Social Spider Research Articles

Ecological momentary assessment models trajectories of expectancy following exposure: A proof-of-concept pilot study

Background and objectivesThe Inhibitory Retrieval Approach to exposure therapy for fears and anxiety emphasizes prediction error as one of several strategies for improving outcomes. Prediction error depends on disconfirmation of expectancies for the feared outcome, and thus exposure strategies that derive from inhibitory retrieval approaches emphasize expectancy violation during exposure. However, research studies examining expectancy violation in exposure therapy have treated expectancy as a stable characteristic, assuming that expectancy following an exposure exercise remains constant over time. This brief report outlines two different uses of a methodology for using ecological momentary assessment (EMA) to assess between-session expectancy following exposure during treatment for anxiety, and reports on pilot trial results. MethodsAdults with social anxiety disorder (N = 12) and spider phobia (N = 31) taking part in larger trials investigating exposure therapy completed EMA questionnaires assessing expectancy for their feared outcome for 2–4 days following each of two exposure sessions. ResultsExpectancy ratings decreased from pre-to post-exposure and remained stable for 2–4 days following exposure. LimitationsThis pilot study used a very limited sample size and should be replicated in a larger sample. ConclusionsExpectancy for feared outcome may be assessed using EMA following exposure sessions. Pilot results suggest that expectancy decreases immediately following exposures and remains stable afterwards.

SSO optimized FOFPID regulator design for performance enhancement of doubly fed induction generator based wind turbine system.

A wind turbine system (WTS) is a highly coupled and nonlinear system where the output power depends upon highly uncertain wind speed. Therefore, the quality of produced power becomes a challenging problem for researchers. Direct Vector Control (DVC) is a powerful and widely utilized power control strategy to deal with winds that vary rapidly and randomly. As a result, this article employed the newly developed Social Spider Optimization (SSO) technique to optimize the design parameters of Fractional-Order Fuzzy Proportional-Integral with Derivative (FOFPID) regulator to maintain the output power of the studied DFIG-based WTS at the rated value under dynamic wind conditions. The suggested FOFPID controller integrates the capabilities of the Fuzzy intelligent regulator and the Fractional-Order controller, enhancing DFIG current control while allowing independent control of active and reactive power. The approach is incorporated within the DVC strategy of the DFIG's rotor-side converter (RSC), replacing the conventional Proportional-Integral (PI) regulator in the internal current loops. Extensive performance evaluations are conducted under various operating conditions, including active power reference changes, parameter uncertainties, and rapid wind speed variations. Comparative analyses with SSO-optimized PID and Fuzzy regulators show that the FOFPID regulator performs better in terms of maximum overshoot, extreme undershoot, settling time, Total Harmonic Distortion (THD), and Weighted Total Harmonic Distortion (WTHD). The suggested FOFPID regulator also displays stronger robustness against parameters mismatch and weather change than other regulator architectures.

An hybrid machine learning and improved social spider optimization based clustering and routing protocol for wireless sensor network

An hybrid machine learning and improved social spider optimization based clustering and routing protocol for wireless sensor network

A histopathological guide for the social spider Stegodyphus dumicola

The study of invertebrate pathology relies on histopathological tools to define visible internal structures and processes in understudied taxa like spiders. Histopathology involves the infiltration of tissues and organ structures with wax or resin, allowing for the visualisation of cellular anatomy and morphological structure, which can lead to the identification of abnormalities (e.g., pathology) and symbioses (e.g., parasites). In this study, southern African social spiders − Stegodyphus dumicola (Araneae: Eresidae) − were histologically prepared whole and their tissue appearances described (eyes, stomach, heart, ovaries, cuticle, stercoral pocket, chelicerae, book lungs, and silk gland). In addition to healthy tissues, an intranuclear, baculovirus-like pathology was identified. The availability of this material and whole-preparation method makes for a valuable histological resource, where few such resources currently exist for spiders.

An Assessment of the Visibility of Particular Swarm Intelligence Technologies in the Resolution of the Object Classification Problem

This article assesses the visibility of five swarm intelligence algorithms in resolving the object classification problem explicitly particle swarm optimization, artificial bee colonies, ant colony optimization, bacterial foraging optimization, and the Social Spider Optimization. 58 articles in total were reviewed and used as the ground on which this assessment was based on. Primarily articles were grouped into two categories namely the articles which directly resolve the object classification problem and those which in directly resolve the object classification problem followed by a further grouping to indicate articles that were directly linked to the object classification problem through swarm technology and finally grouped by the aim. Three aims were observable which are to modify, to improve and to investigate. More than 70% of the articles aimed at either modifying or improving already existing swarm intelligence algorithms. PSO was the most dominant algorithm of the five technologies assessed. Interesting to note was that although all these algorithms were applied there is no formal representation of knowledge in this domain.

Coding complete genomes of an iridovirus and two parvoviruses identified in lab-reared social spiders (Stegodyphus dumicola).

Coding complete genomes of an iridovirus (194,403 nts) and two parvoviruses (4,689, 3,764 nts) were identified in social spiders (Stegodyphus dumicola). The iridovirus and one of the parvovirus are most closely related to those from house crickets (Acheta domesticus), whereas the other is most closely related to one from a social spider.

Novel hybrid data-driven models for enhanced renewable energy prediction

Global power grid management depends on accurate solar energy estimation, yet present prediction techniques frequently suffer from unreliability as a result of abnormalities in solar energy data. Solar radiation projections are affected by variables such as anticipated horizon length, meteorological classification, and power measuring techniques. Therefore, a Solar Wind Energy Prediction System (SWEPS) is proposed as a solution to these problems. It improves renewable energy projections by taking sun trajectories and atmospheric characteristics into account. In addition to using a variety of optimization methods and pre-processing techniques, such as Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Least Absolute Shrinkage Selection Operator (LASSO), and recursive feature addition processes (RFA), complemented by a genetic algorithm for feature selection (GAFS). The SWEPS also makes use of sophisticated machine learning algorithms and Statistical Correlation Analysis (SCA) to find important connections. Neural Network Algorithms (NNA) and other metaheuristic techniques like Cuckoo Search Optimization (CSO), Social Spider Optimization (SSO), and Particle Swarm Optimization (PSO) are adopted in this work to increase the predictability and accuracy of models. Utilizing the strengths of machine learning and deep learning techniques (Artificial Neural Networks (ANN), Decision Trees, Support Vector Machine (SVM), Recurrent Neural Networks (RNN), and Long Short Term Memory (LSTM)) for robust forecasting, as well as meta-heuristic optimization techniques to fine-tune hyper-parameters and achieve near-optimal values and significantly improve model performance, are some of this work contributions to the development of a comprehensive prediction system.

Diagnosing skin cancer using social spider optimization (SSO) and error correcting output codes (ECOC) with weighted hamming distance

Skin cancer is a common disease resulting from genetic defects, and early detection is critical to improve treatment outcomes. Diagnostic programs that use computer aid especially those that use supervised learning are very useful in early diagnosis of skin cancer. This research therefore presents a new approach that integrates optimization methods with supervised learning to improve skin cancer diagnosis using machine vision approach. The presented method is initiated by data pre-processing that involves the removal of unnecessary data. Then, to segment the images, a combination of K-means clustering and social spider optimization technique is employed. The region of interest is then extracted from the segmented image and from this region a convolutional neural network extracts the significant features. To enhance the classification performance as compared with the standard classifiers, this research introduces a new concept of error correcting output codes coupled with a weighted Hamming distance in the group of gamma classifiers. The ability of the proposed approach in segmentation of skin lesions and classifying them was tested using samples from the ISIC-2017 and ISIC-2016 databases. The introduced method obtained state-of-the-art accuracy on both datasets (ISIC-2016: 97.10%, ISIC-2017: 95.17%). In particularly, the accuracy of the introduced approach for both these databases is at least 1.17% higher than the compared methods. This proves the high performance of the suggested method based on the usage of the convolutional neural networks for feature extraction and gamma classifiers with error correcting output codes for classification in skin cancer detection.

Secondary contact zone and genetic introgression in closely related haplodiploid social spider mites.

How frequently hybridisation and gene flow occur in the contact zones of diverging taxa is important for understanding the speciation process. Stigmaeopsis sabelisi and Stigmaeopsis miscanthi high-aggression form (hereafter, S. miscanthi HG) are haplodiploid, social spider mites that infest the Chinese silver grass, Miscanthus sinensis. These two species are closely related and parapatrically distributed in Japan. In mountainous areas, S. sabelisi and S. miscanthi HG are often found in the highlands and lowlands, respectively, suggesting that they are in contact at intermediate altitudes. It is estimated that they diverged from their common ancestors distributed in subtropical regions (south of Japan) during the last glacial period, expanded their distribution into the Japanese Archipelago, and came to have such a parapatric distribution (secondary contact). As their reproductive isolation is strong but incomplete, hybridisation and genetic introgression are expected at their distributional boundaries. In this study, we investigated their spatial distribution patterns along the elevation on Mt. Amagi using male morphological differences, and investigated their hybridisation status using single-nucleotide polymorphisms by MIG-seq. We found their contact zone at altitudes of 150-430 m, suggesting that their contact zone is prevalent in the parapatric area, which is in line with a previous study. Interspecific mating was predicted based on the sex ratio in the contact zone. No obvious hybrids were found, but genetic introgression was detected although it was extremely low.

Solving Equations Systems by Spider Optimization Algorithm

In this paper four algorithms for solving linear equations systems, based on Social Spider Optimization (SSO) algorithm are proposed, and the solving process is being viewed as an optimization problem. Experimental results showed that the proposed algorithms are able to find the solution vector for such a linear system even when math classical methods fail. One of the best improvements of SSO in the case of our problem consists in modifying the search space for each component of the system solution vector depending on the global fitness and the particular fitness of vector solution components.

Exploring changes in social spider DNA methylation profiles in all cytosine contexts following infection

Living at high density and with low genetic diversity are factors that should both increase the susceptibility of organisms to disease. Therefore, group living organisms, especially those that are inbred, should be especially vulnerable to infection and therefore have particular strategies to cope with infection. Phenotypic plasticity, underpinned by epigenetic changes, could allow group living organisms to rapidly respond to infection challenges. To explore the potential role of epigenetic modifications in the immune response to a group-living species with low genetic diversity, we compared the genome-wide DNA methylation profiles of five colonies of social spiders (Stegodyphus dumicola) in their natural habitat in Namibia at the point just before they succumbed to infection to a point at least six months previously where they were presumably healthier. We found increases in genome- and chromosome-wide methylation levels in the CpG, CHG, and CHH contexts, although the genome-wide changes were not clearly different from zero. These changes were most prominent in the CHG context, especially at a narrow region of chromosome 13, hinting at an as-of-yet unsuspected role of this DNA methylation context in phenotypic plasticity. However, there were few clear patterns of differential methylation at the base level, and genes with a known immune function in spiders had mean methylation changes close to zero. Our results suggest that DNA methylation may change with infection at large genomic scales, but that this type of epigenetic change is not necessarily integral to the immune response of social spiders.

Public Health Initiative: Enhancing Stress Evaluation using EEG Data

Study proposes an innovative public health that uses EEG data processing to improve stress assessment. Stress is a widespread health issue that affects people in a variety of demographics. Stress evaluation techniques based on traditional self-reporting are biased and subjective. EEG records patterns of brain activity linked to stress reactions and offers an objective measurement. In this study, we proposed Social Spider Advanced Bidirectional Long ShortTerm Memory (SS-ABiLSTM), an innovative method for improving stress evaluation by integration of EEG data. Initially, EEG data is gathered, and then preprocessed the EEG data using min-max normalization to remove inaccurate records from a dataset. To extract features from the preprocessed data using the Discrete Wavelet Transform (DWT), this reliably extracts frequency-domain information from the data. The most discriminative characteristics for stress assessment to select feature using Recursive Feature Elimination (RFE). By combining these methods, stress assessment models that utilize EEG data have high accuracy. Utilizing the application of SS-ABiLSTM, that addresses stress prevention and management while also improving the ability to interpret EEG data in public health. The proposed methods are to evaluate F1-score (90.1%), precision (88.2%), recall (91.8%), and accuracy (98.97%). The accuracy of the proposed technique is superior to the existing techniques. Finally by using EEG data for stress evaluation in public health shows potential for more individualized care and better mental health results.

Intelligent controller design of an autonomous system using a social spider optimizer for path navigation and obstacle avoidance

This research paper proposes a hybrid fuzzy logic controller for achieving autonomous path navigation and obstacle avoidance through the use of the Social Spider Optimizer algorithm. The proposed controller employs kinematic modelling to determine the mobile robot’s path navigation and utilizes a fuzzy logic system for effective control. The Social Spider Optimizer algorithm optimizes the parameters of the fuzzy controller, while the FLC is responsible for obstacle avoidance. The effectiveness of the proposed controller has been analyzed, and a comparative study has been carried out with optimization techniques like particle swarm optimization (PSO) and cuckoo search optimization (CSO) controllers. The study aims to propose a hybrid fuzzy logic controller, that provides efficient navigation and obstacle avoidance for mobile robots. In a simulation, the starting point is considered as (0,0) and the destination point is set as Xk = 1.1 and Yk = 1.2. The performance of the proposed method is compared with FLC and methods like PSO and CSO. With the SSO-based FLC, the proposed mobile robot identifies the obstacle distance and travels towards the destination with smooth navigation. The results show the efficacy of the proposed controller in comparison to other controllers for mobile robots in terms of path navigation and obstacle avoidance.

A parallel social spider algorithm based on population mining

The social spider algorithm is a meta-heuristic algorithm inspired by the foraging behavior of biological social spiders, and is initially used to solve optimization problems with continuous functions. There exist many challenges when dealing with discrete combinatorial optimization problems. Therefore, a novel parallel social spider algorithm based on population mining is proposed. Its starting point is to mine dynamic heuristic information from the population of social spiders that cause vibrations to guide the spider’s next walk. It proposes the concept of vibration candidate sets and enhances the behavior of artificial spiders. Furthermore, it is implemented in parallel on the graphics processing unit. According to our investigation, it is the first parallel social spider algorithm. The classical traveling salesman problem is solved as an example. By solving 30 small-scale and medium-scale problems and comparing with other algorithms, it shows that its solution quality is significantly better. By solving 17 large-scale problems, compared with the parallel ant colony system and parallel iterative hill climbing algorithm on the GPU, it is found that both the solution quality and performance are also significantly better. Codes are available at https://github.com/BuptCIAGroup/P-SSA.

Comparison analysis of the prediction accuracy of GWO, GM and SSA model in the wind power

Wind power has been occupying the substantial proportion of energy structure since it occurs to people that dependence on fossil fuels with the aims of worldwide energy supply would provoke an avalanche of challenges concerning on environmental degradation like the increase of greenhouse gas emissions. Nevertheless, most of wind power resource cannot be utilized completely as wind power generation have high volatility, which also possess partly repercussion with aims of contributing to the operation of the power system. As the technology has burgeoned, numerous scientists have made great strides in the wind power prediction, which can facilitate the consistent functioning of the electrical grid. However, there are massive difference in the wind power predication effect due to the respective character of algorithm. This essay will introduce the category of multiple relative models and compares the consequence of the improved algorithm based in Grey Wolf Optimizer (GWO), Grey Model (GM) and Social Spider Algorithm (SSA) model by analyzing the relevant table Comparison results that GM model is capable of significantly enhancing the predictive performance to achieve heightened authenticity and decreased presage error in contrast to the previous algorithm.

An enhanced QoS approach for multi-objective optimization using social spider optimization 5G enable IoMT network

An enhanced QoS approach for multi-objective optimization using social spider optimization 5G enable IoMT network

Aerodynamic optimization method of intake grille of active clearance control system for turbines based on modified social spider algorithm (MSSA)

During the operation of an active clearance control (ACC) system of a turbine, the aerodynamic performance of the intake grille indirectly influences its control. To improve the performance, an aerodynamic optimization method is proposed, consisting of parameterization, an optimization algorithm, and a fitness evaluation. During parameterization, its geometry is represented by seven geometric variables. A modified social spider algorithm is used as the optimization algorithm. To evaluate the aerodynamic performance of the grille, a special fitness function is adopted, obtained using an adaptive topological multi-layer feedforward artificial neural network. To verify the feasibility of this method, experiments and numerical calculations are carried out on the original and optimized intake grilles. The results show that the average intake flow rate and average total pressure recovery coefficient of the optimized grille have increased by 17.3% and 4.9%, respectively.

Cost optimization of tall buildings having tube composite columns using social spider algorithm

SummaryThis study aims to develop an algorithmic approach to obtain optimum designs for tall buildings having composite columns and investigate the material cost advantages of these buildings over steel structures. The social spider optimization (SSO) algorithm, a new meta‐heuristic optimization method that has shown promising results in optimizing frame structures, was used to obtain the optimum designs. Concrete‐filled steel tube sections were chosen for composite columns. To define the optimization problem, we considered the material cost of the structure as the objective function, the size of columns (strength, deflection, drift, and geometric limitations) as the constraint functions, and ready steel sections as the design variables. We tested eight different frame structures of varying heights and irregularities to analyze how cost varied according to these parameters. Our results demonstrate that composite columns are a more cost‐effective option than steel structures, even for buildings that are not considered high rises. We found that the difference in cost between the two types of structures increases with building height and irregularity. Additionally, our optimization algorithm was unable to find feasible designs for steel structures taller than 180 m using ready steel profiles.

Covid-19 infection deficiency based on risk prediction using adaptive social spider featured decisive convolution neural network

The novel coronavirus disease which originated has been medical as Covid 19 by the World Health Organization (WHO). The early onset in December 2019. It revealed that. A virus has been the causative factor for this pandemic. A phylogenetic research has been carried out with achievable based on data analytic model. In this situation, individuals with this disease have improved more features produce poor classification accuracy. To resolve this problem, we propose an adaptive social spider featured decisive Convolution Neural network for Covid-19 deficiency infection based on risk prediction to enhance the prediction and evaluation of infectious diseases. Both GIS and the Covid data sets are used in the process. The Social spider Feature Selection (SSFS) selects features depending on Disease deficiency infection rate (DDIR) and Spreading habitual factor (SHF). By selecting these features decisive Convolutional Neural Classifier (DCNN) trains the value to trained set to classify the risk based on the feature predicting weights. This proposed system produce higher prediction rate compared to the other methods as well in precision and recall rate.

Optimizing support vector machine (SVM) by social spider optimization (SSO) for edge detection in colored images

Edge detection in images is a vital application of image processing in fields such as object detection and identification of lesion regions in medical images. This problem is more complex in the domain of color images due to the combination of color layer information and the need to achieve a unified edge boundary across these layers, which increases the complexity of the problem. In this paper, a simple and effective method for edge detection in color images is proposed using a combination of support vector machine (SVM) and the social spider optimization (SSO) algorithm. In the proposed method, the input color image is first converted to a grayscale image, and an initial estimation of the image edges is performed based on it. To this end, the proposed method utilizes an SVM with a Radial Basis Function (RBF) kernel, in which the model's hyperparameters are tuned using the SSO algorithm. After the formation of initial image edges, the resulting edges are compared with pairwise combinations of color layers, and an attempt is made to improve the edge localization using the SSO algorithm. In this step, the optimization algorithm's task is to refine the image edges in a way that maximizes the compatibility with pairwise combinations of color layers. This process leads to the formation of prominent image edges and reduces the adverse effects of noise on the final result. The performance of the proposed method in edge detection of various color images has been evaluated and compared with similar previous strategies. According to the obtained results, the proposed method can successfully identify image edges more accurately, as the edges identified by the proposed method have an average accuracy of 93.11% for the BSDS500 database, which is an increase of at least 0.74% compared to other methods.