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Artificial Intelligence Research
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от экспертов «ДЮК Технологии»
1. Identified clusters of key topics related to Covid-19 in the context of business management
2. Identified key business issues caused by the pandemic
3.  Identified possible areas for further study
An analysis of Covid-19-related articles in the field of business and management in the scientific citation electronic journal Scopus
Research based
on topic modeling methods
14 sources
Topic modeling scheme
A topic model in machine learning and natural language processing is a type of statistical model for identifying abstract "topics" that occur in a collection of documents.
each document consists of several topics
each topic consists of a set of keywords (terms)
All topic models are based on the same basic assumption:
Схема тематического моделирования
Examples of research objectives based on topic modeling methods
EXTRACTION OF RELATED TOPICS FROM TEXTS
Topic modeling allows for the automatic identification of hidden themes raised in analyzed texts and the extraction of keywords that characterize each theme
RECOMMENDATIONS AND INFORMATION SEARCH
creation of a recommendation system and search engine optimization
Text filtering and classification
Automatic filtering and classification of text data based on specific topics. For example, when processing large volumes of text: news, media, etc.
CLUSTERING OF DOCUMENTS
grouping documents by their content based on identified themes to identify similar documents and search for patterns in the data
An overview of research and development in topic modeling
Learn about industry applications and development directions of topic modeling
Обзор исследований и разработок в области тематического моделирования
1. We selected the optimal open neural network for bag detection.
2. Prepared data for further model training
3. Retrained the model
Developing an AI-based model that can analyze video from shopping mall surveillance cameras online and identify bags and other suspicious items left behind by customers.
ML model for detecting abandoned bags in shopping malls
YOLO v8 YOLO
4. Tested on real video data
ML model diagram based on YOLO v8 YOLO
YOLO v8 YOLO (You Only Look Once) is a neural network designed for object detection in images and videos. Instead of the traditional approach of running an input image through a convolutional network multiple times to extract features and then detect objects,  YOLO performs all these steps simultaneously in a single network.
Input image
divided into N*N parts
Containing frameworks + confidence
Class Probability Map
Final recognitions
Data
Trainig
Model
Prediction
1.  We used a single-class Tiny-YOLO model to detect each person in the frame.
2. Used AlphaPose to obtain the skeletal pose
3. We applied the ST-GCN model to predict actions based on every 30 frames of human tracks.
Video camera monitoring to ensure patient safety and optimize clinic operations
ML model for detecting patient falls in hospitals
Tiny-YOLO
4. We created a system that not only ensures patient safety, but also helps optimize clinic operations, prevent potential incidents, and improve overall efficiency.
ML model diagram based on Tiny YOLO
Tiny YOLO (You Only Look Once) is a version of the YOLO model for object detection in images. Tiny YOLO allows for faster image processing without significantly compromising the quality of object detection.
AlphaPose is an open-source system for detecting and analyzing human poses in video with high speed and accuracy.
ST-GCN is a spatiotemporal graph convolutional network. The ST-GCN model is capable of recognizing seven human actions: standing, walking, sitting, lying down, standing up, sitting down, and falling down.
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