The appearance of new concepts in systems and handling of large amounts of information has generated a novel trend in the field of Data Science – Pattern Recognition.
Pattern Recognition is the recognition of patterns and regularities in data and is closely associated with Artificial Intelligence (AI) and Machine Learning (ML). Depending on the use case and type of incoming data, different pattern recognition methods are applied. The data can range from texts, images, sentiments to even sounds.
The three common types of Pattern Recognition are:
1. Statistical Pattern Recognition
This refers to pattern recognition based on statistical historical data. It collects found patterns, processes them, and learns to generalize and apply these rules to new and similar observations.
2. Syntactic/Structural Pattern Recognition
This type of pattern recognition relies on simpler structural sub patterns. The pattern is described in terms of connections between the sub patterns.
3. Neural Pattern Recognition
Artificial Neural Networks are used in this type of pattern recognition. Kloud9 uses Pattern Recognition across Retail, Consumer Goods, Healthcare, and Manufacturing organizations enabling them to make informed business decisions.
In today’s world, Feature Engineering has become an integral part of Data Science as it increases prediction accuracy. Microsoft defines Feature Engineering as, “The process of creating new features from raw data to increase the predictive power of the learning algorithm. Engineered features should capture additional information that is not easily apparent in the original feature set”.
Feature Engineering is performed to:
Kloud9 enables organizations across the Retail, Consumer Goods, Healthcare, and Manufacturing industries to engineer new features that can improve their performance thereby resulting in productivity improvements and cost savings.
Businesses today are dealing with an abundance of data daily. Big Data sometimes gets accumulated in unmanageable datasets forcing organizations to make faster decisions in real-time. One way to process data faster and more efficiently is to detect any abnormal events, changes, or shifts in datasets.
Thus, Anomaly Detection, a technology that relies heavily on Artificial Intelligence (AI) and Machine Learning (ML) are used to identify abnormal behaviour within a given pool of collected data.
Kloud9’s Anomaly Detection use cases include:
Decision-makers across businesses are constantly facing the challenge of predicting the future as accurately as possible when making operational, tactical, and strategic decisions to derive maximum business value. The availability of time-stamped data is extremely critical for this forecasting. Studying time-series data and statistics to make forecasts and strategic decisions using data models is referred to as Time Series Forecasting.
There are two types of Time Series Forecasting:
Machine learning (ML) and Artificial Intelligence (AI) applied to Time Series Data is an efficient and effective way to analyze data, apply a forecasting algorithm, and derive an accurate forecast.
Time Series Forecasting has a range of practical applications in Retail & Consumer Goods, Healthcare and Manufacture, including:
Kloud9’s data experts use Time Series Forecasting to understand business problems and have the appropriate data and forecasting capabilities to find a fitting solution to the problems.
As organizations continue to grow there is an increasing demand to use Artificial Intelligence (AI), Machine Learning (ML), and Neural Networks to teach computers to see defects and issues before they affect business operations. Computer Vision is a component of Artificial Intelligence (AI) that is used in industries ranging from Retail and Consumer Goods, Healthcare and Manufacturing – and the market is continuing to grow. It is expected to reach USD 48.6 billion by 2022.
Computer Vision analyses content and extracts rich information from images and video. AI-enabled Computer Vision runs on advanced algorithms that can analyze visual content in different ways based on certain input and user choices.
Computer Vision often includes Optical Character Recognition (OCR), Image Analysis, and Spatial Analysis.
Some of the established Computer Vision tasks include:
As per Gartner Glossary, “Image recognition technologies strive to identify objects, people, buildings, places, logos, and anything else that has value to consumers and enterprises”.
The inputs are in the form of images and a computer vision algorithm generates a deciphered output.
Some of the established Image Recognition tasks include:
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