Utilizing Support Vector Machine Research Articles

A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants

The safety and public health during nuclear power plant operation can be enhanced by accurately recognizing and diagnosing potential problems when a malfunction occurs. However, there are still obvious technological gaps in fault diagnosis applications, mainly because adopting a single fault diagnosis method may reduce fault diagnosis accuracy. In addition, some of the proposed solutions rely heavily on fault examples, which cannot fully cover future possible fault modes in nuclear plant operation. This paper presents the results of a research in hybrid fault diagnosis techniques that utilizes support vector machine (SVM) and improved particle swarm optimization (PSO) to perform further diagnosis on the basis of qualitative reasoning by knowledge-based preliminary diagnosis and sample data provided by an on-line simulation model. Further, SVM has relatively good classification ability with small samples compared to other machine learning methodologies. However, there are some challenges in the selection of hyper-parameters in SVM that warrants the adoption of intelligent optimization algorithms. Hence, the major contribution of this paper is to propose a hybrid fault diagnosis method with a comprehensive and reasonable design. Also, improved PSO combined with a variety of search strategies are achieved and compared with other current optimization algorithms. Simulation tests are used to verify the accuracy and interpretability of research findings presented in this paper, which would be beneficial for intelligent execution of nuclear power plant operation.

Acceptability analysis method for evaluate data on twitter using support vector machine

The first model centres around believability at the client level, tackling different elements of information stream into a registered validity rating. The next model specifies a methodology to find believability score for singular tweets. We built up the system for validity on Face book by evaluating the validity of: (i) the reliability of the web sources discussing a case, (ii) the dialect style of the articles revealing the case and, (iii) their position. We at that point gathered the preparation information for making a model utilizing Support Vector Machine (SVM). Furthermore the standardization technique is essential advance for purifying information before utilizing the machine learning strategy to order information. The outcome demonstrate that Naïve Bayes to identify the Fake news has precision 96.08%. We distinguish basic examples of transiently agent discussion subgraphs and speak to their subjects utilizing Latent Dirichlet Allocation (LDA) demonstrating. We break down how the information had proliferated, and the moves were made in light of the source. The component retweet was considered as a proportion of examination to upgrade the reliability of the spread information. The performance of our positioning calculation essentially upgraded when we connected re-positioning system.

Brain image clustering by wavelet energy and CBSSO optimization algorithm

Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights.

Fall Detection for Elderly People Using the Variation of Key Points of Human Skeleton

In the area of health care, fall is a dangerous problem for aged persons. Sometimes, they are a serious cause of death. In addition to that, the number of aged persons will increase in the future. Therefore, it is necessary to develop an accurate system to detect fall. In this paper, we present spatiotemporal method to detect fall form videos filmed by surveillance cameras. Firstly, we computed key points of human skeleton. We calculated distances and angles between key points of each two pair sequences frames. After that, we applied Principal Component Analysis (PCA) to unify the dimension of features. Finally, we utilized Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbors (KNN) to classify features. We found that SVM is the best classifier to our method. The results of our algorithm are as follow: accuracy is 98.5%, sensitivity is 97% and the specificity is 100%.

Optimizing of predictive performance for construction projects utilizing support vector machine technique

Construction projects still face the old–new problem of delivering the projects within the predefined time and cost. This problem becomes more complicated with when addendums and variations are con...

Corrigendum: Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine

In the version of this article initially published, there were some errors in Section III, Methods and Section VI, Conclusions. In Preprocessing of Methods, there is a sentence “The informal words may be in the form of slang words or abbreviations that are often used in daily life like cp at (from “cepat” or fast), blum (from “belum” or not yet), and gak (from “tidak” or no).”. The correct sentence is “The informal words may be in the form of slang words or abbreviations that are often used in daily life like cpat (from “cepat” or fast), blum (from “belum” or not yet), and gak (from “tidak” or no).”. In Text Classification of Methods, there is a sentence “Where P(B|A) is the probability of B appearance when A is known? The value P(A|B) is the probability of an appearance if B is known. P(A) is the probability of an appearance, while P(B) is the probability of B appearance.”. The correct sentence is “Where P(B│A) is the probability of the appearance of B when A is known. The value of P(A|B) is the probability of the appearance of A if B is known. P(A) is the probability of the appearance of A, while P(B) is the probability of the appearance of B.”. In Conclusions, a sentence “The accuracy reaches 93.42%; using 25% features with highest TF-IDF” should be changed to “The accuracy reaches 93.65%; using 25% features with highest TF-IDF” based on the results in Fig.3. These errors have been corrected in the PDF versions of the article.

Identifying anticancer peptides by using a generalized chaos game representation.

We generalize chaos game representation (CGR) to higher dimensional spaces while maintaining its bijection, keeping such method sufficiently representative and mathematically rigorous compare to previous attempts. We first state and prove the asymptotic property of CGR and our generalized chaos game representation (GCGR) method. The prediction follows that the dissimilarity of sequences which possess identical subsequences but distinct positions would be lowered exponentially by the length of the identical subsequence; this effect was taking place unbeknownst to researchers. By shining a spotlight on it now, we show the effect fundamentally supports (G)CGR as a similarity measure or feature extraction technique. We develop two feature extraction techniques: GCGR-Centroid and GCGR-Variance. We use the GCGR-Centroid to analyze the similarity between protein sequences by using the datasets 9 ND5, 24 TF and 50 beta-globin proteins. We obtain consistent results compared with previous studies which proves the significance thereof. Finally, by utilizing support vector machines, we train the anticancer peptide prediction model by using both GCGR-Centroid and GCGR-Variance, and achieve a significantly higher prediction performance by employing the 3 well-studied anticancer peptide datasets.

Identifying Citronella Plants From UAV Imagery Using Support Vector Machine

High-resolution imagery taken from Unmanned Aerial Vehicle (UAV) is now often used as an alternative in monitoring the agronomic plants compared to satellite imagery. This paper presents a method to identify Citronella among other plants based on UAV imagery. The method utilizes Support Vector Machine (SVM) to classify Citronella among other plants according to the extraction of texture feature. The implementation of the method was evaluated using two group of datasets: 1) consists of Citronella, Kaffir Lime, other green plants, vacant soil, and buildings, and 2) consists of Citronella and paddy rice plants. The evaluation results show that the proposed method can identify Citronella on the first group of datasets with an accuracy 94.23% and Kappa value 88.48%, whereas on the second group of datasets with an accuracy 100% and Kappa value 100%.

Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine

The online store is changing people’s shopping behavior. Despite the fact, the potential customer’s distrust in the quality of products and service is one of the online store’s weaknesses. A review is provided by the online stores to overcome this weakness. Customers often write a review using languages that are not well structured. Sentiment analysis is used to extract the polarity of the unstructured texts. This research attempted to do a sentiment analysis in the sales review. Sentiment analysis in sales reviews can be used as a tool to evaluate the sales. This research intends to conduct a sentiment analysis in the sales review of Indonesian marketplace by utilizing Support Vector Machine and Naive Bayes. The reviews of the data are gathered from one of Indonesian marketplace, Bukalapak. The data are classified into positive or negative class. TF-IDF is used to feature extraction. The experiment shows that Support Vector Machine with linear kernel provides higher accuracy than Naive Bayes. Support Vector Machine shows the highest accuracy average. The generated accuracy is 93.65%. This approach of sentiment analysis in sales review can be used as the base of intelligent sales evaluation for online stores in the future.

Feature Extraction in JPEG domain along with SVM for Content Based Image Retrieval

Content Based Image Retrieval (CBIR) applies computer vision methods for image retreival purposes from the databases. It is majorly based on the user query, which is in visual form rather than the traditional text form. CBIR is applied in different fields extending from surveillance to remote sensing, E-purchase, medical image processing, security systems to historical research and many others. JPEG, a very commonly used method of lossy compression is used to reduce the size of the image before being stored or transmitted. Almost every digital camera in the market are storing the captured images in jpeg format. The storage industry has seen many major transformations in the past decades while the retrieval technologies are still developing. Though there are some breakthroughs happened in text retrieval, the same is not true for the image and other multimedia retrieval. Specifically image retreival has witnessed many algorithms in the spatial or the raw domain but since majority of the images are stored in the JPEG format, it takes time to decode the compressed image before extracting features and retrieving. Hence, in this research work, we focus on extracting the features from the compressed domain itself and then utilize support vector machines (SVM) for improving the retrieval results. Our proof of concept shows us that the features extracted in compressed domain helps retrieve the images 43% faster than the same set of images in the spatial domain and the accuracy is improved to 93.4% through SVM based feedback mechanism.

Markovian encoding models in human splice site recognition using SVM

Splice site recognition is among the most significant and challenging tasks in bioinformatics due to its key role in gene annotation. Effective prediction of splice site requires nucleotide encoding methods that reveal the characteristics of DNA sequences to provide appropriate features to serve as input of machine learning classifiers. Markovian models are the most influential encoding methods that highly used for pattern recognition in biological data. However, a direct performance comparison of these methods in splice site domain has not been assessed yet. This study compares various Markovian encoding models for splice site prediction utilizing support vector machine, as the most outstanding learning method in the domain, and conducts a new precise evaluation of Markovian approaches that corrects this limitation. Moreover, a novel sequence encoding approach based on third order Markov model (MM3) is proposed. The experimental results show that the proposed method, namely MM3-SVM, performs significantly better than thirteen best known state-of-the-art algorithms, while tested on HS3D dataset considering several performance criteria. Further, it achieved higher prediction accuracy than several well-known tools like NNsplice, MEM, MM1, WMM, and GeneID, using an independent test set of 50 genes. We also developed MMSVM, a web tool to predict splice sites in any human sequence using the proposed approach. The MMSVM web server can be assessed at https://pashaei.shinyapps.io/mmsvm.

Supervised Classification of the Accuracy of the Time Delay Estimation in Ultrasound Elastography.

The accuracy of time-delay estimation (TDE) in ultrasound elastography is usually measured by calculating the value of normalized cross correlation (NCC) at the estimated displacement. NCC value, however, could be very high at a displacement estimate with large error, a well-known problem in TDE referred to as peak-hopping. Furthermore, NCC value could suffer from jitter error, which is due to electric noise and signal decorrelation. Herein, we propose a novel method to assess the accuracy of TDE by investigating the NCC profile around the estimated time-delay. We extract several features from the NCC profile, and utilize support vector machine to classify peak-hopping and jitter error. The results on simulation, phantom, and in vivo data show the significant improvement of the proposed algorithm compared to the state of the art techniques.

Development of a clinical decision support system utilizing support vector machine

Development of a clinical decision support system utilizing support vector machine

Automated and Precise Event Detection Method for Big Data in Biomedical Imaging with Support Vector Machine

This paper proposes a machine learning based method which can detect certain events automatically and precisely in biomedical imaging. We detect one important and not well-defined event, which is called flash, in fluorescence images of Escherichia coli. Given a time series of images, first we propose a scheme to transform the event detection on region of interest (ROI) in images to a classification problem. Then with supervised human labeling data, we develop a feature selection technique to utilize support vector machine (SVM) to solve this classification problem. To reduce the time in training SVM model, a parallel version of SVM training is implemented. On ten stacks of fluorescence images labeled by experts, each of which owns one hundred 512 ·512 images with in total 4906 ROIs and 72056 labeled events, event detection with proposed method takes 19 seconds, while human labeling roughly costs 60 hours. With human labeling as the standard, the accuracy of our method achieves an F-value of about 0.81. This method is much faster than human detection and expects to be more precise with bigger data. It also can be expanded to a series of event detection with similar properties and improve efficiency of detection greatly.

Pattern recognition based on enhanced multifeature parameters for vibration events in φ‐OTDR distributed optical fiber sensing system

AbstractA new multifeature recognition method for optical fiber vibration signals is proposed to solve the difficulty with the present long‐distance distributed optical fiber sensing systems based on phase‐sensitive optical time‐domain reflectometer (φ‐OTDR) in effectively distinguishing vibrational intrusions. Firstly, wide‐band background noise is reduced by spectral subtraction to enhance time‐frequency characteristics of vibration signals. Then, multifeature parameters including short‐time energy ratio, short‐time level crossing rate, vibration duration, and power‐spectrum energy ratio are extracted from the vibration signals. Finally, by utilizing support vector machine classifiers to identify multifeature vectors of different types of vibration signals, patterns of vibration events are recognized accurately. Experiments show that after using this method to process 800 vibration signal samples generated by four different vibration events, namely, taping, striking, shaking, and crushing, the signal‐to‐noise ratio is enhanced by more than 10 dB and the recognition rates of vibration events are over 90%. The experimental results prove that this method can improve the classification accuracy of vibration events detected by φ‐OTDR distributed optical fiber sensing systems with a recognition time below 0.6 s, especially for long distances.

Combining Supervised and Unsupervised Learning for Improved miRNA Target Prediction.

MicroRNAs (miRNAs) are short non-coding RNAs which bind to mRNAs and regulate their expression. MiRNAs have been found to be associated with initiation and progression of many complex diseases. Investigating miRNAs and their targets can thus help develop new therapies by designing anti-miRNA oligonucleotides. While existing computational approaches can predict miRNA targets, these predictions have low accuracy. In this paper, we propose a two-step approach to refine the results of sequence-based prediction algorithms. The first step, which is based on our previous work, uses an ensemble learning approach that combines multiple existing methods. The second step utilizes support vector machine (SVM) classifiers in one- and two-class modes to infer miRNA-mRNA interactions based on both binding features, as well as network features extracted from gene regulatory network. Experimental results using two real data sets from TCGA indicate that the use of two-class SVM classification significantly improves the precision of miRNA-mRNA prediction.

Tibetan Person Attributes Extraction Based on Support Vector Machine

Person attributes extraction is an important branch in Natural Language Processing. At present, Chinese entity attributes extraction method is mainly based on rules, statistics and machine learning methods, and has achieved good results. But the study of Tibetan entity attributes extraction has a great space. The paper uses person attribute, case-auxiliary word, verbs and other related meaningful words in Tibetan corpus as the features, and utilizes support vector machine to train and predict the classifier. It provides support for search engine, information security, machine translation and other researches.

Time series and support vector machines to predict powered-two-wheeler accident risk and accident type propensity: A combined approach

ABSTRACTPredicting road accident probability by exploiting high-resolution traffic data has been a continuously researched topic in the last years. However, there is no specific focus on powered-two-wheelers. Furthermore, urban arterials have not received adequate attention so far because the majority of relevant studies considers freeways. This study aims to contribute to the current knowledge by utilizing support vector machine (SVM) models for predicting powered-two-wheeler (PTW) accident risk and PTW accident type propensity on urban arterials. The proposed methodology is applied on original and transformed time series of real-time traffic data collected from urban arterials in Athens, Greece, for 2006 to 2011. Findings suggest that PTW accident risk and PTW accident type propensity can be adequately defined by the prevailing traffic conditions. When predicting PTW accident risk, the original traffic time series performed better than the transformed time series. On the other hand, when PTW accident type is investigated, neither of the two approaches clearly outperformed the other, but the transformed time series perform slightly better. The results of the study indicate that the combination of SVM models and time-series data can be used for road safety purposes especially by utilizing real-time traffic data.

Route choice modeling with Support Vector Machine

Abstract This paper aims at exploring the possibility of utilizing Support Vector Machine (SVM) to establish route choice model. A widely used non-parametric modelling approach, Neural Network, was used to compare with SVM. A stated preferences survey was conducted among 18 participants. Information about three route attributes including travel time, travel time fluctuations and fuel cost were given to participants for making route choice decisions. The data collected from the survey was used to calibrate and test both NN and SVM. The results show that SVM has similar prediction accuracy with NN but has much more computing efficiency.

Dynamic Router Performance Control Utilizing Support Vector Machines for Energy Consumption Reduction

In this paper, we propose a machine-learning-based novel dynamic performance control method for routers that supports several performance levels. The method utilizes support vector machine (SVM) to determine performance level change points. We utilize a traffic normalization technique with a corresponding performance threshold that allows us to apply the same SVM to different traffic volumes. This technique shortens the learning process for control. Several experiments on real Internet traffic data sequences prove that the method yields higher energy efficiency than conventional methods, that is, our previously proposed frequency decomposition-based method and a conventional traffic prediction method based on auto-regressive moving average model with optimal parameter values given by Akaike’s information criterion. We also evaluate the impact of several key parameters such as traffic measurement interval and the number of packet processing engines. The results clarify the proper ranges of parameter values that attain significant power reductions while keeping packet loss to acceptable levels.