با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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الگوریتم ژنتیک چند هدفه و طرح معماری یادگیری عمیق مبتنی بر CNN برای تشخیص موثر spam
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 18 معمولا ایمیل به عنوان قدرتمندترین رسانه در شبکههای اجتماعی آنلاین در نظر گرفته میشود که امکان گفتگو و ارتباط آنلاین کاربران رسانههای اجتماعی آنلاین را با یکدیگر فراهم می کند، همچنین امکان اشتراک گذاری لینک هم وجود دارد. به ویژه، توییتر به عنوان محبوب ترین شبکه اجتماعی شناخته شده است که بهترین کانال ارتباطی برای به اشتراک گذاشتن اخبار، ایده ها، افکار، نظرات و عقاید فعلی کاربران خود با سایر کاربران رسانه های اجتماعی آنلاین است. علیرغم تلاشهایی که برای مبارزه با عملیات اسپم در شبکه اجتماعی آنلاین انجام شده است، اسپم توییتر دارای عملکرد جدیدی محدود به 140 کاراکتر است. این نه تنها علت اصلی آزار کاربران روزمره است، بلکه اکثر مسائل امنیتی رایانه نیز ناشی از آن است که میلیاردها دلار کاهش بهره وری هزینه را در پی دارد. در این مقاله، یک الگوریتم ژنتیک چندهدفه و یک طرح معماری یادگیری عمیق مبتنی بر CNN (MOGA-CNN-DLAS) برای فرآیند تشخیص اسپم غالب در توییتر پیشنهاد میکنیم. جزئیات تجربی و نتایج و بحث حاصل از MOGA-CNN-DLAS پیشنهادی از نظر دقت ، صحت، فراخوان، FScore، RMSE و MAE مورد ارزیابی قرار گرفتند. این ارزیابی با تغییر نسبت دادههای آموزشی کاربردی از سه مجموعه داده واقعی، مانند مجموعه داده توییتر k100 و ASU انجام شد.
کلمات کلیدی: اسپم توییتر | یادگیری عمیق | شبکه عصبی پیچشی یا همگشتی (CNN) | الگوریتم ژنتیک | آنالیز رسانه های اجتماعی | تشخیص موثر اسپم |
مقاله ترجمه شده |
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Time management: Improving the timing of post-prostatectomy radiotherapy, clinical trials, and knowledge translation
مدیریت زمان: بهبود زمان رادیوتراپی پس از پروستاتکتومی، آزمایشات بالینی و ترجمه دانش-2021 Background: Management of prostate cancer after surgery is controversial. Past studies on adjuvant radiotherapy
(aRT) for higher-risk features have had conflicting results. Through the collaborative conversations of the global
radiation oncology Twitter-based journal club (#RadOnc #JC), we explored this complex topic to share recent
advances, better understand what the global radiation oncology community felt was important and inspire next
steps.
Methods: We selected the recent publication of a landmark international randomized controlled trial (RCT)
comparing immediate and salvage radiotherapy for prostate cancer, RADICALS-RT, for discussion over the
weekend of January 16 to 17, 2021. Coordination included open access to the article and an asynchronous
portion to decrease barriers to participation, cooperation of study authors (CP, MS) who participated to share
deeper insights including a live hour, and curation of related resources and tweet content through a blog post and
Wakelet journal club summary.
Discussion of Results: Our conversations created 2,370,104 impressions over 599 tweets with 51 participants
spanning 11 countries and 5 continents. A quarter of the participants were from the US (13/51) followed by 10%
from the UK (5/51). Clinical or Radiation Oncologists comprised 59% of active participants (16/27) with 62%
(18/29) reporting giving aRT within the last 5 years. Discussion was interdisciplinary with three urologists
(11%), three trainees (11%), and two physiotherapists (7%). Four months after the journal club its article Altmetric
score had increased by 7% (214 to 229). Thematic analysis of tweet content suggested participants wanted
clarification on definitions of adjuvant (aRT) and salvage radiotherapy (sRT) including indications, timing, and
decision-making tools including guidelines; more interdisciplinary and cross-sectoral collaboration including
with patients for study design including survivorship and meaningful outcomes; more effective knowledge
translation including faster clinical trials; and more data including mature results of current trials, particular
high-risk features (Gleason Group 4+, pT4b+, and margin-positive disease), implications of newer technologies
such as PSMA-PET and genomic classifiers, and better explanations for practice pattern variations including
underutilization of radiotherapy. This was further explored in the context of relevant literature.
Conclusion: Together, this global collaborative review on the postoperative management of prostate cancer
suggested a stronger signal for the uptake of early salvage radiation treatment with careful PSA monitoring, more
sensitive PSA triggers, and expected access to radiotherapy. Questions still remain on potential exceptions and barriers to use. These require better decision-making tools for all practice settings, consideration of newer
technologies, more pragmatic trials, and better use of social media for knowledge translation.
Keywords: Prostate radiotherapy | Adjuvant radiation | Salvage radiation | Journal club |
مقاله انگلیسی |
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An analysis of Twitter users’ long term political view migration using cross-account data mining
تجزیه و تحلیل از مهاجرت دیدگاه های طولانی مدت کاربران توییتر با استفاده از داده های متقابل حسابداری-2021 During the 2016 US presidential election, we witnessed a polarized population and an election outcome that
defied the predictions of many media sources. In this study, we conducted a follow-up on political view
migration through tracking Twitter users’ account activity. The study was conducted by following a set of
Twitter users over a four year period. Each year, Twitter user activities were collected and analyzed by our
novel cross-account data mining algorithm. This algorithm through multiple iterations computes a numerical
political score for each user based on their connection to other users and hashtags. We identified a set of
seed users and hashtags using prominent political figures and movements to bootstrap the algorithm. The
political score distribution demonstrates a divided population on political views. We also observed that users
are more moderate in years close to elections (2017 and 2020) compared to years of none election (2018
and 2019). There is an overall migration trend from conservatives to progressives during the four years. This
change in scores across the four year time frame suggests a unique political cycle exclusive to Donald Trump’s
unprecedented presidential term. Our results in a broad sense portray the potential capabilities of a data
collection and scoring algorithm that detected a noticeable political migration and describes the broad social
characteristics of certain politically aligned users on social media platforms.
keywords: شبکه های اجتماعی | سیاست | توییتر | داده کاوی | Social networks | Politics | Twitter | Datamining |
مقاله انگلیسی |
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A real-time deep-learning approach for filtering Arabic low-quality content and accounts on Twitter
یک رویکرد یادگیری عمیق در زمان واقعی برای فیلتر کردن عربی با کیفیت پایین محتوا و حساب های کاربری در توییتر-2021 Social networks have generated immense amounts of data that have been successfully utilized
for research and business purposes. The approachability and immediacy of social media have also
allowed ill-intentioned users to perform several harmful activities that include spamming, promoting,
and phishing. These activities generate massive amounts of low-quality content that often exhibits
duplicate, automated, inappropriate, or irrelevant content that subsequently affects users’ satisfaction
and imposes a significant challenge for other social media-based systems. Several real-time systems
were developed to tackle this problem by focusing on filtering a specific kind of low-quality content. In
this paper, we present a fine-grained real-time classification approach to identify several types of lowquality tweets (i.e., phishing, promoting, and spam tweets) written in Arabic. The system automatically
extracts textual features using deep learning techniques without relying on hand-crafted features that
are often time-consuming to be obtained and are tailored for a single type of low-quality content.
This paper also proposes a lightweight model that utilizes a subset of the textual features to identify
spamming Twitter accounts in a real-time setting. The proposed methods are evaluated on a real-world
dataset (40, 000 tweets and 1, 000 accounts), showing superior performance in both models with
accuracy and F1-scores of 0.98. The proposed system classifies a tweet in less than five milliseconds
and an account in less than a second.
keywords: محتوای کم کیفیت در شبکه های اجتماعی | حساب های اسپم | سیستم تشخیص زمان واقعی | تکنیک های یادگیری عمیق | Low-quality content in social networks | Spam accounts | Real-time detection system | Deep learning techniques |
مقاله انگلیسی |
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Diurnal emotions, valence and the coronavirus lockdown analysis in public spaces
احساسات روزانه ، ظرفیت و تجزیه و تحلیل قرنطینه کرونا در فضاهای عمومی-2021 A large-scale analysis of diurnal and seasonal mood cycles in global social networks has been performed successfully over the past ten years using Twitter, Facebook and blogs. This study describes the application of remote biometric technologies to such investigations on a large scale for the first time. The performance of this research was under real conditions producing results that conform to natural human diurnal and seasonal rhythm patterns. The derived results of this, 208 million data research on diurnal emotions, valence and facial temperature correlate with the results of an analogical Twitter research performed worldwide (UK, Australia, US, Canada, Latin America, North America, Europe, Oceania, and Asia). It is established that diurnal valence and sadness were correlated with one another both prior to and during the period of the coronavirus crisis, and that there are statistically significant relationships between the values of diurnal happiness, sadness, valence and facial temperature and the numbers of their data. Results from the simulation and formal comparisons appear in this article. Additionally the analyses on the COVID-19 screening, diagnosing, monitoring and analyzing by applying biometric and AI technologies are described in Housing COVID-19 Video Neuroanalytics. Keywords: Diurnal emotions | Valence and facial temperature | COVID-19 | Public spaces | Remote biometric technologies | Large-scale data analysis | Worldwide comparisons |
مقاله انگلیسی |
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Analysis of sentiment in tweets addressed to a single domain-specific Twitter account: Comparison of model performance and explainability of predictions
تجزیه و تحلیل احساسات در توییت های خطاب به یک حساب توییتر خاص دامنه: مقایسه عملکرد مدل و توضیح پذیری پیش بینی ها-2021 Many institutions and companies find it valuable to know how people feel about their ventures; hence, scientific
research in sentiment analysis has been intensely developed over time. Automated sentiment analysis can be
considered as a machine learning (ML) prediction task, with classes representing human affective states. Due to
the rapid development of ML and deep learning (DL), improvements in automatic sentiment analysis perfor-
mance are achieved almost every year. Since 2013, Semantic Evaluation (SemEval) has hosted a worldwide
community-acknowledged competition that allows for comparisons of recent innovations. The sentiment analysis
tasks focus on assessing sentiment in Twitter posts authored by various publishers and addressing multiple
subjects. Our study aimed to compare selected popular and recent natural language processing methods using a
new data set of Twitter posts sent to a single Twitter account. For improved comparability of our experiments
with SemEval, we adopted their metrics and also deployed our models on data published for SemEval-2017. In
addition, we investigated if an unsupervised ML technique applied for the detection of topics in tweets can be
leveraged to improve the predictive performance of a selected transformer model. We also demonstrated how a
recent explainable artificial intelligence technique can be used in Twitter sentiment analysis to gain a deeper
understanding of the models’ predictions. Our results show that the most recent DL language modeling approach
provides the highest quality; however, this quality comes at reduced model transparency. keywords: پردازش زبان طبیعی | یادگیری عمیق | تجزیه و تحلیل احساسات | فراگیری ماشین | توضیح پذیری | توییتر | Natural language processing | Deep learning | Sentiment analysis | Machine learning | Explainability | Twitter |
مقاله انگلیسی |
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Bias reduction in the population size estimation of large data sets
کاهش تمایل در برآورد اندازه جمعیت مجموعه داده های بزرگ-2020 Estimation of the population size of large data sets and hard to reach populations
can be a significant problem. For example, in the military, manpower
is limited and the manual processing of large data sets can be time consuming.
In addition, accessing the full population of data may be restricted by
factors such as cost, time, and safety. Four new population size estimators
are proposed, as extensions of existing methods, and their performances are
compared in terms of bias with two existing methods in the big data literature.
These would be particularly beneficial in the context of time-critical
decisions or actions. The comparison is based on a simulation study and
the application to five real network data sets (Twitter, LiveJournal, Pokec,
Youtube, Wikipedia Talk). Whilst no single estimator (out of the four proposed)
generates the most accurate estimates overall, the proposed estimators
are shown to produce more accurate population size estimates for small sample
sizes, but in some cases show more variability than existing estimators in
the literature. Keywords: Relative bias | Twitter | Size estimator | Youtube | Random walk sampling |
مقاله انگلیسی |
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TUTORIAL: AI research without coding: The art of fighting without fighting: Data science for qualitative researchers
آموزش: تحقیقات هوش مصنوعی بدون رمزگذاری: هنر مبارزه بدون جنگ: علم داده برای محققان کیفی-2020 In this tutorial, we show how to scrape and collect online data, perform sentiment analysis, social network
analysis, tribe finding, and Wikidata cross-checks, all without using a single line of programming code. In a stepby-
step example, we use self-collected data to perform several analyses of the glass ceiling. Our tutorial can serve
as a standalone introduction to data science for qualitative researchers and business researchers, who have
avoided learning to program. It should also be useful for experienced data scientists who want to learn about the
tools that will allow them to collect and analyze data more easily and effectively. Keywords: Twitter | Data scraping | Sentiment analysis | Tribe finding | Wikidata |
مقاله انگلیسی |
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Geo-semantic-parsing: AI-powered geoparsing by traversing semantic knowledge graphs
تجزیه جغرافیایی-معنایی: تجزیه و تحلیل ژئوپارسی با هوش مصنوعی با عبور از نمودارهای دانش معنایی-2020 Online social networks convey rich information about geospatial facets of reality. However in most cases,
geographic information is not explicit and structured, thus preventing its exploitation in real-time applications.
We address this limitation by introducing a novel geoparsing and geotagging technique called Geo-Semantic-
Parsing (GSP). GSP identifies location references in free text and extracts the corresponding geographic coordinates.
To reach this goal, we employ a semantic annotator to identify relevant portions of the input text and
to link them to the corresponding entity in a knowledge graph. Then, we devise and experiment with several
efficient strategies for traversing the knowledge graph, thus expanding the available set of information for the
geoparsing task. Finally, we exploit all available information for learning a regression model that selects the best
entity with which to geotag the input text. We evaluate GSP on a well-known reference dataset including almost
10 k event-related tweets, achieving F1=0.66. We extensively compare our results with those of 2 baselines and
3 state-of-the-art geoparsing techniques, achieving the best performance. On the same dataset, competitors
obtain F1 ≤ 0.55. We conclude by providing in-depth analyses of our results, showing that the overall superior
performance of GSP is mainly due to a large improvement in recall, with respect to existing techniques. Keywords: Geoparsing | Geotagging | Artificial intelligence | Knowledge graphs | Twitter |
مقاله انگلیسی |
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Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections
آیا تحلیل های توییتر می توانند نتیجه انتخابات را پیش بینی کنند؟ بینشی از انتخابات مجلس پنجم 2017-2020 Since the beginning of this decade, there has seen an exponential growth in number of internet users using social
media, especially Twitter for sharing their views on various topics of common interest like sports, products,
politics etc. Due to the active participation of large number of people on Twitter, huge amount of data (i.e. big
data) is being generated, which can be put to use (after refining) to analyze real world problems. This paper
takes into consideration the Twitter data related to the 2017 Punjab (a state of India) assembly elections and
applies different social media analytic techniques on collected tweets to extract and unearth hidden but useful
information. In addition to this, we have employed machine learning algorithm to perform polarity analysis and
have proposed a new seat forecasting method to accurately predict the number of seats that a political party is
likely to win in the elections. Our results confirmed that Indian National Congress was likely to emerge winner
and that in fact was the outcome, when results got declared. Keywords: Analytics | Election prediction | Social media | Natural language processing | Machine learning | Sentiment analysis | Twitter |
مقاله انگلیسی |