Web applications are meant to be viewed by human user. While this implies that quality of web app... more Web applications are meant to be viewed by human user. While this implies that quality of web application has importance in our daily life. Web application quality is our prime concern. To ensure the quality of web application, web testing is having a dandy role in Software Testing as well as Web Community. Web Applications are erring because of features provided for rising of web application. In the last years, various web testing problems have been addressed by research work. Several tools, techniques and methods have bee n determined to test w eb application effi caciously. This paper w ill present the contribution of researchers in the field of web application in previous years and state of art of web testing and challenges primarily because of di stributed and heterogeneous nature of we b application.
Dynamic Content of a Web Application gives acclivity to whole set of new challenges in the field ... more Dynamic Content of a Web Application gives acclivity to whole set of new challenges in the field of web application testing. After canvassing several approaches on how to effectively test Dynamic Web Applications, We found that an effective way to counter the web application dynamic content testing exceptions is using the combined approaches of state-based testing and user session based testing. In this research work, we projected a novel approach User Session based State Testing to test the Dynamic Content of a Web Application that can walk through different states of a highly dynamic web application, create a model of the active user session navigational paths and illustrate the dynamic content by means of State Machine. The proposed model captures precisely the distinctive features of the dynamic content of a web application of changing DOM manipulation. User session based state testing technique makes it possible to test and detect faults like asynchronous communication fault, i...
Journal of Retailing and Consumer Services, Jul 1, 2019
The growth of social media has completely revamped the way people interact, communicate and engag... more The growth of social media has completely revamped the way people interact, communicate and engage. These platforms play a key role in facilitating greater outreach and influence. This study proposes a mechanism for measuring the influencer index across popular social media platforms including Facebook, Twitter, and Instagram. A set of features that determine the impact on the consumers are modelled using a regression approach. The underlying machine learning algorithms including Ordinary Least Squares (OLS), K-NN Regression (KNN), Support Vector Regression (SVR), and Lasso Regression models are adapted to compute a cumulative score in terms of influencer index. Findings indicate that engagement, outreach, sentiment, and growth play a key role in determining the influencers. Further, the ensemble of the four models resulted in the highest accuracy of 93.7% followed by the KNN regression with 93.6%. The study has implications across various domains of e-commerce, viral marketing, social media marketing and brand management wherein identification of key information propagators is essential. These influencer indices may further be utilized by e-commerce portals and brands for the purpose of social media promotion and engagement for larger outreach.
International Journal of Ambient Computing and Intelligence
The credible analysis of news on social media due to the fact of spreading unnecessary restlessne... more The credible analysis of news on social media due to the fact of spreading unnecessary restlessness and reluctance in the community is a need. Numerous individual or social media marketing entities radiate inauthentic news through online social media. Henceforth, delineating these activities on social media and the apparent identification of delusive content is a challenging task. This work projected a continuous attention-driven memory-based deep learning model to predict the credibility of an article. To exhibit the importance of continuous attention, research work is presented in accretive exaggeration mode. Initially, long short-term memory (LSTM)-based deep learning model has been applied, which is extended by merging the concept of bidirectional LSTM for fake news identification. This research work proposed a continuous attention mechanism embedded (CAME)-bidirectional LSTM model for predicting the nature of news. Result shows the proposed CAME model outperforms the performanc...
International Journal of Information Technology Project Management
A lucid representation of the hidden structure of real-world application has attracted complex ne... more A lucid representation of the hidden structure of real-world application has attracted complex network research communities and triggered a vast number of solutions in order to resolve complex network issues. In the same direction, initially, this paper proposes a methodology to act on the financial dataset and construct a stock correlation network of four stock indexes based on the closing stock price. The significance of this research work is to form an effective stock community based on their complex price pattern dependencies (i.e., simultaneous fluctuations in stock prices of companies in a time series data). This paper proposes a community detection approach for stock correlation complex networks using the BAT optimization algorithm aiming to achieve high modularity and better-correlated communities. Theoretical analysis and empirical modularity performance measure results have shown that the usage of BAT algorithm for community detection proves to transcend performance in com...
2021 7th International Conference on Signal Processing and Communication (ICSC)
Social media usage has shot up over the past decade which gives us many wonderful opportunities t... more Social media usage has shot up over the past decade which gives us many wonderful opportunities to showcase ourselves, our skills, and field of competences. In short, the facial nature of social media raised opportunities to create and promote one’s own brand to gain influence in the market. Besides exploiting these social media content uplifted the negative impacts of spreading false information as well. As a consequence, falsehood detection in terms of information gain diverted researchers’ attention and the same issue has been addressed in this work. We present an ImageFake ensemble model which makes use of abundant pre-trained CNN models to discover the usage of multimedia features for image-based fake news detection and classification. The pre-trained models used for fake news detection and classification are VGG-16, VGG-19, Inception v3, SqueezeNet, and ResNet-101, and finally the bagging ensemble model is used for selecting the best of the bunch. MediaEval 2015, a Twitter dataset is used for the experiment and to validate multimedia feature usage. The performance of pre-trained CNN models on the particular visual domain is performed and compared and out of all best-considered models in terms of accuracy and execution time is ResNet-101 which is able to achieve 96% training accuracy. The ensemble model is able to get training and validation accuracy 97% and 66% respectively.
Research Anthology on Machine Learning Techniques, Methods, and Applications
Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of the... more Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of these networks. Spammers often use certain techniques to deceive the OSN users for their own benefit. Facebook, one of the leading OSNs, is experiencing such crucial problems at an alarming rate. This chapter presents a methodology to segregate spam from legitimate posts using machine learning techniques: naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The textual, image, and video features are used together, which wasn't considered by the earlier researchers. Then, 1.5 million posts and comments are extracted from archival and real-time Facebook data, which is then pre-processed using RStudio. A total of 30 features are identified, out of which 10 are the best informative for identification of spam vs. ham posts. The entire dataset is shuffled and divided into three ratios, out of which 80:20 ratio of training and testing dataset provides the best result. Also, ...
In the present scenario, Automatic Text Summarization (ATS) is in great demand to address the eve... more In the present scenario, Automatic Text Summarization (ATS) is in great demand to address the ever-growing volume of text data available online to discover relevant information faster. In this research, the ATS methodology is proposed for the Hindi language using Real Coded Genetic Algorithm (RCGA) over the health corpus, available in the Kaggle dataset. The methodology comprises five phases: preprocessing, feature extraction, processing, sentence ranking, and summary generation. Rigorous experimentation on varied feature sets is performed where distinguishing features, namely- sentence similarity and named entity features are combined with others for computing the evaluation metrics. The top 14 feature combinations are evaluated through Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure. RCGA computes appropriate feature weights through strings of features, chromosomes selection, and reproduction operators: Simulating Binary Crossover and Polynomial Mutation. To extr...
International Journal of Operations Research and Information Systems, 2021
Particle swarm optimization (PSO) algorithm is proposed to deal with text summarization for the P... more Particle swarm optimization (PSO) algorithm is proposed to deal with text summarization for the Punjabi language. PSO is based on intelligence that predicts among a given set of solutions which is the best solution. The search is carried out by extremely high-speed particles. It updates particle position and velocity at the end of iteration so that during the development of generations, the personal best solution and global best solution are updated. Calculation within PSO is performed using fitness function which looks into various statistical and linguistic features of the Punjabi datasets. Two Punjabi datasets—monolingual Punjabi corpus from Indian Languages Corpora Initiative Phase-II and Punjabi-Hindi parallel corpus—are considered. The parallel corpus comprises 1,000 Punjabi sentences from the tourism domain while monolingual corpus contains 30,000 Punjabi sentences of the general domain. ROUGE measures evaluate summary where the highest measure, ROUGE-1, is achieved for paral...
2018 Eleventh International Conference on Contemporary Computing (IC3)
Musical genres can be used to distribute and manage music datasets to increase the ease in findin... more Musical genres can be used to distribute and manage music datasets to increase the ease in finding a music piece a person wants to listen to. This paper presents a research for creating a suitable model for genre recognition in audio files using machine learning classifiers on the IRMAS11 https://www.upf.edu/web/mtg/irmas dataset. Python language library pyAudinAnalysls22 https://github.com/tyiannak/pyAudioAnalysis for extracting features from audio files is used. Further, three base classifiers, namely Support Vector Machines (SVM), Decision Tree and Random Forest are also depicted. IRMAS [10] genre dataset provides 6705 audio files of four genres classical, country folk, jazz and pop-rock. Also explored is an ensemble classification model by creating a stack of classifiers for the genre recognition task. Genre classification using SMOTE has been characterized in the confusion matrix. Maximum accuracy of 81.56% using the ensemble classifier is achieved using the proposed methodology.
International Journal of Social Network Mining, 2017
This research paper focuses on the virality of content corresponding to user's reaction and propo... more This research paper focuses on the virality of content corresponding to user's reaction and proposes a multiple metric aware virality model (M2VM) which is beneficial to identify virality of content according to content characteristics and their resultant user reactions. The proposed M2VM assigns virality coefficient to each user reaction based on content properties/characteristics and predict a virality score of target content corresponding to measured user reactions' virality coefficients. To validate our proposed model we have chosen five tutorial-based theme videos of YouTube as case study. Thus in this research work, efforts have been made to analyse and understand users' selection intention towards tutorial videos on the basis of various video metrics. The prime objective of this research work is to analyse video characteristics role in identifying users' selection preference on the basis of video statistics and formulate various video metric corresponding to outcome to conclude the reaction of video characteristics over video virality. The research gives a practical exhibit of YouTube tutorial videos virality with respect to video characteristics mapping with video statistics.
Web applications are meant to be viewed by human user. While this implies that quality of web app... more Web applications are meant to be viewed by human user. While this implies that quality of web application has importance in our daily life. Web application quality is our prime concern. To ensure the quality of web application, web testing is having a dandy role in Software Testing as well as Web Community. Web Applications are erring because of features provided for rising of web application. In the last years, various web testing problems have been addressed by research work. Several tools, techniques and methods have bee n determined to test w eb application effi caciously. This paper w ill present the contribution of researchers in the field of web application in previous years and state of art of web testing and challenges primarily because of di stributed and heterogeneous nature of we b application.
Dynamic Content of a Web Application gives acclivity to whole set of new challenges in the field ... more Dynamic Content of a Web Application gives acclivity to whole set of new challenges in the field of web application testing. After canvassing several approaches on how to effectively test Dynamic Web Applications, We found that an effective way to counter the web application dynamic content testing exceptions is using the combined approaches of state-based testing and user session based testing. In this research work, we projected a novel approach User Session based State Testing to test the Dynamic Content of a Web Application that can walk through different states of a highly dynamic web application, create a model of the active user session navigational paths and illustrate the dynamic content by means of State Machine. The proposed model captures precisely the distinctive features of the dynamic content of a web application of changing DOM manipulation. User session based state testing technique makes it possible to test and detect faults like asynchronous communication fault, i...
Journal of Retailing and Consumer Services, Jul 1, 2019
The growth of social media has completely revamped the way people interact, communicate and engag... more The growth of social media has completely revamped the way people interact, communicate and engage. These platforms play a key role in facilitating greater outreach and influence. This study proposes a mechanism for measuring the influencer index across popular social media platforms including Facebook, Twitter, and Instagram. A set of features that determine the impact on the consumers are modelled using a regression approach. The underlying machine learning algorithms including Ordinary Least Squares (OLS), K-NN Regression (KNN), Support Vector Regression (SVR), and Lasso Regression models are adapted to compute a cumulative score in terms of influencer index. Findings indicate that engagement, outreach, sentiment, and growth play a key role in determining the influencers. Further, the ensemble of the four models resulted in the highest accuracy of 93.7% followed by the KNN regression with 93.6%. The study has implications across various domains of e-commerce, viral marketing, social media marketing and brand management wherein identification of key information propagators is essential. These influencer indices may further be utilized by e-commerce portals and brands for the purpose of social media promotion and engagement for larger outreach.
International Journal of Ambient Computing and Intelligence
The credible analysis of news on social media due to the fact of spreading unnecessary restlessne... more The credible analysis of news on social media due to the fact of spreading unnecessary restlessness and reluctance in the community is a need. Numerous individual or social media marketing entities radiate inauthentic news through online social media. Henceforth, delineating these activities on social media and the apparent identification of delusive content is a challenging task. This work projected a continuous attention-driven memory-based deep learning model to predict the credibility of an article. To exhibit the importance of continuous attention, research work is presented in accretive exaggeration mode. Initially, long short-term memory (LSTM)-based deep learning model has been applied, which is extended by merging the concept of bidirectional LSTM for fake news identification. This research work proposed a continuous attention mechanism embedded (CAME)-bidirectional LSTM model for predicting the nature of news. Result shows the proposed CAME model outperforms the performanc...
International Journal of Information Technology Project Management
A lucid representation of the hidden structure of real-world application has attracted complex ne... more A lucid representation of the hidden structure of real-world application has attracted complex network research communities and triggered a vast number of solutions in order to resolve complex network issues. In the same direction, initially, this paper proposes a methodology to act on the financial dataset and construct a stock correlation network of four stock indexes based on the closing stock price. The significance of this research work is to form an effective stock community based on their complex price pattern dependencies (i.e., simultaneous fluctuations in stock prices of companies in a time series data). This paper proposes a community detection approach for stock correlation complex networks using the BAT optimization algorithm aiming to achieve high modularity and better-correlated communities. Theoretical analysis and empirical modularity performance measure results have shown that the usage of BAT algorithm for community detection proves to transcend performance in com...
2021 7th International Conference on Signal Processing and Communication (ICSC)
Social media usage has shot up over the past decade which gives us many wonderful opportunities t... more Social media usage has shot up over the past decade which gives us many wonderful opportunities to showcase ourselves, our skills, and field of competences. In short, the facial nature of social media raised opportunities to create and promote one’s own brand to gain influence in the market. Besides exploiting these social media content uplifted the negative impacts of spreading false information as well. As a consequence, falsehood detection in terms of information gain diverted researchers’ attention and the same issue has been addressed in this work. We present an ImageFake ensemble model which makes use of abundant pre-trained CNN models to discover the usage of multimedia features for image-based fake news detection and classification. The pre-trained models used for fake news detection and classification are VGG-16, VGG-19, Inception v3, SqueezeNet, and ResNet-101, and finally the bagging ensemble model is used for selecting the best of the bunch. MediaEval 2015, a Twitter dataset is used for the experiment and to validate multimedia feature usage. The performance of pre-trained CNN models on the particular visual domain is performed and compared and out of all best-considered models in terms of accuracy and execution time is ResNet-101 which is able to achieve 96% training accuracy. The ensemble model is able to get training and validation accuracy 97% and 66% respectively.
Research Anthology on Machine Learning Techniques, Methods, and Applications
Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of the... more Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of these networks. Spammers often use certain techniques to deceive the OSN users for their own benefit. Facebook, one of the leading OSNs, is experiencing such crucial problems at an alarming rate. This chapter presents a methodology to segregate spam from legitimate posts using machine learning techniques: naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The textual, image, and video features are used together, which wasn't considered by the earlier researchers. Then, 1.5 million posts and comments are extracted from archival and real-time Facebook data, which is then pre-processed using RStudio. A total of 30 features are identified, out of which 10 are the best informative for identification of spam vs. ham posts. The entire dataset is shuffled and divided into three ratios, out of which 80:20 ratio of training and testing dataset provides the best result. Also, ...
In the present scenario, Automatic Text Summarization (ATS) is in great demand to address the eve... more In the present scenario, Automatic Text Summarization (ATS) is in great demand to address the ever-growing volume of text data available online to discover relevant information faster. In this research, the ATS methodology is proposed for the Hindi language using Real Coded Genetic Algorithm (RCGA) over the health corpus, available in the Kaggle dataset. The methodology comprises five phases: preprocessing, feature extraction, processing, sentence ranking, and summary generation. Rigorous experimentation on varied feature sets is performed where distinguishing features, namely- sentence similarity and named entity features are combined with others for computing the evaluation metrics. The top 14 feature combinations are evaluated through Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure. RCGA computes appropriate feature weights through strings of features, chromosomes selection, and reproduction operators: Simulating Binary Crossover and Polynomial Mutation. To extr...
International Journal of Operations Research and Information Systems, 2021
Particle swarm optimization (PSO) algorithm is proposed to deal with text summarization for the P... more Particle swarm optimization (PSO) algorithm is proposed to deal with text summarization for the Punjabi language. PSO is based on intelligence that predicts among a given set of solutions which is the best solution. The search is carried out by extremely high-speed particles. It updates particle position and velocity at the end of iteration so that during the development of generations, the personal best solution and global best solution are updated. Calculation within PSO is performed using fitness function which looks into various statistical and linguistic features of the Punjabi datasets. Two Punjabi datasets—monolingual Punjabi corpus from Indian Languages Corpora Initiative Phase-II and Punjabi-Hindi parallel corpus—are considered. The parallel corpus comprises 1,000 Punjabi sentences from the tourism domain while monolingual corpus contains 30,000 Punjabi sentences of the general domain. ROUGE measures evaluate summary where the highest measure, ROUGE-1, is achieved for paral...
2018 Eleventh International Conference on Contemporary Computing (IC3)
Musical genres can be used to distribute and manage music datasets to increase the ease in findin... more Musical genres can be used to distribute and manage music datasets to increase the ease in finding a music piece a person wants to listen to. This paper presents a research for creating a suitable model for genre recognition in audio files using machine learning classifiers on the IRMAS11 https://www.upf.edu/web/mtg/irmas dataset. Python language library pyAudinAnalysls22 https://github.com/tyiannak/pyAudioAnalysis for extracting features from audio files is used. Further, three base classifiers, namely Support Vector Machines (SVM), Decision Tree and Random Forest are also depicted. IRMAS [10] genre dataset provides 6705 audio files of four genres classical, country folk, jazz and pop-rock. Also explored is an ensemble classification model by creating a stack of classifiers for the genre recognition task. Genre classification using SMOTE has been characterized in the confusion matrix. Maximum accuracy of 81.56% using the ensemble classifier is achieved using the proposed methodology.
International Journal of Social Network Mining, 2017
This research paper focuses on the virality of content corresponding to user's reaction and propo... more This research paper focuses on the virality of content corresponding to user's reaction and proposes a multiple metric aware virality model (M2VM) which is beneficial to identify virality of content according to content characteristics and their resultant user reactions. The proposed M2VM assigns virality coefficient to each user reaction based on content properties/characteristics and predict a virality score of target content corresponding to measured user reactions' virality coefficients. To validate our proposed model we have chosen five tutorial-based theme videos of YouTube as case study. Thus in this research work, efforts have been made to analyse and understand users' selection intention towards tutorial videos on the basis of various video metrics. The prime objective of this research work is to analyse video characteristics role in identifying users' selection preference on the basis of video statistics and formulate various video metric corresponding to outcome to conclude the reaction of video characteristics over video virality. The research gives a practical exhibit of YouTube tutorial videos virality with respect to video characteristics mapping with video statistics.
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Papers by Anuja Arora