Full Project – Web-based evaluation and deployment of pattern recognizers

Full Project – Web-based evaluation and deployment of pattern recognizers

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The study is based on the aspect of computing known as pattern recognition. Pattern recognition is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bio-informatics, data compression, computer graphics and machine learning Pattern recognition also refers to analysis of a behaviour of non-linear complex systems in absence of fundamental equations describing them. Keilis-Borok(2007) This approach encourages the possibility for a technical analysis and it involves a heuristic search for relationships between available system information and its features which are inaccessible for direct measurement. In some fields the evaluation

of algorithms can be done largely theoretically; a classic example being sorting algorithms. In pattern recognition, however, the performance of an algorithm on large quantities of real-world data is widely accepted to be the most important test. In an ideal world, having designed and implemented a new pattern recognition algorithm, one would be able to inform an evaluation server about the existence of the implementation, and leave the server with the task of evaluating the algorithm on lots of datasets, using widely accepted evaluation methods.


The problem is stated as follows;

The pattern recognizer tools are mostly implemented using traditional desktop and online programming languages but here we seek to create one using web based technologies. Use of web applications is often easier than a machine specific algorithm or software. They are often cross-platform in nature.

The project seeks to create a pattern recognizer and evaluated basically by testing on large datasets.


This study was motivated by the fact that we can exploit various computational theories for real life applications.


The aims and objectives of this study include;

  • Taking a deep dive into the field of pattern recognition.
  • Exploring the web technologies and specific algorithms that can be implemented in real world situations alongside machine learning and pattern recognition trends.
  • Studying and analyzing the related publications and research work in both web technology and pattern recognition fields.
  • Creating a simple pattern recognizer which can be tested by an evaluation software over a server.
  • Evaluate the pattern recognizer software and recommend improvements in later works.


The implementation of this idea and project was done with the php and python programming languages and the front end technologies of html, css and javascript. This was chosen because of its cross-platform nature and its general use for pattern recognition and machine learning of a wide range of datasets.


This study covers simple pattern recognition and machine learning algorithm that can also support training algorithms for efficient evaluation and deployment.


The study is very significant in studying, analyzing and postulating more efficient and trustworthy ways of pattern. It also explores the aspects where the algorithms such as the clustering and classification algorithms can be applied and improved.


  • PATTERN RECOGNITION: Pattern recognition as a field is the automated recognition of patterns and regularities in data.Pattern recognition more specifically defined is the process of recognizing patterns by using machine learning algorithms. It is based on the classification of data based on knowledge already gained or based on the statistical information extracted from patterns, data sets and also their representations.
  • MACHINE LEARNING: Machine learning is a branch of artificial intelligence based on the idea that systems can learn on their own to perform certain tasks without being hard programmed that way. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. It is also described as a method of data analysis that automates analytical model building.
  • EVALUATION SERVER: This is a platform by which a pattern recognition and machine learning program or algorithm can be tested particularly to see the algorithm’s proximity to human interaction.
  • TRAINING ALGORITHM: This is an algorithm that functions in such a way that it draws sensitivity from a provided set of data and draws conclusions and/or generally responds and acts on them.
  • DATASET: A data set is a collection of data that is worked on by a machine learning software to train an automated system.


The study is organized into five different sections/chapters as follows; Chapter One examines the problem statement, the background, the scope and the aims and objectives of the development. The operational terms of this project are also defined in this chapter. Chapter Two gives the review of the previous literature published by scholars and researchers. It explores in-depth research work that have been done in this area of computing. Chapter Three outlines the methodology used in the web based development of this project. It also gives the software development model used to implement the project. Chapter Four will explain the implementation, evaluation and presentation of results. Chapter Five gives good summary findings, recommendation for future works and conclusions.



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Full Project – Web-based evaluation and deployment of pattern recognizers