Minimum width trees and prim algorithm using artificial intelligence

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Ахатов, А., & Улугмуродов, Ш. А. (2022). Minimum width trees and prim algorithm using artificial intelligence. Современные инновационные исследования актуальные проблемы и развитие тенденции: решения и перспективы, 1(1), 141–144. извлечено от https://inlibrary.uz/index.php/zitdmrt/article/view/5271
Шох Аббос Улугмуродов, Jizzakh branch of the National University of Uzbekistan

Assistant of the Department of Computer Science and Programming

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Аннотация

To date, many algorithms have been developed that can be calculated using the Prim algorithm. Artificial intelligence-based methods are a significant drawback. Using artificial intelligence is a very convenient method of minimizing residual trees (MST) to find the shortest path graph optimally.


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MINIMUM WIDTH TREES AND PRIM ALGORITHM USING ARTIFICIAL

INTELLIGENCE

Akhatov Akmal Rustamovich

Samarkand State University Vice-Rector for International Cooperation.

Ulugmurodov Shokh Abbos Bakhodir ugli

Assistant of the Department of Computer Science and Programming,

Jizzakh branch of the National University of Uzbekistan

Abstract:

To date, many algorithms have been developed that can be calculated using

the Prim algorithm. Artificial intelligence-based methods are a significant drawback. Using
artificial intelligence is a very convenient method of minimizing residual trees (MST) to find the
shortest path graph optimally.

Keywords.

phonetics, annotation, segmentation, ai, Minimum spanning tree

.

Minimum Spanning trees: A common problem in communications networks and circuit

design is that of connecting together a set of nodes (communication sites or circuit components)
by a network of minimal total length (where length is the sum of the lengths of connecting wires.
We assume that the network is undirected. To minimize the length of the connecting network, it
never pays to have any cycles since we could break any cycle without destroying connectivity
and decrease the total length). Since the resulting connection graph is connected, undirected, and
acyclic, it is a free tree.[2]

The computational problem is called the minimum spanning tree problem (MST for

short). More formally, given a connected, undirected graph G (V, E), a spanning tree is an
acyclic subset of edges T C E that connects all the vertices together. Assuming that each edge (u,
v of G has a numeric weight or cost, w u, v , (may be zero or negative we define the cost of a
spanning tree T to be the sum of edges in the spanning tree.

A minimum spanning tree (MST) is a spanning tree of minimum weight. Note that the

minimum spanning tree may not be unique, but it is true that if all the edge weights are distinct,
then the MST will be distinct (this is a rather subtle fact, which we will not prove . The figure
below shows three spanning trees for the same graph, where the shaded rectangles indicate the
edges in the spanning tree. The one on the left is not a minimum spanning tree, and the other two
are an interesting observation is that not only do the edges sum to the same value, but in fact the
same set of edge weights appear in the two MST's.[4]


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Cost = 33

Cost = 22

Cost = 22

Figure 13: Spanning trees (the middle and right are minimum spanning trees.


Steiner Minimum trees: Minimum spanning trees are actually mentioned in the U.S. legal

code. The reason is that AT&T was a government supported monopoly at one time, and was
responsible for handling all telephone connections. If a company wanted to connect a collection
of installations by an private internal phone system, AT&T was required (by law) to connect
them in the minimum cost manner, which is clearly a spanning tree or is it?

(a) In dataset 1, 8 devices are identified with issues.(b) In dataset 2, 10 devices are

identified with issues.

By removing the longest edge(s) of the MST, the tree will be transformed to a forest. The

small sub-tree(s) with few number of clusters (nodes) and/or with smaller sized clusters can be
identified as outliers.[5] The initial assumption is: the sub-trees with fewer nodes and smaller
size contain patterns that happen rarely. Therefore, the clusters in these sub-trees are small, far
and different from the clusters in the bigger sub-trees. The process of removing the longest
edge(s) of the MST can also be performed by considering a user-specified threshold. The
detected clusters of outliers can supply domain experts with a better understanding of the system
behavior and facilitate them in the further analysis by mapping the detected patterns to the
corresponding sequences. The proposed approach has been evaluated on smart meter data and
video session data. The results of the evaluation on video session data has been discussed with
the domain experts.


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Fig. 1: (Top-left) The constructed MST before removing the longest edges on smart

meter sampled dataset 1. Edges A and B represent the longest edges of the tree. (Top-right) The
transformation of the constructed MST into a forest with 3 sub-trees after the longest edges are
removed. The sub-trees 1 and 2 are considered as outliers based on their size.[6] (Bottom-left)
The constructed MST before removing the longest edges on video session dataset. (Bottom-
right) The transformation of the constructed MST into a forest with 22 sub-trees after the longest
edges are removed. The sub-trees are ranked from smallest to largest based on their size. The top
10 smallest sub-trees are considered as outliers. Note. The size of a node represents the number
of smart meters or video sessions that are matched with it. The color of a node shows the degree
of the node and is used only for the visualization purposes. The distance between edges range
between [0,1].

Conclusion

In this study, we have presented an outlier detection for sequence datasets. Our approach

combines sequential pattern mining, clustering, and minimum spanning tree to identify outliers.
We have shown that the proposed approach can facilitate the domain experts in identification of
outliers. Building the minimum spanning tree on top the clustering solution can lead to
identifying clusters of outliers. This can reduce the time complexity of the proposed approach.
Moreover, in this study we have looked into collective outliers, sequences of events that based
on their occurrence together assumed to be anomalous, which may help to find the outlying
properties of the detected outliers.

The proposed approach has been applied on two sequence datasets, smart meter data and

video session data. Both datasets contain sequences of event types that either shows the
operational status of a smart meter or the current action that takes place in a viewer‘s video
session.

REFERENCES

1. Akhatov Akmal Rustamovich, Kayumov Oybek Achilovich, Ulug'murodov Shoh Abbos
Baxodir ugli. Scientific and theoretical basis of development and introduction of innovative
methods in inclusive education
2021// Universum: психология и образование 7 (85) // 46-48.
Общество с ограниченной ответственностью «Международный центр науки и
образования»
2. Akhatov A.R.,

Qayumov O.A., Ulugmurodov Sh.A.B.

Working with robot simulation using ros

and gazebo in inclusion learning // ―Фан, таълим ва ишлаб чиқариш интеграциясида рақамли
иқтисодиѐт истиқболлари‖ республика илмий-техник анжуман, ЎзМУ Жиззах филиали,
5-6 май 2021 йил, Жиззах – 194-199 б.
3. Aхatov A.R.,

Ulugʻmurodov Sh.A.B., Primqulov O.D.

Development of algorithms for

determining the breadth of digital economic change in access to open CV, open NI and PCL //
―Фан, таълим ва ишлаб чиқариш интеграциясида рақамли иқтисодиѐт истиқболлари‖
республика илмий-техник анжуман, ЎзМУ Жиззах филиали, 5-6 май 2021 йил, Жиззах –
182-185 б.


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144

4. Ахатов А.Р., Улугмуродов Ш.А., Умаров Х.А. Разработка методов и алгоритмов
системы генерации речи по данным сенсорных устройств считывания с элементами
искусcтвенного интеллекта // «Zamonaviy tadqiqotlar, innovatsiyalarning dolzarb muammolari
va rivojlanish tendensiyalari: yechimlar va istiqbollar» Respublika ilmiy-amaliy konferensiya
toʻplami, O‘zMU Jizzax filiali, Jizzax - 2021-yil 29-30-oktabr – 56-60 б.
5. Ахатов А.Р., Улугмуродов Ш.А. Development of methods and algorithms for a speech
generation system based on data from sensor reading devices with elements of artificial
intelligence //«Zamonaviy tadqiqotlar, innovatsiyalarning dolzarb muammolari va rivojlanish
tendensiyalari: yechimlar va istiqbollar» Respublika ilmiy-amaliy konferensiya toʻplami, O‘zMU
Jizzax filiali, Jizzax - 2021-yil 29-30-oktabr – 60-65 б.
6. Akhatov A.R., Nazarov F. Rashidov A. Mechanisms of Information Reliability In big data and
Blockchain Technologies. // International conference on information science and
communications technologies: applications, trends and opportunities 4-6 November. ICISCT
2021(IEEE).
7. Ruzibaev O., Muhamediyeva D., Ismailov I. Selecting a Suitable Initial Approximation Of
Multi-Component Cross-Diffusion Systems //2021 International Conference on Information
Science and Communications Technologies (ICISCT). – IEEE, 2021. – С. 1-4.
8. D. Khasanov, M. Tojiyev and O. Primqulov, "Gradient descent in machine learning," 2021
International Conference on Information Science and Communications Technologies (ICISCT),
2021, pp. 1-3, doi: 10.1109/ICISCT52966.2021.9670169.

QISHLOQ XOʻJALIK EKINLARINING AVTOMATLASHGAN TASNIFINI

YARATISHDA YUQORI ANIQLIKDAGI KOSMIK TASVIR MATERIALLARINI

QO'LLANISH TAJRIBASI.

Axatov Akmal Rustamovich

Samarqand Davlat universiteti professori

Saydaliyev Bobir Maxamadaliyevich

O‗zbekiston Milliy universitetining Jizzax filiali tayanch doktoranti

Annotatsiya:

Tabiiy resurs salohiyati, er sifati va boshqalar to'g'risidagi ma'lumotlar

bilan boshqaruv organlarini axborot bilan ta'minlash muammosini hal qilish eng zamonaviy
axborot texnologiyalari va sun'iy yo'ldosh tasviri materiallarini jalb qilishni talab qiladi. Qaror
qabul qilish jarayonida foydalaniladigan ma'lumotlarning eng muhim sifatlari ularning
dolzarbligi, to'liqligi va obektivligidir. Masofadan zondlash ma'lumotlari (RSD) bu barcha
afzalliklarga ega ekanligi ushbu maqolada yoritilgan.

Tayanch so‗zlar:

GAT, yer toyifasi, modellashtirish, ekinlarni tuzilishi, ishlov

beriladigan maydonlar, axborot texnologiyalari, sun'iy yo'ldosh tasviri

.

Masofadan zondlash ma'lumotlari (RSD) bu barcha afzalliklarga ega. RSD o'z o'lchamlari

va qamrovi doirasidagi hudud haqidagi barcha ma'lumotlarni o'z ichiga oladi, qamrov bo'ylab
doimiy ma'lumot maydonini va har bir obektning barcha individual xususiyatlarini o'z ichiga
oladi. Masofaviy zondlash eng dolzarb ma'lumotlarni taqdim etadi, bu ayniqsa optimal yechimni
ishlab chiqish uchun vaziyatni tahlil qilish uchun muhimdir. Bu ma'lumotlar zamonaviy
topografik, kadastr va tematik xaritalarni yaratish uchun asos bo'lib xizmat qiladi va aslida
barcha zamonaviy kartografik ma'lumotlarning asosiy manbai hisoblanadi. So'nggi yillarda turli
iqtisodiy muammolarni hal qilish uchun masofaviy zondlash materiallaridan foydalanish sezilarli
darajada oshdi. Zamonaviy axborot maxsulotlarining turlaridan biri bu sun‘iy yo‗ldoshdan
olingan tasvir materiallari bo‗lib, ular turli sohalarda, masalan, qishloq xo‗jaligi, ekologiya,
foydali qazilmalarni qidirish, mudofaa va boshqalarda muvaffaqiyatli qo‗llanilishi mumkin[1-6].
Shu bilan birga, sun'iy yo'ldoshdan suratga olish materiallaridan foydalanish ulushi aerofotosurat
materiallaridan foydalanishga nisbatan tezroq o'sib bormoqda.

Библиографические ссылки

Akhatov Akmal Rustamovich, Kayumov Oybck Achilovich, Ulug'murodov Shoh Abbos Baxodir ugli. Scientific and theoretical basis of development and introduction of innovative methods in inclusive education 2021// Universum: психология и образование 7 (85) // 46-48. Общество с ограниченной ответственностью «Международный центр науки и образования»

Akhatov A.R., Qayumov О.A., Ulugmurodov Sh.A.B. Working with robot simulation using ros and gazebo in inclusion learning // “Фан, таълим ва ишлаб чикариш интсграциясида ракамли иктисодиёт истикболлари” республика илмий-техник анжуман, УзМУ Жиззах филиали, 5-6 май 2021 йил, Жиззах - 194-199 б.

Axatov A.R., Ulug'murodov Sh.A.B., Primqulov O.D. Development of algorithms for determining the breadth of digital economic change in access to open CV, open N1 and PCL // “Фан, таълим ва ишлаб чикариш интсграциясида ракамли иктисодиёт истикболлари” республика илмий-техник анжуман, УзМУ Жиззах филиали, 5-6 май 2021 йил, Жиззах -182-185 б.

Ахатов А.Р., Улугмуродов Ш.А., Умаров Х.А. Разработка методов и алгоритмов системы генерации речи по данным сенсорных устройств считывания с элементами искусственного интеллекта // «Zamonaviy tadqiqotlar, innovatsiyalarning dolzarb muammolari va rivojlanish tcndcnsiyalari: yechimlar va istiqbollar» Rcspublika ilmiy-amaliy konferensiya to’plami, O’zMU Jizzax filiali, Jizzax - 2021-yil 29-30-oktabr - 56-60 6.

Ахатов A.P., Улугмуродов Ш.А. Development of methods and algorithms for a speech generation system based on data from sensor reading devices with elements of artificial intelligence //«Zamonaviy tadqiqotlar, innovatsiyalarning dolzarb muammolari va rivojlanish tcndcnsiyalari: yechimlar va istiqbollar» Rcspublika ilmiy-amaliy konferensiya to‘plami, O’zMU Jizzax filiali, Jizzax - 2021-yil 29-30-oktabr - 60-65 6.

Akhatov A.R., Nazarov F. Rashidov A. Mechanisms of Information Reliability In big data and Blockchain Technologies. // International conference on information science and communications technologies: applications, trends and opportunities 4-6 November. ICISCT 2021 (IEEE).

Ruzibacv O., Muhamcdiyeva D., Ismailov I. Selecting a Suitable Initial Approximation Of Multi-Component Cross-Diffusion Systems //2021 International Conference on Information Science and Communications Technologies (ICISCT). - IEEE, 2021. - C. 1-4.

D. Khasanov, M. Tojiyev and O. Primqulov, "Gradient descent in machine learning," 2021 International Conference on Information Science and Communications Technologies (ICISCT), 2021, pp. 1-3, doi: 10.1109/ICISCT52966.2021.9670169.

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