Demirkıran, Ferhat

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Demirkıran, Ferhat
F.,Demirkıran
F. Demirkıran
Ferhat, Demirkıran
Demirkiran, Ferhat
F.,Demirkiran
F. Demirkiran
Ferhat, Demirkiran
Demirkiran,F.
Demirkiran, F.
Job Title
Araş. Gör.
Email Address
Ferhat.demırkıran@khas.edu.tr
Main Affiliation
Management Information Systems
Status
Former Staff
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Author: Demirkiran,F.

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    Citation - Scopus: 0
    Fine-Tuning Wav2vec2 for Classification of Turkish Broadcast News and Advertisement Jingles
    (Institute of Electrical and Electronics Engineers Inc., 2023) Demirkiran,F.; Demirkıran, Ferhat; Oner,O.; Komecoglu,Y.; Guven,R.; Komecoglu,B.B.; Management Information Systems
    The accurate classification of news and commercial jingles is essential for the automated generation of broadcast flow. Currently, in press companies, editors manually label the start and end times of news and advertisements, which incurs both cost and time loss. Although the method of extracting fingerprints of news and commercial jingles has been employed to detect jingles on a channel basis and automatically classify news and commercial music, this approach falls short when it comes to classifying new jingles produced by channels. In this study, we created a new dataset by extracting segments of commercial and news jingles from TV channels in Turkey. We analyzed the most effective second interval for classifying news or commercials, resulting in an impressive accuracy score of 98.18%. By leveraging this dataset and conducting extensive analysis, we have made significant progress in accurately classifying news and commercial jingles. This research can potentially save press companies costs and time by automating the classification process. © 2023 IEEE.
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    Garbage In, Garbage Out: a Case Study on Defective Product Prediction in Manufacturing
    (Institute of Electrical and Electronics Engineers Inc., 2023) Colhak,F.; Dağ, Hasan; Ucar,B.E.; Demirkıran, Ferhat; Saygut,I.; Duzgun,B.; Demirkiran,F.; Dag,H.; Management Information Systems
    Despite their potential business value and invest-ments, data science projects often fail owing to a lack of preparedness, implementation challenges, and poor data quality. This study aimed to develop a machine learning model for predicting defective products in the dyeing process within the manufacturing domain. However, inadequate importance given to data by the involved factory, insufficient data quality, and the lack of the necessary technical infrastructure for data science projects have hindered attaining desired results. This study emphasizes to academic researchers and industry experts the significance of data quality and technical infrastructure, highlights how these deficiencies can impact the success of a data science project, and provides several recommendations. © 2023 IEEE.