Deep Learning Based Sentiment Classification on User-Generated Big Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1047-1056
Author(s):  
Akshi Kumar ◽  
Arunima Jaiswal
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Soft Computing ◽  
Sentiment Classification ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Computing Technique ◽  
Proposed Model ◽  
Soft Computing Techniques

Background: Sentiment analysis of big data such as Twitter primarily aids the organizations with the potential of surveying public opinions or emotions for the products and events associated with them. Objective: In this paper, we propose the application of a deep learning architecture namely the Convolution Neural Network. The proposed model is implemented on benchmark Twitter corpus (SemEval 2016 and SemEval 2017) and empirically analyzed with other baseline supervised soft computing techniques. The pragmatics of the work includes modelling the behavior of trained Convolution Neural Network on wellknown Twitter datasets for sentiment classification. The performance efficacy of the proposed model has been compared and contrasted with the existing soft computing techniques like Naïve Bayesian, Support Vector Machines, k-Nearest Neighbor, Multilayer Perceptron and Decision Tree using precision, accuracy, recall, and F-measure as key performance indicators. Methods: Majority of the studies emphasize on the utilization of feature mining using lexical or syntactic feature extraction that are often unequivocally articulated through words, emoticons and exclamation marks. Subsequently, CNN, a deep learning based soft computing technique is used to improve the sentiment classifier’s performance. Results: The empirical analysis validates that the proposed implementation of the CNN model outperforms the baseline supervised learning algorithms with an accuracy of around 87% to 88%. Conclusion: Statistical analysis validates that the proposed CNN model outperforms the existing techniques and thus can enhance the performance of sentiment classification viability and coherency.

scholarly journals Soft Computing Techniques Based Automatic Licence Plate Recognition Systems for Indian Vehicles

2019 ◽  
Vol 8 (12) ◽  
pp. 470-474
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Traffic Management ◽  
Support Vector ◽  
Hybrid Technique ◽  
Management Problem ◽  
Computing Technique ◽  
Soft Computing Techniques ◽  
Recognition Systems ◽  
Licence Plate Recognition

Automobile industries are growing exponentially in last decade in India. Growth in the vehicle numbers results in much more road accidents and traffic management problem. Not only this, long queues at toll plazas and parking lot is also a major issue of concern. Problem of traffic management and long queues can be solved by automatic licence plate recognition systems. In this paper, an automatic Licence Plate Recognition Systems based on soft computing techniques are presented. Indian vehicle with licence plates were used for testing the implemented systems. Firstly the licence plate image is extracted from the vehicle image and the characters are segmented from the extracted licence plate image and then features are extracted from the segmented characters which are used for the recognition. Soft computing techniques random forest, neural network, support vector machine, and convolutional neural network are used for the implementation pusrpose. The results obtained for the applied soft computing technique are compared to the last. The future scope is the hybrid technique solution to the problem

Performance Analysis of DE over K-Means Proposed Model of Soft Computing

2016 ◽  
pp. 211-224
Author(s):  
Kapil Patidar ◽  
Manoj Kumar ◽  
Sushil Kumar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Soft Computing ◽  
Big Data Analysis ◽  
Learning Approach ◽  
Real World Data ◽  
Computing Technique ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Soft Computing Technique

In real world data increased periodically, huge amount of data is called Big data. It is a well-known term used to define the exponential growth of data, both in structured and unstructured format. Data analysis is a method of cleaning, altering, learning valuable statistics, decision making and advising assumption with the help of many algorithm and procedure such as classification and clustering. In this chapter we discuss about big data analysis using soft computing technique and propose how to pair two different approaches like evolutionary algorithm and machine learning approach and try to find better cause.

scholarly journals A Fuzzy Based Classification – An Experimental Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 4634-4638
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Soft Computing ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Hybrid Technique ◽  
Classification Rate ◽  
Data Set ◽  
Computing Technique ◽  
Increase In Accuracy

Soft Computing has become popular in developing systems that encloses human expertise. Imaging technologies and clinical cytology has improved in disease diagnosis. Exact detection is extremely important for proper treatment and cure of disease. Two soft computing technique Neural Network and Support Vector Machine are used for classification of Caridotocography data set. This paper clearly explains the advantages of hybrid technique, when Fuzzy is combined with Neural Network and Support Vector Machine it is clearly noticed that there is an increase in accuracy of classification rate.

scholarly journals Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3608
Author(s):  
Chiao-Sheng Wang ◽  
I-Hsi Kao ◽  
Jau-Woei Perng
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Permanent Magnet ◽  
Support Vector ◽  
One Dimensional ◽  
Proposed Model ◽  
Wide Range ◽  
Fault State

The early diagnosis of a motor is important. Many researchers have used deep learning to diagnose motor applications. This paper proposes a one-dimensional convolutional neural network for the diagnosis of permanent magnet synchronous motors. The one-dimensional convolutional neural network model is weakly supervised and consists of multiple convolutional feature-extraction modules. Through the analysis of the torque and current signals of the motors, the motors can be diagnosed under a wide range of speeds, variable loads, and eccentricity effects. The advantage of the proposed method is that the feature-extraction modules can extract multiscale features from complex conditions. The number of training parameters was reduced so as to solve the overfitting problem. Furthermore, the class feature map was proposed to automatically determine the frequency component that contributes to the classification using the weak learning method. The experimental results reveal that the proposed model can effectively diagnose three different motor states—healthy state, demagnetization fault state, and bearing fault state. In addition, the model can detect eccentric effects. By combining the current and torque features, the classification accuracy of the proposed model is up to 98.85%, which is higher than that of classical machine-learning methods such as the k-nearest neighbor and support vector machine.

scholarly journals Soft Computing Techniques for Weather Change Predictions in Delhi

2019 ◽  
Vol 8 (4) ◽  
pp. 793-800
Keyword(s):  
Soft Computing ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Research Work ◽  
Absolute Error ◽  
Support Vector ◽  
Squared Error ◽  
Proposed Model ◽  
Soft Computing Techniques

Weather forecasting and warning is the application of science and technology to predict the state of the weather for a future time of a given location. The emergence of adverse effects of weather has endangered the life of general public in previous years. The unpredicted flood and super cyclone in many places have created havoc. The government and private agencies are working on its behaviours but still it is challenging and incomplete. But, the application of soft computing techniques in weather prediction has made a significant perfomance now a days. This research work presents the comparative study of soft computing techniques like MultiLayer Perceptron(MLP), Support Vector Machine(SVM) and J48 Decision Tree for forecasting the weather of Delhi with ten years data comprising of temperature, dew, humidity, air pressure, wind speed and visibility. This paper tries to describe the comparison among above models using four different error values like Relative Absolute Error(RAE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE) and Root Relative Squared Error(R2 ) with a proposed model by defining new algorithm. Further the performance can be enhanced if textmining will be applied in this proposed model.

scholarly journals Using Convolution Neural Network for Defective Image Classification of Industrial Components

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hao Wu ◽  
Zhi Zhou
Keyword(s):  
Neural Network ◽  
Experimental Study ◽  
Deep Learning ◽  
Large Scale ◽  
Intelligent System ◽  
Convolution Neural Network ◽  
Lens Distortion ◽  
Proposed Model ◽  
Industrial Cameras

Computer vision provides effective solutions in many imaging relation problems, including automatic image segmentation and classification. Artificially trained models can be employed to tag images and identify objects spontaneously. In large-scale manufacturing, industrial cameras are utilized to take constant images of components for several reasons. Due to the limitations caused by motion, lens distortion, and noise, some defective images are captured, which are to be identified and separated. One common way to address this problem is by looking into these images manually. However, this solution is not only very time-consuming but is also inaccurate. The paper proposes a deep learning-based artificially intelligent system that can quickly train and identify faulty images. For this purpose, a pretrained convolution neural network based on the PyTorch framework is employed to extract discriminating features from the dataset, which is then used for the classification task. In order to eliminate the chances of overfitting, the proposed model also employed Dropout technology to adjust the network. The experimental study reveals that the system can precisely classify the normal and defective images with an accuracy of over 91%.

Genes expression classification through Histone Modification using Temporal Neural Network

2019 ◽  
Vol 12 ◽  
Author(s):  
Rajit Nair ◽  
Amit Bhagat
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Deep Learning ◽  
Histone Modification ◽  
Convolution Neural Network ◽  
Proposed Model ◽  
Genes Expression ◽  
Expression Classification ◽  
Improved Accuracy ◽  
Deep Learning Model

Histones are one of the important proteins present in our body. So histone modification will regulate the genes expression. Many computational methods are based on histone modification that predicts gene expression. The prediction of genes expression helps us in discovering epigenetic drugs for different types of diseases like cancer. Some of the methods are already developed to show the relation between histone and genes expression but failed in many aspects. This paper presents a work based on deep learning method that is namely Temporal Convolution Neural Network (TCN) and it classifies genes expression using histones as input. Temporal Convolution Neural Network basically extract features from input histones and predict the gene expression as on or off through temporal convolution neural network. This work also shows how proposed model generates improved accuracy than other machine learning and deep learning model working in this area. The comparison of the proposed work is done with other algorithms which are SVM, Logistic Regression, Convolutional Neural Network and Deep Chrome Neural Network.

Literature review and analysis on big data stream classification techniques

2020 ◽  
Vol 24 (3) ◽  
pp. 205-215
Author(s):  
B. Srivani ◽  
N. Sandhya ◽  
B. Padmaja Rani
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Data Classification ◽  
Support Vector ◽  
Complex Data ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Data Stream Classification ◽  
Big Data Classification

Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodologies, like deep learning based techniques, Convolutional Neural Network (CNN) based techniques, K-Nearest Neighbor (KNN) based techniques, Neural Network (NN) based techniques, fuzzy based techniques, and Support vector based techniques, and so on. Moreover, a detailed study is made by concerning the parameters, like evaluation metrics, implementation tool, employed framework, datasets utilized, adopted classification methods, and accuracy range obtained by various techniques. Eventually, the research gaps and issues of various big data classification schemes are presented.

scholarly journals Empirical Analysis of Cardiovascular Diseases using Machine Learning and Soft Computing Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 3944-3948 ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Soft Computing ◽  
Accurate Result ◽  
Research Community ◽  
Support Vector ◽  
Disease Prediction ◽  
Soft Computing Techniques ◽  
Artificial Neural Network Ann

Cardiovascular diseases are a one of the most exigent issue in healthcare domain. There have been various multidisciplinary approaches proposed and applied to reduce the mortality rate. As per literature and current study machine learning and soft computing techniques are efficient and widely accepted approaches in research community. This paper identifies and compares the various techniques of machine learning using Random Forest (RF), Support Vector Machine (SVM), XGBoost and Artificial Neural Network (ANN) and uncovers the F1 score, recall, precision to predict efficient and more accurate result. The results are further compared with existing benchmark models and showed significant improvement in heart disease prediction of patient.

scholarly journals Recognition of Handwritten Digits using Convolutional Neural Network and Linear Binary Pattern

2019 ◽  
Vol 9 (1) ◽  
pp. 4368-4372
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Handwritten Documents ◽  
Python Language ◽  
Digital Format ◽  
Proposed Model ◽  
Human Effort

Over the past few years there has been a tremendous developments observed in the field of computer technology and artificial intelligence, especially the use of machine learning concepts in Research and Industries. The human effort can be further more reduced in recognition, learning, predicting and many other areas using machine learning and deep learning. Any information which has been handwritten documents consisting of digits in digital form like images, recognizing such digits is a challenging task. The proposed system can recognize any handwritten digits in the document which has been converted into digital format. The proposed model includes Convolutional Neural Network (CNN), a deep learning approach with Linear Binary Pattern (LBP) used for feature extraction. In order to classify more effectively we also have used Support Vector Machine to recognize mere similar digits like 1 and 7, 5 and 6 and many others. The proposed system CNN and LBP is implemented on python language; also the system is tested with different images of handwritten digits taken from MNIST dataset. By using proposed model we could able to achieve 98.74% accuracy in predicting the digits in image format.

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