3,616 research outputs found
A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder
With the advancement in technology, digital images can easily be transmitted
and stored over the Internet. Encryption is used to avoid illegal interception
of digital images. Encrypting large-sized colour images in their original
dimension generally results in low encryption/decryption speed along with
exerting a burden on the limited bandwidth of the transmission channel. To
address the aforementioned issues, a new encryption scheme for colour images
employing convolutional autoencoder, DNA and chaos is presented in this paper.
The proposed scheme has two main modules, the dimensionality conversion module
using the proposed convolutional autoencoder, and the encryption/decryption
module using DNA and chaos. The dimension of the input colour image is first
reduced from N M 3 to P Q gray-scale image using the
encoder. Encryption and decryption are then performed in the reduced dimension
space. The decrypted gray-scale image is upsampled to obtain the original
colour image having dimension N M 3. The training and
validation accuracy of the proposed autoencoder is 97% and 95%, respectively.
Once the autoencoder is trained, it can be used to reduce and subsequently
increase the dimension of any arbitrary input colour image. The efficacy of the
designed autoencoder has been demonstrated by the successful reconstruction of
the compressed image into the original colour image with negligible perceptual
distortion. The second major contribution presented in this paper is an image
encryption scheme using DNA along with multiple chaotic sequences and
substitution boxes. The security of the proposed image encryption algorithm has
been gauged using several evaluation parameters, such as histogram of the
cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. encryption
Entropy in Image Analysis III
Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Video anomaly detection and localization by local motion based joint video representation and OCELM
Nowadays, human-based video analysis becomes increasingly exhausting due to the ubiquitous use of surveillance cameras and explosive growth of video data. This paper proposes a novel approach to detect and localize video anomalies automatically. For video feature extraction, video volumes are jointly represented by two novel local motion based video descriptors, SL-HOF and ULGP-OF. SL-HOF descriptor captures the spatial distribution information of 3D local regions’ motion in the spatio-temporal cuboid extracted from video, which can implicitly reflect the structural information of foreground and depict foreground motion more precisely than the normal HOF descriptor. To locate the video foreground more accurately, we propose a new Robust PCA based foreground localization scheme. ULGP-OF descriptor, which seamlessly combines the classic 2D texture descriptor LGP and optical flow, is proposed to describe the motion statistics of local region texture in the areas located by the foreground localization scheme. Both SL-HOF and ULGP-OF are shown to be more discriminative than existing video descriptors in anomaly detection. To model features of normal video events, we introduce the newly-emergent one-class Extreme Learning Machine (OCELM) as the data description algorithm. With a tremendous reduction in training time, OCELM can yield comparable or better performance than existing algorithms like the classic OCSVM, which makes our approach easier for model updating and more applicable to fast learning from the rapidly generated surveillance data. The proposed approach is tested on UCSD ped1, ped2 and UMN datasets, and experimental results show that our approach can achieve state-of-the-art results in both video anomaly detection and localization task.This work was supported by the National Natural Science Foundation of China (Project nos. 60970034, 61170287, 61232016)
Multimedia
The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications
A deep learning enabler for non-intrusive reduced order modeling of fluid flows
In this paper, we introduce a modular deep neural network (DNN) framework for
data-driven reduced order modeling of dynamical systems relevant to fluid
flows. We propose various deep neural network architectures which numerically
predict evolution of dynamical systems by learning from either using discrete
state or slope information of the system. Our approach has been demonstrated
using both residual formula and backward difference scheme formulas. However,
it can be easily generalized into many different numerical schemes as well. We
give a demonstration of our framework for three examples: (i) Kraichnan-Orszag
system, an illustrative coupled nonlinear ordinary differential equations, (ii)
Lorenz system exhibiting chaotic behavior, and (iii) a non-intrusive model
order reduction framework for the two-dimensional Boussinesq equations with a
differentially heated cavity flow setup at various Rayleigh numbers. Using only
snapshots of state variables at discrete time instances, our data-driven
approach can be considered truly non-intrusive, since any prior information
about the underlying governing equations is not required for generating the
reduced order model. Our \textit{a posteriori} analysis shows that the proposed
data-driven approach is remarkably accurate, and can be used as a robust
predictive tool for non-intrusive model order reduction of complex fluid flows.Comment: 36 pages, 21 figure
Improved Reptile Search Optimization Algorithm using Chaotic map and Simulated Annealing for Feature Selection in Medical Filed
The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search Algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles’ encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic optimizer, namely the Improved Reptile Search Algorithm (IRSA), is proposed to overcome these limitations and adapt the RSA to solve the feature selection problem. Two main improvements adding value to the standard RSA; the first improvement is to apply the chaos theory at the initialization phase of RSA to enhance its exploration capabilities in the search space. The second improvement is to combine the Simulated Annealing (SA) algorithm with the exploitation search to avoid the local optima problem. The IRSA performance was evaluated over 20 medical benchmark datasets from the UCI machine learning repository. Also, IRSA is compared with the standard RSA and state-of-the-art optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization algorithm (GOA) and Slime Mould Optimization (SMO). The evaluation metrics include the number of selected features, classification accuracy, fitness value, Wilcoxon statistical test (p-value), and convergence curve. Based on the results obtained, IRSA confirmed its superiority over the original RSA algorithm and other optimized algorithms on the majority of the medical datasets
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