List of Articles Recurrent Neural Network

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Utilizing Gated Recurrent Units to Retain Long Term Dependencies with Recurrent Neural Network in Text Classification
  Nidhi Chandra Laxmi  Ahuja Sunil Kumar Khatri Himanshu Monga
  10.52547/jist.9.34.89
  The classification of text is one of the key areas of research for natural language processing. Most of the organizations get customer reviews and feedbacks for their products for which they want quick reviews to action on them. Manual reviews would take a lot of time a More

  The classification of text is one of the key areas of research for natural language processing. Most of the organizations get customer reviews and feedbacks for their products for which they want quick reviews to action on them. Manual reviews would take a lot of time and effort and may impact their product sales, so to make it quick these organizations have asked their IT to leverage machine learning algorithms to process such text on a real-time basis. Gated recurrent units (GRUs) algorithms which is an extension of the Recurrent Neural Network and referred to as gating mechanism in the network helps provides such mechanism. Recurrent Neural Networks (RNN) has demonstrated to be the main alternative to deal with sequence classification and have demonstrated satisfactory to keep up the information from past outcomes and influence those outcomes for performance adjustment. The GRU model helps in rectifying gradient problems which can help benefit multiple use cases by making this model learn long-term dependencies in text data structures. A few of the use cases that follow are – sentiment analysis for NLP. GRU with RNN is being used as it would need to retain long-term dependencies. This paper presents a text classification technique using a sequential word embedding processed using gated recurrent unit sigmoid function in a Recurrent neural network. This paper focuses on classifying text using the Gated Recurrent Units method that makes use of the framework for embedding fixed size, matrix text. It helps specifically inform the network of long-term dependencies. We leveraged the GRU model on the movie review dataset with a classification accuracy of 87%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Data-Driven Sliding Mode Control Based on Projection Recurrent Neural Network for HIV Infection: A Singular Value Approach
  Ashkan  Zarghami mehdi  Siahi Fereidoun Nowshiravan Rahatabad
  In the present study, drug treatment of HIV infection is investigated using a Data-Driven Sliding Mode Control (DDSMC) combined with a Projection Recurrent Neural Network (PRNN). The major objective is to establish the control law that eliminates the need for HIV infect More

  In the present study, drug treatment of HIV infection is investigated using a Data-Driven Sliding Mode Control (DDSMC) combined with a Projection Recurrent Neural Network (PRNN). The major objective is to establish the control law that eliminates the need for HIV infection mathematical formulae and ensures that the physical limits of the actuator are reached. This is accomplished by creating the concepts of model-free adaptive control, in which the relation between input and output is described using local dynamic linearized models based on quasi-partial derivatives. To determine the DDSMC law, a performance index is first defined based on the fulfillment of a discrete-time exponential reaching condition. By turning this index into a quadratic programming problem, the dynamics of the PRNN are extracted based on projection theory. The closed-loop system is explicitly determined using the optimizer output equation and the closed-loop stability analysis is evaluated using the singular value approach. The simulation results reveal that the proposed algorithm has robust performance in conducting the state variables of HIV infection to the healthy equilibrium point in the face of model uncertainty and external disturbances when compared to one of the newest control techniques. Manuscript profile