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Detection of Ransomware Based on Recurrent Neural Network (RNN)
2021Ransomware, as a specific type of malignant programming, has as of late rose as a significant danger in PC security. With a capacity to bolt out client access to their substance, late ransomware assaults have caused serious effect at an individual and hierarchical level. Explicit auxiliary properties of ransomware can additionally improve the nature of
Saketh Kanumuri +3 more
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RECURRENT NEURAL NETWORKS (RNNS)
Recurrent Neural Networks (RNNs) are a specialized class of neural networks designed to process sequential data. Unlike traditional feedforward networks, RNNs utilize internal memory to maintain contextual information across time steps, making them ideal for tasks such as language modeling, time series forecasting, and speech recognition.Waseem Ahmad, Vishal Goyal, Dr Surender
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Recurrent Neural Networks (RNNs)
2019Recurrent neural networks (RNNs) are another specialized scheme of neural network architectures. RNNs are developed to solve learning problems where information about the past (i.e., past instants/events) is directly linked to making future predictions. Such sequential examples play up frequently in many real-world tasks such as language modeling where
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Mixing-RNN: A Recommendation Algorithm Based on Recurrent Neural Network
2019Collaborative filtering algorithms have been used by recommender systems for item (e.g., movie) recommendation. However, traditional collaborative filtering algorithms face challenges to provide accurate recommendation when users’ interest and context suddenly changed.
Enhan Liu +4 more
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Recurrent Neural Networks (RNNs)
2017Recurrent neural networks (RNNs) are models that were created to tackle problems within the scope of pattern recognition and are fundamentally built on the same concepts with respect to feed-forward MLPs. The difference is that although MLPs by definition have multiple layers, RNNs do not and instead have a directed cycle through which the inputs are ...
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Recurrent Neural Network (RNN) to Analyse Mental Behaviour in Social Media
International Journal of Software Science and Computational Intelligence, 2021A recent British study of people between the ages of 14 and 35 has shown that social media has a negative impact on mental health. The purpose of the paper is to detect people with mental disorders' behaviour in social media in order to help Twitter users in overcoming their mental health problems such as anxiety, phobia, depression, paranoia.
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Recurrent Neural Networks (RNN) or Sequence Models
2019In this chapter, we will explore and discuss the basic architecture of sequence models (Recurrent Neural Networks). In particular, we will Build and train sequence models, and a commonly used variant known as Long Short-Term Memory Models (LSTMs). Apply sequence models to Natural Language Processing (NLP) problems, including text synthesis.
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LSTM Recurrent Neural Network (RNN) for Anomaly Detection in Cellular Mobile Networks
2019Anomaly detection can show significant behavior changes in the cellular mobile network. It can explain much important missing information and which can be monitored using advanced AI (Artificial Intelligent) applications/tools. In this paper, we have proposed LSTM (Long Short-Term Memory) based RNN (Recurrent Neural Network) which can model a time ...
S. M. Abdullah Al-Mamun, Mehmet Beyaz
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Malware Attacks Detection in IoT Using Recurrent Neural Network (RNN)
Intelligent Automation & Soft ComputingIoT (Internet of Things) devices are being used more and more in a variety of businesses and for a variety of tasks, such as environmental data collection in both civilian and military situations. They are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their ...
Abeer Abdullah Alsadhan +5 more
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