Publications

(2022). k-NN-p: A paired index structure for k-Nearest Neighbor Search Algorithms over High Dimensional Data and Large Data Sets. Information Sciences (under-review).

(2022). Rapidly Predicting Kohn-Sham Total Energy Using Data-centric AI. Nature Scientific Reports.

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(2022). Regeneration of Lithium-ion Battery Impedance using a Novel Machine Learning Framework and Minimal Empirical Data. Journal of Energy Storage.

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(2022). Applying Data-Centric AI to Improve a Single-cell RNA-seq Pipeline. In IEEE International Conference on Data Mining (ICDM) (under-review).

(2022). State of Charge and Temperature-Dependent Impedance Spectra Regeneration of Lithium-ion Battery by Duplex Learning Modeling. Journal of Energy Storage (under-review).

(2022). Data on Machine Learning regenerated Lithium-ion battery impedance. Data in Brief (under-review).

(2021). Data Expressiveness and Its Use in Data-centric AI. In NeurIPS Data-Centric AI, DCAI'21.

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(2021). Rare-class Learning over Mg-Doped ZnO Nanoparticles. Chemical Physics.

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(2021). A DFT Study on Stability and Electronic Structure of AlN Nanotubes . Materials Today Communications.

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(2021). Predicting Atom Types of Anatase TiO2 Nanoparticles with Machine Learning. Key Engineering Materials.

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(2021). Measuring the Proximity of Medical Treatment Areas with Text Mining. European Journal of Science and Technology.

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(2020). Tailoring the structural properties and electronic structure of anatase, brookite and rutile phase TiO2 nanoparticles: DFTB calculations. Computational Materials Science.

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(2019). Study of Structural and Optoelectronic Properties of Hexagonal ZnO Nanoparticles. In Bilecik Seyh Edebali University Journal of Science.

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(2019). Controlling structural and electronic properties of ZnO NPs: Density-functional tight-binding method. Bilge International Journal of Science and Technology Research.

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(2019). Size Dependent Electronic Structure and Structural Properties of Cupric Oxide (CuO) NanoParticles. International Natural Science, Engineering and Material Technologies Conference.

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(2018). Using data analytics to optimize public transportation on a college campus. In IEEE international conference on data science and advanced analytics (DSAA).

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(2017). A novel approach to optimization of iterative machine learning algorithms: Over heap structure. In IEEE International Conference on Big Data (Big Data).

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(2017). Improving expectation maximization algorithm over stellar data. In IEEE International Conference on Big Data (Big Data).

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(2017). Case Study: Clustering Big Stellar Data with EM. In IEEE/ACM International Conference on Big Data Computing, Applications and Technologies.

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(2017). A NOVEL APPROACH TO OPTIMIZATION OF ITERATIVE MACHINE LEARNING ALGORITHMS: OVER HEAP STRUCTURE. Indiana University, ProQuest Dissertations Publishing.

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(2017). Using data to build a better EM: EM* for big data. International Journal of Data Science and Analytics.

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(2016). Em*: An em algorithm for big data. In The IEEE International Conference on Data Science and Advanced Analytics (DSAA).

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(2016). EMPLOYING SOFTWARE ENGINEERING PRINCIPLES TO ENHANCE MANAGEMENT OF CLIMATOLOGICAL DATASETS FOR CORAL REEF ANALYSIS. In The International Workshop on Climate Informatics (CI).

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(2015). Red-RF: Reduced random forest for big data using priority voting & dynamic data reduction. In IEEE International Congress on Big Data (IEEE BigData).

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(2014). A new set of Random Forests with varying dynamic data reduction and voting techniques. In The IEEE International Conference on Data Science and Advanced Analytics (DSAA).

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(2014). Studying the milky way galaxy using paraheap-k. IEEE Computer.

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