Journal of Intelligent & Fuzzy Systems - Volume 41, issue 5

Authors: Priyanga, V.T | Sanjanasri, J.P | Menon, Vijay Krishna | Gopalakrishnan, E.A | Soman, K.P

Article Type: Research Article

Abstract: The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine …learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis. Show more

Keywords: Fake news, social media, word embedding, cosine similarity, Auto-encoders, network analysis

DOI: 10.3233/JIFS-189865

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5441-5448, 2021

Authors: Chirgaiya, Sachin | Sukheja, Deepak | Shrivastava, Niranjan | Rawat, Romil

Article Type: Research Article

Abstract: The decisions and approaches of renowned personality used to impress the real world are to a great extent adapted to how others have seen or assessed the world with opinion and sentiment. Examples could be any opinion and sentiment of people view about Movie audits, Movie surveys, web journals, smaller scale websites, and informal organizations. In this research classifies the movie review into its correct category, classifier model is proposed that has been trained by applying feature extraction and feature ranking. The focus is on how to examine the sentiment expression and classification of a given movie review on a …scale of (–) negative and (+) positive sentiments analysis for the IMDB movie review database. Due to the lack of grammatical structures to comments on movies, natural language processing (NLP) has been used to implement proposed model and experimentation is performed to compare the present study with existing learning models. At the outset, our approach to sentiment classification supplements the existing movie rating systems used across the web to an accuracy of 97.68%. Show more

Keywords: Machine learning, artificial intelligence, movie reviews, sentiment analysis

DOI: 10.3233/JIFS-189866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5449-5456, 2021

Authors: Sil, Riya | Alpana, | Roy, Abhishek | Dasmahapatra, Mili | Dhali, Debojit

Article Type: Research Article

Abstract: It is essential to provide a structured data feed to the computer to accomplish any task so that it can process flawlessly to generate the desired output within minimal computational time. Generally, computer programmers should provide a structured data feed to the computer program for its successful execution. The hardcopy document should be scanned to generate its corresponding computer-readable softcopy version of the file. This process also proves to be a budget-friendly approach to disengage human resources from the entire process of record maintenance. Due to this automation, the workload of existing manpower is reduced to a significant level. This …concept may prove beneficial for the delivery of any type of services to the ultimate beneficiary (i.e., citizen) in a minimal time frame. The administration has to deal with various issues of citizens due to the pressure of a huge population who seek legal help to resolve their issues, thereby leading to the filing of large numbers of pending legal cases at several courts of the country. To assist the victims with prompt delivery of justice and legal professionals in reducing their workload, this paper proposed a machine learning based automated legal model to enhance the efficiency of the legal support system with an accuracy of 94%. Show more

Keywords: Machine learning, image processing, document analysis, argument, text summarization

DOI: 10.3233/JIFS-189867

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5457-5466, 2021

Authors: Lalitha, S. | Gupta, Deepa

Article Type: Research Article

Abstract: Automatic recognition of human affective state using speech has been the focus of the research world for more than two decades. In the present day, with multi-lingual countries like India and Europe, population are communicating in various languages. However, majority of the existing works have put forth different strategies to recognize affect from various databases, with each comprising single language recordings. There exists a great demand for affective systems to serve the context of mixed-language scenario. Hence, this work focusses on an effective methodology to recognize human affective state using speech samples from a mixed language framework. A unique cepstral …and bi-spectral speech features derived from the speech samples classified using random forest (RF) are applied for the task. This work is first of its kind with the proposed approach validated and found to be effective on a self-recorded database with speech samples comprising from eleven various diverse Indian languages. Six different affective states of angry, fear, sad, neutral, surprise and happy are considered. Three affective models have been investigated in the work. The experimental results demonstrate the proposed feature combination in addition to data augmentation show enhanced affect recognition. Show more

Keywords: Affective state, cepstral, mixed-lingual, recognition, Indian languages

DOI: 10.3233/JIFS-189868

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5467-5476, 2021

Authors: Ojha, Chinmayee | Venugopalan, Manju | Gupta, Deepa

Article Type: Research Article

Abstract: Fast growth of technology and the tremendous growth of population has made millions of people to be active participants on social networking forums. The experiences shared by the participants on different websites is highly useful not only to customers to make decisions but also helps companies to maintain sustainability in businesses. Sentiment analysis is an automated process to analyze the public opinion behind certain topics. Identifying targets of user’s opinion from text is referred to as aspect extraction task, which is the most crucial and important part of Sentiment Analysis. The proposed system is a rule-based approach to extract aspect …terms from reviews. A sequence of patterns is created based on the dependency relations between target and its nearby words. The system of rules works on a benchmark of dataset for Hindi shared by Akhtar et al., 2016. The evaluated results show that the proposed approach has significant improvement in extracting aspects over the baseline approach reported on the same dataset. Show more

Keywords: Sentiment analysis (SA), aspect term, sequential pattern, dependency parser (DP), part of speech (POS)

DOI: 10.3233/JIFS-189869

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5477-5485, 2021

Authors: Banerjee, Tulika | Yagnik, Niraj | Hegde, Anusha

Article Type: Research Article

Abstract: Human communication is not limited to verbal speech but is infinitely more complex, involving many non-verbal cues such as facial emotions and body language. This paper aims to quantitatively show the impact of non-verbal cues, with primary focus on facial emotions, on the results of multi-modal sentiment analysis. The paper works with a dataset of Spanish video reviews. The audio is available as Spanish text and is translated to English while visual features are extracted from the videos. Multiple classification models are made to analyze the sentiments at each modal stage i.e. for the Spanish and English textual datasets as …well as the datasets obtained upon coalescing the English and Spanish textual data with the corresponding visual cues. The results show that the analysis of Spanish textual features combined with the visual features outperforms its English counterpart with the highest accuracy difference, thereby indicating an inherent correlation between the Spanish visual cues and Spanish text which is lost upon translation to English text. Show more

Keywords: Multimodal analysis, natural language processing, non-verbal cues, classification algorithms, cultural-shift

DOI: 10.3233/JIFS-189870

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5487-5496, 2021

Authors: Dhanani, Jenish | Mehta, Rupa | Rana, Dipti

Article Type: Research Article

Abstract: Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O  (n 2 ). However, there is a limited number of pairs consisting of strong relevance among …the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems. Show more

Keywords: Legal document recommender systems, Pairwise similarity, Graph Clustering, Semantic similarity

DOI: 10.3233/JIFS-189871

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5497-5509, 2021

Authors: Richa, | Bedi, Punam

Article Type: Research Article

Abstract: Recommender System (RS) is an information filtering approach that helps the overburdened user with information in his decision making process and suggests items which might be interesting to him. While presenting recommendation to the user, accuracy of the presented list is always a concern for the researchers. However, in recent years, the focus has now shifted to include the unexpectedness and novel items in the list along with accuracy of the recommended items. To increase the user acceptance, it is important to provide potentially interesting items which are not so obvious and different from the items that the end user …has rated. In this work, we have proposed a model that generates serendipitous item recommendation and also takes care of accuracy as well as the sparsity issues. Literature suggests that there are various components that help to achieve the objective of serendipitous recommendations. In this paper, fuzzy inference based approach is used for the serendipity computation because the definitions of the components overlap. Moreover, to improve the accuracy and sparsity issues in the recommendation process, cross domain and trust based approaches are incorporated. A prototype of the system is developed for the tourism domain and the performance is measured using mean absolute error (MAE), root mean square error (RMSE), unexpectedness, precision, recall and F-measure. Show more

Keywords: Recommender system, cross domain, serendipity, trust, fuzzy sets

DOI: 10.3233/JIFS-189872

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5511-5523, 2021

Authors: Pathak, Vinay | Singh, Karan

Article Type: Research Article

Abstract: Due to the rapid growth in sensor technology and embedded technology, wireless body area network WBANs plays a vital role in monitoring the human body system and the surrounding environment. It supports many healthcare applications on the one hand and are very much help full in pandemic scenarios. It has become the most innovative health care area, which is intriguing to many researchers because of its vast future prospective and potential. Data collected by different wireless sensors or nodes is very personal, critical, and important because of human life involvement. WBANs can minimize human to human contact, which helps stop …the spread of severe infectious diseases. The biggest concern is the maintenance of privacy and accuracy of data is still a hot area of research due to nature of attacks, which are changing day by day and increasing, as well as for the sake of better performance. A suitable security mechanism is a way to address above issues, for achieving data security, it is expedient to propose a mechanism. It is essential to update the patient’s regular data. WBANs help to deliver truthful reports related to the patient’s health regularly and individually. This paper proposes an algorithm that shows a better result than the existing algorithm in their previous works. This work is all about proposing a mechanism which needs comparatively less resource. Only authentic entities can interact with the server, which has become obligatory for both sides, keeping data safe. Several authentication schemes have been proposed or discussed by different researchers. This paper has proposed a Secure and Efficient WBANs Authentication Mechanism (SEAM). This security framework will take care of the authentication and the security of transmitted data. Show more

Keywords: WBANs, wearable sensors, eHealth privacy & security, threat, WSN, security

DOI: 10.3233/JIFS-189873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5525-5534, 2021

Authors: Ayub, Mohammed | El-Alfy, El-Sayed M.

Article Type: Research Article

Abstract: The World-Wide Web technology has become an indispensable part in human’s life for almost all activities. On the other hand, the trend of cyberattacks is on the rise in today’s modern Web-driven world. Therefore, effective countermeasures for the analysis and detection of malicious websites is crucial to combat the rising threats to the cyber world security. In this paper, we systematically reviewed the state-of-the-art techniques and identified a total of about 230 features of malicious websites, which are classified as internal and external features. Moreover, we developed a toolkit for the analysis and modeling of malicious websites. The toolkit has …implemented several types of feature extraction methods and machine learning algorithms, which can be used to analyze and compare different approaches to detect malicious URLs. Moreover, the toolkit incorporates several other options such as feature selection and imbalanced learning with flexibility to be extended to include more functionality and generalization capabilities. Moreover, some use cases are demonstrated for different datasets. Show more

Keywords: Web security, malicious websites, malicious URL, machine learning, feature extraction, toolkits

DOI: 10.3233/JIFS-189874

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5535-5549, 2021