Hybrid Algorithm Research Articles

A family of TENOA-THINC-MOOD schemes based on diffuse-interface method for compressible multiphase flows

In this work, we develop a family of hybrid algorithms to simulate compressible multi-phase flows by solving the quasi-conservative five-equation model. The general numerical framework utilizes targeted essentially non-oscillatory schemes with adaptive dissipation (TENOA) to boost the resolution of high-frequency waves, whereas the Tangent of Hyperbola for INterface Capturing (THINC) scheme is activated near the shock and contact discontinuities as well as material interfaces to maintain their sharpness, based on a novel indicator. Also, the newly derived THINC reconstruction scheme is both simpler and better at preserving the flow symmetry. The robustness of our numerical approach is further fortified through the adaptation of a modified a posteriori multidimensional optimal order detection (MOOD) technique. Moreover, to mitigate carbuncle phenomenon encountered in two-dimensional simulations, a hybrid Riemann solver is also proposed based on the low Mach modification around shock for the Harten-Lax-van Leer contact (HLLC) approximate Riemann solver. Numerical results of the challenging one- and two-dimensional benchmark cases demonstrate the superiority of the proposed methods regarding oscillation-free, interface-sharpening, low-dissipation and robustness properties.

A Comparative Investigation of Hybrid MPPT Methods for Enhancing Solar Power Generation in Renewable Energy Systems

Photovoltaic (PV) systems are among the types of renewable energy that are frequently employed. Since the characteristics of the solar cell depend on the amount of insolation and temperature, it is necessary to use MPPT “Maximum Power Point Tracking” to move the operating voltage close to the maximum power point under changing weather conditions. This article aims to design a photovoltaic energy system based on boost converter control to obtain maximum power using a hybrid algorithm based on artificial neurons (ANN). Additionally included is a proportional-integral (PI) controller, which improves the performance of the ANN-MPPT controller; this method is quick and precise for tracking the maximum power point (MPP) in the face of variations in temperature and solar radiation. The efficiency of the tracking algorithm was calculated and compared to one of the traditional methods, the incremental conductance (INC) method, in addition to comparing it to other hybrid methods and by simulating the system using MATLAB/Simulation and analyzing the results. This study unequivocally proves the superiority of the hybrid ANN+PI strategy; the efficiency reached 99.91%. This approach excels at tracking maximum power accuracy by leveraging the adaptive learning capabilities of neural networks, ensuring maximum power even in the face of changing environmental conditions.

Design optimization method of pipeline parameter based on improved artificial neural network

The rationality of pipeline design is directly related to its energy efficiency, reliability, and safety. Pipeline vibration may lead to negative effects such as mechanical loss and fatigue damage. Therefore, this study utilizes pipeline optimization design to mitigate these effects. Recently, neural networks have been widely used in structure design optimization. In the study, a backpropagation neural network (BP) combined with a variant slime mould algorithm (SMA) is utilized to solve the pipeline structure design optimization problem. Pipeline transport plays a crucial role in the efficient movement of various commodities, including but not limited to gas, oil, water, and other liquid substances. The interaction between liquid and pipeline can cause pipeline vibration and even damage. Therefore, based on the simulation model considering FSI (fluid-structure interaction), machine learning methods such as BP can predict vibration characteristics of fluid-conveying pipelines. However, existing research has shown that BP has insufficient parsing ability in structure mechanics problems, especially in solving the overall characteristics of complex structures (such as maximum structural strain). This study proposes an Arithmetic-based slime mould algorithm (ACSMA) with an adaptive decision strategy and a chaotic mapping strategy. A hybrid algorithm named ACSMA-BP is presented to promote the model's prediction ability. At last, to verify the effectiveness of the proposed pipeline structure design optimization approach, the ACSMA-BP model is utilized to complete a structure design optimization case for a simulated pipeline. The numerical results indicate that compared with AOA, CWOA, ESSAWOA, NGS_WOA, and RSA, the ACSMA has the best optimization ability.

Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach

BackgroundAssessing left ventricular diastolic function (LVDF) with echocardiography as per ASE guidelines is tedious and time-consuming. The study aims to develop a fully automatic approach of this procedure by a lightweight hybrid algorithm combining deep learning (DL) and machine learning (ML). MethodsThe model features multi-modality input and multi-task output, measuring LV ejection fraction (LVEF), left atrial end-systolic volume (LAESV), and Doppler parameters: mitral E wave velocity (E), A wave velocity (A), mitral annulus e’ velocity (e’), and tricuspid regurgitation velocity (TRmax). The algorithm was trained and tested on two internal datasets (862 and 239 echocardiograms) and validated using three external datasets, including EchoNet-Dynamic and CAMUS. The ASE diastolic function decision tree and total probability theory were used to provide diastolic grading probabilities. ResultsThe algorithm, named MMnet, demonstrated high accuracy in both test and validation datasets, with Dice coefficients for segmentation between 0.922 and 0.932 and classification accuracies between 0.9977 and 1.0. The mean absolute errors (MAEs) for LVEF and LAESV were 3.7 % and 5.8 ml, respectively, and for LVEF in external validation, MAEs ranged from 4.9 % to 5.6 %. The diastolic function grading accuracy was 0.88 with hard criteria and up to 0.98 with soft criteria which account for the top two probability in total probability theory. ConclusionsMMnet can automatically grade ASE diastolic function with high accuracy and efficiency by annotating 2D videos and Doppler images.

A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions.

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss.

An improved chaotic GWO-LGBM hybrid algorithm for emotion recognition

To address the challenges associated with the unbalanced search abilities and low search accuracy of the Gray Wolf Optimization (GWO) algorithm, we propose an enhanced version termed the Improved Chaotic Gray Wolf Optimization Algorithm (ICGWO) based on cubic mapping. Initially, the population positions of gray wolves were perturbed to expedite the convergence speed. Subsequently, a nonlinear convergence factor was introduced to enhance both search ability and efficiency. Finally, within the algorithm, a dynamic inertia-weighting strategy was implemented to improve convergence speed and solution accuracy. Experimental simulations encompassing 23 classical test functions demonstrate that the ICGWO algorithm exhibits superior convergence speed, solution accuracy, and stability compared to other swarm intelligence optimization algorithms and certain enhanced GWO algorithms. In comparison to GWO, utilizing the ICGWO method for hyperparameter optimization within the Light Gradient Boosting Machine (LGBM) for emotional recognition led to notable enhancements in average accuracy and F1 score by 7.29 % and 3.92 % respectively for valence, and 4.33 % and 3.23 % respectively for arousal. Additionally, the standard deviation of the ICGWO-LGBM was less than one-tenth of the standard deviation of GWO-LGBM in both conditions. These results underscore the strong optimization capabilities and high stability of the ICGWO algorithm.

Hybrid Elephant Herding Optimization Approach for Cluster Head Selection And Secure Data Transmission In Wsn Using Hybrid Approach Cryptography Techniques

Introduction: The wireless nature of sensor networks makes safe transfer of data from one node to another a major challenge in communications. Sensing tasks connect these sensor nodes which have limitations of memories and energies. Cryptography techniques are utilised to handle critical issues of security in these networks. The performance of large-scale networks is enhanced in this case by optimisation algorithm mimicking natural behaviours.Methods: This work uses H-EHO (Hybrid Elephant Herding Optimisation technique based on Individual strategies to enhance cluster head selections in WSNs (Wireless Sensor Networks) and thus extend networks’ lifetime. WSNs complete cluster head selection processes, and proposed optimisation approach which selects cluster heads based on tracking of sensor nodes for enhancements. The clan operators of optimisation algorithms are adjusted to handle random walk scale factors of elephants. Clusters of WSNs elect updated sensor nodes in principle. Hybrid algorithm HSR19, a novel security symmetric technique offers greater security during data transfers. It offers integrity, confidentiality, and authentication for cryptographic primary keys. Results: The output of the simulation demonstrates the energy consumption, network longevity, end to end delay, and secure data transfer metrics. The results for choosing an effective and time-efficient cluster head selection process for WSNs are improved by contrasting the two approaches. Conclusion: This comparison also shows the efficiency of communication devices in terms of calculation times for encoding, decoding and energies consumed for various file sizes

Probabilistic prediction-based multi-objective optimization approach for multi-energy virtual power plant

Virtual power plants (VPPs) are encountering multiple challenges due to market uncertainties and power network instability. In this paper, a novel probabilistic prediction-based multi-objective optimization framework for VPP is proposed to maximize operating profit while minimizing pollutant emissions and voltage deviations in the distribution network, which considers the uncertainties of wind power and electricity price. In this framework, the VPP that participates in the energy and ancillary service markets firstly aggregates the wind farms, the electric vehicle charging stations (EVCS), and the combined cooling, heating, and power subsystems to improve the utilization efficiency and operational flexibility of multiple energy sources. Then, a new Pareto optimizer, called multi-objective hybrid sand cat swarm optimization and strength firefly algorithm, is proposed to tackle the multi-objective optimization model of VPP. The proposed hybrid algorithm utilizes the advantages of sand cat swarm optimization and strength firefly algorithm mechanisms to facilitate local exploitation and global exploration. Finally, a new deep reinforcement learning probabilistic prediction approach based on quantile regression deep deterministic policy gradient is modeled to evaluate the uncertainties. The proposed models and methods have been thoroughly discussed on a modified distributed network. It is calculated that compared with the VPP without EVCS, the operating profit of the proposed VPP increases by 18.69%, and the emissions and voltage deviation of the proposed VPP are reduced by 3.42% and 10.44%, respectively. Experimental results also prove that the performance of the proposed Pareto optimizer and probabilistic prediction approach is superior to other benchmark techniques.

An efficient hybrid spectrum sensing algorithm to enhance the performance of optical NOMA waveforms using 256-QAM

Abstract The ever-increasing demand for bandwidth in optical networks necessitates efficient spectrum utilization. To address this challenge, this paper proposes a novel hybrid spectrum sensing algorithm tailored explicitly for 256-QAM (Quadrature Amplitude Modulation) optical communication waveforms. The proposed algorithm combines the strengths of energy detection and cyclostationary feature detection to overcome the limitations of individual methods. Energy detection (ED) provides fast and low-complexity sensing, while cyclostationary feature detection offers higher accuracy and sensitivity. First, ED is employed for rapid initial spectrum assessment. Subsequently, matched filter (MF) detection is selectively applied only to frequency bands identified as potentially occupied by primary users based on the energy detection results. This selective approach significantly reduces computational complexity while maintaining high detection accuracy. The results demonstrate significant improvements in detection accuracy, sensitivity, and computational efficiency compared to existing methods. In particular, the hybrid algorithm performs better in scenarios where weak 256-QAM signals coexist with strong primary users, showcasing its effectiveness in dynamic spectrum-sharing applications. This work contributes significantly to optical spectrum sensing by offering an efficient and accurate solution for advanced radio systems. The proposed hybrid algorithm paves the way for improved spectrum utilization and facilitates the development of high-performance, next-generation optical networks. The projected method obtained a gain of −200 as compared with the existing methods.

Quality Optimizing Teaching Decisions in Flipped Classrooms Through Data-Driven Strategies

In the context of digitalized educational research, the rapid advancement of internet technologies and big data has catalyzed the transformation of traditional teaching models. The flipped classroom, recognized for its flexibility and efficiency, has garnered significant attention. However, the challenge of scientifically optimizing teaching decisions in flipped classrooms to maximize educational outcomes remains critical. Previous studies have achieved some progress in optimizing teaching decisions within flipped classrooms, yet they often suffer from a lack of methodological diversity, inadequate consideration of multi-level constraints, and struggle to adapt to dynamic teaching environments. Addressing these deficiencies, this research introduces a hybrid evolutionary algorithm combining differential evolution and greedy backtracking. Defined and classified constraints within flipped classroom teaching decisions, the construction of constraint networks, and the creation of multi-level decision spaces are transformed and solved through this hybrid algorithm, offering a systematic optimization strategy. Case analysis confirms the effectiveness and practicality of the proposed method, aiming to bolster decision-making in flipped classrooms and advance the development of digitalized teaching.

Accurate machine-learning-based prediction of aerodynamic and heat transfer coefficients for cylindrical biomass particles

Cylindrical biomass (straw) particles play a crucial role in the numerical simulations of biomass co-firing in coal-fired boilers, wherein the aerodynamic and heat transfer coefficients of these particles are particularly important. Despite the existence of models proposed thus far for various specific conditions, developing a universal model for cylindrical particles remains challenging in multiphase flow systems. In this paper, a total of 1092 validated computational fluid dynamics (CFD) datasets of cylindrical drag, lift, torque coefficients and average Nusselt number are acquired based on direct numerical simulations of OpenFOAM body-fitted meshes, and a hybrid algorithmic framework of N+1 convolutional neural network (CNN) machine learning optimality is established. where multiple single-tree models (referred to as N models) are combined with an additional CNN layer (referred to as +1 model).The framework inherits the advantages of a single-tree models while significantly improving the overall algorithmic generalisation performance, with the aim of allowing efficient use of time and minimal expertise in the fluid modelling process. The mean relative absolute errors for drag, lift, torque coefficient, and the average Nusselt number was evaluated, and the results demonstrated a reduction of 75.19 %, 89.48 %, 89.53 %, and 85.70 %, respectively, in the mean relative absolute errors compared to the extremely randomised tree single-tree model. We extended the model scope to different aspect ratios (1 ≤ Ar ≤ 30), angles of incidence (0° ≤ θ ≤ 90°), and Reynolds numbers (1 ≤ Re ≤ 4000), and conducted random predictions. Compared with traditional mathematical relationships, the proposed framework reduced the average relative error to 0.99 %. The simultaneous application of models for the prediction of unknown parameters was validated against relevant early literature, which demonstrated that the average relative error of drag coefficients for the predicted values remained within 2.07 %. The N+1 (CNN) hybrid algorithm framework exhibited high accuracy and excellent adaptability, furnishing a substantial quantity of data to support the Euler–Lagrange model of non-spherical biomass particle multiphase flow in industrial applications.

Combining A Hybrid Genetic Algorithm with A Fuzzy Logic Classifier Enhances Heart Disease Diagnosis

Because cardio illness has a significant impact on death rates worldwide, diagnosing heart disease is an essential field of healthcare. The study proposes a unique method, the HDD-GA-FL model, to enhance Heart Disease Diagnosis (HDD) that combines a hybrid genetic algorithm (GA) along with a fuzzy logic classifier (FL). The suggested hybrid system aims to overcome the difficulties brought on by the intricacy and ambiguity involved in diagnosing cardiac disease. Fuzzy logic classifiers are used to analyze ambiguous medical data, while genetic algorithms are used for choosing features and optimization. Combining these two methods provides a strong foundation for precise and effective diagnosis. Experiments on an extensive dataset with different clinical factors and cases of heart disease are conducted to assess the efficacy of the hybrid strategy. Compared to conventional diagnostic techniques, there have been significant improvements in diagnostic reliability and accuracy. When navigating the complex feature space involved in diagnosing heart illness, the combination of GA and FLC performs better than alone. It can capture minute associations and trends in the data. The suggested hybrid system has great potential for real-world application in clinical settings, providing doctors with an invaluable instrument for accurately diagnosing and detecting cardiac problems early on. This method advances the latest developments in cardiac healthcare by utilizing the complementary strengths of fuzzy logic classifiers and genetic algorithms, eventually improving patient outcomes and lowering healthcare costs.

Hybrid differential evolution algorithm for Falkner-Skan flow with rotation

A hybrid differential evolution algorithm is used in this work to study the rotating transport of Falkner-Skan flow. The problem is modeled as an equivalent optimization problem by using Padé rational approximation functions. The primary model of the governing partial differential equations is imposed to subjugate the error between profiles. A hybrid evolutionary algorithm based on differential evolution and a convergent version of Nelder-Mead direct search algorithm is employed to perform global exploratory search along with an enhanced exploitation to improve the accuracy of the proposed solution scheme. The resulting scheme is named as evolutionary Padé approximation (EPA) scheme. The performance of the proposed EPA scheme on the Falkner-Skan boundary value problem is investigated by considering various values of the rotation parameters. The developed optimizer in EPA scheme was able to minimize the residuals up to10−10. Results are displayed graphically in order to study the effect of various types of parameters. EPA scheme determined that angular velocity increases or decreases accordingly as fluid parameter (β) and the rotation parameter (λ) but shows inverse behavior with respect to power law index(n). Similarly, the response of velocity profile along y−axis was decreasing function of β as well as n but increasing function of λ. The performance of the proposed EPA scheme has been demonstrated by comparing results with a hybrid neural network scheme and found in excellent agreement.

Artificial Intelligence Advancements for Accurate Groundwater Level Modelling: An Updated Synthesis and Review

Artificial Intelligence (AI) methods, including Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs), Support Vector Machines (SVMs), Deep Learning (DL), Genetic Programming (GP) and Hybrid Algorithms, have proven to be important tools for accurate groundwater level (GWL) modelling. Through an analysis of the results obtained in numerous articles published in high-impact journals during 2001–2023, this comprehensive review examines each method’s capabilities, their combinations, and critical considerations about selecting appropriate input parameters, using optimisation algorithms, and considering the natural physical conditions of the territories under investigation to improve the models’ accuracy. For example, ANN takes advantage of its ability to recognise complex patterns and non-linear relationships between input and output variables. In addition, ANFIS shows potential in processing diverse environmental data and offers higher accuracy than alternative methods such as ANN, SVM, and GP. SVM excels at efficiently modelling complex relationships and heterogeneous data. Meanwhile, DL methods, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), are crucial in improving prediction accuracy at different temporal and spatial scales. GP methods have also shown promise in modelling complex and nonlinear relationships in groundwater data, providing more accurate and reliable predictions when combined with optimisation techniques and uncertainty analysis. Therefore, integrating these methods and optimisation techniques (Hybrid Algorithms), tailored to specific hydrological and hydrogeological conditions, can significantly increase the predictive capability of GWL models and improve the planning and management of water resources. These findings emphasise the importance of thoroughly understanding (a priori) the functionalities and capabilities of each potentially beneficial AI-based methodology, along with the knowledge of the physical characteristics of the territory under investigation, to optimise GWL predictive models.

A hybrid intrusion detection approach based on message queuing telemetry transport (MQTT) protocol in industrial internet of things

AbstractThe number of attacks against Industrial Internet of Things (IIoT) devices has increased over the past years, particularly on widely used communication protocols like Message Queuing Telemetry Transfer (MQTT). The fast increase in IIoT applications brings both critical challenges and technical gaps in cybersecurity. On the other hand, traditional cyber‐attack detection approaches scrap to address and support the run‐time responsibilities of IIoT environments. This study presents a hybrid Genetic Algorithm and Random Forest (GA_RF) method for detecting cyber‐attacks in Industrial Control Machines (ICS) that use MQTT protocol in the IIoT environment. This architecture integrates ICS with edge devices and cloud servers, using a GA_RF algorithm to detect anomalies in data collected by sensors. Normal data is processed locally and then sent to the cloud for storage and return, ensuring continuous monitoring and security. Also, the MQTT‐IOT‐IDS2020 dataset as a real test case was applied for prediction of the proposed GA_RF method with compare to some other powerful machine and deep learning models. The experimental results show that the proposed GA_RF method has an optimum accuracy of 99.87%–100% for detecting cyber‐attacks. This hybrid algorithm also achieved 0–0.0015 in Mean Absolute Error (MAE) and 100% in Precision, Recall, and F‐score factors. This result led to the proposed architecture, which connects the ICS to a server while running GA_RF on the IIoT environment. In conclusion, this study indicates the effectiveness of GA_RF and aims to improve security by using the MQTT protocol in IIoT.

Advanced image analysis for medical diagnostics: a system for segmentation and classification using level set methods and AI algorithms

This work aims to implement and utilize an advanced computer system for image analysis and processing through artificial intelligence. The system will evaluate images from multiple sources. As a result, a comprehensive e-Medicus system will be developed to capture and analyze X-ray data and classify cancer cells. This innovative tool, with its unique features tailored for medical facilities, will assist them in capturing and analyzing X-ray images and CT scan results. The e-Medicus system offers several benefits for medical facilities, including efficient automation of photo analysis, tracking changes in a patient's condition over time, and facilitating the identification of medical changes and data classification. A multimedia presentation of the change process will use the contour set function, allowing for topological changes in solution properties. The system integrates novel procedures and algorithms from theoretical computer science and numerical mathematics, leveraging neural networks, genetic algorithms, semantic networks, image ontology, rough set theory, contour set methods, and hybrid algorithms. These developed algorithms enhance methods and concepts in image segmentation, gathering, transmitting, storing, extracting, and effectively presenting information.

Quantum annealer accelerates the variational quantum eigensolver in a triple-hybrid algorithm

Hybrid algorithms that combine quantum and classical resources have become commonplace in quantum computing. The variational quantum eigensolver (VQE) is routinely used to solve prototype problems. Currently, hybrid algorithms use no more than one kind of quantum computer connected to a classical computer. In this work, a novel triple-hybrid algorithm combines the effective use of a classical computer, a gate-based quantum computer, and a quantum annealer. The solution of a graph coloring problem found using a quantum annealer reduces the resources needed from a gate-based quantum computer to accelerate VQE by allowing simultaneous measurements within commuting groups of Pauli operators. We experimentally validate our algorithm by evaluating the ground state energy of H2 using different IBM Q devices and the DWave Advantage system requiring only half the resources of standard VQE. Other larger problems we consider exhibit even more significant VQE acceleration. Several examples of algorithms are provided to further motivate a new field of multi-hybrid algorithms that leverage different kinds of quantum computers to gain performance improvements.

Application of hybrid chaotic particle swarm optimization and slime mould algorithm to optimally estimate the parameter of fuel cell and solar PV system

Through the use of fuel cells and photovoltaic (PV) solar cells, green energy has become one of the most popular developments in electricity generation in recent years. Accurate modeling of fuel cell and PV cell is critical in the simulation and analysis of energy systems that use both sources. Due to the complexity and nonlinearity of these hybrid systems, the model must be optimized under different operating conditions. In this article, a novel metaheuristic efficient hybrid algorithm is proposed for this purpose. A Hybrid Chaotic Particle Swarm Optimization and Slime Mould Algorithm (HCPSOSMA) has been proposed to estimate the PV/fuel cell system parameters. The hybrid HCPSOSMA algorithm is superior to other algorithms in terms of higher accuracy in searching for optimal solutions and better explorative capability. The problem formulation is based on minimizing the Root Mean Square Error (RMSE) between the calculated values and measured values for both the triple-diode PV model and the proton exchange membrane (PEM) fuel cell. The results show that the hybrid algorithm outperforms other algorithms in determining the best PV/PEMFC parameters that produce accurate V–I and P–I characteristic curves and the RMSE is about 4 × 10–12. Finally, non-parametric tests based on Friedman's ranking, Wilcoxon's rank sum test and Kruskal-Wallis test are implemented to prove the superiority of the proposed hybrid algorithm in the parameter estimation.

Medical image segmentation approach based on hybrid adaptive differential evolution and crayfish optimizer

Image segmentation plays a pivotal role in medical image analysis, particularly for accurately isolating tumors and lesions. Effective segmentation improves diagnostic precision and facilitates quantitative analysis, which is vital for medical professionals. However, traditional segmentation methods often struggle with multilevel thresholding due to the associated computational complexity. Therefore, determining the optimal threshold set is an NP-hard problem, highlighting the pressing need for efficient optimization strategies to overcome these challenges. This paper introduces a multi-threshold image segmentation (MTIS) method that integrates a hybrid approach combining Differential Evolution (DE) and the Crayfish Optimization Algorithm (COA), known as HADECO. Utilizing two-dimensional (2D) Kapur’s entropy and a 2D histogram, this method aims to enhance the efficiency and accuracy of subsequent image analysis and diagnosis. HADECO is a hybrid algorithm that combines DE and COA by exchanging information based on predefined rules, leveraging the strengths of both for superior optimization results. It employs Latin Hypercube Sampling (LHS) to generate a high-quality initial population. HADECO introduces an improved DE algorithm (IDE) with adaptive and dynamic adjustments to key DE parameters and new mutation strategies to enhance its search capability. In addition, it incorporates an adaptive COA (ACOA) with dynamic adjustments to the switching probability parameter, effectively balancing exploration and exploitation. To evaluate the effectiveness of HADECO, its performance is initially assessed using CEC’22 benchmark functions. HADECO is evaluated against several contemporary algorithms using the Wilcoxon signed rank test (WSRT) and the Friedman test (FT) to integrate the results. The findings highlight HADECO’s superior optimization abilities, demonstrated by its lowest average Friedman ranking of 1.08. Furthermore, the HADECO-based MTIS method is evaluated using MRI images for knee and CT scans for brain intracranial hemorrhage (ICH). Quantitative results in brain hemorrhage image segmentation show that the proposed method achieves a superior average peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) of 1.5 and 1.7 at the 6-level threshold. In knee image segmentation, it attains an average PSNR and FSIM of 1.3 and 1.2 at the 5-level threshold, demonstrating the method’s effectiveness in solving image segmentation problems.

The Construction of an English Network Course Based on the Theory of Hybrid Real-Time Synchronization Algorithm

At present, the construction of online courses is an important content in the process of educational informatization. At present, there are two problems in the construction of online courses: from the teaching point of view, the design of teaching activities and teaching processes in online courses is not enough, and the teaching effect is not good. Technically, the development of most courses is basically one-off and cannot be updated quickly. Due to the small scale, limited access and strong pertinence of SPOC, it has obvious advantages to implement the online and offline mixed teaching mode of college English based on SPOC in college English teaching. This paper mainly studies the research and analysis of English online courses based on the hybrid real-time synchronization algorithm. This paper combines OFDM synchronization algorithm with SPOC hybrid algorithm teaching to analyze and study it. According to the research, the influence of English online teaching course is very great, and the effect is more obvious than that of traditional teaching.