ADS Using NER Systems

2021 ◽  
Vol 9 (11) ◽  
pp. 992-997
Author(s):  
Mithilesh Bade
Keyword(s):  
Vital Role ◽  
Mining Machine ◽  
Svm Classifier ◽  
Arbitrary Field ◽  
Vector Machines ◽  
Abstract Data ◽  
Index Terms ◽  
Bidirectional Lstm ◽  
The Moment ◽  
Different Sources

Abstract: Data accessible over the net is generally unstructured. Offers distributed by different sources like banks, digital wallets, merchants, etc., are one of the foremost gotten to advertising data in today’s world. This information gets gotten to by millions of people on a every day premise and is effortlessly deciphered by people, but since it is generally unstructured and differing, utilizing an algorithmic way to extricate significant data out of these offers is hard. Distinguishing the basic offer substances (for occasion, its amount, the item on which the offer is pertinent, the merchant giving the offer, etc.) from these offers plays a vital role in focusing on the proper clients to make strides deals.This work presents and assesses different existing Named Substance Recognizer (NER) models which can distinguish the desired substances from offer feeds. We moreover propose a novel NER demonstration constructed by two-level stacking of Conditional Arbitrary Field, Bidirectional LSTM and Spacy models at the primary level and an SVM classifier at the moment. The proposed cross breed demonstrate has been tried on offer feeds collected from different sources and has appeared better performance within the offered space when compared to the existing models. Index Terms—Named Substance Acknowledgment, Information Mining, Machine Learning, Stanford NER, Bidirectional LSTM, Spacy, Bolster Vector Machines.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

Hyperspectral imaging for the elimination of visual ambiguity in corrosion detection and identification of corrosion sources

2021 ◽  
pp. 147592172110416
Author(s):  
Dayakar N Lavadiya ◽  
Hizb Ullah Sajid ◽  
Ravi K Yellavajjala ◽  
Xin Sun
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Sulfate Solution ◽  
Vital Role ◽  
Mitigation Strategies ◽  
Svm Classifier ◽  
Corrosive Media ◽  
Detection And Identification ◽  
Coated Surfaces ◽  
Visual Ambiguity

The similarity in the hue of corroded surfaces and coated surfaces, dust, vegetation, etc. leads to visual ambiguity which is challenging to eliminate using existing image classification/segmentation techniques. Furthermore, existing methods lack the ability to identify the source of corrosion, which plays a vital role in framing the corrosion mitigation strategies. The goal of this study to employ hyperspectral imaging (1) to detect corroded surfaces under visually ambiguous scenarios and (2) identify the source of corrosion in such scenarios. To this end, three different corrosive media, namely, (1) 1M hydrochloric acid (HCl), 2) 3.5 wt.% sodium chloride solution (NaCl), and (3) 3 wt.% sodium sulfate solution (Na2SO4), are employed to generate chemically distinctive corroded surfaces. The hyperspectral imaging sensor is employed to obtain the visible and near infrared (VNIR) spectra (397 nm–1004 nm) reflected by the corroded/coated surfaces. The intensity of the reflectance in various spectral bands are considered as the descriptive features in this study, and the training and test datasets were generated consisting of 35,000 and 15,000 data points, respectively. SVM classifier is trained and then its efficacy on the test data is assessed. Furthermore, validation datasets are employed and the generalization ability of the trained SVM classifier is verified. The results from this study revealed that the SVM classifier achieved an overall accuracy of 94% with the misclassifications of 18% and 13% in the case of NaCl and Na2SO4 corrosion, respectively. Reflectance spectra obtained in the VNIR region was found to eliminate the visual ambiguity between the corroded and coated surfaces and, identify the source of corrosion accurately. Further, the range of key wavelengths of the spectra that play an important role in the distinguishability of coating and chemically distinctive corroded surface were identified to be 500–520 nm, 660–680 nm, 760–770 nm, and 830–850 nm.

PLCζ, a sperm-specific PLC and its potential role in fertilization

2007 ◽  
Vol 74 ◽  
pp. 23-36 ◽  
Author(s):  
Christopher M. Saunders ◽  
Karl Swann ◽  
F. Anthony Lai
Keyword(s):  
Intracellular Calcium ◽  
Membrane Fusion ◽  
Causative Agent ◽  
Calcium Concentration ◽  
Potential Role ◽  
Vital Role ◽  
The Other ◽  
Sperm Protein ◽  
Dramatic Rise ◽  
The Moment

A dramatic rise in intracellular calcium plays a vital role at the moment of fertilization, eliciting the resumption of meiosis and the initiation of embryo development. In mammals, the rise takes the form of oscillations in calcium concentration within the egg, driven by an elevation in inositol trisphosphate. The causative agent of these oscillations is proposed to be a recently described phosphoinositide-specific phospholipase C, PLCζ, a soluble sperm protein that is delivered into the egg following membrane fusion. In the present review, we examine some of the distinctive structural and functional characteristics of this crucial enzyme that sets it apart from the other known forms of mammalian PLC.

scholarly journals Dog Face Detection Using YOLO Network

MENDEL ◽  
2020 ◽  
Vol 26 (2) ◽  
pp. 17-22
Author(s):  
Alzbeta Tureckova ◽  
Tomas Holik ◽  
Zuzana Kominkova Oplatkova
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Training Procedure ◽  
The Gaze ◽  
Real World Application ◽  
Human Face Detection ◽  
Deep Convolution Neural Network ◽  
Bounding Boxes ◽  
The Moment ◽  
Different Sources

This work presents the real-world application of the object detection which belongs to one of the current research lines in computer vision. Researchers are commonly focused on human face detection. Compared to that, the current paper presents a challenging task of detecting a dog face instead that is an object with extensive variability in appearance. The system utilises YOLO network, a deep convolution neural network, to~predict bounding boxes and class confidences simultaneously. This paper documents the extensive dataset of dog faces gathered from two different sources and the training procedure of the detector. The proposed system was designed for realization on mobile hardware. This Doggie Smile application helps to snapshot dogs at the moment when they face the camera. The proposed mobile application can simultaneously evaluate the gaze directions of three dogs in scene more than 13 times per second, measured on iPhone XR. The average precision of the dogface detection system is 0.92.

scholarly journals Management in the heat-supplying organizations on the basis of balance models

2018 ◽  
Vol 170 ◽  
pp. 01112 ◽  
Author(s):  
Evgeny Evseev ◽  
Tatiana Kisel
Keyword(s):  
Power Efficiency ◽  
Balance Method ◽  
Financial Resources ◽  
Capital Repair ◽  
Heating Systems ◽  
Financial Transparency ◽  
The Moment ◽  
Generating Equipment ◽  
Financial Balance ◽  
Different Sources

Development of the heating systems, which are the part of engineering infrastructure of the cities and which are necessary for providing comfortable conditions for the cities' activity, is governed by the municipal authorities. Nowadays some requirements, connected with the need of increase in power efficiency are imposed in relation to the heating systems. The actions, aimed on the increase in the efficiency of heating systems, are usually connected with carrying out capital repair and updating of heating systems and the heat-generating equipment. That demands considerable financial resources and that in turn means the need of involvement of an investor. However at the moment investments into the heat-supplying enterprises are not attractive for the investor because of the lack of financial transparency and the mechanism of return of investments. In the article the authors offer the application of the balance method for formation of financial balance of the heat-supplying enterprise, The method will allow to consider the attraction of resources from three different sources and all the directions of expenditure of resources, including the mechanism of rationing of profit of the heat supplier. That guarantees the existence of the transparent mechanism of investments return.

scholarly journals Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

2003 ◽  
Vol 15 (7) ◽  
pp. 1667-1689 ◽  
Author(s):  
S. Sathiya Keerthi ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Heuristic Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Linear Svm ◽  
Generalization Errors

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.

EFFECTS OF SVM PARAMETER OPTIMIZATION BASED ON THE PARAMETER DESIGN OF TAGUCHI METHOD

2011 ◽  
Vol 20 (03) ◽  
pp. 563-575 ◽  
Author(s):  
MEI LING HUANG ◽  
YUNG HSIANG HUNG ◽  
EN JU LIN
Keyword(s):  
Taguchi Method ◽  
Parameter Design ◽  
Support Vector ◽  
Svm Classifier ◽  
Liver Disorders ◽  
Second Stage ◽  
Vector Machines ◽  
Class Labels ◽  
Two Stages ◽  
Decision Boundaries

Support Vector Machines (SVMs) are based on the concept of decision planes that define decision boundaries, and Least Squares Support Vector (LS-SVM) Machine is the reformulation of the principles of SVM. In this study a diagnosis on a BUPA liver disorders dataset, is conducted LS-SVM with the Taguchi method. The BUPA Liver Disorders dataset includes 345 samples with 6 features and 2 class labels. The system approach has two stages. In the first stage, in order to effectively determine the parameters of the kernel function, the Taguchi method is used to obtain better parameter settings. In the second stage, diagnosis of the BUPA liver disorders dataset is conducted using the LS-SVM classifier; the classification accuracy is 95.07%; the AROC is 99.12%. Compared with the results of related research, our proposed system is both effective and reliable.

scholarly journals Machine Learning-Based Crop Drought Mapping System by UAV Remote Sensing RGB Imagery

2020 ◽  
Vol 08 (01) ◽  
pp. 71-83 ◽  
Author(s):  
Jinya Su ◽  
Matthew Coombes ◽  
Cunjia Liu ◽  
Yongchao Zhu ◽  
Xingyang Song ◽  
...  
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Field Measurements ◽  
Vital Role ◽  
Learning System ◽  
Bayesian Optimization ◽  
Growth And Yield ◽  
Support Vector ◽  
Svm Classifier ◽  
Spectral Intensities

Water stress has adverse effects on crop growth and yield, where its monitoring plays a vital role in precision crop management. This paper aims at initially exploiting the potentials of UAV aerial RGB image in crop water stress assessment by developing a simple but effective supervised learning system. Various techniques are seamlessly integrated into the system including vegetation segmentation, feature engineering, Bayesian optimization and Support Vector Machine (SVM) classifier. In particular, wheat pixels are first segmented from soil background by using the classical vegetation index thresholding. Rather than performing pixel-wise classification, pixel squares of appropriate dimension are defined as samples, from which various features for pure vegetation pixels are extracted including spectral and color index (CI) features. SVM with Bayesian optimization is adopted as the classifier. To validate the developed system, a Unmanned Aerial Vehicle (UAV) survey is performed to collect high-resolution atop canopy RGB imageries by using DJI S1000 for the experimental wheat fields of Gucheng town, Heibei Province, China. Two levels of soil moisture were designed after seedling establishment for wheat plots by using intelligent irrigation and rain shelter, where field measurements were to obtain ground soil water ratio for each wheat plot. Comparative experiments by three-fold cross-validation demonstrate that pixel-wise classification, with a high computation load, can only achieve an accuracy of 82.8% with poor F1 score of 71.7%; however, the developed system can achieve an accuracy of 89.9% with F1 score of 87.7% by using only spectral intensities, and the accuracy can be further improved to 92.8% with F1 score of 91.5% by fusing both spectral intensities and CI features. Future work is focused on incorporating more spectral information and advanced feature extraction algorithms to further improve the performance.

TOPTMH: TOPOLOGY PREDICTOR FOR TRANSMEMBRANE α-HELICES

2010 ◽  
Vol 08 (01) ◽  
pp. 39-57 ◽  
Author(s):  
REZWAN AHMED ◽  
HUZEFA RANGWALA ◽  
GEORGE KARYPIS
Keyword(s):  
Markov Models ◽  
Structural Characteristics ◽  
Transmembrane Helix ◽  
3D Structure ◽  
Prediction Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Sequence Information ◽  
Vector Machines ◽  
Prediction Approach

Alpha-helical transmembrane proteins mediate many key biological processes and represent 20%–30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods to predict the location and orientation of transmembrane helix segments using sequence information are essential. We present TOPTMH, a new transmembrane helix topology prediction method that combines support vector machines, hidden Markov models, and a widely used rule-based scheme. The contribution of this work is the development of a prediction approach that first uses a binary SVM classifier to predict the helix residues and then it employs a pair of HMM models that incorporate the SVM predictions and hydropathy-based features to identify the entire transmembrane helix segments by capturing the structural characteristics of these proteins. TOPTMH outperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark.

scholarly journals Correlation Kernels for Support Vector Machines Classification with Applications in Cancer Data

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Hao Jiang ◽  
Wai-Ki Ching
Keyword(s):  
Support Vector Machines ◽  
Positive Semidefinite ◽  
Superior Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Correlation Kernel ◽  
Cancer Data ◽  
Tumor Tissues ◽  
Vector Machines

High dimensional bioinformatics data sets provide an excellent and challenging research problem in machine learning area. In particular, DNA microarrays generated gene expression data are of high dimension with significant level of noise. Supervised kernel learning with an SVM classifier was successfully applied in biomedical diagnosis such as discriminating different kinds of tumor tissues. Correlation Kernel has been recently applied to classification problems with Support Vector Machines (SVMs). In this paper, we develop a novel and parsimonious positive semidefinite kernel. The proposed kernel is shown experimentally to have better performance when compared to the usual correlation kernel. In addition, we propose a new kernel based on the correlation matrix incorporating techniques dealing with indefinite kernel. The resulting kernel is shown to be positive semidefinite and it exhibits superior performance to the two kernels mentioned above. We then apply the proposed method to some cancer data in discriminating different tumor tissues, providing information for diagnosis of diseases. Numerical experiments indicate that our method outperforms the existing methods such as the decision tree method and KNN method.

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