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BACKGROUND AND OBJECTIVES: The accurate and scientific assessment of the risk to issue an insurance policy is one of the most critical and important stages of risk assessment frameworks. This leads companies to identify high-risk customers and determine the policy rates in accordance with their risks, and as a result, the claims will be covered appropriately through the insurance premiums. In this paper, a new method is presented to define the concept of risk factor in more practical, flexible and accurate way. In this method, which is based on an unsupervised clustering algorithm, initially, every single factor is examined based on different ranges and their corresponding impact on customer loss levels. Then, considering their connection with the ranges of other factors in terms of creating similar levels of customer loss, they are combined to form a package. Thus, different packages are created, each of which is considered a risk factor and comprise the ranges of factors affecting different levels of loss. METHODS: The k-means clustering method was used to divide insurers into clusters with similar risks, which correspond to the risk packages associated with the customers’ risk level. The number of desired clusters should be determined in advance, which is the main challenge of using this algorithm. Two main approaches for validation, namely the silhouette score and the elbow method, were presented. FINDINGS: Based on the elbow plot and silhouette coefficient, as well as considering the practical and realistic evaluation needed by insurance companies, four clusters were obtained. Cluster 2 and 3 are similar and can be merged to form a cluster of medium risk level. Therefore, three clusters were considered the best outcome for categorizing insurance policyholders. CONCLUSION: The risk packages can be introduced from the examination of the 3 clusters including People with high, medium and low age (confidence interval) with low price car whose gender is male can be introduced as the highest level of risk; People with medium and high ages (confidence interval) with medium and high car prices can be considered as medium risks, and Middle-aged and older people (confidence interval) with expensive cars were considered the lowest level of risk. From the results of these risk packages, it can be concluded that although a significant population of older policyholders falls into the first package (first cluster), they have the highest level of risk. On the other hand, the older people in the third package (even though their average age is the highest among the clusters) have the lowest level of risk. Another important point is that the risk level decreases as income increases simultaneously with age. [ABSTRACT FROM AUTHOR]

The ever-increasing of renewable distributed generation sources in distribution networks, as well as increasing network size, have faced agent-based protection schemes with a heavy communicational load. Accordingly, despite the fast and reliable nature of multi-agent systems, they have the possibility of improper performance, especially in centralized protection systems. This paper presents an intelligent self-healing method that has the ability to replace common multi-agent systems during fault conditions. Therefore, protection tasks are performed in a single control level, without dependence on higher communicational levels, to clear the fault. Decentralized operation of this structure is provided by using intelligent electronic devices and distributed communications. In this way, the proposed scheme is described with high-speed peer-to-peer communication capability using the IEC-61850 GOOSE protocol. Then, a penetration-free algorithm, without the help of a central controller, is provided by using GOOSE message capabilities, to prevent any electricity interruption due to insufficient protection settings. Finally, by planning different scenarios and simulating a practical distribution network via ETAP software, the accuracy of the proposed algorithm has been proven. [ABSTRACT FROM AUTHOR]

This paper uses the unique biometric features of fingerprints to generate random cryptographic keys. The main aspects of the security of the generated key include the privacy of the fingerprint and the randomness and complexity of the key generation algorithm. In the proposed method, first, the unique fingerprint features, which include Minutiae points, are extracted from the fingerprint image. Then, to increase the statistical properties and complexity of the algorithm, the Euclidean distance and the angle of all the points of Minutiae relative to each other are calculated and stored. In the next step, after normalizing to 8-bit numbers, these data are moved by permutation operations and combined. In the following, the proposed method is used to increase the level of security and the ability to be random from the non-linear operations of 8-bit S-boxes S0 and S1 used in the CLEFIA block cipher. Statistical analyzes performed on the generated keys show the acceptable random nature of the keys. Therefore, the proposed structure for generating a random key can be used in encrypting digital signals with large volumes of data such as image and sound. [ABSTRACT FROM AUTHOR]

In this paper, a new fractional-order fuzzy controller named proportional fuzzy ا integral fuzzy + deferential fuzzy (FP + Fla + FDp) is proposed. The proposed controller is single gain, integrate, derivative and fractional order in its structure. The controller has a structure with two adjustable fractions and is simply designed. This controller performs the stability process in a short time. The response time can also be changed by adjusting the coefficient of time scale a and p, which makes it more flexible and efficient compared to the classic PID controller. On the other hand, in the design of the proposed controller, the optimal adjustment of the controller gain and fuzzy members has turned into an optimization problem, which is done by a hybrid algorithm based on the vcs and the HBC colony (ABC) based on a function in the time domain. In the proposed hybrid algorithm, attempts have been made to take advantage of local and final search as the strengths of these controllers to dramatically reduce the possibility of locating in local points. Investigations of different load variation scenarios, better performance of the proposed controller facing load disturbances from the root mean square, rise and fall time, will show the number of oscillations, and the fall time of the frequency changes. [ABSTRACT FROM AUTHOR]

Background. Cervical cancer begins in superficial cells and over time can invade deeper tissues and surrounding tissues. This paper presents a creative idea of using an ensemble classification algorithm that improves the predictive performance of an artificial intelligence system based on cervical cancer screening. This study aimed to classify Pap-smear images by different machine learning methods to achieve high accuracy detection. Methods. This study was performed on 917 Pap-smear images from the Herlev public database. In the feature extraction stage, 20 geometric features and 76 texture features were extracted. After that, using ensemble classification method, the images were classified into two categories (i.e., normal and abnormal) and then into seven categories (i.e., superficial epithelial, intermediate epithelial, columnar epithelial, mild dysplasia, moderate dysplasia, severe dysplasia and carcinoma) and the accuracy of the proposed method was evaluated. Results. The algorithm in the ensemble classification was able to achieve accuracy of 99.9% with a processing time of 0.028 second in the two-class classification and accuracy of 76.5% with a processing time of 0.033 second in the seven-class classification. Conclusion. Based on the results, the designed algorithm can be used as a computer aided diagnostic tool to increase the accuracy and speed of predicting the risk of cervical cancer. Practical Implications. Cervical cancer is one of the most common cancers among women. Early diagnosis of the disease can save various costs and prevent the patients’ frequent visits to medical centers. This research proposed an artificial intelligence method for automatic classification of cervical cells and improving the accuracy of diagnosis. [ABSTRACT FROM AUTHOR]

As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

One of the most important parts of infrared search and tracking systems is image processing, which detects the targets in infrared images. In this paper, a new infrared small target detection algorithm is proposed. The proposed method uses heuristic optimization algorithms to find small targets in infrared images. In this way, the particle swarm optimization algorithm is used as one of the best heuristic optimization algorithms. The performance of the proposed algorithm is evaluated using real and simulated infrared images. These images include a variety of false response sources like high-intensity edges, cloudy background, complex sea-sky background, and a target close to high-intensity background clutter. The experimental results are compared with the four common methods of infrared small target detection. The experimental results show the effectiveness and performance of the proposed algorithm. Also, the runtime of the proposed method is comparable to other commonly used methods, and this makes it usable in real-time programs. [ABSTRACT FROM AUTHOR]

The accurate awareness of the users as the most vital need for media has always been a concern for communication researchers. Given the emergence of new media, traditional techniques no longer have the ability to meet this need, and the use of modern methods is essential in understanding users' behavior. The present study used the Social Network Analysis (SNA) technique to investigate the user's behavior of VOD platforms. In this research, the site of CinemaMarket selected as the sample, and a dataset of its user’s purchases formed. Using Gephi software, the dataset was visualized and the members were clustered based on logical algorithms. By analyzing three detected communities, two different strategies were founded for the development of the users of this platform. Based on these strategies, platform managers can adapt their media policies to load new content on the platform and offer targeted content; as a result, they can meet their user's needs. [ABSTRACT FROM AUTHOR]

Role and Reference Grammar (RRG) provides a workable mechanism, namely “interclausal relations hierarchy”, for the analysis of complex constructions. It is used as a general rule for interlinguistic studies. This hierarchy consists of "semantic continuum" and "syntactic continuum" and is based on iconicity, which means that constructions with high semantic correlations are expressed in the form of stronger syntactic relations and vice versa. In this hierarchy, causative constructions are also included, which were the focus of the present study. In this research, causative constructions were divided into 3 types: causative [type 1] or direct causatives, causative [type 2] or indirect causatives, and jussives. We first examined their syntactic semantic forms and interactions in causative constructions. The juncture-nexus relations and semantic hierarchy were discussed in the syntactic and semantic dimensions, respectively. The findings indicated that the direct causatives were in nuclear co-subordination relations, while the indirect causatives and jussives were in core co-subordination relations. We also proposed a constructional scheme for direct causation in Persian. The interaction of the two levels of syntax and semantics in causative constructions was also explained in the form of linking algorithms. Finally, it was found that Persian language acts in a way that it confirms the "interclausal relation hierarchy" and thus, its explanatory adequacy could be corroborated. [ABSTRACT FROM AUTHOR]