Results 41 to 50 of about 28,490,308 (324)
Sentiment Analysis of Tweets Using Supervised Machine Learning Techniques Based on Term Frequency [PDF]
Journal of Information Technology Management, 2021 World of technology provides everyone with a great outlet to give their opinion, using social media like Twitter and other platforms. This paper employs machine learning methods for text analysis to obtain sentiments of reviews by the people on twitter ...
Deepti Aggarwal +5 more
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Using a linguistic computer software to explore the N-grams in ESL college compositions [PDF]
Asian Journal of English Language Studies (AJELS), 2017 This research made an initial exploration on the English N-grams commonly used by college students in their ESL writing classes. Previous studies found out that there are several types of N-grams; however, this research zeroed in on three- and four-word ...
Rey John Castro Villanueva
doaj
N-gram MalGAN: Evading machine learning detection via feature n-gram
Digital Communications and Networks, 2022 In recent years, many adversarial malware examples with different feature strategies, especially GAN and its variants, have been introduced to handle the security threats, e.g., evading the detection of machine learning detectors. However, these solutions still suffer from problems of complicated deployment or long running time.
Enmin Zhu +4 more
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N-Gram Similarity and Distance [PDF]
, 2005 In many applications, it is necessary to algorithmically quantify the similarity exhibited by two strings composed of symbols from a finite alphabet. Numerous string similarity measures have been proposed. Particularly well-known measures are based are edit distance and the length of the longest common subsequence.
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Detection of algorithmically generated malicious domain names using masked N-grams
Expert systems with applications, 2019 Malware detection is a challenge that has increased in complexity in the last few years. A widely adopted strategy is to detect malware by means of analyzing network traffic, capturing the communications with their command and control (C&C) servers ...
J. Selvi +2 more
semanticscholar +1 more source
, 2009 In language modeling, n-gram models are probabilistic models of text that use some limited amount of history, or word dependencies, where n refers to the number of words that participate in the dependence relation.
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Recasting the discriminative n-gram model as a pseudo-conventional n-gram model for LVCSR [PDF]
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 ABSTRACT Discriminative n-gram language modeling has been used to re-rank candidate recognition hypotheses for performance improvements in large vocabulary continuous speech recognition (LVCSR). Discriminative n-gram modeling is defined in a linear framework.
Zhengyu Zhou, Helen M. Meng
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Fast Structural Alignment of Biomolecules Using a Hash Table, N-Grams and String Descriptors
Algorithms, 2009 This work presents a generalized approach for the fast structural alignment of thousands of macromolecular structures. The method uses string representations of a macromolecular structure and a hash table that stores n-grams of a certain size for ...
Robert Preissner +6 more
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IEEE Access, 2022 E-learning has gained further importance and the amount of e-learning research and applications has increased exponentially during the COVID-19 pandemic.
Fatih Gurcan +2 more
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