scholarly journals An Efficient Soft Computing Approach for Text Identification using Artificial Intelligence Model

2021 ◽  
Vol 9 (VI) ◽  
pp. 3860-3866
Author(s):  
Shashi Bhushan
Keyword(s):  
Artificial Intelligence ◽  
Soft Computing ◽  
Feature Vector ◽  
Similar Work ◽  
Input Text ◽  
Proposed Model ◽  
Soft Computing Techniques ◽  
Pos Tagger ◽  
Vector Matrix ◽  
Computing Approach

This paper presents an enhanced system in the field of text identification using Soft computing techniques. The model designed in this work analyzes the blogs or input text and classifies the personality into five major categories; Neuroticism, Extraversion, Openness, Conscientiousness and Agreeableness. The blog or text is first passed through POS tagger then a feature vector matrix is generated according to the attributes of the personality chart. Each column of FVM is calculated in its domain that improves the final result of personality identification. The result of the proposed model is improvement over similar work by other researchers [1, 2, 3].

scholarly journals A Survey of the Various Steganography Techniques Using Soft Computing Techniques

2021 ◽  
Vol 9 (VII) ◽  
pp. 1616-1619
Author(s):  
Er. Savita Devi
Keyword(s):  
Artificial Intelligence ◽  
Soft Computing ◽  
Secret Data ◽  
Artificial Intelligence Techniques ◽  
Individual Privacy ◽  
Soft Computing Techniques

Steganography is the method of storing information by hiding that information’s existence. It can be used to carry out hidden exchanges and hence can enhance individual privacy. Steganography aims at communicating the secret data in an appropriate multimedia carrier. In this paper, the various techniques used to perform Steganography in a secure way are studied and reviewed. In this the various Artificial Intelligence techniques used for steganography are reviewed and analyzed.

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 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 ENHANCED SOFT COMPUTING APPROACHES FOR INTRUSION DETECTION SCHEMES IN SOCIAL MEDIA NETWORKS

2019 ◽  
Vol 2019 (2) ◽  
pp. 69-79 ◽  
Author(s):  
Dr. Sathesh A.
Keyword(s):  
Social Network ◽  
Intrusion Detection ◽  
Soft Computing ◽  
Detection System ◽  
Low Cost ◽  
Feature Reduction ◽  
Computing Technique ◽  
The Social ◽  
Soft Computing Techniques ◽  
Computing Approach

The soft computing methods play a vital role in identifying the malicious activities in the social network. The low cost solutions and the robustness provided by the soft computing in the identifying the unwanted activities make it a predominant area of research. The paper combines the soft computing techniques and frames an enhanced soft computing approach to detect the intrusion that cause security issues in the social network. The proffered method of the paper employs the enhanced soft computing technique that combines the fuzzy logic, decision tree, K means -EM and the machine learning in preprocessing, feature reduction, clustering and classification respectively to develop a security approach that is more effective than the traditional computations in identifying the misuse in the social networks. The intrusion detection system developed using the soft computing approach is tested using the KDD-NSL and the DARPA dataset to note down the security percentage, time utilization, cost and compared with the other traditional methods.

scholarly journals Optimized Hybrid Soft Computing Model for Weather Predictions in Delhi

2019 ◽  
Vol 8 (4) ◽  
pp. 9793-9798
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Weather Forecasting ◽  
Statistical Prediction ◽  
Energy Prediction ◽  
Advantages And Disadvantages ◽  
Computing Model ◽  
Proposed Model ◽  
Soft Computing Techniques ◽  
Weather Parameters

Soft computing techniques have become very popular now-a-days as these techniques have replaced the traditional and statistical prediction mechanisms in weather forecasting, stock market prediction, crop prediction, solar energy prediction, and predictions in physics and chemistry etc. Each model has its advantages and disadvantages. Hybrid soft computing model is the mechanism of designing the models by exploiting the advantages of two or more models and suppressing their disadvantages. If the advantages of two or more number of models will be taken together in the new proposed model, then the accuracy in the prediction will be enhanced with decrease in error rate. This paper intends to design a hybrid model by taking the advantages of J48 Decision Tree and Fuzzy Logic andit is used to predict the weather parameters in Delhi with better accuracy.

scholarly journals Hybrid Maintainability Prediction using Soft Computing Techniques

2021 ◽  
pp. 350-356
Author(s):  
Manju Duhan ◽  
Pradeep Kumar Bhatia
Keyword(s):  
Soft Computing ◽  
Software Maintenance ◽  
Software Metrics ◽  
Fuzzy Model ◽  
Object Oriented ◽  
Dynamic Binding ◽  
Proposed Model ◽  
Soft Computing Techniques ◽  
Run Time ◽  
Dynamic Metrics

Effective software maintenance is a crucial factor to measure that can be achieved with the help of software metrics. In this paper, authors derived a new approach for measuring the maintainability of software based on hybrid metrics that takes advantages of both i.e. static metrics and dynamic metrics in an object-oriented environment whereas, dynamic metrics capture the run time features of object-oriented languages i.e. run time polymorphism, dynamic binding etc. which is not covered by static metrics. To achieve this, the authors proposed a model based on static and hybrid metrics to measure maintainability factor by using soft computing techniques and it is found that the proposed neuro-fuzzy model was trained well and predict adequate results with MAE 0.003 and RMSE 0.009 based on hybrid metrics. Additionally, the proposed model was validated on two test datasets and it is concluded that the proposed model performed well, based on hybrid metrics.

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