5. Declaring Global Variables in LocServer.java -- 5.1.6. Creating Live Monitor Layout -- 5.1.7. Retrieving the Last Index from the Cloud -- 5.1.8. Local Monitoring Server Implementation -- 5.1.8.1. Calling getNumberOfItems -- 5.1.8.2. Server Thread for Android Values -- 5.1.8.3. Server Thread for PC Values -- 5.1.8.4. Stopping the Server -- 5.1.8.5. Uploading Android Values to the Cloud -- 5.1.9. Filling Localhost Databases -- 5.1.10. Analytics -- 5.1.11. Classification -- 5.1.11.1. Filling Cloudant Databases -- 5.1.11.2. Retrieving Pre-Labelled Values from Cloudant Database or Localhost -- 5.1.11.3. Classifying Network Parameters Using K-Nearest Neighbour (KNN) -- 5.1.11.4. Performing K-Nearest Neighbour (KNN) -- 5.1.11.5. Classifying Network Parameters Using Multilayer Perceptron (MLP) -- 5.1.11.6. Performing Multilayer Perceptron (MLP) Classification -- 5.1.12. Regression -- 5.1.12.1. Using the Sliding Window Method -- 5.1.12.2. Filling Cloudant Databases -- 5.1.12.3. Retrieving Streaming Values from Cloudant Database -- 5.1.12.4. Multiple Linear Regression Models for Network data -- 5.1.12.5. Performing Multiple Linear Regression (MLR) -- 5.1.12.6. Multilayer Perceptron Models for Network data -- 5.1.12.7. Performing Multilayer Perceptron (MLP) Regression -- 5.1.13. Downloading to .csv Files -- 5.1.13.1. Browse Button -- 5.1.13.2. Download the Last N Samples Button -- 5.1.13.2. Download by Specific Date Button -- 5.1.14. Local Download and Analytics -- 5.1.15. Making the GUI User-Friendly -- 5.2. Testing the Local Server -- 5.2.1. Local Live Monitoring -- 5.2.2. Downloading Functionalities -- 5.2.3. Performing Analytics.
5.3. Servlet Program for Network Analytics, Monitoring and Data Retrieval.
Record Nr. UNINA-9911009290703321
Fowdur Tulsi Pawan  
New York : , : Nova Science Publishers, Incorporated, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui