MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI.,
“AXBOROT TIZIMLARI VA TEXNOLOGIYALARI” KAFEDRASI.,
SUN’IY INTELLEKT., s.b.ergashev@gmail.com
S.B. Ergashev
MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI
SUN’IY INTELLEKT
“AXBOROT TIZIMLARI VA TEXNOLOGIYALARI” KAFEDRASI.
SUN’IY INTELLEKT FANI
S.B. Ergashev
s.b.ergashev@gmail.com
MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI “AXBOROT TIZIMLARI VA TEXNOLOGIYALARI” KAFEDRASI. SUN’IY INTELLEKT
S.B. Ergashev
MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI.,
“AXBOROT TIZIMLARI VA TEXNOLOGIYALARI” KAFEDRASI., SUN’IY INTELLEKT.
s.b.ergashev@gmail.com
S.B. Ergashev
MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI
SUN’IY INTELLEKT
“AXBOROT TIZIMLARI VA TEXNOLOGIYALARI” KAFEDRASI.
SUN’IY INTELLEKT FANI
S.B. Ergashev
s.b.ergashev@gmail.com
MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI.
“AXBOROT TIZIMLARI VA TEXNOLOGIYALARI” KAFEDRASI.
SUN’IY INTELLEKT. S.B. Ergashev
s.b.ergashev@gmail.com
MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI
SUN’IY INTELLEKT
“AXBOROT TIZIMLARI VA TEXNOLOGIYALARI” KAFEDRASI.
SUN’IY INTELLEKT FANI
S.B. Ergashev
s.b.ergashev@gmail.com
MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI “AXBOROT TIZIMLARI VA TEXNOLOGIYALARI” KAFEDRASI. SUN’IY INTELLEKT
S.B. Ergashev
MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI.,
“AXBOROT TIZIMLARI VA TEXNOLOGIYALARI” KAFEDRASI., SUN’IY INTELLEKT.
s.b.ergashev@gmail.com
S.B. Ergashev
MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI
SUN’IY INTELLEKT
“AXBOROT TIZIMLARI VA TEXNOLOGIYALARI” KAFEDRASI.
SUN’IY INTELLEKT FANI
S.B. Ergashev
s.b.ergashev@gmail.com
MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI.
“AXBOROT TIZIMLARI VA TEXNOLOGIYALARI” KAFEDRASI.
SUN’IY INTELLEKT. S.B. Ergashev
s.b.ergashev@gmail.com
This document summarizes a student project using a neural network for character recognition. The project aims to develop software that can recognize English characters by processing input characters, training a neural network algorithm, and modifying the characters. The methodology involves 4 phases - pre-processing the image, segmenting the image into individual characters, extracting features, and performing classification and recognition using an artificial neural network. The literature review summarizes several papers on using neural networks for handwritten character recognition in various languages.
Artificial neural networks (ANNs) are processing systems inspired by biological neural networks. They consist of interconnected processing elements that dynamically change their outputs based on external inputs. While much simpler than actual brains, some ANNs have accurately modeled systems like the retina. ANNs are initially trained on large datasets to learn input-output relationships, then make predictions on new inputs. They are nonlinear, adaptable systems suited for parallel processing tasks.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
An artificial neural network (ANN) is a computational model inspired by the human brain that can learn from large amounts of data to detect patterns and relationships. ANNs are formed from hundreds of artificial neurons connected by coefficients that are organized in layers. The power of ANNs comes from connecting neurons, with each neuron consisting of a weighted input, transfer function, and single output. ANNs learn by adjusting the weights between neurons to minimize error and reach a specified level of accuracy when trained on data. Once trained, ANNs can be used to make predictions on new input data.
Introduction to Graph neural networks @ Vienna Deep Learning meetupLiad Magen
Graphs are useful data structures that can be used to model various sorts of data: from molecular protein structures to social networks, pandemic spreading models, and visually rich content such as websites & invoices. In the recent few years, graph neural networks have done a huge leap forward. It is a powerful tool that every data scientist should know. In this talk, we will review their basic structure, show some example usages, and explore the existing (python) tools.
This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work.
Below topics are explained in this neural network presentation:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
Learn more at: https://www.simplilearn.com
[MICCAI 2021 - Poster] MT-UDA: Towards unsupervised cross-modality medical im...Ziyuan Zhao
The document proposes a new framework called MT-UDA for unsupervised cross-modality medical image segmentation using limited labeled source data. MT-UDA leverages unlabeled data from both the source and target domains using two teacher models to transfer semantic and structural knowledge to a student model. Experimental results on a cardiac image dataset show MT-UDA improves segmentation performance on the target domain compared to using only limited labeled source data. Ablation studies demonstrate the importance of transferring both semantic and structural knowledge for label-efficient unsupervised domain adaptation.
Neural networks are computing systems inspired by biological neural networks in the brain. They are composed of interconnected artificial neurons that process information using a connectionist approach. Neural networks can be used for applications like pattern recognition, classification, prediction, and filtering. They have the ability to learn from and recognize patterns in data, allowing them to perform complex tasks. Some examples of neural network applications discussed include face recognition, handwritten digit recognition, fingerprint recognition, medical diagnosis, and more.
План конспект уроку на тему «Від теорем і аксіом до ознак паралельності прямих»Максим Павленко
Мета уроку: познайомити учнів з методами міркувань та доведення;
- виробити вміння формулювати і доводити ознаки паралельності прямих;
- реалізовувати ідею співробітництва на уроці;
- сприяти розвитку колективної праці, активізуючи взаємодію між дітьми;
- сприяти адаптації теоретичних знань учнів до соціальної практики.
Методи:
словесні: розповідь, обговорення в групах, коментар до виконання завдань, пояснення, стратегії формування та розвитку творчої компетентності;
наочні: робота з електронним підручником;
практичні: робота в групах
Засоби навчання: НІТ, ПК, мультимедійна установка, наочний матеріал.
Тип уроку: засвоєння нових знань.
This document summarizes a student project using a neural network for character recognition. The project aims to develop software that can recognize English characters by processing input characters, training a neural network algorithm, and modifying the characters. The methodology involves 4 phases - pre-processing the image, segmenting the image into individual characters, extracting features, and performing classification and recognition using an artificial neural network. The literature review summarizes several papers on using neural networks for handwritten character recognition in various languages.
Artificial neural networks (ANNs) are processing systems inspired by biological neural networks. They consist of interconnected processing elements that dynamically change their outputs based on external inputs. While much simpler than actual brains, some ANNs have accurately modeled systems like the retina. ANNs are initially trained on large datasets to learn input-output relationships, then make predictions on new inputs. They are nonlinear, adaptable systems suited for parallel processing tasks.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
An artificial neural network (ANN) is a computational model inspired by the human brain that can learn from large amounts of data to detect patterns and relationships. ANNs are formed from hundreds of artificial neurons connected by coefficients that are organized in layers. The power of ANNs comes from connecting neurons, with each neuron consisting of a weighted input, transfer function, and single output. ANNs learn by adjusting the weights between neurons to minimize error and reach a specified level of accuracy when trained on data. Once trained, ANNs can be used to make predictions on new input data.
Introduction to Graph neural networks @ Vienna Deep Learning meetupLiad Magen
Graphs are useful data structures that can be used to model various sorts of data: from molecular protein structures to social networks, pandemic spreading models, and visually rich content such as websites & invoices. In the recent few years, graph neural networks have done a huge leap forward. It is a powerful tool that every data scientist should know. In this talk, we will review their basic structure, show some example usages, and explore the existing (python) tools.
This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work.
Below topics are explained in this neural network presentation:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
Learn more at: https://www.simplilearn.com
[MICCAI 2021 - Poster] MT-UDA: Towards unsupervised cross-modality medical im...Ziyuan Zhao
The document proposes a new framework called MT-UDA for unsupervised cross-modality medical image segmentation using limited labeled source data. MT-UDA leverages unlabeled data from both the source and target domains using two teacher models to transfer semantic and structural knowledge to a student model. Experimental results on a cardiac image dataset show MT-UDA improves segmentation performance on the target domain compared to using only limited labeled source data. Ablation studies demonstrate the importance of transferring both semantic and structural knowledge for label-efficient unsupervised domain adaptation.
Neural networks are computing systems inspired by biological neural networks in the brain. They are composed of interconnected artificial neurons that process information using a connectionist approach. Neural networks can be used for applications like pattern recognition, classification, prediction, and filtering. They have the ability to learn from and recognize patterns in data, allowing them to perform complex tasks. Some examples of neural network applications discussed include face recognition, handwritten digit recognition, fingerprint recognition, medical diagnosis, and more.
План конспект уроку на тему «Від теорем і аксіом до ознак паралельності прямих»Максим Павленко
Мета уроку: познайомити учнів з методами міркувань та доведення;
- виробити вміння формулювати і доводити ознаки паралельності прямих;
- реалізовувати ідею співробітництва на уроці;
- сприяти розвитку колективної праці, активізуючи взаємодію між дітьми;
- сприяти адаптації теоретичних знань учнів до соціальної практики.
Методи:
словесні: розповідь, обговорення в групах, коментар до виконання завдань, пояснення, стратегії формування та розвитку творчої компетентності;
наочні: робота з електронним підручником;
практичні: робота в групах
Засоби навчання: НІТ, ПК, мультимедійна установка, наочний матеріал.
Тип уроку: засвоєння нових знань.
MIRZO ULUGʻBEK NOMIDAGI OʻZBEKISTON MILLIY UNIVERSITETINING JIZZAX FILIALI. Kafedra Axborot tizimlari va texnologiyalari.
AXBOROT TIZIMLARINI LOYIHALASH.
S.B. Ergashev
More from MIRZO ULUG‘BEK NOMIDAGI O‘ZBEKISTON MILLIY UNIVERSITETI JIZZAX FILIALI (11)
3. БАНК
WOODGROVE 3
NEYRON TARMOQ
Neyron tarmoq - bu inson
miyasining ishlash usulini taqlid
qiluvchi jarayon orqali ma'lumotlar
to'plamidagi asosiy munosabatlarni
aniqlashga intiladigan bir qator
algoritmlardir. Shu ma'noda neyron
tarmoqlar organik yoki sun'iy
tabiatga ega bo'lgan neyronlar
tizimlariga ishora qiladi.. Odam miyasining soddalashtirilgan modeli.
Kirishlari chiqishda yaxshi natijalarga aylantiradi.
MPS
O’ZMUJF
4. БАНК
WOODGROVE
SUN'IY INTELLEKT BILAN ISHLAYDIGAN ENG
MASHHUR KOMPANIYALAR
Google Tesla Netflix Amazon Facebook Apple
Googlening sun’iy intellektdan foydalanishi uning sun’iy neyron tarmoqlari inson miyasi ma’lumotlarni qayta ishlash
usulini taqlid qiladigan Deep Learning֥ga qiziqishi bilan bog‘liq. Shuningdek tabiiy tilni qayta ishlashab ovozli va matn
kiritish imkonini beruvchi Google Assistant orqali sun’iy intellektdan mukammal foydalanadi. Bu ovozli buyruqlar, ovozli
qidiruv, ovozli qurilmani boshqarishdan real vaqtda tarjima qilishgacha bo'lgan turli xizmatlarni osonlashtiradi.
Facebook foydalanuvchilarning qiziqishlariga tarif berish va bashorat qilish uchun machine learning
foydalanadi. Foydalanuvchining qiziqishlarini, yoqtirgan do'stlarini va ma'lumotlarini joylashuvini baholash orqali
Facebook o'z foydalanuvchisi Facebook Watch xususiyatlaridan bahramand bo'lishida, his qiladigan ma'lumotlar
kontentlarni tanlash uchun foydalanadi. Ushbu bashorat qilish qobiliyatlari kelajakdagi boshqa xatti-harakatlarni
bashorat qilish uchun ham ishlatilishi mumkin, masalan foydalanuvchi sotib olishi mumkin bo'lgan mahsulotlar! Bu esa
o'z navbatida reklama beruvchilarga ajoyib imkoniyatlar beriladi.
4
MPS
O’ZMUJF
5. БАНК
WOODGROVE
SUN'IY INTELLEKT BILAN ISHLAYDIGAN ENG
MASHHUR KOMPANIYALAR
Suniy intllektdan Tesla kompaniyasi asosan ikkita yo'nalishga foydalandi: Elektr quvvati va avtonom
haydash. Kompaniya tomonidan ishlab chiqarilgan sun'iy intellekt chiplari orqali avtomobillar nafaqat
avtomagistrallar, balki mahalliy ko'chalar va svetoforlar orqali ham harakatlana olishini ta'minlashni maqsad
qilgan.
Tesla tizim yaxshi ishlashini nazorat qilish uchun avtomobillarga ikkitadan SI chiplari bilan ta’minladi. Har bir SI
chiplar avtomobilni mos ravishda harakatlanishi uchun yo'l harakati holatini alohida baholaydi. Keyin ikkala
chipning bahosi tizim tomonidan moslashtiriladi va agar ikkalasidan kiritilgan ma'lumotlar bir xil bo'lsa, unga
amal qilinadi.
Netflix shou va filmlarini oldindan ishlab chiqarish jarayonida sun'iy intellektdan foydalanadi , masalan, suratga
olish uchun eng zo'r joyni aniqlash uchun bo'lajak aktyorlar va ularning joylashuvini skanerlash orqali.
SI Amazon ovozli boshqariladigan virtual yordamchisi Alexa bo'lib, foydalanuvchi so'rovlarini boshqarish va
mahsulotlarga buyurtma berish yoki aqlli uy qurilmalarini boshqarish kabi amallarni bajarish uchun machine
learningdan foydalanadi.
Apple kompaniyasi machine learningdan foydalandi. Bundan tashqari Apple Machine learningni orgatuvchi
asbolar to’plami, ovozli boshqaruv va qulay interfeys kabi funksiyalari SI qo‘llanilgan.
5
MPS
O’ZMUJF
6. БАНК
WOODGROVE
MPS
O’ZMUJF
BIOLOGIK HUJAYRA VA NEYRON
6
Kirish dendritlar tomonidan qabul qilinadi va
neyronning asosiy tanasiga (yadro) uzatiladi,
kirish signali akson tomon uzatiladi. Akson
terminallar tomon uzatish liniyasidir. Akson
terminallari keyingi neyronning dendritiga
ulangan.
Tirik organizmlarda miya neyron tarmog'ining
boshqaruv birligi bo'lib, u ko'rish, his qilish,
harakat va eshitish vazifasini bajaruvchi turli
bo'linmalarga javob beradi.
Miyada taxminan 10¹¹ neyron mavjud bo'lib, ular
organizmning butun markaziy asab tizimining
bloklaridir.
7. БАНК
WOODGROVE
BIO NEYRONING SIGNALALMASHINISH JARAYONI
7
MPS
O’ZMUJF
Neyronlar odatda vazifalariga qarab uch turga bo'linadi.
Sensor neyronlar sensorli organlarning hujayralariga ta'sir qiluvchi teginish, tovush yoki
yorug'lik kabi ogohlantirishlarga javob beradi va ular orqa miya yoki miyaga signal
yuboradi.
Dvigatel neyronlari mushaklarning qisqarishidan tortib bez chiqishigacha bo'lgan hamma
narsani nazorat qilish uchun miya va orqa miya signallarini oladi.
Interneyronlar neyronlarni miya yoki orqa miyaning bir mintaqasidagi boshqa neyronlar
bilan bog'laydi. Bir nechta neyronlar bir-biriga bog'langanda, ular neyron zanjiri deb
ataladi.
8. БАНК
WOODGROVE
SUN’IY NEYRON TUZILISHI
8
Z
X1
X2
X3
1
chiqish
kirish
W1
W2
W3
b
synapses
Kirish - bu o'quv jarayoni uchun modelga
kiritilgan xususiyatlar to'plami.
Sinaptik og'irlik ikki tugun o'rtasidagi
bog'lanishning kuchi yoki amplitudasini anglatadi,
bu biologiyada bir neyronning boshqasiga ta'sir
qilish hajmiga mos keladi.
O'tkazish funktsiyasi - O'tkazish funktsiyasining
vazifasi faollashtirish funktsiyasini qo'llash uchun
bir nechta kirishlarni bitta chiqish qiymatiga
birlashtirishdir.
Chiqish tugunlarning vaznli yig'indisidir.
MPS
O’ZMUJF
a
𝐙 = 𝐗𝟏𝐖𝟏 + 𝐗𝟐𝐖𝟐 + 𝐗𝟑𝐖𝟑 + 𝐛
𝐚 = 𝐟(𝐙)
x qiymatlari kirishlarga, ya'ni asl xususiyatlar yoki oldingi yashirin qatlamdagi kirishlarga tegishli.
Har bir qatlamda, shuningdek, ma'lumotlarni yaxshiroq moslashtirishga yordam beradigan egilish b mavjud.
Neyron a qiymatini keyingi qatlamda ulangan barcha neyronlarga uzatadi yoki uni yakuniy qiymat sifatida qaytaradi.
9. БАНК
WOODGROVE
TO’LIQ BOG’LAMLI NEYRON TARMOQ
9
To'liq bog'langan neyron tarmoq bir qatlamdagi har bir
neyronni boshqa qatlamdagi har bir neyron bilan
bog'laydigan bir qator to'liq bog'langan qatlamlardan iborat.
W1
W2
W3
Y
X
Kirish
qatlami
Yashirin
qatlam 1
Yashirin
qatlam 2
Chiqish
qatlami
W1
W2 W3
Y
X
Kirish
qatlami
Yashirin
qatlam 1
Yashirin
qatlam 2
Chiqish
qatlami
MPS
O’ZMUJF
10. БАНК
WOODGROVE
QAYTAFIKRLASH (MULOHAZA)
10
kirish Chiqish
qayta fikir (mulohaza)
qayta fikir (mulohaza)
Raqobat yoki
cheklanish
Agar elementlar chiqishining qayta
aloqasi bir xil qatlamdagi ishlov
berish elementlariga kirish sifatida
qaytarilsa, u yondan lekadigan
qayta ishlashnuvchi teskari aloqa
deb ataladi.
MPS
O’ZMUJF
11. БАНК
WOODGROVE
O'Z FIKRIGA EGAYAGONATUGUN
(SINGLE NODE WITH ITS OWN FEEDBACK)
11
chiqish
kirish
fikr-mulohaza
MPS
O’ZMUJF
Chiqishlarni bir xil qatlamga yoki oldingi qatlam tugunlariga kirish sifatida qaytarish mumkin bo'lsa, bu
qayta aloqa tarmoqlariga olib keladi. Takroriy tarmoqlar yopiq halqalarga ega bo'lgan qayta aloqa
tarmoqlaridir. Rasmda o'ziga teskari aloqaga ega bo‘lgan bitta neyronning takrorlanuvchi tarmog‘i
ko'rsatilgan.
12. БАНК
WOODGROVE
TAKRORIY NEYRON TARMOQLARI (RNN)
12
Recurrent Neural Network (RNN -
Takrorlanuvchi Neyron Tarmoq) ikki
tomonlama bo‘ladi ya’ni ma'lumotlar
faollashtirilgandan so'ng neyronlarga qaytib
kelishi mumkin.
RNNlar nutq tarjimalari, signallarni qayta
ishlash, vosita boshqaruvi, tabiiy til interfeysi,
matnni bashorat qilish zarur bo'lgan ilovalarda
qo'llaniladi.
MPS
O’ZMUJF