On Chaotic Neural Network Design: A New Framework

Qin, Ke

doi:10.1007/s11063-016-9525-y

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…As a result, it is characterized by erratic behavior. Because of the security concerns, cryptography applications [12] are attracted to the chaotic activity of neurons. According to the research, the ANN model may be expanded to describe complex reversible encoders as well.…”

Section: A Artificial Neural Network and Cloud Computingmentioning

confidence: 99%

Medical Image Cryptanalysis using Adaptive, Lightweight Neural Network based Algorithm for IoT based Secured Cloud Storage

Narayana¹,

Lakshmi²,

Sayal³

2022

IJACSA

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Currently available modern medical system generates large amounts of data, such as computerized patient data and digital medical pictures, which must be kept securely for future reference. Existing storage technologies are not capable of storing large amounts of data efficiently. It is a key and abrogating topic of specialized, social, and medical significance and a key and abrogating subject of general interest. The results of cars, purchasers, and Internet of Things industrybased and essential segments, sensors, and other daily objects are fused with a network of the Internet and solid information abilities that promise to alter the way we operate and live future. The suggested work demonstrates a symmetric-key lightweight technique for secure data transmission of images and text, which uses an image encryption system and a reversible data hiding system to demonstrate the program's implementation. On the other hand, cloud storage services can meet demand due to features such as flexibility and availability. Cloud computing is enabled by amazing internet innovation as well as cutting-edge electrical equipment. Even though medical images may be stored on the cloud, most cloud service providers only save client data in plain text. As part of their overall strategy, cloud users must take responsibility for protecting medical data. Because attackers' increasing computing power and creativity are opening up more and more areas in this mathematical form, most existing image encryption schemes are vulnerable to the plaintext attack of choice. This article presents an image encryption method inspired by an Adaptive IoT-based Hopfield Neural Network (AIHNN) that can resist other assaults while optimizing and improving the system through continuous learning and updating.

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Section: A Artificial Neural Network and Cloud Computingmentioning

confidence: 99%