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Machine Learning Models for Pattern ...

Aziz, Zagroz,

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Machine Learning Models for Pattern Identification and Predictive Analytics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Models for Pattern Identification and Predictive Analytics // Zagroz Aziz.
Author:	Aziz, Zagroz,
Description:	1 electronic resource (155 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Mean square errors. -
Online resource:	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 /
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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.
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