Online misogyny has become an increasing worry for Arab women who experience genderbased online abuse on a daily basis. Misogyny automatic detection systems can assist in the prohibition of anti-women Arabic toxic con...
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Community detection in the complex network is the process of finding optimal clusters of vertices that are similar in characteristics. To study the properties and functions of complex networks, community detection pla...
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ISBN:
(数字)9789811604256
ISBN:
(纸本)9789811604249
Community detection in the complex network is the process of finding optimal clusters of vertices that are similar in characteristics. To study the properties and functions of complex networks, community detection plays a crucial role. Community detection is generally categorized as an optimization problem, and due to its inherent property it can’t be solved by the traditional optimization method, and over the past few decades, various algorithms have been proposed to address this problem in multiple fields such as power system, physics, biology, or sociology. In this paper, we present a critical survey on various algorithms for community detection currently available such as genetic algorithm, evolutionary algorithms, a nature-inspired algorithm, deep learning algorithm. This survey paper outlines the challenges and constraints of different state-of-arts community algorithms detection by utilizing contemporary techniques like deep neural networks, genetic algorithms, and various topological features based methodologies. This paper includes two genetic algorithms one is based in safe and balanced initialization while the other is based in clustering co-efficient and common finding for these two algorithms are they both can be further improved for the detection of overlapping communities and also we discussed community detection using internal force between vertices this can method can further include the dynamic strategies to uncover the communities so that it will work for the dynamic network and increase the practicality of the method this paper also include community detection method using edges betweenness centrality this method can further be used improved in the detection of directed and weighted networks lastly community detection using deep neural network auto-encoder is used to for dimension reduction and further K-means is used for the detection of community in the network this method can be further improved in term of scaling of the model and can be furth
In this paper we present our approach and system description on Task 7a in ProfNer-ST: Identification of profession & occupation in Health related Social Media. Our main contribution is to show the effectiveness o...
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At the moment, the vast majority of Portuguese archives with an online presence use a software solution to manage their finding aids: e.g. Digitarq or Archeevo. Most of these finding aids are written in natural langua...
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Nowadays, the news ecosystem has shifted from traditional print media to social media outlets. It has resulted in the inaccuracy and irrelevancy in updating information by people which is commonly known as fake news. ...
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The problem of power system load forecasting is the basis for the realization of safe, economical, and efficient power system operation. Based on statistical analysis of a large amount of historical load data, accordi...
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Legal practice can gain much from advances in machine learning and A.I. technology. Common law countries such as the United States, the United Kingdom, Canada, and Australia, rely on judicial precedent to decide what ...
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Massive volumes of high-frequency and high-volume data are constantly being generated by the vast amount of available tracking sensors of moving objects. This phenomenon can be strongly observed in the maritime domain...
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Between 1973 and 1985, a civic-military dictatorship ruled in Uruguay. Systematic violations of human rights marked this period. Project *** aims to develop tools and methodologies to analyze historical documents from...
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While searching for solutions for Reinforcement learning problems Policy Search algorithms crawl through a Search Space to efficiently find a successful policy. In contrast to Deep learning methods, which rely on hund...
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