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Schizophrenia is a chronic mental illness that can negatively affect emotions, thoughts, social interaction, motor behavior, attention, and perception. Early diagnosis is still challenging and is based on the disease's symptoms. However, electroencephalography (EEG) signals yield incredibly detailed information about the activities and functions of the brain. In this study, a hybrid algorithm approach is proposed to improve the search performance of the marine predator algorithm (MPA) based on chaotic maps. For evaluating the performance of the proposed chaotic-based marine predator algorithm (CMPA), benchmark datasets are used. The results of the suggested variation method on the benchmarks show that the Sine Chaotic-based MPA (SCMPA) significantly outperforms the other MPA variants. The algorithm was verified using a public dataset consisting of 14 subjects. Moreover, the proposed SCMPA is essential for EEG electrode selection because it minimizes model complexity and selects the best representative features for providing optimal solutions. The extracted features for each subject were used in the decision tree (DT), random forest (RF), and extra tree (ET) methods. Performance measures showed that the proposed model was successful at differentiating schizophrenia patients (SZ) from healthy controls (HC). In the end, it was demonstrated that the feature selection technique SCMPA, which is the subject of this research, performs significantly better in regard to classification using EEG signals. [ABSTRACT FROM AUTHOR]

The Kepler Optimisation Algorithm (KOA) is a recently proposed algorithm that is inspired by Kepler’s laws to predict the positions and velocities of planets at a given time. However, although promising, KOA can encounter challenges such as convergence to sub-optimal solutions or slow convergence speed. This paper proposes an improvement to KOA by integrating chaotic maps to solve complex engineering problems. The improved algorithm, named Chaotic Kepler Optimization Algorithm (CKOA), is characterized by a better ability to avoid local minima and to reach globally optimal solutions thanks to a dynamic diversification strategy based on chaotic maps. To confirm the effectiveness of the suggested approach, in-depth statistical analyses were carried out using the CEC2020 and CEC2022 benchmarks. These analyses included mean and standard deviation of fitness, convergence curves, Wilcoxon tests, as well as population diversity assessments. The experimental results, which compare CKOA not only to the original KOA but also to eight other recent optimizers, show that the proposed algorithm performs better in terms of convergence speed and solution quality. In addition, CKOA has been successfully tested on three complex engineering problems, confirming its robustness and practical effectiveness. These results make CKOA a powerful optimisation tool in a variety of complex real-world contexts. After final acceptance, the source code will be uploaded to the Github account: nawal.elghouate@usmba.ac.ma. [ABSTRACT FROM AUTHOR]

The security of digital images is increasingly important as the Internet is a growing concern due to the ease with which images can be shared via the Internet. These images need to be protected, and cryptography is the way to achieve this--chaos-based encryption, to be more specific. This work describes a technique of image encryption for colour images using a logistic map with a two-dimensional S-Box and the Hénon 2D map for encryption. The given method S-Box is cleared by several important parameters of evaluation of S-Box and is estimated by such indicators as resistance to the differential attacks, histograms, correlations, and entropy. The results show good efficiency, which can be seen in figures of UACI being equal to 34. 5847%, NPCR at 98. 87%, and entropy at 7. 9994 of the Lena images, 9985 of the Lena images. [ABSTRACT FROM AUTHOR]

Electro-hydraulic actuator system (EHAS) has imposed a challenge in the research community for accurate mathematical modeling and identification due to non-linearities. In this paper, autoregressive exogenous (ARX) structure is used for EHAS modeling and identification is performed by exploiting the competency of atomic physics-based chaotic atom search optimization (CASO) that adapts ten chaotic maps (Chebyshev, Circle, Gauss, Iterative, Logistic, Piecewise, Sine, Singer, Sinusoidal and Tent) in position update of atom search optimization (ASO). The fitness/merit function of the EHAS model is developed in mean-square error (MSE) sense between desired and approximated values. Simulations and analysis show that ASO with a chaotic logistic map (CASO5) performs better than the ASO and its other chaotic variants, as well as other recently introduced metaheuristics for diverse variations in the system model. Statistics based on MSE, learning plots, results of autonomous trials and average fitness analyses verify the consistency and reliability of the CASO5 for the identification of the EHAS model. [ABSTRACT FROM AUTHOR]

This research presents a novel hybrid approach, which combines the White Shark Optimizer (WSO) metaheuristic algorithm with chaotic maps integrated into the binarization process. Inspired by the predatory behavior of white sharks, WSO has shown great potential to navigate complex search spaces for optimization tasks. On the other hand, chaotic maps are nonlinear dynamical systems that generate pseudo-random sequences, allowing for better solution diversification and avoiding local optima. By hybridizing WSO and chaotic maps through adaptive binarization rules, the complementary strengths of both approaches are leveraged to obtain high-quality solutions. We have solved the Set Covering Problem (SCP), a well-known NP-hard combinatorial optimization challenge with real-world applications in several domains, and experimental results indicate that LOG and TENT chaotic maps are better after statistical testing. This hybrid approach could have practical applications in telecommunication network optimization, transportation route planning, and resource-constrained allocation. [ABSTRACT FROM AUTHOR]

This work proposes multiple variants of the Multiverse Optimizer Algorithm (MVO) with the use of Chaotic Maps and Fuzzy Logic. These variants come from the Fuzzy-Chaotic Multiverse Optimizer Algorithm (FCMVO), and the purpose is to analyze the behavior of the algorithm in some benchmark mathematical functions, to compare in which cases can perform best between the variants presented. The use of chaotic maps resides from some of the most used in the literature on optimization algorithms, and the main use is to adjust some of the parameters of behavior in the MVO algorithm; and other implementation is the use of Fuzzy Logic for dynamical adaptation of two parameters in the algorithm. In this study, we are presenting an extended comparison between the variants obtained from diverse chaotic maps, that in previous studies, we delimited to six variants which we called Elitist FCMVO. This comparison is done by evaluating the resulting variants from a set of three chaotic maps and comparing also with two different fuzzy inference systems (Mamdani and Sugeno types). The main objective is to present the obtained variants and compare which are the best chaotic maps in the FCMVO algorithm, to improve further in other study cases. [ABSTRACT FROM AUTHOR]

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems. This approach aims to leverage the strengths of multiple algorithms, enhancing solution quality, convergence speed, and robustness, thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks. In this paper, we introduce a hybrid algorithm that amalgamates three distinct metaheuristics: the Beluga Whale Optimization (BWO), the Honey Badger Algorithm (HBA), and the Jellyfish Search (JS) optimizer. The proposed hybrid algorithm will be referred to as BHJO. Through this fusion, the BHJO algorithm aims to leverage the strengths of each optimizer. Before this hybridization, we thoroughly examined the exploration and exploitation capabilities of the BWO, HBA, and JS metaheuristics, as well as their ability to strike a balance between exploration and exploitation. This meticulous analysis allowed us to identify the pros and cons of each algorithm, enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance. In addition, the BHJO algorithm incorporates Opposition-Based Learning (OBL) to harness the advantages offered by this technique, leveraging its diverse exploration, accelerated convergence, and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm. Moreover, the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems, providing a comprehensive assessment of its efficacy and applicability in diverse problem domains. Similarly, the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms, where mean and standard deviation values were utilized as evaluation metrics. This rigorous comparison aimed to assess the performance of the BHJO algorithm about its counterparts, shedding light on its effectiveness and reliability in solving optimization problems. Finally, the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn's test. The resulting numerical values revealed the BHJO algorithm's competitiveness in tackling intricate optimization problems, affirming its capability to deliver favorable outcomes in challenging scenarios. [ABSTRACT FROM AUTHOR]

In the era of digital communication, ensuring the security of transmitted images is paramount, particularly for online and real-time applications where data privacy and integrity are essential. This paper introduces a novel image encryption algorithm designed to address the rigorous demands of such contexts, prioritizing both security and speed. Employing SHA-512 as the secret key and leveraging block-based encryption techniques, the proposed algorithm offers security and adaptability to various image sizes and aspect ratios, catering to the diverse needs of modern applications. The algorithm enhances security by incorporating chaotic systems for key generation and permutation while maintaining high-speed encryption and decryption processes. Performance evaluation demonstrates the algorithm's resilience against common cryptographic attacks, underscoring its effectiveness in safeguarding sensitive image data in dynamic online and real-time contexts. Notably, the algorithm achieves encryption speeds of up to 438.49 Mbps for images sized 1080 × 1920, with an encryption time of just 0.108 s as well as 0.26 s for an image sized 2611 × 1800. For a Lena color image sized 512 × 512, the proposed algorithm results in an entropy of 7.999252, an NPCR value of 99.6365, a UACI value of 33.4542 and encryption time of 0.0254 s. With adaptability to diverse image characteristics and a robust defense against potential threats, the proposed algorithm emerges as a promising contender for ensuring the integrity and confidentiality of digital images in today's interconnected world. [ABSTRACT FROM AUTHOR]

In the digital age, where data is a valuable commodity, securing sensitive information has become a growing concern. Image encryption techniques play an essential role in protecting visual data from unauthorized access and ensuring privacy. However, with limited computing capacity in Internet of Things (IoT) devices, standard encryption algorithms are not feasible, rendering lightweight methods mandatory. This study proposes a novel Corner Traversal algorithm, an alternative to existing pixel scrambling techniques. The proposed algorithm demonstrably outperforms its counterparts in both higher confusion and lower time complexity, making it remarkably efficient. Integrated with chaos-based diffusion methods, this algorithm forms a comprehensive encryption scheme. The proposed lightweight image encryption scheme utilizing the Corner Traversal algorithm successfully passed rigorous statistical and differential security analysis. Compared to similar schemes, the proposed encryption scheme employing the Corner Traversal algorithm in the confusion phase distinguishes itself through exceptional NPCR (99.6093 for Lenna) and UACI (33.4648 for Lenna) values. Combined with other evaluation criteria, this method demonstrably meets the stringent security requirements of IoT systems. [ABSTRACT FROM AUTHOR]

Ionic liquids (ILs) have attracted considerable interest due to their unique properties and prospective uses in various industries. However, their potential toxicity, particularly regarding enzyme inhibition, has become a growing concern. In this study, a QSAR model was proposed to predict the enzyme inhibition toxicity of ILs. A dataset of diverse ILs with corresponding toxicity data against three enzymes was compiled. Molecular descriptors that capture the physicochemical, structural, and topological properties of the ILs were calculated. To optimize the selection of descriptors and develop a robust QSAR model, the chaotic spotted hyena optimization algorithm, a novel nature-inspired metaheuristic, was employed. The proposed algorithm efficiently searches for an optimal subset of descriptors and model parameters, enhancing the predictive performance and interpretability of the QSAR model. The developed model exhibits excellent predictive capability, with high classification accuracy and low computation time. Sensitivity analysis and molecular interpretation of the selected descriptors provide insights into the critical structural features influencing the toxicity of ILs. This study showcases the successful application of the chaotic spotted hyena optimization algorithm in QSAR modelling and contributes to a better understanding of the toxicity mechanisms of ILs, aiding in the design of safer alternatives for industrial applications. [ABSTRACT FROM AUTHOR]

Modern-day's digital world witnesses large-scale transmissions of various media forms (including images) in resource-constrained environments. The sensitive nature of transmitted images has highlighted the importance of image security. Substitution-Permutation Network (SPN) is a popular encryption design catering to the special needs of images. Several reviews on image encryption schemes exist in literature, but reviews focusing on cryptanalysis of image encryption schemes are rare. This motivated us to conduct this systematic review (period: 2019–2023), exploring the trends of cryptanalysis of SPN-based image encryption schemes. This review presents the state-of-the-art in the domain of design and analysis of image encryption. We also identify and highlight the weak designs in existing schemes and provide suggestions for overcoming these weaknesses to prevent potential cryptanalytic attacks. Ultimately, our goal is to contribute to the ongoing efforts to improve security and resilience of image encryption schemes, offering a significant resource for researchers working in this area. [ABSTRACT FROM AUTHOR]

Chaotic maps, despite their deterministic nature, can introduce controlled randomness into optimization algorithms. This chaotic map behaviour helps overcome the lack of mathematical validation in traditional stochastic methods. The chaotic optimization algorithm (COA) uses chaotic maps that help it achieve faster convergence and escape local optima. The effective use of these maps to find the global optimum would be possible only with a complete understanding of them, especially their fixed points. In chaotic maps, fixed points repeat indefinitely, disrupting the map's characteristic unpredictability. While using chaotic maps for global optimization, it is crucial to avoid starting the search at fixed points and implement corrective measures if they arise in between the sequence. This paper outlines strategies for addressing fixed points and provides a numerical evaluation (using Newton's method) of the fixed points for 20 widely used chaotic maps. By appropriately handling fixed points, researchers and practitioners across diverse fields can avoid costly failures, improve accuracy, and enhance the reliability of their systems. [ABSTRACT FROM AUTHOR]

This paper presents an optimal tuning of a proportional integral differential (PID) controller used to maintain glucose concentration at a desired set point. The PID controller synthesizes an appropriate feed rate profile for an E. coli fed-batch cultivation process. Mathematical models are developed based on dynamic mass balance equations for biomass, substrate, and product concentration of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation for bacterial phytase extracellular production. For model parameter identification and PID tuning, a hybrid metaheuristic technique—chaotic electromagnetic field optimization (CEFO)—is proposed. In the hybridization, a chaotic map is used for the generation of a new electromagnetic particle instead of the electromagnetic field optimization (EFO) search strategy. The CEFO combines the exploitation capability of the EFO algorithm and the exploration power of ten different chaotic maps. The comparison of the results with classical EFO shows the superior behaviour of the designed CEFO. An improvement of 30% of the objective function is achieved by applying CEFO. Based on the obtained mathematical models, 10 PID controllers are tuned. The simulation experiments show that the designed controllers are robust, resulting in a good control system performance. The closed-loop transient responses for the corresponding controllers are similar to the estimated models. The settling time of the control system based on the third PID controller for all estimated models is approximately 9 min and the overshoot is approximately 15%. The proposed CEFO algorithm can be considered an effective methodology for mathematical modelling and achievement of high quality and better performance of the designed closed-loop system for cultivation processes. [ABSTRACT FROM AUTHOR]

The Aquila Optimization (AO) algorithm is a newly established swarm-based method that mimics the hunting behavior of Aquila birds in nature. However, in complex optimization problems, the AO has shown a slow convergence rate and gets stuck in the local optimal region throughout the optimization process. To overcome this problem, a hybrid with AO and twelve chaotic maps has been proposed to adjust its main parameter. This new mechanism, namely the Chaotic Aquila Optimization (CAO) algorithm, is employed with chaotic maps with the AO algorithm. The proposed chaotic AO (CAO) approach takes seriously a variety of chaotic maps while setting the main AO parameter, which helps in managing exploration and exploitation. To validate the performance of the CAO algorithm, estimates for CEC 2005 and CEC 2022 test functions and the first chaotic map results are compared with the AO algorithm to select the best results of the CAO algorithm, and then CAO results are compared with nine popular optimization algorithms such as FFA, AVOA, MGO, AGTO, SSA, GWO, MVO, SCA, TSA, and AO. Moreover, statistical analyses such as the Wilcoxon rank-sum test and the t-test are performed to analyze the significant difference between the proposed CAO and other algorithms. Furthermore, the proposed CAO has been employed to solve six real-world engineering problems. The results demonstrate the CAO’s superiority and capability over other algorithms in solving complex optimization problems. The results demonstrate that CAO achieved outstanding performance and effectiveness in solving an extensive variety of optimization problems.

Reinforcement learning, specifically $ Q $-learning, has gained a pleth-ora of attention from researchers in recent decades due to its remarkable performance in various applications. This study proposes a novel Reinforcement Learning-inspired Tunicate Swarm Algorithm (RLTSA) that employs a $ Q $-learning approach to enhance the convergence accuracy and local search efficacy of tunicates in TSA while preventing their local optimal entrapment. Firstly, a novel Chaotic Quasi Reflection Based Learning (CQRBL) strategy with ten chaotic maps is proposed to improve convergence reliability. Then, $ Q $-learning is introduced and embedded with TSA by dynamically switching the learning mechanisms of CQRBL and ROBL strategies at different stages for distinct problems. These two strategies in the $ Q $-learning approach significantly improve the efficiency of the proposed algorithm. The performance of RLTSA is evaluated on a set of 33 distinct functions, including the CEC'05 and CEC'19 test functions, as well as four engineering design problems, and its outcomes are statistically and graphically tested against the TSA and seven other eminent meta-heuristics. In addition, statistical tests, notably the Friedman, Wilcoxon rank-sum, and $ t $-tests, have been employed to exemplify the dominance of the RLTSA. The experimental findings disclose that RLTSA outperforms the competing algorithms in the realm of real-world engineering design problems. [ABSTRACT FROM AUTHOR]

This paper proposes a model for encrypted images that depend on chaotic maps. This scheme uses eight chaotic maps to perform the encryption process: Logistic, Gauss, Circle, Sine, Singer, Piecewise, Tent, and Chebyshev. The two major processes of the suggested model are chaotic confusion and pixel diffusion. Chaotic maps are used to permute the pixel positions during the confusion process. In the diffusion process, the value of the image pixel is changed. To evaluate the suggested model, some performance metrics were used, such as execution time, peak signal-to-noise ratio, entropy, key sensitivity, noise attack, the number of pixels change rate (NPCR), unified average changing intensity (UACI), histogram analysis, and cross-correlation. According to experimental analysis, images encrypted with the suggested system have correlation coefficient values that are almost zero, NPCR of 99.6%, UACI of 32.9%, the key space of 10^(80), the histogram analysis showed that the encrypted images have almost similar pixels, an execution time of 0.1563 ms, the, and entropy of 7.9973. All prior results have verified the robustness and efficiency of the suggested algorithm. [ABSTRACT FROM AUTHOR]

Abstract Power systems exhibit nonlinearity. causing dynamic instability and complex power oscillations. This research proposes an innovative strategy using the Novel Bat Algorithm (NBA) to achieve ideal Power System Stabilizers (PSSs) in a multimachine power system. The approach shifts electromechanical modes to specific areas in the s-plane. Enhancing the multi-machine power system and establishing stabilizer parameters for dynamic performance. The study examines the designed approach aptitude for standard lead-lag PSSs configurations. In order to elevate the global search problem and transfer some static operators for the optimum optimization process. the chaos mapping. also known as CNBA. is introduced into NBA. Four different forms of chaos maps are compared in experiments to resolve unconstrained mathematical issues in order to illustrate CNBA performance. In any other case. the challenge of designing PSS under a wide range of loading situations is transformed into an optimization challenge with the damping ratio of electromechanical modes with low damping as the target function. The optimal stabilizers’ gains are gotten by employing the CNBA algorithm. Second plan. an effective technique is astutely established to delineate the PSS location and quantity using CNBA and another side using participation factor. To examine the efficacy of the proposed CNBA-based PSS on a large system; it is tested on the interconnected of New-England/New-York (16 generators and 68 buses) power grid. and verified by comparative study with NBA through eigenvalue analysis and nonlinear simulation to provide evidence the algorithmic competence of CNBA. The CNBA approach yields a minimum damping ratio of 37%. which is consistent with the its eigenvalue. In contrast, the NBA approach achieves a minimum damping ratio of 31%. The simulation results reveal the fine performance of the proposed CNBA-PSS in a convincing manner and its capacity to provide an excellent damping for inter-area and local oscillations under diverse operating cases compared to NBA-PSS then in the case of PSS location.

The growing demand for maintaining secure communication channels with the advancements in digital technologies has led to an intensified interest in designing reliable image encryption schemes. Despite various encryption schemes that have been used, some are considered to have insecurity regarding data transmission and multimedia. Motivated by this concern, this paper proposes a new color image encryption algorithm of a multi-key generation-based nD-Hyperchaotic (HC) system. The new algorithm achieves Shannon’s confusion and diffusion principles using a two S-box generation approach, where the first S-box is generated from the proposed nD-HC system and the resulting sequence is used to increase encryption complexity, while the second S-box is generated from (n + i)D-HC system. Afterward, mathematical analysis is carried out to showcase the robustness and efficiency of the proposed algorithm, as well as its resistance to visual, statistical, differential, and brute-force attacks. The proposed scheme successfully passes all NIST SP 800 suite tests. The cryptographic system demonstrated by the proposed scheme has proven to have outstanding performance through simulation tests, which indicates promising potential applicability aspects in secure and real-time image communication settings. [ABSTRACT FROM AUTHOR]

The Arnold's Cat Map (ACM) is one of the chaotic transformations, which is utilized by numerous scrambling and encryption algorithms in Information Security. Traditionally, the ACM is used in image scrambling whereby repeated application of the ACM matrix, any image can be scrambled. The transformation obtained by the ACM matrix is periodic; therefore, the original image can be reconstructed using the scrambled image whenever the elements of the matrix, hence the key, is known. The transformation matrices in all the chaotic maps employing ACM has limitations on the choice of the free parameters which generally require the area-preserving property of the matrix used in transformation, that is, the determinant of the transformation matrix to be ± 1. This reduces the number of possible set of keys which leads to discovering the ACM matrix in encryption algorithms using the brute-force method. Additionally, the period obtained is small which also causes the faster discovery of the original image by repeated application of the matrix. These two parameters are important in a brute-force attack to find out the original image from a scrambled one. The objective of the present study is to increase the key space of the ACM matrix, hence increase the security of the scrambling process and make a brute-force attack more difficult. It is proved mathematically that area-preserving property of the traditional matrix is not required for the matrix to be used in scrambling process. Removing the restriction enlarges the maximum possible key space and, in many cases, increases the period as well. Additionally, it is supplied experimentally that, in scrambling images, the new ACM matrix is equivalent or better compared to the traditional one with longer periods. Consequently, the encryption techniques with ACM become more robust compared to the traditional ones. The new ACM matrix is compatible with all algorithms that utilized the original matrix. In this novel contribution, we proved that the traditional enforcement of the determinant of the ACM matrix to be one is redundant and can be removed. [ABSTRACT FROM AUTHOR]

Despite the ease of digital image distribution, storage, and replication, averting identity theft, privacy breaches, and ownership issues can be challenging. Medical image encryption plays a vital role in ensuring the confidentiality of sensitive medical data and safeguarding patient privacy. This research addresses these concerns by introducing a novel approach, RISE, to medical image security by using the fusion of chaotic keys and a secret-sharing technique. The key advancement is the use of a Rubik's cube-based bit-plane shuffling technique to reduce the complexity of strong image encryption, adding a unique dimension to the field of medical image security. Another distinguishing aspect of our approach is the strategic use of segmentation to encrypt only the sensitive part of the image and reduce the time complexity. This area is encrypted using a chaotic key with a Rubik's cube-based bit-plane shuffling algorithm, followed by the implementation of the confusion process. The encrypted image is shared using a K-N Secret sharing method, which provides authentication and high robustness. The final decrypted image is enhanced using super-resolution to provide better information outputs. The proposed technique offers excellent security and produces better outcomes while being simple. The average NPCR and UACI scores of the proposed encryption technique are 99.47, and 49.90, respectively, and the entropy is 7.995, underscoring the robustness and effectiveness of our proposed approach. It has a high key bit sensitivity and average time complexity. The result analysis further ensures resistance against crop attacks or data loss, positioning it as a formidable contender in the landscape of modern image security. [ABSTRACT FROM AUTHOR]

AES is yet one of the prominent cryptographic algorithms of the 21st century with the reputation of excellent performance and reliability. AES revolves around the S-Box, a nonlinear substitutive table that is essential to attain the level of cryptosecurity. In this paper, there are several methods discussed as how to increase AES S-Box functionality with regard to new cryptographic threats and their effectiveness. We detail the consequences of raising S-Box length and size, dynamical S-Box producing, higher nonlinearity, and efficient methods of S-Box calculation by means of hardware and software. Furthermore, it explains the issues with these improvements and how they impact security measures & relevant computations. Here it is possible to state that applying all these modern approaches, it is possible to strengthen the AES S-Box essentially and ensure compliance with present day demands to cryptographic solutions. [ABSTRACT FROM AUTHOR]

Power systems exhibit nonlinearity. causing dynamic instability and complex power oscillations. This research proposes an innovative strategy using the Novel Bat Algorithm (NBA) to achieve ideal Power System Stabilizers (PSSs) in a multimachine power system. The approach shifts electromechanical modes to specific areas in the s-plane. Enhancing the multi-machine power system and establishing stabilizer parameters for dynamic performance. The study examines the designed approach aptitude for standard lead-lag PSSs configurations. In order to elevate the global search problem and transfer some static operators for the optimum optimization process. the chaos mapping. also known as CNBA. is introduced into NBA. Four different forms of chaos maps are compared in experiments to resolve unconstrained mathematical issues in order to illustrate CNBA performance. In any other case. the challenge of designing PSS under a wide range of loading situations is transformed into an optimization challenge with the damping ratio of electromechanical modes with low damping as the target function. The optimal stabilizers' gains are gotten by employing the CNBA algorithm. Second plan. an effective technique is astutely established to delineate the PSS location and quantity using CNBA and another side using participation factor. To examine the efficacy of the proposed CNBA-based PSS on a large system; it is tested on the interconnected of New-England/New-York (16 generators and 68 buses) power grid. and verified by comparative study with NBA through eigenvalue analysis and nonlinear simulation to provide evidence the algorithmic competence of CNBA. The CNBA approach yields a minimum damping ratio of 37%. which is consistent with the its eigenvalue. In contrast, the NBA approach achieves a minimum damping ratio of 31%. The simulation results reveal the fine performance of the proposed CNBA-PSS in a convincing manner and its capacity to provide an excellent damping for inter-area and local oscillations under diverse operating cases compared to NBA-PSS then in the case of PSS location. [ABSTRACT FROM AUTHOR]

The three party authenticated key agreement protocol assists two parties in affirming one another and agreeing on a shared session key with the assistance of a trusted server. Chaos-based cryptography has seen considerable progress due to the sound characteristics of chaotic systems. Nowadays, many researchers are actively working in this direction and have proposed various three party authenticated key agreement protocols based on chaotic maps. In this paper, we analysed the Zheng et al. 's scheme (IEEE Access 8:66150–66162, 2020, https://doi.org/10.1109/ACCESS.2020.2979251) and found that it is vulnerable to various attacks like verification table theft attack, impersonation attacks and also it does not provide anonymity to users. Zheng et al.'s scheme also has flaw in registration phase and uses timestamps for key freshness. In this article, we proposed a three party chaotic based authenticated key agreement protocol which is secured against aforementioned attacks. Authors are also compared the proposed scheme with other comparable and existing schemes in terms of security features, computation cost, and communication cost. The comparison analysis shows that the proposed scheme has more security features at a lower cost in computing and communication. We have also demonstrated the security of the proposed protocol within the framework of the random oracle model. [ABSTRACT FROM AUTHOR]

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Transferring data through unsecured communication channels exposes it to the risk of sabotage and theft by unauthorized persons. Therefore, secure and modern methods must be provided to protect this data upon transmission. This study suggests a new way to use deoxyribonucleic acid (DNA) coding and chaotic maps to create two security levels for data protection. The first level includes generating a key using a one-dimensional logistic map and then converting the values to DNA codes. In contrast, the image color data is converted to DNA codes. After that, the XOR operation is applied between key and color codes by using four rules (2, 4, 6, and 8) from the encoding DNA rules. The second level includes generating three keys via a three-dimensional logistic map. After that, an XOR operation is performed between the keys and the result of the first level. Finally, the result is an encrypted image with two levels of security. The proposed method shows a high safety rate, which achieved an Entropy value rate of 7.997 and a Number of Pixels Changing Rate (NPCR) of 100% on the test image group. [ABSTRACT FROM AUTHOR]

Frequency hopping is a main technique for wireless communication, avoiding interference and interception. This paper provides novel hardware design for frequency-hopping pseudorandom bit generator (PRBG).PRBG design by chaotic maps on FPGA.Two proposed methods in this work first combine chaotic maps in a cascade manner called fixed point cascade chaotic maps (FPCCM-FHSS), and second, the conjunction of chaotic maps done in an XORed manner called fixed point an XOR chaotic method (FPXORCM-FHSS).The results were the first eight NIST randomness tests.Frequency indicates that all p-values larger than 0.01 are needed to achieve better randomness,Second and third were die-hard tests, many distribution tests with significant p-values (p

Video steganography is a method for concealing information within a video without substantially changing its visual content. The utilization of high-definition videos has garnered considerable interest from industries. H.265/HEVC, the recent video coding technology, is a promising field for video steganography. In this paper, a high concealment capacity based on three chaotic maps for the HEVC video standard is proposed, where the confidential data will be encrypted using two chaotic maps and then concealed in randomly selected Discrete Cosine Transform (DCT) coefficients of Transform Blocks (TBs), which are also randomly chosen using one chaotic map. The technique used in the DCT domain to achieve superior embedding capacity at the same visual quality compared with the state-of-the-art schemes in the compressed domain, and the use of three novel chaotic maps to protect the secret information and get uncrackable security level are the significant contributions of this paper. The simulation findings proved that the proposed approach has an average concealing capacity reaching 41.3 Kbits/frame. This payload exceeds what recent cutting-edge techniques could achieve in 1280 x 720 video frame dimensions with a ) of -0.009 dB and a Bit Rate Increase (BRI) of 0.0747 at a Quantization Parameter (QP) value of 32. Furthermore, the critical space size of the suggested scheme is , which makes it very secure against all types of brute-force attacks.

Abstract The Mountain Gazelle Optimizer (MGO) algorithm has become one of the most prominent swarm-inspired meta-heuristic algorithms because of its outstanding rapid convergence and excellent accuracy. However, the MGO still faces premature convergence, making it challenging to leave the local optima if early-best solutions neglect the relevant search domain. Therefore, in this study, a newly developed Chaotic-based Mountain Gazelle Optimizer (CMGO) is proposed with numerous chaotic maps to overcome the above-mentioned flaws. Moreover, the ten distinct chaotic maps were simultaneously incorporated into MGO to determine the optimal values and enhance the exploitation of the most promising solutions. The performance of CMGO has been evaluated using CEC2005 and CEC2019 benchmark functions, along with four engineering problems. Statistical tests like the t-test and Wilcoxon rank-sum test provide further evidence that the proposed CMGO outperforms the existing eminent algorithms. Hence, the experimental outcomes demonstrate that the CMGO produces successful and auspicious results.

Image ciphering techniques usually transform a given plain image data into a cipher image data resembling noise, serving as an indicator of the presence of secret image data. However, the transmission of such noise-like images could draw attention, thereby attracting the attackers and may face several possible attacks. This paper presents an approach for generating a visually meaningful image encryption (VMIE) scheme that combines three layers of security protection: encryption, digital signature, and steganography. The present scheme is dedicated to achieving a balanced performance in robustness, security and operational efficiency. First, the original image is partially encrypted by using the RSA cryptosystem and modified Hénon map (MHM). In the second stage, a digital signature is generated for the partially encrypted image by employing a hash function and the RSA cryptosystem. The obtained digital signature is appended to the partially encrypted image produced after implementing the zigzag confusion in the above partially encrypted image. Further, to achieve better confusion and diffusion, the partially encrypted image containing a digital signature undergoes through the application of 3D Arnold cat map (ARno times), to produce the secret encrypted image (Sr5). To ensure the security and robustness of the proposed technique against various classical attacks, the hash value obtained from the SHA-256 hash function and carrier images is utilized to generate the initial conditions Mh10 and Mh20 for modified Hénon map, and initial position Zip=(zrow,zcol) for zigzag confusion. In the proposed algorithm, the digital signature is utilized for both purposes to verify the sender’s authenticity and to enhance the encryption quality. The carrier image undergoes lifting wavelet transformation, and its high-frequency components are utilized in the embedding process through a permuted pattern of MHM, resulting in a visually meaningful encrypted image. The proposed scheme achieves efficient visual encryption with minimal distortion and ensures lossless image quality upon decryption (infinite PSNR), balancing high level of security along with a good computational efficiency.

In this paper, the generation of new dragon fractal shapes with chaotic iteration parameters is introduced as the main component of a new efficient approach for different cryptographic applications. This process involves applying a chaotic map, which is considered the initiator pattern, to generate different chaotic dragon fractal (ChDrFr) shapes in lieu of lines (which are classically used to generate dragon fractals). This is the new concept of this paper. The used chaotic maps are sensitive to their initial conditions and are characterized by randomness; hence, the resulting scheme is highly secure. As the resulting ChDrFr shapes have sparse structures, the spaces are packed with random values generated from another 5D hyper chaotic map. For encryption applications based on the substitution approach, one of the five generated ChFrDr shapes can be used to construct a chaotic fractal (ChFr) S-Box, while the other four ChDrFr shapes can be used for diffusion purposes. As an application to these new ChDrFr shapes and the ChFr S-Box, we introduce in this paper a new highly secure image encryption algorithm. A Henon chaotic map is used as the initiator of the ChDrFr shapes. The integer wavelet transform (IWT) is used to generate an approximation and three detail sub-bands for the original image. As the approximation sub-band contains a considerable amount of information about the original image, the above-described ChFr S-Box is used as a replacement for each pixel's value in this sub-band. Then, the resultant substituted image is diffused with one of the generated ChFrDr shapes. The other three ChDrFr shapes are XORed with the details sub-images. Numerical simulation is applied to ensure the efficacy of encrypted images against different attacks. In particular, the correlation coefficient between the initial and the generated images is shown to be nearly zero. Moreover, tests reveal that the information entropy of the encrypted images and UACI were close to their optimum values. The properties of the newly proposed ChDrFr-based encryption algorithm are compared to the ones obtained by other encryption algorithms, and the results prove the superiority of this newly proposed algorithm to other types of encryption methods. [ABSTRACT FROM AUTHOR]

This article proposes a dynamic economic load dispatch (DELD) including wind power involvement process, which is capable to produce least possible generation cost of electrical systems by integrating large-scale wind power penetration. The primary component of hybridized power system comprises of conventional thermal generators and wind generators. To expedite the convergence flexibility and escape the clarifications from the confined optimal purpose, quasi-oppositional based learning (Q-OBL) is combined with HBA, which results in quasi oppositional chaotic honey badger algorithm (QOHBA). Moreover, chaotic strategy is integrated with QOHBA to boost the convergence speed as well as enhance the performance of the basic honey badger algorithm (HBA). The proposed QOCHBA is suggested to explain wind-based shared DELD problem to reduce thermal-wind generation energy cost. The superiority and expediency of the prospective technique is proved through its operation in two benchmark investigation systems. We use probability density functions (PDF) for wind energy (WE) forecasting to assessment dynamic working reserve necessities, based on the readjust of uncertainty in the prediction. To establish the efficacy of the recommended QOCHBA method in explaining non-linear, non-convex, and constrained DELD problem, the suggested structures are implemented on benchmark of 10-unit in addition to 15-unit test methods. [ABSTRACT FROM AUTHOR]

Recently, digital images have been widely used in several applications through advanced wearable devices and networks. Despite several benefits of digital images, such as easy distribution, storage, and reproduction, it is difficult to prevent the issue of identity theft, privacy leakage, and ownership conflicts. We present a wavelet‐based encryption technique, MieWC, to solve the above issues using multiple chaotic keys. This work uses multiple keys for the encryption process, which ensures high security and authenticity. Lorenz and logistic maps are used for chaotic key generation for diffusion and confusion processes. Further, the technique is tested for two different wavelet transforms, including integer wavelet transform (IWT) and discrete wavelet transform, with IWT giving better results. Hence, it is being used for further analysis. The proposed technique is easy to implement and provides high security with improved results. It has low time complexity and high key bit sensitivity. The result analysis further ensures resistance against crop attacks. [ABSTRACT FROM AUTHOR]

Over the past few years, the use of chaotic maps have surfaced to become an important topic in the field of image encryption. The majority of chaos-based algorithms employed single or multiple rounds of confusion and diffusion. However, such algorithms increase the security and computational cost of the encryption process. On the other hand,a higher computational process is required to maintain security. To overcome this issue, a robust image encryption method using the features of two dimensional coupling map (2D-CM) and Discret Wavelet Transform(DWT) is proposed. Additionally, low-frequency component of DWT used for encryption can significantly save storage space thereby reducing computational overheads without compromising the security of the encryption. Finally, the cover image is combined with higher frequency coefficients to obtain the whole image, which will be totally secure against external attacks. The proposed encryption technique undergoes various security analyses, including Information Entropy (I en) , Correlation Coefficients (CC), Number of Pixel Change Rate (NPCR), Unified Average Change Intensity (UACI) and computational complexity and histogram analysis. The NPCR value and UACI obtained for all the images under simulation are greater than 99% and 33, respectively. Furthermore, results show that the proposed method has low correlation, high key sensitivity and sufficient key space (2 256) to withstand against external attacks. [ABSTRACT FROM AUTHOR]

Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to the data. Nowadays most of the hospitals use electronic methods to store and transmit data with basic security measures, but these methods are still vulnerable. There is no perfect solution that solves the security problems in any industry, especially healthcare. So, to cope with the arising need to increase the security of the data from being manipulated the proposed method uses a hybrid image encryption technique to hide the data in an image so it becomes difficult to sense the presence of data in the image while transmission. It combines Least Significant Bit (LSB) Algorithm using Arithmetic Division Operation along with Canny edge detection to embed the patient data in medical images. The image is subsequently encrypted using keys of six different chaotic maps sequentially to increase the integrity and robustness of the system. Finally, an encrypted image is converted into DNA sequence using DNA encoding rule to improve reliability. The experimentation is done on the Chest XRay image, Knee Magnetic Resonance Imaging (MRI) image, Neck MRI image, Lungs Computed Tomography (CT) Scan image datasets and patient medical data with 500 characters, 1000 characters and 1500 characters. And, it is evaluated based on time coefficient of encryption and decryption, histogram, entropy, similarity score (Mean Square Error), quality score (peak signal-to-noise ratio), motion activity index (number of changing pixel rate), unified average changing intensity, image similarity score (structure similarity index measurement) between original and encrypted images. Also, the proposed technique is compared with other recent state of arts methods for 500 characters embedding and performed better than those techniques. The proposed method is more stable and embeds comparatively more data than other recent works with lower Mean Square Error value of 4748.12 which is the main factor used to determine how well the data is hidden and cannot be interpreted easily. Also, it achieved a Peak Signal-Noise Ratio (PSNR) value of 71.34 dB, which is superior than other recent works, verifying that the image quality remains uncompromising even after being encrypted. [ABSTRACT FROM AUTHOR]

The classification of time series using machine learning (ML) analysis and entropy-based features is an urgent task for the study of nonlinear signals in the fields of finance, biology and medicine, including EEG analysis and Brain–Computer Interfacing. As several entropy measures exist, the problem is assessing the effectiveness of entropies used as features for the ML classification of nonlinear dynamics of time series. We propose a method, called global efficiency (GEFMCC), for assessing the effectiveness of entropy features using several chaotic mappings. GEFMCC is a fitness function for optimizing the type and parameters of entropies for time series classification problems. We analyze fuzzy entropy (FuzzyEn) and neural network entropy (NNetEn) for four discrete mappings, the logistic map, the sine map, the Planck map, and the two-memristor-based map, with a base length time series of 300 elements. FuzzyEn has greater GEFMCC in the classification task compared to NNetEn. However, NNetEn classification efficiency is higher than FuzzyEn for some local areas of the time series dynamics. The results of using horizontal visibility graphs (HVG) instead of the raw time series demonstrate the GEFMCC decrease after HVG time series transformation. However, the GEFMCC increases after applying the HVG for some local areas of time series dynamics. The scientific community can use the results to explore the efficiency of the entropy-based classification of time series in "The Entropy Universe". An implementation of the algorithms in Python is presented. [ABSTRACT FROM AUTHOR]

Digital images are information carriers in the latest era of technology. Therefore, securing digital images or keeping data, videos, or confidential documents during communication through the internet is challenging because an adversary always endeavors to acquire any related information about digital images or reserved data. Chaotic systems are immaculate for developing new cryptosystems due to their exceptional characteristics. In this paper, our primary focus is to introduce a procedure to generate unique secret key streams using a two-dimensional logistic map and create a sequence of positive integers with the Arnold Cat map. Confusion and diffusion are the two fundamental principles of any image encryption scheme. In our proposed image encryption scheme, confusion is done using a series of positive integers to devastate the correlation among the adjacent pixels of plain images in all directions, and diffusion is done with secret key streams. The consequences of the security and statistical analysis depict that the proposed image encryption scheme is safe against numerous attacks. [ABSTRACT FROM AUTHOR]

Scheduling tasks in the cloud system is the main issue that needs to be addressed in order to improve customer satisfaction and system performance. This paper proposes DCOHHOTS, a novel multi-objective task scheduling algorithm based on a modified Harris hawks optimizer. In overall, this paper has two main stages. As the first step, DCOHHO is introduced as a new version of Harris Hawks Optimizer. Using the Differential Evolution algorithm, an optimal configuration is selected from the chaotic map, the opposition-based learning, and the ratio of the population. In order to improve the performance of the Harris Hawks Optimizer, this optimal configuration is applied to initialize the hawk's position. In the second stage, DCOHHOTS, a DCOHHO-based Task Scheduling algorithm, is proposed. Multi-objective behavior in the proposed task scheduling algorithm optimizes resource utilization to decrease the makespan, energy consumption, and execution cost. Moreover, prioritizing tasks before submitting them to the scheduler is done using the hierarchical process in the DCOHHOTS algorithm. For the purpose of investigating the performance of the proposed DCOHHO algorithm, a number of experiments are conducted using 20 standard functions and twelve algorithms. The experimental results demonstrate that the DCOHHO algorithm is superior at determining the optimal test function solutions. Additionally, makespan, execution cost, resource utilization, and energy efficiency of DCOHHOTS task scheduling algorithms are analyzed. Compared to existing algorithms, the proposed algorithm saves up to 16% energy in heavy loads. Additionally, resource utilization has increased by 17%. Compared to the conventional algorithm, the proposed algorithm reduced makepan and execution cost by 26% and 8%, respectively. [ABSTRACT FROM AUTHOR]

This article addresses this challenge and proposes a novel approach based on the modified cuckoo search and chaos theory. This article describes a novel approach for multilevel biomedical image segmentation based on the modified cuckoo search and chaos theory which is the major contribution to the literature. The modified cuckoo search approach helps to model the Lévy flight efficiently and the incorporation of the chaos theory helps to maintain the diversity in the population. The proposed approach helps to determine the optimal threshold values for a given number of thresholds. Four different objective functions are used to get the realistic segmented output which is essential in biomedical image analysis. Moreover, detailed analysis is also helpful in understanding the suitable objective function for biomedical image segmentation. This work also helps to choose suitable chaotic maps with different optimization algorithms. Hybridization of chaos theory and modified cuckoo search helps to overcome the local optima and to find the global optima cost-effectively. The chaos theory is incorporated in this proposed work to replace some solutions with some chaotic sequences to enhance the associated randomness with various phases which is beneficial to overcome the premature convergence and its related issues. The optimum setup is determined by investigating the effect of different chaotic maps along with some standard metaheuristic optimization approaches. Both qualitative and quantitative approaches are used to evaluate and compare the proposed approach. The proposed algorithm is compared with four state-of-the-art approaches. The obtained results clearly show that the proposed approach outperforms some state-of-the-art approaches in terms of both quantitative results and segmented output. On average, the proposed approach can achieve 255.8331 MSE, 24.07047 PSNR, 291.6077 mean, 0.038869 SD, 0.688655 SSIM, and 16.05358 s execution time (all for nine clusters). It can be observed that the proposed approach can determine the optimal set of clusters comparatively faster on most occasions, especially for the higher number of clusters. [ABSTRACT FROM AUTHOR]

A novel color image encryption based on permutation and bit-wise exclusive OR (XOR) operation is introduced in this work. The main objective of the proposed scheme is to provide techniques for safe transmission of image data. A novel technique for generating relation between image pixels and key initial values is proposed in this work to minimize the image attacks, as the sensitivity of key to image pixels is enhanced in such a way that if an attacker slightly changes the image pixel, key values are changed too. For this purpose, two random numbers are derived from novel convolution codes technique. These random numbers are then utilized as initial values of four-dimensional (4-D) dynamical system for generation of the chaotic sequences. These sequences are then deployed in confusion and diffusion phase for segmented images. For the illustration of security level, the proposed scheme is validated via different analysis that includes histogram, correlation, entropy, differential attacks (number of pixel change rate (NPCR), unified average changing intensity (UACI)), key space and key sensitivity analysis. Proposed technique achieves encrypted images RGB (red, green, blue) channel entropy values lie between 7.99791 and 7.9990, NPCR values between 99.5873% and 99.7341%, UACI values between 33.051% and 33.422% and Peak signal noise ratio (PSNR) values between 7.8218 and 9.3821. Also, in key sensitivity analysis a slight alteration in key does not provide any information for the decryption of image. The simulation analysis of proposed scheme confirmed the high sensitivity, image pixels uniform distribution and randomness. Also, the classical attack analysis is performed to check security of proposed ciphered image. [ABSTRACT FROM AUTHOR]

The need for high-speed and secure image encryption has become increasingly critical in the digital age, driven by the rapid growth of digital data, the rapid advancement of internet technologies, and the limitations of traditional encryption algorithms. This paper addresses this need by proposing a novel high-speed and secure image encryption that leverages the flexibility and parallelism of Field-Programmable Gate Arrays (FPGAs). Firstly, we present the LTB map, an enhanced chaotic map that synergistically combines three distinct 1D chaotic maps (the Logistic, Tent, and Bernoulli shift maps) into a single framework. The resulting LTB map exhibits an expanded key space and a heightened level of complexity and unpredictability compared to its constituent maps operating independently. Furthermore, the LTB map adopts a 32-bit fixed-point representation and involves simple operations like addition, subtraction, and multiplication, resulting in a resource-efficient implementation on FPGA platform. Additionally, we propose a new image encryption algorithm based on the LTB map. The LTB map plays a pivotal role in scrambling pixel values and positions during the confusion and diffusion operations, thereby ensuring robust security. By leveraging the inherent parallelism of the LTB map and parallelizing encryption operations, coupled with efficient hardware implementation on FPGAs, our encryption method achieves high-speed performance while maintaining strong security properties.

This research presents a novel hybrid approach, which combines the White Shark Optimizer (WSO) metaheuristic algorithm with chaotic maps integrated into the binarization process. Inspired by the predatory behavior of white sharks, WSO has shown great potential to navigate complex search spaces for optimization tasks. On the other hand, chaotic maps are nonlinear dynamical systems that generate pseudo-random sequences, allowing for better solution diversification and avoiding local optima. By hybridizing WSO and chaotic maps through adaptive binarization rules, the complementary strengths of both approaches are leveraged to obtain high-quality solutions. We have solved the Set Covering Problem (SCP), a well-known NP-hard combinatorial optimization challenge with real-world applications in several domains, and experimental results indicate that LOG and TENT chaotic maps are better after statistical testing. This hybrid approach could have practical applications in telecommunication network optimization, transportation route planning, and resource-constrained allocation.