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AN ALGORITHM FOR EXTRACTING INFORMATICS FEATURES FROM
DATA USING ARTIFICIAL INTELLIGENCE METHODS
Begmatov Komiljon Uktam uglu,
Qarshi State Technical University,
Student of the Department of Telecommunication Technologies
Annotation. The article reviews algorithms for extracting informatics features
from data using artificial intelligence methods. Extracting informatics features is the
process of identifying important and useful information from a data set. This process
is carried out, in particular, using natural language processing (NLP), machine
learning (ML), and deep learning (DL) technologies. The article covers in detail the
stages of data collection, preprocessing, feature extraction, and selection, as well as
algorithms for extracting informatics features using machine learning and deep
learning methods (e.g., SVM, KNN, CNN, RNN).
Keywords: Artificial intelligence, data, computer science, algorithm, natural
language processing (NLP), machine learning (ML), deep learning (DL), technology,
machine learning, deep learning methods, SVM, KNN, CNN, RNN, text, image and
audio, format.
Аннотация. Аннотация. В статье рассматриваются алгоритмы
извлечения признаков информатики из данных с использованием методов
искусственного интеллекта. Извлечение признаков информатики — это
процесс выявления важной и полезной информации из набора данных. Этот
процесс осуществляется, в частности, с использованием технологий обработки
естественного языка (NLP), машинного обучения (ML) и глубокого обучения
(DL). В статье подробно рассматриваются этапы сбора данных,
предварительной обработки, извлечения и отбора признаков, а также
алгоритмы извлечения признаков информатики с использованием методов
машинного обучения и глубокого обучения (например, SVM, KNN, CNN, RNN).
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Ключевые слова: Искусственный интеллект, данные, информатика,
алгоритм, обработка естественного языка (NLP), машинное обучение (ML),
глубокое обучение (DL), технология, машинное обучение, методы глубокого
обучения, SVM, KNN, CNN, RNN, текст, изображение и аудио, формат.
Algorithms are used to identify important features from data in various formats,
such as text, images, and audio, and are of great importance for effective data analysis
and decision-making. The article provides a detailed understanding of the practical
application of informatics features extraction technologies using artificial intelligence
and their role in data analysis.
In data analysis, extracting important information from data sets or texts is one
of the main tasks of artificial intelligence (AI) technologies. Extracting information
from data sets involves identifying important and useful information. This process is
carried out, in particular, using natural language processing (NLP), machine learning
(ML), and deep learning methods.
Basic Steps in Extracting Information from Data. The basic steps in extracting
information from data are as follows.
Data Collection and Aggregation. The first step in extracting information from
data is to collect data. Data can be in different formats: text, images, video, or audio.
Specific algorithms and AI methods are used for each format. For example, NLP is
used for text data, convolutional neural networks (CNN) for images, and recurrent
neural networks (RNN) for audio data.
Data Preprocessing. Before data extraction, the data must be cleaned,
normalized, and encoded. This process includes the following.
Text cleaning. Cleaning texts from stop words, special characters, and non-
standard words.
Lexical normalization: Lemmatization or stemming of words.
Tokenization. Dividing text into small pieces (tokens).
Feature Selection and Extraction.
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In feature selection, the main and computationally important parts of the data
are separated using a specific algorithm.
TF-IDF (Term Frequency-Inverse Document Frequency): Used to identify
important words for textual data. This method takes into account how often a word
occurs in a document and how widespread it is in the entire data set.
Word Embeddings (Word2Vec, GloVe): Represents words using vectors,
taking into account the semantic meaning of words, which provides understanding of
words in relation to each other.
Feature Selection. When selecting features, not all features are equally
important. Some features may be more important than others in identifying
information. The following methods are used in feature selection.
Mutual Information: Measures the relationship between features.
PCA (Principal Component Analysis): Combines similar features and reduces
the dimensionality of the data.
Algorithms and Methods. Machine Learning (ML) Algorithms. Machine
learning algorithms are used to extract informational features from data. The following
methods are widely used.
KNN (K-Nearest Neighbors): Used to group objects with similar features.
SVM (Support Vector Machine): Effective in separating categories and
selecting features.
Random Forest. Determines the relationship between features through many
decision trees.
Deep Learning Methods. Deep learning can extract informational features from
complex data. Convolutional neural networks (CNN) and recurrent neural networks
(RNN) are especially effective in analyzing text, image, and audio data.
CNN (Convolutional Neural Networks). Used to extract features from images
and video data. In each layer, important elements of the image are extracted.
RNN (Recurrent Neural Networks). Used to extract features from sequential
data, such as text or time series data. Advanced models such as LSTM (Long Short-
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Term Memory) or GRU (Gated Recurrent Unit) help extract informatics features by
remembering past states.
Emerging Feature Analysis. The extracted features are then analyzed and based
on them, model outputs or decisions are made. Machine learning or deep learning
models are often used in feature analysis. Classification, regression, and other
forecasting methods are performed using the analyzed features.
Practical Applications. The practical applications of informatics feature
extraction are very wide. Here are some examples. Text data analysis: Data
classification, sentiment analysis, extracting important information.
Image analysis. Object detection, face recognition, image classification.
Medical data. Identify diseases by extracting important features from medical
images and laboratory results.
Audio data. Speech recognition, music analysis, voice command recognition.
Data mining algorithms using artificial intelligence techniques are used to
identify important and useful features from various types of data. Machine learning and
deep learning algorithms play an important role in automating and making this process
efficient. Proper analysis and extraction of data provides greater accuracy and
efficiency.
REFERECEN:
1.
Daminova B. E., Bozorova I. J., Jumayeva N. X. FORMATION OF TEXT
DATA PROCESSING SKILLS //Экономика и социум. – 2024. – №. 4-2 (119). – С.
110-119.
2.
Daminova B. E. et al. USE OF ONLINE ELECTRONIC DICTIONARIES IN
ENGLISH LANGUAGE LESSONS //Экономика и социум. – 2024. – №. 5-1 (120).
– С. 193-196.
3.
Daminova B. E. et al. ADVANTAGES OF USING MULTIMEDIA
RESOURCES IN ENGLISH LANGUAGE LESSONS //Экономика и социум. –
2024. – №. 5-1 (120). – С. 207-210.
4.
Daminova B. E. et al. SCIENTIFIC AND METHODOLOGICAL SUPPORT
OF EDUCATIONAL INFORMATION INTERACTION IN THE EDUCATIONAL
MODERN EDUCATION AND DEVELOPMENT
Выпуск журнала №-26
Часть–6_ Май –2025
85
PROCESS BASED ON INTERACTIVE ELECTRONIC EDUCATIONAL
RESOURCES: USING THE EXAMPLE OF TEACHING ENGLISH //Экономика и
социум. – 2024. – №. 5-1 (120). – С. 233-236.
5.
Daminova B. E. et al. THE ROLE AND FEATURES OF THE USE OF
INFORMATION TECHNOLOGY IN TEACHING A FOREIGN LANGUAGE
//Экономика и социум. – 2024. – №. 5-1 (120). – С. 184-188.
6.
Daminova B. E. et al. USING THE GOOGLE CLASSROOM WEB SERVICE
AND PREPARING INTERACTIVE PRESENTATIONS //Экономика и социум. –
2024. – №. 5-1 (120). – С. 216-225.
7.
Daminova B. E., Bozorova I. J., Jumayeva N. X. CREATION OF
ELECTRONIC LEARNING MATERIALS USING MICROSOFT WORD
PROGRAM //Экономика и социум. – 2024. – №. 4-2 (119). – С. 104-109. 1. – S.
1169-1172.
8.
Daminova B. E. et al. APPLICATION OF MODERN INFORMATION AND
COMMUNICATION TECHNOLOGIES IN TEACHING ENGLISH //Экономика и
социум. – 2024. – №. 5-1 (120). – С. 197-201.
9.
Daminova B. E. et al. SOFTWARE TOOLS FOR CREATING MULTIMEDIA
RESOURCES IN TEACHING ENGLISH //Экономика и социум. – 2024. – №. 5-1
(120). – С. 202-206.
10.
Daminova B. E. et al. THE MAIN ADVANTAGES, PROBLEMS AND
DISADVANTAGES OF USING MULTIMEDIA IN TEACHING FOREIGN
LANGUAGES //Экономика и социум. – 2024. – №. 5-1 (120). – С. 189-192.
11.
Даминова Б. Э. и др. ОБРАБОТКА ВИДEОМАТEРИАЛОВ ПРИ
РАЗРАБОТКE ОБРАЗОВАТEЛЬНЫХ РEСУРСОВ //Экономика и социум. –
2024. – №. 2-2 (117). – С. 435-443.
12.
Daminova B. E. GAUSS AND ITERATION METHODS FOR SOLVING A
SYSTEM OF LINEAR ALGEBRAIC EQUATIONS //Экономика и социум. – 2024.
– №. 2 (117)-1. – С. 235-239.
13.
Daminova B. E., Oripova M. O. METHODS OF USING MODERN
METHODS BY TEACHERS OF MATHEMATICS AND INFORMATION
MODERN EDUCATION AND DEVELOPMENT
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Часть–6_ Май –2025
86
TECHNOLOGIES IN THE CLASSROOM //Экономика и социум. – 2024. – №. 2
(117)-1. – С. 256-261.
14.
Daminova B. E. et al. USE OF ELECTRONIC EDUCATIONAL
RESOURCES IN THE PROCESS OF TEACHING A FOREIGN LANGUAGE
//Экономика и социум. – 2024. – №. 5-1 (120). – С. 230-232.
15.
Daminova B. E. et al. USING COMPUTER PRESENTATIONS IN
TEACHING FOREIGN LANGUAGES //Экономика и социум. – 2024. – №. 5-1
(120). – С. 211-215.
16.
Daminova B. E. et al. USING DIGITAL TECHNOLOGIES IN FOREIGN
LANGUAGE LESSONS //Экономика и социум. – 2024. – №. 5-1 (120). – С. 226-
229.
