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Optimal algorithm for color medical encryption and compression images based on DNA coding and a hyperchaotic system in the moments

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2024年 50卷

作者： Bencherqui, Ahmed Tahiri, Mohamed Amine Karmouni, Hicham Alfidi, Mohammed Motahhir, Saad Abouhawwash, Mohamed Askar, S. S. Wen, Shuhuan Qjidaa, Hassan Sayyouri, Mhamed Sidi Mohamed Ben Abdellah Fez Univ Natl Sch Appl Sci Engn Syst & Applicat Lab Fes Morocco Sidi Mohamed Ben Abdellah Fez Univ Fac Sci Elect Signals & Informat Syst Lab Fes Morocco Cadi Ayyad Univ Natl Sch Appl Sci Marrakech Morocco Michigan State Univ Coll Engn Dept Computat Mat Sci & Engn CMSE E Lansing MI 48824 USA Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt King Saud Univ Coll Sci Dept Stat & Operat Res POB 2455 Riyadh 11451 Saudi Arabia Yanshan Univ Yanshan Peoples R China

Currently, visual data security plays a significant role in various fields, especially in medical imaging. Addressing the challenges associated with limited key space and vulnerability to different types of attacks within current encryption schemes, this work proposes an optimal compression-encryption scheme for large medical images that incorporates elements of archimedes' optimization algorithm, discrete orthogonal Hahn moments, chaotic systems, and DNA coding. The primary aim of this study is to develop an optimal and exceptionally resilient compression-encryption scheme capable of countering various attack types effectively. This approach is structured into three principal phases: a compression phase harnessing the efficiencies of Hahn's discrete orthogonal moments (HMs) in signal and image representation, coupled with the archimedes optimization algorithm (AO) to ensure optimal tuning of polynomial parameters (a, b) for superior image reconstruction quality. The encryption phase is performed on the compressed image, using hyperchaotic memristive 4-D (HCM-4D), adapted logistics map (ALM) and DNA coding. Initially, the adapted logistics map is responsible for generating random sequences linked to the compressed image. Subsequently, chaotic sequences originating from the hyperchaotic 4D memristive system govern both random sequences and DNA processes. The optimization phase, facilitated by the AO algorithm, focuses on minimizing the value of the objective function (correlation) on the compressed and encrypted images. Ultimately, the image with the lowest correlation value is designated as the optimal compressed-encrypted representation. The simulation results clearly illustrate the resilience of the AO algorithm when juxtaposed with other optimization algorithms, especially with respect to convergence speed and computational efficiency. On the other hand, the proposed compression approach demonstrated exceptional efficiency in compressing medical images, offering

关键词： Moments Image compression-encryption Chaotic system archimedes optimization algorithm

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Deep Learning-Based FOPID Controller for Cascaded DC-DC Converters

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Computer Systems Science & Engineering 2023年第8期46卷 1503-1519页

作者： S.Hema Y.Sukhi Department of EEE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of EEE R.M.K.Engineering CollegeKavaraipettaiChennai601206India

Smart grids and their technologies transform the traditional electric grids to assure safe,secure,cost-effective,and reliable power ***-linear phenomena in power systems,such as voltage collapse and oscillatory phenomena,can be investigated by chaos ***,renewable energy resources,such as wind turbines,and solar photovoltaic(PV)arrays,have been widely used for electric power *** design of the controller for the direct Current(DC)converter in a PV system is performed based on the linearized model at an appropriate operating ***,these operating points are everchanging in a PV system,and the design of the controller is usually accomplished based on a low irradiance *** study designs a fractional-order proportional-integrated-derivative(FOPID)controller using deep learning(DL)with quasi-oppositional archimedes optimization algorithm(FOPID-QOAOA)for cascaded DC-DC converters in micro-grid *** presented FOPIDQOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost *** addition,the proposed model develops a FOPID controller using a stacked sparse autoencoder(SSAE)model to regulate the converter output *** tune the hyper-parameters related to the SSAE model,the QOAOA is derived by the including of the quasi-oppositional based learning(QOBL)with traditional ***,an objective function with the including of the integral of time multiplied by squared error(ITSE)is considered in this *** validating the efficiency of the FOPID-QOAOA method,a sequence of simulations was performed under distinct aspects.A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.

关键词： Micro-grid deep learning archimedes optimization algorithm FOPID controller objective function

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The implementation of a regularized radial basis function model for predicting California bearing capacity

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 2359-2380页

作者： Zhan, Weiwei Chongqing Vocat Coll Transportat Sch Rd Bridge & Architecture Chongqing 402247 Peoples R China

The characterization of soil resistance properties is of paramount importance in the realm of pavement design. Indices such as dynamic modulus, resilient modulus, and California Bearing Ratio (CBR) are commonly employed to ascertain these properties. The CBR test is an experimental methodology implemented in laboratory or field settings to assess the subgrade's shear strength and stiffness modulus. Due to the exorbitant expenses linked with these assessments, it is deemed essential to implement distinct methodologies to predict their results. The advancement of artificial intelligence (AI) methodologies has enabled the production of innovative models and algorithms. Implementing these techniques enables research professionals to choose predictive methodologies over empirical ones. This study employs an AI methodology to assess the mechanical properties of the CBR index. The radial basis function (RBF) technique was used as a problem-solving methodology to create a machine-learning model. The methodology entails the utilization of sampling experiments to tackle a precisely defined problem. Three different meta-heuristic optimisation techniques were used in this study, namely, the archimedes optimization algorithm (AOA), bald eagle search optimization (BESO), and Ebola optimization search (EOS), to achieve optimal outcomes. Furthermore, the entire procedure is subjected to evaluation by employing evaluators. According to the results, RBEO obtained the most suitable values in presented metrics, such as R2 and RMSE, equal to 0.9969 and 0.629, respectively. In general, EOS indicated the most desirable accuracy when coupled with RBF compared to BESO and AOA.

关键词： California bearing ratio Radial basis function archimedes optimization algorithm Bald eagle search optimization Ebola optimization search

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Development of lightweight intrusion model in Industrial Internet of Things using deep learning technique

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DISCOVER APPLIED SCIENCES 2024年第7期6卷 346页

作者： Sinha, Raj Thakur, Padmanabh Gupta, Sandeep Shukla, Anand Lovely Profess Univ Sch Comp Applicat Phagwara Punjab India Graph Era Deemed Be Univ Dept Elect Engn Dehra Dun 248002 Uttarakhand India Wollega Univ Nekemte Ethiopia

Nowadays, the IDS is being used in conjunction with the IIoT system to reduce the security risk, but on the other hand, the false rate of the IDS is very high. Therefore, in this work, a pre-training method, making use of both a deep neural network and a deep auto-encoder, has been proposed for the quick prediction of assaults with increased accuracy and a reduced false rate. The replicas were expanded using hyperparameter optimization (HPO) techniques. The proposed model delivers an alternative to deep learning construction replicas through an HPO procedure incorporating the archimedes optimization algorithm. This optimization technique can be used to determine the hyperparameter value and the ideal categorical hyperparameter combination for improved detection performance. The DS2OS dataset is used alongside numerous other indicators to evaluate the efficacy of the developed model. The various existing techniques of assault detection have also been considered to show the effectiveness of the proposed model. Through the comparative evaluation of the outcomes, it is shown that the developed model provides better performance than the other existing models. Eventually, it is discovered that the suggested security paradigm is successful in fending off a variety of internal and external threats. A hybridization of DNN and DAE is considered for accurate assaults detection. archimedes optimization algorithm has been used to improv the detection performance. The percentage accuracy and precision of the developed model are 96.57 and 98.57. The percentage recall and F-measure of the developed hybrid model are 98.69 and 98.35.

关键词： Industrial Internet of Things Intrusion detection organization archimedes optimization algorithm Cyber-attacks

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Prediction of pile settlement using hybrid support vector regressor

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 2103-2120页

作者： Li, Yinan Li, Tianzhai Xinyang Agr & Forestry Univ Coll Landscape Architecture & Art Xinyang 464000 Henan Peoples R China Xinyang Vocat & Tech Coll Architectural Engn Inst Xinyang 464000 Henan Peoples R China

Pile settlement is the downward displacement of a foundational pile in construction, used to support structures by transferring loads to stable soil or rock layers beneath the surface. Accurately predicting pile settlement is critical for successful foundation design in civil engineering. In recent years, machine learning techniques have been utilized to predict pile settlement with improved accuracy compared to traditional analytical methods. This study has considered three different optimization algorithms combined with Support vector regression (SVR) to enhance its predictive capabilities further. These algorithms include archimedes optimization (AO), Marine Predators algorithm (MPA), and Augmented Grey Wolf Optimizer (AGWO). The proposed SVR-based models are trained and tested using field data from several projects with varying soil conditions. The input parameters include pile diameter, length, soil properties, and load characteristics. In addition, various statistical measures are used to evaluate each model's performance. In conclusion, the proposed SVR models, including optimization algorithms, particularly AGWO, MPA, and AO, provide a robust and accurate prediction of pile settlement. In comparing the models with each other, SVAO was able to obtain the most appropriate values compared to the other two models, with R-2 = 0.997 and RMSE = 0.201. In general, these models can be used as a reliable tool in foundation design for predicting pile settlement and ensuring the safety and stability of structures.

关键词： Pile settlement Support vector regression archimedes optimization algorithm Marine predators' algorithm Augmented grey wolf optimizer

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AOA Based CKF Adaptive algorithm for AUV Navigation

AOA Based CKF Adaptive Algorithm for AUV Navigation

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OCEANS Hampton Roads Conference

作者： Zhai, Ning Zhang, Xin Mu, Xiaokai Zhang, Di Zhou, Liqian Lu, Feiyang He, Bo Ocean Univ China Fac Informat Sci & Engn Qingdao Peoples R China Harbin Engn Univ Coll Shipbldg Engn Harbin Peoples R China Harbin Engn Univ Qingdao Innovat & Dev Ctr Qingdao Peoples R China

ISBN: (数字)9781665468091

ISBN: (纸本)9781665468091

High precision navigation is an important prerequisite to realizing various functions of the Autonomous Underwater Vehicle (AUV). Observation noise is not fixed, and the uncertainty of observation noise would lead to the accumulation of navigation errors. Therefore, this paper proposes an archimedes optimization algorithm (AOA) based Cubature Kalman Filter (CKF) adaptive navigation algorithm. The real experimental data of the self-developed underwater vehicle are processed to evaluate the performance of the algorithm. Experimental results show that the algorithm can improve the navigation accuracy compared with the traditional method.

关键词： Autonomous Underwater Vehicle Integrated Navigation Kalman Filter archimedes optimization algorithm

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Stackelberg Game of Electricity Retailer Based on Demand Response 4

Stackelberg Game of Electricity Retailer Based on Demand Res...

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4th International Conference on Smart Power and Internet Energy Systems (SPIES)

作者： Zheng, Jian Zhang, Junfang Bi, Yue Zhong, Xiaomin Zhu, Kaiwen Wang, Luyue Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China State Grid Liaoning Elect Power Co Ltd Inst Econ & Technol Shenyang Peoples R China

ISBN: (纸本)9781665489577

With the formation of the market-oriented competition pattern on the electricity selling side, electricity retailers are always faced with huge risks caused by the uncertainty of electricity prices in the spot market. It is urgent to optimize the revenue risk to improve the market competitiveness of electricity retailers. As an important tool to deal with spot market risk, demand response has been increasingly widely used. This paper considers the electricity purchase of electricity retailers in the spot market and sells electricity to power consumers through the time-of-use price. On this basis, a Stackelberg game model between an electricity retailer and power consumers based on demand response is constructed. In order to solve the problem, the archimedes optimization algorithm is adopted. The results of the cases demonstrate that the proposed model can achieve a win-win situation between electricity retailers and power consumers, which provides guidance for electricity retailers when making decisions.

关键词： electricity retailer demand respond Stackelberg game archimedes optimization algorithm

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Estimation of Solar Cell Circuit Model Parameters Using an algorithm Based on Artificial Intelligence 10

Estimation of Solar Cell Circuit Model Parameters Using an A...

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10th Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Samani, Aydin Khodsuz, Masume Islamic Azad Univ Dept Elect Engn Sci & Res Branch Tehran Iran Univ Sci & Technol Mazandaran Fac Elect & Comp Engn Behshahr Iran

ISBN: (纸本)9798350300338

In this article, it is intended to estimate the parameters of the equivalent circuit of the two-diode model of the PV system by using the method based on artificial intelligence. Based on this, at first, using the particle swarm algorithm as a suitable method, 7 parameters of the two-diode model were optimized. In order to check the performance of the particle swarm method, different strategies were considered for it, including PSO algorithm with random inertia coefficient, time-varying inertia coefficient, time-varying learning coefficients, contraction coefficient and average. Next, the archimedes optimization method as a new algorithm in this field was proposed and investigated, and the parameters of the two-diode model were optimized based on it. In order to check the accuracy of the proposed methods, the results obtained from the optimization of the parameters of the two-diode PV system model were compared with some optimization methods.

关键词： solar cell system two diode circuit model particle swarm algorithm archimedes optimization algorithm mean square error

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Prediction of Yoga Pose from YouTube Dataset using Skeleton Feature Extraction Based ISDL Model

Prediction of Yoga Pose from YouTube Dataset using Skeleton ...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Syedinamulla Vishwanatha, C.R. Dhareshwar, Shweta Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350313413

The ancient Indian discipline of yoga has spread over the world as a means of improving one's physical, mental, and spiritual health. Computational probing has a huge potential in all areas of society, thanks to the rapid development of related technologies. It has been shown that pose detection methods may be utilised to both recognize the postures and help people do yoga with greater precision. Due to the difficulty of detecting posture in real time and the limited availability of datasets, posture recognition is a difficult task. This study use a deep learning model to categorise yoga postures according to their difficulty. This research suggests a methodology for automatic detection and classification based on deep learning. The steps of the proposed technique are called preprocessing, feature extraction, and classification. At first, a preprocessing stage is executed to remove any excess noise from the edges. The next step is an equalisation based on the histogram to isolate the usable parts of the image. Skeleton-based models are used to extract features;an improved synergic deep learning (ISDL) model is then used to categorise input photographs into different postures;and last, the archimedes optimisation algorithm (AOA) is used to further refine the model. The provided SDL model is validated using data extracted from YouTube videos. Experiments showed that the projected ISDL model outperformed the state-of-the-art models in accuracy. © 2023 IEEE.

关键词： archimedes optimization algorithm Histogrambased equalization Spiritual wellness Synergic deep learning Yoga Pose Prediction

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Optimal Configuration Scheme of Energy Storage in Regional Power Grid Under the Goal of 'Double Carbon' 5

Optimal Configuration Scheme of Energy Storage in Regional P...

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5th International Conference on Power and Energy Technology, ICPET 2023

作者： Liang, Haiping Tao, Rongrong Su, Haifeng Niu, Shengsuo Li, Shihang Wang, Jinying North China Electric Power University Department of Electrical Engineering Baoding China

ISBN: (纸本)9798350339673

With the release of the 'carbon peak, carbon neutrality' program, energy transformation has been further promoted, and carbon emission reduction has been fully assisted. At the same time, wind power and photovoltaics have gradually been widely used in power systems. In this context, this paper proposes a bi-level optimization model of energy storage system under wind and solar access. Integrating planning with operational issues, the inner layer considers the cost of wind and power curtailment with the goal of minimizing the operating cost of the unit;the outer layer is aimed at minimizing the total cost of energy storage planning, taking into account network losses and energy storage benefits. Finally, the correctness and feasibility of the scheme and model are verified by the simulation analysis of archimedes optimization algorithm in the improved 39-bus system. © 2023 IEEE.

关键词： 'double carbon' archimedes optimization algorithm bi-level optimization capacity configuration energy-storage system photovoltaic wind power

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