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Twitter is one of the most popular and renowned online social networks spreading information which although dependable could lead to spreading improbable and misleading rumors causing irreversible damage to individuals and society. In the present paper, a novel approach for detecting rumor-based conversations of various world events such as real-world emergencies and breaking news on Twitter is investigated. In this study, three aspects of information dissemination including linguistic style used to express rumors, characteristics of people involved in propagating information and structural features are studied. Structural features include features of reply tree and user graph. Structural features were extracted as new features in order to enhance the efficiency of the rumor conversations detection. These features provide valuable clues on how a source tweet is transmitted and responds over time. Experimental results indicate that the new features are effective in detecting rumors and that the proposed method is better than other methods as F1-score increased by 4%. Implementation of the proposed method was carried out on Twitter datasets collected during five breaking news stories. [ABSTRACT FROM AUTHOR]

With the increasing development of social networks, they have turned into important research platforms. Influence maximization is one of the most important research issues in the field of social networks. This problem detects influential k-node with the greatest influence spread. The influence maximization faces two important challenges, time efficiency and optimal selection of seed nodes. In order to solve such challenges, we propose an algorithm based on optimal pruning and scoring adjustment, which is called IMBC for short. The IMBC (Influence Maximization Based on Community structure) algorithm uses optimal pruning and a minimum of dominating nodes to improve time efficiency. In addition, for optimal selection of seed nodes, the IMBC algorithm modulates the scores of nodes with a high Rich-Club coefficient. In order to select influential nodes, we first select an optimal set using the minimum dominating nodes and node scores, with the aim of optimal pruning in influence spread calculations. Because large-scale social networks have many nodes, optimal pruning reduces computational overhead. Then, the seed nodes are selected based on the scoring adjustment. Scoring adjustment is done to avoid the Rich Club phenomenon because avoiding this phenomenon causes a large amount of diffusion in social networks. The experimental results show that the proposed algorithm performs better than the algorithms presented in recent years in influence spread and runtime. Therefore, the IMBC algorithm is a balance between quality and efficiency. Also, in the PGP dataset results, the PHG algorithm with as much as a 5.08% increase in influence spread, and the runtime has decreased by 97%. [ABSTRACT FROM AUTHOR]

• The kernel-based version of the least square support vector regression estimator is defined. Unlike most methods, this process aims to utilize all the data-related statistical features to generate a dose determination tool proportional to the data set. • This defined kernel is one of the few tasks that covers the spectrum of discrete and continuous data together. • The defined model covers a wide range of doses, and patients who need very high doses are seen by the proposed model. A novel research field in bioinformatics is pharmacogenomics and the corresponding applications of artificial intelligence tools. Pharmacogenomics is the study of the relationship between genotype and responses to medical measures such as drug use. One of the most effective drugs is warfarin anticoagulant, but determining its initial treatment dose is challenging. Mistakes in the determination of the initial treatment dose can result directly in patient death. Some of the most successful techniques for estimating the initial treatment dose are kernel-based methods. However, all the available studies use pre-defined and constant kernels that might not necessarily address the problem's intended requirements. The present study seeks to define and present a new computational kernel extracted from a data set. This process aims to utilize all the data-related statistical features to generate a dose determination tool proportional to the data set with minimum error rate. The kernel-based version of the least square support vector regression estimator was defined. Through this method, a more appropriate approach was proposed for predicting the adjusted dose of warfarin. This paper benefits from the International Warfarin Pharmacogenomics Consortium (IWPC) Database. The results obtained in this study demonstrate that the support vector regression with the proposed new kernel can successfully estimate the ideal dosage of warfarin for approximately 68% of patients. [ABSTRACT FROM AUTHOR]

Background: It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high coexpressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes. Results: We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%. Conclusions: We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as nonhubs, intermediately connected, party hubs and date hubs. This study also confirms the possibility of predicting non-hubs, party hubs and date hubs based on their biological features with acceptable accuracy. If such a hypothesis is correct for other species as well, similar methods can be applied to predict the roles of proteins in those species. [ABSTRACT FROM AUTHOR]

A recommender system is a model that automatically recommends some meaningful cases (such as clips/films/goods/items) to the clients/people/consumers/users according to their (previous) interests. These systems are expected to recommend the items according to the users' interests. There are two traditional general recommender system models, i.e., Collaborative Filtering Recommender System (ColFRS) and Content-based Filtering Recommender System (ConFRS). Also, there is another model that is a hybrid of those two traditional recommender systems; it is called Hybrid Recommender System (HRS). An HRS usually outperforms simple traditional recommender systems. The problems such as scalability, cold start, and sparsity belong to the main problems that any recommender system should solve. The memory-based (modeless) recommender systems benefit from good accuracies. But they suffer from a lack of admissible scalability. The model-based recommender systems suffer from a lack of admissible accuracies. But they benefit from good scalability. In this paper, it is tried to propose a hybrid model based on an automatically improved ontology to solve the scalability, cold start, and sparsity problems. Our proposed HRS also uses an innovative approach of clustering as an augmented section. When there are enough ratings, it uses a collaborative filtering approach to predict the missing ratings. When there are not enough ratings, it uses a content-based filtering approach to predict the missing ratings. In the content-based filtering section of our proposed HRS, ontology concepts are used to improve the accuracy of ratings' prediction. If our target client is severely sparse, we cannot trust even the ratings predicted by the content-based filtering section of our proposed HRS. Therefore, our proposed HRS uses additive clustering to improve the prediction of the missing ratings if the target client is severely sparse. It is experimentally shown that our model outperforms many of the newly developed recommender systems. [ABSTRACT FROM AUTHOR]

With the development of online social networking applications, microblogs have become a necessary online communication network in daily life. Users are interested in obtaining personalized recommendations related to their tastes and needs. In some microblog systems, tags are not available, or the use of tags is rare. In addition, user-specified social relations are extremely rare. Hence, sparsity is a problem in microblog systems. To address this problem, we propose a new framework called Pblog to alleviate sparsity. Pblog identifies users' interests via their microblogs and social relations and computes implicit similarity among users using a new algorithm. The experimental results indicated that the use of this algorithm can improve the results. In online social networks, such as Twitter, the number of microblogs in the system is high, and it is constantly increasing. Therefore, providing personalized recommendations to target users requires considerable time. To address this problem, the Pblog framework groups similar users using the analytic hierarchy process (AHP) method. Then, Pblog prunes microblogs of the target user group and recommends microblogs with higher ratings to the target user. In the experimental results section, the Pblog framework was compared with several other frameworks. All of these frameworks were run on two datasets: Twitter and Tumblr. Based on the results of these comparisons, the Pblog framework provides more appropriate recommendations to the target user than previous frameworks. [ABSTRACT FROM AUTHOR]

Despite all the developments in recommender systems and utilizations of successful application models in the industry, it can be said that there is still a need to improve various parts of these systems in order to enhance their effectiveness and scope of application. Recommender Systems (RS) are well-known in the field of e-commerce and are expected to provide customers with important goods and items (including music and movies). In traditional recommender systems suffer from important challenges and problems such as cold start, scalability, and data dispersion. Recently, some of these challenges have been successfully overcomed to an acceptable extent by the advantages of the combined use of these methods. The K-nearest neighbors (KNN)-based Recommender Systems (KRS) are among the most powerful recommender engines that are currently available. In these systems, the rating of a target item is predicted based on the average rating of similar items, where the similarity is defined based on a similarity measure and the average rating of items is treated as a feature. In this paper, KRS is developed by combining the following approaches: (a) using the mean and variance of the item ratings as item features to identify the similar items (i.e., item-based KRS or IKRS); (b) using the mean and variance of the user ratings as the user features to identify the similar users (i.e., user-based KRS or UKRS); (c) using weighted average to combine the neighboring user/item ratings; and (d) using ensemble learning. In this study, some methods, i.e., EVMRS (Ensemble Variance-Mean based Recommender System) and EWVMRS (Ensemble Weighted Variance-Mean based Recommender System) are discussed and an improved EWVMRSG (Ensemble Weighted Variance-Mean Based Recommender system enriched by Gaussian mixture model (GMM)) is expanded, which are all user-based and involve using Mean Distance as the measure of similarity between the users/items to find neighboring users/items, but then they use unweighted average, weighted average, and weighted averaging based on the full-covariance GMM, respectively, for prediction. Experimental evaluations show that the EVMRS, EWVMRS, and EWVMRSG methods, which all use ensemble learning, are the most accurate methods among those developed and evaluated in this study. Depending on the dataset, the EWVMRSG mothed achieves a 20–30% lower absolute error than that of the original Mean based Recommender Systems (MRS). In terms of execution time, the proposed method is comparable to the original MRS models and is much faster than the Slope-one, P-kNN, and C-kNN RSs. [ABSTRACT FROM AUTHOR]

The Decision tree algorithm is a very popular classifier for reasoning through recursive partitioning of the data space. To choose the best attributes for splitting, the range of each continuous attribute should be split into two or more intervals. Then partitioning criteria are calculated for each value. Fuzzy partitioning can be used to reduce sensitivity to noise and increase tree stability. Also, tree-building algorithms face memory limitations as they need to keep the entire training dataset in the main memory. In this paper, we introduced a fuzzy decision tree approach based on fuzzy sets. To avoid storing the entire training dataset in the main memory and overcome the memory limitations, the algorithm incrementally builds FDTs. Membership functions are automatically generated. The Fuzzy Information Gain (FIG) is then used as the fast split attribute selection criterion, and leaf expansion is performed only on the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm can overcome memory limitations and balance accuracy and complexity while reducing the complexity of the tree. [ABSTRACT FROM AUTHOR]

Influence maximization problem aims to find a set of nodes with the highest diffusion in social networks in order to maximize diffusion in the graph by this set. A set of these nodes can be used to diffuse news, viruses , marketing and etc. To solve this problem, some algorithms have been proposed to help identify a set of nodes. Due to the low accuracy and high run time in selecting the set of nodes by the proposed algorithms, further studies should be performed in this area. This paper intends to solve the Richclub problem in selecting nodes, reduce the search space to decrease computational overhead, and achieve increases the algorithm accuracy by selecting high-charisma nodes. The Charismatic Transmission in Influence Maximization (CTIM) algorithm reduces the computational overhead by using community structure and pruning criteria. In this algorithm, seed nodes are selected using nodes that have high charismatic power. Also, these nodes have a high correlation with influential nodes in other communities, which causes optimal diffusion in social networks. As a result a set of seed nodes are selected by calculating the influence spread, which increases the algorithm accuracy. In all size ranges of seed sets and different data sets, experiment results show The CTIM algorithm performs well in influence spread. Also in The douban dataset significantly outperforms the PHG algorithm to as much as a 6.56% increase in influence spread and the execution time has decreased by 99%. [ABSTRACT FROM AUTHOR]

In this paper we use cellular learning automata integrated with a normalized gradient based motion detection algorithm in fuzzy domain to detect and track the moving objects in a video. A sequence of the video frames with a preset interval are first converted to gray scale images and then based on the first frame a first order gradient is calculated in fuzzy domain. Normalization process is then performed and the gray levels are ranged between 0 and 255 for each pixel. A sequence of primary motion detected images are calculated from the normalized first order gradient images subject to a minimum threshold value for difference between every two sequent gradient images. If the condition of a minimum difference is met, each primary motion detected image is therefore computed as the products of the second order gradient of the image by the difference of the current frame with the average. Two terms of the products provide larger values for larger motions while vibrations and slight shakings are avoided. Resulting motion images are normalized to the gray scale range [0 : 255]. Then, cellular learning automata algorithm is used to reconstruct and form the object(s) of interest based on a set of rules. Objects are detected by a contour after the reconstruction. Detected objects in the sequential frames are used for the tracking purpose. This algorithm can be used for controlling and supervising a secured public place viewed by a fixed camera. The objects may be considered as the traversing people, animals, cars or any carrying baggage and the algorithm can be parameterized for the objects of interest. Finally, all parameters of the method as the threshold values, score, penalty and the number of evolution cycles are analyzed to find the optimum values for the dataset under analysis. Comprehensive experiments are performed to show the capability and efficiency of the proposed method while it is stated that developing this code in MATLAB constrains working in offline processing mode. [ABSTRACT FROM AUTHOR]

Recommender Systems (RS) are expected to suggest the accurate goods to the consumers. Cold start is the most important challenge for RSs. Recent hybrid RSs combine ConF and ColF. We introduce an ontological hybrid RS where the ontology has been employed in its ConF part while improving the ontology structure by its ColF part. In this paper, a new hybrid approach is proposed based on the combination of demographic similarity and cosine similarity between users in order to solve the cold start problem of new user type. Also, a new approach is proposed based on the combination of ontological similarity and cosine similarity between items in order to solve the cold start problem of new item type. The main idea of the proposed method is to expand user/item profiles based on different strategies to build higher-performing profiles for users/items. The proposed method has been evaluated on a real dataset and the experimentations indicate the proposed method has the better performance comparing with the state of the art RS methods, especially in the case of the cold start. [ABSTRACT FROM AUTHOR]