Performance Evaluation and Social Optimization of an Energy-Saving Virtual Machine Allocation Scheme Within a Cloud Environment

Achieving greener cloud computing is non-negligible for the open-source cloud platform.In this paper,we propose a novel virtual machine allocation scheme with a sleep-delay and establish a corresponding mathematical model.Taking into account the number of tasks and the state of the physical machine,we construct a two-dimensional Markov chain and derive the average latency of tasks and the energy-saving degree of the system in the steady state.Moreover,we provide numerical experiments to show the effectiveness of the proposed scheme.Furthermore,we study the Nash equilibrium behavior and the socially optimal behavior of tasks and carry out an improved adaptive genetic algorithm to obtain the socially optimal arrival rate of tasks.Finally,we present a pricing policy for tasks to maximize the social profit when managing the network resource within the cloud environment.

Metaheuristic Optimization with Deep Learning Enabled Smart Grid Stability Prediction

Due to the drastic increase in global population as well as economy,electricity demand becomes considerably high.The recently developed smart grid(SG)technology has the ability to minimize power loss at the time of power distribution.Machine learning(ML)and deep learning(DL)models can be effectually developed for the design of SG stability techniques.This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability(SSODLSA-SGS)pre-diction model.Primarily,class imbalance data handling process is performed using Synthetic minority oversampling technique(SMOTE)technique.The SSODLSA-SGS model involves two stages of pre-processing namely data nor-malization and transformation.Besides,the SSODLSA-SGS model derives a deep belief-back propagation neural network(DBN-BN)model for the pre-diction of SG stability.Finally,social spider optimization(SSO)algorithm can be applied for determining the optimal hyperparameter values of the DBN-BN model.The design of SSO algorithm helps to appropriately modify the hyperparameter values of the DBN-BN model.A series of simulation analyses are carried out to highlight the enhanced outcomes of the SSODLSA-SGS model.The extensive comparative study reported the enhanced performance of the SSODLSA-SGS algorithm over the other recent techniques interms of several measures.

Optimizing Social Impact in Organizations:Concepts,Levers and Barriers

With the socio-economic change that has taken place over the last years,in addition to an increase in sustainability regulation,stakeholders have gained importance and organizations are more active in relation to generating social impact,but society demands more and better social impact from organizations.The objectives of this paper are to clarify the concepts of impact and social impact optimization,and to detect levers and barriers to help organizations optimize the social impact that they generate.A qualitative approach based on interviews with social impact leaders from organizations with different forms(big companies,small and medium-sized enterprises,corporate foundations,b-corps,community foundations,public and private foundations,associations and investing firms)is applied,together with focus groups with stakeholders from those organizations that are best practices.

Field Weakening Control of a Separately Excited DC Motor Using Neural Network Optemized by Social Spider Algorithm

This paper presents the speed control of a separately excited DC motor using Neural Network (NN) controller in field weakening region. In armature control, speed controller has been used in outer loop while current controller in inner loop is used. The function of NN is to predict the field current that realizes the field weakening to drive the motor over rated speed. The parameters of NN are optimized by the Social Spider Optimization (SSO) algorithm. The system has been implemented using MATLAB/SIMULINK software. The simulation results show that the proposed method gives a good performance and is feasible to be applied instead of others conventional combined control methods.

Improved Hybrid Swarm Intelligence for Optimizing the Energy in WSN

In this current century,most industries are moving towards automation,where human intervention is dramatically reduced.This revolution leads to industrial revolution 4.0,which uses the Internet of Things(IoT)and wireless sensor networks(WSN).With its associated applications,this IoT device is used to compute the receivedWSN data from devices and transfer it to remote locations for assistance.In general,WSNs,the gateways are a long distance from the base station(BS)and are communicated through the gateways nearer to the BS.At the gateway,which is closer to the BS,energy drains faster because of the heavy load,which leads to energy issues around the BS.Since the sensors are battery-operated,either replacement or recharging of those sensor node batteries is not possible after it is deployed to their corresponding areas.In that situation,energy plays a vital role in sensor survival.Concerning reducing the network energy consumption and increasing the network lifetime,this paper proposed an efficient cluster head selection using Improved Social spider Optimization with a Rough Set(ISSRS)and routing path selection to reduce the network load using the Improved Grey wolf optimization(IGWO)approach.(i)Using ISSRS,the initial clusters are formed with the local nodes,and the cluster head is chosen.(ii)Load balancing through routing path selection using IGWO.The simulation results prove that the proposed optimization-based approaches efficiently reduce the energy through load balancing compared to existing systems in terms of energy efficiency,packet delivery ratio,network throughput,and packet loss percentage.

Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization

We propose multi-objective social learning pigeon-inspired optimization(MSLPIO)and apply it to obstacle avoidance for unmanned aerial vehicle(UAV)formation.In the algorithm,each pigeon learns from the better pigeon but not necessarily the global best one in the update process.A social learning factor is added to the map and compass operator and the landmark operator.In addition,a dimension-dependent parameter setting method is adopted to improve the blindness of parameter setting.We simulate the flight process of five UAVs in a complex obstacle environment.Results verify the effectiveness of the proposed method.MSLPIO has better convergence performance compared with the improved multi-objective pigeon-inspired optimization and the improved non-dominated sorting genetic algorithm.

An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images

In the digestion of amino acids,carbohydrates,and lipids,as well as protein synthesis from the consumed food,the liver has many diverse responsibilities and functions that are to be performed.Liver disease may impact the hormonal and nutritional balance in the human body.The earlier diagnosis of such critical conditions may help to treat the patient effectively.A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver.The proposed approach can recognize the abnormalities with better accuracy without training,unlike in supervisory procedures requiring considerable computational efforts for training.In the earlier stages,the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better segmentation.Then,the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted—Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information,which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts.It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%.The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.

Detection of leaf folder and yellow stemborer moths in the paddy field using deep neural network with search and rescue optimization

In agriculture,insect pests must be identified at the initial stage of infestation to avoid their spread in the field.Leaf folders(cnaphalocrocis medinalis)and yellow stemborers(scirpophaga incertulas)are destructive pests of paddy crops,which are causing severe yield loss.Manual identification of insect pests in the crop is time-consuming,tedious,and ineffective.This paper focuses on a light trap based four-layer deep neural network with search and rescue optimization(DNN-SAR)method to identify leaf folders and yellow stemborers.Light traps are designed to lure the insects in the paddy field and the images of trapped insects are analyzed using the proposed detection method.In the DNN-SAR,images are contrastenhanced using deer hunting algorithm,impulse noise is removed with fast average group filter,and segmented using social ski-driver optimization.The search and rescue optimization algorithm is used for the selection of optimal weights in the deep neural network,which has improved the convergence rate,lowered the complexity of learning,and improved the accuracy of detection.The proposed method outperformed the existing methods and achieved 98.29%pest detection accuracy.

Pricing Policy for a Dynamic Spectrum Allocation Scheme with Batch Requests and Impatient Packets in Cognitive Radio Networks

In cognitive radio networks(CRNs),multiple secondary users may send out requests simultaneously and one secondary user may send out multiple requests at one time,i.e.,request arrivals usually show an aggregate manner.Moreover,a secondary user packet waiting in the buffer may leave the system due to impatience before it is transmitted,and this impatient behavior inevitably has an impact on the system performance.Aiming to investigate the influence of the aggregate behavior of requests and the likelihood of impatience on a dynamic spectrum allocation scheme in CRNs,in this paper a batch arrival queueing model with possible reneging and potential transmission interruption is established.By constructing a Markov chain and presenting a transition rate matrix,the steady-state distribution of the queueing model along with a dynamic spectrum allocation scheme is derived to analyze the stochastic behavior of the system.Accordingly,some important performance measures such as the loss rate,the balk rate and the average delay of secondary user packets are given.Moreover,system experiments are carried out to show the change trends of the performance measures with respect to batch arrival rates of secondary user packets for different impatience parameters,different batch sizes of secondary user packets,and different arrival rates of primary user packets.Finally,a pricing policy for secondary users is presented and the dynamic spectrum allocation scheme is socially optimized.