scholarly journals Intelligent Prediction Model: Optimized Neural Network for Lean Manufacturing Technology

2021 ◽  
Author(s):  
Jobin M V ◽  
Keyword(s):  
Neural Network ◽  
Organizational Performance ◽  
Organizational Support ◽  
Lean Manufacturing ◽  
Particle Swarm ◽  
Lead Times ◽  
Optimal Weights ◽  
Proposed Model ◽  
Management Commitment ◽  
Initial Process

Lean manufacturing (LM) is a method, which focuses on reducing wastes and increasing the productivity within manufacturing firms. Several analyses were performed on LM technology depending on minimal lead times, enhanced quality and reduced operating costs. However, limitation exists in understanding its role to develop managing commitment, worker involvement and in turn its organizational performance. This paper intends to propose a new Neural Network (NN) based intelligent prediction framework. The initial process is manual labeling or response validation, which is carried out by utilizing the responses attained for each questions under each factors including lean awareness, employee involvement, management commitment, lean technology, Organizational Performance (OP) and Organizational Support (OS). Subsequently, NN is exploited for prediction process, where the features (received responses) are given as input and the labeling values attained are set as target. Further, in order to improve the prediction performance, the NN training is performed by a new Hybrid Particle Swarm and Pigeon Optimization (HPS-PO) algorithm via tuning the optimal weights. In fact, the proposed algorithm is the combination of Particle Swarm Optimization (PSO) and Pigeon Optimization Algorithm (POA), respectively. Finally, the performance of the proposed model is examined over conventional methods in terms of prediction analysis and error analysis.

Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate

2022 ◽  
pp. 1301-1312
Author(s):  
M. Outanoute ◽  
A. Lachhab ◽  
A. Selmani ◽  
H. Oubehar ◽  
A. Snoussi ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Relative Humidity ◽  
Back Propagation ◽  
Particle Swarm ◽  
Simulated Data ◽  
Swarm Optimization ◽  
Generalization Ability ◽  
Optimal Weights ◽  
Hidden Layer

In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately.

scholarly journals Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5609 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Deep Learning Neural Network

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

scholarly journals Butterfly-PSO Based Energy Efficient Data Analysis of Wireless IoT Devices

2019 ◽  
Vol 8 (3) ◽  
pp. 5779-5784
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Data Analysis ◽  
Model Comparison ◽  
Cluster Head ◽  
Back Propagation ◽  
Particle Swarm ◽  
Cluster Center ◽  
Swarm Optimization ◽  
Proposed Model

Paper collecting data from various sources for research observation, security, etc. are depend on IOT networks. As IOT device are remotely which transform information from nearby area and lifespan of this network rely on energy uses for communication. So this paper proposed a neural network and genetic algorithm combination for increasing the life span of the network. Error Back Propagation neural network was trained to identify best set of nodes for the cluster center selection. This machine learning based data selection increase the cluster selection accuracy of the BFPSO (Butterfly Particle Swarm Optimization). As combination get reduce by neural network data analysis so less number of population need to be developed for BFPSO algorithm which ultimately increase the accuracy of device selection. Various set of region size and number of nodes were developed to evaluate proposed model. Comparison of proposed model NN-BFPSO-CHS (Neural Network Butterfly Particle Swarm Optimization based Cluster Head Selection) was done with previous existing methods on different evaluation parameters and it was obtained that proposed model has improved all set of parameters

scholarly journals A Tourist Flow Prediction Model for Scenic Areas Based on Particle Swarm Optimization of Neural Network

2020 ◽  
Vol 34 (4) ◽  
pp. 395-402
Author(s):  
Nan Chen ◽  
Yi Liang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Short Term Memory ◽  
Service Industry ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Flow Prediction ◽  
Proposed Model ◽  
Tourist Flow

In recent years, China has been expanding domestic demand and promoting the service industry. This is a mixed blessing for the further development of tourism. To make accurate prediction of tourist flow, this paper proposes a tourist flow prediction model for scenic areas based on the particle swarm optimization (PSO) of neural network (NN). Firstly, a system of influencing factors was constructed for the tourist flow in scenic areas, and the factors with low relevance were eliminated through grey correlation analysis (GCA). Next, the long short-term memory (LSTM) NN was optimized with adaptive PSO, and used to establish the tourist flow prediction model for scenic areas. After that, the workflow of the proposed model was introduced in details. Experimental results show that the proposed model can effectively predict the tourist flow in scenic areas, and provide a desirable prediction tool for other fields.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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