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Tag: Learningbased
Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems (Advances in Intelligent Decision-Making, Systems Engineering, and Project Management)
Price: $115.00
(as of Dec 29,2024 07:13:13 UTC – Details)
Publisher : CRC Press; 1st edition (June 17, 2022)
Language : English
Hardcover : 92 pages
ISBN-10 : 1032147253
ISBN-13 : 978-1032147253
Item Weight : 8.4 ounces
Dimensions : 0.26 x 6.14 x 9.21 inches
In the field of industrial engineering, the ability to quickly and accurately diagnose faults in systems is crucial for maintaining efficiency and productivity. Traditional methods of fault diagnosis can be time-consuming and often rely on manual intervention, which can lead to delays and increased downtime.However, with the recent advancements in machine learning technology, there is now a more efficient and effective way to diagnose faults in industrial engineering systems. Machine learning algorithms can analyze vast amounts of data in real-time, allowing for faster and more accurate fault detection.
In the book “Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems (Advances in Intelligent Decision-Making, Systems Engineering, and Project Management),” experts in the field explore the applications of machine learning in fault diagnosis. They discuss how these technologies can be used to optimize maintenance schedules, predict potential failures, and improve overall system performance.
By leveraging the power of machine learning, industrial engineers can proactively identify and address issues before they escalate, ultimately leading to cost savings and improved operational efficiency. This book serves as a valuable resource for professionals looking to stay ahead of the curve in the rapidly evolving field of industrial engineering.
#Machine #LearningBased #Fault #Diagnosis #Industrial #Engineering #Systems #Advances #Intelligent #DecisionMaking #Systems #Engineering #Project #Management,rnn
Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (NLP) Applications
Price:$79.99– $48.99
(as of Dec 26,2024 13:00:40 UTC – Details)
From the brand
Browse our NLP & LLM books
Sharing the knowledge of experts
O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
Publisher : O’Reilly Media; 1st edition (January 12, 2021)
Language : English
Paperback : 422 pages
ISBN-10 : 149207408X
ISBN-13 : 978-1492074083
Item Weight : 1.5 pounds
Dimensions : 7 x 1 x 9.2 inches
Text analytics using Python is a powerful tool for extracting valuable insights from unstructured text data. In this post, we will explore blueprints for implementing machine learning-based solutions for common real-world Natural Language Processing (NLP) applications.1. Sentiment Analysis: Sentiment analysis is a common NLP task that involves determining the sentiment (positive, negative, or neutral) expressed in a piece of text. To implement sentiment analysis using Python, you can start by pre-processing the text data (removing stop words, punctuation, and special characters), tokenizing the text, and then applying machine learning algorithms such as Naive Bayes or Support Vector Machines to classify the sentiment of the text.
2. Named Entity Recognition (NER): Named Entity Recognition is another important NLP task that involves identifying and classifying named entities in text data, such as names of people, organizations, and locations. To implement NER using Python, you can use pre-trained NER models like spaCy or train your own NER model using machine learning algorithms such as Conditional Random Fields or Recurrent Neural Networks.
3. Text Classification: Text classification involves categorizing text data into predefined categories or labels. Common applications of text classification include spam detection, topic categorization, and sentiment analysis. To implement text classification using Python, you can use machine learning algorithms such as Logistic Regression, Random Forest, or Neural Networks to train a classifier on labeled text data.
4. Text Summarization: Text summarization is the task of generating a concise summary of a longer piece of text. There are two main approaches to text summarization: extractive summarization, which involves selecting and rearranging sentences from the original text, and abstractive summarization, which involves generating new sentences to summarize the text. To implement text summarization using Python, you can use pre-trained models like BERT or GPT-3 for abstractive summarization or implement algorithms like TextRank for extractive summarization.
In conclusion, text analytics using Python offers a wide range of machine learning-based solutions for common real-world NLP applications. By leveraging the power of machine learning algorithms and Python libraries such as NLTK, spaCy, and scikit-learn, you can build robust and scalable text analytics solutions to extract valuable insights from text data.
#Blueprints #Text #Analytics #Python #Machine #LearningBased #Solutions #Common #Real #World #NLP #Applications
Machine Vision Inspection Systems, Machine Learning-based Approaches, Hardcov…
Machine Vision Inspection Systems, Machine Learning-based Approaches, Hardcov…
Price : 72.00
Ends on : N/A
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Machine Vision Inspection Systems: Enhancing Quality Control with Machine LearningMachine vision inspection systems have revolutionized the manufacturing industry by providing automated solutions for quality control. These systems use cameras and image processing algorithms to detect defects and deviations in products, ensuring that only high-quality items are produced.
One of the key advancements in machine vision inspection systems is the integration of machine learning algorithms. By utilizing machine learning, these systems can continuously improve their accuracy and efficiency in detecting defects. Machine learning algorithms can analyze large amounts of data to identify patterns and trends, allowing the system to make more informed decisions about product quality.
Hardcover: A Robust Solution for Industrial Applications
For industrial applications where ruggedness and durability are essential, hardcover machine vision inspection systems are the ideal choice. These systems are designed to withstand harsh environmental conditions, such as high temperatures, dust, and vibrations, ensuring reliable performance in challenging manufacturing environments.
By combining the robustness of hardcover systems with the advanced capabilities of machine learning-based approaches, manufacturers can achieve unparalleled levels of quality control and efficiency in their production processes. With machine vision inspection systems, machine learning algorithms, and hardcover technology working together, manufacturers can enhance their quality control processes and streamline their operations for maximum productivity and profitability.
#Machine #Vision #Inspection #Systems #Machine #Learningbased #Approaches #Hardcov..
