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Journals / Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi / 2020 / Cilt: 12 - Sayı: 3

Storage Requirement Estimation For Electronic Document Management System With Artificial Neural Networks

Yapay Sinir Ağları ile Elektronik Belge Yönetim Sistemi için Depolama Gereksinimi Tahmini

Zeynep ÇETİNKAYA
Erdal ERDAL
Atilla ERGÜZEN
Pages
65–72
Publication Date
2020-12-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this study using Kırıkkale University Electronic document management systems data, different ANN models were created, the most suitable models were determined, and the required storage area was estimated for the future periods.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Electronic document management systems are defined as the protection and management of the contents, formats and relational features of all kinds of documents created by an institution in the process of carrying out its activities.
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Key Findings & Data:With every transaction and activity transferred to electronic environment in institutions, the infrastructure and investments that should be allocated for Electronic document management systems storage areas increase and the forecast of this increase becomes more important over time.
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Conclusion & Impact:In this study using Kırıkkale University Electronic document management systems data, different ANN models were created, the most suitable models were determined, and the required storage area was estimated for the future periods.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Sinir Ağları · Talep Tahmini · Algoritma

Abstract

Electronic document management systems are defined as the protection and management of the contents, formats and relational features of all kinds of documents created by an institution in the process of carrying out its activities. Storage areas are one of the important elements for electronic document management systems. With every transaction and activity transferred to electronic environment in institutions, the infrastructure and investments that should be allocated for Electronic document management systems storage areas increase and the forecast of this increase becomes more important over time. Artificial neural networks (ANN) approach has been used in many areas in recent years. Estimation studies in different fields have been made with ANN and it has been observed that successful results have been obtained. In this study, an ANN model is proposed to be used in estimating the storage area required for electronic document management systems. In this study using Kırıkkale University Electronic document management systems data, different ANN models were created, the most suitable models were determined, and the required storage area was estimated for the future periods.

Özet

Elektronik belge yönetim sistemleri, bir kurumun faaliyetlerini yürütme sürecinde oluşturduğu her türlü belgenin içerik, format ve ilişkisel özelliklerinin korunması ve yönetilmesi olarak tanımlanmaktadır. Depolama alanları, elektronik belge yönetim sistemleri için önemli unsurlardan biridir. Kurumlarda elektronik ortama aktarılan her işlem ve faaliyetle birlikte Elektronik belge yönetim sistemleri depolama alanlarına tahsis edilmesi gereken altyapı ve yatırımlar artmakta ve bu artışın tahmini zamanla daha da önem kazanmaktadır. Yapay sinir ağları (YSA) yaklaşımı son yıllarda pek çok alanda kullanılmaktadır. YSA ile farklı alanlarda tahmin çalışmaları yapılmış ve başarılı sonuçlar elde edildiği görülmüştür. Bu çalışmada, elektronik belge yönetim sistemleri için gerekli depolama alanının tahmin edilmesinde kullanılmak üzere yapay bir sinir ağı modeli önerilmiştir. Kırıkkale Üniversitesi Elektronik belge yönetim sistemleri verilerinin kullanıldığı bu çalışmada, farklı YSA modelleri oluşturulmuş, en uygun modeller belirlenmiş ve gelecek dönemler için gerekli depolama alanı tahmin edilmiştir.

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Cite This Article

ÇETİNKAYA, Z., ERDAL, E., ERGÜZEN, A. (2020). Storage Requirement Estimation For Electronic Document Management System With Artificial Neural Networks. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, Cilt: 12 - Sayı: 3, 65–72. https://ajindex.com/articles/889280

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