Chaotic Mapping Function Research Articles

CMRLCCOA: Multi-Strategy Enhanced Coati Optimization Algorithm for Engineering Designs and Hypersonic Vehicle Path Planning.

The recently introduced coati optimization algorithm suffers from drawbacks such as slow search velocity and weak optimization precision. An enhanced coati optimization algorithm called CMRLCCOA is proposed. Firstly, the Sine chaotic mapping function is used to initialize the CMRLCCOA as a way to obtain better-quality coati populations and increase the diversity of the population. Secondly, the generated candidate solutions are updated again using the convex lens imaging reverse learning strategy to expand the search range. Thirdly, the Lévy flight strategy increases the search step size, expands the search range, and avoids the phenomenon of convergence too early. Finally, utilizing the crossover strategy can effectively reduce the search blind spots, making the search particles constantly close to the global optimum solution. The four strategies work together to enhance the efficiency of COA and to boost the precision and steadiness. The performance of CMRLCCOA is evaluated on CEC2017 and CEC2019. The superiority of CMRLCCOA is comprehensively demonstrated by comparing the output of CMRLCCOA with the previously submitted algorithms. Besides the results of iterative convergence curves, boxplots and a nonparametric statistical analysis illustrate that the CMRLCCOA is competitive, significantly improves the convergence accuracy, and well avoids local optimal solutions. Finally, the performance and usefulness of CMRLCCOA are proven through three engineering application problems. A mathematical model of the hypersonic vehicle cruise trajectory optimization problem is developed. The result of CMRLCCOA is less than other comparative algorithms and the shortest path length for this problem is obtained.

Bearing Fault Diagnosis based on Convolution Neural Network with Logistic Chaotic Map

AbstractBearing is the most basic component of motor, and prone to failure. Bearing fault diagnosis is paramount for improving the reliability and safety in motor‐drive systems. Therefore, convolutional neural network (CNN) is proposed with Logistic chaotic map and its corresponding fault diagnosis approach, which can effectively advance the accuracy of bearing fault diagnosis. Specifically, the Logistic chaotic map and Sigmoid function are combined into a non‐monotonic excitation function, which is employed to the full connection layer of the CNN. The proposed chaotic CNN can solve two issues that the conventional neural network inclines to get the local minimum value and the gradient of Sigmoid excitation function disappears. It is applied to fault data from the center of Western Reserve University and from the American Society for Mechanical Failure Prevention technology (in noiseless and noisy conditions). The results indicate the diagnosis accuracy of the algorithm outperforms other classical bearing diagnosis algorithms. Moreover, the chaotic CNN exhibits better anti‐noise performance.

An Improved Chaotic Grey Wolf Optimization Algorithm (CGWO)

Grey Wolf Optimization (GWO) is a new type of swarm-based technique for dealing with realistic engineering design constraints and unconstrained problems in the field of metaheuristic research. Swarm-based techniques are a type of population-based algorithm inspired by nature that can produce low-cost, quick, and dependable solutions to a wider variety of complications. It is the best choice when it can achieve faster convergence by avoiding local optima trapping. This work incorporates chaos theory with the standard GWO to improve the algorithm's performance due to the ergodicity of chaos. The proposed methodology is referred to as Chaos-GWO (CGWO). The CGWO improves the search space's exploration and exploitation abilities while avoiding local optima trapping. Using different benchmark functions, five distinct chaotic map functions are examined, and the best chaotic map is considered to have great mobility and ergodicity characteristics. The results demonstrated that the best performance comes from using the suitable chaotic map function, and that CGWO can clearly outperform standard GWO.

Development of a COVID-19 Patients’ Fatality Prediction System Using Swarm Intelligent Convolution Neural Network

Aims: This work aims to develop a system that can be used to accurately and timely predict the fatality of a positively tested COVID-19 patient through the use of a deep learning technique – a swarm intelligent convolutional neural network.
 Methodology: The dataset used in this study was acquired from the Kaggle repository database. The dataset contains the Lung Chest X-Ray images of COVID-19 patients. The images were pre- processed to obtain the desired image quality for further processing. This was followed by segmenting the pre-processed images. An Enhanced Firefly Algorithm (EFA) was formulated by applying the roulette wheel selection procedure to model the movement process of the firefly as a deterministic process to assist the standard Firefly Algorithm (FA) and application of Chaotic Sinusoidal Map Function to model the attractive process of the firefly which establishes a balance between exploration and exploitation in FA. The EFA was applied to optimize Convolution Neural Network (CNN) hyper-parameters (number of layers, number of filters per layer, filter size and batch size). The segmented result was subsequently presented to EFA-CNN feature extraction and prediction of COVID-19 patient fatality cases. The formulated deep learning models (EFA-CNN and CNN) were implemented using Matrix Laboratory 2020a software. The implemented models were evaluated using specificity, sensitivity, false positive rate, accuracy, and recognition time/rate to determine the performance of the developed models.
 Results: The findings revealed that the EFA-CNN model performs better in the prediction of COVID-19 patients’ fatality compared to the CNN model. It was also discovered that the formulated EFA applied to select optimal values of the hyper-parameters for the CNN architecture accounted for improved recognition accuracy and reduced recognition time of the developed COVID-19 Patients’ Fatality Prediction System.
 Conclusion: The system developed will assist both the government and healthcare workers in providing the needed computational capability for the prediction of the fatality level of a positively tested COVID-19 patient.

Reliable Machine Learning Model for IIoT Botnet Detection

Due to the growing number of Internet of Things (IoT) devices, network attacks like denial of service (DoS) and floods are rising for security and reliability issues. As a result of these attacks, IoT devices suffer from denial of service and network disruption. Researchers have implemented different techniques to identify attacks aimed at vulnerable Internet of Things (IoT) devices. In this study, we propose a novel features selection algorithm FGOA-kNN based on a hybrid filter and wrapper selection approaches to select the most relevant features. The novel approach integrated with clustering rank the features and then applies the Grasshopper algorithm (GOA) to minimize the top-ranked features. Moreover, a proposed algorithm, IHHO, selects and adapts the neural network’s hyper parameters to detect botnets efficiently. The proposed Harris Hawks algorithm is enhanced with three improvements to improve the global search process for optimal solutions. To tackle the problem of population diversity, a chaotic map function is utilized for initialization. The escape energy of hawks is updated with a new nonlinear formula to avoid the local minima and better balance between exploration and exploitation. Furthermore, the exploitation phase of HHO is enhanced using a new elite operator ROBL. The proposed model combines unsupervised, clustering, and supervised approaches to detect intrusion behaviors. The N-BaIoT dataset is utilized to validate the proposed model. Many recent techniques were used to assess and compare the proposed model’s performance. The result demonstrates that the proposed model is better than other variations at detecting multiclass botnet attacks.

Optimizing boiler combustion parameters based on evolution teaching-learning-based optimization algorithm for reducing NOx emission concentration.

How to reduce a boiler's NOx emission concentration is an urgent problem for thermal power plants. Therefore, in this paper, we combine an evolution teaching-learning-based optimization algorithm with extreme learning machine to optimize a boiler's combustion parameters for reducing NOx emission concentration. Evolution teaching-learning-based optimization algorithm (ETLBO) is a variant of conventional teaching-learning-based optimization algorithm, which uses a chaotic mapping function to initialize individuals' positions and employs the idea of genetic evolution into the learner phase. To verify the effectiveness of ETLBO, 20 IEEE congress on Evolutionary Computation benchmark test functions are applied to test its convergence speed and convergence accuracy. Experimental results reveal that ETLBO shows the best convergence accuracy on most functions compared to other state-of-the-art optimization algorithms. In addition, the ETLBO is used to reduce boilers' NOx emissions by optimizing combustion parameters, such as coal supply amount and the air valve. Result shows that ETLBO is well-suited to solve the boiler combustion optimization problem.

Enhancement of the CAST Block Algorithm Based on Novel S-Box for Image Encryption.

due to the rapid growth of data communication and multimedia system applications, security becomes a critical issue in the communication and storage of images. This study aims to improve encryption and decryption for various types of images by decreasing time consumption and strengthening security. An algorithm is proposed for encrypting images based on the Carlisle Adams and Stafford Tavares CAST block cipher algorithm with 3D and 2D logistic maps. A chaotic function that increases the randomness in the encrypted data and images, thereby breaking the relation sequence through the encryption procedure, is introduced. The time is decreased by using three secure and private S-Boxes rather than using six S-Boxes, as in the traditional method. Moreover, the CAST encryption algorithm was modified to be used on the private keys and substitution stage (S-Boxes), with the keys and S-Boxes of the encryption algorithm being generated according to the 2D and 3D chaotic map functions. The proposed system passed all evaluation criteria, including (MSE, PSNR, EQ, MD, SC, NC, AD, SNR, SIM, MAE, Time, CC, Entropy, and histograms). Moreover, the results also illustrate that the created S-Boxes passed all evaluation criteria; compared with the results of the traditional method that was used in creating S-Box, the proposed method achieved better results than other methods used in the other works. The proposed solution improves the entropy which is between (7.991-7.999), reduces the processing time which is between (0.5-11 s/Images), and improves NCPR, which is between (0.991-1). The proposed solution focuses on reducing the total processing time for encryption and decryption and improving transmission security. Finally, this solution provides a fast security system for surgical telepresence with secure real-time communication. The complexity of this work needs to know the S-Box creation method used, the chaotic method, the values of the chaotic parameters, and which of these methods was used in the encryption process.

FTPS: Efficient fault‐tolerant dynamic phrase search over outsourced encrypted data with forward and backward privacy

SummaryWith the popularity of cloud computing, more and more users store sensitive information in cloud servers. In order to protect the data over the cloud server, symmetric encryption with keyword search has been developed and the phrase search has been proposed subsequently to overcome the inefficiency produced by single/multi‐keyword search. However, most existing phrase search schemes fails to support fault‐tolerant search which is essential to users. Therefore, this paper proposes a fault‐tolerant dynamic phrase search scheme with forward privacy and backward privacy (FTPS). Piecewise Linear Chaotic Map and minhash function are used to blur information, and Bloom filter based index is constructed to realize efficient search and dynamic update simultaneously. Security analysis proves that FTPS can properly preserve the privacy of search user, and experimental results show that FTPS is practical.

An ACO-Based Clustering Algorithm With Chaotic Function Mapping

To overcome shortcomings when the ant colony optimization clustering algorithm (ACOC) deal with the clustering problem, this paper introduces a novel ant colony optimization clustering algorithm with chaos. The main idea of the algorithm is to apply the chaotic mapping function in the two stages of ant colony optimization: pheromone initialization and pheromone update. The application of chaotic mapping function in the pheromone initialization phase can encourage ants to be distributed in as many different initial states as possible. Applying the chaotic mapping function in the pheromone update stage can add disturbance factors to the algorithm, prompting the ants to explore new paths more, avoiding premature convergence and premature convergence to suboptimal solutions. Extensive experiments on the traditional and proposed algorithms on four widely used benchmarks are conducted to investigate the performance of the new algorithm. These experiments results demonstrate the competitive efficiency, effectiveness, and stability of the proposed algorithm.

A new symmetrical cryptosystem based cellular automata and chaotic map function

Multimedia data including text, audio, image, and video are increasingly transmitted over networks or stored in digital format. Therefore, secure transmission and storage are required to preserve it. This is why the utilization of cryptography has become imperative to establish new approaches for rising security. The cryptographic systems rely on cellular automata (CA) and chaotic systems, which are fundamental in various cryptographic applications such as key generation and image encryption. Some of them are complex and others have little key space. In this paper, a novel research work based on cellular automata and chaotic logistic mapping is proposed. Overall, the paper aims to propose a new cryptographic system that produces encrypted data with random characteristics. A novel technique is adopted to develop the evolution of configurations in elementary cellular automata (ECA) which makes the obtained result more random. Simulation results demonstrate that the proposed approach has a high level of security and resists various attacks. In addition, it outperforms some existing image encryption algorithms.

Using Sequence-to-Sequence Models for Carrier Frequency Offset Estimation of Short Messages and Chaotic Maps

Deep Learning methods have produced good carrier frequency offset estimations for short message sequences in comparison with methods based on the Fast Fourier Transform. However, these performance gains were observed for short ranges of frequency offsets, sequences with predefined pilot symbols and periodic modulation schemes. Chaotic modulation has an advantage over periodic signals in offering security through the continuous changes produced by parameterising the chaotic map function. However, synchronisation of chaotic map parameters in coherent receivers is dependent on the carrier recovery of phase and frequency which dramatically reduces the demodulation performance under high noise levels. This article presents a stacked sequence-to-sequence neural network architecture for blind carrier frequency offset estimation of both periodic and chaotic modulation schemes. The results obtained demonstrate better performance than conventional methods in low SNR for the Additive White Gaussian Noise channel. While this technique operates without feature engineering, the results demonstrate that data augmentation produces a higher degree of accuracy for such models, indicating the benefit of integration with conventional signal pre-processing steps as part of the deep learning pipeline. The proposed neural network architecture is shown to perform carrier frequency offset estimation, not only for the selected periodic modulations, but also in the case of highly non-linear chaotic maps. This suggests the applicability of deep learning methods for synchronisation in waveforms that employ chaotic modulation schemes for secure communication and for applications where short and sporadic messaging are required (e.g., Internet of Things).

An improved chaotic ideal gas molecular movement algorithm for engineering optimization problems

AbstractRecently, the ideal gas molecular movement (IGMM) optimization algorithm was introduced by the authors. It is inspired by the movement and collision behaviour of the ideal gas molecules in an isolated medium. Although the IGMM could perform a high potential in determining the global optimum, improving its convergence behaviour came to the attention of the present investigation to deal with any type of especially highly nonlinear optimization problems. For that purpose, two actions took place hybridly. One was the simulation of the vibrational motion of gas molecules, especially at the early stages of the optimization process. The second simultaneous move concerned the non‐repetitive nature of chaotic maps which could diversify the molecules, reduce the threat of premature convergence and improve the convergence speed of the IGMM algorithm. Therefore, this article investigates 10 different chaotic map functions and a random number generator along with four different Vibrational‐based Chaotic IGMM (VCIGMM) strategies to still improve the speed of convergence. The results of applying the proposed algorithm to various numerical and engineering benchmark problems, intensely show that the chaotic maps, merged with vibrational motion of gas molecules, significantly improved the performance of the IGMM. It could considerably outperform some of the well‐known meta‐heuristic optimization algorithms in the literature.

Intelligent physiological signal infosecurity: Case study in photoplethysmography (PPG) signal

A wrist-based photoplethysmography (PPG) tool offers a simple and non-invasive approach for applications in vital sign monitoring and healthcare. However, in a telecare network, these physiological signals ensure the authorisation demands for the domain of medical applications. Hence, this study proposes an intelligent method for PPG signal encryption and decryption. This method combines chaotic map and radial basis function network (RBFN) into symmetric cryptography with an adaptive scheme. The sine-power chaotic map is used as a key generator of 256 non-ordered numbers (key space) to set the private cipher codes. Two RBFNs are used to train an encryptor and a decryptor with the authorised cipher codes. Its substitution-based infosecurity scheme can change the numerical values of PPG raw data for each encrypted communication, and the decrypted PPG data are further applied for time- and frequency-domain analyses in clinical applications, such as heart and respiration rate analysis and arterial stiffness and upper extremity vascular disease evaluations. Through experimental results, the security levels are validated using the number of pixel change rate (NPCR), unified averaged changed intensity (UACI), and correlation analysis. The average NPCR, UACI, and correlation coefficient (CC) are 96.57%, 35.43%, and 0.005, respectively, between the plain PPG and the encrypted PPG data against hacker attacks. The larger-the-better of NPCR and UACI indexes and the smaller-the-better of CC index are obtained to evaluate the efficiency of the proposed cryptography method. The encrypted PPG also guarantees physiological signals of good quality in clinical applications. In addition, the performance of RBFN-based method is superior in adaptive learning capability than that of the traditional learning method in real-time applications.

A Novel Blockchain and Bi-Linear Polynomial-Based QCP-ABE Framework for Privacy and Security over the Complex Cloud Data.

As a result of the limited resources available in IoT local devices, the large scale cloud consumer’s data that are produced by IoT related machines are contracted out to the cloud. Cloud computing is unreliable, using it can compromise user privacy, and data may be leaked. Because cloud-data and grid infrastructure are both growing exponentially, there is an urgent need to explore computational sources and cloud large-data protection. Numerous cloud service categories are assimilated into numerous fields, such as defense systems and pharmaceutical databases, to compute information space and allocation of resources. Attribute Based Encryption (ABE) is a sophisticated approach which can permit employees to specify a higher level of security for data stored in cloud storage facilities. Numerous obsolete ABE techniques are practical when applied to small data sets to generate cryptograms with restricted computational properties; their properties are used to generate the key, encrypt it, and decrypt it. To address the current concerns, a dynamic non-linear polynomial chaotic quantum hash technique on top of secure block chain model can be used for enhancing cloud data security while maintaining user privacy. In the proposed method, customer attributes are guaranteed by using a dynamic non- polynomial chaotic map function for the key initialization, encryption, and decryption. In the proposed model, both organized and unorganized massive clinical data are considered to be inputs for reliable corroboration and encoding. Compared to existing models, the real-time simulation results demonstrate that the stated standard is more precise than 90% in terms of bit change and more precise than 95% in terms of dynamic key generation, encipherment, and decipherment time.

Using Unique Node ID TO Control IPv6 ID Spoofing

Intemet Protocol (IP) has a resent version knowns as (IPv6). Computers on networks could be located and identified via IP. Meanwhile, by monitoring the routing data traffic across internet. Protecting an Identity of IPV6 Packet against ID Spoofing Based on Cryptographic and Steganography techniques are proposed. One of the most considered point is the reliable communication by using security side especially in IPv6 network applications. The faced problems in general cases are DoS attack, IP-Spoofing, and commonly passive attacks types. In this paper an approach is suggested depending on generate an unique identity for each node randomly. In this work, nine experiments are implemented, which are the proposed method based on generate ipv6 address, which is passed to the chaotic logistic map and RSA function with SHA 2. The results show the proposed system provides high randomness for generated ID, and it hides identity of node inside packet.

A Chaotic Krill Herd Optimization Algorithm for Global Numerical Estimation of the Attraction Domain for Nonlinear Systems

Nowadays, solving constrained engineering problems related to optimization approaches is an attractive research topic. The chaotic krill herd approach is considered as one of most advanced optimization techniques. An advanced hybrid technique is exploited in this paper to solve the challenging problem of estimating the largest domain of attraction for nonlinear systems. Indeed, an intelligent methodology for the estimation of the largest stable equilibrium domain of attraction established on quadratic Lyapunov functions is developed. The designed technique aims at computing and characterizing a largest level set of a Lyapunov function that is included in a particular region, satisfying some hard and delicate algebraic constraints. The formulated optimization problem searches to solve a tangency constraint between the LF derivative sign and constraints on the level sets. Such formulation avoids possible dummy solutions for the nonlinear optimization solver. The analytical development of the solution exploits the Chebyshev chaotic map function that ensures high search space capabilities. The accuracy and efficiency of the chaotic krill herd technique has been evaluated by benchmark models of nonlinear systems. The optimization solution shows that the chaotic krill herd approach is effective in determining the largest estimate of the attraction domain. Moreover, since global optimality is needed for proper estimation, a bound type meta-heuristic optimization solver is implemented. In contrast to existing strategies, the synthesized technique can be exploited for both rational and polynomial Lyapunov functions. Moreover, it permits the exploitation of a chaotic operative optimization algorithm which guarantees converging to an expanded domain of attraction in an essentially restricted running time. The synthesized methodology is discussed, with several examples to illustrate the advantageous aspects of the designed approach.

Chaotic-based grey wolf optimizer for numerical and engineering optimization problems

Grey wolf optimizer (GWO) is a recently proposed optimization algorithm inspired from hunting behavior of grey wolves in wild nature. The main challenge of GWO is that it is easy to fall into local optimum. Owing to the ergodicity of chaos, this paper incorporates the chaos theory into the GWO to strengthen the performance of the algorithm. Three different chaotic strategies with eleven various chaotic map functions are investigated and the most suitable one is regarded as the proposed chaotic GWO. Extensive experiments are made to compare the proposed chaotic GWO against other metaheuristics including adaptive differential evolution (JADE), cellular genetic algorithm, artificial bee colony, evolutionary strategy, biogeography-based optimization, comprehensive learning particle swarm optimization, and GWO. In addition, the proposal is also successfully applied to practical engineering problems. Experimental results demonstrate that the chaotic GWO is better than its compared metaheuristics on most of test problems and engineering optimization problems.

A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task

This work presents a hybrid model that combines support vector regression (SVR), empirical mode decomposition (EMD), the krill herd (KH) algorithm and a chaotic mapping function. EMD is used to decompose input time series data into components with several intrinsic mode functions (IMFs) and one residual, to capture the trends in the input data. SVR is used to forecast separately IMFs and the residual owing to its effectiveness in solving nonlinear regression and time series problems. The KH algorithm is used to select the parameters in the SVR models. The Tent chaotic mapping function is hybridized with the KH algorithm to prevent premature convergence and increase the accuracy of the whole model. Two real-world datasets from the New South Wales (NSW, Australia) market and the New York Independent System Operator (NYISO, USA) are used to demonstrate the performance of the proposed EMD-SVRCKH model. The experimental results reveal that the proposed model provides competitive advantages over other models and offers greater forecasting accuracy.

A novel image security technique based on nucleic acid concepts

Image security is very important to protect from any unauthorised user access. In this paper a two-phase secure image encryption has been proposed using the concepts of Deoxyribonucleic acid (DNA) and Ribonucleic acid (RNA). First, the plain image is encrypted based on DNA rules, DNA XOR operator, and chaotic map function in DNA phase and initial cipher image is created. Next, the cipher image is used as the input of RNA phase so that codons truth table for RNA and secret key are exploited for further image encryption. The proposed method has high resistance against common attacks in the field and achieved entropy of 7.9994. The implementation of the proposed method is available on https://github.com/mehdim7/RNA-DNA-Image-Encryption.

A novel method of dynamic S-box design based on combined chaotic map and fitness function

This paper introduces a method of generating a strong S-box by using combined chaotic system and fitness function. Our method presents an algorithm which has two stages: static S-box generation and dynamic S-box optimization. Chaotic sequences generation, sequences discretization are involved in the first stage. Moreover, the proposed static S-box is converted into a dynamic S-box with the help of fitness function. The optimal S-box satisfies the following criteria: bijectivity, nonlinearity, strict avalanche criteria, output bits independence criterion, equiprobable inputs/outputs XOR distribution. Extensive experiments demonstrate that the proposed algorithm has excellent performance in cryptography and attacks resistance.