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Showing posts with label Artificial Intelligence. Show all posts
Showing posts with label Artificial Intelligence. Show all posts

Monday, 16 December 2019

12/16/2019 05:20:00 pm

Predictive Maintenance Using Machine learning Techniques



Understanding Predictive Maintenance Applications

Predictive maintenance nowadays gaining popularity among enterprises that predict failure of the system, and the actions could include corrective actions, the replacement of the system, or even planned failure. This helps enterprises to cost savings, greater predictability, and the improved availability of the systems. Predictive maintenance sidesteps both the limits and maximizes the use of its sources. Predictive maintenance also detects the irregularities and failure patterns and provide real-time alerts. These signals can facilitate the efficient maintenance of those components. AI-enabled Predictive Maintenance is uncommon in that instead of just predicting impending failure, it also attempts to provide outcome-focused instructions for operations and maintenance from analytics. Let’s explore the areas where predictive maintenance can be used:
Predictive maintenance covers diverse application areas, such as -
  • Manufacturing industry
  • Information and technology
  • Aerospace
  • Heavy-Machinery sector
  • Predicting the future performance of a subsystem or a component to make RUL (Remaining Useful Life) estimation.
In this use case, we will guide you through how to build a machine learning platform for predictive maintenance.

Business Challenge for Enabling Predictive Maintenance

  • Monitoring of Assets in Real-Time via sensor data patterns to predict the breakdown of Assets.
  • Production systems deteriorate with time and need maintenance.
  • The regular way to keep the system good is to apply preventive maintenance practices, in the case of clearly detected malfunctions or equipment breakdowns. All this affects the quality, cost and in general, productivity.
Other than this, the uncertainty of machine reliability at any given time also impacts on product/production delivery times.

Predictive Maintenance Analytics Pipeline

Collecting targeted data
The targeted data reside in remote locations and get into the analysis pipeline including sensors, meters, supervisory control, etc. Collect data from all of the remote data sources to learn and continually make better, more informed business decisions.
Determining Analytics Pipeline
Establish an Advanced Analytics Pipeline based on the specific operation. Cloud analytics should be balanced to reduce the burden of streaming perishable PdM data on Cloud Deployment. Follow a distributed approach to detect and respond to local events at Cloud dataflow consumer step, take immediate action on Streaming data, while simultaneously integrating additional data sources in the Cloud.

Technology Stack -

  • Python
  • Flask
  • Cloud IoT Core
  • Cloud Pub/Sub

Source: XenonStack/Use-Cases
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Friday, 13 December 2019

12/13/2019 05:03:00 pm

Advanced Threat Analytics and Intelligence

Overview of Advanced Threat Analytics and Intelligence

The security aspect has changed dramatically over recent years. The cyber-attacks nowadays have become more pervasive, persistent, and proficient than ever at escaping and contaminating traditional security architecture. Cyber threats have become more complex and complicated. Many companies meet stealthy attacks in their systems. These attacks are targeted towards intellectual property and consumer information theft or encryption of important data for ransom. Therefore, to protect your IT assets, you must know what is coming, secure your digital interactions, detect, and manage inevitable breaches, and safeguard business chain and regulative compliance.
Threat Detection is the art of identifying attacks on a computer. While there are a large variety of Cyber Security attacks, most of them fit into one of four categories -
  • Probe
  • Denial of Service (DoS)
  • User to Root
  • Remote to User
Hence, companies are looking for Cyber Security Services and Solutions to ensure the security of their IT network. In this use case, we will guide you through how we built an effective cybersecurity and threat detection system using machine learning.

Apache Metron Overview

Apache Metron is a cybersecurity application framework that provides the ability to ingest, process and store various security data feeds at a scale level to detect cyber anomalies and enable organizations to take action against them rapidly.

Apache Spot Architecture for Cyber Security

Apache Spot is a cybersecurity project, aimed to bring Advanced Analytics to all IT Telemetry data on an open, scalable platform. Apache Spot expedites the threat detection, investigation, and remediation via machine learning and consolidates all enterprise security data into a comprehensive IT telemetry hub based on open data models.

Threat Detection Using Deep Learning

A multi-layered Deep Learning-based system is very robust, scalable and adaptable. All the identified incidents & patterns are denoted by a risk score, to help investigate the breach, control data loss and take precautionary actions for the future.

Threat Detection Using Machine Learning

A Machine Learning-based Threat Detection system automates the process of extracting insights from file samples through better generalization at identifying unknown variations. It also helps in reducing human analysis time.

Challenges to Real-Time Cyber Threat Intelligence

  • To perform Real-Time Threat Intelligence on trillions of messages per year.
  • Storing and Processing the unstructured security data.
  • Combine Machine Learning and Predictive Analytics to perform Real-Time Threat Analytics.

Solution Offerings for Threat Detection and Cyber Security

Threat Analytics and Intelligence by automating the process of Threat Detection and Analysis. Following steps are performed to Automate the process -
  • Network Dataset
  • Pre-Processing of Data
  • Feature Extraction
  • Reduce Data Amount
  • Improve Accuracy
  • Avoid Overfitting

Training and Testing of Data Using Classification Models

  • Decision Tree
  • Random Forest
  • Naive Bayes
  • KNN
  • Result Analysis
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Wednesday, 24 May 2017

5/24/2017 05:58:00 pm

Overview of Artificial Intelligence and Role of Natural Language Processing in Big Data



Artificial Intelligence Overview




AI refers to ‘Artificial Intelligence’ which means making machines capable of performing intelligent tasks like human beings. AI performs automated tasks using intelligence.

The term Artificial Intelligence has two key components -
    • Automation  
    • Intelligence

Goals of Artificial Intelligence







Stages of Artificial Intelligence


Stage 1 - Machine Learning - It is a set of algorithms used by intelligent systems to learn from experience.

Stage 2 - Machine Intelligence - These are the advanced set of algorithms used by machines to learn from experience. Eg - Deep Neural Networks.

ArtificiaI Intelligence technology is currently at this stage.

Stage 3 - Machine Consciousness - It is self-learning from experience without the need of external data.





Types of Artificial Intelligence



ANI - Artificial Narrow Intelligence - It comprises of basic/role tasks such as those performed by chatbots, personal assistants like SIRI by Apple and Alexa by Amazon.

AGI - Artificial General Intelligence - Artificial General Intelligence comprises of human-level tasks such as performed by self-driving cars by Uber, Autopilot by Tesla. It involves continual learning by the machines.

ASI - Artificial Super Intelligence - Artificial Super Intelligence refers to intelligence way smarter than humans.

What Makes System AI Enabled









Difference Between NLP, AI, ML, DL & NN



AI or Artificial Intelligence - Building systems that can do intelligent things.

NLP or Natural Language Processing - Building systems that can understand language. It is a subset of Artificial Intelligence.

ML or Machine Learning - Building systems that can learn from experience. It is also a subset of Artificial Intelligence.

NN or Neural Network - Biologically inspired network of Artificial Neurons.

DL or Deep Learning - Building systems that use Deep Neural Network on a large set of data. It is a subset of Machine Learning.



What is Natural Language Processing?


Natural Language Processing (NLP) is “ability of machines to understand and interpret human language the way it is written or spoken”.

The objective of NLP is to make computer/machines as intelligent as human beings in understanding language.



The ultimate goal of NLP is to the fill the gap how the humans communicate(natural language) and what the computer understands(machine language).

There are three different levels of linguistic analysis done before performing NLP -

Syntax - What part of given text is grammatically true.
Semantics - What is the meaning of given text?
Pragmatics - What is the purpose of the text?

NLP deal with different aspects of language such as

  • Phonology - It is systematic organization of sounds in language.
  • Morphology - It is a study of words formation and their relationship with each other.


Approaches of NLP for understanding semantic analysis

  • Distributional - It employs large-scale statistical tactics of Machine Learning and Deep Learning.
  • Frame - Based - The sentences which are syntactically different but semantically same are represented inside data structure (frame) for the stereotyped situation.
  • Theoretical - This approach is based on the idea that sentences refer to the real word (the sky is blue) and parts of the sentence can be combined to represent whole meaning.
  • Interactive Learning - It involves pragmatic approach and user is responsible for teaching the computer to learn the language step by step in an interactive learning environment. 


The true success of NLP lies in the fact that humans deceive into believing that they are talking to humans instead of computers.

Why Do We Need NLP?


With NLP, it is possible to perform certain tasks like Automated Speech and Automated Text Writing in less time.

Due to the presence of large data (text) around, why not we use the computers untiring willingness and ability to run several algorithms to perform tasks in no time.

These tasks include other NLP applications like Automatic Summarization (to generate summary of given text) and Machine Translation (translation of one language into another)

Process of NLP


In case the text is composed of speech, speech-to-text conversion is performed.

The mechanism of Natural Language Processing involves two processes:
  • Natural Language Understanding
  • Natural Language Generation

Natural Language Understanding


NLU or Natural Language Understanding tries to understand the meaning of given text. The nature and structure of each word inside text must be understood for NLU. For understanding structure, NLU tries to resolve following ambiguity present in natural language:

  • Lexical Ambiguity - Words have multiple meanings
  • Syntactic Ambiguity - Sentence having multiple parse trees.
  • Semantic Ambiguity - Sentence having multiple meanings
  • Anaphoric Ambiguity - Phrase or word which is previously mentioned but has a different meaning.


Next, the meaning of each word is understood by using lexicons (vocabulary) and set of grammatical rules.

However, there are certain different words having similar meaning (synonyms) and words having more than one meaning (polysemy).

Natural Language Generation


It is the process of automatically producing text from structured data in a readable format with meaningful phrases and sentences. The problem of natural language generation is hard to deal with. It is subset of NLP

Natural language generation divided into three proposed stages:-

1. Text Planning - Ordering of the basic content in structured data is done.
2. Sentence Planning - The sentences are combined from structured data to represent the flow of information.
3. Realization - Grammatically correct sentences are produced finally to represent text.

Difference Between NLP and Text Mining or Text Analytics


Natural language processing is responsible for understanding meaning and structure of given text.

Text Mining or Text Analytics is a process of extracting hidden information inside text data through pattern recognition.



Natural language processing is used to understand the meaning (semantics) of given text data, while text mining is used to understand structure (syntax) of given text data.

As an example - I found my wallet near the bank. The task of NLP is to understand in the end that ‘bank’ refers to financial institute or ‘river bank'.

What is Big Data?


According to the Author Dr. Kirk Borne, Principal Data Scientist, Big Data Definition is described as big data is everything, quantified, and tracked.

For More Details on Big Data, Please Read - Ingestion And Processing of Data For Big Data and IoT Solutions

NLP for Big Data is the Next Big Thing


Today around 80 % of total data is available in the raw form. Big Data comes from information stored in big organizations as well as enterprises. Examples include information of employees, company purchase, sale records, business transactions, the previous record of organizations, social media etc.

Though human uses language, which is ambiguous and unstructured to be interpreted by computers, yet with the help of NLP, this huge unstructured data can be harnessed for evolving patterns inside data to better know the information contained in data.

NLP can solve big problems of the business world by using Big Data. Be it any business like retail, healthcare, business, financial institutions.

What is Chatbot?


Chatbots or Automated Intelligent Agents


  • These are the computer program you can talk to through messaging apps, chat windows or through voice calling apps
  • These are intelligent digital assistants used to resolve customer queries in a cost-effective, quick, and consistent manner.

Importance of Chatbots

Chatbots are important to understanding changes in digital customer care services provided and in many routine queries that are most frequently enquired.

Chatbots are useful in a certain scenario when the customer service requests are specific in the area and highly predictable, managing a high volume of similar requests, automated responses.

Working of Chatbot


Knowledge Base - It contains the database of information that is used to equip chatbots with the information needed to respond to queries of customers request.

Data Store - It contains interaction history of chatbot with users.

Continue Reading About AI & NLP  At: XenonStack.com/Blog



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