Recognition analysis engine
RECOGNITION ANALYSIS ENGINE
Recognition analysis engine CAS
From a data analysis point of view, ‘cognition’ means to search for the features of formal rules and objects to describe them. Saltlux’s Cognitive Engine is the world’s best cognitive engine based on machine learning and deep learning incorporating artificial intelligence technology that allows computers, like humans, to learn to perceive or predict various perspectives on data.
By analyzing large and external big data with machine learning and deep learning, the cognitive engine can quickly find characteristics, meanings, and associations among data that humans have a hard time finding or analyzing. Furthermore, it provides complex system analysis of ultra-capacity data, fusion analysis between voice and text, and fusion analysis between image and text.
< Recognition analysis engine -Cognitive Engine block diagram >
< Big data collection engine concept map >
Machine learning and deep learning can be used to analyze semantic networks in data such as individual name recognition analysis, emotional recognition analysis, knowledge / social network analysis, speech recognition fusion analysis, and image recognition fusion analysis, etc.
Object name recognition analysis feature
Machine learning-based object name recognition analysis feature automatically extracts (boundary distinctions) objects (company name, person name, area name, date, time, amount) from the data, classifies them, and automatically analyzes the relationship between the objects in real time.
< Object name recognition analysis feature (real building screen – Korea Press Foundation) >
Emotional recognition analysis feature
It is a high-quality, high-precision sentiment analysis feature in which machine learning and deep learning are used to identify morphemes, sentence structures, object names, and meanings, which leads to sentiment analysis by topic and positive/negative trends analysis.
< Emotional recognition analysis feature >
Knowledge/Social Network Analysis feature
It extracts semantic social networks and analyzes its structure from the data held by the massive web, social media, email, and large companies. It uses machine learning to analyze the knowledge and mutual influence flowing through the network as follows: centrality analysis, cluster analysis, shortest path analysis, key player analysis, core node analysis, thematic related node analysis, related core node analysis, etc. This function enables the real-time analysis of the data circulating on the knowledge network.
< Knowledge/social network analysis feature – Statistical analysis & central tendency analysis >
Voice Recognition & Text Integration Analysis Feature
It receives the real-time voice data of the user, converts it into text, and performs analysis in connection with the non-structured big data analysis function. And the test data possessed by the user and the data converted into text using speech recognition are fused and analyzed.
< Text analysis through real-time speech recognition – Issue analysis >