What is AI pattern recognition? Methods, benefits, challenges and real-life example

Data acquisition

AI models are trained on high-quality data to identify accurate patterns. Using an openly accessible dataset can streamline the process of data acquisition while maintaining strict quality checks. In a scenario where manual data collection is not possible, synthetic datasets are collected to train the AI model.

Pre-processing

Raw data is refined in this step to eliminate imperfections and enhance the clarity of the data needed for accurate prediction. Techniques such as smoothing and normalization are vital while modifying the image to reduce the effect of lighting variation while preparing data for further analysis.

Feature extraction

Input data is processed and simplified to extract the most relevant insights while eliminating the irrelevant data. This step addresses the challenge of processing high-dimensional data by ensuring the most important information is extracted and forwarded for classification.

Classification

Extracted features are compared with the information in the database in this step. Each feature is assigned to the most appropriate category. Classifications are done through supervised learning, where AI models have predefined pattern categories and distinguishing parameters. Parameters are decided based on input data, and the desired output is produced using the models’ pattern recognition capability.

Statistical pattern recognition

This model relies on statistical techniques and historical data to learn features from examples by collecting and processing observations. Features are selected in a way that they don’t overlap. The machine uses the learned pattern for further processing and training. The pattern is grouped based on its features, and the number of features determines how the pattern is viewed as a point in d-dimensional space. The goal is to find relationships between variables. This approach is mainly used for prediction or classification.

Syntactic pattern recognition

It relies on primitives such as letters of the alphabet. It is used for recognizing complex patterns by adopting a hierarchical perspective. Syntactic pattern recognition is a subfield of pattern recognition that focuses on the rational and structural characteristics of a pattern rather than depending upon statistical features. Turning complex patterns into simpler hierarchical sub-patterns, it becomes easy to identify roads, rivers, or humans in images, or syntax and grammar in text.

Neural pattern recognition

It is a branch of artificial intelligence that uses neural networks to identify and interpret patterns. Neural networks function based on parallel subunits, also called neurons, that simulate human decision-making. Neural pattern recognition is the most popular form of machine learning using neural networks and deep Convolutional Neural Networks (CNN) to solve classification tasks.

Template matching

This technique is used to identify the similarities between two entities of the same type. Thus, template-matching models discover similarities based on a reference template in a sample. It is commonly used in digital image processing to detect small sections that match the template image. Some of the most popular application areas are quality control in manufacturing, medical image processing, facial recognition, and robot navigation.

Fuzzy-based approach

This approach uses fuzzy logic principles that describe patterns with fuzzy sets rather than crisp sets. A fuzzy-based approach defines fuzzy sets for features, designs fuzzy rules, and uses speculations to classify data. It is beneficial when the difference between the patterns is unclear. This technique is applied in areas where data is ambiguous, such as control systems and medical diagnosis.

High computational demands

Challenge: Training AI models for pattern recognition requires processing power, memory, computational resources, and specialized hardware. Thus, increasing infrastructure cost and energy consumption.

Solution: Using a cloud-based platform for AI workloads can address the computational challenge. Implementing optimization techniques such as pruning and quantization reduces resource wastage.

Data privacy concerns

Challenge: Pattern recognition system uses sensitive and personal data such as voice recordings or facial images to analyze behavior patterns, raising data privacy and security issues.

Solution: Adopting a data anonymization technique and implementing privacy-protecting machine learning methods like federated learning ensures data privacy and compliance. Moreover, implementing strong encryption, access controls, and regular audits can enhance data privacy.

Data quality

Challenge: Pattern recognition works well when the data provided is accurate and high-quality. If the data is incomplete, biased, or unrepresentative, it can result in poor accuracy and skewed predictions.

Solution: Ensure the quality of data is good and representative by implementing data cleaning, processing, and validation techniques. Conducting regular audits and using synthetic and open-source data can enhance the quality of data.

Model transparency

Challenge: Deep neural networks operate as “black boxes,” making the decision-making process complex and raising ethical concerns.

Solution: Implementing techniques that make model behaviour more interpretable without compromising on the quality, enabling transparency and adherence to regulatory compliance.

Fraud detection

Financial organizations use pattern recognition to find anomalies in transactions that identify suspicious behavior. This technique is used in industries such as finance, banking, and e-commerce. For instance, a sudden high volume of transactions from a particular location can be identified by a pattern recognition system and trigger an alert of suspicious activity.

Medical diagnostics

This technique is applied in medical imaging to identify tumors and analyze medical scans such as X-rays, MRI, and CT scans. Clinical Decision Support Systems (CDSS) are useful in diagnosing patients based on their visual symptoms, and Computer-Aided Detection Systems (CAD) use algorithms to assist doctors when predicting medical images.

Voice recognition

Pattern recognition is used to identify patterns in speech. This system is commonly applied in car navigation systems and voice search engines to understand and respond effectively to customer requests. Speech recognition techniques allow people with visual impairments to hear and send back the text.

Chatbot

Machine learning algorithms are used by chatbots to classify text based on the pattern of the sentence and produce effective results. AI model identifies all possible patterns to respond to user input. Chatbots use machine learning and natural language processing systems to interpret user intents and generate appropriate responses. It uses pattern recognition to analyze customer queries, helps patients in preliminary symptom analysis, and screens resumes by recognizing patterns in applicant qualifications and experience.

Stock Market Prediction

Deep learning models are used in predicting future stock values. Traders use stock price charts and chat patterns to identify future price movements and take data-driven decisions. For instance, a trader uses charting software to observe a head and shoulders pattern, characterized by a peak (head) between two lower peaks (shoulders), as a potential bearish reversal signal.

Emotion recognition systems

Machine learning in pattern recognition is used to identify human emotions by analyzing the facial expressions, voice, text, and physiological signals. The goal is to indicate the mood, opinion, and intent of an audience or customers. Customer support services use Speech Emotion Recognition (SER) to identify customer emotion and provide agents with real-time feedback for faster conflict resolution, reduced churn, and improved service quality.

Visual image search

Pattern recognition is used in visual image search, allowing AI models to process and interpret visual information to find relevant images based on visual features. E-commerce platforms use pattern recognition in visual search to allow customers to select products by uploading images, making product discovery easier and more intuitive.

Text pattern recognition

Machine learning is used to generate, analyze, and translate text, and then patterns are used to create text messages. Techniques like Optical Character Recognition are used to text recognition of words to classify documents and detect sensitive text passages automatically. For instance, retailers can analyze customer feedback to proactively address issues, prioritize product improvements based on customer demand, and enhance overall customer experience.

Autonomous vehicles and navigation

Self-driving car uses pattern recognition in visuals and sensor data to identify objects, road signs, and lane boundaries. This proactive analysis allows vehicles to make real-time decisions for safe navigation. For example, Tesla uses computer vision and sensors to navigate roads, maintain proper lanes while driving, and avoid obstacles, ensuring safer and more efficient driving without human intervention.