Journal of Intelligent & Fuzzy Systems - Volume 38, issue 5

Authors: Ajeena Beegom, A.S. | Chinmayan, P.

Article Type: Research Article

Abstract: In natural language processing, the problem of finding the intended meaning or “sense” of a word which is activated by the use of that word in a particular context is generally known as word sense disambiguation (WSD) problem. The solution to this problem impacts many other fields of natural language processing including sentiment analysis and machine translation. Here, WSD problem is modelled as a combinatorial optimization problem where the goal is to find a sequence of meanings or senses that maximizes the semantic meaning among the targeted words. In this work, an algorithm is proposed that uses a combinatorial version …of particle swarm optimization algorithm for solving WSD problem. The test results show that the algorithm performs better than existing methods. Show more

Keywords: Word sense disambiguation, particle swarm optimization, knowledge-based approach, combinatorial PSO

DOI: 10.3233/JIFS-179701

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6193-6200, 2020

Authors: Akhtar, Nadeem | Sufyan Beg, M.M. | Hussain, Md. Muzakkir

Article Type: Research Article

Abstract:  Most extractive multi-document summarization (MDS) methods relies on extraction of content relevant sentences ignoring sentence relationships. In this work, we propose a unified framework for extractive MDS that also considers sentence relationships. We argue that adding a sentence to the summary increases summary score by relevance score of the new sentence plus some additional score which depends on the relationships of new sentence with other summary sentences. The quantification of additional score depends on how coherent the new sentence is with respect to the existing sentences in the summary. Simultaneously, some score is decreased from the summary score due to …the redundancy which depends on overlap between new and existing summary sentences. To find the exact solution, sentence extraction problem is modeled as integer linear problem. The sentence relevance score is found using content and surface features of the sentence using topic model and regression framework. To find the relative coherence score, transition probabilities in the entity grid model are used. Redundancy between sentences is found using support vector regression that uses sentence overlapping features. The proposed method is evaluated on DUC datasets over query based multi-document summarization task. DUC 2006 dataset is used as training and development set for tuning parameters. Experimental results produce ROUGE score comparable to the state-of-the-art methods demonstrating the effectiveness of the proposed method. Show more

Keywords: Multi-document summarization, topic model, support vector regression, entity grid, rouge

DOI: 10.3233/JIFS-179702

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6201-6210, 2020

Authors: El-Alfy, El-Sayed M. | Al-Azani, Sadam

Article Type: Research Article

Abstract:  With the proliferation of social media and mobile technology, huge amount of unstructured data is posted daily online. Consequently, sentiment analysis has gained increasing importance as a tool to understand the opinions of certain groups of people on contemporary political, cultural, social or commercial issues. Unlike western languages, the research on sentiment analysis for dialectical Arabic language is still in its early stages with several challenges to be addressed. The main goal of this study is twofold. First, it compares the performance of core machine learning algorithms for detecting the polarity in imbalanced Arabic tweet datasets using neural word embedding …as a feature extractor rather than hand-crafted or traditional features. Second, it examines the impact of using various oversampling techniques to handle the highly-imbalanced nature of the sentiment data. Intensive empirical analysis of nine machine learning methods and six oversampling methods has been conducted and the results have been discussed in terms of a wide range of performance measures. Show more

Keywords: Social network, sentiment analysis, polarity detection, word embedding, machine learning, imbalanced dataset, Arabic tweets

DOI: 10.3233/JIFS-179703

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6211-6222, 2020

Authors: Sanagar, Swati | Gupta, Deepa

Article Type: Research Article

Abstract: Sentiment analysis research has evolved over the years to extract relevant information from opinionated raw text. Sentiment lexicon is a compiled list of sentiment words and a core component of sentiment analysis tasks. These words play a key role in domain adaptation. Domain adaptation is challenging due to variation in sentiments across the domains. We propose a solution to this research problem by presenting a genre-level sentiment lexicon adaptation approach. The model uses a language domain sense to represent the genre pertaining to the distinct characteristics of the communicated text. The approach addresses the generalization of knowledge at the genre …level by learning the multi-source domain lexicon for the selected source domains. The novelty of our approach lies in the genre level relevancy of the source lexicon to the target domains. The model uses unlabeled training data for the source and target domain sentiment lexicon learning. The lexicon adaptation is demonstrated on a long list of target domains that address the three domain adaptation challenges. Experimental results have proved that the model learns the relevant scores and polarities of sentiment words, in addition, it identifies new domain-based sentiment words. The model is evaluated in comparison with standard baselines. Show more

Keywords: Lexicon adaptation, sentiment lexicon, domain adaptation, multiple source, transfer learning, sentiment analysis

DOI: 10.3233/JIFS-179704

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6223-6234, 2020

Authors: Richa, | Bedi, Punam

Article Type: Research Article

Abstract: Group recommender system provides suggestions for a group of users by exploring the choices of individual users of the group. Popularity of group recommender systems is increasing because many activities such as listening to music, watching movies, traveling, etc. are normally performed in groups rather individually. Group recommender systems like personal recommender systems also suffer from cold start and sparsity issues. The cold start and sparsity issues result into inaccurate recommendation computation which degrades the recommendation quality. To handle the cold start and sparsity issues in a Group Recommender System (GRS), this paper proposes to use cross domain approach and …introduces Cross Domain Group Recommender System (CDGRS). The recommendations provided by trustworthy and reputed users in the group enhance the acceptance towards the presented recommendations as compared to the other individuals in the group. We have combined the social factors e.g. trust and reputation to get influential user in the group recommendation. A prototype of the system is developed for tourism domain that incorporates four sub-domains i.e. restaurants, hotels, tourist places and shopping places. The performance of CDGRS is compared with GRS. Spearman’s Correlation Coefficient, MAE, RMSE, Precision, Recall and F-measure are used to find the accuracy of the generated recommendations. Show more

Keywords: Recommender system, cross domain, group recommender system, multi-agent system, user influence

DOI: 10.3233/JIFS-179705

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6235-6246, 2020

Authors: Sujitha, P. | Simon, Philomina

Article Type: Research Article

Abstract: For the last three decades human activity recognition has shown a huge technological advancement due to less expensive RGB-D cameras and the increase in the large volume of video data. As a result of the increase in number of surveillance cameras, manual annotation becomes difficult and need for automatic recognition and annotation of video arises. In this paper, we introduce a computationally and storage efficient method for recognizing human activities from depth videos and a new frame selection method based on the mean value of motion energy. We extract normal vectors from the points in the boundary curve. Then polynormals …are obtained by sequentially attaching the normals from a neighborhood of each of the points in the boundary curve. These polynormals from a spatio-temporal cuboid constructed from the input video and it is pooled to form the Super Normal vectors. These Super Normal vectors are the final feature vectors, which are given as input to the classifier. The classifier used is lib-linear SVM. The results on MSRAction3D dataset show that the algorithm we put forward is fast and the accuracy obtained is comparable with the existing methods. The method which we proposed here gives an accuracy of 88% while taking whole frames and 89.82% when frame selection method is applied. The proposed method is also tested on UTD-MHAD dataset. Show more

Keywords: Motion energy, depth videos, frame selection, boundary curves, polynormal, dictionary learning

DOI: 10.3233/JIFS-179706

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6247-6255, 2020

Authors: Alpana, | Chand, Satish

Article Type: Research Article

Abstract: Coal is a primary natural resource of fuel that is efficiently used for electricity generation, steel or iron production, and household usage. Characterization is needed for industries to understand the quality of coal before shipping. Currently, industries follow chemical, microscopical, and machine-based analysis as the gold standard for coal characterization. These conventional analyses of coal are an indispensable method over the years and have tested by high skilled petrologists. Though, these types of optical or machine-dependent recognition of coal category samples are quite slow, expensive, and restricted by subjective analyses with less accuracy. The main aim of this research is …to propose an accurate, time, and cost-effective machine learning-based automated characterization system by analyzing coal color and textural features. This paper comes up with a quantitative approach toward the characterization of dissimilar types of coal for better utilization in industries. The proposed ensemble learning coal characterization method provides an accuracy of around 97% and takes less computational time than conventional methods. Hence, the proposed automated coal characterization system provides support to industries in the development of computer-aided assessment of coal category samples. Show more

Keywords: Coal, HSV, GLCM, image processing, machine learning

DOI: 10.3233/JIFS-179707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6257-6267, 2020

Authors: Ramachandran, Sivakumar | Kochitty, Shymol | Vinekar, Anand | John, Renu

Article Type: Research Article

Abstract: The identification of landmark features such as optic disc is of high prognostic significance in diagnosing various ophthalmic diseases. A retinal fundus photograph provides a non-invasive observation of the optic disc. The wide variability present in fundus images poses difficulties in its detection and further analysis. The reported work is a part of the fundus image screening for the diagnosis of Retinopathy of Prematurity (ROP), a sight threatening disorder seen in preterm infants. The diagnostic procedure for this disease estimates blood vessel tortuosity in a pre-defined area around the optic disc. Hence accurate optic disc localization is very important for …the disease diagnosis. In this paper, we present an optic disc localization technique using a deep neural network based framework. The proposed system relies on the underlying architecture of YOLOv3, a fully convolutional neural network pipeline for object detection and localization. The new approach is tested in 10 different data sets and has achieved an overall accuracy of 99.25%, outperforming other deep learning-based OD detection methods. The test results guarantees the robustness of the proposed technique, and hence may be deployed to assist medical experts for disease diagnosis. Show more

Keywords: Optic disc, deep learning, convolutional neural network, retinal images, ROP diagnosis

DOI: 10.3233/JIFS-179708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6269-6278, 2020

Authors: Shiji, T. P. | Remya, S. | Lakshmanan, Rekha | Pratab, Thara | Thomas, Vinu

Article Type: Research Article

Abstract: Intelligent lesion detection system for medical ultrasound images are aimed at reducing physicians’ effort during cancer diagnosis process. Automatic separation and classification of tumours in ultrasound images is challenging owing to the low contrast and noisy behavior of the image. A Computer aided detection (CAD) system that automatically segment and classify breast tumours in ultrasound (US) images is proposed in this paper. The proposed method is invariant to scale changes and does not require an operator defined initial region of interest. Wavelet modulus maxima points of the US image are analyzed to extract the tumour seed point. The lesions segmented …using a region-based approach are classified using a support vector machine (SVM) classifier. Evaluation of various performance measures show that the performance of the proposed CAD system is promising. Show more

Keywords: Breast ultrasound, Shearlet transform, tumour detection, wavelet modulus maxima, SVM classifier

DOI: 10.3233/JIFS-179709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6279-6290, 2020

Authors: Ravikumar, Sourav | Vinod, Dayanand | Ramesh, Gowtham | Pulari, Sini Raj | Mathi, Senthilkumar

Article Type: Research Article

Abstract:  Human-Elephant Conflict (HEC) and its mitigation have always been a serious conservation issue in India. It occurs mainly due to the encroachment of forests by humans as part of societal development. Consequently, these human settlements are highly affected by the intrusion of wild elephants as they cause extensive crop-raiding, injuries and even death in many cases. HEC is a growing problem in rural areas of India which shares a border with forests and other elephant habitats. Based on the studies, it is very explicit that HEC is an important conservation issue which affects the peaceful co-existence of both humans and …elephants near the forest areas. The desirable solution for this problem would be to facilitate co-existence among humans and elephants, but this often fails because of technical difficulties. Hence, this paper presents an end-to-end technological solution to facilitate smoother coexistence of humans and elephants. The proposed work deploys a live video surveillance system along with deep learning strategies to effectively detect the presence of elephants. From the numerical analysis, it is revealed that the post-training accuracy of the deep learning model used in the proposed approach is evaluated at 98.7% and outperforms an an out-of-the-box image detector. The layered approach used in the proposed work improves resource management which is a major bottleneck in real-time deployment scenarios. Show more

Keywords: Human elephant conflict, machine learning, convolutional neural network, support vector machine

DOI: 10.3233/JIFS-179710

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6291-6298, 2020