Handling Water Reflections for Computer Vision in Aquaculture

2018 ◽  
Vol 61 (2) ◽  
pp. 469-479 ◽  
Author(s):  
Chao Zhou ◽  
Chuanheng Sun ◽  
Kai Lin ◽  
Daming Xu ◽  
Qiang Guo ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Image Texture ◽  
Support Vector ◽  
Type I

Abstract. In aquaculture, almost all images collected of an aquaculture scene contain reflections, which often affect the results and accuracy of machine vision. Classifying these images and obtaining images of interest are key to subsequent image processing. The purpose of this study was to identify useful images and remove images that had a substantial effect on the results of image processing for computer vision in aquaculture. In this study, a method for classification of reflective frames based on image texture and a support vector machine (SVM) was proposed for an actual aquaculture site. Objectives of this study were to: (1) develop an algorithm to improve the speed of the method and to ensure that the method has a high classification accuracy, (2) design an algorithm to improve the intelligence and adaptability of the classification, and (3) demonstrate the performance of the method. The results show that the average classification accuracy, false positive rate, and false negative rate for two types of reflective frames (type I and II) were 96.34%, 4.65%, and 2.23%, respectively. In addition, the running time was very low (1.25 s). This strategy also displayed considerable adaptability and could be used to obtain useful images or remove images that have substantial effects on the accuracy of image processing results, thereby improving the applicability of computer vision in aquaculture. Keywords: Aquaculture, Genetic algorithm, Gray level-gradient co-occurrence matrix, Principal component analysis, Reflection frame, Support vector machine.

A hybrid firefly and support vector machine classifier for phishing email detection

Kybernetes ◽  
2016 ◽  
Vol 45 (6) ◽  
pp. 977-994 ◽  
Author(s):  
Oluyinka Aderemi Adewumi ◽  
Ayobami Andronicus Akinyelu
Keyword(s):  
Support Vector Machine ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Support Vector ◽  
Data Set ◽  
Content Type ◽  
Phishing Attacks ◽  
Positive Rate ◽  
E Mail

Purpose – Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars has been lost by many companies and individuals. The global impact of phishing attacks will continue to be on the increase and thus a more efficient phishing detection technique is required. The purpose of this paper is to investigate and report the use of a nature inspired based-machine learning (ML) approach in classification of phishing e-mails. Design/methodology/approach – ML-based techniques have been shown to be efficient in detecting phishing attacks. In this paper, firefly algorithm (FFA) was integrated with support vector machine (SVM) with the primary aim of developing an improved phishing e-mail classifier (known as FFA_SVM), capable of accurately detecting new phishing patterns as they occur. From a data set consisting of 4,000 phishing and ham e-mails, a set of features, suitable for phishing e-mail detection, was extracted and used to construct the hybrid classifier. Findings – The FFA_SVM was applied to a data set consisting of up to 4,000 phishing and ham e-mails. Simulation experiments were performed to evaluate and compared the performance of the classifier. The tests yielded a classification accuracy of 99.94 percent, false positive rate of 0.06 percent and false negative rate of 0.04 percent. Originality/value – The hybrid algorithm has not been earlier apply, as in this work, to the classification and detection of phishing e-mail, to the best of the authors’ knowledge.

scholarly journals An Image Detection Method Based on Parameter Optimization of Support Vector Machine

2021 ◽  
Vol 15 ◽  
pp. 306-314
Author(s):  
Zhanshen Feng
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Parameter Optimization ◽  
Visual Processing ◽  
Image Data ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Image Detection

With the progress and development of multimedia image processing technology, and the rapid growth of image data, how to efficiently extract the interesting and valuable information from the huge image data, and effectively filter out the redundant data, these have become an urgent problem in the field of image processing and computer vision. In recent years, as one of the important branches of computer vision, image detection can assist and improve a series of visual processing tasks. It has been widely used in many fields, such as scene classification, visual tracking, object redirection, semantic segmentation and so on. Intelligent algorithms have strong non-linear mapping capability, data processing capacity and generalization ability. Support vector machine (SVM) by using the structural risk minimization principle constructs the optimal classification hyper-plane in the attribute space to make the classifier get the global optimum and has the expected risk meet a certain upper bound at a certain probability in the entire sample space. This paper combines SVM and artificial fish swarm algorithm (AFSA) for parameter optimization, builds AFSA-SVM classification model to achieve the intelligent identification of image features, and provides reliable technological means to accelerate sensing technology. The experiment result proves that AFSA-SVM has better classification accuracy and indicates that the algorithm of this paper can effectively realize the intelligent identification of image features.

scholarly journals CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yun Zuo ◽  
Jianyuan Lin ◽  
Xiangxiang Zeng ◽  
Quan Zou ◽  
Xiangrong Liu
Keyword(s):  
Reactive Oxygen Species ◽  
False Positive ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Reactive Oxygen ◽  
Support Vector ◽  
Oxygen Species ◽  
Negative Rate ◽  
Positive Rate

Abstract Background Carbonylation is a non-enzymatic irreversible protein post-translational modification, and refers to the side chain of amino acid residues being attacked by reactive oxygen species and finally converted into carbonyl products. Studies have shown that protein carbonylation caused by reactive oxygen species is involved in the etiology and pathophysiological processes of aging, neurodegenerative diseases, inflammation, diabetes, amyotrophic lateral sclerosis, Huntington’s disease, and tumor. Current experimental approaches used to predict carbonylation sites are expensive, time-consuming, and limited in protein processing abilities. Computational prediction of the carbonylation residue location in protein post-translational modifications enhances the functional characterization of proteins. Results In this study, an integrated classifier algorithm, CarSite-II, was developed to identify K, P, R, and T carbonylated sites. The resampling method K-means similarity-based undersampling and the synthetic minority oversampling technique (SMOTE-KSU) were incorporated to balance the proportions of K, P, R, and T carbonylated training samples. Next, the integrated classifier system Rotation Forest uses “support vector machine” subclassifications to divide three types of feature spaces into several subsets. CarSite-II gained Matthew’s correlation coefficient (MCC) values of 0.2287/0.3125/0.2787/0.2814, False Positive rate values of 0.2628/0.1084/0.1383/0.1313, False Negative rate values of 0.2252/0.0205/0.0976/0.0608 for K/P/R/T carbonylation sites by tenfold cross-validation, respectively. On our independent test dataset, CarSite-II yield MCC values of 0.6358/0.2910/0.4629/0.3685, False Positive rate values of 0.0165/0.0203/0.0188/0.0094, False Negative rate values of 0.1026/0.1875/0.2037/0.3333 for K/P/R/T carbonylation sites. The results show that CarSite-II achieves remarkably better performance than all currently available prediction tools. Conclusion The related results revealed that CarSite-II achieved better performance than the currently available five programs, and revealed the usefulness of the SMOTE-KSU resampling approach and integration algorithm. For the convenience of experimental scientists, the web tool of CarSite-II is available in http://47.100.136.41:8081/

scholarly journals Classification of Biodegradable Substances Using Balanced Random Trees and Boosted C5.0 Decision Trees

2020 ◽  
Vol 17 (24) ◽  
pp. 9322
Author(s):  
Alaa M. Elsayad ◽  
Ahmed M. Nassef ◽  
Mujahed Al-Dhaifallah ◽  
Khaled A. Elsayad
Keyword(s):  
Decision Trees ◽  
Roc Curve ◽  
Molecular Descriptor ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Random Trees ◽  
Support Vector ◽  
Statistical Measures ◽  
Qsar Models

Substances that do not degrade over time have proven to be harmful to the environment and are dangerous to living organisms. Being able to predict the biodegradability of substances without costly experiments is useful. Recently, the quantitative structure–activity relationship (QSAR) models have proposed effective solutions to this problem. However, the molecular descriptor datasets usually suffer from the problems of unbalanced class distribution, which adversely affects the efficiency and generalization of the derived models. Accordingly, this study aims at validating the performances of balanced random trees (RTs) and boosted C5.0 decision trees (DTs) to construct QSAR models to classify the ready biodegradation of substances and their abilities to deal with unbalanced data. The balanced RTs model algorithm builds individual trees using balanced bootstrap samples, while the boosted C5.0 DT is modeled using cost-sensitive learning. We employed the two-dimensional molecular descriptor dataset, which is publicly available through the University of California, Irvine (UCI) machine learning repository. The molecular descriptors were ranked according to their contributions to the balanced RTs classification process. The performance of the proposed models was compared with previously reported results. Based on the statistical measures, the experimental results showed that the proposed models outperform the classification results of the support vector machine (SVM), K-nearest neighbors (KNN), and discrimination analysis (DA). Classification measures were analyzed in terms of accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, F1 score, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUROC).

scholarly journals Wearable Device-based Fall Detection System for Elderly Care Using Support Vector Machine (SVM) classifier

2018 ◽  
Vol 7 (4.36) ◽  
pp. 488
Author(s):  
Nur Syazarin Natasha Abd Aziz ◽  
Salwani Mohd Daud ◽  
Nurul Iman Mohd Sa’at
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Elderly Care ◽  
False Negative ◽  
False Negative Rate ◽  
Fall Detection ◽  
Wearable Device ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction And Selection

Fall is an increasing problem as people ageing. It may happen to anyone, but their incidence does increase with age. Hence, the elderly will be facing catastrophic consequences due to falls. Nevertheless, there are still vulnerable in its accuracy in categorizing and differentiating the Activities Daily Living (ADL) and falls as most of the existing systems cause false alarm. This paper presents the research and simulation of wearable device-based fall detection approach by addressing the building of wearable device-based fall detection system for elderly care by using mobile devices. Two main phases involve in this research: online phase and offline phase. Online phase covers in data acquisition step whereby the raw data of simulated fall by participants is collected via built-in-tri-axial accelerometer in a smartphone, then automatically sent towards the computer via wireless communication. Meanwhile, offline phase covers data pre-processing, feature extraction and selection and data classification where these steps are handled in offline mode. Support Vector Machine (SVM) classifier was employed, and evaluated in the analysis. Overall accuracy rate, sensitivity, specificity as well as False Positive Rate (FPR) and False Negative Rate (FNR) were calculated. The findings suggest that SVM with Polynomial (order 5) method which achieved 68.91% overall accuracy as well as producing only 24.46% FPR is the most precise model for fall detection system in this paper. This approach has the potential to be implemented and deploy in real mobile application in future.   

scholarly journals Error Rates and Powers in Genome-Scale RNAi Screens

2009 ◽  
Vol 14 (3) ◽  
pp. 230-238 ◽  
Author(s):  
Xiaohua Douglas Zhang ◽  
Shane D. Marine ◽  
Marc Ferrer
Keyword(s):  
High Throughput Screening ◽  
Control Method ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Error Rates ◽  
Type I ◽  
Small Interfering Rnas ◽  
Screening Experiments ◽  
Genome Scale

For hit selection in genome-scale RNAi research, we do not want to miss small interfering RNAs (siRNAs) with large effects; meanwhile, we do not want to include siRNAs with small or no effects in the list of selected hits. There is a strong need to control both the false-negative rate (FNR), in which the siRNAs with large effects are not selected as hits, and the restricted false-positive rate (RFPR), in which the siRNAs with no or small effects are selected as hits. An error control method based on strictly standardized mean difference (SSMD) has been proposed to maintain a flexible and balanced control of FNR and RFPR. In this article, the authors illustrate how to maintain a balanced control of both FNR and RFPR using the plot of error rate versus SSMD as well as how to keep high powers using the plot of power versus SSMD in RNAi high-throughput screening experiments. There are relationships among FNR, RFPR, Type I and II errors, and power. Understanding the differences and links among these concepts is essential for people to use statistical terminology correctly and effectively for data analysis in genome-scale RNAi screens. Here the authors explore these differences and links. (Journal of Biomolecular Screening 2009:230-238)

scholarly journals Support vector machine and deep-learning object detection for localisation of hard exudates

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Veronika Kurilová ◽  
Jozef Goga ◽  
Miloš Oravec ◽  
Jarmila Pavlovičová ◽  
Slavomír Kajan
Keyword(s):  
Support Vector Machine ◽  
False Positive ◽  
High Reliability ◽  
False Positive Rate ◽  
False Negative ◽  
Support Vector ◽  
Retinal Images ◽  
Svm Classifier ◽  
Hard Exudates ◽  
Positive Rate

AbstractHard exudates are one of the main clinical findings in the retinal images of patients with diabetic retinopathy. Detecting them early significantly impacts the treatment of underlying diseases; therefore, there is a need for automated systems with high reliability. We propose a novel method for identifying and localising hard exudates in retinal images. To achieve fast image pre-scanning, a support vector machine (SVM) classifier was combined with a faster region-based convolutional neural network (faster R-CNN) object detector for the localisation of exudates. Rapid pre-scanning filtered out exudate-free samples using a feature vector extracted from the pre-trained ResNet-50 network. Subsequently, the remaining samples were processed using a faster R-CNN detector for detailed analysis. When evaluating all the exudates as individual objects, the SVM classifier reduced the false positive rate by 29.7% and marginally increased the false negative rate by 16.2%. When evaluating all the images, we recorded a 50% reduction in the false positive rate, without any decrease in the number of false negatives. The interim results suggested that pre-scanning the samples using the SVM prior to implementing the deep-network object detector could simultaneously improve and speed up the current hard exudates detection method, especially when there is paucity of training data.

Comparative study of ultrasonographic findings with the operative findings of biliary surgery

2020 ◽  
Vol 22 (1) ◽  
pp. 25-29
Author(s):  
Zubayer Ahmad ◽  
Mohammad Ali ◽  
Kazi lsrat Jahan ◽  
ABM Khurshid Alam ◽  
G M Morshed
Keyword(s):  
Bile Duct ◽  
Common Bile Duct ◽  
Imaging Modality ◽  
False Positive Rate ◽  
Gallstone Disease ◽  
False Negative ◽  
False Negative Rate ◽  
Biliary Disease ◽  
Biliary Surgery ◽  
Operative Findings

Background: Biliary disease is one of the most common surgical problems encountered all over the world. Ultrasound is widely accepted for the diagnosis of biliary system disease. However, it is a highly operator dependent imaging modality and its diagnostic success is also influenced by the situation, such as non-fasting, obesity, intestinal gas. Objective: To compare the ultrasonographic findings with the peroperative findings in biliary surgery. Methods: This prospective study was conducted in General Hospital, comilla between the periods of July 2006 to June 2008 among 300 patients with biliary diseases for which operative treatment is planned. Comparison between sonographic findings with operative findings was performed. Results: Right hypochondriac pain and jaundice were two significant symptoms (93% and 15%). Right hypochondriac tenderness, jaundice and palpable gallbladder were most valuable physical findings (respectively, 40%, 15% and 5%). Out of 252 ultrasonically positive gallbladder, stone were confirmed in 249 cases preoperatively. Sensitivity of USG in diagnosis of gallstone disease was 100%. There was, however, 25% false positive rate detection. Specificity was, however, 75% in this case. USG could demonstrate stone in common bile duct in only 12 out of 30 cases. Sensitivity of the test in diagnosing common bile duct stone was 40%, false negative rate 60%. In the series, ultrasonography sensitivity was 100% in diagnosing stone in cystic duct. USG could detect with relatively good but less sensitivity the presence of chronic cholecystitis (92.3%) and worm inside gallbladder (50%). Conclusion: Ultrasonography is the most important investigation in the diagnosis of biliary disease and a useful test for patients undergoing operative management for planning and anticipating technical difficulties. Journal of Surgical Sciences (2018) Vol. 22 (1): 25-29

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification

Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

2020 ◽  
Vol 13 (2) ◽  
pp. 153-164
Author(s):  
Narina Thakur ◽  
Deepti Mehrotra ◽  
Abhay Bansal ◽  
Manju Bala
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Time Window ◽  
Optimal Solution ◽  
Similarity Score ◽  
Learning Objects ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Computationally Efficient ◽  
User Query

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

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