Ptilonorhynchus Research Articles

Design and Implementation of Sports Network Teaching Platform Based on Discrete Similarity Method

Sport is the most important topic in education, which individually shows motor skills in health activities. Athletic training should focus graduates on sports, low-involvement task activities, and physical fitness or exercise. Teaching concepts and methodologies are innovative, coaching methods and procedures, evaluation of coaching sessions in sports-all this is accompanied by development in the sports hall and successful improvement of sports performance. Here, he provides extraordinary assistance to students by predicting early school leaving and improving the potential application of wireless platforms in sports programs and changing the nature of sports, including visualization and repetition, and incorporating it into sports training. A sports network learning platform based on the discrete similarity approach is proposed for design and implementation in this paper. First, data is collected from a real-time data set. After that, the data submitted for preprocessing removes noise and imperfect records, which can be removed using a global constant or a most likely learning method using an Adaptive Savitsky-Golay Filtering (ASGF) method. The preprocessing method is fed into the feature extraction utilizing Multi‐Hypothesis Fuzzy‐Matching Radon Transform (MHFMRT). MHFMRT extract statistical features such us kurtosis, variance, energy, mean and standard deviation. Then, the Similarity-Based Convolutional Neural Network (SBCNN) method is optimized by the Chaotic Satin Bower Bird Optimization (CSBBO) algorithm, which effectively classifies the visualization and a satisfactory training platform of the sports network. The proposed system is executed in the MATrix LABoratory (MATLAB) platform and the performance of the proposed methods attains 28.5%, 27.9% and 29.5% higher accuracy, 28%, 21% and 26.5% higher precision, 21.2%, 28.75% and 21.3% higher recall compared with the existing methods like Recurrent Neural model to analyze the effect of Physical Training and Treatment in Relation to Sports Injuries (PTT-RSI-RNN), recognizing Sports Activities from Video Frames using Deformable Convolution and Adaptive Multiscale Features (SAVF-DCN) and Sports Match Prediction model for training and Exercise using attention-based LSTM network (SMPE-LSTM) respectively

Health Monitoring Technology for Hydraulic Engineering Gates Based on Multi-Sensor Data Fusion

An essential part of the hydraulic engineering systems, hydraulic gates regulate water flow in dams and other structures. To avoid structural problems, they must ensured to be consistent. Multi-sensor data fusion has become a key tool for health monitoring with advances in machine learning (ML) and sensor technologies. This study aims to provide a novel multi-sensor data fusion health monitoring system for hydraulic engineering gates. To provide more effective and precise real-time monitoring of hydraulic engineering gates, a unique Dynamic Satin Bowerbird search-driven Bidirectional Long Short-Term Memory (DSB-BiLSTM) model were proposed. The hydraulic monitoring dataset was collected from Kaggle website, comprising information and some attributes obtained from hydraulic engineering gates. To improve data quality, noise reduction, and data normalization are used. Time-series analysis techniques extract important information such as vibration patterns and pressure variations. The DSB-BiLSTM model leverages bidirectional LSTM to progress time-dependent data, and the Satin Bowerbird search optimizes sensor selection. The method shows better accuracy and efficiency in identifying potential faults compared to traditional models. When compared to four attributes such as pump, cooling, valve and accumulator with the metrics outcomes of 0.9901, 0.9925, 0.9950, and 0.9890 in accuracy, 0.9930, 0.9960, 0.9902, and 0.9889 in precision, 0.9920, 0.9960, 0.9911, and 0.9890 in recall, and 0.9915, 0.9955, 0.9905, and 0.9889 in F1-score, the DSB-BiLSTM model performs better than all other approaches that were examined. This suggests better overall hydraulic engineering gate health monitoring performance. The proposed DSB-BiLSTM model is a show potential solution for real-time health monitoring of hydraulic gates, provided that it enhances accuracy and reduces computational complexity.

Correction: Chaotic Satin Bowerbird Optimizer Based Advanced AI Techniques for Detection of COVID‑19 Diseases from CT Scans Images

Correction: Chaotic Satin Bowerbird Optimizer Based Advanced AI Techniques for Detection of COVID‑19 Diseases from CT Scans Images

Chaotic Satin Bowerbird Optimizer Based Advanced AI Techniques for Detection of COVID-19 Diseases from CT Scans Images

Chaotic Satin Bowerbird Optimizer Based Advanced AI Techniques for Detection of COVID-19 Diseases from CT Scans Images

Satin bowerbird algorithm with an adaptive constriction factor for enhanced photovoltaic integration in distribution feeders

—This paper introduces an enhanced Satin bowerbird optimizer (SBO), specifically tailored for the integration of photovoltaic distributed generation (DG) units into radial distribution systems. The upgraded SBO version involves two pivotal adjustments. Firstly, the positional updating mechanism of the newly generated bower is modified to enable exploration around the iteration's elite bower. Secondly, an adaptive constriction factor is introduced, progressively decreasing during iterations to concentrate the search in promising areas. These modifications significantly amplify the exploration capacity of each new bower in the SBO algorithm. The proposed upgraded SBO aims at minimizing costs related to CO2 emissions from the grid and those linked with photovoltaic units, in addition to energy losses. The variability of photovoltaic DG units is represented using the Beta Probability Density Function (PDF) to portray distinct solar irradiation conditions experienced daily. The enhanced SBO version undergoes testing on a practical Nigerian distribution system, the Ajinde 62-bus network, and a standard IEEE 69 nodes system. Simulation results underscore the effectiveness of the upgraded SBO version, revealing substantial reductions in energy losses and emissions. Specifically, for the Ajinde 62-bus network, the proposed SBO version achieves a noteworthy 31 % reduction in the combined yearly costs of energy losses and emissions compared to the initial case. Additionally, for the IEEE 69-bus system, it attains a considerable reduction of 35 %. Furthermore, the simulation results illustrate the competitive performance of the suggested SBO version when compared to Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms, as well as the standard SBO.

HGANet-23: a novel architecture for human gait analysis based on deep neural network and improved satin bowerbird optimization

HGANet-23: a novel architecture for human gait analysis based on deep neural network and improved satin bowerbird optimization

Meta Heuristic Technique with Reinforcement Learning for Node Deployment in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are vital in applications like environmental monitoring, smart homes, and battlefield surveillance. Comprising small devices with limited resources, WSNs require efficient node deployment for power optimization and prolonged network lifetime, ensuring sufficient coverage and connectivity. This study introduces an Intelligent Satin Bower Bird Optimizer augmented with reinforcement learning (ISBO-RL), enhancing coverage and connectivity. ISBO-RL focuses on optimal sensor placement for improved coverage and connectivity, using an Optimum Position Finding (OPF) method to identify key sensor node locations. Reinforcement learning is integrated into the ISBO algorithm, allowing nodes to adapt based on performance and changing conditions. Experimental results on diverse platforms highlight ISBO-RL’s efficacy and its superior coverage and connectivity performance as compared to other algorithms. ISBO-RL represents a significant advancement in the field of Wireless Sensor Networks, offering a promising solution to address the challenges of efficient node deployment and network optimization in various critical applications.

Feature selection using modified chaotic satin bowerbird algorithm with deep transfer learning for Multispectral Image Classification

Feature selection using modified chaotic satin bowerbird algorithm with deep transfer learning for Multispectral Image Classification

Frequency Control of Interconnected Power System via SBO assisted Simultaneous Tuning Strategy in Presence of DFIG

To achieve the objectives of the automatic generation control (AGC) of wind integrated interconnected power system (WIIPS), better tuning and control strategies are required. This paper suggests simultaneous tuning strategy for the AGC and wind system controllers to improve the stability of the WIIPS. The bulk amount of wind is integrated into the grid using the doubly fed induction generator (DFIG) and its simplified model is used to simulate the WIIPS. Initially, classical controllers are opted in AGC mechanism to reduce the frequency disturbances caused by the load and wind generation uncertainties. Further, classical controllers of WIIPS are replaced with cascade controllers to enhance the system frequency profile during the disturbances. The optimal set of solutions of the classical and cascade controllers are examined by the satin bowerbird optimizer (SBO) with the help of integral square error as fitness function of the optimization problem. Comparisons are provided to show the merits of the WIIPS over normal interconnected power system, cascade controller over classical controller and simultaneous tuning strategy over regular tuning mechanism.

Frequency Control of Interconnected Power System via SBO assisted Simultaneous Tuning Strategy in Presence of DFIG

To achieve the objectives of the automatic generation control (AGC) of wind integrated interconnected power system (WIIPS), better tuning and control strategies are required. This paper suggests simultaneous tuning strategy for the AGC and wind system controllers to improve the stability of the WIIPS. The bulk amount of wind is integrated into the grid using the doubly fed induction generator (DFIG) and its simplified model is used to simulate the WIIPS. Initially, classical controllers are opted in AGC mechanism to reduce the frequency disturbances caused by the load and wind generation uncertainties. Further, classical controllers of WIIPS are replaced with cascade controllers to enhance the system frequency profile during the disturbances. The optimal set of solutions of the classical and cascade controllers are examined by the satin bowerbird optimizer (SBO) with the help of integral square error as fitness function of the optimization problem. Comparisons are provided to show the merits of the WIIPS over normal interconnected power system, cascade controller over classical controller and simultaneous tuning strategy over regular tuning mechanism.

RETRACTED: Optimized wavelet and feature set of EEG signal for Parkinson disease classification

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Satin bowerbird optimizer-neural network for approximating the capacity of CFST columns under compression

Concrete-filled steel tube columns (CFSTCs) are important elements in the construction sector and predictive analysis of their behavior is essential. Recent works have revealed the potential of metaheuristic-assisted approximators for this purpose. The main idea of this paper, therefore, is to introduce a novel integrative model for appraising the axial compression capacity (Pu) of CFSTCs. The proposed model represents an artificial neural network (ANN) supervised by satin bowerbird optimizer (SBO). In other words, this metaheuristic algorithm trains the ANN optimally to find the best contribution of input parameters to the Pu. In this sense, column length and the compressive strength of concrete, as well as the characteristics of the steel tube (i.e., diameter, thickness, yield stress, and ultimate stress), are considered input data. The prediction results are compared to five ANNs supervised by backtracking search algorithm (BSA), earthworm optimization algorithm (EWA), social spider algorithm (SOSA), salp swarm algorithm (SSA), and wind-driven optimization. Evaluating various accuracy indicators showed that the proposed model surpassed all of them in both learning and reproducing the Pu pattern. The obtained values of mean absolute percentage error of the SBO-ANN was 2.3082% versus 4.3821%, 17.4724%, 15.7898%, 4.2317%, and 3.6884% for the BSA-ANN, EWA-ANN, SOSA-ANN, SSA-ANN and WDA-ANN, respectively. The higher accuracy of the SBO-ANN against several hybrid models from earlier literature was also deduced. Moreover, the outcomes of principal component analysis on the dataset showed that the yield stress, diameter, and ultimate stress of the steel tube are the three most important factors in Pu prediction. A predictive formula is finally derived from the optimized SBO-ANN by extracting and organizing the weights and biases of the ANN. Owing to the accurate estimation shown by this model, the derived formula can reliably predict the Pu of concrete-filled steel tube columns.

Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment

Intrusion Detection in the Industrial Internet of Things (IIoT) concentrations on the security and safety of critical structures and industrial developments. IIoT extends IoT principles to industrial environments, but linked sensors and devices can be deployed for monitoring, automation, and control of manufacturing, energy, and other critical systems. Intrusion detection systems (IDS) in IoT drive to monitor network traffic, device behavior, and system anomalies for detecting and responding to security breaches. These IDS solutions exploit a range of systems comprising signature-based detection, anomaly detection, machine learning (ML), and behavioral analysis, for identifying suspicious actions like device tampering, unauthorized access, data exfiltration, and denial-of-service (DoS) attacks. This study presents an Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning (IID-SBODL) model for IIoT Environment. The IID-SBODL technique initially preprocesses the input data for compatibility. Next, the IID-SBODL technique applies Echo State Network (ESN) model for effectual recognition and classification of the intrusions. Finally, the SBO algorithm optimizes the configuration of the ESN, boosting its capability for precise identification of anomalies and significant security breaches within IIoT networks. By widespread simulation evaluation, the experimental results pointed out that the IID-SBODL technique reaches maximum detection rate and improves the security of the IIoT environment. Through comprehensive experimentation on both UNSW-NB15 and UCI SECOM datasets, the model exhibited exceptional performance, achieving an average accuracy of 99.55% and 98.87%, precision of 98.90% and 98.93%, recall of 98.87% and 98.80%, and F-score of 98.88% and 98.87% for the respective datasets. The IID-SBODL model contributes to the development of robust intrusion detection mechanisms for safeguarding critical industrial processes in the era of interconnected and smart IIoT environments.

Research on Speech Communication Enhancement of English Web-based Learning Platform based on Human-computer Intelligent Interaction

This study presents a novel web-based learning platform that leverages human-computer intelligent interaction to enhance English communication skills. The platform integrates cutting-edge technologies to create an immersive learning experience, combining natural language processing, speech recognition, and interactive exercises. Learners engage in real-time conversations with virtual tutors, receive personalized feedback, and access a vast repository of educational resources. The platform not only facilitates language acquisition but also encourages self-paced learning, making it a valuable tool for both educators and students. By harnessing the power of artificial intelligence, this web-based platform represents a significant advancement in the realm of English language education. To overcome these issues this paper proposed SVM with an improved satin Bower bird optimization algorithm (SVM-ISBBO). SVM-ISBBO uses fog computing services that minimize the latency and speeds up the process, effectively handling huge wearable devices. In this proposed work SVM-ISBBO monitors the students communication, vocal parameters, blood pressure, etc, and these values are obtained from wearable sensor devices and their notifications are sent back to teachers. Teachers diagnosed the student information and sent back the alert notifications to the students for taking proper medications. All this information is stored in fog-based cloud storage in a secure manner. The accuracy rate of KNN got 78.56%, NB got 81.74%, SVM got 85.15% and the proposed work of SVM-ISBBO got 92.34%.

Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS.

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still presents a significant challenge. This study develops a machine learning (ML) model utilizing deep neural network (DNN) and optimization algorithms, namely earthworm optimization algorithm (EOA), wildebeest herd optimization (WHO), biogeography-based optimization (BBO), satin bowerbird optimizer (SBO), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO), to solve the extrapolation problem in the construction of flood susceptibility models. Quang Nam Province was chosen as a case study as it is subject to the significant impact of intense flooding, and Nghe An Province was selected as the region for extrapolation of the flood susceptibility model. Root mean square error (RMSE), receiver operating characteristic (ROC), the area under the ROC curve (AUC), and accuracy (ACC) were applied to assess and compare the fit of each of the models. The results indicated that the models in this study are a good fit in establishing flood susceptibility maps, all with AUC > 0.9. The deep neural network (DNN)-BBO model enjoyed the best results (AUC = 0.99), followed by DNN-WHO (AUC = 0.99), DNN-SBO (AUC = 0.98), DNN-EOA (AUC = 0.96), DNN-GOA (AUC = 0.95), and finally, DNN-PSO (AUC = 0.92). In addition, the models successfully solved the extrapolation problem. These new models can modify their behavior to evaluate flood susceptibility in different regions of the world. The models in this study distribute a first point of reference for debate on the solution to the extrapolation problem, which can support urban planners and other decision-makers in other coastal regions in Vietnam and other countries.

Proposing hybrid prediction approaches with the integration of machine learning models and metaheuristic algorithms to forecast the cooling and heating load of buildings

Accurate prediction of heating and cooling loads in residential buildings is crucial for both researchers and practitioners. This study employs advanced forecasting techniques, utilizing support vector regression and extreme gradient boosting methods, enhanced by state-of-the-art metaheuristic algorithms. The metaheuristic optimizers applied include the Satin Bowerbird Optimizer, Ant Lion Optimizer, Artificial Ecosystem-Based Optimizer, Slime Mold Optimizer, Moth-Flame Optimizer, and Particle Swarm Optimizer. These optimizers are strategically used to refine the forecasting models, optimizing their parameters for increased precision. To evaluate the models, a suite of statistical indices is used, including mean square error, root mean square error, Mean Absolute Percentage Error, Mean Absolute Error, Relative Absolute Error, coefficient of correlation, Coefficient of Determination, and Normalized Mean Square Error. This comprehensive analysis assesses the effectiveness of the forecasting methods. The study's findings highlight that the combination of the Satin Bowerbird Optimizer and extreme gradient boosting is particularly effective, achieving high coefficients of determination for cooling and heating load predictions (0.95826 and 0.938048, respectively). This hybrid algorithm shows remarkable performance, with minimal error values and consistent convergence across various datasets. In summary, this research underscores the efficiency of integrating sophisticated machine learning models with metaheuristic optimization techniques, particularly identifying a potent hybrid algorithm for the accurate prediction of heating and cooling loads in residential buildings.

Multi-objective modified satin Bowerbird optimization algorithm used for simulation-based energy consumption optimization of yearly energy demand of lighting and cooling in a test case room

An effective method of optimizing building energy performance through simulation is introduced in this study. The Modified Satin Bowerbird Optimization Algorithm and the DesignBuilder simulation tools are used to find optimal solutions for improving building energy efficiency. Five distinct climatic zones of China are examined in terms of decision variables including window size, overhang characteristics, and building orientation. The objective functions are based on the energy demand for lighting and cooling over a year. Analyses of optimization employing either a single-criterion or a multi-criteria approach delve into the interactions between these functions. According to the results, buildings had a reduction of 24.0 %–42.7 % in their overall yearly energy consumption. 22.9 %, 24.0 %, 35.1 %, 37.2 %, and 42.7 % yearly overall energy consumption reduction are specific to Harbin, Beijing, Guangzhou, Shanghai, and Kunming, respectively. Under varying weather conditions, architectural decision variables have a significant impact on energy consumption.

Clinical Dataset Classification Using Feature Ranking And Satin Bower Bird Optimized SVMs

Abstract A clinical decision support system is a computer-based system that is designed to assist healthcare providers with clinical decision-making by analyzing electronic health records and other healthcare information systems to provide real-time support to clinicians at the point of care. A novel classification framework for clinical datasets in which the relevant features are selected by ranking them based on Fisher’s Score and a wrapper-based Satin Bower Bird Optimization algorithm with the combination of accuracy, G-mean and F-Score measured by support vector machine (SVM) as the fitness function is proposed. The classification is performed using an SVM classifier in which the hyperparameters of the SVM classifier are optimized using the Satin Bower Bird Optimization that improves the classification performance. In the context of statistical analysis, the research undergoes a non-parametric Friedman Test. This study selects relevant attributes from three clinical datasets from the Machine Learning Repository maintained by the University of California Irvine and achieved an accuracy of 86% for the Breast Cancer Wisconsin (Diagnostic) dataset, 89% for the Diabetic Retinopathy Debrecen dataset, and 91% for the EEG Eye State dataset respectively. When compared with other machine learning classifiers the proposed approach performed well with feature selection compared with other machine learning classifiers.

An efficient 3D IC partitioning approach using satin bowerbird optimization for reduced TSV count and improved heat dissipation

Net list partitioning achieves paramount importance in the physical architecture design step of Three Dimensional (3D) Very Large Scale Integrated (VLSI) circuits. The performance of all subsequent steps like floor layout, placement, pin assignment, and routing in the physical architecture design of the VLSI circuit are heavily affected by the outcomes of partitioning steps. Wire length between gates is reduced by 3D Integrated Circuit (IC) due to compact footprint as well as vertical inter connections among dies. This reduced wire length in turns responsible for less energy consumption and reduced connection delays. In this paper, Satin Bowerbird Optimization (SBO) is applied to solve 3D IC partitioning challenges. The suggested 3D IC partitioning technique is aimed at reducing the number of Through Silicon Vias (TSV) and measurements have also been taken to reduce heat dissipation. The presence of a significant amount of TSVs expands the chip area. 3D ICs have considerably greater densities of power because of advances in the technology and a vastly increased variety of components with high frequency. The heat generated by the used power has an impact on the performance and dependability of the chip. In this experiment, SBO is used as a multi-objective optimization method to achieve two objectives simultaneously - TSV count minimization and heat dissipation reduction. The performance of the suggested SBO - based approach is assessed utilizing the Giga Scale Research Center (GSRC) benchmark circuits. The applicability of the suggested technique is judged by comparing the outcomes with thermal-aware multilevel hyper-graph partitioning method. The findings of the comparison reveal that the suggested technique outperforms the thermal-aware multilevel hyper-graph partitioning approach by lowering the number of TSV count, maximum area requirement, and power density by 16.99%, 9.94% and 29.10% respectively. The performance of the experiment is also compared with existing Simulated Annealing and Tabu search based techniques. In both cases, the proposed method achieves better result by lowering TSV by 26.03% and 27.31%, and reducing area by 6.59% and 6.59%, respectively.

Improved Satin Bowerbird optimization algorithm to optimize school buildings with Elman neural networks and life cycle cost: a case study in China

ABSTRACT Improving the energy efficiency, indoor air quality, and energy storage of public buildings, especially schools, is crucial due to the associated energy consumption and costs. In this study, a retrofitting approach is presented, which employs the Improved Satin Bowerbird optimization algorithm, Elman neural network, and life cycle cost analysis to optimize the insulation thickness of the exterior walls and roof of two school building models in China. The study focuses on two objective functions, namely annual heating load and indoor thermal discomfort due to overheating, using five design variables. To estimate the objective function values, 95 Elman neural networks are trained and verified, with models 1 and 2 achieving r2 values of 0.9851 and 0.9827, respectively. The optimization technique is employed to specify the Efficient fronts, with the choice criteria being to minimize the life cycle cost. Compared to the starting condition, model 1 reduces yearly heating load and life cycle cost by 27.5% and 29.5%, respectively. Similarly, model 2 lowers yearly heating load and life cycle cost by 22.1% and 25.3%, respectively. The results demonstrate the success of the approach in enhancing energy efficiency and reducing costs in school buildings, both of which are crucial areas for public investment.