Solution

Supporting company growth with cutting-edge AI technology based on academic research

Since our founding in 2009, CHOWA GIKEN Corporation(CGC) has been developing and utilizing general-purpose AI engines
in cooperation with research institutions. By customizing these engines to meet our clients' needs, we are able to build specialized AI solutions that achieve outstanding results. At CHOWA GIKEN Corporation(CGC), we provide our knowledge of
AI and our AI engines as a service to support the continuous growth and transformation of your company.

Linguistic engines
Lango

Tect classification engine
This engine classifies the sentences into appropriate categories based on their content. It extracts features based on N-Gram、Word2Vec、Doc2Vec、Deep learning, etc. and classifies sentences properly.
Emotional Analysis Engine
The engine analyzes the emotions such as positive, negative, nutoral, etc.
Sentence summary engine
The engine understands the key points of a sentence and generates summary sentences. Evaluates the importance of words based on morphological analysis, syntactic analysis and LexRank, and produces summarized sentences.
Feature word extraction engine

The engine extracts feature words which represent a sentence with using TFIDF and AIC ratings of word importance.
Conversation-generating engine

The engine is an interactive response engine that understands the content of the conversation and responds appropriately. It learns the conversation data using RNN and responds to the content of the conversation.

Cases studies of Lango

Image Engine
Visee

Object recognition engine
The engine detects the position of various objects in an image and recognizes the types(classed) of objects such as objects and faces, etc. By combining the YOLO algorithm, R-CNN, SVM and other classifiers, it recognizes objects at high speed.
Image Classifiction Engine
The engine categorizes images into appropriate categories based on characteristics such as object, face, etc. It uses CNNs based on the latest research, such as ResNet and Inception, to provide highly accurate analysis with no degradation in performance over layers.

Case studies of Visee

Numeric Engine
Furas

Prediction Engine
The engine predicts the future, such as sales and visitor numbers, based on past data. It performs feature extraction and investigation of prior distributions of data to fit client's data, and predicts the data using Bayesian estimation and random forests.
Recommendation Engine
The engine identifies hobbies and thoughts based on past user behavior and recommends events, coupon information, etc. It makes recommendations using memory-based methods that learn feature vectors of each user through collaborative filtering, and model-based methods that build predictive models using algorithms such as Bayesian networks.
Optimization Engine
The engine searches for the best combination and creates efficient shift proposals and other tasks in spite of constraints. It creates efficient shifts and schedules through optimization algorithms, including meta-heuristics such as genetic algorithms.
Anomaly Detection Engine
The engine detects anomalies based on past accumulated data and notifies the user. Density ratio estimation and one-class SVM are used to detect values that are out of the data distribution.