MLP TALAN

MLP TALAN capabilities

• Classification

  Forecasting and Classification

  Forecasting

  Forecasting using machine learning allows you to predict future values ​​based on historical data. For example, models can be configured to predict stock prices, product demand, or user behavior. Using regression algorithms, neural networks and other methods, it is possible to obtain highly accurate forecasts that help businesses plan their strategies and make informed decisions.

  The main advantages of forecasting include:

  • Increasing the accuracy of business planning.
  • Optimization of resources and costs.
  • Reduction of risks associated with unpredictable changes.

  Classification

  Classification is the process of dividing data into specific categories or groups. This is especially useful in tasks where you need to determine which class a new data sample belongs to. Examples include:

  • Fraud detection systems.
  • Sorting email into spam and non-spam.
  • Medical diagnoses based on symptoms and medical history.

  Classification models use various algorithms such as decision trees, nearest neighbor methods, and deep neural networks to provide accurate recognition and classification of data.

  The main advantages of classification include:

  • Process automation and human error reduction.
  • Increasing efficiency in various industries such as healthcare, finance and marketing.
  • Ability to quickly process large volumes of data.

  Use in various industries

  Machine learning prediction and classification are used in many industries:

  • Finances: Forecasting of stock prices, credit scoring.
  • Health care: Disease diagnosis, forecasting epidemics.
  • Marketing: Consumer behavior analysis, advertising targeting.
  • Logistics: Route optimization, demand forecasting for transport services.
• Recognition

  Pattern Recognition

  Facial recognition

  Face recognition is one of the most common applications of machine learning in the field of computer vision. Using deep neural networks such as Convolutional Neural Networks (CNN), systems can learn to identify and verify individuals based on images or videos.

  The main advantages of face recognition include:

  • Safety: Use in access and security control systems.
  • Convenience: Simplifying authentication processes for users.
  • Personalization: Adaptation of content and services based on identified users.

  Speech recognition

  Speech recognition allows systems to convert spoken language into text, which is the basis for voice assistants, automated support systems and more. Using recurrent neural networks (RNN) and transformers, such systems can accurately interpret and process natural language.

  The main advantages of speech recognition include:

  • Accessibility: Improving the accessibility of technology for people with disabilities.
  • Automation: Simplifying interaction with equipment and programs.
  • Analytics: The ability to analyze phone conversations and other audio recordings to improve the service.

  Text recognition

  Text recognition includes recognition of handwritten or printed text on images (OCR), text analysis for key phrases and topics (NLP), and much more. This allows for the automation of processes that previously required manual data entry and processing.

  Key benefits of text recognition include:

  • Fastness: Significant acceleration of document processing.
  • Accuracy: Reducing the number of errors when entering data.
  • Analytics: Advanced analysis of textual information for decision making.

  Use in various industries

  Pattern recognition is used in many fields:

  • Safety: Video surveillance systems and access control.
  • Health care: Analysis of medical images and audio recordings for diagnosis.
  • Finances: Analyzing Text Data for Fraud Detection.
  • Entertainment: Creating interactive games and applications with gesture and face recognition.
• Clustering

  Clustering

  Grouping or clustering is the process of automatically assigning objects to groups or clusters based on their characteristics. Objects in one cluster are highly similar to each other and significantly different from objects in other clusters. Clustering allows you to simplify complex data sets, making them more understandable and convenient for analysis.

  Methods of Grouping

  Існує кілька основних методів групування, які широко використовуються:

  • K-means method: Divides the data into K clusters, where each object belongs to the cluster with the closest mean value. This method is fast and effective for large datasets.
  • Hierarchical grouping: Creates a cluster tree (dendrogram) that shows how objects are grouped into groups at different levels. It can be agglomerative (bottom-up) or divisive (top-down).
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Detects clusters of any shape based on point density, which is especially useful for complex noisy datasets.
  • Gaussian Mixture Model (GMM): Represents the data as a mixture of several Gaussian distributions, allowing the creation of clusters with elliptical shapes.

  Benefits of Grouping

  • Detection of Hidden Structures: Helps find natural groupings in data, which can be useful for marketing research, customer segmentation, bioinformatics, etc.
  • Refutation of data: Transforms the complexity of large data sets, making them more accessible for further analysis and visualization.
  • Improving Efficiency: Enables automation of processes that previously required manual intervention, such as object classification in large databases.

  Use in various industries

  Grouping is used in many industries, such as:

  • Marketing: Customer segmentation for the development of targeted advertising campaigns.
  • Health care: Analysis of medical images and data to identify groups of patients with similar characteristics.
  • Bioinformatics: Discovery of genes with similar expression in large genomic data.
  • Finances: Detection of transaction anomalies to prevent fraud.
• Recommendations

  Recommendations

  Recommender systems use various machine learning algorithms to analyze large amounts of data about user behavior, preferences, and interaction history. The main approaches include:

  • Collaborative Filtering: Analyzes the behavior of a large number of users in order to find similar patterns. Based on this, the system recommends items to the user that other users with similar preferences liked. There are two primary types:
    • User-based: Recommendations based on similarity between users.
    • Item-based: Recommendations based on similarities between items (products, movies, etc.).
  • Content Filtering: Uses item attributes (eg, movie genres, product characteristics) to build a user profile and make recommendations based on the similarity of those attributes to items already liked.
  • Hybrid Models: Combines several approaches to achieve greater accuracy and reliability. For example, you can combine collaborative and content filtering to get better results.

  Advantages of Recommendation Systems

  • Personalization: Provide users with recommendations tailored to their individual preferences, improving the user experience.
  • Increase Interaction: Increase the time users spend on the platform by offering relevant content.
  • Sales increase: Help businesses increase sales by offering products or services that users may be interested in.
  • Marketing effectiveness: Improve the effectiveness of marketing campaigns by targeting specific groups of users based on their preferences.

  Use in various industries

  Recommender systems are used in many fields, such as:

  • Electronic Commerce: Product recommendation based on the user's purchase history and views.
  • Streaming Services: Suggestions for movies, TV shows or music based on user preferences.
  • Social networks: Recommend friends, groups, or content to increase user interaction and engagement.
  • News Platforms: Personalization of news content so that users receive news and articles relevant to them.
  • Online Education: Recommend courses or educational materials that match the user's interests and level of training.
• Anomalies

  Detection of Anomalies

  Anomaly detection, also known as anomaly detection, is the identification of elements in data that do not conform to expected patterns or behavior. Such items may indicate errors, fraud, system malfunctions, or other issues that require attention.

  Methods of Detecting Nonconformities

  There are several inconsistency detection methods that are widely used in machine learning:

  • Statistical Methods: Use statistical models to determine deviations from the normal distribution of data. For example, methods of Z-scores or hypothesis testing.
  • Machine Learning: Uses algorithms to train models based on historical data. The main approaches include:
    • Methods based on clustering: Use algorithms such as K-means or DBSCAN to detect mismatches that do not belong to any cluster.
    • Methods based on classification: Training models for anomaly detection using algorithms such as Forest Isolation or Support Vector (SVM).
    • Deep learning: Using neural networks to detect complex patterns in large data sets. Autoencoders and recurrent neural networks (RNN) are popular methods.
  • Data Based Rules: Using expert knowledge to define rules and thresholds to detect anomalies.

  Benefits of Discrepancy Detection

  • Increasing Security: Detection of potential threats and fraud, allowing timely action.
  • Improvement of Data Quality: Identifying and correcting errors in data to ensure its accuracy and reliability.
  • Optimization of System Operation: Identification of malfunctions and anomalies in the operation of systems for their quick correction.
  • Support for Decision-Making: Providing accurate and relevant data to make informed decisions.

  Use in various industries

  Discrepancy detection is used in many industries, such as:

  • Finances: Detection of fraudulent transactions and anomalies in financial transactions.
  • Health care: Detection of deviations in medical data that may indicate diagnostic errors or incorrect treatment.
  • Cyber ​​security: Identifying anomalous activities in network traffic or system logs that may indicate cyber-attacks.
  • The industry: Monitoring the state of the equipment and detecting anomalies that may indicate potential breakdowns or malfunctions.
  • Internet of Things (IoT): Detecting anomalous behavior in data from sensors and devices to ensure their reliable operation.
• Automation

  Automation of Tasks

  Automation through machine learning involves using algorithms to perform routine tasks without human intervention. Machine learning enables systems to learn from data, recognize patterns, and make predictions that enable automation of processes in various areas of activity.

  Automation examples

  • Data Processing: Automatic collection, cleaning and analysis of large volumes of data. For example, systems can automatically extract information from unstructured data such as text or images.
  • Marketing: Automating advertising campaigns and targeting by analyzing user behavior to deliver personalized offers.
  • Customer Support: Using chatbots to answer typical customer queries, which reduces the workload on support staff.
  • Financial services: Automation of transaction processing, risk management and fraud detection processes.
  • Manufacture: Use of predictive maintenance to predict and prevent equipment breakdowns, automation of product quality control.

  Benefits of Automation

  • Productivity increase: Automation enables tasks to be completed faster and with fewer resources, increasing overall productivity.
  • Cost reduction: Automation of routine tasks reduces the need for manual labor, which reduces operational costs.
  • Accuracy Improvement: Automated systems are less prone to human error, which increases the accuracy and reliability of processes.
  • Ability to Focus on Strategic Tasks: Release from routine tasks allows employees to focus on more important and creative aspects of work.
  • Flexibility and Adaptability: Machine learning systems can quickly adapt to changes in data or processes, providing flexibility in response to new challenges.

  Use in various industries

  • Electronic Commerce: Automation of recommender systems, inventory management and order processing.
  • Health care: Automation of medical image analysis, electronic medical record keeping and prescription management.
  • Logistics and Transportation: Automation of vehicle routing, inventory management and demand forecasting.
  • Banking Sector: Automation of credit application processing, transaction monitoring and asset management.
  • Education: Automation of evaluation of student works, provision of personalized educational recommendations and management of educational processes.
• Solutions

  Solutions and Implementation

  Models work

  Artificial intelligence-based image recognition models can greatly improve manufacturing processes by visually scanning and detecting potentially defective products. These models use deep learning algorithms to analyze product images in real time, providing high accuracy and speed of defect detection.

  Learning Models

  The process of training image recognition models begins with uploading and labeling images of products at various stages of production. Images are labeled as “optimal” (no defects) or “defective” (with various types of defects). This process involves several steps:

  • Collection of Images: Фотографування продуктів на всіх етапах виробництва.
  • Image Marking: Marking defective and optimal products either manually or using automated systems.
  • Model training: Using labeled images to train an image recognition model, where the model learns to recognize quality characteristics and product defects.

  Detection of Defects

  Once trained, the model can detect product defects with high accuracy. This is achieved by uploading images of both optimal and defective products. Basic steps include:

  • Analysis of Images: The model analyzes product images looking for known defects.
  • Classification: The model classifies products as “optimal” or “defective” based on the identified characteristics.
  • Signaling: If the model detects a defect, the system can signal it, allowing a quick response to the problem.

  Implementation in Production

  Integrating image recognition models into the manufacturing process can significantly improve production efficiency. Key aspects of implementation include:

  • Special Integration: Implementation of models in the production line using cameras and sensors for continuous scanning of products.
  • Annotations to Images: Using annotations to improve the accuracy of models that can adapt to new types of defects.
  • Automatic Removal: Systems can automatically remove defective products from the production line, preventing low-quality products from reaching the end consumer.
  • Monitoring and Adaptation: Constantly monitor the performance of models and adapt them to changes in the production process or new types of defects.

  The use of AI-based image recognition models in manufacturing allows not only to improve the quality of products, but also to reduce the costs of inspection and elimination of defects.

• Drones

  Solutions for drones.

  Autonomous control

  MLP TALAN provides the ability to create autonomous drones that can be controlled independently without human intervention. Using deep learning algorithms, the platform can teach drones to perform the following tasks:

  • Route planning: Drones can automatically plan optimal routes to reach a given target, taking into account current conditions such as weather, terrain and obstacles.
  • Obstacles Avoidance: Machine learning models allow drones to identify and avoid obstacles in their path in real-time, improving flight safety and efficiency.

  Object Recognition

  MLP TALAN can be trained to recognize and classify various objects based on the analysis of images and videos coming from drone cameras. This allows you to perform different tasks:

  • Monitoring and Intelligence: Drones can be used to monitor areas, detect and identify objects such as buildings, vehicles or people.
  • Rescue Operations: In emergency situations, drones can quickly scan large areas to identify victims and provide information to emergency services.

  Forecasting Weather Conditions

  MLP TALAN is able to predict weather conditions, which is an important factor for planning and executing drone flights:

  • Route Optimization: Forecasting weather conditions helps avoid dangerous areas, such as areas with strong wind or precipitation, ensuring safe and efficient flights.
  • Accident Prevention: Using forecasts to alert operators of potential weather hazards that may affect drone flight.

  Condition Control

  MLP TALAN allows for real-time monitoring of the technical condition of drones, which helps prevent breakdowns:

  • Equipment Diagnostics: The system can analyze data from the drone's sensors to detect anomalies that may indicate potential malfunctions.
  • Maintenance Planning: Automatic determination of the need for maintenance based on the analysis of the condition of the equipment, which allows to reduce the risks of unexpected breakdowns.

  Data Analysis

  MLP TALAN provides powerful capabilities for collecting and analyzing data obtained from drones:

  • Agricultural monitoring: Analyze field images to detect crop condition, identify plant pests or diseases, allowing farmers to take timely action to improve yields.
  • Infrastructure Inspection: Using drones to inspect infrastructure such as bridges, power lines and oil pipelines to detect damage and carry out necessary repairs.

  Advantages of using MLP TALAN in Drones

  • High Accuracy: Machine learning algorithms ensure high accuracy of object detection and event prediction.
  • Efficiency and Speed: Automation of processes allows you to significantly speed up the execution of tasks and increase their efficiency.
  • Cost reduction: The use of drones reduces the need for manual labor and reduces the cost of surveying and monitoring large areas.
  • Safety: Autonomous management and risk prediction increase the overall security of operations.

  MLP TALAN is a universal platform that opens up new opportunities for the use of drones in various fields of activity, providing reliability, efficiency and an innovative approach to the performance of complex tasks.

  More details
• Military affairs

  Military solutions

  MLP TALAN can significantly improve the functionality and efficiency of drones in various scenarios:

  Autonomous control

  Using machine learning models for autonomous drone control, including route planning and obstacle avoidance. With TALAN's MLP, drones can make autonomous decisions about the best route, adapting to changing conditions in real time.

  Object Recognition

  Image and video analysis to recognize objects such as buildings, vehicles or people, enabling the use of drones for monitoring, reconnaissance and rescue operations. This ensures accuracy and speed of object detection in conditions where it is critical.

  Forecasting Weather Conditions

  Forecasting weather conditions to optimize flight routes and prevent emergency situations. MLP TALAN can predict weather changes, which allows you to avoid danger zones and ensure safe and efficient flights.

  Condition Control

  Monitoring the technical condition of drones in real time in order to detect malfunctions and prevent breakdowns. This allows you to keep drones in working condition, reducing the risk of accidents and increasing the reliability of operations.

  Data Analysis

  Data collection and analysis to identify trends and optimize operational processes, such as product delivery or agricultural monitoring. MLP TALAN can analyze large volumes of data coming from drones to improve efficiency and accuracy in various applications.

  Use of MLP TALAN in Military Affairs

  MLP TALAN offers a wide range of possibilities for use in the military sphere:

  Intelligence and Surveillance

  Automatic intelligence analysis to identify and classify potential threats, monitoring situations in real time. This helps you detect and respond to threats quickly and accurately.

  Combat Operations Management

  Prediction of the results of combat operations and optimization of military operations based on the analysis of large volumes of data. MLP TALAN helps commanders make informed decisions that improve operational outcomes.

  Cyber ​​security

  Detection of anomalies and threats in cyberspace, ensuring the protection of military information systems. MLP TALAN can monitor and analyze cyberspace to detect threats and prevent attacks.

  Support for Decision-Making

  Providing analytical support to commanders in making decisions based on real data and forecasts. This ensures more efficient management of resources and strategy.

  Modeling and Simulations

  Using models to simulate various scenarios and train military units. MLP TALAN allows you to create realistic simulations to prepare the military for various situations.

  Following and Tracking Combat Objectives

  MLP TALAN can be used to detect, locate and track combat targets. This includes:

  • Targets Identifying: Automatic detection and identification of enemy objects such as tanks, aircraft or infantry using image and video analysis.
  • Target Tracking: Continuous real-time tracking of target movements, allowing military units to effectively plan and execute attacks.
  • Fire Support: Providing precise coordinates and characteristics of targets for artillery and air strikes, increasing the accuracy and effectiveness of fire.

  Advantages of using MLP TALAN

  • Accuracy and Reliability: High accuracy of predictions and decisions due to advanced machine learning algorithms.
  • Flexibility: Ability to adapt to different scenarios and tasks without the need for reprogramming.
  • Fastness: Fast processing of large volumes of data in real time, which is especially important in military and operational applications.
  • Efficiency: Optimization of resources and reduction of costs thanks to the automation of routine processes.
  • Demonstration video (click on the link to download) – MLP Talan – object detection for drones.

  For more information, visit www.orbdrones.com

  More details
• Licensing

  MLP TALAN Licensing Policy

  MLP TALAN license - 3,000 euro*.

  Teaching

  • 4.50 euros* per hour of training or pre-processing
  • 15% discount for projects over 100 hours.

  It is possible to buy as a subscription MLP TALAN.

  Subscription: The client pays for the opportunity to use the software product for each month or for a specified period of time.

  Please note that these prices may vary according to the terms and conditions and the license agreement between the end user and Talan Systems.

  Other licensing options with additional services are also possible.

  *Does not include value-added tax and other national taxes.