A Novel Hybrid Optimization Algorithm for Materialized View Selection from Data Warehouse Environments

Responding to complex analytical queries in the data warehouse(DW)is one of the most challenging tasks that require prompt attention.The problem of materialized view(MV)selection relies on selecting the most optimal views that can respond to more queries simultaneously.This work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from DWs.The proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique(ECHT).The constraints such as self-adaptive penalty,epsilon(ε)-parameter and stochastic ranking(SR)are considered for constraint handling.These two constraints helped the proposed model select the finest views that minimize the objective function.Further,a novel and effective combination of Ebola and coot optimization algorithms named hybrid Ebola with coot optimization(CHECO)is introduced to choose the optimal MVs.Ebola and Coot have recently introduced metaheuristics that identify the global optimal set of views from the given population.By combining these two algorithms,the proposed framework resulted in a highly optimized set of views with minimized costs.Several cost functions are described to enable the algorithm to choose the finest solution from the problem space.Finally,extensive evaluations are conducted to prove the performance of the proposed approach compared to existing algorithms.The proposed framework resulted in a view maintenance cost of 6,329,354,613,784,query processing cost of 3,522,857,483,566 and execution time of 226 s when analyzed using the TPC-H benchmark dataset.

Cooperative Channel and Optimized Route Selection in Adhoc Network

Over the last decade,mobile Adhoc networks have expanded dramati-cally in popularity,and their impact on the communication sector on a variety of levels is enormous.Its uses have expanded in lockstep with its growth.Due to its instability in usage and the fact that numerous nodes communicate data concur-rently,adequate channel and forwarder selection is essential.In this proposed design for a Cognitive Radio Cognitive Network(CRCN),we gain the confidence of each forwarding node by contacting one-hop and second level nodes,obtaining reports from them,and selecting the forwarder appropriately with the use of an optimization technique.At that point,we concentrate our efforts on their channel,selection,and lastly,the transmission of data packets via the designated forwarder.The simulation work is validated in this section using the MATLAB program.Additionally,steps show how the node acts as a confident forwarder and shares the channel in a compatible method to communicate,allowing for more packet bits to be transmitted by conveniently picking the channel between them.We cal-culate the confidence of the node at the start of the network by combining the reliability report for thefirst hop and the reliability report for the secondary hop.We then refer to the same node as the confident node in order to operate as a forwarder.As a result,we witness an increase in the leftover energy in the output.The percentage of data packets delivered has also increased.

Towards complex scenes: A deep learning-based camouflaged people detection method for snapshot multispectral images

Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.

Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.

Competitive Swarm Optimization with Encryption Based Steganography for Digital Image Security

Digital image security is a fundamental and tedious process on shared communication channels.Several methods have been employed for accomplishing security on digital image transmission,such as encryption,steganography,and watermarking.Image stenography and encryption are commonly used models to achieve improved security.Besides,optimal pixel selection process(OPSP)acts as a vital role in the encryption process.With this motivation,this study designs a new competitive swarmoptimization with encryption based stenographic technique for digital image security,named CSOES-DIS technique.The proposed CSOES-DIS model aims to encrypt the secret image prior to the embedding process.In addition,the CSOES-DIS model applies a double chaotic digital image encryption(DCDIE)technique to encrypt the secret image,and then embedding method was implemented.Also,the OPSP can be carried out by the design of CSO algorithm and thereby increases the secrecy level.In order to portray the enhanced outcomes of the CSOES-DIS model,a comparative examination with recent methods is performed and the results reported the betterment of the CSOES-DIS model based on different measures.

Optimal Selection Model of Equipment Design Scheme Based on Set Pair Analysis

Selecting the optimal one from similar schemes is a paramount work in equipment design.In consideration of similarity of schemes and repetition of characteristic indices,the theory of set pair analysis(SPA)is proposed,and then an optimal selection model is established.In order to improve the accuracy and flexibility,the model is modified by the contribution degree.At last,this model has been validated by an example,and the result demonstrates the method is feasible and valuable for practical usage.

Strategy Selection for Moving Target Defense in Incomplete Information Game

As a core component of the network,web applications have become one of the preferred targets for attackers because the static configuration of web applications simplifies the exploitation of vulnerabilities by attackers.Although the moving target defense(MTD)has been proposed to increase the attack difficulty for the attackers,there is no solo approach can cope with different attacks;in addition,it is impossible to implement all these approaches simultaneously due to the resource limitation.Thus,the selection of an optimal defense strategy based on MTD has become the focus of research.In general,the confrontation of two players in the security domain is viewed as a stochastic game,and the reward matrices are known to both players.However,in a real security confrontation,this scenario represents an incomplete information game.Each player can only observe the actions performed by the opponent,and the observed actions are not completely accurate.To accurately describe the attacker’s reward function to reach the Nash equilibrium,this work simulated and updated the strategy selection distribution of the attacker by observing and investigating the strategy selection history of the attacker.Next,the possible rewards of the attacker in each confrontation via the observation matrix were corrected.On this basis,the Nash-Q learning algorithm with reward quantification was proposed to select the optimal strategy.Moreover,the performances of the Minimax-Q learning algorithm and Naive-Q learning algorithm were compared and analyzed in the MTD environment.Finally,the experimental results showed that the strategy selection algorithm can enable defenders to select a more reasonable defensive strategy and achieve the maximum possible reward.

Spectral Match Optimization Based on Grey Relational Analysis

A spectral match optimization based on grey incidence matrix was put forward to evaluate camouflage painting design.A synthetic degree of incidence (SDI) was defined to comprehensively reflect the spectrum similarity based on theory of grey incidence analysis.A grey optimization model for camouflage painting sheme was constructed on the basis of SDI and grey incidence matrix.Its weight values were determined according to area percentages of all components in the camouflage scene,and a quantitative ordering for various schemes could be obtained according to the evaluation coefficients.Experiment results show that the method mentioned in this paper can provide a quantitative basis for the camouflage decision-making,and it can also be used in other camouflage scheme selection.

面向工业互联网平台的二维制造服务协作优化

工业互联网平台被公认为智能制造的必要推动者,使物理制造资源得以虚拟化,并允许资源以服务的形式进行协作。作为平台的核心功能,制造服务协作优化致力于为制造任务提供高质量的服务协作解决方案。这种优化与任务的功能和数量要求密不可分,在编排服务时必须满足这些要求。然而,现有的制造服务协作优化方法主要关注服务之间针对功能需求的横向协作,很少考虑纵向协作来覆盖所需的数量。为了解决这一差距,本文提出了一种结合功能和数量协作的二维服务协作方法。首先,提出了一种描述服务的多粒度制造服务建模方法。在此基础上,建立了二维制造服务协同优化模型。在垂直维度上,多个功能等效的服务组成一个服务集群来完成一个子任务;在水平维度上,互补服务集群协作完成整个任务。服务的选择和所选服务的金额分配是模型中的关键问题。为了解决这个问题,设计了一种具有多个局部搜索算子的多目标模因算法。将该算法嵌入竞争机制来动态调整本地搜索算子的选择概率。实验结果表明,与常用算法相比,该算法在收敛性、解质量和综合度量方面具有优势。

Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data

The tendency toward achieving more sustainable and green buildings turned several passive buildings into more dynamic ones.Mosques are the type of buildings that have a unique energy usage pattern.Nevertheless,these types of buildings have minimal consideration in the ongoing energy efficiency applications.This is due to the unpredictability in the electrical consumption of the mosques affecting the stability of the distribution networks.Therefore,this study addresses this issue by developing a framework for a short-term electricity load forecast for a mosque load located in Riyadh,Saudi Arabia.In this study,and by harvesting the load consumption of the mosque and meteorological datasets,the performance of four forecasting algorithms is investigated,namely Artificial Neural Network and Support Vector Regression(SVR)based on three kernel functions:Radial Basis(RB),Polynomial,and Linear.In addition,this research work examines the impact of 13 different combinations of input attributes since selecting the optimal features has a major influence on yielding precise forecasting outcomes.For the mosque load,the(SVR-RB)with eleven features appeared to be the best forecasting model with the lowest forecasting errors metrics giving RMSE,nRMSE,MAE,and nMAE values of 4.207 kW,2.522%,2.938 kW,and 1.761%,respectively.

Accurate Machine Learning Predictions of Sci-Fi Film Performance

A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut.Our study aims to harness the power of available data to estimate a film’s early success rate.With the vast resources offered by the internet,we can access a plethora of movie-related information,including actors,directors,critic reviews,user reviews,ratings,writers,budgets,genres,Facebook likes,YouTube views for movie trailers,and Twitter followers.The first few weeks of a film’s release are crucial in determining its fate,and online reviews and film evaluations profoundly impact its opening-week earnings.Hence,our research employs advanced supervised machine learning techniques to predict a film’s triumph.The Internet Movie Database(IMDb)is a comprehensive data repository for nearly all movies.A robust predictive classification approach is developed by employing various machine learning algorithms,such as fine,medium,coarse,cosine,cubic,and weighted KNN.To determine the best model,the performance of each feature was evaluated based on composite metrics.Moreover,the significant influences of social media platforms were recognized including Twitter,Instagram,and Facebook on shaping individuals’opinions.A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms.The findings of this study demonstrate that the chosen algorithms offer more precise estimations,faster execution times,and higher accuracy rates when compared to previous research.By integrating the features of existing prediction models and social media sentiment analysis models,our proposed approach provides a remarkably accurate prediction of a movie’s success.This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release,allowing them to tailor their promotional activities accordingly.Furthermore,the adopted research lays the foundation for developing even more accurate prediction models,considering the ever-increasing significance of social media platforms in shaping individ-uals’opinions.In conclusion,this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films,opening new avenues for the film industry.

森林优化特征选择算法的增强与扩展

特征选择作为一种重要的数据预处理方法,不但能解决维数灾难问题,还能提高算法的泛化能力.各种各样的方法已被应用于解决特征选择问题,其中,基于演化计算的特征选择算法近年来获得了更多的关注并取得了一些成功.近期研究结果表明,森林优化特征选择算法具有更好的分类性能及维度缩减能力.然而,初始化阶段的随机性、全局播种阶段的人为参数设定,影响了该算法的准确率和维度缩减能力;同时,算法本身存在着高维数据处理能力不足的本质缺陷.从信息增益率的角度给出了一种初始化策略,在全局播种阶段,借用模拟退火控温函数的思想自动生成参数,并结合维度缩减率给出了适应度函数;同时,针对形成的优质森林采取贪心算法,形成一种特征选择算法EFSFOA(enhanced feature selection using forest optimization algorithm).此外,在面对高维数据的处理时,采用集成特征选择的方案形成了一个适用于EFSFOA的集成特征选择框架,使其能够有效处理高维数据特征选择问题.通过设计对比实验,验证了EFSFOA与FSFOA相比在分类准确率和维度缩减率上均有明显的提高,高维数据处理能力更是提高到了100 000维.将EFSFOA与近年来提出的比较高效的基于演化计算的特征选择方法进行对比,EFSFOA仍具有很强的竞争力.

基于镜像选择的改进鲸鱼优化算法

针对鲸鱼优化算法收敛速度慢、精度低、易陷入局部最优解的缺点,提出了一种基于镜像选择的改进鲸鱼优化算法(Whale optimization algorithm based-on mirror selection,WOA-MS)。具体改进包括:(1)为了平衡全局搜索和局部开采,提出了一种基于Branin函数的自适应非线性惯性权重;(2)为了提高算法的个体质量和收敛速度,提出了一种镜像选择方法。通过对若干种测试函数进行优化,并与其他三种算法的实验结果进行比较,证明了WOA-MS具有良好的优化性能。

Center for Plant Conservation's Best Practice Guidelines for the reintroduction of rare plants

Recent estimates indicate that one-fifth of botanical species worldwide are considered at risk of becoming extinct in the wild. One available strategy for conserving many rare plant species is reintroduction, which holds much promise especially when carefully planned by following guidelines and when monitored long-term. We review the Center for Plant Conservation Best Reintroduction Practice Guidelines and highlight important components for planning plant reintroductions. Before attempting reintroductions practitioners should justify them, should consider alternative conservation strategies, understand threats, and ensure that these threats are absent from any recipient site. Planning a reintroduction requires considering legal and logistic parameters as well as target species and recipient site attributes.Carefully selecting the genetic composition of founders, founder population size, and recipient site will influence establishment and population growth. Whenever possible practitioners should conduct reintroductions as experiments and publish results. To document whether populations are sustainable will require long-term monitoring for decades, therefore planning an appropriate monitoring technique for the taxon must consider current and future needs. Botanical gardens can play a leading role in developing the science and practice of plant reintroduction.

A Novel Improved Artificial Bee Colony and Blockchain-Based Secure Clustering Routing Scheme for FANET

Flying Ad hoc Network(FANET)has drawn significant consideration due to its rapid advancements and extensive use in civil applications.However,the characteristics of FANET including high mobility,limited resources,and distributed nature,have posed a new challenge to develop a secure and ef-ficient routing scheme for FANET.To overcome these challenges,this paper proposes a novel cluster based secure routing scheme,which aims to solve the routing and data security problem of FANET.In this scheme,the optimal cluster head selection is based on residual energy,online time,reputation,blockchain transactions,mobility,and connectivity by using Improved Artificial Bee Colony Optimization(IABC).The proposed IABC utilizes two different search equations for employee bee and onlooker bee to enhance convergence rate and exploitation abilities.Further,a lightweight blockchain consensus algorithm,AI-Proof of Witness Consensus Algorithm(AI-PoWCA)is proposed,which utilizes the optimal cluster head for mining.In AI-PoWCA,the concept of the witness for block verification is also involved to make the proposed scheme resource efficient and highly resilient against 51%attack.Simulation results demonstrate that the proposed scheme outperforms its counterparts and achieves up to 90%packet delivery ratio,lowest end-to-end delay,highest throughput,resilience against security attacks,and superior in block processing time.

Elite Opposition Based Metaheuristic Framework for Load Balancing in LTE Network

In present scenario of wireless communications,Long Term Evolution(LTE)based network technology is evolved and provides consistent data delivery with high speed andminimal delay through mobile devices.The traffic management and effective utilization of network resources are the key factors of LTE models.Moreover,there are some major issues in LTE that are to be considered are effective load scheduling and traffic management.Through LTE is a depraved technology,it is been suffering from these issues.On addressing that,this paper develops an Elite Opposition based Spider Monkey Optimization Framework for Efficient Load Balancing(SMO-ELB).In this model,load computation of each mobile node is done with Bounding Theory based Load derivations and optimal cell selection for seamless communication is processed with Spider Monkey Optimization Algorithm.The simulation results show that the proposed model provides better results than exiting works in terms of efficiency,packet delivery ratio,Call Dropping Ratio(CDR)and Call Blocking Ratio(CBR).

QoS Constrained Network Coding Technique to Data Transmission Using IoT

The research work presents,constrained network coding technique to ensure the successful data transmission based composite channel cmos technology using dielectric properties.The charge fragmentation and charge splitting are two components of the filtered switch domino(FSD)technique.Further behavior of selected switching is achieved using generator called conditional pulse generator which is employed in Multi Dynamic Node Domino(MDND)technique.Both FSD and MDND technique need wide area compared to existing single nodekeeper domino technique.The aim of this research is to minimize dissipation of power and to achieve less consumption of power.The proposed research,works by introducing the method namely Interference and throughput aware Optimized Multicast Routing Protocol(IT-OMRP).The main goal of this proposed research method is to introduce the system which can forward the data packets towards the destination securely and successfully.To achieve the bandwidth and throughput in optimized data transmission,proposed multicast tree is selected by Particle Swarm Optimization which will select the most optimal host node as the branches of multi cast tree.Here node selection is done by considering the objectives residual energy,residual bandwidth and throughput.After node selection multi cast routing is done with the concern of interference to ensure the reliable and successful data transmission.In case of transmission range size is higher than the coverage sense range,successful routing is ensured by selecting secondary host forwarders as a backup which will act as intermediate relay forwarders.The NS2 simulator is used to evaluate research outcome from which it is proved that the proposed technique tends to have increased packet delivery ratio than the existing work.

基于Vague集的最优路径选择方法（英文）

Optimal route selection is an important function of vehicle traffic flow guidance system. Its core is to determine the index weight for measuring the route merits and to determine the evaluation method for selecting route. In this paper, subjective weighting method which relies on driver preference is used to determine the weight and the paper proposes the multi-criteria weighted decision method based on vague sets for selecting the optimal route. Examples show that, the usage of vague sets to describe route index value can provide more decision-making information for route selection.

Digital twin-driven green material optimal selection and evolution in product iterative design

In recent years,green concepts have been integrated into the product iterative design in the manufacturing field to address global competition and sustainability issues.However,previous efforts for green material optimal selection disregarded the interaction and fusion among physical entities,virtual models,and users,resulting in distortions and inaccuracies among user,physical entity,and virtual model such as inconsistency among the expected value,predicted simulation value,and actual performance value of evaluation indices.Therefore,this study proposes a digital twin-driven green material optimal selection and evolution method for product iterative design.Firstly,a novel framework is proposed.Subsequently,an analysis is carried out from six perspectives:the digital twin model construction for green material optimal selection,evolution mechanism of the digital twin model,multi-objective prediction and optimization,algorithm design,decision-making,and product function verification.Finally,taking the material selection of a shared bicycle frame as an example,the proposed method was verified by the prediction and iterative optimization of the carbon emission index.

Multi-Period Telser's Safety-First Portfolio Selection Problem in a Defined Contribution Pension Plan

This paper investigates a multi-period portfolio optimization problem for a defined contribution pension plan with Telser's safety-first criterion.The plan members aim to maximize the expected terminal wealth subject to a constraint that the probability of the terminal wealth falling below a disaster level is less than a pre-determined number called risk control level.By Tchebycheff inequality,Lagrange multiplier technique,the embedding method and Bellman's principle of optimality,the authors obtain the conditions under which the optimal strategy exists and derive the closed-form optimal strategy and value function.Special cases show that the obtained results in this paper can be reduced to those in the classical mean-variance model.Finally,numerical analysis is provided to analyze the effects of the risk control level,the disaster level and the contribution proportion on the disaster probability and the value function.The numerical analysis indicates that the disaster probability in this paper is less than that in the classical mean-variance model on the premise that the value functions are the same in two models.