scholarly journals Computational learning of features for automated colonic polyp classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kangkana Bora ◽  
M. K. Bhuyan ◽  
Kunio Kasugai ◽  
Saurav Mallik ◽  
Zhongming Zhao
Keyword(s):  
Cross Validation ◽  
Statistical Significance ◽  
Colonic Polyps ◽  
Colonic Polyp ◽  
Least Square ◽  
Support Vector ◽  
Feature Descriptor ◽  
Ranking Algorithm ◽  
Feature Based ◽  
Fold Cross Validation

AbstractShape, texture, and color are critical features for assessing the degree of dysplasia in colonic polyps. A comprehensive analysis of these features is presented in this paper. Shape features are extracted using generic Fourier descriptor. The nonsubsampled contourlet transform is used as texture and color feature descriptor, with different combinations of filters. Analysis of variance (ANOVA) is applied to measure statistical significance of the contribution of different descriptors between two colonic polyps: non-neoplastic and neoplastic. Final descriptors selected after ANOVA are optimized using the fuzzy entropy-based feature ranking algorithm. Finally, classification is performed using Least Square Support Vector Machine and Multi-layer Perceptron with five-fold cross-validation to avoid overfitting. Evaluation of our analytical approach using two datasets suggested that the feature descriptors could efficiently designate a colonic polyp, which subsequently can help the early detection of colorectal carcinoma. Based on the comparison with four deep learning models, we demonstrate that the proposed approach out-performs the existing feature-based methods of colonic polyp identification.

scholarly journals Determination of Total Flavonoid Content in Medicinal Plant Leaves Powder Using Infrared Spectroscopy and Chemometrics

2020 ◽  
Vol 20 (5) ◽  
pp. 1044
Author(s):  
Lestyo Wulandari ◽  
Bayu Dwi Permana ◽  
Nia Kristiningrum
Keyword(s):  
Medicinal Plant ◽  
Cross Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Calibration Model ◽  
Plant Leaves ◽  
Flavonoid Content ◽  
Significant Difference ◽  
Fold Cross Validation

Flavonoid is phenolic compounds consisting of fifteen carbon atoms and is commonly found in plants. Infrared (IR) spectroscopy combined with chemometrics, has been developed for a simple analysis of flavonoid in the medicinal plant leaves powder. IR spectra of selected medicinal plant powder were correlated with flavonoid content using chemometrics. The chemometric methods used for calibration analysis were Partial Least Square (PLS), Principal Component Regression (PCR), and Support Vector Regression (SVR). After the calibration model was formed, it was then validated using Leave-One-Out-Cross-Validation (LOOCV) and 2-fold cross-validation. In this study, the PLS of the Near-infrared (NIR) calibration model showed the best calibration with R-Square and RMSEC values of 0.9676524 and 0.0978202, respectively. The LOOCV of PLS of the NIR calibration model has the R-square and RMSE values of 0.9850164 and 0.067663, respectively. The 2-fold cross-validation gave the R-square and RMSE values of 0.9857071 and 0.2104665, respectively. PLS of the NIR calibration model was further used to predict unknown flavonoid content in commercial samples. The significance of flavonoid content that has been measured by NIR and UV-Vis spectrophotometry was evaluated with paired samples T-test. The flavonoid content that has been measured with both methods gave no significant difference.

scholarly journals Diagnosis of Elevator Faults with LS-SVM Based on Optimization by K-CV

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Zhou Wan ◽  
Shilin Yi ◽  
Kun Li ◽  
Ran Tao ◽  
Min Gou ◽  
...  
Keyword(s):  
Cross Validation ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Wire Rope ◽  
Least Square ◽  
Support Vector ◽  
Guide Rail ◽  
Vibration Signals ◽  
Energy Characteristics ◽  
Fold Cross Validation

Several common elevator malfunctions were diagnosed with a least square support vector machine (LS-SVM). After acquiring vibration signals of various elevator functions, their energy characteristics and time domain indicators were extracted by theoretically analyzing the optimal wavelet packet, in order to construct a feature vector of malfunctions for identifying causes of the malfunctions as input of LS-SVM. Meanwhile, parameters about LS-SVM were optimized by K-fold cross validation (K-CV). After diagnosing deviated elevator guide rail, deviated shape of guide shoe, abnormal running of tractor, erroneous rope groove of traction sheave, deviated guide wheel, and tension of wire rope, the results suggested that the LS-SVM based on K-CV optimization was one of effective methods for diagnosing elevator malfunctions.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

scholarly journals Intelligent Prediction of Sieving Efficiency in Vibrating Screens

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Bin Zhang ◽  
Jinke Gong ◽  
Wenhua Yuan ◽  
Jun Fu ◽  
Yi Huang
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Prediction Performance ◽  
Least Square ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Screen Length ◽  
Vibrating Screen ◽  
Working Parameters ◽  
Better Than

In order to effectively predict the sieving efficiency of a vibrating screen, experiments to investigate the sieving efficiency were carried out. Relation between sieving efficiency and other working parameters in a vibrating screen such as mesh aperture size, screen length, inclination angle, vibration amplitude, and vibration frequency was analyzed. Based on the experiments, least square support vector machine (LS-SVM) was established to predict the sieving efficiency, and adaptive genetic algorithm and cross-validation algorithm were used to optimize the parameters in LS-SVM. By the examination of testing points, the prediction performance of least square support vector machine is better than that of the existing formula and neural network, and its average relative error is only 4.2%.

scholarly journals IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Cheng Yan ◽  
Guihua Duan ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
Infectious Diseases ◽  
Cross Validation ◽  
Sequence Similarity ◽  
Least Square ◽  
Computational Method ◽  
Receptor Interaction ◽  
Virus Receptor ◽  
Receptor Interactions ◽  
Leave One Out ◽  
Fold Cross Validation

Abstract Background Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited. Result In this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF). Conlusion The experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.

scholarly journals Pengenalan Wajah Manusia berbasis Algoritma Local Binary Pattern

2017 ◽  
Vol 17 (2) ◽  
pp. 29-38
Author(s):  
Ratih Purwati ◽  
Gunawan Ariyanto
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Cross Validation ◽  
Support Vector ◽  
Fold Cross Validation

Face Recognition merupakan teknologi komputer untuk mengidentifikasi wajah manusia melalui gambar digital yang tersimpan di database. Wajah manusia dapat berubah bentuk sesuai dengan ekspresi yang dimilikinya. Wajah manusia dapat berubah bentuk sesuai dengan eskpresi yang dimilikinya. Ekspresi wajah manusia memiliki kemiripan satu sama lain sehingga untuk mengenali suatu ekspresi adalah kepunyaan siapa akan sedikit sulit. Pengenalan wajah terus menjadi topik aktif di zaman sekarang pada penelitian bidang computer vision. Penggunaan wajah manusia sering kita jumpai pada fitur-fitur aplikasi media sosial seperti Snapchat, Snapgram dari Instagram dan banyak aplikasi sosial media lainnya yang menggunakan teknologi tersebut. Pada penelitian ini dilakukan analisa pengenalan ekpresi wajah manusia dengan pendekatan fitur alogaritma Local Binary Pattern dan mencari pengembangan alogaritma dasar Local Binary Pattern yang paling optimal dengan cara menggabungkan metode Hisogram Equalization, Support Vector Machine, dan K-fold cross validation sehingga dapat meningkatkan pengenalan gambar wajah manusia pada hasil yang terbaik. Penelitian ini menginput beberapa database wajah manusia seperti JAFFE yang merupakan gambar wajah manusia wanita jepang yang berjumlah 10 orang dengan 7 ekspresi emosional seperti marah, sedih, bahagia, jijik, kaget, takut dan netral ke dalam sistem. YALE yaitu merupakan gambar wajah manusia orang Amerika. Serta menggunakan dataset CALTECH yang merupakan gambar manusia yang terdiri dari 450 gambar dengan ukuran 896 x 592 piksel dan disimpan dalam format JPEG. Kemudian data tersebut di sesuaikan dengan bentuk tekstur wajah masing-masing. Dari hasil penggabungan ketiga metode diatas dan percobaan-percobaan yang sudah dilakukan, didapatkan hasil yang paling optimal dalam pengenalan wajah manusia yaitu menggunakan dataset JAFFE dengan resolusi 92 x 112 piksel dan dengan tingkat penggunaan processor yang tinggi dapat mempengaruhi waktu kecepatan komputasi dalam proses menjalankan sistem sehingga menghasilkan prediksi yang lebih tepat.

Abstract 473: Identification of Apolipoproteins Using Feature Selection Technique

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Hua Tang ◽  
Hao Lin
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Support Vector ◽  
Feature Subset ◽  
Risk Markers ◽  
Dipeptide Composition ◽  
Accurate Identification ◽  
Feature Selection Technique ◽  
Physiological Importance ◽  
Fold Cross Validation

Objective: Apolipoproteins are of great physiological importance and are associated with different diseases such as dyslipidemia, thrombogenesis and angiocardiopathy. Apolipoproteins have therefore emerged as key risk markers and important research targets yet the types of apolipoproteins has not been fully elucidated. Accurate identification of the apoliproproteins is very crucial to the comprehension of cardiovascular diseases and drug design. The aim of this study is to develop a powerful model to precisely identify apolipoproteins. Approach and Results: We manually collected a non-redundant dataset of 53 apoliproproteins and 136 non-apoliproproteins with the sequence identify of less than 40% from UniProt. After formulating the protein sequence samples with g -gap dipeptide composition (here g =1~10), the analysis of various (ANOVA) was adopted to find out the best feature subset which can achieve the best accuracy. Support Vector Machine (SVM) was then used to perform classification. The predictive model was evaluated using a five-fold cross-validation which yielded a sensitivity of 96.2%, a specificity of 99.3%, and an accuracy of 98.4%. The study indicated that the proposed method could be a feasible means of conducting preliminary analyses of apoliproproteins. Conclusion: We demonstrated that apoliproproteins can be predicted from their primary sequences. Also we discovered the special dipeptide distribution in apoliproproteins. These findings open new perspectives to improve apoliproproteins prediction by considering the specific dipeptides. We expect that these findings will help to improve drug development in anti-angiocardiopathy disease. Key words: Apoliproproteins Angiocardiopathy Support Vector Machine

The Animal Classification: An Evaluation of Different Transfer Learning Pipeline

Mekatronika ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 27-31
Author(s):  
Ken-ji Ee ◽  
Ahmad Fakhri Bin Ab. Nasir ◽  
Anwar P. P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman ◽  
Nur Hafieza Ismail
Keyword(s):  
Transfer Learning ◽  
Classification System ◽  
Cross Validation ◽  
Support Vector ◽  
Svm Classifier ◽  
Average Classification Accuracy ◽  
Validation Technique ◽  
Search Approach ◽  
Fold Cross Validation

The animal classification system is a technology to classify the animal class (type) automatically and useful in many applications. There are many types of learning models applied to this technology recently. Nonetheless, it is worth noting that the extraction of the features and the classification of the animal features is non-trivial, particularly in the deep learning approach for a successful animal classification system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards animal classification applications are somewhat limited. The present study aims to determine a suitable TL-conventional classifier pipeline for animal classification. The VGG16 and VGG19 were used in extracting features and then coupled with either k-Nearest Neighbour (k-NN) or Support Vector Machine (SVM) classifier. Prior to that, a total of 4000 images were gathered consisting of a total of five classes which are cows, goats, buffalos, dogs, and cats. The data was split into the ratio of 80:20 for train and test. The classifiers hyper parameters are tuned by the Grids Search approach that utilises the five-fold cross-validation technique. It was demonstrated from the study that the best TL pipeline identified is the VGG16 along with an optimised SVM, as it was able to yield an average classification accuracy of 0.975. The findings of the present investigation could facilitate animal classification application, i.e. for monitoring animals in wildlife.

scholarly journals EMCM: A Novel Binary Edge-Feature-Based Maximum Clique Framework for Multispectral Image Matching

2019 ◽  
Vol 11 (24) ◽  
pp. 3026
Author(s):  
Bin Fang ◽  
Kun Yu ◽  
Jie Ma ◽  
Pei An
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Maximum Clique ◽  
Feature Space ◽  
Multispectral Image ◽  
Feature Descriptor ◽  
Ranking Algorithm ◽  
Feature Based ◽  
Edge Feature

Seeking reliable correspondence between multispectral images is a fundamental and important task in computer vision. To overcome the nonlinearity problem occurring in multispectral image matching, a novel, edge-feature-based maximum clique-matching frame (EMCM) is proposed, which contains three main parts: (1) a novel strong edge binary feature descriptor, (2) a new correspondence-ranking algorithm based on keypoint distinctiveness analysis algorithms in the feature space of the graph, and (3) a false match removal algorithm based on maximum clique searching in the correspondence space of the graph considering both position and angle consistency. Extensive experiments are conducted on two standard multispectral image datasets with respect to the three parts. The feature-matching experiments suggest that the proposed feature descriptor is of high descriptiveness, robustness, and efficiency. The correspondence-ranking experiments validate the superiority of our correspondences-ranking algorithm over the nearest neighbor algorithm, and the coarse registration experiments show the robustness of EMCM with varied interferences.

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