Yazar "Raheem, Abd-ur" seçeneğine göre listele

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    An investigation of the causal relationship between sunspot groups and coronal mass ejections by determining source active regions
    (Royal Astronomical Society, 2021) Raheem, Abd-ur; Çavuş, Hüseyin; Çoban, Gani Çağlar; Kınacı, Ahmet Cumhur; Wang, Haimin; Wang, Jason T. L.
    Although the source active regions of some coronal mass ejections (CMEs) were identified in CME catalogues, vast majority of CMEs do not have an identified source active region. We propose a method that uses a filtration process and machine learning to identify the sunspot groups associated with a large fraction of CMEs and compare the physical parameters of these identified sunspot groups with properties of their corresponding CMEs to find mechanisms behind the initiation of CMEs. These CMEs were taken from the Coordinated Data Analysis Workshops (CDAW) data base hosted at NASA's website. The Helioseismic and Magnetic Imager (HMI) Active Region Patches (HARPs) were taken from the Stanford University's Joint Science Operations Center (JSOC) data base. The source active regions of the CMEs were identified by the help of a custom filtration procedure and then by training a long short-term memory network (LSTM) to identify the patterns in the physical magnetic parameters derived from vector and line-of-sight magnetograms. The neural network simultaneously considers the time series data of these magnetic parameters at once and learns the patterns at the onset of CMEs. This neural network was then used to identify the source HARPs for the CMEs recorded from 2011 till 2020. The neural network was able to reliably identify source HARPs for 4895 CMEs out of 14604 listed in the CDAW data base during the aforementioned period.
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    Can Solar Cycle 25 Be a New Dalton Minimum?
    (Springer, 2021) Çoban, Gani Çağlar; Raheem, Abd-ur; Çavuş, Hüseyin; Asghari-Targhi, Mahboubeh
    The aim of this study is to predict solar activity for the next 10 years (Solar Cycle 25) using a deep learning technique known as a stateful Long Short-Term Memory (LSTM) network. To achieve this goal the number of daily sunspots observed by the American Association of Variable Star Observers (AAVSO) organization from 1945 to 2020 is used as training data for a stateful LSTM model. Time slices are produced by dividing the data between 1945–2020; then data are predicted and examined for the test years of the network trained on these time slices. The mean and smoothed values are calculated from the estimated daily data, compared with the actual mean, and smoothed values, including standard deviations, and the prediction accuracy of the model is examined. Finally, the number of daily sunspots for 10 years of Cycle 25 is estimated. The results are discussed by calculating the mean and smoothed values. The predicted Solar Cycle 25 shows features of a new Dalton Minimum together with Cycle 24. We conclude that Cycle 25 will be the marker of a new Dalton Minimum period.
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    Correlation between sunspots and interplanetary shocks measured by ACE during 1998-2014 and some estimations for the 22nd solar cycle and the years between 2015 and 2018 with artificial neural network using the Cavus 2013 model
    (Elsevier Sci Ltd, 2020) Cavus, Huseyin; Araz, Gokhan; Coban, Gani Caglar; Raheem, Abd-ur; Karafistan, Aysel I.
    The Advanced Composition Explorer (ACE) spacecraft has measured 235 solar-based interplanetary (IP) shock waves between the years of 1998-2014. These were composed of 203 fast forward (FF), 6 slow forward (SF), 21 fast reverse (FR) and 5 slow reverse (SR) type shocks. These data can be obtained from the Interplanetary Shock Database of Harvard-Smithsonian Centre for Astrophysics. The Solar Section of American Association of Variable Star Observers (AAVSO) is an organization that counts the number of the sunspots. The effects of interplanetary shock waves on some physical parameters can be computed using a hydrodynamical model. There should be some correlations between these effects and the sunspot variations. The major objective of this paper is twofold. The first one is to search these correlations with sunspots given in the database of AAVSO. As expected, high correlations between physical parameters and sunspots have been obtained and these are presented in tables below. The second objective is to make an estimation of these parameters for the 22nd solar cycle and the years between 2015 and 2018 using an artificial neural network. Predictions have been made for these years where no shock data is present using artificial intelligence. The correlations were observed to increase further when these prediction results were included. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
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    The investigation of the relationship between the physical parameters of sunspots and solar coronal mass ejections using artificial intelligence algorithms
    (Çanakkale Onsekiz Mart Üniversitesi, 2021) Raheem, Abd-ur; Çavuş, Hüseyin
    Güneş yüzeyinde bulunan manyetik olarak aktif bölgeler çeşitli değişikliklere uğrayarak, gezegenler arası ortamda şok dalgalarına, uzay hava durumu tahminleri ve diğer ilgili çalışmalar için önemli bir faktör olan Koronal Kütle Atımlarına (CME) neden olurlar. Şimdiye kadar CME'ler için kaynak aktif bölgeler bizim bilgimize göre manuel olarak tanımlandıkları için CME'ler için kaynak aktif bölgelere ilişkin bilgiler çok az sayıda ve CME'lerin büyük çoğunluğu için kaynak aktif bölgeler halen bilinmemektdedir. Bu çalışma, çok sayıda CME için kaynak aktif bölgeleri belirlemek, makine öğrenimi ve diğer özel filtreleme süreçlerine dayalı yeni bir teknik önermekte ve tanımlama sürecine büyük bir otomasyon getirmektedir. Bu çalışmada ilgili CME'ler için önceden bilinen kaynak Helio-sismik ve Manyetik Görüntüleyici (HMI) Aktif Bölgelerin (HARP'ler) manyetik verileri, makine öğrenimi algoritmalarını eğitmek için kullanılmıştır. Daha önce bilinen kaynak aktif bölgelerin vektör ve görüş hattı manyetogramları kullanılarak HARP'lere ait 17 manyetik parametre verisi toplananmış ve bu HARP'lere ait bu manyetik parametrelerdeki değişiklikleri eş zamanlı olarak öğrenebilen bir Uzun-Kısa süreli bellek Ağı (LSTM) kullanılarak bir mekanizma önerilmiştir. Geliştirilen LSTM modeli daha sonra 2010 ve 2020 boyunca bilinen tüm CME'lere ait kaynak HARP'leri belirlemek için kullanılmıştır. Bu teknik kullanılarak 4895 CME olayı için kaynak aktif bölgeler belirlenmiş ve bunun bir sonucu olarak CME'ler için tanımlanmış kaynak aktif bölgelerin bir veri tabanı elde edilmiştir. CME'lerin ortaya çıkmasına ilişkin fikir edinmek için tanımlanan HARP'lerin manyetik parametreleri daha sonra ilgili CME'lere göre analiz edilmiş ve analiz sonuçları bu tezde verilmiştir.