Çankaya GCRIS Standart veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.
 

Eş, Sinan

Profile Picture
Name Variants
Job Title
Arş. Gör.
Email Address
Main Affiliation
Bilgisayar Mühendisliği
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals Report Points

SDG data could not be loaded because of an error. Please refresh the page or try again later.
Scholarly Output

3

Articles

0

Citation Count

0

Supervised Theses

1

Filters

2005 - 20062
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Master Thesis
    A computational analysis of a language structure in natural language text processing
    (2005) Eş, Sinan; Bilgisayar Mühendisliği
    Text categorization or classification is a general task of classifying un-organized natural language texts according to specific subject matter or category. Electronic mail (e-mail) filtering is a binary text classification problem which the user emails can be classified as legitimate (non-spam) or un-wanted mail (spam). In this study, we tried to find a filtering solution that is able to automatically classify emails into spam and legitimate categories. In order to automatically and efficiently classify emails as spam or legitimate we took advantage of some Machine Learning methods and some novel ideas from Information Retrieval
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 0
    Spam mail filtering using Bayesian classifier and heuristics
    (2006) Eş, Sinan; Eş, S.; Aşkun, A.R.; Hassanpour, Reza; Hassanpour, R.; 56475; Bilgisayar Mühendisliği; Yazılım Mühendisliği
    Commercials and virus infected files transferred by electronic mails (e-mail) have been a main source of time and resource loss during the past decade and email filtering as a binary text classification problem has attracted the attention of many researchers recently. In this study, we have tried to find a filtering solution which is able to automatically classify emails into spam and legitimate categories. In order to automatically and efficiently classify emails as spam or legitimate we took advantage of some Bayesian stochastic algorithms and machine learning based heuristic methods. Our approach includes some novel ideas from Information Retrieval.