東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Machine Learning Models for Pattern ...

Aziz, Zagroz,

FindBook

Google Book

Amazon

博客來

Machine Learning Models for Pattern Identification and Predictive Analytics /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Models for Pattern Identification and Predictive Analytics // Zagroz Aziz.
作者:	Aziz, Zagroz,
面頁冊數:	1 electronic resource (155 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
標題:	Mean square errors. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31324687
ISBN:	9798384203599

Machine Learning Models for Pattern Identification and Predictive Analytics /
Aziz, Zagroz,

Machine Learning Models for Pattern Identification and Predictive Analytics /Zagroz Aziz. - 1 electronic resource (155 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

V poslednich desetiletich zaznamenalo mnozstvi generovanych dat exponencialni rust. Rychly rust dat vedl techniky dolovani dat k tomu, aby hraly vyznamnou roli pri stanovovani zakladnich linii, meritek, cilu, analyzy a lepsiho porozumeni konceptum. Ziskavani uzitecnych informaci v datech vsak vyzaduje prislusne techniky a postupy. Naproti tomu proces identifikace vzoru v entitach nezpracovanych dat se stava narocnejsi. Vzorce jsou klicem k porozumeni, analyze, predpovidani a rozhodovani. Na druhou stranu, hledani vzorcu vyzaduje nejprve analyzu dat, strojove uceni, umelou inteligenci a statisticke modely. Tento proces se nazyva prediktivni analytika, coz je proces vyuzivani dat k predpovidani budoucich vysledku. V teto praci byly predstaveny ruzne nove pripadove studie s pouzitim ruznych datovych sad. Kazda datova sada prosla predzpracovanim. Predzpracovani dat zahrnuje extrakci dat, sber, profilovani, redukci, hadky, obohaceni a validaci pred tim, nez jsou studovana. Kazda pripadova studie predstavuje urciteproblemy a poskytuje reseni, prinasi lepsi analyticke porozumeni, vylepseni systemu, zlepseni vysledku a presnosti. Krome toho bylo k reseni trendu a vzorcu v datech pouzito nekolik statistickych algoritmu a modelu a algoritmu strojoveho uceni. Dale poskytujeme statistickou analyzu pomoci ruznych metodologii k identifikaci vzorcu provozu, indikaci vykonu site a kvality sluzeb. Krome toho jsme navrhli chovani, detekci a predikci anomalii, ktere pomahaji provozovatelum siti porozumet a predvidat takove chovani site. Vsechny tyto studie byly vyhodnoceny a posouzeny pomoci nekolika vykonnostnich metrik a parametru.

English

ISBN: 9798384203599Subjects--Topical Terms:

3562318
Mean square errors.

Machine Learning Models for Pattern Identification and Predictive Analytics /
LDR:04733nmm a22004333i 4500 001 2430762
005 20250603111612.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798384203599
035 $a (MiAaPQD)AAI31324687
035 $a (MiAaPQD)CzechTech114256
035 $a AAI31324687
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Aziz, Zagroz, $e author. $3 3819250
245 1 0 $a Machine Learning Models for Pattern Identification and Predictive Analytics / $c Zagroz Aziz.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (155 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
500 $a Advisors: Bestak, Robert.
502 $b Ph.D. $c Czech Technical University $d 2024.
520 $a V poslednich desetiletich zaznamenalo mnozstvi generovanych dat exponencialni rust. Rychly rust dat vedl techniky dolovani dat k tomu, aby hraly vyznamnou roli pri stanovovani zakladnich linii, meritek, cilu, analyzy a lepsiho porozumeni konceptum. Ziskavani uzitecnych informaci v datech vsak vyzaduje prislusne techniky a postupy. Naproti tomu proces identifikace vzoru v entitach nezpracovanych dat se stava narocnejsi. Vzorce jsou klicem k porozumeni, analyze, predpovidani a rozhodovani. Na druhou stranu, hledani vzorcu vyzaduje nejprve analyzu dat, strojove uceni, umelou inteligenci a statisticke modely. Tento proces se nazyva prediktivni analytika, coz je proces vyuzivani dat k predpovidani budoucich vysledku. V teto praci byly predstaveny ruzne nove pripadove studie s pouzitim ruznych datovych sad. Kazda datova sada prosla predzpracovanim. Predzpracovani dat zahrnuje extrakci dat, sber, profilovani, redukci, hadky, obohaceni a validaci pred tim, nez jsou studovana. Kazda pripadova studie predstavuje urciteproblemy a poskytuje reseni, prinasi lepsi analyticke porozumeni, vylepseni systemu, zlepseni vysledku a presnosti. Krome toho bylo k reseni trendu a vzorcu v datech pouzito nekolik statistickych algoritmu a modelu a algoritmu strojoveho uceni. Dale poskytujeme statistickou analyzu pomoci ruznych metodologii k identifikaci vzorcu provozu, indikaci vykonu site a kvality sluzeb. Krome toho jsme navrhli chovani, detekci a predikci anomalii, ktere pomahaji provozovatelum siti porozumet a predvidat takove chovani site. Vsechny tyto studie byly vyhodnoceny a posouzeny pomoci nekolika vykonnostnich metrik a parametru.
520 $a In the last decades,the amount of data generated has seen exponential growth.The rapid growth of data led the data mining techniques to play a significant role in establishing base lines, benchmarks, objectives, analyzing and better understanding concepts. However,mining useful information in data requires relevant techniques and procedures. Incontrast,the process of pattern identification in the raw data entities becomes more challenging. Patterns are the key to understanding, analyzing, predicting and decision making. On the other hand, finding patterns needs first data analysis, machine learning, artificial intelligence, and statistical models.This process is called predictive analytics, which is the process of employ in data to forecast future outcomes. In this thesis, different novel case studies have been introduce dusing different datasets. Each dataset has gone through preprocessing. Data preprocessing includes data extraction, collection, profiling, reduction, wrangling, enrichment and validation before being studied. Every case study introduces certain problems and provides solutions, brings in better analytical understanding, system enhancement,outcomes and accuracy improvement. In addition, several statistical and machine learning algorithms and model shave been employed to address the trends and patterns in the data.Furthermore, we provide a statistical analysis using different methodologies to identify traffic patterns, indicate the network performance and quality of service. Moreover,we projected anomaly behavior, detection and prediction that helps network operators to understand and fore cast such network behaviors. All these studies have been evaluated and assessed using several performance metrics and parameters.
546 $a English
590 $a School code: 3804
650 4 $a Mean square errors. $3 3562318
650 4 $a Precipitation. $3 3680799
650 4 $a Communication. $3 524709
650 4 $a Telecommunications systems. $3 649821
650 4 $a Standard scores. $3 3683230
650 4 $a Clustering. $3 3559215
650 4 $a Access control. $3 1458437
650 4 $a Rain. $3 3560372
650 4 $a Computer science. $3 523869
650 4 $a Electrical engineering. $3 649834
650 4 $a Meteorology. $3 542822
650 4 $a Statistics. $3 517247
690 $a 0459
690 $a 0800
690 $a 0984
690 $a 0544
690 $a 0557
690 $a 0463
710 2 $a Czech Technical University. $e degree granting institution. $3 3819251
720 1 $a Bestak, Robert $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-03B.
790 $a 3804
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31324687