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Matnlarni intellektual taxlil qilish masalalari
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ЎЗБЕКИСТОН РЕСПУБЛИКАСИ
ОЛИЙ ВА ЎРТА МАХСУС ТАЪЛИМ ВАЗИРЛИГИ
ЎЗБЕКИСТОН РЕСПУБЛИКАСИ ФАНЛАР АКАДЕМИЯСИ
В.И.
РОМАНОВСКИЙ НОМИДАГИ МАТЕМАТИКА ИНСТИТУТИ
ЎЗБЕКИСТОН МИЛЛИЙ УНИВЕРСИТЕТИ
ТОШКЕНТ ДАВЛАТ ТРАНСПОРТ УНИВЕРСИТЕТИ
БУХОРО ДАВЛАТ УНИВЕРСИТЕТИ
Бухоро фарзанди, Беруний номидаги Давлат мукофоти лауреати, кўплаб
ёш изланувчиларнинг ўз йўлини топиб олишида раҳнамолик қилган етук
олим, физика
-
математика фанлари доктори Ғайбулла Назруллаевич
Салиховнинг 90 йиллик юбилейларига бағишланади
АМАЛИЙ МАТЕМАТИКА ВА
АХБОРОТ ТЕХНОЛОГИЯЛАРИНИНГ
ЗАМОНАВИЙ МУАММОЛАРИ
ХАЛҚАРО ИЛМИЙ
-
АМАЛИЙ АНЖУМАН
МАТЕРИАЛЛАРИ
2022
йил
, 11-12
май
БУХОРО –
2022
ТАШКИЛИЙ ҚЎМИТА
Фахрий раислар
:
Аюпов Шавкат
В.И.Романовский номидаги Математика Институти
директори, академик
Маджидов Иномжон
М.Улуғбек номидаги Ўзбекистон Миллий Университети
ректори
Абдурахманов Одил
Тошкент давлат транспорт университети ректори
Хамидов Обиджон
Бухоро давлат университети ректори
Раислар
:
Розиқов Ўткир
ЎзФА Математика Институти илм
-
фан бўйича директор
ўринбосари, профессор
Арипов Мирсаид
ЎзМУ, профессор
Шадиметов Холматвай
Тошкент
давлат транспорт университети
,
профессор
Дурдиев Дурдимурод
ЎзФА Математика Институти Бухоро бўлими
мудири, профессор
Раис ўринбосарлари:
Ҳаётов
Абдулло
В.И.Романовский номидаги Математика Институти,
профессор
Худойберганов Мирзоали
ЎзМУ, ф.
-
м.ф.д.
Эшанқулов Ҳамза
БухДУ, факультет декани, т.ф.ф.д. (PhD)
ТАШКИЛИЙ ҚЎМИТА АЪЗОЛАРИ
Жўраев А.Т.
БухДУ, проректор
Жумаев Р
.
Ғ
.
БухДУ, проректор
Зарипов Г.Т.
БухДУ, доцент
Жумаев Ж.
БухДУ, доцент
Расулов Т.Ҳ.
БухДУ, профессор
Жалолов О.И.
БухДУ, кафедра мудири, доцент
Шафиев Т.Р.
БухДУ, кафедра мудири, т.ф.ф.д.(PhD)
Бабаев С.С.
БухДУ, ф.
-
м.ф.ф.д.(PhD)
Ахмедов Д.М
В.И.Романовский номидаги Математика институти, (PhD)
Болтаев А.Қ
В.И.Романовский номидаги Математика институти, (PhD)
Дурдиев У.Д.
БухДУ, доцент
Дилмуродов Э.Б.
БухДУ, доцент
Жумаев Ж.Ж.
ЎзФА Математика Институти Бухоро бўлинмаси, (PhD)
Зарипова Г.К.
БухДУ, доцент
Сайидова Н.С.
БухДУ, доцент
Бакаев И.И.
Рақамли технологиялар ва сунъий интеллектни
ривожлантириш илмий
-
тадқиқот институти, (PhD)
Шадманов И.У.
Математика Институти Бухоро бўлинмаси, (PhD)
Хаятов Х.У.
БухДУ, катта ўқитувчи
Хазратов Ф.Х.
БухДУ, катта ўқитувчи
Эргашев А.А.
БухДУ, катта ўқитувчи
Авезов А.А
БухДУ, катта ўқитувчи
449
the control of the reliability of customs declarations is in the field of economic interests of any state, due to
the fact that customs payments, as a rule, make up a considerable part of the revenue side of the state budget.
For example, over the past five years, customs payments amounted to 13-18% of the total revenue part of
the state budget of the Republic of Uzbekistan [1].
Therefore, the task of investigating methods for identifying unreliable customs declarations is
relevant.
Information reliability control methods
The task of identifying unreliable customs declarations is a special case of the general and, as you
know, ancient task of identifying false information, i.e. how to distinguish "truth" from "falsehood".
Numerous scholars have been interested in this problem, starting with the ancient Greek philosopher
Aristotle (384 B.C.). Also famous are Leibniz's "sufficient reason" Law, Godel's Incompleteness Theorem,
Kolmogorov's Superposition Theorem, Satoshi Nakamoto's Blockchain System Technology, Xin Luna
Dong's "Knowledge-based Reliability" Theory, and others.
Not only scientists of natural or secular sciences were interested in this problem. This refers to the
scholars who were involved in determining the authenticity of hadith in Islam. The legendary Muhammad
ibn Ismoil al-Bukhari (IX century) is considered to be one of them. He is said to have devoted his entire
life to collecting and analyzing hadith, and he worked to develop methods that allowed him to identify
"authentic" hadith. Having analyzed over 600,000 hadiths, he chose only 7,275 (1.21%) for his book, Al-
Jami as-Sahih, which has been considered the most reliable book for over 11 centuries.
According to the conditions of Imam Bukhari, the criteria for the authenticity of hadith consist of
two groups of conditions:
- Conditions for the source of the hadith;
- conditions for the content of the hadith.
Based on the concept of Imam Bukhoriy, the authors of this paper have developed criteria for the
reliability of customs information, consisting of two groups and six conditions:
- conditions for the source of customs information;
- conditions for the content of customs information.
Conclusion.
In conclusion, we would like to note that the above-mentioned criteria for ensuring the reliability of
customs information are implemented in the automated information systems of the State Customs
Committee of the Republic of Uzbekistan and have shown their effectiveness. As a result of the functioning
of this system during 2020 revealed 201 thousand 611 unreliable cargo customs declarations and charged
to the state budget additional 36.7 billion UZS customs payments.
REFERENCES
1.
Saidov A.A.
Classical methods of controlling the reliability of information and peculiarities of their
application to the customs case // Monograph. -Tashkent. -2021.
–
498 p.
https://elibrary.ru/item.asp?id=47759425.
MATNLARNI INTELLEKTUAL TAXLIL QILISH MASALALARI
Samandarov B.S., To‘xtabaev U.A., Ispanova J.P.
Berdaq nomidagi Qoraqalpoq davlat universiteti,
O’zbekiston
Keyingi yillarda lingvistik tahlil va matnni qayta ishlash sohasida ko‘plagan tadqiqotlar olib borilib
[1], bunda statistik usullar yordamida mualli
fning uslubini o‘rganish yoki axborot tizimlarida yangiliklar
yoki blog materiallari reytingini o‘rganish, foydalanuvchi xatti
-harakatlarini modellashtirish tadqiq qilib
kelinmoqda [2]. Bugungi kunda Internet va boshqa manbalarda axborotlar keskin o‘sishda
davom etmoqda
va bu axborotlarning asosiy qismi matn ko‘rinishida shakllantirilmoqda. Ushbu katta xajmdagi
ma’lumotlardan foydalisini ajratib olish uchun maxsus usul va mexanizlar zaruriyati kelib chiqadi [3].
Matndan belgilarni ajratib olish (Feature Extraction)
–
Data Mining sohasining keng uchrashadigan
masalasi bo‘lib, aynan belgilarni generatsiya qilish bosqichida qo‘llaniladi. Shundan kelib chiqib, bugungi
kunda matnlarni intellektual tahlil qilish Text Mining nomini oldi. Bu holatda Feature Extraction
–
NLP
(
NLP, Natural Language Processing
–
tabiiy tilga ishlov berish
) sohosiga tegishli bo‘lib, sun’iy intellekt
va matematik lingvistikaning alohida sohasi sanaladi. Bugungi kunda NLP ko‘lami nutqni aniqlash va
matnni tarjima qilishdan bosh
lanib, bashoratli matn kiritish va mashina va odam o‘rtasida aloqa
o‘rnatishgacha qo‘llaniladi. Bunda mashinani o‘qitish (Machine Learning) yordamida matn ma’lumotlarini
tanib olish va tahlil qilish vazifalari hal qilinadi hamda matndan belgilarni ajratib olish va data scientist
450
mutaxassis ma’lumotlarni tayyorlashning (Data Preparation) ushbu bosqichini qanday amalga oshirishi
tahlil qilinadi.
Matndan belgilarni ajratib olishda dastlab uni ML (Machine Learning) algoritmlari yordamida ishlov
berishga yaroqli
ko‘rinishga keltirish zarurati kelib chiqadi. Buni amalga oshirish uchun matnlarni bir
nechta bosqichda qayta ishlash zarurati kelib chiqadi:
▪
Tokenlash
–
matnning uzun qismlarini kichikroq qismlarga bo‘lish (paragraflar, jumlalar, so‘zlar).
TextMingda tokenlash matnni qayta ishlashning birinchi sanaladi.
▪
Normallashtirish
–
matnni «tozalangan» shaklga keltirish, ya’ni, so‘zlarning yagona reestri, tinish
b
elgilarining yo‘qligi, shifrlangan qisqartmalar, raqamlarning og‘zaki yozilishi va boshqalar;
▪
Stemlash
-
qo‘shimchalarni (qo‘shimcha, prefiks, tugatish) olib tashlash orqali so‘zni o‘z ildiziga
olib kelish;
▪
Lemmalash
-
so‘zni semantik kanonik shakliga qis
qartirish;
▪
Tozalash
-
semantik yukni ko‘tarmaydigan to‘xtash so‘zlarini (so‘z birikmalari, qo‘shma gaplar va
boshqalar) olib tashlash.
Ushbu amallar orqali matnlar zarur belgilarni ajratib olish uchun sonli qayta ishlashga tayyor
ko‘rinishga keltiriladi.
B
izga sir emaski, ko‘plagan kompaniyalarda juda katta miqdorda foydalanilmaydigan ma’lumotlar
(dark data ) mavjud. IBM kompaniyasi hisob-
kitoblariga ko‘ra datchiklar va analog raqamli konvertorlar
tomonidan shakllantirilayotgan ma’lumotlarning taxminan 90%
xech qachon ishlatilmaydi. Xulosa qilib
ayttadigan bo‘lsak, strukturalanmagan foydalanilmaydigan ma’lumotlardan foydali ma’lumotlarni ajratib
olib ularga ishlov berish eng istiqbolli usullardan biri bo‘lib qoladi.
ADABIYOTLAR
1.
В.Э.Пашковский, В.Р.Пиотровская, Р.Г.Пиотровский
.
Психиатрическая лингвистива Изд.
4-
е. –
М.: ЛЕНАНД, 2015. –
168 с.
2.
Gir´on J., Ginebra J., Riba A.
Bayesian Analysis of a Multinomial Sequence and Homogeneity of
Literary Style // The American Statistician. 2005. Vol. 59. Issue 1. PP. 19
–
30
3.
Faiyaz Ahmad, Yassar, Amreen Ahmad.
Automatic Summarization of Textual Document //
International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-9
Issue-1, November 2019. PP 2486-2491
PREDICTING AND CLASSIFYING OF PUPILS' KNOWLEDGE USING MACHINE
LEARNING ALGORITHMS
Samandarov E.K.
National university of Uzbekistan, Tashkent, Uzbekistan
Educational Data Mining is a rising discipline that aims to develop methods to explore data from
educational contexts, i
n order to understand students’ behavior, interests and results in a better way. The
EDM can be considered as the intersection of three main areas: education, statistics and informatics. This
intersection among these three areas also generates other sub-fields, narrowly related to EDM, such as
computer-based education, learning analysis, data mining (DM) and machine learning (ML).
As seen in Fig. 1, machine learning is critical in educational data
mining. It gives the capacity to forecast in the educational sector. One
advantage of this approach is that it can identify recurring queries.
There are four main types of machine learning: supervised,
unsupervised, semi-supervised and reinforcement. The supervised
learning is the most popular paradigm for machine learning, it aims to
build a model based on observation data and desired results; This model
allows the best approach to the relationship between input and output
observable in the data. Supervised learning problems can be grouped
into regression and classification problems. While, unsupervised
learning uses only input observations data without having
corresponding outputs. So, the goal of unsupervised learning is to
determine the patterns or clusters hidden in the data from unlabeled data. Unsupervised learning problems
can be seen as a problem of clustering or association. The semi-supervised learning falls between the two
previous types of learning. Indeed, data labeling is a very expensive operation and requires presence of
human experts. However, the availability of labels in some observations, even if they are missing in the
most cases, gives to the semi-supervised algorithms the best chance for model construction. The semi-
supervised learning algorithms exploit the idea that, even if group memberships of unlabeled data are
Dusmukhametov A.I., Saidov A.A., Khakimova F.A.PROBLEMATIC ISSUES OF CUSTOMS
CONTROL ORGANIZATION RELATED TO THE USE OF ARTIFICIAL INTELLIGENCE
METHODS ............................................................................................................................................. 437
Ergashev A.А., Kayumova N.N.
MA`LUMOTLAR BAZASINING TAHLILIY IMKONIYATINI
OSHIRISH .............................................................................................................................................. 438
Eshankulov H.I., Sultonov H. TAQSIMLANGAN AXBOROT TIZIMLARNING
ARXITEKTURASI
......................................................................................................................439
Eshonqulov H.I. IDEOLOGY OF ONTOLOGY WEB LANGUAGE .................................................... 440
Ibragimov S. MODELING INDIVIDUAL LIFE TRAJECTORIES BY GRAPH .................................. 441
Ibragimov Sh.M. ARTIFICIAL INTELLIGENCE
–
DEVELOPMENT PROSPECTS.......................... 443
Ismoilova D.
ASSOTSATSIYA QOIDASINI O’RGANISH VA QO’LLASH
....................................... 443
Polvonov S.Z., Akramov O. I. PYTHONDA LOGISTIK REGRESSIYA ALGORITMINI AMALGA
OSHIRISH .............................................................................................................................................. 444
Qobilov K.H., Olimov N.N., Toyirova U.I.
SUN’IY INTELLEKT MASALALARINI YECHISH
MODELLARI ......................................................................................................................................... 446
Risqaliyev J.D.
SUN’IY INTELLEKTDA MANTIQIY REGRESSIYANING O‘RNI
.......................... 447
Ro’zimatov S. Sh., Rahimov A. G’.
TA'LIM TIZIMIDAGI SUN'IY AQLNING KELAJAGI .............. 448
Saidov A.A., Khakimova F.A., Abdurakhmanov T.T. APPLICATION OF THE CONDITIONS OF
IMAMA BUKHARIY TO MODERN INFORMATION CHALLENGES ............................................. 448
Samandarov B.S., To‘xtabaev U.A., Ispanova J.P.
MATNLARNI INTELLEKTUAL TAXLIL QILISH
MASALALARI ...................................................................................................................................... 449
Samandarov E.K. PREDICTING AND CLASSIFYING OF PUPILS' KNOWLEDGE USING
MACHINE LEARNING ALGORITHMS .............................................................................................. 450
Xazratov F. X., Rufatov J. Z., Boltayev S.B. BIG DATA VA MA`LUMOTLAR TAHLILI TURLI
SOHALARDA QO`LLANILISHI........................................................................................................... 451
Бакаев И.И., Бакаева Р.И. СОЗДАНИЕ АЛГОРИТМА ТОКЕНИЗАЦИИ НА ОСНОВЕ БАЗА
ЗНАНИЙ ДЛЯ УЗБЕКСКОГО ЯЗЫКА.
............................................................................................. 452
Болтаев Т.Б., Ибрагимов С.И. СПЕЦИФИКАЦИЯ ИНТЕЛЛЕКТУАЛЬНОГО АНАЛИЗА
ПРОЦЕССА ОБУЧЕНИЯ НА ОСНОВЕ МОДЕЛИ ДИДАКТИКИ
.................................................. 453
Гаращенко А.В., Эргашев Н.Х
.
ФОРМИРОВАНИЕ ИНДИВИДУАЛЬНОЙ ОБРАЗОВАТЕЛЬНОЙ
ТРАЕКТОРИИ НА ОСНОВЕ МНОГОУРОВНЕВОЙ CNN
-
LSTM СИСТЕМЫ НЕЙРОННЫХ
СЕТЕЙ
........................................................................................................................................455
Ғайбулов Қ.М
.
ҚАРOРНИ ҚЎЛЛАБ
-
ҚУВВАТЛАШ ТИЗИМЛАРИНИ (ҚҚҚТ) ҚУРИЛИШ
МАТЕРИАЛЛАРНИ ТАНЛАШГА ҚЎЛЛАШ
................................................................................... 456
Кодиров З., Студенкова Д., Косимов Д. ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ПОМОЩЬ
ПРЕПОДАВАТЕЛЮ
............................................................................................................................. 457
Сеитназаров К.К., Туремуратова Б.К. ОСОБЕННОСТИ ИЗУЧЕНИЯ ИНТЕЛЛЕКТУАЛЬНЫХ
АГЕНТОВ ДЛЯ ПРОГРАММИРОВАНИЯ
........................................................................................ 457
Туремуратова Б.К., Кенесбаева Д.А. ОСНОВНЫЕ НАПРАВЛЕНИЯ РАБОТ В ОБЛАСТИ
ИСКУССТВЕННОГО ИНТЕЛЛЕКТА
................................................................................................ 458
Эргашев А.
A.,
Холиков А
.
О
.
МИЖОЗ С
E
РВ
E
Р Т
E
ХНОЛОГИЯЛАРИДА ИЛОВАЛАРНИ
ИШЛАТИШ УЧУН
MICROSOFT AZURE
АСОСИДАГИ БУЛУТ Т
E
ХНОЛОГИЯЛАРИДАН
ФОЙДАЛАНИШ.
.................................................................................................................................. 459
VII
ШЎЪБА. АХБОРОТ ХАВФСИЗЛИГИ. INFORMATION SECURITY.
................... 460
Adizova Z.M., Davletov J. K. PYTHON DASTURLASH TILI ORQALI AXBOROT XAVSIZLIGINI
TAMINLASH. ........................................................................................................................................ 460
Eshonqulov Sh. XODIMLARNI FACE ID YORDAMIDA BIOMETRIK AVTORIZATSIYADAN
O‘TKAZISH AXBOROT TIZIMINI TASHKIL ETISHNING TEXNIK TA
LABLARI ....................... 460
Matyakubov A.S., Tadjiev R.N., Komilov R.K. KIRUVCHI VA CHIQUVCHI TARMOQ TRAFIGINI
TEKSHIRISH VA
BOSHQARISHNING ILG’OR USULLARI
............................................................ 461
Mavlonov Sh. H., Baxramov M. S. KIBERJINOYATCHILIKKA QARSHI KIBERXAVFSIZLIK ..... 462
Mavlonov Sh.H. ZAMONAVIY RAQAMLI TEXNOLOGIYALARDAN FOYDALANISHDA
KIBERJINOYATCHILIKNING OLDINI OLISH.................................................................................. 463
Mirzakulov J. POSTGRESQL - DATABASE FOR HIGH PROTECTION. .......................................... 464
Nurullayev M.M. KRIPTOGRAFIK KALITLARNI SHAKLLANTIRISH UCHUN TASODIFIY
SONLARNI GENERATSIYALASHDA SMARTFON SENSORLARIDAN FOYDALANISH ........... 465
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