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PubMed-indexed neurosurgical research productivity of Iraq-based neurosurgeons
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Background:

Research is a central component of neurosurgical training and practice and is increasingly viewed as a quintessential indicator of academic productivity. In this study, we focus on identifying the current status and challenges of neurosurgical research in Iraq.

Methods:

An online PubMed Medline database search was conducted to identify all articles published by Iraq-based neurosurgeons between 2003 and 2020. Information was extracted in relation to the following parameters: authors, year of publication, author’s affiliation, author’s specialty, article type, article citation, journal name, journal impact factor, and topic. This data were then tabulated and analyzed.

Results:

Between 2003 and 2021, a total of 52 PubMed indexed papers were published from Iraq. All publications have been clustered in the period of 2012–2020. From 2012 to 2016, only four papers were published, one per year. The number of publications increased from 2017 to 2021, with an average of 12 publications per year. The most common article type was “case reports” (n = 14). Neurotrauma (n = 10) and vascular neurosurgery (n = 10) were the two most common topics. Most of the studies came from the city of Bagdad (n = 46), with just nine studies coming from peripheral governorates. The Neurosurgery Teaching Hospital in Bagdad was the neurosurgery center with the highest research output (n = 38).

Conclusion:

The number of publications per year has been showing a, relatively, promising trend since 2012. However, to promote sustained growth in academic productivity, a strategic plan that acknowledges the political, financial, and health-system-related challenges are urgently needed.

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Improve topic modeling algorithms based on Twitter hashtags
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Abstract<p>Today with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned</p> ... Show More
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BCI-Based Smart Room Control using EEG Signals
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Face-based Gender Classification Using Deep Learning Model
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Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

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Genetic –Based Face Retrieval Using Statistical Features
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Mon Apr 15 2019
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