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    A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices
    (Tubitak Scientific & Technological Research Council Turkey, 2022) Memis, Samet; Enginoglu, Serdar; Erkan, Ugur
    Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy parameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM) and classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity measures of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) and a generalised eigenvalue-based similarity measure. Then, it applies an SDM method based on the aggregation operator of fpfs-matrices to a decision matrix and classifies the given test sample. Afterwards, we perform an experimental study using 15 UCI datasets to manifest the success of our approach and compare FPFS-AC with the well-known and state-of-the-art classifiers (kNN, SVM, fuzzy kNN, EigenClass, and BM-fuzzy kNN) in terms of accuracy, precision, recall, macro F-score, micro F-score, and running time. Moreover, we statistically analyse the experimentally obtained data. Experimental and statistical results show that FPFS-AC outperforms the state-of-the-art classifiers in all the datasets concerning the five performance metrics.
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    Determination of Some Biological Parameters of Helicoverpa armigera Hubner (Lepidoptera; Noctuidae) on Cotton in Manisa Province
    (Univ Namik Kemal, 2020) Memis, Samet; Ozpinar, Ali
    The study was conducted on the cotton fields of Sehzadeler district in Manisa province with the purpose of determining some biological parameters of Helicoverpa armigera Hubner (Lepidoptera; Noctuidae) in 2018 and 2019. In both years, 5 different experimental parcels were selected to represent the cotton fields of Sehzadeler district. In 2018, cotton was sowed in 14th - 21st of April, one chemical control was applied against Aphis gossypii, Empoasca spp, and Tetranychus urticae. In 2019, cotton was sowed in 25-27 April with two different cultivars (Carisma and BA 440). A total of 7 insecticides applications were made with 5 times against A. gossypii, Empoasca spp, and Bemisia tabaci, two times against H. armigera. Adult population development of H. armigera was followed with delta and funnel traps. Samplings were made once a week, the number of adults were recorded and the number of H. armigera eggs and larvae and eggs of Chrysoperla carnea (Stephens) (Neuroptera: Chrysopidae) were counted on 3 different points on 3 m plant lines in all parcels. In the first year, a total of 16 adults were captured with 2 from Karaagacli, 1 from Selimsahlar, 2 from Yenimahmudiye, 10 from Mutevelli and 1 from Veziroglu. Because of the low population density of adults, the data were not evaluated. In 2019, adult flight of H. armigera has started at the first week of July on all parcels and two peak points, in the middle of August and at the end of September, were recorded. Adult flight has continued until the harvest date (19.10.2019). A total of 434 adults were captured on all traps with 101 from Karaagacli, 83 from Selimsahlar, 55 from Yenimahmudiye, 77 from Mutevelli and 118 from Veziroglu throughout the sampling period. The highest number of H. armigera adults captured per parcel was on Karagacli and Veziroglu, which was sowed with BA 440 cultivar. On the other hand, the number of H. armigera adults captured were higher in funnel traps than delta traps in all parcels. Number of C. carnea eggs were higher than other predators on 3 m plant lines, even with 2 insecticide applications against H. armigera and other pests. A parallel trend was observed between the egg and larval population of H. armigera and the number of C. carnea eggs.
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    Improved Adaptive Weighted Mean Filter for Salt-and-Pepper Noise Removal
    (Institute of Electrical and Electronics Engineers Inc., 2020) Erkan, Ugur; Thanh, Dang N. H.; Enginoglu, Serdar; Memis, Samet
    In this study, we propose an improved adaptive weighted mean filter (IAWMF) to remove salt-and-pepper noise. The most prominent advantage of IAWMF is its ability to take into account the weights of noise-free pixels in the adaptive window. Hence, the new grey value occurs closer to the original grey value of the centre pixel than the grey value computed by the adaptive weighted mean filter (AWMF). Moreover, the proposed method utilises the advantage of AWMF to reduce the error of detecting noisy pixels. In the experiments, we compare the denoising results of the proposed method with other state-of-the-art image denoising methods. The results confirm that IAWMF outperforms other methods. © 2020 IEEE.
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    Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal
    (Springer, 2019) Enginoglu, Serdar; Erkan, Ugur; Memis, Samet
    In this study, we propose a new method, i.e. Adaptive Riesz Mean Filter (ARmF), by operationalizing pixel similarity for salt-and-pepper noise (SPN) removal. Afterwards, we compare the results of ARmF, A New Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Based on Pixel Density Filter (BPDF), Modified Decision-Based Unsymmetric Trimmed Median Filter (MDBUTMF) and Decision-Based Algorithm (DBA) by using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Image Enhancement Factor (IEF), and Visual Information Fidelity (VIF) for 20 traditional test images (Lena, Cameraman, Barbara, Baboon, Peppers, Living Room, Lake, Plane, Hill, Pirate, Boat, House, Bridge, Elaine, Flintstones, Flower, Parrot, Dark-Haired Woman, Blonde Woman, and Einstein), 40 test images in the TESTIMAGES Database, and 200 RGB test images from the UC Berkeley Dataset ranging in noise density from 10% to 90%. Moreover, we compare the running time of these algorithms. These results show that ARmF outperforms the methods mentioned above. We finally discuss the need for further research.