Search Results (310)

Human activity recognition (HAR) systems are essential in healthcare, surveillance, and sports analytics, enabling automated movement analysis. This research presents a novel HAR system combining graph structures with deep neural networks to capture both spatial and temporal patterns in activities. While CNN-based models excel at spatial feature extraction, they struggle with temporal dynamics, limiting their ability to classify complex actions. To address this, we applied the Firefly Optimization Algorithm to fine-tune the hyperparameters of both the graph-based model and a CNN baseline for comparison. The optimized graph-based system, evaluated on the UCF101 and Kinetics-400 datasets, achieved 88.9% accuracy with balanced precision, recall, and F1-scores, outperforming the baseline. It demonstrated robustness across diverse activities, including sports, household routines, and musical performances. This study highlights the potential of graph-based HAR systems for real-world applications, with future work focused on multi-modal data integration and improved handling of occlusions to enhance adaptability and performance. Full article

This study innovatively develops a multi-objective Markal-Macro model, which simultaneously considers three objectives: minimizing carbon emissions from energy consumption, minimizing carbon emissions from production processes, and maximizing societal welfare. Based on the Cobb–Douglas production function, we construct a production function of carbon emission and use it as a coupling equation of the Markal-Macro model (Markal is the abbreviation of market allocation, and Macro is the abbreviation of macroeconomy). This enables the coupling of the endogenous variables of carbon emissions and those related to maximizing societal welfare. By collecting relevant data on energy consumption, production outputs, and key economic indicators, five different scenarios are established. To enhance the computational efficiency of the simulation, we introduce a Firefly Algorithm into the penalty function method. The objective of our simulation is to explore the optimal carbon peak pathway for China. The results indicate that under the baseline scenario, China can achieve its carbon peak by 2029, with the peak value reaching approximately 12.5 billion tons of carbon dioxide. Finally, based on the simulation results, this study provides specific policy recommendations for China’s carbon peak pathway, addressing aspects such as industrial structure, energy consumption structure, the share of clean energy, economic growth targets, and the growth of emission reduction expenditures, while considering regional five-year plans and regional carbon peak strategies. From the aspect of the practical contributions, this article not only provides a set of methods for policymakers to make the Carbon Peak Implementation Plan but also offers an optimal path to improve the sustainable development for China. Full article

With the popularity of higher education and the evolution of the workplace environment, graduate education has become a key choice for students planning their future career paths. Therefore, this study proposes to use the data processing ability and pattern recognition ability of machine learning models to analyze the relevant information of graduate applicants. This study explores three different models—backpropagation neural networks (BPNN), random forests (RF), and logistic regression (LR)—and combines them with the firefly algorithm (FA). Through data selection, the model was constructed and verified. By comparing the verification results of the three composite models, the model whose evaluation results were closest to the actual data was selected as the research result. The experimental results show that the evaluation result of the BPNN-FA model is the best, with an R value of 0.8842 and the highest prediction accuracy. At the same time, the influence of each characteristic parameter on the prediction result was analyzed. The results show that CGPA has the greatest influence on the evaluation results, which provides the evaluation direction and evaluation results for the evaluators to analyze the level of students’ scientific research ability, as well as providing impetus to continue to promote the combination of education and artificial intelligence. Full article

Rapid technology advances have made managing charging loads and optimizing routes for electric vehicle (EV) fleets, especially in cities, increasingly important. IoT sensors in EV charging stations and cars enhance prediction and optimization algorithms with real-time data on charging behaviors, traffic, vehicle locations, and environmental factors. These IoT data enable the GNN-ViGNet hybrid deep learning model to anticipate electric vehicle charging needs. Data from 400,000 IoT sensors at charging stations and vehicles in Texas were analyzed to identify EV charging patterns. These IoT sensors capture crucial parameters, including charging habits, traffic conditions, and other environmental elements. Frequency-Aware Dynamic Range Scaling and advanced preparation methods, such as Categorical Encoding, were employed to improve data quality. The GNN-ViGNet model achieved 98.9% accuracy. The Forecast Accuracy Rate (FAR) and Charging Load Variation Index (CLVI) were introduced alongside Root-Mean-Square Error (RMSE) and Mean Square Error (MSE) to assess the model’s predictive power further. This study presents a prediction model and a hybrid Coati–Northern Goshawk Optimization (Coati–NGO) route optimization method. Routes can be real-time adjusted using IoT data, including traffic, vehicle locations, and battery life. The suggested Coati–NGO approach combines the exploratory capabilities of Coati Optimization (COA) with the benefits of Northern Goshawk Optimization (NGO). It was more efficient than Particle Swarm Optimization (919 km) and the Firefly Algorithm (914 km), reducing the journey distance to 511 km. The hybrid strategy converged more quickly and reached optimal results in 100 rounds. This comprehensive EV fleet management solution enhances charging infrastructure efficiency, reduces operational costs, and improves fleet performance using real-time IoT data, offering a scalable and practical solution for urban EV transportation. Full article

Protecting AC microgrids (MGs) is a challenging task due to their dual operating modes—grid-connected and islanded—which cause sudden variations in fault currents. Traditional protection methods may no longer ensure network security. This paper presents a novel approach to protection coordination in AC MGs using non-standard features of directional over-current relays (DOCRs). Three key optimization variables are considered: Time Multiplier Setting (TMS), the plug setting multiplier’s (PSM) maximum limit, and the standard characteristic curve (SCC). The proposed model is formulated as a mixed-integer nonlinear programming problem and solved using four metaheuristic techniques: the genetic algorithm (GA), Imperialist Competitive Algorithm (ICA), Harmonic Search (HS), and Firefly Algorithm (FA). Tests on a benchmark IEC MG with distributed generation and various operating modes demonstrate that this approach reduces coordination times compared to existing methods. This paper’s main contributions are threefold: (1) introducing a methodology for assessing the optimal performance of different standard curves in MG protection; (2) utilizing non-standard characteristics for optimal coordination of DOCRs; and (3) enabling the selection of curves from both North American and European standards. This approach improves trip time performance across multiple operating modes and topologies, enhancing the reliability and efficiency of MG protection systems. Full article

For chicken carcass breast blood-related defects (CBDs), which occur with high frequency, the visual features are approximated in terms of the similarity of the composition of these defects, making it challenging to classify them, either manually or automatically, using conventional machine vision. The aim of this paper was to introduce a method of CBD classification based on hyperspectral imaging combined with Convolutional Neural Networks (CNNs). To process hyperspectral data, the Improved Firefly Band Selection Algorithm was constructed with the 1-D CNN CBD classification model as the objective function, achieving a reduction in the dimensionality of hyperspectral data. The multidimensional data CBD classification models were developed based on YOLOv4 and Faster R-CNN, incorporating the 1-D CNN CBD classification model and the feature fusion layer. The combination of hyperspectral data and CNN can effectively accomplish the classification of CBDs, although different model architectures emphasize classification speed and accuracy differently. The multidimensional data YOLOv4 CBD classification model achieves an mAP of 0.916 with an inference time of 41.8 ms, while the multidimensional data Faster R-CNN CBD classification model, despite having a longer inference time of 58.2 ms, reaches a higher mAP of 0.990. In practical production scenarios, the appropriate classification model can be selected based on specific needs. Full article

This research introduces a hybrid approach combining bio- and nature-inspired metaheuristic algorithms to enhance scheduling efficiency and minimize energy consumption in Underwater Acoustic Sensor Networks (UASNs). Five hybridized algorithms are designed to efficiently schedule nodes, reducing energy costs compared to existing methods, and addressing the challenge of unscheduled nodes within the communication network. The hybridization techniques such as Elephant Herding Optimization (EHO) with Genetic Algorithm (GA), Firefly Algorithm (FA), Levy Firefly Algorithm (LFA), Bacterial Foraging Algorithm (BFA), and Binary Particle Swarm Optimization (BPSO) are used for optimization. To implement these optimization techniques, the Scheduled Routing Algorithm for Localization (SRAL) is introduced, aiming to enhance node scheduling and localization. This framework is crucial for improving data delivery, optimizing Route REQuest (RREQ) and Routing Overhead (RO), while minimizing Average End-to-End (AE2E) delays and localization errors. The challenges of node localization, RREQ reconstruction at the beacon level, and increased RO, along with End-to-End delays and unreliable data forwarding, have a significant impact on overall communication in underwater environments. The proposed framework, along with the hybridized metaheuristic algorithms, show great potential in improving node localization, optimizing scheduling, reducing energy costs, and enhancing reliable data delivery in the Internet of Underwater Things (IoUT)-based network. Full article

Media visual sculpture is a landscape element with high carbon emissions. To reduce carbon emission in the process of creating and displaying visual art and structures (visual communication), geo-polymer concrete (GePC) is considered by designers. It has emerged as an environmentally friendly substitute for traditional concrete, boasting reduced carbon emissions and improved longevity. This research delves into the prediction of the compressive strength of GePC (CSGePC) employing various soft computing techniques, namely SVR, ANNs, ANFISs, and hybrid methodologies combining Genetic Algorithm (GA) or Firefly Algorithm (FFA) with ANFISs. The investigation utilizes empirical datasets encompassing variations in concrete constituents and compressive strength. Evaluative metrics including RMSE, MAE, R², VAF, NS, WI, and SI are employed to assess predictive accuracy. The results illustrate the remarkable precision of all soft computing approaches in predicting CSGePC, with hybrid models demonstrating superior performance. Particularly, the FFA-ANFISs model achieves a MAE of 0.8114, NS of 0.9858, RMSE of 1.0322, VAF of 98.7778%, WI of 0.9236, R² of 0.994, and SI of 0.0358. Additionally, the GA-ANFISs model records a MAE of 1.4143, NS of 0.9671, RMSE of 1.5693, VAF of 96.8278%, WI of 0.8207, R² of 0.987, and SI of 0.0532. These findings underscore the effectiveness of soft computing techniques in predicting CSGePC, with hybrid models showing particularly promising results. The practical application of the model is demonstrated through its reliable prediction of CSGePC, which is crucial for optimizing material properties in sustainable construction. Additionally, the model’s performance was compared with the existing literature, showing significant improvements in predictive accuracy and robustness. These findings contribute to the development of more efficient and environmentally friendly construction materials, offering valuable insights for real-world engineering applications. Full article

In this paper, we construct a laser 3D imaging simulation model based on the 3D imaging principle of electro–optical crystal modulation. Unlike the traditional 3D imaging simulation method, this paper focuses on the laser scattering characteristics of the target scene. To accurately analyze and simulate the scattering characteristic model of the target under laser irradiation, we propose a BRDF (Bidirectional Reflectance Distribution Function) model fitting algorithm based on the hybrid BBO–Firefly model, which can accurately simulate the laser scattering distribution of the target at different angles. Finally, according to the fitted scattering characteristic model, we inverted the target imaging gray map. We used the laser 3D imaging restoration principle to reconstruct the 3D point cloud of the target to realize the laser 3D imaging of the target. Full article

This article investigates the application of eXtreme gradient boosting (XGBoost) and hybrid metaheuristics optimisation techniques to predict the axial load bearing capacity of fully grouted rock bolting systems. For this purpose, a comprehensive dataset of 72 pull-out tests was built, considering various influential parameters such as three water-to-grout (W/G) ratios, five ranges of curing time (CT), three different grout admixtures with two different fly ash (FA) contents, and two different diameter confinements (DCs). Additionally, to find out the effect of the mechanical behaviour of grouts on the performance of fully grouted rock bolting systems, seventy-two uniaxial compression strength (UCS) samples were cast and tested simultaneously with pull-out samples. The UCS samples were prepared with the same details as the pull-out samples to avoid any inconsistency. The results highlight that peak load values generally increase with longer curing times, lower W/G, and higher UCS and DC values. The main novelty of this paper lies in its data-driven approach, using various XGBoost models. This method offers a time-, cost-, and labour-efficient alternative to traditional experimental methods for predicting rock bolt performance. For this purpose, after building the dataset and dividing it randomly into two training and testing datasets, five different XGBoost models were developed: a standalone XGBoost model and four hybrid models incorporating Harris hawk optimisation (HHO), the jellyfish search optimiser (JSO), the dragonfly algorithm (DA), and the firefly algorithm (FA). These models were subsequently evaluated for their ability to predict peak load values. The results demonstrate that all models effectively predicted peak load values, but the XGBoost-JSO hybrid model demonstrated superior performance, achieving the highest R-squared coefficients of 0.987 and 0.988 for the training and testing datasets, respectively. Sensitivity analysis revealed that UCS values were the most influential parameter, while FA content had the least impact on the maximum peak load values of fully cementitious grouted rock bolts. Full article

Over the past decade, wireless mesh networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation, and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches. This motivated us to develop the Maximum Entropy Genetic Algorithm (MEGA) to address the issue of mesh router node placement in WMNs. To assess the proposed method, we conducted experiments across various scenarios with different settings, focusing on key metrics such as network connectivity and user coverage. The simulation results showed the comparative performance of MEGA in relation to other prominent algorithms, such as the Coyote Optimization Algorithm (COA), Firefly Algorithm (FA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), revealing MEGA’s effectiveness and usability in determining optimal locations for mesh routers. Full article

Background/Objectives: Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. Methods: This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI). Results: The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%. Conclusions: This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease. Full article

The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding the optimal hyperparameters (also known as hyperparameter optimization) is currently a great challenge, directly affecting the predictive performance of the deep learning models. The Beluga Whale Optimization (BWO) algorithm is a swarm intelligence optimization algorithm that can be used to optimize hyperparameters of deep learning models. However, it is easy to fall into local minima. This paper analyzed the drawbacks of BWO and proposed an improved BWO algorithm, named FAMBWO (Firefly Assisted Multi-strategy Beluga Whale Optimization). Our proposed FAMBWO was compared with 11 state-of-the-art swarm intelligence optimization algorithms on 30 benchmark functions, and the results showed that our improved algorithm had faster convergence speed and better solutions on almost all benchmark functions. Then we proposed an automated machine learning framework FAMBWO-MA-BiLSTM for TEC prediction, where MA-BiLSTM is for TEC prediction and FAMBWO for hyperparameters optimization. We compared it with grid search, random search, Bayesian optimization algorithm and beluga whale optimization algorithm. Results showed that the MA-BiLSTM model optimized by FAMBWO is significantly better than the MA-BiLSTM model optimized by grid search, random search, Bayesian optimization algorithm, and BWO. Full article

With the increasing popularity of Android smartphones, malware targeting the Android platform is showing explosive growth. Currently, mainstream detection methods use static analysis methods to extract features of the software and apply machine learning algorithms for detection. However, static analysis methods can be less effective when faced with Android malware that employs sophisticated obfuscation techniques such as altering code structure. In order to effectively detect Android malware and improve the detection accuracy, this paper proposes a dynamic detection model for Android malware based on the combination of an Improved Zebra Optimization Algorithm (IZOA) and Light Gradient Boosting Machine (LightGBM) model, called IZOA-LightGBM. By introducing elite opposition-based learning and firefly perturbation strategies, IZOA enhances the convergence speed and search capability of the traditional zebra optimization algorithm. Then, the IZOA is employed to optimize the LightGBM model hyperparameters for the dynamic detection of Android malware multi-classification. The results from experiments indicate that the overall accuracy of the proposed IZOA-LightGBM model on the CICMalDroid-2020, CCCS-CIC-AndMal-2020, and CIC-AAGM-2017 datasets is 99.75%, 98.86%, and 97.95%, respectively, which are higher than the other comparative models. Full article

Modern power systems are growing in complexity due to the installation of large generators, long transmission lines, the addition of inertialess renewable energy resources (RESs) with zero inertia, etc., which can all severely degrade the system frequency stability. This can lead to under-/over-frequency load shedding, damage to turbine blades, and affect frequency-sensitive loads. In this study, we propose a methodology to improve the two critical frequency stability indices, i.e., the frequency nadir and the rate of change of frequency (RoCoF), by formulating an optimization problem. The size and placement location of battery energy storage systems (BESSs) are considered to be the constraints for the proposed optimization problem. Thereafter, the optimization problem is solved using the three metaheuristic optimization algorithms: the particle swarm optimization, firefly, and bat algorithm. The best performing algorithm is then selected to find the optimal sizing and placement location of the BESSs. The analyses are all performed on the IEEE 9-bus and IEEE 39-bus test systems. Several scenarios which consider multiple generator outages, increased/decreased loading conditions, and the addition of RESs are also considered for both test systems in this study. The obtained results show that under all scenarios, the proposed method can enhance system frequency compared to the existing method and without BESSs. The proposed method can be easily upscaled for a larger electrical network for obtaining the optimized BESS size and location for the improvement of the system frequency stability. Full article