The basic principle of Machine learning is to develop algorithm which predict output by using statistical analysis from received input data.
The processes involved in machine learning are similar to that of data mining and predictive modeling. Both require searching through data to look for patterns and adjusting program actions accordingly. Many people are familiar with machine learning from shopping on the internet and being served ads related to their purchase. This happens because recommendation engines use machine learning to personalize online ad delivery in almost real time. Beyond personalized marketing, other common machine learning use cases include fraud detection, spam filtering, network security threat detection, predictive maintenance and building news feeds.
Aerofolic has extensive experience in supporting predictive analysis project. Predictive analytics is a branch of data analytics which relies on both new and chronological data to forecast trends, behavior and activities. Predictive analytics needs proper understanding of mathematical and statistical principles that underlie modern data techniques. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value -- or score -- on the likelihood of a particular event happening..
Predictive analytics software applications use variables that can be measured and analyzed to predict the likely behavior of individuals, machinery or other entities. For example, an insurance company is likely to take into account potential driving safety variables, such as age, gender, location, type of vehicle and driving record, when pricing and issuing auto insurance policies.
Multiple variables are combined into a predictive model capable of assessing future probabilities with an acceptable level of reliability. The software relies heavily on advanced algorithms and methodologies, such as logistic regression models, time series analysis and decision trees.
Predictions models fall into three wide categories are: technical analysis, fundamental analysis, technological methods
Some of the areas of application of Predictive analysis are
- Stock Market Prediction/Finanace
- Sports Gaming Prediction System
- Medical Prediction system
- Customer Behaviour Prediction System:
- Power Generation and Energy Consumption Prediction
- Thermal Performance Prediction System
In order to become suitable for digital processing, an image function f(x,y) must be digitized both spatially and in amplitude. Typically, a frame grabber or digitizer is used to sample and quantize the analogue video signal. Hence in order to create an image which is digital, we need to covert continuous data into digital form. There are two steps in which it is done:
The sampling rate determines the spatial resolution of the digitized image, while the quantization level determines the number of grey levels in the digitized image. A magnitude of the sampled image is expressed as a digital value in image processing. The transition between continuous values of the image function and its digital equivalent is called quantization.
The number of quantization levels should be high enough for human perception of fine shading details in the image. The occurrence of false contours is the main problem in image which has been quantized with insufficient brightness levels.
In this lecture we will talk about two key stages in digital image processing. Sampling and quantization will be defined properly. Spatial and grey-level resolutions will be introduced and examples will be provided. An introduction on implementing the shown examples in MATLAB will be also given in this lecture.
- ACQUISITION– It could be as simple as being given an image which is in digital form. The main work involves:
- IMAGE ENHANCEMENT– It is amongst the simplest and most appealing in areas of Image Processing it is also used to extract some hidden details from an image and is subjective.
- IMAGE RESTORATION– It also deals with appealing of an image but it is objective(Restoration is based on mathematical or probabilistic model or image degradation).
- COLOR IMAGE PROCESSING– It deals with pseudo color and full color image processing color models are applicable to digital image processing.
- WAVELETS AND MULTI-RESOLUTION PROCESSING– It is foundation of representing images in various degrees.
- IMAGE COMPRESSION-It involves in developing some functions to perform this operation. It mainly deals with image size or resolution.
- MORPHOLOGICAL PROCESSING-It deals with tools for extracting image components that are useful in the representation & description of shape
- SEGMENTATION PROCEDURE-It includes partitioning an image into its constituent parts or objects. Autonomous segmentation is the most difficult task in Image Processing.
- REPRESENTATION & DESCRIPTION-It follows output of segmentation stage, choosing a representation is only the part of solution for transforming raw data into processed data.
- OBJECT DETECTION AND RECOGNITION-It is a process that assigns a label to an object based on its descriptor.
a) Scaling b) Color conversion(RGB to Gray or vice-versa)
Natural Language Processing
Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken. NLP is a component of artificial intelligence (AI).
The development of NLP applications is challenging because computers traditionally require humans to "speak" to them in a programming language that is precise, unambiguous and highly structured, or through a limited number of clearly enunciated voice commands. Human speech, however, is not always precise -- it is often ambiguous and the linguistic structure can depend on many complex variables, including slang, regional dialects and social context.
Uses of natural language processing
Most of the research being done on natural language processing revolves around search, especially enterprise search. This involves allowing users to query data sets in the form of a question that they might pose to another person. The machine interprets the important elements of the human language sentence, such as those that might correspond to specific features in a data set, and returns an answer.
NLP can be used to interpret free text and make it analyzable. There is a tremendous amount of information stored in free text files, like patients' medical records, for example. Prior to deep learning-based NLP models, this information was inaccessible to computer-assisted analysis and could not be analyzed in any kind of systematic way. But NLP allows analysts to sift through massive troves of free text to find relevant information in the files.
Sentiment analysis is another primary use case for NLP. Using sentiment analysis, data scientists can assess comments on social media to see how their business's brand is performing, for example, or review notes from customer service teams to identify areas where people want the business to perform better.
Google and other search engines base their machine translation technology on NLP deep learning models. This allows algorithms to read text on a webpage, interpret its meaning and translate it to another language
Importance of NLP
The advantage of natural language processing can be seen when considering the following two statements: "Cloud computing insurance should be part of every service level agreement" and "A good SLA ensures an easier night's sleep -- even in the cloud." If you use national language processing for search, the program will recognize that cloud computing is an entity, that cloud is an abbreviated form of cloud computing and that SLA is an industry acronym for service level agreement.
Main features of NLP are:
- Semantic search
- Correction of Spelling
- Faceted search group
- Advanced techniques
- Fuzzy matching
Natural language processing helps to convert Speech to text conversion and text to speech conversion for AI Customer Service Chatbot which is usually used in Banking, Finance, Ecommerce and many other sectors.
Artificial Neural Network
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons.
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer "what if" questions. Other advantages include:
- Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
- Self-Organization: An ANN can create its own organization or representation of the information it receives during learning time.
- Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
- Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage