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Register a new userLecture3 (V1) :integrate- Lists -Data Frames - Dr. Eng Fadi CHAABAN
محاضرة3 (V1) : تكامل- قوائم - إطار بيانات - د. م. فادي شعبان
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Tishreen University محاضرة
Publication date
2020
fields
Mathematical Statistics
and research's language is
العربية
Created by
Fadi CHAABAN
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تكامل- قوائم - إطار بيانات integrate Lists Data Frames تكامل- قوائم - إطار بيانات integrate Lists Data Frames
integrate Lists Data Frames integrate Lists Data Frames integrate Lists Data Frames integrate Lists Data Frames
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plot من أكثر توابع الرسم استخداماً في R وهو تابع عام Generic Function، أي أنه
يمتلك العديد من الطرائق حتى يلائم الكائنات الممررة له.
سلسلة محاضرات في لغة البرمجة الإحصائية R
مقدمة عن لغة R - الأوامر الخاصة في لغة البرمجة
Rstudio & R و تطبيقات عملية
R-Studio
الاوامر الخاصة في لغة البرمجة R
ايعازات عامة
الكائنات Objects –
الأشعة Vectors
المصفوفات Matrices
Video image data can be analyzed and processed in many ways. This research explores the extent at which spiking neurons, which are designed along the Hodgkin-Huxley model, are suitable for this task. The simulations reported in this research
consid
er integrate-and-fire neurons constant and alternating input currents, as well as pixel-intensity driven inputs. Currently, the simulation software employs 64 independently operating spiking neurons that process image data taken every 25 ms. In order to define the response of these neurons, the experiments were done on 100 digital images which include different illuminations, contrast, and saturation situations. The results show that the integrate-and-fire-neuron is highly sensitive to the changes in the intensity of pixels if its parameters are properly set. So in many applications, such as "Saliency Maps", which highly depend on the intensity values of a set of pixels, a neural network made of this neuron will perfectly fit.
With the rise of research on toxic comment classification, more and more annotated datasets have been released. The wide variety of the task (different languages, different labeling processes and schemes) has led to a large amount of heterogeneous da
tasets that can be used for training and testing very specific settings. Despite recent efforts to create web pages that provide an overview, most publications still use only a single dataset. They are not stored in one central database, they come in many different data formats and it is difficult to interpret their class labels and how to reuse these labels in other projects. To overcome these issues, we present a collection of more than thirty datasets in the form of a software tool that automatizes downloading and processing of the data and presents them in a unified data format that also offers a mapping of compatible class labels. Another advantage of that tool is that it gives an overview of properties of available datasets, such as different languages, platforms, and class labels to make it easier to select suitable training and test data.
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