scholarly journals Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine

2020 ◽  
Vol 12 (18) ◽  
pp. 7569
Author(s):  
Guoquan Zhang ◽  
Guohao Li ◽  
Jing Peng
Keyword(s):  
Risk Assessment ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Fresh Produce ◽  
Assessment Model ◽  
Cold Chain ◽  
Risk Indicators ◽  
Support Vector ◽  
Monitoring Model ◽  
Assessment And Monitoring

The sustainability and profitability of fresh produce supply chains are contingent upon several risk factors. This work, therefore, examines several risk indicators that affect the quality and safety of fresh produce in transit, including technological, biological, sustainability, environmental, and emergency risks. Then, we developed a risk assessment and monitoring model that employs a machine learning algorithm, a support vector machine, based on historical monitoring data. The proposed methodology was then applied to simulation and numerical analysis to assess the risks incurred in the strawberry cold chain. After training, the algorithm predicted the risks incurred during transportation with an average accuracy of 90.4%. Therefore, the developed methodology can effectively and accurately perform a risk assessment. Furthermore, the risk assessment model can be applied to other fresh produce due to comprehensive risk indicators. Decision-makers in fresh produce logistics companies can use the developed methodology to identify and mitigate risks incurred, thus improving food safety, reducing product loss, maximizing profits, and realizing sustainable development.

scholarly journals Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination

2020 ◽  
Vol 16 (1) ◽  
pp. 155014772090363 ◽  
Author(s):  
Ying Liu ◽  
Lihua Huang
Keyword(s):  
Risk Assessment ◽  
Support Vector Machine ◽  
Supply Chain ◽  
Credit Risk ◽  
Financial Analysis ◽  
Learning Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Machine Model ◽  
Supply Chain Finance

Recently, support vector machines, a supervised learning algorithm, have been widely used in the scope of credit risk management. However, noise may increase the complexity of the algorithm building and destroy the performance of classifier. In our work, we propose an ensemble support vector machine model to solve the risk assessment of supply chain finance, combined with reducing noises method. The main characteristics of this approach include that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noise and class noise to achieve an optimal clean set, and (2) support vector machine classifiers, based on the improved particle swarm optimization algorithm, are seen as component classifiers. Then, we obtained the final classification results by combining finally individual prediction through AdaBoosting algorithm on the new sample set. Some experiments are applied on supply chain financial analysis of China’s listed companies. Results indicate that the credit assessment accuracy can be increased by applying this approach.

A paratactic subjective-objective weighting methods and SVM risk assessment model applied in textile and apparel safety

2015 ◽  
Vol 32 (5) ◽  
pp. 472-485 ◽  
Author(s):  
Xiongying Wu ◽  
Lihong Chen ◽  
Shuhui Pang ◽  
Xuemei Ding
Keyword(s):  
Risk Assessment ◽  
Empirical Analysis ◽  
Safety Evaluation ◽  
Assessment Model ◽  
Risk Indicators ◽  
Support Vector ◽  
Potential Hazard ◽  
Risk Assessment Model ◽  
Content Type ◽  
Textile And Apparel

Purpose – The purpose of this paper is to explore a descriptive framework for a more structured and objective evaluation of the risk situation of textile and apparel, also to find the best set of methods or optimal scientific grounds for the safety evaluation of textile and apparel. Design/methodology/approach – Risk analysis theory is used to analyze potential hazard of textile and apparel, weight is given to risk indicators using subjective and objective weighting method, respectively, grading standards of safe risk of textile and apparel is made. Finally a safety risk assessment model of textile and apparel based on support vector machine (SVM) is built, and empirical analysis is also made. Findings – Quantitative and highly reliable evaluation of textile and apparel risks, relatively easy grading classification and simplicity in operating the evaluation process are the advantages that promote the application of risk assessment model based on SVM for textile and apparel, and empirical analysis showed considerably good applicability. Practical implications – The research is useful to ensure safety textile and apparel in market, also contributing to the sustainable development of textile industries in future. Originality/value – SVM as a risk assessment method provided safety evaluation to toxic and harmful substance and small parts in textile and apparel, which can be an effective tool to monitor textile and apparel safety.

scholarly journals Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Akash Saxena ◽  
Shalini Shekhawat
Keyword(s):  
Support Vector Machine ◽  
Air Quality ◽  
Industrial Development ◽  
Learning Algorithm ◽  
Ambient Air ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Mathematical Framework ◽  
Primary Concern

With the development of society along with an escalating population, the concerns regarding public health have cropped up. The quality of air becomes primary concern regarding constant increase in the number of vehicles and industrial development. With this concern, several indices have been proposed to indicate the pollutant concentrations. In this paper, we present a mathematical framework to formulate a Cumulative Index (CI) on the basis of an individual concentration of four major pollutants (SO2, NO2, PM2.5, and PM10). Further, a supervised learning algorithm based classifier is proposed. This classifier employs support vector machine (SVM) to classify air quality into two types, that is, good or harmful. The potential inputs for this classifier are the calculated values of CIs. The efficacy of the classifier is tested on the real data of three locations: Kolkata, Delhi, and Bhopal. It is observed that the classifier performs well to classify the quality of air.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

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