Social Spider Research Articles

Antimicrobial volatiles emitted by members of the nest microbiome of social spiders.

Microbes produce and respond to a range of structurally and functionally diverse volatiles. Many microbial volatiles have antimicrobial properties. Since volatiles can diffuse through complex 3D systems like spider nests, they are promising pathogen protection for social arthropods. Here, we analyzed the volatilomes of five nest microbiome members of the Namibian, social spider Stegodyphus dumicola, namely the bacteria Massilia sp. IC2-278, Massilia sp. IC2-477, Sphingomonas sp. IC-11, Streptomyces sp. IC-207, and the fungus Aureobasidium sp. CE_32, and tested their antimicrobial activity against two putative spider pathogens, namely Bacillus thuringiensis and Purpureocillium lilacinum. Most nest microbiome members released volatilomes with antibacterial and/or antifungal activities under in vitro conditions. The analysis of their volatilomes using GC/Q-TOF revealed that they include numerous antimicrobial volatiles. We tested the antimicrobial activity of five pure volatile compounds found in the volatilomes and revealed that all of them were antibacterial and/or antifungal. We could not identify the same antimicrobial volatiles as in a previous in situ study, but our results indicate that social spider-associated microorganisms as a source of antimicrobial volatiles are important for pathogen inhibition. Additionally, we showed the influence of the volatilomes on the antibiotic sensitivity of B. thuringiensis offering novel approaches to counter antibiotic resistance.

Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach

Nowadays, Artificial Intelligence (AI) based modeling is the major consideration to build efficient, automated, and smart systems for our today's needs. Many companies are benefited from these modeling methods for their marketing efforts. Each firm has expected to increase its product development in an innovative way to improve its business growth. Successful firm marketing is to offer the right product to the right person at the right time. To market the product to the customer successfully, it is needed to segment the customer by finding their behavioral patterns. The customer behaviors and their purchasing patterns are used to generate profit for the company. Customer segmentation is the process of grouping customers based on commonalities. Developing an efficient AI-based customer segmentation to improve digital marketing growth is a challenging task. In this paper, an unsupervised deep learning model called a Self-organizing map with an Improved social spider optimization approach has been used for efficient customer segmentation. The customer data are analyzed by a feature engineering process using a swarm intelligence model called Modified social spider optimization to select the behavioral features of the customer. Then, the customers are clustered using Self Organizing neural network (SONN). Based on the clusters, the customers are classified using the Deep neural network (DNN) model. The experimental results prove the performance of the proposed model with high clustering and segmentation capability to improve the business profit in marketing.

Multi objective short term hydro-thermal- CHP scheduling using social spider algorithm

Potential of cheap and emission free hydro electric generation and that of another non linear efficient distributed energy resource namely combined heat and power (CHP) are utilized in the present dissertation. Water discharge from cascaded multi-reservoir system, its net head and power generation from such a hydroelectric system bears a nonlinear relationship. Such a hydro system with water transport delay among reservoirs is considered here. Optimal utilization of these sources to serve demands over short term of 24 h of a day by scheduling their respective generations so as to achieve multi objectives of cost effective production, reduced emission and transmission loss have been studied using a bio-inspired social spider algorithm. Outcome of the first test system containing hydro-thermal units shows the efficacy of the utilized optimization method while compared to other existing optimization techniques. The second test system is designed for the first time to exhibit the usefulness of the used algorithm in hydro-chp scheduling concept. Provided Statistical analysis justifies the significance of the data of the test systems.

Diverging cooperative prey capture strategies in convergently evolved social spiders

Sociality in spiders has evolved independently multiple times, resulting in convergently evolved cooperative breeding and prey capture. In all social spiders, prey is captured by only a subset of group members and then shared with other, non-attacking group members. However, spiders' propensity to attack prey may differ among species due to species-specific trade-offs between risks, costs and benefits of prey capture involvement. We explored whether engagement in prey attack differs among three social Stegodyphus species, using orthopteran prey, and found substantial differences. Stegodyphus mimosarum Pavesi, 1883 had a low prey acceptance rate, was slow to attack prey, and engaged very few spiders in prey attack. In S. sarasinorum Karsch, 1892, prey acceptance was high, independently of prey size, but more spiders attacked when prey was small. While medium-sized prey had higher acceptance rate in S. dumicola Pocock, 1898, indicating a preference, the number of attackers was not affected by prey size. Our results suggest that the three species may have different cooperative prey capture strategies. In S. mimosarum and S. dumicola, whose geographical ranges overlap, these strategies may represent niche specialization, depending on whether their respective cautious and choosy approaches extend to other prey types than orthopterans, while S. sarasinorum may have a more opportunistic approach. We discuss factors that can affect social spiders' foraging strategy, such as prey availability, predation pressure, and efficiency of the communal web to ensnare prey. Future studies are required to investigate to which extent species-specific cooperative foraging strategies are shaped by ontogeny, group size, and plastic responses to environmental factors.

A Proactive Approach to assess web application security through the integration of security tools in a Security Orchestration Platform

The increasing number of attacks leads to a growing research and development interest in cybersecurity systems. As a response to the increasingly distributed nature of attacks, many organizations have demonstrated willingness to exchange information concerning threats, incidents, and mitigation strategies with security detection tools and techniques. Various security detection techniques such as signature recognition, anomaly detection, etc fail to completely detect complicated attacks. The current situation can be dealt with as a significant tool that helps auditors and administrators to manage and identify distributed threats. In this paper, a novel social spider communicating behavior-based swarm intelligent open-source Orchestrated Continuous Vulnerability Assessment (OCVA) scanning tool is proposed. The proposed OCVA tool addresses the requirement of orchestration of continuous vulnerability assessment of all automated cybersecurity detection processes. It scans, monitors, visualizes, analyzes, mitigates, and remediates the vulnerabilities of the network, assets, and web applications. It helps the developers and security auditors overcome challenges by providing the desired visualizations and analytics of the vulnerable assets. Two case studies are conducted on the basis of the algorithmic comparative analysis with BRICK, Fuzzing, ACO, PSO and GA based vulnerability scanners along with the tool based comparative evaluation with W3af, ZAP, Wapiti, and Arachni in terms of vulnerability detection rate, accuracy, false positive rate, true positive rate and consistency. The results indicate that the proposed OCVA tool outperforms in terms of accuracy, vulnerability remediation rate, and consistency in both.

Color contour texture based peanut classification using deep spread spectral features classification model for assortment identification

Agriculture is one of great economic growth in India by producing variety of peanuts for various co products development. Peanut Crop and weed identification is an important step in developing a highly efficient automotive peanut production system. Most of the exiting classification fix the threshold margins to select the inappropriate features cause classification inaccuracy. Deep learning, as a powerful image processing technology, can extract conventional and characteristic features in layers. Spectral information is used as the input of the network, and the output spectral characteristics of the network are directly passed to the subsequent classifiers to achieve pixel-level classification. To resolve the classification categorization problem, we propose a Color contour texture based peanut classification using deep spread spectral features classification model for variety identification. Initial the Cascading subset Feature Filtering (CSFF) is applied to select the shape textures, color gradient rate form histogram equivalence and returns the feature scalar values. Based in CSFF the feature weight is attained to Social Spider Optimization to select the Impact Features Weight (SSO-IFW). The optimal object segmentation war applied based on cross fold object verification to select the shape projection features. Then Ada Boosting classifier generate the type of object structural for identifying type definition to generate pattern to produce peanut characteristic clusters. Based on Image Features recognition clusters, deep spread spectral neural classification is applied to train the features with soft-max logical activation function with Adaptive Convolution Neural Network (ACNN) to categorize the peanuts based on feature characteristics using image recognition, 20 varieties of peanut are collected through scanner type dataset to train the images into deep neural network. This produce high classification accuracy up to 89 %, high precision, recall rate under F-measure with low false rate. This achieves high classification than other existing methods.

Improved social spider algorithm for partial disassembly line balancing problem considering the energy consumption involved in tool switching

As the waste products have a variety of connection structure characteristics, the energy consumed in tool switching in the disassembly process is considered to better comprehensively optimise the energy consumption index. A mixed-integer non-linear programming (MINLP) model of multi-objective partial disassembly line balancing problem (PDLBP) is constructed to minimise four optimisation objectives which are the number of workstations, workstation load, number of the tools are switched, and energy consumption. Based on the characteristics of PDLBP, we constructed an energy consumption matrix of tool switching and proposed a multi-objective improved social spider algorithm (ISSA). The random movement and mask change operations of ISSA were improved, and the artificial spiders were added to enhance the global optimisation capabilities of ISSA. ISSA was applied to optimise two typical benchmark instances, which have different scales, respectively. And the computational results were compared with several algorithms of existing literature to verify the superiority of ISSA. Finally, ISSA was applied to a partial disassembly instance of a printer, which considered the energy consumed in tool switching. Then, multiple better disassembly schemes were provided for decision-makers.

Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models

ABSTRACT Resilient modulus ( M R ) plays the most critical role in the evaluation and design of flexible pavement foundations. M R is utilised as the principal parameter for representing stiffness and behaviour of flexible pavement foundation in experimental and semi-empirical approaches. To determine M R , cyclic triaxial compressive experiments under different confining pressures and deviatoric stresses are needed. However, such experiments are costly and time-consuming. In the present study, an extreme gradient boosting-based ( X G B ) model is presented for predicting the resilient modulus of flexible pavement foundations. The model is optimised using four different optimisation methods (particle swarm optimisation ( P S O ), social spider optimisation ( S S O ), sine cosine algorithm ( S C A ), and multi-verse optimisation ( M V O )) and a database collected from previously published technical literature. The outcomes present that all developed designs have good workability in estimating the M R of flexible pavement foundation, but the P S O − X G B models have the best prediction accuracy considering both training and testing datasets.

Network Intrusion Detection and Mitigation Using Hybrid Optimization Integrated Deep Q Network

Secure communication between network resources is a key issue, and researchers are persistently developing new intrusion detection schemes to detect intruders to enhance security. However, the traditional intrusion detection techniques have security challenges due to their poor detection performance. Hence, an effective intrusion detection model, namely spider monkey social optimization algorithm (SMSOA)-based Deep Q network is introduced for the detection of intrusion in the network. Here, the intrusion is detected based on the following three steps, such as pre-processing, feature fusion step, and detection. Estimated value replaced the missed value using missing value imputation in the pre-processing phase. Jaccard index by means of deep belief network (DBN) is performed in feature fusion. Deep Q network is used for intrusion detection process, where SMSOA method is used for training. The developed SMSOA scheme is performed by the integration of the social optimization algorithm (SOA) and spider monkey optimization (SMO). In addition, the attack mitigation process prevents the intruder enter into the network. Moreover, the developed model attained a better performance with respect to f-measure, recall values, and precision of 0.9455, 0.9585, and 0.9590, respectively.

A data-driven self-adaptive parameter tuning framework for composite automobile part optimization design

Population-based heuristic optimization algorithms are wildly used in the automobile optimization design. However, the hyper-parameter tuning has a significant effect on the performance of the most of the heuristic algorithms. In order to take full advantages of the heuristic optimization algorithms, this article proposes a data-driven framework for self-adaptive parameters tuning, which named DSPT. The DSPT framework divides the optimization process into two phases. In the learning phase, the knowledge is learned from abundant benchmark functions. The specifically designed performance metrics are used to relate the characteristics of different problems and algorithm performances. In the optimizing phase, the characteristics of a new problem are firstly extracted. According to the knowledge gained from the learning phase and the problem characteristics gained in this phase, rather than predetermined parameters based on experience, the key parameters are tuned automatically. Therefore, the optimization can continue more efficiently. Based on the newly proposed social spider inspired particle swarm optimization algorithm, the proposed framework is successfully applied to the multi-scale lightweight design of four different composite automobile parts.

Hunger state and not personality determines task participation in a spider society

Task specialization is a hallmark of social success as it can minimize among-individual conflict in task participation, and thereby optimize colony productivity. Task specialization is therefore predicted to manifest as a consequence of social evolution. While age or caste in some species explains task participation, a recent hypothesis states that personality type determines differential task participation and thereby facilitates specialization. The social spider Stegodyphus sarasinorum exhibits no castes or conspicuous dominance hierarchies or age polyethism yet shows consistent among-individual differences in prey capture. We tested whether one personality trait (boldness) determines task specialization by exploring the determinants causing consistent participation in prey attack. By integrating a personality type and individual hunger, we tested two mutually exclusive hypotheses. (1) Among-individual differences in personality determine consistent differences in participation in prey capture, leading to specialization. (2) Individuals are flexible, and individual hunger state determines the propensity to capture prey. We found that hunger state was the only significant determinant of attacking prey. By reversing individual hunger state, we were able to dramatically increase or decrease individual attack propensities. Spiders exhibited consistent among-individual differences in boldness, yet this personality type did not predict foraging participation. Therefore, we found no evidence for behavioural specialization in prey capture. Our study emphasizes the importance of internal state as a mechanism underlying variable task participation, suggesting caution in attributing behavioural specialization to personality without appropriate integration of state-dependent effects.

Behavior of the Social Spider Technique on Network Reconfiguration

The goal of this paper is to offer a new strategy for solving the network reconfiguration problem with the aim of decreasing real power loss and enhancing the voltage profile in the distribution system. Social spider optimization (SSO), a new swarm algorithm, is employed to concurrently reconfigure and find the best network. The proposed method was tested on 30-bus mesh and 33-bus radial distribution systems at fixed load levels. To show the performance and efficacy of the suggested method, it was compared to optimization methodology, such as the genetic algorithm, harmony search algorithm, Kruskal's maximal spanning tree, discrete evolutionary programming, and cuckoo search algorithm. The findings reveal that SSO is a strategy worth investigating for tackling the network reconfiguration problem.

A machine learning-based weather prediction model and its application on smart irrigation

Weather prediction is paramount for many applications and scenarios, among them is agriculture. In order to efficiently irrigate the crops with the exact needed water amount, weather forecasting can be used to optimize the quantity of required irrigation water such that the crops are neither dried up nor over-irrigated. This paper proposes a Machine Learning (ML)-based weather forecasting model, which utilizes the Social Spider Algorithm-Least Square-Support Vector Machine (SSA-LS-SVM) algorithm. The simulation results are used to predict the prime weather and soil parameters such as the atmospheric temperature, pressure, and soil humidity for 24, 48, and 72 hours based on previous 39 days’ hourly data for Amman city. The predicted values showed low relative mean square errors compared with the actual values and the LS-SVM predictor.

The role of inter-individual intolerance in group cohesion and the transition to sociality in spiders.

Conspecific tolerance is key for maintaining group cohesion in animals. Understanding shifts from conspecific tolerance to intolerance is therefore important for understanding transitions to sociality. Subsocial species disperse to a solitary lifestyle after a gregarious juvenile phase and display conspecific intolerance as adults as a mechanism to maintain a solitary living. The development of intolerance towards group members is hypothesized to play a role in dispersal decisions in subsocial species. One hypothesis posits that dispersal is triggered by factors such as food competition with the subsequent development of conspecific intolerance, rather than conspecific intolerance developing prior to and potentially driving dispersal. Consistent with this hypothesis, we show that intolerance (inferred by inter-individual distance) developed post-dispersal in the subsocial spider Stegodyphus lineatus. The development of conspecific intolerance was delayed when maintaining spiders in groups showing plasticity in this trait, which is advantageous when trade-offs are not fixed over time. However, major evolutionary transitions, such as the transition to sociality, can permanently modify trade-offs and cause derived adaptations by the evolution of new or modified traits or evolutionary loss of traits that become redundant. Sociality in spiders has evolved repeatedly from subsocial ancestors, and social life in family groups combined with a lack of interaction with competing groups suggests relaxed selection for the development of conspecific intolerance. In the social Stegodyphus sarasinorum we found no evidence for the development of conspecific intolerance, consistent with the loss of this trait. Instead, we found evidence for conspecific attraction, which is likely to govern group cohesion.

A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia.

One of the major reasons of mortality in human beings is cancer, and there is an absolute necessity for doctors to identify and treat a person suffering from it. Leukemia is a group of blood cancers that usually originates in the bone marrow and results in very high number of abnormal cells. For the diagnosis of cancer, microarray data serves as an important clinical application and serves as a great aid to the entire medical community. The dimensionality of the microarray data is too high, and so selection of suitable genes is quite an important step for the improvement of data classification. Therefore, for the prediction and diagnosis of cancer, there is an utmost necessity to select the most informative genes. In this work, Minimum Redundancy Maximum Relevance (MRMR), Signal to Noise Ratio (SNR), Multivariate Error Weight Uncorrelated Shrunken Centroid (EWUSC), and multivariate correlation-based feature selection (CFS) are chosen as initial feature selection techniques. Then, to select the most informative genes, five different kinds of evolutionary optimization techniques too are incorporated here such as African Buffalo Optimization (ABO), Artificial Bee Colony Optimization (ABCO), Cockroach Swarm Optimization (CSO), Imperialist Competitive Optimization (ICO), and Social Spider Optimization (SSO). Finally, the optimized values are fed through classification process and the best results are obtained when multivariate CFS with SSO is utilized and classified with Probabilistic Neural Network (PNN), and a high classification accuracy of 95.70% is obtained.

Enhanced UAV-aided vehicular delay tolerant network (VDTN) routing for urban environment using a bio-inspired approach

The evolution of VDTN routing came to solve the problem of routing in sparse Vehicular Ad-hoc Networks (VANETs). Recently, the implication of Unmanned Aerial Vehicles (UAVs) in VANET communication has gathered quicker information dissemination than Roadside Units (RSUs) in urban VANETs, where ground vehicles’ mobility is restricted by the topology of city roads. Gradually, UAVs are emerging for VDTNs: UAV-aided VDTN (UVDTN) routing field has been introduced for sparse areas in order to reduce the network gaps between Vehicle-to-Vehicle (V2V) connections. Meanwhile, the conventional UVDTN routing protocols expose the short contact opportunity time in the case of Vehicle-to-UAV (V2U) and UAV-to-Vehicle (U2V) connections. This constraint entails more congested bundles’ flooding rates and a higher number of lost bundle copies compared to V2V-based forwarding. In this paper, a bio-inspired UVDTN routing protocol is conceived to improve the bundles’ forwarding between ground vehicles and UAVs in terms of flooding optimization and Quality-of-Service (QoS) performances for urban VANET environments. For this purpose, a swarm-inspired approach called the Social Spider Optimization (SSO) is proposed. SSO seeks to find a better selection of next Store-Carry-and-Forward (SCF) relay nodes specifically for V2U and U2V connections. In order to control the bundle replication rate and enhance bundles’ delivery ratio and delay, the proposed bio-inspired UVDTN protocol adjusts the SCF selection decision. The latter is based on predefined historical knowledge-based forwarding parameters and urban geographic topology information. The proposed bio-inspired routing protocol is simulated using the Opportunistic Network Environment (ONE) simulator and compared to a few VDTN and UVDTN routing protocols.

Energy aware fault tolerant clustering with routing protocol for improved survivability in wireless sensor networks

Wireless Sensor Networks (WSN) remain a hot research topic due to its wide range of application areas like environmental monitoring, military surveillance, healthcare, and disaster management. Network survivability is one of the challenging issues in WSN that needs to be maximized for long-run operation of the network. It can be proficiently improved by effective utilization of available energy since the nodes in WSN operate only via in-built batteries. The studies conducted earlier have mainly focused on the design of clustering and routing protocols to reduce energy consumption in the network. With this motivation, the current research paper presents a novel Energy-Aware Fault Tolerant Clustering with Routing for Improved Survivability (EAFTC-RIS) technique in WSN. The proposed EAFTC-RIS technique intends to select the Cluster Heads (CHs) and optimal routes to the destination in an optimal manner with fault tolerant mechanism. The proposed EAFTC-RIS technique primarily designs Moth Flame Optimization (MFO)-based clustering scheme for CH selection and cluster construction. Also, fault tolerant mechanism is considered to further enhance the survivability of the network. In addition, Social Spider Optimization (SSO)-based routing approach is also employed for optimal selection of routes in WSN. Clustering, routing, and fault tolerant mechanism designs, with multiple input variables, result in enhanced network survivability. The proposed EAFTC-RIS technique was validated for its performance and the outcomes were inspected under various aspects. The simulation outcomes demonstrate the superiority of EAFTC-RIS technique over recent approaches namely, EAFTC-RIS, FUCCAR-GWSO, FBECS, MLSLEEP, and Bee Cluster.

Twitter Sentiment Analysis Using Social-Spider Lex Feature-Based Syntactic-Senti Rule Recurrent Neural Network Classification

Social platforms have become one of the major sources of unstructured text. Investigating the unstructured text and interpreting the meaning is a complex job. Sentiment Analysis is an emerging approach as the social platforms have lot of opinionated data. 1 It uses language processing, classification of texts and linguistics to retrieve the opinions from the text. Twitter is a micro blogging site which is popular amongst the social users as it is a vast open data-platform and it witnesses lot of sentiments. Twitter Sentiment Analysis is a process of automatic mining of user tweets for opinions, emotions, attitude to derive useful insights into community opinions and classify the opinions as well. Due to the enormous increase in the number of collaborative tweets, it has become complex to identify the terms that carries sentiments. Also, the unstructured tweets may have non-relevant terms and reduce the classification accuracy. To address these issues, we propose a Social-Spider Lex Feature Ensemble Model-Based Syntactic-Senti Rule prediction Recurrent Neural Network Classifier (S2LFEM-S2RRNN) to obtain better classification accuracy. Twitter is used as source of data and we have extracted the tweets using Twitter API. Initially, data pre-processing is done to remove unwanted data, symbols and content terms are extracted to improvise the dataset. Then, the significant lexical content terms are extracted employing the proposed Social Spider Lex Feature Ensemble Model (S2LFEM) based on Syntactic-Senti Rule Prediction. The semantics 4 of the terms are analysed on the verbs, subjectivity of the tweet patterns to count the overall weightage of tweets. Based on tweet weightage Recurrent Neural Network is trained to classify the tweets int to positive, negative and neutral. The experiment results show that the proposed classifier outperforms the existing models for sentiment classification in terms of accuracy with a performance score 94.1%.

WITHDRAWN: Cancer prediction using simplex method based social spider optimization technique

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Antimicrobial Volatiles emitted by Members of the Nest Microbiome of Social Spiders

Antimicrobial Volatiles emitted by Members of the Nest Microbiome of Social Spiders