Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.

Review of Fabric Defect Detection Based on Computer Vision

In textile inspection field,the fabric defect refers to the destruction of the texture structure on the fabric surface.The technology of computer vision makes it possible to detect defects automatically.Firstly,the overall structure of the fabric defect detection system is introduced and some mature detection systems are studied.Then the fabric detection methods are summarized,including structural methods,statistical methods,frequency domain methods,model methods and deep learning methods.In addition,the evaluation criteria of automatic detection algorithms are discussed and the characteristics of various algorithms are analyzed.Finally,the research status of this field is discussed,and the future development trend is predicted.

Weed Classification Using Particle Swarm Optimization and Deep Learning Models

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity.To address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image classification.Throughout this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed datasets.The Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed images.An effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification accuracy.The proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed classification.This work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

Posterior probability calculation procedure for recognition rate comparison

This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition rate comparison procedures and discusses their limitations. A new method, the posterior probability calculation（PPC） procedure is then proposed based on Bayesian technique. The paper analyzes the basic principle, process steps and computational complexity of the PPC procedure. In the Bayesian view, the posterior probability represents the credible degree（equal to confidence level） of the comparison results. The posterior probability of correctly selecting or sorting the competing recognition algorithms is derived, and the minimum sample size requirement is also pre-estimated and given out by the form of tables. To further illustrate how to use our method, the PPC procedure is used to prove the rationality of the experiential choice in one application and then to calculate the confidence level with the fixed-size datasets in another application. These applications reveal the superiority of the PPC procedure, and the discussions about the stopping rule further explain the underlying statistical causes. Finally we conclude that the PPC procedure achieves all the expected functions and be superior to the traditional methods.

Performance Evaluating Model for Computer Network Application Environments

This paper presents a new measuring method based on the simulating technology and measuring technology, researches and analyzes deeply its network performance evaluating model, measuring methods, evaluating algorithms and system implementation. Experimental results argue that this method can define expediently different models of manual simulated loads and configure neatly different environments of network applications, can develop adequately characteristics of widespread applications and accuracy in simulating technology, as well as reality, reliability and better adaptability in measuring technology.

Competition system for automatically evaluating edge detection algorithms based on the evolutionary game theory

An ensemble learning algorithm based on game theory is proposed to evaluate algorithms of image analysis and image feature extraction.A competition system is established to implement the algorithm for evaluating the applicability and efficiency of different edge detection algorithms.Through the game in the algorithm competition system,the most suitable algorithm as a winner in the competition can be selected.A group of optimal parameters for the corresponding edge detection can also be found.Firstly,based on the evolutionary game theory,a strategy of the competition of edge extraction algorithms is developed.Secondly,after selecting the most suitable algorithm from the candidates,the overall parameters are optimized.Experiments show that for a specific class of images,several candidate algorithms can be used as a class of preference algorithms based on the final evolutionary result.When analyzing the images,the priority algorithm can be recommended as the best edge detection algorithm from these reference algorithms.It is more effective than traditional methods in determining an algorithm and choosing parameters.

Algorithms mention in full-text content of article from NLP domain:A comparative analysis between English and Chinese

Algorithms play an increasingly important role in scientific work,especially in data-driven research.Investigating the mention of algorithms in full-text paper helps us understand the use and development of algorithms in a specific domain.Current research on the mention of algorithms is limited to the academic papers in one language,which is hard to comprehensively investigate the use of algorithms.For example,in papers of Chinese conference,is the mention of algorithms consistent with it in English conference papers?In order to answer this question,this paper takes NLP as an example,and compares the mention frequency,mention location and mention time of the top10 data-mining algorithms between the papers of the famous international conference,Annual Meeting of the Association for Computational Linguistics(ACL),and the Chinese conference,China National Conference on Computational Linguistics(CCL).The results show that compared with ACL,the mention frequency of top10 data-mining algorithms in CCL is slightly lower and the mention time is slightly delayed,while the distribution of mention location is similar.This study can provide a reference for the research related to the mention,citation and evaluation of knowledge entities.

A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm

In this paper,a bioinspired path planning approach for mobile robots is proposed.The approach is based on the sparrow search algorithm,which is an intelligent optimization algorithm inspired by the group wisdom,foraging,and anti-predation behaviors of sparrows.To obtain high-quality paths and fast convergence,an improved sparrow search algorithm is proposed with three new strategies.First,a linear path strategy is proposed,which can transform the polyline in the corner of the path into a smooth line,to enable the robot to reach the goal faster.Then,a new neighborhood search strategy is used to improve the fitness value of the global optimal individual,and a new position update function is used to speed up the convergence.Finally,a new multi-index comprehensive evaluation method is designed to evaluate these algorithms.Experimental results show that the proposed algorithm has a shorter path and faster convergence than other state-ofthe-art studies.