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Subject: Regression

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    Article
    Citation - WoS: 8
    Citation - Scopus: 10
    Employability of It Graduates From the Industry's Perspective: a Case Study in Turkey
    (Springer, 2013) Turhan, Cigdem; Akman, Ibrahim
    The qualifications that constitute the employability and identity of graduates are viewed differently by the academic community and the industry. Currently, it is observed for Information Technologies (IT) sector that the demands of the industry are not always satisfied by the perceived standards of the graduates. To provide feedback to the corresponding departments, a survey regarding employer expectations and factors affecting these expectations has been conducted among a number of senior professionals and managers working in the IT sector in Turkey regarding this inconsistency. The employer expectations are considered in two empirical categories as competencies and adequacies. The multiple regression analysis technique has been used to analyze the survey data. Based on the analysis, recommendations are provided to IT departments as well as their students to better fulfill the demands of the industry.
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    Conference Object
    Citation - Scopus: 2
    Comparison of Gross Calorific Value Estimation of Turkish Coals Using Regression and Neural Networks Techniques
    (2012) Ozbayoglu,A.M.; Ozbayoglu,M.E.; Ozbayoglu,G.
    Gross calorific value (GCV) of coals was estimated using artificial neural networks, linear and non-linear regression techniques. Proximate and ultimate analysis results were collected for 187 different coal samples. Different input data sets were compared, such as both proximate and ultimate analysis data, and only proximate analysis data and only ultimate analysis data. It was observed that the best results were obtained when both proximate analysis and ultimate analysis results were used for estimating the gross calorific value. When the performance of artificial neural networks and regression analysis techniques were compared, it was observed that both artificial neural networks and regression techniques were promisingly accurate in estimating gross calorific values. In general, most of the models estimated the gross calorific value within ±3% of the expected value.
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    Conference Object
    Citation - Scopus: 1
    A Hybrid Data-Driven and Fuzzy MCDM Approach for Employee Selection
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sadeghzadeh, K.; Bahreini, P.; Kao, Y.-L.; Yilmaz, I.; Erdebilli, B.; Aghsami, A.; Bahrini, A.
    Employee selection, a cornerstone of human resource management, critically shapes organizational performance and long-term effectiveness. While traditional approaches primarily rely on expert-based evaluations, this study proposes a novel hybrid framework that integrates Multi-Criteria Decision-Making methods with data mining techniques to reduce the dimensionality of the number of criteria or variables considered. By integrating backward regression with fuzzy Multi-Criteria Decision-Making methods, our framework reduces model complexity and captures criteria interdependencies, while fuzzy logic addresses ambiguity in expert judgment, a gap often overlooked in prior research. The methodology first uses backward regression modeling with the employee attrition rate as the response variable to identify core criteria. Subsequently, the fuzzy Decision-Making Trial and Evaluation Laboratory analyzes interrelationships between criteria, followed by the fuzzy Analytic Network Process for weighting criteria and ranking candidates. We validate our approach using real-world recruitment data - including expert interview scores and historical attrition - from a company specializing in electronic attendance systems. The AI-generated rankings are benchmarked against these expert-based evaluations to assess alignment with human judgment. Initially, 17 criteria were systematically reduced to 11 core factors, resulting in a streamlined yet robust evaluation system. Our findings emphasize that 'Time-of-service,' 'Requested-wage,' 'Teamwork,' and 'Leadership' are the most critical criteria influencing effective IT personnel selection. © 2025 IEEE.