Novel Heuristic Recurrent Neural Network Framework to Handle Automatic Telugu Text Categorization from Handwritten Text Image

In the near future, the digitization and processing of the current paper documents describe efficient role in the creation of a paperless environment. Deep learning techniques for handwritten recognition have been extensively studied by various researchers. Deep neural networks can be trained quickly thanks to a lot of data and other algorithmic advancements. Various methods for extracting text from handwritten manuscripts have been developed in literature. To extract features from written Telugu Text image having some other neural network approaches like convolution neural network (CNN), recurrent neural networks (RNN), long short-term memory (LSTM). Different deep learning related approaches are widely used to identification of handwritten Telugu Text; various techniques are used in literature for the identification of Telugu Text from documents. For automatic identification of Telugu written script efficiently to eliminate noise and other semantic features present in Telugu Text, in this paper, proposes Novel Heuristic Advanced Neural Network based Telugu Text Categorization Model (NHANNTCM) based on sequence-to-sequence feature extraction procedure. Proposed approach extracts the features using RNN and then represents Telugu Text in sequence-to-sequence format for the identification advanced neural network performs both encoding and decoding to identify and explore visual features from sequence of Telugu Text in input data. The classification accuracy rates for Telugu words, Telugu numerals, Telugu characters, Telugu sentences, and the corresponding Telugu sentences were 99.66%, 93.63%, 91.36%, 99.05%, and 97.73% consequently. Experimental evaluation describe extracted with revealed which are textured i.e. TENG shown considerable operations in applications such as private information protection, security defense, and personal handwriting signature identification

GAN Base feedback analysis system for industrial IOT networks

The internet, like automated tools, has grown to better our daily lives. Interacting IoT products and cyber-physical systems. Generative Adversarial Network's (GANs') generator and discriminator may have different inputs, allowing feedback in supervised models. AI systems use neural networks, and adversarial networks analyse neural network feedback. Cyber-physical production systems (CPPS) herald intelligent manufacturing . CPPS may launch cross-domain attacks since the virtual and real worlds are interwoven. This project addresses enhanced Cyber-Physical System(CPS) feedback structure for Denial-of-Service (DoS) defence . Comparing sensor-controller and controller-to-actuator DoS attack channels shows a swapping system modelling solution for the CPS's complex response feedback. Because of the differential in bandwidth between the two channels and the suspects' limited energy, one person can only launch so many DoS assaults. DoS attacks are old and widespread. Create a layered switching paradigm that employs packet-based transfer techniques to prevent assaults. The discriminator's probability may be used to assess whether feedback samples came from real or fictional data. Cognitive feedback can assess GA feedback data

Ensemble-based cryptography for soldiers’ health monitoring using mobile ad hoc networks

Information technology acts an important role in gathering, transmitting with executing data from areas of disaster-prone such as the battlefield and international borders. In addition to the country’s security, the soldier needs protection by defending himself with advanced weapons such as a bomb detector. This paper provides the capability to track the whereabouts and health of soldiers who have been lost or injured on the battlefield. It assists in reducing the time, searching and rescuing operation efforts of the military control room. This paper implements a system for health-condition monitoring that sends soldiers’ health parameters, such as the electrocardiogram (ECG), blood oxygen level, pulse rate, and temperature, to the control room via a Mobile Ad hoc Network (MANET). Body parameters are sensed utilizing various body sensors fixed to the bodies of soldiers. The body parameters are broadcasted to the control room via MANET devices at the path. To preserve the health parameters data of soldiers from enemies while data transmission, this paper also proposes a cryptographic ensemble approach. This approach combines Symmetric Key Encryption, and Identity Based Encryption (IBE) with Identity Based Signature (IBS). The experimental result shows proposed cryptographic ensemble provides high security compared with existing MANET security algorithms