scholarly journals Predicting sensory evaluation of spinach freshness using machine learning model and digital images

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248769
Author(s):  
Kento Koyama ◽  
Marin Tanaka ◽  
Byeong-Hyo Cho ◽  
Yusaku Yoshikawa ◽  
Shige Koseki
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sensory Evaluation ◽  
Color Space ◽  
Local Features ◽  
Support Vector ◽  
Nondestructive Technique ◽  
Color Features ◽  
Artificial Neural ◽  
Four Levels

The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spinach freshness was evaluated. Images of spinach leaves were taken using a smartphone camera after different storage periods. Twelve sensory panels ranked spinach freshness into one of four levels using these images. The rounded value of the average from all twelve panel evaluations was set as the true label. The spinach image was removed from the background, and then converted into a gray scale and CIE-Lab color space (L*a*b*) and Hue, Saturation and Value (HSV). The mean value, minimum value, and standard deviation of each component of color in spinach leaf were extracted as color features. Local features were extracted using the bag-of-words of key points from Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels.

scholarly journals Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Tuan Vu Dinh ◽  
Hieu Nguyen ◽  
Xuan-Linh Tran ◽  
Nhat-Duc Hoang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Soil Erosion ◽  
Differential Evolution ◽  
Influencing Factors ◽  
Support Vector ◽  
Adaptive Differential Evolution ◽  
Artificial Neural

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

scholarly journals Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

2019 ◽  
Vol 67 (6) ◽  
pp. 1991-2003 ◽  
Author(s):  
Edyta Puskarczyk
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
P Wave ◽  
Well Logs ◽  
Support Vector ◽  
Western Slope ◽  
Data Set ◽  
Artificial Neural ◽  
Gas Bearing

Abstract Unconventional oil and gas reservoirs from the lower Palaeozoic basin at the western slope of the East European Craton were taken into account in this study. The aim was to supply and improve standard well logs interpretation based on machine learning methods, especially ANNs. ANNs were used on standard well logging data, e.g. P-wave velocity, density, resistivity, neutron porosity, radioactivity and photoelectric factor. During the calculations, information about lithology or stratigraphy was not taken into account. We apply different methods of classification: cluster analysis, support vector machine and artificial neural network—Kohonen algorithm. We compare the results and analyse obtained electrofacies. Machine learning method–support vector machine SVM was used for classification. For the same data set, SVM algorithm application results were compared to the results of the Kohonen algorithm. The results were very similar. We obtained very good agreement of results. Kohonen algorithm (ANN) was used for pattern recognition and identification of electrofacies. Kohonen algorithm was also used for geological interpretation of well logs data. As a result of Kohonen algorithm application, groups corresponding to the gas-bearing intervals were found. Analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in gas-saturated beds is present. It is concluded that ANN appeared to be a useful and quick tool for preliminary classification of members and gas-saturated identification.

scholarly journals Fault Diagnosis in Centrifugal Pump using Support Vector Machine and Artificial Neural Network

2021 ◽  
Author(s):  
Nagendra Singh Ranawat ◽  
◽  
Pavan Kumar Kankar ◽  
Ankur Miglani ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Centrifugal Pump ◽  
Support Vector ◽  
Feature Ranking ◽  
Chi Square ◽  
Artificial Neural

Centrifugal pumps are commonly utilized in thermo-fluidic systems in the industry. Being a rotating machinery, they are prone to vibrations and their premature failure may affect the system predictability and reliability. To avoid their premature breakdown during operation, it is necessary to diagnose the faults in a pump at their initial stage. This study presents the methodology to diagnose fault of a cent rifugal pump using two distinct machine learning techniques, namely, Support vector machine (SVM) and Artificial neural network (ANN). Different statistical features are extracted in the time and the frequency domain of the vibration signal for different working conditions of the pump. Furthermore, to decrease the dimensionality of the obtained features different feature ranking (FR) methods, namely, Chi-square, ReliefF and XGBoost are employed. ANN technique is found to be more efficient in classifying faults in a centrifugal pump as compared to the SVM, and Chi-square and XGBoost ranking techniques are better than ReliefF at sorting more relevant features. The results presented in thus study demonstrate that an ANN based machine learning approach with Chi-square and XGBoost feature ranking techniques can be used effectively for the fault diagnosis of a centrifugal pump.

scholarly journals Classification of color features in butterflies using the Support Vector Machine (SVM)

2021 ◽  
Vol 2 (02) ◽  
pp. 83-87
Author(s):  
Dhian Satria Yudha Kartika ◽  
Hendra Maulana
Keyword(s):  
Support Vector Machine ◽  
Pieris Rapae ◽  
Color Space ◽  
Danaus Plexippus ◽  
Research Process ◽  
Extraction Process ◽  
Junonia Coenia ◽  
Support Vector ◽  
Color Quantization ◽  
Color Features

Research in digital images is expanding widely and includes several sectors. One sector currently being carried out research is in insects; specifically, butterflies are used as a dataset. A total of 890 types of butterflies divided into ten classes were used as a dataset and classified based on color. Ten types of butterflies include Danaus plexippus, Heliconius charitonius, Heliconius erato, Junonia coenia, Lycaena phlaeas, Nymphalis antiopa, Papilio cresphontes, Pieris rapae, Vanessa atalanta, Vanessa cardui. The process of extracting color features on butterfly wings uses the RGB method to become HSV color space with color quantization (CQ). The purpose of adding CQ is that the computation process is carried out faster without reducing the image's information. In the color feature extraction process, the image is converted into 3-pixel sizes and normalized. The process of normalizing the dataset has the aim that the value ranges in the dataset have the same value. The 890 butterfly dataset was classified using the Support Vector Machine (SVM) method. Based on this research process, the accuracy of the 256x160 pixel size is 72%, the 420x315 pixel is 75%, and the 768x576 pixel is 75%. The test results on a system with a 768x576 pixel get the highest results with a precision value of 74.6%, a recall of 72%, and an f-measure of 73.2% Keywords—image processing; classification; butterflies; color features; features extraction

scholarly journals Komparasi Support Vector Machine, Logistic Regression Dan Artificial Neural Network Dalam Prediksi Penyakit Jantung

2021 ◽  
Vol 7 (3) ◽  
pp. 329
Author(s):  
Fitri Handayani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Support Vector ◽  
Artificial Neural

Penyakit jantung adalah salah satu penyakit yang menyebabkan resiko kematian cukup tinggi di dunia. Kolesterol, diabetes, tekanan darah tinggi merupakan faktor-faktor pemicu terjadinya penyakit jantung. Perlu deteksi sejak ini mengenai prediksi penyakit jantung pada setiap individu agar pencegahan dan pengobatan dapat segera dilakukan demi tingkat Kesehatan yang lebih baik. Berbagai metode dapat dilakukan untuk melakukan deteksi penyakit jantung, baik dengan metode tradisional dan metode yang memanfaatkan teknologi. Saat ini mulai banyak bermunculan system pendeteksi penyakit jantung dengan memanfaatkan algoritma machine learning. Algoritma machine learning dianggap mudah untuk diaplikasikan untuk mengklasifikasikan apakah seseorang terkena penyakit jantung. Penelitian ini mencoba melakukan klasifikasi penyakit jantung menggunakan dataset public dari UCI menggunakan tiga algorima machine learning, yaitu Support Vector Machine (SVM), Logistic Regression (LR) dan Artifiacial Neural Network (ANN). Ketiga algorima tersebut diuji menggunakan empat skenario pembagian data training dan testing yang berbeda, yaitu 90:10, 80:20, 70:40 dan 60:40. Dari hasil eksperimen didapatkan hasil akurasi tertinggi pada metode Logistic Regression sebesar 86% menggunakan skenario pembagian data 80:20.

scholarly journals A Machine Learning-based Damage Prediction Techniques for Structural Health Monitoring

2021 ◽  
Vol 12 (2) ◽  
pp. 3392-3405
Author(s):  
M Vishnu Vardhana Rao, Et. al.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Rough Set ◽  
Feature Reduction ◽  
Support Vector ◽  
Damage Prediction ◽  
Vibration Data ◽  
Artificial Neural

Nowadays, the Structural Building Health Damage Monitoring System (SBHDMS) is a crucial technology for predicting the civil building structures' health. SBHDMS contains abnormal changes in the buildings in terms of damage levels. Natural Disasters like Earthquakes, Floods, and cyclones affect the unusual changes in the buildings. If the building undergoes any natural disaster, the sensors capture the vibration data or change the buildings' structure. Due to the vibration data, these unusual changes can be analyzed. Here sensors or Machine Learning based Building Damage Prediction (MLBDP) are used for capturing and collecting the vibration data. This paper proposes a Novel Rough Set based Artificial Neural Network with Support Vector Machine (RAS) metaheuristic method. RAS method is used to predict the damaged building's vibration data levels captured by the sensors. For the feature reduction subset, we use one of the essential pre-processing method called the Rough set theory (RST) strategy. RAS has two contributions. The first one is the Support Vector Machine (SVM) classification method used for identifying the structures of the buildings. The artificial Neural Network (ANN) method used to predict the buildings' damage levels is the second contribution. The proposed method (RAS) is accurately predicting the conditions of the construction building structure and predicting the damage levels, without human intervention. Comparing the results states that the proposed method accuracy is better than SVM's classification methods, ANN. The prediction analysis depicts that the RAS method can effectively detect the damage levels.

scholarly journals Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1020 ◽  
Author(s):  
Yong Kown ◽  
Seung Baek ◽  
Young Lim ◽  
JongCheol Pyo ◽  
Mayzonee Ligaray ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Chlorophyll A ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Support Vector ◽  
Landsat 8 ◽  
Artificial Neural

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms.

scholarly journals Exploring Statistical Parameters of Machine Learning Techniques for Detection and Classification of Brain Tumor

2019 ◽  
Vol 8 (10) ◽  
pp. 4118-4124
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Random Forest ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Computerized System ◽  
The Brain

A computerized system can improve the disease identifying abilities of doctor and also reduce the time needed for the identification and decision-making in healthcare. Gliomas are the brain tumors that can be labeled as Benign (non- cancerous) or Malignant (cancerous) tumor. Hence, the different stages of the tumor are extremely important for identification of appropriate medication. In this paper, a system has been proposed to detect brain tumor of different stages by MR images. The proposed system uses Fuzzy C-Mean (FCM) as a clustering technique for better outcome. The main focus in this paper is to refine the required features in two steps with the help of Discrete Wavelet Transform (DWT) and Independent Component Analysis (ICA) using three machine learning techniques i.e. Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The final outcome of our experiment indicated that the proposed computerized system identifies the brain tumor using RF, ANN and SVM with 100%, 91.6% and 95.8%, accuracy respectively. We have also calculated Sensitivity, Specificity, Matthews’s Correlation Coefficient and AUC-ROC curve. Random forest shows the highest accuracy as compared to Support Vector Machine and Artificial Neural Networks.

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

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