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Image denoising methods are of fundamental importance in image processing and artificial intelligence systems. In this review, we analyze the traditional and state of the art mathematical models for computational color image denoising. These algorithms are divided into methods that are based on the partial differential equations, low rank, sparse representation and recent developments based on deep learning models. These algorithms also compared in terms of image quality measures. Our analysis and review of the computational color image denoising filters indicate that the convolutional neural networks from the deep learning domain obtain high quality restorations in terms of image quality despite the higher computational complexity.

Social media has become one of the most popular sources of information. People communicate with each other and share their ideas, commenting on global issues and events in a multilingual environment. While social media has been popular for several years, recently, it has given an exponential rise in online data volumes because of the increasing popularity of local languages on the web. This allows researchers of the NLP community to exploit the richness of different languages while overcoming the challenges posed by these languages. Urdu is also one of the most used local languages being used on social media. In this paper, we presented the first-ever event detection approach for Urdu language text. Multiclass event classification is performed by popular deep learning (DL) models, i.e.,Convolution Neural Network (CNN), Recurrence Neural Network (RNN), and Deep Neural Network (DNN). The one-hot-encoding, word embedding, and term-frequency inverse document frequency- (TF-IDF-) based feature vectors are used to evaluate the Deep Learning(DL) models. The dataset that is used for experimental work consists of more than 0.15 million (103965) labeled sentences. DNN classifier has achieved a promising accuracy of 84% in extracting and classifying the events in the Urdu language script.

Graph-based methods have been widely used by the document image analysis and recognition community, as the different objects and the content in document images is best represented by this powerful structural representation. Designing of novel computation tools for processing these graph-based structural representations has always remained a hot topic of research. Recently, Graph Neural Network (GNN) have been used for solving different problems in the domain of document image analysis and recognition. In this article we take forward the state of the art by presenting a new approach to gather the symbolic and numeric information from the nodes and edges of a graph. We use this information to learn a Graph Neural Network (GNN). The experimentation on the recognition of handwritten letters and graphical symbols shows that the proposed approach is an interesting contribution to the growing set of GNN-based methods for document image analysis and recognition.

The recent success of graph neural networks (GNNs) in the area of pattern recognition (PR) has increased the interest of researchers to use these frameworks in non-euclidean structures. This non-euclidean structure includes graphs or manifolds that are called geometric deep learning (GDL). It has opened a new direction for researchers to deal with graphs using deep learning in document processing, outperforming conventional methods. We propose a Deep Graph Neural Network (DGNN) classifier-based on additive angular margin loss for the classification task in document analysis. Another contribution of this work is to investigate the performance of a DGNN as a classifier using different loss functions, which helps to minimize the loss for the document analysis problem. We compare additive angular margin loss, Cosine angular margin loss, and multiplicative angular margin loss. Furthermore, we give a comparison between the mentioned loss functions and the Softmax loss function. We also present the comparisons of results using different graph edit distance (GED) methods. Our quantitative results suggest, that by applying the additive angular marginal loss function makes more compact intra-class ability and increases the inter-class discrepancy which enhances the discriminating power of the DGNN. Enhancing the decision boundaries between the classes increase the intra-class compactness and inter-class discrimination power of the model.

Work on the problem of handwritten text recognition in Urdu script has been an active research area. A significant progress is made in this interesting and challenging field in the last few years. In this study, the authors presented a comprehensive survey for a number of offline and online handwritten text recognition systems for Urdu script written in Nastaliq font style from 2004 to 2019. Following features make their contribution worthwhile and unique among the reviews of a similar kind: (i) their review classifies the existing studies based on types of recognition systems used for Urdu handwritten text, (ii) it covers a very different outlook of the recognition process of the Urdu handwritten text at different granularity levels (e.g. character, word, ligature, or sentence level), (iii) this review article also presents each of surveyed articles in following dimensions: the task performed, its granularity level, dataset used, results obtained, and future dimensions, and (iv) lastly it gives the summary of the surveyed articles according to the granularity levels, publishing years, related tasks or subtasks, and types of classifiers used. In the end, major challenges and tasks related to Urdu handwritten text recognition approaches are also discussed in detail.

In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task.

The first-order fuzzy differential equation has two possible solutions depending on the definition of differentiability. The definition of differentiability changes as the product of the function and its first derivative changes its sign. This switching of the derivative&rsquo, s definition is handled with the application of min, max operators. In this paper, a numerical technique for solving fuzzy initial value problems is extended to solving higher-order fuzzy differential equations. Fuzzy Taylor series is used to develop the fuzzy differential transformation method for solving this problem. This leads to a single solution for higher-order differential equations.

We applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 &times, 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One of the challenging and critical issues for the correct visualization of Urdu numerals is shape similarity between some of the digits. This issue was resolved using t-SNE, by exploiting local and global structures of the large data set at different scales. The global structure consists of geometrical features and local structure is the pixel-based information for each class of Urdu digits. We introduce a novel approach that allows the fusion of these two independent spaces using Euclidean pairwise distances in a highly organized and principled way. The fusion matrix embedded with t-SNE helps to locate each data point in a two (or three-) dimensional map in a very different way. Furthermore, our proposed approach focuses on preserving the local structure of the high-dimensional data while mapping to a low-dimensional plane. The visualizations produced by t-SNE outperformed other classical techniques like principal component analysis (PCA) and auto-encoders (AE) on our handwritten Urdu numeral dataset.

Today, many organizations are transforming their traditional telephone services into Voice over Internet Protocol (VoIP) systems. These services are simple to implement, but they are often vulnerable to attacks because they are packet-switched IP networks to support the circuit-switched used for voice communication. SIP is widely used as a signaling protocol to facilitate video and voice communication, as well as for more multimedia applications. However, it is not protected against various types of attacks because of its open nature and lack of a clear line of defense against the growing number of security threats. Among these risks, registration hijacking assaults, known by its harmful effect, attack both the User Agent Server (UAS) and the User Agent Client (UAC). In particular, the REGISTER message is evaluated as one of the main reasons of registration hijacking assaults in SIP. An attacker who deactivates the SIP registration of a valid user and replaces it with the logical address of the hacker. This allows the hacker to block incoming calls as well as redirect, replay or end calls at will. In this survey, we present a complete study of the registration attack against SIP, communicating its different alternatives and analyzing its consequences. We have also categorized current solutions based on the different registration hijacking attack approaches they face, their types, and their targets. In addition, We conduct an in-depth review of the robustness and inefficiency of these solutions, as well as an in-depth analysis of each one’s basic assumptions to better understand their limitations. Finally, we recommend protecting the UAC registration method against registration-hijacking by using the Media Access Control (MAC) address to improve the efficiency of the studied solutions.

Health information technology revolutionized the world with its great expansion and widespread in the domain of health care system. Most of the developed countries adopted advanced technology in their vaccination systems. Vaccination systems of many developing countries still lack the use of technology eventually causing mismanagement and corruption to occur in vaccination campaigns. Issues like mismanagement and corruption not only affect vaccination campaigns but also cause further diffusion of a disease. Pakistan is also one of such countries where vaccination system is prone to these and many other issues and hence it does not help in disease eradication. For example, polio remains alive in Pakistan because Pakistan’s Polio vaccination system is faced with many problems and the biggest one is security of vaccination teams. Corruption, mismanagement, unawareness among public and life-threat to vaccination teams are the main problems of current polio vaccination system of Pakistan. To overcome these flaws and to make an idyllic system with the new advanced technology, we propose technology oriented secure polio vaccination system. The proposed system is more secure and removes flaws in the current system. We model our proposed system using Colored Petri Nets (CPNs) which is a state-of-the-art tool for formal modeling.

It is the age of the social web, where people express themselves by giving their opinions about various issues, from their personal life to the world&rsquo, s political issues. This process generates a lot of opinion data on the web that can be processed for valuable information, and therefore, semantic annotation of opinions becomes an important task. Unfortunately, existing opinion annotation schemes have failed to satisfy annotation challenges and cannot even adhere to the basic definition of opinion. Opinion holders, topical features and temporal expressions are major components of an opinion that remain ignored in existing annotation schemes. In this work, we propose OpinionML, a new Markup Language, that aims to compensate for the issues that existing typical opinion markup languages fail to resolve. We present a detailed discussion about existing annotation schemes and their associated problems. We argue that OpinionML is more robust, flexible and easier for annotating opinion data. Its modular approach while implementing a logical model provides us with a flexible and easier model of annotation. OpinionML can be considered a step towards &ldquo, information symmetry&rdquo, It is an effort for consistent sentiment annotations across the research community. We perform experiments to prove robustness of the proposed OpinionML and the results demonstrate its capability of retrieving significant components of opinion segments. We also propose OpinionML ontology in an effort to make OpinionML more inter-operable. The ontology proposed is more complete than existing opinion ontologies like Marl and Onyx. A comprehensive comparison of the proposed ontology with existing sentiment ontologies Marl and Onyx proves its worth.

Spams are becoming a serious threat for the users of online social networks especially for the ones like of twitter. twitter’s structural features make it more volatile to spam attacks. In this paper, we propose a spam detection approach for twitter based on sentimental features. We perform our experiments on a data collection of 29K tweets with 1K tweets for 29 trending topics of 2012 on twitter. We evaluate the usefulness of our approach by using five classifiers i.e. BayesNet, Naive Bayes, Random Forest, Support Vector Machine (SVM) and J48. Naive Bayes, Random Forest, J48 and SVM spam detections performance improved with our all proposed features combination. The results demonstrate that proposed features provide better classification accuracy when combined with content and user-oriented features.

On Twitter, hashtags are used to summarize topics of the tweet content and to help search tweets. However, hashtags are created in a free style and thus heterogeneous, increasing difficulty of their usage. Therefore, it is important to evaluate that if they really represent the content they are attached with? In this work, we perform detailed experiments to find answer for this question. In addition to this, we compare different semantic relatedness measures to find this similarity between hashtags and tweets. Experiments are performed using ten different measures and Adapted Lesk is found to be the best.

International audience; Opinion mining is one of the most challenging tasks of the field of information retrieval. Research community has been publishing a number of articles on this topic but a significant increase in interest has been observed during the past decade especially after the launch of several online social networks. In this paper, we provide a very detailed overview of the related work of opinion mining. Following features of our review make it stand unique among the works of similar kind: (1) it presents a very different perspective of the opinion mining field by discussing the work on different granularity levels (like word, sentences, and document levels) which is very unique and much required, (2) discussion of the related work in terms of challenges of the field of opinion mining, (3) document level discussion of the related work gives an overview of opinion mining task in blogosphere, one of most popular online social network, and (4) highlights the importance of online social networks for opinion mining task and other related sub-tasks.