IZTECH Research Centers Collection / İYTE Araştırma Merkezleri Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/11147/2636

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Department: search.filters.department.İzmir Institute of Technology. RectorateLanguage: search.filters.language.en

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  • Article
    Citation - WoS: 8
    Citation - Scopus: 14
    An End-To Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things
    (IEEE, 2021) Nakıp, Mert; Karakayalı, Kubilay; Güzeliş, Cüneyt; Rodoplu, Volkan; 01. Izmir Institute of Technology
    We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and embedded feature selection methods, our architecture enables the automatic selection of features dynamically based on feature importance score calculation and gamma-gated feature selection units that are trained jointly and end-to-end with the forecaster. We compare the performance of our FSF architecture on the problem of forecasting IoT device traffic against the following existing (feature selection, forecasting) technique pairs: Autocorrelation Function (ACF), Analysis of Variance (ANOVA), Recurrent Feature Elimination (RFE) and Ridge Regression methods for feature selection, and Linear Regression, Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), 1 Dimensional Convolutional Neural Network (1D CNN), Autoregressive Integrated Moving Average (ARIMA), and Logistic Regression for forecasting. We show that our FSF architecture achieves either the best or close to the best performance among all of the competing techniques by virtue of its dynamic, automatic feature selection capability. In addition, we demonstrate that both the training time and the execution time of FSF are reasonable for IoT applications. This work represents a milestone for the development of predictive networks for IoT in smart cities of the near future.
  • Article
    Citation - WoS: 9
    Citation - Scopus: 13
    X-Ray Fluorescence Spectroscopic Determination of Heavy Metals and Trace Elements in Aerial Parts of Origanum Sipyleum L From Turkey
    (Pharmacotherapy Group, 2016) Durmuşkahya, Cenk; Alp, Hakan; Hortooğlu, Zehra Sinem; Toktaş, Ümit; Kayalar, Hüsniye; 01. Izmir Institute of Technology
    Purpose: To determine the heavy metal and trace element composition of the powdered aerial parts of Origanum sipyleum L. and its water extract. Methods: The heavy metal and trace elements content of the powdered plant material and 2% aqueous extract were evaluated by x-ray fluorescence spectroscopy with silicon drift detector SDD at a resolution of 145 eV and 10,000 pulses. The process conditions were 0.1 g sample weight, process time of 300 s at a voltage of 25 kV and 50 kV, and at a current of 0.5 and 1.0 mA under helium atmosphere. Results: The major elements, K, Ca and Na, known as macronutrients, constituted 11990, 10490 and 970 ppm of the powdered drug and 8910, 2991 and 810 ppm of the water extract, respectively. Among other constituents, arsenic, lead and uranium levels were < 1, 2.1 and < 3 ppm, respectively, in the powdered material while in the aqueous extract, the levels were < 1, < 2 and 200 ppm, respectively. Conclusion: O. sipyleum is a potential source of macro- and micronutrients from which useful food additives and health supplements can be derived.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 7
    Elemental Analysis of Galium Incanum Sm Subsp Centrale Ehrend by X-Ray Fluorescence Spectroscopy
    (Pharmacotherapy Group, 2013) Kayalar, Hüsniye; Durmuşkahya, Cenk; Hortooğlu, Zehra Sinem; 01. Izmir Institute of Technology
    Purpose: To evaluate the content of trace elements in Galium incanum SM. subsp. centrale Ehrend. Methods: The air-dried aerial parts of the plant material were used and its water extract (2 % w/v infusion) was analysed for trace elements using x-ray fluorescence (XRF) spectrometry. Results: The aqueous extract depicted significant concentrations of macro- and micro-nutrients with heavy metal and metal oxide content of 4.07 - 6.02 and 3.19 - 4.01 % for powdered plant material and water extract, respectively. The contents of Ca (22840 ppm) and K (8204 ppm) were the highest among all the elements while Zn (45.9 ppm) and Fe (328 ppm) were also detected in significant amounts. Zn, Mn and Cu showed the highest content while those of Mg, Al, K, Ca, Fe and P lowest in the water extract. Conclusion: The presence of significant levels of Ca, K, Na, Fe, Zn, Mg, Mn and Cu in G. incanum subsp. centrale showed that this plant has notable nutrient elements. The traditional use of Gallium species as a diuretic may be attributed to its rich content of potassium.