Public and governmental concerns over online rumors’ widespread diffusion and deceptive impact on social media have increased. For users to obtain accurate information and preserve social peace, finding and controlling social media rumors is challenging. Automatically, identifying fake news (FN) is a critical yet challenging topic that is still little understood because the consequences are so high. The text, visual features, the acceptance of the user’s reply, stance, and social context are a few aspects of FN that are universally acknowledged. Current research has concentrated on modifying results to one specific trait, which has been partially the reason for their success. This article proposes Fakefind, a convolutional neural network (CNN)$+$recurrent neural networks (RNNs) hybrid model that integrates multimodal features for efficient rumor detection (RD). Additionally, the stance is extracted from indirectly implied postreply pairs using a CNN-based knowledge extractor (KE), and the stance representations are integrated for FN detection (FND). Extensive research findings are based on three multimedia rumor datasets from Weibo, Fakeddit, and PHEME. The outcomes show how well the recommended Fakefind identifies rumors with multimodal content.

The Sign Language Recognition system intends to recognize the Sign language used by the hearing and vocally impaired populace. The interpretation of isolated sign language from static and dynamic gestures is a difficult study field in machine vision. Managing quick hand movement, facial expression, illumination variations, signer variation, and background complexity are amongst the most serious challenges in this arena. While deep learning-based models have been used to accomplish the entirety of the field's state-of-the-art outcomes, the previous issues have not been fully addressed. To overcome these issues, we propose a Hybrid Neural Network Architecture for the recognition of Isolated Indian and Russian Sign Language. In the case of static gesture recognition, the proposed framework deals with the 3D Convolution Net with an atrous convolution mechanism for spatial feature extraction. For dynamic gesture recognition, the proposed framework is an integration of semantic spatial multi-cue feature detection, extraction, and Temporal-Sequential feature extraction. The semantic spatial multi-cue feature detection and extraction module help in the generation of feature maps for Full-frame, pose, face, and hand. For face and hand detection, GradCam and Camshift algorithm have been used. The temporal and sequential module consists of a modified auto-encoder with a GELU activation function for abstract high-level feature extraction and a hybrid attention layer. The hybrid attention layer is an integration of segmentation and spatial attention mechanism. The proposed work also involves creating a novel multi-signer, single, and double-handed Isolated Sign representation dataset for Indian and Russian Sign Language. The experimentation was done on the novel dataset created. The accuracy obtained for Static Isolated Sign Recognition was 99.76%, and the accuracy obtained for Dynamic Isolated Sign Recognition was 99.85%. We have also compared the performance of our proposed work with other baseline models with benchmark datasets, and our proposed work proved to have better performance in terms of Accuracy metrics.

Cervical cancer is one of the common cancers among women and it causes significant mortality in many developing countries. Diagnosis of cervical lesions is done using pap smear test or visual inspection using acetic acid (staining). Digital colposcopy, an inexpensive methodology, provides painless and efficient screening results. Therefore, automating cervical cancer screening using colposcopy images will be highly useful in saving many lives. Nowadays, many automation techniques using computer vision and machine learning in cervical screening gained attention, paving the way for diagnosing cervical cancer. However, most of the methods rely entirely on the annotation of cervical spotting and segmentation. This paper aims to introduce the Faster Small-Object Detection Neural Networks (FSOD-GAN) to address the cervical screening and diagnosis of cervical cancer and the type of cancer using digital colposcopy images. The proposed approach automatically detects the cervical spot using Faster Region-Based Convolutional Neural Network (FR-CNN) and performs the hierarchical multiclass classification of three types of cervical cancer lesions. Experimentation was done with colposcopy data collected from available open sources consisting of 1,993 patients with three cervical categories, and the proposed approach shows 99% accuracy in diagnosing the stages of cervical cancer.

Abstract Location recommending frameworks plays a very significant role in suggesting the users with new places to visit especially when users are visiting unfamiliar areas. Most of the existing recommender systems do not consider the fact that different users have different behavior while checking in. Some approaches do not consider the essential factors while providing recommendations. These systems lack adaptability and hence they provide poor recommendations. An adaptive approach to provide users with a personalized recommendation has been proposed in this paper. We have considered three features namely, user activeness feature, user similarity feature, and the spatial feature. In addition to this, we have also considered the location popularity for a given timeslot. We have divided the users into inactive and active based on the degree of activeness on social networks using fuzzy c‐means clustering. We have provided two strategies based on the activeness of the user. A two‐dimensional Gaussian kernel density estimation strategy is used for the active user. A one‐dimensional power‐law function strategy is used for inactive users. Moreover, we have integrated the time‐based popularity of the location and probability estimation based on the similarity between the users. To evaluate the proposed model, we have used a large‐scale Foursquare dataset.

The web contains a huge volume of data, and it's populating every moment to the point that human beings cannot deal with the vast amount of data manually or via traditional tools. Hence an advanced tool is required to filter such massive data and mine the valuable information. Recommender systems are among the most excellent tools for such a purpose in which collaborative filtering is widely used. Collaborative filtering (CF) has been extensively utilized to offer personalized recommendations in electronic business and social network websites. In that, matrix factorization is an efficient technique; however, it depends on past transactions of the users. Hence, there will be a data sparsity problem. Another issue with the collaborative filtering method is the cold start issue, which is due to the deficient information about new entities. A novel method is proposed to overcome the data sparsity and the cold start problem in CF. For cold start issue, Recommender System with Linked Open Data (RS-LOD) model is designed and for data sparsity problem, Matrix Factorization model with Linked Open Data is developed (MF-LOD). A LOD knowledge base “DBpedia” is used to find enough information about new entities for a cold start issue, and an improvement is made on the matrix factorization model to handle data sparsity. Experiments were done on Netflix and MovieLens datasets show that our proposed techniques are superior to other existing methods, which mean recommendation accuracy is improved.

Within the graph mining context, frequent subgraph identification plays a key role in retrieving required information or patterns from the huge amount of data in a short period. The problem of finding frequent items in traditional mining changed to the innovation of subgraphs that recurrently occur s in graph datasets containing a single huge graph. Majority of the existing methods target static graphs, and the distributed solution for dynamic graphs has not been explored. But, in modern applications like Facebook, robotics utilizes large evolving graphs. The goal is to design a method to find recurrent subgraphs from a single large evolving graph. In this research paper, a novel approach is proposed called DFSME, which uses SPARK to discover frequent subgraphs from an evolving graph in a distributed environment. DFSME maintains a set of subgraphs between frequent and infrequent subgraphs, which is used to decrease the search space. Our experiments with synthetic and real-world datasets authorize the effectiveness of DFSME for mining of recurrent subgraphs from huge evolving graph datasets.

Summary Sentiment analysis and opinion mining has become a major tool for collecting information from customer reviews on user sentiments and emotions, especially for online video streaming services and social networks. The increasing use of smartphones has popularized subscription to various streaming services that provide streaming media and video‐on‐demand. These applications offer a gateway to analyze user reviews by introducing sentiment analysis in the mobile environment. Online user reviews can hold a lot of useful information and help predict user interests. Analysis of user reviews can provide substantive information for business processing. Sentiment classification of these reviews is a commonly used analysis technique. Usually, these reviews are given in a text format, with every word in each considered a feature, so selection should focus on optimal features from all available features present in the reviews. This study employs machine learning algorithms to extract the best features from the training review data set. Then, the selected features are fed into the convolutional neural network and other fully connected layers for further processing. The proposed approach is evaluated with the standard evaluation metrics, such as precision, accuracy, recall, and f‐measure, using three distinct benchmark data sets: polarity, Rotten Tomatoes, and IMDb. This work has also employed a pretrained sentiment analysis model over an Android application framework to classify reviews on a Smartphone without the need for any cloud or server‐side API.

Tag recommender schemes suggest related tags for an untagged resource and better tag suggestions to tagged resources. Tagging is very important if the user identifies the tag that is more precise to use in searching interesting blogs. There is no clear information regarding the meaning of each tag in a tagging process. An user can use various tags for the same content, and he can also use new tags for an item in a blog. When the user selects tags, the resultant metadata may comprise homonyms and synonyms. This may cause an improper relationship among items and ineffective searches for topic information. The collaborative tag recommendation allows a set of freely selected text keywords as tags assigned by users. These tags are imprecise, irrelevant, and misleading because there is no control over the tag assignment. It does not follow any formal guidelines to assist tag generation, and tags are assigned to resources based on the knowledge of the users. This causes misspelled tags, multiple tags with the same meaning, bad word encoding, and personalized words without common meaning. This problem leads to miscategorization of items, irrelevant search results, wrong prediction, and their recommendations. Tag relevancy can be judged only by a specific user. These aspects could provide new challenges and opportunities to its tag recommendation problem. This paper reviews the challenges to meet the tag recommendation problem. A brief comparison between existing works is presented, which we can identify and point out the novel research directions. The overall performance of our ontology-based recommender systems is favorably compared to other systems in the literature.

In this paper, we proposed a novel approach based on topic ontology for tag recommendation. The proposed approach intelligently generates tag suggestions to blogs. In this approach, we construct topic ontology through enriching the set of categories in existing small ontology called as Open Directory Project. To construct topic ontology, a set of topics and their associated semantic relationships is identified automatically from the corpus-based external knowledge resources such as Wikipedia and WordNet. The construction relies on two folds such as concept acquisition and semantic relation extraction. In the first fold, a topic-mapping algorithm is developed to acquire the concepts from the semantic of Wikipedia. A semantic similarity-clustering algorithm is used to compute the semantic similarity measure to group the set of similar concepts. The second is the semantic relation extraction algorithm, which derives associated semantic relations between the set of extracted topics from the lexical patterns between synsets in WordNet. A suitable software prototype is created to implement the topic ontology construction process. A Jena API framework is used to organize the set of extracted semantic concepts and their corresponding relationship in the form of knowledgeable representation of Web ontology language. Thus, Protégé tool provides the platform to visualize the automatically constructed topic ontology successfully. Using the constructed topic ontology, we can generate and suggest the most suitable tags for the new resource to users. The applicability of topic ontology with a spreading activation algorithm supports efficient recommendation in practice that can recommend the most popular tags for a specific resource. The spreading activation algorithm can assign the interest scores to the existing extracted blog content and tags. The weight of the tags is computed based on the activation score determined from the similarity between the topics in constructed topic ontology and content of the existing blogs. High-quality tags that has the highest activation score is recommended to the users. Finally, we conducted experimental evaluation of our tag recommendation approach using a large set of real-world data sets. Our experimental results explore and compare the capabilities of our proposed topic ontology with the spreading activation tag recommendation approach with respect to the existing AutoTag mechanism. And also discuss about the improvement in precision and recall of recommended tags on the data sets of Delicious and BibSonomy. The experiment shows that tag recommendation using topic ontology results in the folksonomy enrichment. Thus, we report the results of an experiment mean to improve the performance of the tag recommendation approach and its quality.