A Multiple Model Approach to Modeling Based on Fuzzy Support Vector Machines

A new multiple models(MM) approach was proposed to model complex industrial process by using Fuzzy Support Vector Machines(F -SVMs). By applying the proposed approach to a pH neutralization titration experiment, F -SVMs MM not only provides satisfactory approximation and generalization property, but also achieves superior performance to USOCPN multiple modeling method and single modeling method based on standard SVMs.

Support vector classifier based on principal component analysis

Support vector classifier （SVC） has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC. A principal component analysis （PCA） is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently, and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC. Furthermore, a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines. Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically, but also improves the identify rates effectively.

Fault depth estimation using support vector classifier and features selection

Depth estimation of subsurface faults is one of the problems in gravity interpretation. We tried using the support vector classifier （SVC） method in the estimation. Using forward and nonlinear inverse techniques, detecting the depth of subsurface faults with related error is possible but it is necessary to have an initial guess for the depth and this initial guess usually comes from non-gravity data. We introduce SVC in this paper as one of the tools for estimating the depth of subsurface faults using gravity data. We can suppose that each subsurface fault depth is a class and that SVC is a classification algorithm. To better use the SVC algorithm, we select proper depth estimation features using a proper features selection （FS） algorithm. In this research, we produce a training set consisting of synthetic gravity profiles created by subsurface faults at different depths to train the SVC code to estimate the depth of real subsurface faults. Then we test our trained SVC code by a testing set consisting of other synthetic gravity profiles created by subsurface faults at different depths. We also tested our trained SVC code using real data.

Exploiting multi-context analysis in semantic image classification

As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image classification methods do not overcome the so-called semantic gap problem in which low-level visual features cannot represent the high-level semantic content of images. Image classification using visual and textual information often performs poorly since the extracted textual features are often too limited to accurately represent the images. In this paper, we propose a semantic image classification ap- proach using multi-context analysis. For a given image, we model the relevant textual information as its multi-modal context, and regard the related images connected by hyperlinks as its link context. Two kinds of context analysis models, i.e., cross-modal correlation analysis and link-based correlation model, are used to capture the correlation among different modals of features and the topical dependency among images induced by the link structure. We propose a new collective classification model called relational support vector classifier (RSVC) based on the well-known Support Vector Machines (SVMs) and the link-based cor- relation model. Experiments showed that the proposed approach significantly improved classification accuracy over that of SVM classifiers using visual and/or textual features.

Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern era.Twitter,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment analysis.In recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets.Opinions or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing opinions.The process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning methods.The deep learning-based model long-short-term memory(LSTM)performed better than machine learning approaches.Long short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.

An Ophthalmic Evaluation of Central Serous Chorioretinopathy

Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal diseases.In this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods.Thefirst approach,which was focused on image quality,improves medical image accuracy.An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be minimized.The total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.

An optical brain-to-brain interface supports rapid information transmission for precise locomotion control

Brain-to-brain interfaces(BtBIs) hold exciting potentials for direct communication between individual brains. However,technical challenges often limit their performance in rapid information transfer. Here, we demonstrate an optical brain-to-brain interface that transmits information regarding locomotor speed from one mouse to another and allows precise, real-time control of locomotion across animals with high information transfer rate. We found that the activity of the genetically identified neuromedin B(NMB) neurons within the nucleus incertus(NI) precisely predicts and critically controls locomotor speed. By optically recording Ca2+ signals from the NI of a "Master" mouse and converting them to patterned optogenetic stimulations of the NI of an "Avatar" mouse, the Bt BI directed the Avatar mice to closely mimic the locomotion of their Masters with information transfer rate about two orders of magnitude higher than previous Bt BIs. These results thus provide proof-of-concept that optical Bt BIs can rapidly transmit neural information and control dynamic behaviors across individuals.

Modeling the Facies of Reservoir Using Seismic Data with Missing Attributes by Dissimilarity Based Classification

Using seismic attributes as features for classification in feature space, in various aims such as seismic facies analysis, is conventional for the purpose of seismic interpretation. But sometimes seismic data may have no attributes or it is hard to define a small and relevant set of attributes in some applica- tions. Therefore, employing techniques that perform facies modeling without using attributes is neces- sary. In this paper we present a new method for facies modeling of seismic data with missing attributes that called dissimilarity based classification. In this method, classification is based on dissimilarities and facies modeling will be done in dissimilarity space. In this space dissimilarities consider as new features instead of real features. A support vector machine as a powerful classifier was employed in both feature space （feature-based） and dissimilarity space （feature-less） for facies analysis. The proposed feature-less and feature-based classification is applied on a real seismic data from an Iranian oil field. Facies model- ing using seismic attributes provide better results, but the feature-less classification outcome is also satis- factory and the facies correlation is acceptable. Indeed, the power of attributes to discriminate different facies causes to that facies analysis using attributes provide more reliable results comparing to feature- less facies analysis.

Evaluation of Radarsat-2 quad-pol SAR time-series images for monitoring groundwater irrigation

Groundwater assists farmers to irrigate crops for fulfilling the crop-water requirement.Indian agriculture system is characterized by three cropping seasons known as Kharif(monsoon),Rabi(post-monsoon)and summer(pre-monsoon).In tropical countries like India,monitoring cropping practices using optical remote sensing during Kharif and Rabi seasons is constraint due to the cloud cover,which can be well addressed by microwave remote sensing.In the proposed research,the strength of C-band polarimetric Synthetic Aperture Radar(SAR)time series images were evaluated to classify groundwater irrigated croplands for the Kharif and Rabi cropping seasons of the year 2013.The present study was performed in the Berambadi experimental watershed of Kabini river basin,southern peninsular India.A total of fifteen polarimetric variables were estimated includes four backscattering coefficients(HH,HV,VH,VV)and eleven polarimetric indices for all Radarsat-2 SAR images.The cumulative temporal sum(seasonal and dual-season)of these parameters was supervised classified using Support Vector Machine(SVM)classifier with intensive ground observation samples.Classification results using the best equation(highest accuracy and kappa)shows that the Kharif,Rabi and irrigated double croplands are respectively 9.58 km2(20.6%),16.14 km2(34.7%)and 6.22 km2(13.4%)with a kappa coefficient respectively 0.84,0.74 and 0.94.