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Our AI & Data Science Success Stories
Delve into our case studies showcasing how we've harnessed the transformative power of AI and Data Science in solving real-world challenges.
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Introduction to AI and Data Science
Artificial Intelligence
Artificial Intelligence (AI) is a field of computer science focused on creating machines capable of performing tasks that typically require human intelligence. AI systems are designed to process data, learn from experiences, and adapt to new inputs, enabling them to perform complex tasks autonomously or assist humans in various activities.
Data Science
Data Science is a multidisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from data, both structured and unstructured. It involves various techniques like statistical analysis, machine learning, and data visualization to understand and solve complex problems, making informed decisions based on data.
Our Computer Vision Projects
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![DarkNet]()
DarkNet
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![mediapipe]()
mediapipe
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![Google
vision Api]()
google vision api
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![nvidia cuda]()
nvidia cuda
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![FFmpeg]()
ffmpeg
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![Pytorch]()
pytorch
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![tensorflow]()
tensorflow
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![opencv]()
opencv
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![amazon
rekognition]()
amazon rekognition
Automatic human body measurement
Detection of cars on overhead images
Online exercises quality evaluation
Football player performance evaluation
Car presence detection and discrimination
Realtime playing card recognition
Fabric Defects Detection
Palm recognition
Automatic human body measurement
Project challenge
The objective of this project was to develop a method for accurately measuring the dimensions of the human body. This precision is crucial for creating realistic and personalized 3D avatars, applicable in diverse fields.
Our approach
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Integration of Advanced Pose Estimation
We utilized OpenPose, a cutting-edge pose estimation technology, to accurately capture and map the human body's posture and movements.
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Segmentation of Body Parts
Employing BodyPix, we segmented the body into distinct parts. This segmentation was vital for isolating and precisely analyzing each section of the body.
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Application of Curve Fitting Techniques
To model the body's dimensions accurately, we implemented sophisticated curve fitting methods. This step was critical in ensuring the measurements closely mirrored the individual's actual body shape.
Detection of cars on overhead images
Project challenge
The primary aim of this project was to calculate the density of cars in a chosen region. This analysis was crucial for understanding traffic patterns, aiding urban planning, and developing smart city initiatives.
Our approach
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Implementation of Object Detection
We utilized YOLO (You Only Look Once), an advanced object detection system, to accurately identify and count cars within various urban images.
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Processing Map Tiles
Using the Google API, we processed map tiles to obtain detailed geographical data of the chosen region. This step was essential for accurately pinpointing the locations where car density needed to be calculated.
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Map Visualization
To effectively present our findings, we integrated Leaflet.js for map visualization. This allowed us to display the car density data on interactive maps, making the information easily accessible and understandable.
Online exercises quality evaluation
Project challenge
The main aim of this project was to develop a web application for home workouts that evaluates exercises in real-time. The application should allow users to select an example exercise and then attempt to replicate it, providing immediate feedback on their performance.
Our approach
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Integration of Pose Estimation
We implemented pose estimation using MediaPipe POSE to accurately track and analyze the user's movements during exercises.
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Applying Time Series Comparison
To assess the accuracy of the user's exercise performance, we utilized the Dynamic Time Warping (DTW) algorithm.
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Enabling Real-Time Video Stream Processing
The application was developed to process video streams in real time, ensuring that users receive immediate and relevant feedback as they perform the exercises.
Football player performance evaluation
Project challenge
The primary goal of this project was to develop a sophisticated system capable of quantifying athletic performance with high accuracy. This involved tracking and analyzing the movements of football players during games to provide valuable insights into their performance.
Our approach
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CNN-Based Field Marking Recognition
We utilized Convolutional Neural Networks (CNN) to identify key points and lines on the football field. This technology allowed us to establish a reference framework for accurately tracking the players' movements.
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Custom Camera Calibration
Fully automatic camera calibration algorithm for the entire video that uses keypoint and line data for all frames of the video. This allows accurate camera calibration even on frames with a high zoom, where few keypoints and few lines are visible.
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Object Detection and Tracking
Our system employed advanced object detection and tracking algorithms. These were instrumental in monitoring the athletes' positions and movements throughout the game.
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Pose Estimation
We implemented a lightweight pose estimation model. This model was designed to recognize the projection point of an athlete's pelvis onto the field, a key metric in assessing movement efficiency and agility.
Results
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Accurate Performance Metrics
The system reliably calculated essential metrics like speed and acceleration, offering coaches and players valuable insights for training and strategy development.
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Efficient Resource Usage
Despite its complexity, the pose estimation model was optimized to require less than 200 MB of GPU memory and function effectively at low resolutions, ensuring its applicability in various environments without the need for high-end hardware.
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Enhanced Training and Strategy
The insights provided by our system have been instrumental in enhancing training regimes and in-game strategies, directly contributing to improved athletic performance.
Car presence detection and discrimination
Project challenge
Our primary challenge was to develop a validation module capable of reducing false positive violations generated by in-field sensors in parking violation detection systems.
Our approach
1 Designing the Validation Module
We created a specialized module focused on accurately identifying and validating parking violations, distinguishing between true and false positives.
2 Technology Integration
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Multilabel Image Classification: This technique was used to analyze visible car surfaces, a key factor in identifying parking violations accurately.
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Siamese CNN for Car Matching: We employed Siamese Convolutional Neural Networks to match cars effectively, ensuring precise vehicle identification throughout the monitoring process.
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Motion Detection: Incorporating motion detection technology allowed us to track vehicle movements, an essential component in determining parking violations
Results
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Enhanced Accuracy
The implementation of our solution led to a significant reduction in false positive violations, thereby increasing the system's accuracy.
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Improved Reliability
The enhanced system reliability fostered greater trust and efficiency in parking violation enforcement.
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Operational Efficiency
Our solution streamlined the process of parking violation detection, making it more efficient and effective for users and enforcement agencies.
Realtime playing card recognition
Project challenge
The primary objective of this project was to develop a playing card recognition module. This module is a key component of a system designed for real-time analysis of casino games. Its purpose is to accurately identify and classify playing cards during live games, enhancing the monitoring and analysis capabilities within a casino environment.
Our approach
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Creation of a Synthetic Dataset
We developed a synthetic dataset specifically tailored for training deep learning models. This dataset was instrumental in training our object detection system to recognize a wide variety of playing card designs.
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Implementation of YOLOv4 Object Detector
We utilized the YOLOv4 object detection framework, known for its efficiency and accuracy in real-time object recognition tasks.
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Real-Time Video Stream Processing
We integrated our system with real-time video stream processing capabilities. This allowed the module to analyze and identify playing cards in live scenarios, a critical requirement for real-time game analysis in casinos.
Results
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High Recognition Accuracy
It can recognize cards with almost any design, demonstrating a high level of accuracy and adaptability.
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Versatile Detection Capabilities
The module is capable of detecting playing cards from any angle and in various environments, showcasing its effectiveness in diverse and dynamic casino settings.
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Performance Under Low Image Quality
Even with low image quality, the system can successfully detect and classify playing cards, ensuring reliability and consistency in challenging visual conditions.
Fabric Defects Detection
Project challenge
The goal of this project was to create a reliable, automated tool capable of identifying various defects in fabrics used in polymer composite material production. This tool aimed to enhance quality control, reduce waste, and improve the efficiency of the manufacturing process.
Our approach
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Dataset Preparation
We generated a synthetic dataset using advanced image processing techniques. This dataset was tailored to represent a wide range of fabric defects, providing a robust foundation for training our Machine learning model.
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Model Development and Training
We employed the U-net architecture, a powerful image semantic segmentation tool, within the TensorFlow framework. This approach allowed for precise segmentation and identification of defects in the fabric materials
Palm recognition
Project challenge
The project aimed to develop a module capable of validating user-submitted hand photos for an online palmistry service. The module's key objectives were to ensure that the hand is present in the image, the entire hand is visible, it is correctly oriented, and the lines on the hand are clearly discernible.
Our approach
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Photo Validation Criteria Establishment
We defined specific criteria for photo validation: presence of the hand, visibility of the entire hand, correct orientation, and clarity of hand lines.
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Technology Utilization
We employed TensorFlow to develop a heatmap-based points and lines recognition system. This technology was chosen for its precision in identifying and analyzing specific features in images, which is crucial for validating the key aspects of hand photos.
Our Data Science Projects
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![Jupyter]()
jupyter
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![scikit learn]()
scikit learn
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![scipy]()
scipy
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![pandas]()
pandas
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![postgis]()
postgis
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![Pytorch]()
pytorch
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![tensorflow]()
tensorflow
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![xgboost]()
xgboost
Demand prediction based on historical data
Structural health monitoring of carbon fiber aircraft structures
Realtime muscle fatigue estimation
Evaluation of human body hydration
Demand prediction based on historical data
Structural health monitoring of carbon fiber aircraft structures
Realtime muscle fatigue estimation
Evaluation of human body hydration
Demand prediction based on historical data
Project challenge
The goal of this project was to create a predictive model that could accurately forecast the demand for transportation services. The model aimed to consider various factors such as seasonality, national holidays, and weather conditions, enabling transport companies to plan the rational placement of vehicles and improve service efficiency.
Our approach
In tackling the challenge of accurately predicting transportation service demand, our approach was multi-faceted and data-driven. We focused on three key areas:
1 Data Collection and Analysis
Historical data on transportation service demand was meticulously analyzed, focusing on patterns influenced by seasonality, national holidays, and weather conditions.
2 Model Development
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An autoregression model was developed to capture the time-dependent nature of transportation demand.
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Random Forest regression, known for its effectiveness in handling complex, non-linear data patterns, was employed to enhance the model's accuracy.
3 Integration of Weather Data
A third-party API was utilized to integrate real-time weather data into the model, acknowledging the significant impact of weather conditions on transportation demand.
Structural health monitoring of carbon fiber aircraft structures
Project challenge
The goal of this project was to create a machine learning-based system that could process sensor data obtained from electrical sensors embedded in carbon-fiber airplane panels to detect low-velocity impact damage. The system was designed not only to recognize patterns indicating damage but also to estimate the damage's location and size. This innovation aimed to reduce maintenance costs and increase the safety of aircraft operations.
Our approach
To achieve our goal of developing a reliable damage detection system for aircraft, we focused on the following critical stages.
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System Development
A machine learning system was developed using TensorFlow. This system was designed to analyze sensor data and identify patterns corresponding to structural damage.
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Damage Estimation
The system was equipped with capabilities to accurately estimate the location and size of detected damage, providing essential information for effective maintenance.
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Synthetic Dataset Creation
A synthetic dataset, essential for training the machine learning model, was generated using Finite Element Method (FEM) simulation. This dataset ensured comprehensive coverage of potential damage scenarios.
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Full-Scale Testing
The system underwent full-scale testing to validate its effectiveness in a real-world setting.
Realtime muscle fatigue estimation
Project challenge
The project aimed to create a system that could instantly analyze EMG data to determine the degree of muscle fatigue during exercises. This tool was intended to aid research in sports medicine, using the Bitalino MuscleBIT EMG device for data collection.
Our approach
In developing a real-time muscle fatigue estimation tool, our approach encompassed the following steps:
1 Data Collection and Preprocessing:
- EMG data was recorded using the Bitalino MuscleBIT device and Open Signal application, capturing raw signals at 1000 hertz during maximum repetition exercises.
- Preprocessing included mean normalization and bandpass filtering, though the model proved insensitive to noise, rendering frequency filtering unnecessary.
2 Muscle Fatigue Detection Methodology:
- A convolutional neural network (CNN) was employed to process EMG data slices, focusing on slices with significant EMG amplitude.
- Ground truth for muscle fatigue was established based on the assumption that initial exercise repetitions indicated zero fatigue and final ones indicated maximum fatigue.
3 Model Development and Alternative Approach
- The CNN model consisted of layers designed for effective pattern recognition in EMG data.
- An alternative approach using EMG spectral analysis was also explored, assessing muscle fatigue through shifts in signal spectrum frequency.
Results
As a result, the binary accuracy of EMG slice classification for the validation data is 91 %, and for test data is 89 %. Compared to the traditional approach based on shift of the signal spectrum, the CNN-based approach gives smoother dependences on the degree of muscle fatigue over time and is better consistent with the subjects' feelings of muscle fatigue
Evaluation of human body hydration
Project challenge
The goal was to create a model using data from a biomedical wearable device to accurately predict the hydration level of individuals. This tool aimed to assist patients with kidney failure or ESRD in monitoring their condition and planning their dialysis sessions more effectively.
Our approach
In developing a model for assessing human body hydration levels, our approach was comprehensive, encompassing various stages. Here's a breakdown of each key stage:
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Data Collection
Data was collected from patients wearing the device during dialysis sessions and overnight, including device signals and ultrafiltration volume from the dialysis machine.
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Data Preprocessing
The preprocessing steps included data synchronization, temperature-based data selection, low-pass filtering, outlier detection using an Isolation Forest model, and signal smoothing.
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Feature Engineering
Based on skin reflection data at different wavelengths, 92 initial features were selected for analysis, informed by literature reviews and laboratory experiments.
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Model Development
Two regression models, Theil-Sen and multi-layer perceptron (MLP), were considered. Models were trained individually for each patient, as well as in general and gender-specific formats.
Results
We meticulously analyzed the outcomes to ensure the model met the high standards required for medical applications, especially for patients with critical needs. Here's a detailed overview of the key results:
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Model Performance
The MLP model outperformed the Theil-Sen regression, exhibiting a lower root mean squared error. Individual patient models yielded more accurate results compared to generalized models.
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Accuracy Metrics
The average R2-score across patients was 0.8, with accuracy metrics ranging from 0.1 to 0.15, indicating high precision as evaluated by the clinical team.
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Real-World Applicability
The model's predictions on overnight data, though not directly testable for accuracy, were deemed reasonable and consistent with clinical expectations.
![Evaluation of human body
hydration: Results](/images/ai-cases/data-science/ai-cases-body-hydration-results.png")
Other Artificial Intelligence Projects
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![scipy]()
scipy
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![c++]()
c++
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![sympy]()
sympy
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![google
or-tool]()
google or-tool
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![coin-or]()
coin-or
Logistics system for technical service
Custom camera calibration algorithm
Estimation of the machining time on a CNC machine
Logistics system for technical service
Custom camera calibration algorithm
Estimation of the machining time on a CNC machine
Logistics system for technical service
Project challenge
The project aimed to develop a sophisticated route optimization system for multiple vehicles. This system will consider specific time windows, adhere to constraints on the length of the working day, and determine the most efficient dates for scheduling work orders.
Our approach
In this project, we adopted a multifaceted approach, combining advanced algorithmic strategies with high-performance computing and data analytics to tackle the complex challenge of multi-vehicle route optimization. Here's a closer look at the key components of our approach and implementation:
Algorithm Development
A route optimization solution was created using a combination of the Simulated Annealing Algorithm and the Greedy Algorithm, implemented in C++ for computational efficiency.
Computational Strategy
The project leveraged multiprocessor computations to handle the intensive computational demands of route optimization.
Time Matrix Calculation
The Open Source Routing Machine (OSRM) was employed for the initial calculation of the time matrix, essential for route planning.
Traffic Data Integration
A regression model trained on historical traffic data was used to adjust the time matrix, accounting for traffic variations and enhancing route accuracy.
Custom camera calibration algorithm
Project challenge
The primary goal of this project was to create an advanced algorithm as part of a football analytics system to accurately determine the positions of athletes on the field. This tool was designed to consider key field markings, such as yard lines, to enhance the precision of player positioning data.
Our approach
Our approach combined advanced computational techniques with a deep understanding of American football dynamics. Here's a closer look at the key components of our approach and implementation.
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Algorithm Development
We developed an algorithm that leverages field keypoints and yard lines to determine player positions with high accuracy.
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Field Marking Recognition
We implemented a system to recognize and interpret field markings, including points and lines, crucial for accurate player positioning.
Technologies used
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Perspective projection with Pinhole camera model, using OpenCV
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Solution of the nonlinear optimization problem to determine parameters of a camera based on the result of field marking recognition (points and lines) using SciPy library
Custom Object tracking algorithm for yard lines
Estimation of the machining time on a CNC machine
Project challenge
The goal of this project was to decrease the time needed to estimate the production cost of each detail with CNC machines. Previously it took several days to do this. We decided to create an automated system that could instantly provide customers with estimates for machining mechanical parts on CNC machines. This system aimed to determine the type of equipment required, the size of the blank, the list of operations for machining the part, the number of tool changes, and the machining time for each operation.
Our approach
We focused on developing a comprehensive solution that integrates advanced technological capabilities with user-friendly access. Here's a detailed look at each aspect of our approach and implementation:
Our approach
We focused on developing a comprehensive solution that integrates advanced technological capabilities with user-friendly access. Here's a detailed look at each aspect of our approach and implementation:
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System Development
Developed an automated estimation system integrated into a web platform, allowing customers to upload part designs and receive immediate cost and time estimates.
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Feature Analysis
Implemented shape recognition and classification algorithms to analyze the 3D designs of mechanical parts and identify key machining features.
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Operational Calculations
Created a methodology to calculate the necessary operations, including the number of tool changes and the time required for each operation, based on the part's design.
