In the process of transforming data into knowledge, our AI assist you.
In Bioinformatics, Artificial Intelligence plays a significant role. Through machine-learning, we discover unknown matters. Recently, persistent technological advancements of big data and deep-learning elevate the possibility.
AI technology unveils and discovers meanings and values of complicated data not only from multi-omics but also from documents, videos, and networks.
Insilicogen’s AI technology handles character data, sequential data, video imaging, and unstructured big data to discover hidden knowledge and implements techniques such as structuring, interconnecting, machine-learning, feature selection, and extraction to support integrated understanding.
Artificial Intelligence Recommender System
Beyond the existing collaborative filtering and content-based filtering, we provide a solution that customizes products in the company based on the latest recommendation system that combines existing machine learning and deep learning technique. We suggest optimal recommendation results to achieve the target indicator based on product meta-information, customer meta-information, and target indicator information. The recommender system applies in various fields such as product recommendation as well as content preference.
- Wide & Deep Learning for Recommender System
- Deep FM
Image Detection, Segmentation, and Classification
We can apply the latest deep learning algorithms that detect objects from the image in real-time and divide the significant areas from the image by using segmentation technology. Additionally, we generalize the images in various ways through computer image preprocessing technology to apply the latest deep learning algorithms with the highest accuracy possible. For data labeling, we have a technology that provides an interface for image area labeling for automatic area recognition.
- Image Detection (YOLO v3)
- Image Classification
- Image Segementation (U-Net)
Through semantic modeling, we integrate the clients' complex data and open sources for continuous data accumulation and creation, and find insight.
Analysis of data structure and reduction of dimension
Data refinement, transformation and preprocessing
Feature selection and extraction
An optimized machine-learning model Development and assessment
Extract feature map using DNN and create a deep-learning model by applying architecture
Biological data related text sequence, Images, Videos, natural language processing for predictable categorization and assessing accuracy
Visualize web-based analysis results
Provide data result of real-time dynamic programming, which is available online
Web application and mobile app development
Development of artificial intelligence and analysis algorithms at mobile edge