Ant Colony Neural Network Research Articles

E-commerce and Mobile Application Development in the Sports Industry

In order to achieve user recommendations that best match their current contextual needs, the author proposes a mobile service QoS (Quality of Service) hybrid recommendation model based on sports user situational awareness. Cluster the users and service items covered by mobile users based on their location contextual information according to the classification principle of autonomous systems, forming a collaborative filtering and recommendation mechanism for mobile user service items; In response to the cold start problem of new users and new projects in traditional QoS recommendation methods, prediction and recommendation of missing QoS attribute preference values are based on User based and Item based CF; In response to the problem of difficult determination of mixed recommendation weights caused by massive data and uneven distribution of service QoS attribute values in mobile network environments, MapReduce based ant colony neural network weight training is used for CF mixed recommendation. Experimental results have shown that the Hadoop sports industry has improved the operational efficiency of algorithms and reduced the time for global QoS preference prediction; And by comparing the operation results of the 10% and 100% sub datasets, it can also be seen that the acceleration ratio of the algorithm will continuously improve with the increase of data volume, thereby improving the operational efficiency of the recommendation algorithm. It has been proven that the MapReduce based ant colony neural network weight training method significantly reduces the global computation time of the algorithm and improves the operational efficiency of the recommendation system.

Multi-attribute Feature Fusion Algorithm of IoT Communication Big Data Based on Ant Colony Neural Network

Abstract To address the issues of time-consuming and inaccurate feature fusion in traditional Internet of Things (IoT) communication big data multi-attribute fusion algorithms, a new algorithm based on the ant colony neural network is proposed. Modeling and feature decomposition processing is carried out on IoT communication big data, communication data information and data attribute characteristics are optimized into different data groups according to a data clustering algorithm, and a rough set calculation method is used to calculate information attribute characteristics. The genetic algorithm is used to improve the ant colony neural network, improve the search ability, extract the multi-attribute characteristics of IoT communication big data, filter the current big data’s characteristic attribute demand through the comparison method, and achieve multi-attribute feature fusion of IoT communication big data through the similarity measurement method. The experimental results show that the fusion error of the IoT communication big data multi-attribute feature fusion algorithm based on the ant colony neural network is small and relatively stable. During the 600 MB data fusion process, the data fusion processing time of the method in this paper is 150 ms, and the average energy consumption ratio is below 2 %, which reflects better IoT communication big data multi-attribute feature fusion performance.

Multi-component analyses of raspberry: Optimization of extraction procedure and network pharmacology

The contents of ellagic acid and kaempferol-3-O-rutinoside, the chief active components of raspberry, are considered the quality control indices of raspberry. This work employed the ant colony neural network (ACO-BPNN) to optimize their extraction processes, and the combination of network pharmacology and molecular docking technology to unveil the potential pharmacological effects of these components. Based on the single-factor test (ultrasonic time, ethanol concentration, ultrasonic temperature, and solid-liquid ratio), a factorial experiment with 4-factors and 3-levels was conducted in parallel for 3 times. The multi-factor analysis of variance results revealed high-order interactions among the factors. Then, the ACO-BPNN model was established to characterize the complex relationship of experimental data. After further verification, relative errors were all less than 8 %, implying the model's effectiveness and reliability. Moreover, with the network pharmacology, 66 key targets were screened out and mainly concentrated in PI3K-AKT, MAPK, and Ras signal pathways. Molecular docking revealed the binding sites between active components and key targets.

Retracted: Construction of Financial Management Early Warning Model Based on Improved Ant Colony Neural Network.

[This retracts the article DOI: 10.1155/2021/6748920.].

Construction of Financial Management Early Warning Model Based on Improved Ant Colony Neural Network.

With the advent of the era of economic globalization, the world capital market is also facing financial risks. It is necessary to have a corresponding financial management early warning model to reduce economic losses. This paper uses the combination of ant colony algorithm and neural network algorithm to build a neural network improved by ant colony algorithm model. By setting relevant assumptions, the financial statements and annual report texts are predicted and analyzed and compared with the original static data forecasting model. Compared with traditional methods, the time series sequencing analysis used in this paper makes the result prediction more accurate. This allows one year's data to be used to predict the data for the next two years. This research can provide a corresponding reference for the optimization of financial management early warning system.

A health management system for large vertical mill

The large vertical mill has complicated structure and tens of thousands of parts, which is a critical grinding equipment for slag and cinder. As large vertical mill always works in severe conditions, the on-line monitoring, timely fault diagnosis, and trend prediction are very important guarantees for the safe service and saving maintaining costs. To address this issue, the health management system for large vertical mill is developed. More specifically, in order to manage reservoirs of state-related running data, the intrinsic physic data, and diagnosis knowledge base, an entity-relationship-model-based database is first constructed. Based on the fault diagnosis reasoning of experts, the fault tree is developed and the fault diagnosis rules are derived. Especially, a hybrid condition prognosis method based on backtracking search optimization algorithm and neural network is developed, and in comparison with traditional back propagation neural network and ant colony neural network, the developed backtracking search optimization algorithm and neural network gets superior hybrid prediction performance in prediction accuracy and training efficiency. Finally, the health management system, including the functions of condition monitoring, fault diagnosis, and trend prediction for large vertical mill is implemented using Microsoft Visual Studio C # and Microsoft SQL Server.

A Novel Wireless Sensor Node Positioning Algorithm Based on Ant Colony Optimization Algorithm and Neural Network

This paper aims to enhance the positioning accuracy of wireless sensor network (WSN) nodes. For this purpose, a WSN node positioning algorithm was proposed based on artificial bee colony (ABC) algorithm and the neural network (NN). First, the parameters between three anchor nodes and the target node were measured. Then, the ABC and NN were introduced to simulate and predict the ranging error, and the weight was determined according to the results. In the proposed algorithm, the cluster structure was effectively combined with the NN model. The weight of backpropagation NN was optimized by the ant colony optimization (ACO) algorithm. Then, the ACO-optimized NN was used to fuse the data collected by WSN nodes. The simulation results show that the proposed algorithm can improve the positioning accuracy of WSN nodes and reduce the time of the search. The research findings shed new light on the positioning of WSN nodes.

Mill Load Control System Based on the Improved Ant Colony Neural Network

Mill Load Control System Based on the Improved Ant Colony Neural Network

Inconsistency Detection in Software Component Source Code using Ant Colony Optimization and Neural Network Algorithm

Objectives: Inconsistency detection is one of the major challenges in source code for the software developers. There is the need of consistent identifiers to reduce the code inconsistencies. So, developers should either have the knowledge to create conceptual identifiers or the knowledge to detect the inconsistencies in source code. Methods/Statistical Analysis: There is the availability of a list of tools for the detection of different types of inconsistencies. But the existing tools are not much appropriate for Semantic, Syntactic Inconsistencies and Part of Speech tagging. Findings: In the paper, an autonomous tool Automatic Bad Code Detector (ABCD) is developed to detect semantic, syntactic and part of speech inconsistency in the source code. ABCD tool identifies the inconsistencies in the source code based on the detected Code Clones. These Clones are detected by matching the test code with Code Repository. A java project based code repository is considered for experimentation. ABCD is evaluated for different java projects in order to find inconsistencies in source code. In ABCD tool main inconsistency detector are Ant Colony Optimization and Neural Network Back Propagation algorithm. Further, ABCD is useful in re-implementing the new versions of the java code. Applications/Improvements: The current concept is evaluated for the Semantic, Syntactic, POS-Word and POS-Phrase inconsistencies based on evaluation parameter of precision. The efficiency of ABCD is evaluated as an overall value for the precision, recall and f-measure.

Third International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design

Jon McCormack: Codeform: A Balancing Act between Variation and Utility in Evolutionary Art 000 Evolutionary Computation (EC) is a broad class of computational techniques inspired by nature, and in particular by Darwinian evolution. EC is used for searchand-optimization problems ranging from business and engineering to graphical art, as described in this special section. It may be familiar to long-time readers, as Leonardo has previously published two special sections on evolutionary art, in Leonardo Vol. 35, No. 2, and Vol. 36, No. 1. The basic principles are those of genetics. In EC, as in nature, there is a population of individuals, and each has some genetic material (a genotype) that affects the appearance or behavior of the individual. Among the individuals of the population, some are “better” than others and give rise to new individuals by mating or mutation. Others are discarded. Over time, the population improves. In nature, better refers to the ability to survive and reproduce in a harsh environment. In EC, if the problem is one of engineering or business, better may be defined in computational terms. In an artistic domain, better may be defined with reference to a theory of aesthetic value or it may be implemented by interactive selection of individuals by a user based purely on subjective preference. The use of EC in artistic domains is called evolutionary art. It has been used as a tool in the creation of many types of art, including music and musical timbre, graphical art and animation, 3D design, architecture and poetry. Evolutionary art is appealing to artists for several reasons. It can be used for exploring the implications of an aesthetic theory or for generating unexpected ideas. It allows a novel workflow very different from the typical workflow of most artists. It can allow peripheral, low-value tasks to be automated, leaving the artist to focus on core issues. The EvoMUSART conference was born as a workshop under the Evostar (Evo*) umbrella in 2003 with the aim of helping the consolidation of evolutionary art and music research. Its scope is evolutionary and spans bioinspired music, sound, art and design. Here, bio-inspired refers to methods that are related to but slightly outside of EC, including particle swarm optimization, ant colony optimization and neural networks. After 12 editions, the event is now a full international conference and has included more than 400 papers. The papers in this special Leonardo section represent the latest work on three projects selected from those presented at EvoMUSART 2014, including the Best Paper Award winner and runner-up. They reflect some of the main topics of evolutionary art, both interactive and noninteractive. Eisenmann et al. report on a novel mode of interaction for 2D and 3D evolutionary art. Their system allows the artist to express preferences for particular parts of the evolving designs rather than for whole designs. It uses sensitivity analysis to perform the necessary “backward mapping” from parts of the design to the genotype. It also aims to reduce “visual load” on the artist. Case studies with several artists are used to explore the strengths and weaknesses of the system. Machado et al. propose another new mode of interaction: Their system allows the artist to specify the goals for a noninteractive generative art system using artificial ants, interactively and with real-time feedback. Along the way, they learn more about what types of goals are obtainable to artists. Finally, McCormack describes an interesting encoding for simple animated creatures, which was employed in an interactive museum exhibit to give each visitor a personalized experience. He uses it as the jumping-off point for a discussion of what properties of search spaces are desirable for artistic uses.

Optimal spatial allocation of irrigation water under uncertainty using the bilayer nested optimisation algorithm and geospatial technology

ABSTRACTThe optimal spatial allocation of irrigation water under uncertainty has become a serious concern because of irrigation water shortage and uncertain factors that affect irrigation water allocation. In this study, an optimal multi-objective model for irrigation water allocation under uncertainty is developed to maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation. The original and optimal plantation structure, irrigation mode and soil water content are acquired through geospatial technology. A bilayer nested optimisation (BLNO) algorithm is designed to produce multiple individuals of design vectors using an ant colony neural network algorithm for an outer optimisation. Meanwhile, a continuous adaptive ant colony (CAAC) algorithm is used for inner optimisation to calculate the interval values of the uncertain model. The crop distribution and irrigation mode are obtained to parameterise the planting area and the water demand of each crop and each block in the multi-objective model. This model is then solved and analysed. Compared to the optimal schemes obtained from an inexact two-stage fuzzy-stochastic programming and the CAAC, respectively, BLNO can effectively and efficiently solve the optimal spatial allocation of irrigation water under uncertainty. This method can spatially maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation using lower and upper bound maps whilst visually obtaining the exact crop type, reasonable irrigation method and precise water demand for each block and for the entire irrigated area.

On the Application of Ant Colony Neural Network for the Highway Traffic Safety Facilities System

Highway traffic safety facilities system evaluation plays an important role to relieve highway safety problem. From the perspective of system engineering, the influence factors of the current situation are comprehensively considered, the freeway traffic safety facilities system evaluation index system is established, and the application of ant colony neural network evaluation model is constructed. The purpose is to provide the beneficial references for freeway traffic safety facilities system evaluation.

Application of Ant Colony Algorithms and Neural Networks in Fault Diagnosis

Neural network technology is widely applied due to its computational simplicity and versatility. But, this method has some weak points, for example, slow convergence, less accurate and easy to fall into local minimum points. Combined ant colony algorithm and neural network for fault diagnosis, it can overcome the limitations of a single fault diagnosis method. Ant colony neural network method is applied to gearbox fault diagnosis, the results show that the diagnosis with characteristics of high precision, strong scientific and practical wider.

A Combinatorial Algorithm of Ant Colony Optimization and Neural Network Algorithm for Channel Assignment Problem

A Combinatorial Algorithm of Ant Colony Optimization and Neural Network Algorithm for Channel Assignment Problem

Research on SPA Forecasting Method Based on RNN and Ant Colony Optimization Algorithm

To reduce the limitation of conventional forecasting methods, the set pair analysis theory is introduced to describe the dynamic system, and the forecasting model based on SPA have been built. And corresponding algorithm has also been put forward based on ant colony neural network. Meanwhile, the node number m of recursive network input layer, parameter q of the recursive network. Finally, a case study has been carried out by using calculating and comparing analysis with forecasting accuracy of relevant forecasting models has been made. The results show that the accuracy of SPA forecasting model based on recurrent neural network and random gray ant colony optimization algorithm is not only greater than any other single forecasting models, but also superior to other traditional combinational forecasting models.

A Performance Evaluation of Shape Based Image Retrieval Using Heuristic Function

Heuristic function plays an important role in content based image retrieval. The heuristic function used for feature selection and feature optimization for retrieval process. The feature selection process are depends on feature extraction process. The content based image consists of three types of features such as color, texture and shape. The shape feature is very important feature for image retrieval. The extraction of shape feature various authors used different method such ad Gabor filter, wavelet transform function and Fourier descriptor. Now in current research trend MPEG-7 feature descriptor are mostly authors are used. In this paper descried the review of content based image retrieval based on shape based feature and optimization technique such as ANT colony optimization, genetic algorithm and neural network. The empirical evaluation result shows that ANT colony optimization technique is better optimization technique in compared with other such as genetic and neural network.

The Prediction Model of the Mechanical Properties of Composite Materials Based on Ant Colony Neural Network

In this paper, a prediction model of the mechanical properties of composite materials has been proposed based on the ant colony neural network. The mechanical properties of the materials are the common problems that the various materials must be involved in the practical applications. The testing of the mechanical properties of the composite materials is of great significance to the development and the progress of the theory and the practice of composite materials. The ant colony algorithm takes advantage of the optimization mechanisms of ant colony, which has a strong ability to find the global optimal solution. The candidate group mechanism is added in the ant colony algorithm and the weights of the artificial neural network are trained through using the improved ant colony algorithm. This model has a strong adaptive ability and can be used in the prediction of the mechanical properties of composite materials. Then, the efficiency of the testing of mechanical properties can be improved.

Electromagnetic Nondestructive Testing in Cracked Defects of Oil-Gas Casing Based on Ant Colony Neural Network

In this paper, a new method on quantitative analysis of magnetic flux leakage signal by ant colony neural network is proposed. Firstly, the parameters of the magnetic flux leakage signal which can reflect the various characteristics of cracked defects are determined by finite element method (FEM) simulation. Secondly, based on the study of the ant colony algorithm, the neural network model is established for the magnetic flux leakage signals processing. Finally, in the simulated working environment, the performance of the neural network is tested with the different signal features as input. The experimental results proved the feasibility of the ant colony neural network, verified the increases of the convergence rate and the accuracy of the neural network, and improved the efficiency as well as the quality of the quantitative analysis for the magnetic flux leakage signals.

Ant Colony Algorithm and Application in Inspection of Concrete Structure Defects

BP neural network detecting concrete defect, convergence is slower and accuracy is not high. In order to overcome the defect of BP algorithm, using a combination of Ant Colony optimization algorithm and BP neural network method, a mathematical model of Ant Colony neural network was established, enables Ant Colony neural network training, and verify the validity of the method. And concluded: using ant Colony neural network identification of concrete defects, the identification of the location more effective than on size.

Coal Mine Gas Prediction Model Based on Ant Colony Neural Network

In order to improve the prediction accuracy and prediction speed of coal mine gas emission, ant colony algorithm combining with neural network is used for prediction models design. Choose an important factor influencing gas emission, establish of its neural network prediction model. Select the network mean square error as the objective function, through the ant colony algorithm iteration achieve optimal BP network weights, and use the optimized BP network for gas emission prediction. Simulation results show that the method has high fitting prediction accuracy.