Success Stories

Cortechma teams’ proud to work on projects in variety of industries using cutting-edge Artificial Intelligence technologies and analytical solutions. Company has an extended portfolio of completed projects from concept to completion and deployment.

We completed the following end to end sample projects in different sectors and industries:

Financial & Banking Industry:

• Credit evaluation and risk management using state-of-the-art machine learning, deep learning, and statistical techniques.

Achievements:  Developed and deployed a set of algorithms for credit cards applicants’ evaluation and bank accounts monitoring & risk management with high performance and accuracy.

Technologies: Deep Neural Networks, Long-Short-Term-Memory Neural Networks (LSTM), Xgboost, SVM, Naïve Bayesian, Random Forest, AWS, Sagemaker.

• Development of novel machine learning and deep learning methodologies and algorithms for stock market analysis and prediction.

Achievements:  Prediction of major stock indices and companies’ stock such as Google, Microsoft, and Amazon that achieved significantly higher accuracy in comparison with the existing works.

Technologies: Time Series Forecasting, LSTM, Genetic Algorithms, Tensorflow, Keras, R, Matlab, Hadoop & Spark.

• Developed and implemented a novel intelligent software platform for algorithmic stock exchange and trading, and portfolio management.

Achievements:  A set of AI-based algorithms are developed and deployed to deliver the optimum personalized investment portfolio for highest profit and lowest risks. Algorithmic trading in real-time is another part of this project that is released successfully. A user-friendly dashboard is also designed and prepared for this project.

Technologies: Machine Learning & Deep Learning algorithms, Power BI.

• Development of software powered by AI for public sentiment analysis in financial market analysis and prediction.

Achievements:  Sentiment analysis is performed successfully using data resources such as social media (Twitter). The results are used as additional input features to predictive models for financial market analysis and prediction.

Technologies: Natural Language Processing, NLTK.

Manufacturing & High speed Assembly Industries:

• Development of all cycles of machine learning projects included data collection and preparation, build, test, validation, staging and final deployment of multiple predictive models based on the leading-edge algorithms for manufacturing systems monitoring and prediction.

Achievements: Promotion of the machinery yields and overall production efficiency in contact lens assembly and production lines.

Technologies: Python and its machine learning libraries (Scikit-learn, Scipy, Pandas, Numpy, Matplotlib), deep learning libraries (Tensorflow, Keras) using Pycharm and Jupyter Notebooks. (Time Series Forecasting methods, ML models, LSTM, Azure Clouds ML Studio, SQL database)

• Building and release of machine learning and deep learning software solutions for production rate prediction, automatic root-cause analysis, and predictive maintenance planning in manufacturing plants.

Achievements: We achieved a high accuracy level for production part per minute (PPM) rate prediction in 30, 60 and 120 minutes time interval ahead. These estimations are used beside the results of anomaly detection algorithms for designing automatic root-cause analysis routines, and ultimately predictive maintenance planning.

Technologies: Clustering, classifications, time series analysis and prediction, anomaly detection using algorithms such as, KMeans, PCA, SVM, Deep ANN, Decision trees and Random Forest (CART), KNN, CNN and LSTM NN, Azure Clouds, and Databricks Platform.

Bio Science, Healthcare and Medical Industries:

• Covid-19 like symptoms detection using deep learning predictive models.

Achievements: Trained models can classify subjects to “Infected by Covid” and “Healthy” categories with high accuracy using subjects’ recent biological data.

Technologies: Convolutional Neural Networks (CNN), LSTM, Google Cloud platform (GCP).

• Infection symptoms analysis and prediction using machine learning algorithms.

Achievements: Developed algorithms can recognize specific patterns due to the   Infection symptoms.

Technologies: Machine Learning Supervised & Un-Supervised algorithms, Google Cloud platform (GCP).

• Health monitoring and alert system implementation by machine learning technologies.

Achievements: Historical and real-time biological data collected from sensors attached to the subject’s body are used to extract specific patterns and detect anomalies that can trigger alerts for preventive actions.

Technologies: Machine Learning and Deep Learning algorithms, Google Cloud platform (GCP).

Heavy Industries:

• Developed AI / ML / DL algorithms in steel hot and cold rolling mills industry for:
• Intelligent and industrial Process Control
• Adaptive Graphical User Interface System

Achievements: Improvement of performance, flexibility, and accuracy of production.

• Developed and implemented intelligent predictive models for failure mode detection and predictive maintenance in steel rolling mill industry.

Achievements:  Considerably increase in production efficiency by reduction of machinery downtimes.

• Designing of rolling mill digital twin included process simulation and its industrial control systems.

Achievements: Analysis & prediction of production output and process optimization.

Technologies: ML, Deep Learning, Matlab and Simulink, Adaptive Controls.