Satin Bowerbird Optimization Algorithm Research Articles

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%.

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.

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.

Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study

Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study

Hybrid improved capuchin search algorithm for plant image thresholding.

With the development and wider application of meta-heuristic optimization algorithms, researchers increasingly apply them to threshold optimization of multi-level image segmentation. This paper explores the performance and effects of Capuchin Search Algorithm (CAPSA) in threshold optimization. To solve problems of uneven distribution in the initial population of Capuchin Search Algorithm, low levels of global search performance and premature falling into local optima, this paper proposes an improved Capuchin Search Algorithm (ICAPSA) through a multi-strategy approach. ICAPSA uses chaotic opposite-based learning strategy to initialize the positions of individual capuchins, and improve the quality of the initial population. In the iterative position updating process, Levy Flight disturbance strategy is introduced to balance the global optimization and local exploitation of the algorithm. Finally, taking Kapur as the objective function, this paper applies ICAPSA to multi-level thresholding in the plant images, and compares its segmentation effects with the original CAPSA, the Fuzzy Artificial Bee Colony algorithm (FABC), the Differential Coyote Optimization Algorithm (DCOA), the Modified Whale Optimization Algorithm (MWOA) and Improved Satin Bowerbird Optimization Algorithm (ISBO). Through comparison, it is found that ICAPSA demonstrates superior segmentation effect, both in the visual effects of image segmentation and in data comparison.

Solar Radiation Prediction Using Satin Bowerbird Optimization with Modified Deep Learning

Solar energy will be a great alternative to fossil fuels since it is clean and renewable. The photovoltaic (PV) mechanism produces sunbeams’ green energy without noise or pollution. The PV mechanism seems simple, seldom malfunctioning, and easy to install. PV energy productivity significantly contributes to smart grids through many small PV mechanisms. Precise solar radiation (SR) prediction could substantially reduce the impact and cost relating to the advancement of solar energy. In recent times, several SR predictive mechanism was formulated, namely artificial neural network (ANN), autoregressive moving average, and support vector machine (SVM). Therefore, this article develops an optimal Modified Bidirectional Gated Recurrent Unit Driven Solar Radiation Prediction (OMBGRU-SRP) for energy management. The presented OMBGRU-SRP technique mainly aims to accomplish an accurate and time SR prediction process. To accomplish this, the presented OMBGRU-SRP technique performs data preprocessing to normalize the solar data. Next, the MBGRU model is derived using BGRU with an attention mechanism and skip connections. At last, the hyperparameter tuning of the MBGRU model is carried out using the satin bowerbird optimization (SBO) algorithm to attain maximum prediction with minimum error values. The SBO algorithm is an intelligent optimization algorithm that simulates the breeding behavior of an adult male Satin Bowerbird in the wild. Many experiments were conducted to demonstrate the enhanced SR prediction performance. The experimental values highlighted the supremacy of the OMBGRU-SRP algorithm over other existing models.

An Ensemble of Deep Learning Enabled Brain Stroke Classification Model in Magnetic Resonance Images.

Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. Magnetic resonance imaging (MRI) techniques is a commonly available imaging modality used to diagnose brain stroke. Presently, machine learning (ML) and deep learning (DL) models can be extremely utilized for disease detection and classification processes. Amongst the available approaches, the convolutional neural network (CNN) models have been widely used for computer vision and image processing issues such as ImageNet, facial detection, and digit classification. In this article, a novel computer aided diagnosis (CAD) based brain stroke detection and classification (CAD-BSDC) model has been developed for MRI images. The proposed CAD-BSDC technique aims in classifying the provided MR brain image as normal or abnormal. The CAD-BSDC technique involves different subprocesses such as preprocessing, feature extraction, and classification. Firstly, the input image undergoes preprocessing using adaptive thresholding (AT) technique for improving the image quality. Followed by, an ensemble of feature extractors such as MobileNet, CapsuleNet, and EfficientNet models are used. Besides, the hyperparameter tuning of the deep learning models takes place using the improved dragonfly optimization (IDFO) algorithm. Moreover, satin bowerbird optimization (SBO) based stacked autoencoder (SAE) is used for the classification of brain stroke. The design of optimal SAE using the SBO algorithm shows the novelty of the work. The performance of the presented technique was validated utilizing benchmark dataset which includes T2-weighted MR brain image collected from the axial axis with size of 256 × 256. The simulation outcomes indicated the promising efficiency of the proposed CAD-BSDC technique over the latest state of art approaches in terms of various performance measures.

Lung cancer diagnosis in CT images based on Alexnet optimized by modified Bowerbird optimization algorithm

ObjectiveCancer is the uncontrolled growth of abnormal cells that do not function as normal cells. Lung cancer is the leading cause of cancer death in the world, so early detection of lung disease will have a major impact on the likelihood of a definitive cure. Computed Tomography (CT) has been identified as one of the best imaging techniques. Various tools available for medical image processing include data collection in the form of images and algorithms for image analysis and system testing. MethodsThis study proposes a new diagnosis system for lung cancer based on image processing and artificial intelligence from CT-scan images. In the present study, after noise reduction based on wiener Filtering, Alexnet has been utilized for diagnosing healthy and cancerous cases. The system also uses optimum terms of different features, including Gabor wavelet transform, GLCM, and GLRM to be used in replacing with the network feature extraction part. The study also uses a new modified version of the Satin Bowerbird Optimization Algorithm for optimal designing of the Alexnet architecture and optimal selection of the features. ResultsSimulation results of the proposed method on the RIDER Lung CT collection database and the comparison results with some other state-of-the-art methods show that the proposed method provides a satisfying tool for lung cancer diagnosis. The comparison results show that the proposed method with 95.96% accuracy shows the highest value toward the others. The results also show that a higher harmonic mean value for the proposed method with higher F1-score of the method toward the others. Plus, the highest test recall results (98.06%) of the proposed method indicate its higher rate of relevant instances that are retrieved for the images. ConclusionTherefore, using the proposed method can provide an efficient tool for optimal diagnosis of the Lung Cancer from the CT Images. Significancethis shows that using the proposed method as a new deep-learning-based methodology, can provide higher accuracy and can resolve the big problem of optimal hyperparameters selection of the deep-learning-based methodology techniques for the aimed case.

Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO

To improve the accuracy of park load forecasting, a combined forecasting method for short-term park load is proposed based on complementary ensemble empirical mode decomposition (CEEMD), sample entropy, the satin bower bird optimization algorithm (SBO), and the least squares support vector regression (LSSVR) model. Firstly, aiming at the random fluctuation of park load series, the modes with different characteristic scales are divided into low-frequency and high-frequency according to the calculation of sample entropy, which is based on the decomposition of historical park load data modes by CEEMD. The low-frequency is forecast by multiple linear regression (MLR), and the high-frequency component is the training input of the LSSVR forecasting model. Secondly, the SBO algorithm is adopted to optimize the regularization parameters and the kernel function width of LSSVR. Then, the park load forecasting model of each sequence component is built. The forecast output of each sequence component is superimposed to get the final park load forecast value. Finally, a case study of a park in Liaoning Province has been performed with the results proving that the proposed method significantly outperforms the state-of-art in reducing the difficulty and complexity of forecasting effectively, also eliminating the defect of large reconstruction error greatly through the decomposed original sequence by the ensemble empirical model.

Bio-Inspired Computing-A Dive into Critical Problems, Potential Architecture and Techniques

The integration and development of electronics in the recent years have impacted a major development on the world and humans, one among that is nanotechnology. Nanotechnology has achieved a greater progress in biomedical engineering in diagnosis and treatment, leading to the introduction of nanomaterials for drug delivery, prostheses and implanting. This work describes the Bio-Nano-tools that are developed based on iron oxide properties, automated tools used in the tumor detection, satin bowerbird optimization (SBO) technique employed in diagnosis of skin cancer. This work also highlights the post introduction development of nanomaterials like combination of nanotechnology with Artificial Intelligence (AI) and its impact, advancement of nanomaterials based on their operations, shapes and characteristics that leading to the growth of nanostructures with operations control properties. The paper also highlights the improvement of silicon neuromorphic photonic processors and parallel simulators in the development of bio inspired computing. We are hopeful that this review article provides future directions in Bio-Inspired Computing.
 HIGHLIGHTS
 
 In processing of medical images, noise plays a challenging role. So, reduction of noise is important, with the data that is analyzed in our review, it is shown that noise reduction can be achieved using Gradient and Feature Adaptive Contour (GFAC) model, with effective results
 There are many algorithms that are used for skin cancer detection, as highlighted in our review. Amongst all the methods, the particle swarm optimization (PSO) algorithm shows impressive results when compared to other models in terms of feature extraction in dermoscopy images
 Satin bowerbird optimization (SBO) algorithm helps in improving the CNN efficiency. The optimal justification of the hyper parameter numbers in convolutional neural network (CNN) for skin cancer diagnosis can be achieved using an SBO algorithm

Satin Bowerbird optimization algorithm for the Application of Optimal power flow of power system with FACTS devices

The following research paper, addresses the issue of the optimal power flow ( OPF) of power system dealt by the proposal of adapting flexible ac transmission FACTS devices with Satin Bowerbird optimization (SBBOA) algorithm. The SBBOA is bio-inspired algorithm; it is carried out based totally on the principle of ‘female-attracted-by-male’ for breeding. The algorithm that is proposed is tested by usingIEEE-30 bus and IEEE-57 bus test systems with FACT devices of two different types. The following are the two types of FACT devices that are kept at fixed locations:
 FACT devices that are kept at fixed locations:
 Thyristor controlled series capacitor and
 Thyristor controlled phase shifter
 The objective of the present study aims at four different functions. They are:
 (a) Minimizing the cost of fuel
 (b) Minimizing active power loss during transmission
 (c) Reduction of emission and
 (d) Minimizing the combination of economic and environmental cost.
 The SBBOA give the finest simulation outcomes than lately proposed optimization algorithms given in the literature.

Optimal probabilistic location of DGs using Monte Carlo simulation based different bio-inspired algorithms

Stochastic nature of load demand has a great impact on the performance of electrical power system. As a result, planning of electrical power system considering load uncertainties became inevitable. This paper presents Monte Carlo simulation based different bio-inspired algorithms, grey wolf optimization (GWO), manta ray foraging optimization (MRFO), satin bower bird optimization (SBO) and whale optimization (WOA) to optimize locations of three DG units under load uncertainties considering 500 scenarios. Each scenario includes 50 iterations which means that for each run we have 25,000 iterations and 500 characteristics for different load value. Two objectives are achieved. Firstly, statistically finding the optimal probabilistic location of three DG units under load uncertainties in IEEE 33-bus and IEEE 69-bus radial distribution system based on Monte Carlo simulation integrated with different bio-inspired algorithms. Secondly, comparing between the performances of four different bio-inspired algorithms. Three objective functions are considered, minimizing active power loss, minimizing voltage deviation and maximizing voltage stability index. The active and reactive power demand are normally distributed using normal distribution function. The optimal probabilistic location is investigated considering two cases under load uncertainties, optimizing location of three DG units generally and optimizing location of one DG unit assuming two optimum locations for the other two units extracted from case I. The obtained results (after placing DG units) are compared to the base case (DG units are not connected) and compared to each other according to the optimization technique. The results show that, SBO algorithm superiors other algorithms almost in all cases. Comes next GWO which provide good results generally. However, the good performance obtained by MRFO, it consumes twice the time of other algorithms. WOA however fast convergence, it provides results worse than other algorithms. The system is applied to the well-known IEEE 33-bus and IEEE 69-bus radial distribution system.

Computer-aided diagnosis of skin cancer based on soft computing techniques.

Skin cancer is a type of disease in which malignant cells are formed in skin tissues. However, skin cancer is a dangerous disease, and an early detection of this disease helps the therapists to cure this disease. In the present research, an automatic computer-aided method is presented for the early diagnosis of skin cancer. After image noise reduction based on median filter in the first stage, a new image segmentation based on the convolutional neural network optimized by satin bowerbird optimization (SBO) has been adopted and its efficiency has been indicated by the confusion matrix. Then, feature extraction is performed to extract the useful information from the segmented image. An optimized feature selection based on the SBO algorithm is also applied to prune excessive information. Finally, a support vector machine classifier is used to categorize the processed image into the following two groups: cancerous and healthy cases. Simulations have been performed of the American Cancer Society database, and the results have been compared with ten different methods from the literature to investigate the performance of the system in terms of accuracy, sensitivity, negative predictive value, specificity, and positive predictive value.

An efficient VLSI circuit partitioning algorithm based on satin bowerbird optimization (SBO)

In partitioning, a circuit is recursively divided into several subcircuits so that each can be efficiently and independently designed with the main objective of reducing the cut-cost. Partitioning is a first step in the very large-scale integration (VLSI) physical design process, and all the other physical design steps such as floorplanning, placement, pin assignment, and routing depend on its outcome. The partitioning determines the overall quality of the final layout, since the use of an incorrect partitioning can degrade the performance of all subsequent phases of the physical design process. A novel partitioning algorithm based on the concept of satin bowerbird optimization (SBO) is proposed herein. The bioinspired SBO algorithm chooses the initial partitions randomly then utilizes the concepts of fitness value evaluation and group migration to improve the cut-cost. The performance of the proposed algorithm is evaluated using parameters such as the cut-cost and time complexity based on simulations with ISCAS’85 benchmark circuits. The performance parameters of the circuits, i.e., area, power, and delay, are evaluated before and after partitioning. The proposed bioinspired SBO algorithm is compared with similar bioinspired algorithms such as ant colony optimization, particle swam optimization, genetic, and firefly algorithms.

An Optimized Methodical Energy Management System for Residential Consumers Considering Price-Driven Demand Response Using Satin Bowerbird Optimization

Home energy management system (HEMS) is a section of demand response (DR), that plays an imperative role in the residential areas towards appliance management for the enhancement of energy efficiency and grid stability. In this article, a methodical home energy management system (Methodical-HEMS) was proposed based upon K-means, a machine learning algorithm and satin bowerbird optimization (SBO) algorithm to optimize the scheduling of appliances within a 24-h period. The K-means algorithm is used for defining the discrete comfort window (DCW) for schedulable appliance, while SBO algorithm is used for defining the suitable time slots for the schedulable appliance to operate within the DCW. Methodical-HEMS is considered for a single home with the day ahead time of use pricing, to minimize the overall electricity bill (EB) and to satisfy the consumer’s comfort. The performance of Methodical-HEMS is evaluated with other heuristic algorithms, including a particle swarm optimization algorithm, grey wolf optimization algorithm, artificial bee colony algorithm and genetic algorithm. The simulation outcomes demonstrate that, the SBO based HEMS algorithm effectually reduces the overall EB from ₹ 29.14/day to ₹ 22.84/day, minimizes the peak-to-average ratio by 10.28% and remains uncompromising on the consumer’s comfort.

A complex-valued encoding satin bowerbird optimization algorithm for global optimization

A complex-valued encoding satin bowerbird optimization algorithm for global optimization

Real-Power Rescheduling of Generators for Congestion Management Using a Novel Satin Bowerbird Optimization Algorithm

In this paper, an efficient meta-heuristic satin bowerbird optimization (SBO) algorithm is presented for congestion management (CM) in the deregulated power system. The main objective of CM is to relieve congestion in the transmission lines using a generation rescheduling-based approach, while satisfying all the constraints with minimum congestion cost. The SBO is a nature-inspired algorithm, developed based on the ‘male-attracts-the-female for breeding’ principle of the specialized stick structure mechanism of satin birds. The proposed approach is effectively tested on small and large test systems, namely, modified IEEE 30-bus, modified IEEE 57-bus, and IEEE 118-bus test systems. The constraints like line loading, line limits, generator limits, and bus voltage impact, etc., are incorporated into this study. The proposed technique gives superior results with regards to congestion cost and losses compared with various recent optimization algorithms.

Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation

Accurate software development effort estimation is crucial to efficient planning of software projects. Due to complex nature of software projects, development effort estimation has become a challenging issue which must be seriously considered at the early stages of project. Insufficient information and uncertain requirements are the main reasons behind unreliable estimations in this area. Although numerous effort estimation models have been proposed during the last decade, accuracy level is not satisfying enough. This paper presents a new model based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and satin bower bird optimization algorithm (SBO) to reach more accurate software development effort estimations. SBO is a novel optimization algorithm proposed to adjust the components of ANFIS through applying small and reasonable changes in variables. The proposed hybrid model is an optimized neuro-fuzzy based estimation model which is capable of producing accurate estimations in a wide range of software projects. The proposed optimization algorithm is compared against other bio inspired optimization algorithms using 13 standard test functions including unimodal and multimodal functions. Moreover, the proposed hybrid model is evaluated using three real data sets. Results show that the proposed model can significantly improve the performance metrics.