scholarly journals Low-Density Polyethylene (LDPE) Food Packaging Defect Classification using Local Binary Pattern (LBP)

2021 ◽  
Vol 2129 (1) ◽  
pp. 012052
Author(s):  
N Sabri ◽  
H N Abdull Hamed ◽  
M A Isa ◽  
N S Ghazali ◽  
Z Ibrahim
Keyword(s):  
Image Classification ◽  
Food Packaging ◽  
Confusion Matrix ◽  
Primary Phase ◽  
Small Sample ◽  
Human Vision ◽  
Classification Model ◽  
Low Density Polyethylene ◽  
Support Vector ◽  
Low Density

Abstract The motivation of this research is to automate the current food packaging inspection process by implementing the non-destructive approach. The current practices require human intervention where human vision tends to overlook the faulty on the package resulting in accuracy dilemma. Human also may be exhausted due to repeated activities. This paper provides the primary phase for effective automation of image classification solution implemented using Weka software. An evaluation of the performance of the Support Vector Machine (SVM), K-nearest Neighbour (KNN) and Random Forest (RF) classification models for Low-Density Polyethylene (LDPE) food packaging defect image classification using a small sample of dataset and Linear Binary Pattern (LBP) as feature extraction algorithm is investigated. Four criteria have been used to evaluate the performance of each classification model which is accuracy, sensitivity, specificity and precision obtained from the confusion matrix table. The results indicate that SVM performs better than RF and KNN with 95% accuracy, 95% sensitivity, 72% specificity and 95% precision in classifying LDPE food packaging defect images.

scholarly journals Low-Density Polyethylene Films Carrying ferula asafoetida Extract for Active Food Packaging: Thermal, Mechanical, Optical, Barrier, and Antifungal Properties

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Razieh Niazmand ◽  
Bibi Marzieh Razavizadeh ◽  
Farzaneh Sabbagh
Keyword(s):  
Polymer Matrix ◽  
Food Packaging ◽  
Barrier Properties ◽  
Low Density Polyethylene ◽  
Low Density ◽  
Optical Barrier ◽  
Leaf Extracts ◽  
Polyethylene Films ◽  
Active Food Packaging ◽  
Ferula Asafoetida

The physical, thermal, mechanical, optical, microstructural, and barrier properties of low-density polyethylene films (LDPE) containing ferula asafoetida leaf and gum extracts were investigated. Results showed a reduction in elasticity and tensile strength with increasing extract concentration in the polymer matrix. The melting temperature and enthalpy increased with increasing concentration of extracts. The films containing extracts had lower L∗ and a∗ and higher b∗ indices. The films containing leaf extract had more barrier potential to UV than the gum extracts. The oxygen permeability in films containing 5% of leaf and gum extracts increased by 2.3 and 2.1 times, respectively. The morphology of the active films was similar to bubble swollen islands, which was more pronounced at higher concentrations of gum and leaf extracts. FTIR results confirmed some chemical interactions of ferula extracts with the polymer matrix. At the end of day 14th, the growth rate of Aspergillus niger and Saccharomyces cerevisea in the presence of the PE-Gum-5 reduced more than PE-Leaf-5 (3.7 and 2.4 logarithmic cycles, respectively) compared to the first day. Our findings showed that active LDPE films have desire thermo-mechanical and barrier properties for food packaging.

scholarly journals Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1994
Author(s):  
Qian Ma ◽  
Wenting Han ◽  
Shenjin Huang ◽  
Shide Dong ◽  
Guang Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Confusion Matrix ◽  
Object Oriented ◽  
Low Complexity ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Multispectral Images ◽  
High Complexity ◽  
Structure Complexity

This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.

scholarly journals Polyethylene Migration from Food Packaging on Cheese Detected by Raman and Infrared (ATR/FT-IR) Spectroscopy

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3872
Author(s):  
Klytaimnistra Katsara ◽  
George Kenanakis ◽  
Zacharias Viskadourakis ◽  
Vassilis M. Papadakis
Keyword(s):  
Vibrational Spectroscopy ◽  
Food Packaging ◽  
Time Frame ◽  
Low Density Polyethylene ◽  
Low Density ◽  
Ft Ir ◽  
Comparative Results ◽  
Short Time ◽  
And Storage ◽  
Ft Ir Spectroscopy

For multiple years, food packaging migration has been a major concern in food and health sciences. Plastics, such as polyethylene, are continuously utilized in food packaging for preservation and easy handling purposes during transportation and storage. In this work, three types of cheese, Edam, Kefalotyri and Parmesan, of different hardness were studied under two complementary vibrational spectroscopy methods, ATR-FTIR and Raman spectroscopy, to determine the migration of low-density polyethylene from plastic packaging to the surface of cheese samples. The experimental duration of this study was set to 28 days due to the degradation time of the selected cheese samples, which is clearly visible after 1 month in refrigerated conditions at 4 °C. Raman and ATR-FTIR measurements were performed at a 4–3–4–3 day pattern to obtain comparative results. Initially, consistency/repeatability measurement tests were performed on Day0 for each sample of all cheese specimens to understand if there is any overlap between the characteristic Raman and ATR-FTIR peaks of the cheese with the ones from the low-density polyethylene package. We provide evidence that on Day14, peaks of low-density polyethylene appeared due to polymeric migration in all three cheese types we tested. In all cheese samples, microbial outgrowth started to develop after Day21, as observed visually and under the bright-field microscope, causing peak reverse. Food packaging migration was validated using two different approaches of vibrational spectroscopy (Raman and FT-IR), revealing that cheese needs to be consumed within a short time frame in refrigerated conditions at 4 °C.

scholarly journals Landslide Mapping Using Multiscale LiDAR Digital Elevation Models

2020 ◽  
Vol 26 (4) ◽  
pp. 405-425
Author(s):  
Javed Miandad ◽  
Margaret M. Darrow ◽  
Michael D. Hendricks ◽  
Ronald P. Daanen
Keyword(s):  
Image Classification ◽  
Vegetation Index ◽  
Confusion Matrix ◽  
Object Oriented ◽  
Support Vector ◽  
Classification Approach ◽  
Resolution Image ◽  
Kappa Value ◽  
The Individual ◽  
Segmentation Image

ABSTRACT This study presents a new methodology to identify landslide and landslide-susceptible locations in Interior Alaska using only geomorphic properties from light detection and ranging (LiDAR) derivatives (i.e., slope, profile curvature, and roughness) and the normalized difference vegetation index (NDVI), focusing on the effect of different resolutions of LiDAR images. We developed a semi-automated object-oriented image classification approach in ArcGIS 10.5 and prepared a landslide inventory from visual observation of hillshade images. The multistage work flow included combining derivatives from 1-, 2.5-, and 5-m-resolution LiDAR, image segmentation, image classification using a support vector machine classifier, and image generalization to clean false positives. We assessed classification accuracy by generating confusion matrix tables. Analysis of the results indicated that LiDAR image scale played an important role in the classification, and the use of NDVI generated better results. Overall, the LiDAR 5-m-resolution image with NDVI generated the best results with a kappa value of 0.55 and an overall accuracy of 83 percent. The LiDAR 1-m-resolution image with NDVI generated the highest producer accuracy of 73 percent in identifying landslide locations. We produced a combined overlay map by summing the individual classified maps that was able to delineate landslide objects better than the individual maps. The combined classified map from 1-, 2.5-, and 5-m-resolution LiDAR with NDVI generated producer accuracies of 60, 80, and 86 percent and user accuracies of 39, 51, and 98 percent for landslide, landslide-susceptible, and stable locations, respectively, with an overall accuracy of 84 percent and a kappa value of 0.58. This semi-automated object-oriented image classification approach demonstrated potential as a viable tool with further refinement and/or in combination with additional data sources.

scholarly journals KLASIFIKASI TEKS SOSIAL MEDIA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE (Studi Kasus Penusukan Wiranto)

2019 ◽  
Vol 2 (2) ◽  
pp. 43
Author(s):  
Lalu Mutawalli ◽  
Mohammad Taufan Asri Zaen ◽  
Wire Bagye
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Big Data ◽  
Mass Communication ◽  
Confusion Matrix ◽  
Classification Model ◽  
Support Vector ◽  
Large Set ◽  
Appearance Time ◽  
Data Production

In the era of technological disruption of mass communication, social media became a reference in absorbing public opinion. The digitalization of data is very rapidly produced by social media users because it is an attempt to represent the feelings of the audience. Data production in question is the user posts the status and comments on social media. Data production by the public in social media raises a very large set of data or can be referred to as big data. Big data is a collection of data sets in very large numbers, complex, has a relatively fast appearance time, so that makes it difficult to handle. Analysis of big data with data mining methods to get knowledge patterns in it. This study analyzes the sentiments of netizens on Twitter social media on Mr. Wiranto stabbing case. The results of the sentiment analysis showed 41% gave positive comments, 29% commented neutrally, and 29% commented negatively on events. Besides, modeling of the data is carried out using a support vector machine algorithm to create a system capable of classifying positive, neutral, and negative connotations. The classification model that has been made is then tested using the confusion matrix technique with each result is a precision value of 83%, a recall value of 80%, and finally, as much as 80% obtained in testing the accuracy.

Multi-Class Taxonomy of Well Integrity Anomalies Applying Inductive Learning Algorithms: Analytical Approach for Artificial-Lift Wells

2021 ◽  
Author(s):  
Mostafa Sa'eed Yakoot ◽  
Adel Mohamed Salem Ragab ◽  
Omar Mahmoud
Keyword(s):  
Decision Tree ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Oil And Gas Industry ◽  
Classification Model ◽  
Gradient Boosting ◽  
Support Vector ◽  
Risk Category ◽  
Well Integrity ◽  
Extreme Gradient Boosting

Abstract Well integrity has become a crucial field with increased focus and being published intensively in industry researches. It is important to maintain the integrity of the individual well to ensure that wells operate as expected for their designated life (or higher) with all risks kept as low as reasonably practicable, or as specified. Machine learning (ML) and artificial intelligence (AI) models are used intensively in oil and gas industry nowadays. ML concept is based on powerful algorithms and robust database. Developing an efficient classification model for well integrity (WI) anomalies is now feasible because of having enormous number of well failures and well barrier integrity tests, and analyses in the database. Circa 9000 dataset points were collected from WI tests performed for 800 wells in Gulf of Suez, Egypt for almost 10 years. Moreover, those data have been quality-controlled and quality-assured by experienced engineers. The data contain different forms of WI failures. The contributing parameter set includes a total of 23 barrier elements. Data were structured and fed into 11 different ML algorithms to build an automated systematic tool for calculating imposed risk category of any well. Comparison analysis for the deployed models was performed to infer the best predictive model that can be relied on. 11 models include both supervised and ensemble learning algorithms such as random forest, support vector machine (SVM), decision tree and scalable boosting techniques. Out of 11 models, the results showed that extreme gradient boosting (XGB), categorical boosting (CatBoost), and decision tree are the most reliable algorithms. Moreover, novel evaluation metrics for confusion matrix of each model have been introduced to overcome the problem of existing metrics which don't consider domain knowledge during model evaluation. The innovated model will help to utilize company resources efficiently and dedicate personnel efforts to wells with the high-risk. As a result, progressive improvements on business, safety, environment, and performance of the business. This paper would be a milestone in the design and creation of the Well Integrity Database Management Program through the combination of integrity and ML.

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