Yazar "Peker, Huseyin" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • [ X ]
    Öğe
    EFFECTS EXAMINATION OF THE FACTORS AFFECTING CHOICE OF TYPE OF FURNITURE WITH DATA MINING TECHNIQUE (DECISION TREE)
    (Health & Environment Assoc, 2018) Bardak, Timucin; Peker, Huseyin; Bardak, Selahattin
    Data mining is the process of analyzing data from different perspectives and summarizing it into beneficial information. Data mining is a very important technique in determining customer behavior. However, the work done on this subject is limited. By analyzing customer behavior, consumer needs can be identified and satisfaction can be increased at the same time. In this study, factors (age, gender, marital status, child status) affecting the selection of the furniture type (classical and modern furniture) will be analyzed using decision tree which is one of the techniques of data mining. Our analysis is intended to guide future research and to assist in the accumulation of knowledge on the implementation of data mining techniques.
  • [ X ]
    Öğe
    IMPREGNATION IN WOOD OF PYRITE (FeS2) MATERIAL AND COMPRESSIVE STRENGTH
    (Health & Environment Assoc, 2017) Bardak, Selahattin; Bardak, Timucin; Morkan, Bulent; Tan, Huseyin; Peker, Huseyin
    In this study, it is aimed to have the ability of being identifying to be impregnated with pyrite substance that is the one of the resources of our country of spruce wood (Picea orientalis (L.) Link) in various concentration (1%, 3%, 5%) with boron compounds up to standart of ASTM 1413-76 and identifying the areas of usage in outdoor furniture industry. For that purpose, it is identified that occurrence changes in mechanical features (compression strentgh). According to the experiment results, control samples compression strengths value (32.43 N/mm(2)) is determined as 3% (the highest) pyrite + Boric acide (48.14 N/mm(2)) and 1% (the lowest) Pyrite (36.81 N/mm(2)). When the pyrite material was used both alone and in combination with boron compounds, it increased the compression strengths according to the control sample.
  • [ X ]
    Öğe
    Predicting Effects of Selected Impregnation Processes on the Observed Bending Strength of Wood, with Use of Data Mining Models
    (North Carolina State Univ Dept Wood & Paper Sci, 2021) Bardak, Selahattin; Bardak, Timucin; Peker, Huseyin; Sozen, Eser; Cabuk, Yildiz
    Wood materials have been used in many products such as furniture, stairs, windows, and doors for centuries. There are differences in methods used to adapt wood to ambient conditions. Impregnation is a widely used method of wood preservation. In terms of efficiency, it is critical to optimize the parameters for impregnation. Data mining techniques reduce most of the cost and operational challenges with accurate prediction in the wood industry. In this study, three data-mining algorithms were applied to predict bending strength in impregnated wood materials (Pinus sylvestris L. and Millettia laurentii). Models were created from real experimental data to examine the relationship between bending strength, diffusion time, vacuum duration, and wood type, based on decision trees (DT), random forest (RF), and Gaussian process (GP) algorithms. The highest bending strength was achieved with wenge (Millettia laurentii) wood in 10 bar vacuum and the diffusion condition during 25 min. The results showed that all algorithms are suitable for predicting bending strength. The goodness of fit for the testing phase was determined as 0.994, 0.986, and 0.989 in the DT, RF, and GP algorithms, respectively. Moreover, the importance of attributes was determined in the algorithms.