Search

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 method of data-driven controller (DDC) design using Simultaneous Perturbation Stochastic Approximation Algorithm (SPSA) and Neural Network (NN) training is presented. This method can be used to control a variety of linear and nonlinear systems. In the simultaneous perturbation stochastic approximation algorithm, the controller is assumed to have a fixed structure and its parameters must be estimated. In this paper, a Proportional, Integral, and Derivative controller (PID) is considered and the parameters that should be estimated by the proposed algorithm are proportional, integral and derivative terms of this controller. In the proposed method, the simultaneous perturbation stochastic approximation algorithm is quantified by using neural network training which increases the convergence speed and also improves the performance of the algorithm against system input changes. Simulations performed on cement grinding particle size distribution process and pitch angle control of aircraft show the high efficiency and potential of the proposed method. [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 growing use of internet and the existence of vulnerable points in networks have made the use of intrusion detection systems as one of the most important security elements. Intrusion detection is essentially a classification problem and it is the identification of effective features such as important issues in the classification This paper presents a novel method for selecting effective features in network intrusion detection based on an estimation of distribution algorithm that uses a probabilistic dependency tree to identify important interactions between features. To evaluate the performance of the proposed method, the NSL- KDD dataset is used, in which the packets are divided into five normal types and intrusive types of DOS, U2R, R2L and Prob. The performance of the proposed algorithm has been compared alone and in combination with other feature selection algorithms such as forward selection, backward selection and genetic algorithm. Moreover, the effect of algorithm parameters like population size on intrusion detection accuracy is tested. Based on this analysis and also considering the intra-class accuracy of different feature selection methods studied in this paper, an effective subset of features for intrusion detection is identified. [ABSTRACT FROM AUTHOR]

Numerical solution of conjugate heat transfer (CHT) problems in multi-region domains is a challenging issue in terms of efficiency and stability due to inherent coupling between governing equations in separate sub-domains. This paper aims at comparing two distinct strategies for coupling energy equations at interface of regions: partitioned and monolithic. While the former enforces strong coupling via iterative procedures, the latter suggests simultaneous solution of energy equation throughout all regions at once. More precisely, the primary objective of this study is to compare computational costs for three finite volume- based CHT solvers which share a same method for handling pressure-velocity coupling in the fluid, i.e. semi-implicit projection method, and are distinguished from the coupling strategy for energy equation at interface of interacting sub-domains. The first method applies simultaneous solution of energy equation across all regions while second and third ones use separate solver for each region with difference in handling iterative loops. The accuracy and convergence rate of three algorithms are assessed via transient solution of conjugate free convection in fluid and conduction in vertical wall. Finally, it is concluded that, in contrast to partitioned solver in OpenFOAM for CHT problems, employing a separate loop to enforce energy-energy coupling per iteration, can be much more beneficial in terms of computational costs. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive algorithm for spectral unmixing in hyperspectral images (HSIs) is proposed. Many of the existing spectral unmixing algorithms, under the assumption of the linear model for the spectral mixing phenomenon, attempt to estimate the signatures of available materials in the observed HSI image. Then, based on the similarity between the estimated spectral signatures and the available spectral signatures in the spectral library, they identify the materials in the HSI and estimate their relative abundances. While the spectral library, as prior knowledge, has not been directly considered in the founding of existing algorithms, the proposed method is directly concentrated on the spectral signatures library. Assuming the linear spectral mixing model, the proposed method takes a set of spectral signatures which are probably present in the observed HSI. Then, based on a non-statistical approach, the normalized least mean square (NLMS) adaptive algorithm is engaged to estimate a weight vector for each spectral signature in the selected set in such a way that each weight vector and its corresponding spectral signature are non-orthogonal whereas the weight vector of each spectral signature is almost orthogonal to the other spectral signatures. A synthetic dataset of hyperspectral images is considered to evaluate the performance of the proposed method. The evaluation results show that the proposed method outperforms its counterparts in low signal to noise ratio (SNR). [ABSTRACT FROM AUTHOR]

Due to the increasing load growth and expansion and significant changes in structure of electricity distribution networks, the issues related to the optimization of these networks face many challenges. Optimal placement and capacity of Distribution substations and their feeder networks are among the important issues of optimization of distribution networks from technical and economic viewpoints. In this paper, considering two important factors including loss reduction and improvement of reliability in distribution networks, a new multi-objective function was presented for optimal placement of location and capacity of Distribution substations based on their service area. The proposed objective function includes all construction and operation costs of equipment, loss costs, and reliability index (Energy Not Supplied). The Artificial Bee Colony (ABC) algorithm was applied to optimize the proposed objective function with the purpose of determining the optimal placement and capacity of substations of two sample distribution networks. A comparison of results obtained from optimization of the proposed objective function by the Artificial Bee Colony (ABC) algorithm with results of other algorithms in two sample networks indicates the efficiency and effectiveness of the proposed algorithm and objective function. [ABSTRACT FROM AUTHOR]

Network on-chip is a communication subsystem within an integrated circuit that provides communication between processors in the on-chip system. There are several different ways to get from one node to another. Therefore, there must be a routing algorithm to find the route to the destination. This paper presents an algorithm based on the reduction of the passing path to reach a packet from origin to destination which is able to increase the reliability, reduce latency, power consumption and increase network efficiency on the chip. And this is when most of the fault-tolerant networks presented in this field increase parameters such as delay, power consumption and circuit complexity in order to achieve higher reliability. The proposed method improves network performance with minimal hardware changes and circuit complexity. The path passed by the packet is reduced to reach the destination, which means passing through fewer links and routers and less chance of encountering faulty links and routers and increasing network reliability. Also, passing fewer links and routers will reduce network latency and power consumption. [ABSTRACT FROM AUTHOR]

One of the important issues in coordination of protective relays is minimization of the time interval between the operation of main and backup overcurrent (OC) relays. Overcurrent relays coordination problem due to the large number of variables and the nature of the objective functions can be introduced as a complex optimization problem that necessitates the need for an efficient optimization method with appropriate accuracy and speed. Given that for the realization of protective purposes including increased relays operation speed, selectivity, support, reliability and stability, different objective functions can be described, therefore, providing a multi-objective mathematical problem in optimizing protective problems may be necessary. In this regard, this article due to the capabilities of the MOPSO, proposes a multi-objective optimization structure, in which the SQP by increasing the speed and not increasing the search space is added to MOPSO. In this paper, several objective functions are proposed based on protection goals and optimal adjustment points have been extracted using the proposed multi-objective MOPSO-SQP. The simulations have been implemented and carefully analyzed on several typical power systems. The simulation results confirm the efficiency of the proposed algorithm in ensuring the optimal coordination of protective overcurrent relays in power system. [ABSTRACT FROM AUTHOR]

Organizations should consider process improvement for promoting their performance. The main problem is the high volume of process dataset along with the wide variety of their features that leads to increase the complexity in process improvement methods. Previous methods cannot improve processes in a high volume of process dataset. Data mining approach can support improvement methods by identifying valuable hidden patterns in high volume of process dataset. In this paper a framework of using data mining techniques for extracting valuable hidden patterns in high volume of process dataset is developed for presenting the improvement suggestions. In order to evaluate the proposed framework, a real set of processes along with their features were gathered. After that, using classification, clustering, and features selection algorithms, valuable patterns in high volume of process dataset were identified. After evaluating these patterns, improvement suggestions were recommended by the identified patterns. The results showed that identified patterns can support processes improvement activities with recommending the improvement suggestions. [ABSTRACT FROM AUTHOR]

Nowadays, determining the contribution of the individual consumers to the harmonic contamination is required for improving power quality. Due to changes occurred in lines, transformers, loads and generators over time, network operating conditions and impedance of different sectors are frequently varied resulting in the change in the contribution of harmonic sources. In this paper, an intelligent-based three-step method is developed to continuously determine the contribution of each harmonic source to the harmonic voltage of different buses without any access to the voltage phase data at different buses in the network. In this algorithm, firstly, the K-means clustering method is used for pre-processing of the measured data in order to reduce the background harmonic destructive effect on accuracy of the harmonic contribution determination. After calculating the harmonic contribution, the K-nearest neighbor method is used to generalize the results, and subsequently to create a continuous harmonic contribution matrix (HCM). Finally, the method is applied to a standard power network-based calculation example. The results demonstrate the capability of the proposed algorithm to evaluate the effects of harmonic sources in power networks. [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]

Hub location problem is further used in transportation and telecommunication networks (airlines, post delivery services, etc.) so origin-destination pairs, receive or send commodities via special facilities called Hub. Hub median problem with multiple allocation is an NP-hard problem which includes both locating hub facilities and allocating non-hub nodes to hubs as minimizes total transportation and location costs. In this paper, the hub median problem is considered in an environment which network flow varies during the time periods and the capacities of hubs and arcs are unlimited. Also opening and closing hubs in different periods of planning horizon are permitted. The model and the proposed algorithm for this problem were considered to Iran airlines network using real passenger flows data. Computational results state that the dynamic network compared with static model has lower cost and whatever the number of time periods in dynamic case increases, the cost will be reduced as well. [ABSTRACT FROM AUTHOR]

Background and Aim Breast cancer is the abnormal cell growth in the breast. In both benign and malignant masses, there is rapid and high cell growth. Nowadays, due to the development of technologies, the diagnosis of diseases has become non-invasive and physicians attempts to diagnose the disease without surgery and based on internal organ images. Methods & Materials In this study, by using images prepared from the Digital Database for Screening Mammography (DDSM), a new method is proposed for detecting cancerous masses in the mammographic images using geometric features extraction and optimization of Support Vector Machine (SVM) parameters to classify breast cancer masses automatically. First, images were pre-processed and then boundaries were determined using threshold method. Next, morphological operators were used to improve these boundaries and the segmentation of images was carried out to classify cancerous masses. Finally, by using the SVM parameter optimization method, Grasshopper Optimization Algorithm (GOA), and 4-fold crossvalidation method, data were classified into two groups of benign and malignant (cancer) masses. Ethical Considerations Images from DDSM database were used in this research, all images are open access in this database. Results The accuracy, sensitivity and specificity values for applying the Radial Basis Function (RBF) kernel in SVM classifier (before optimization process) were obtained 97%, 100% and 96, respectively. After optimization of SVM parameters by the GOA, it was reported 100% for all accuracy, sensitivity and specificity indices for applying linear kernel function, indicating the high accuracy of the proposed method. The average values of accuracy, sensitivity and specificity indices for applying all three SVM kernel functions after optimization were 95.83, 100 and 94.81%, respectively. Conclusion The extracted geometrical features from breast cancer masses are highly efficient for model training and the diagnosis of breast cancer. The GOA could improve the overall accuracy of the proposed method by optimizing the SVM parameters. The results showed the higher performance of the proposed method compared to other methods. [ABSTRACT FROM AUTHOR]

Background and Aim Chronic Lymphocytic Leukemia (CLL) is the most commonly occurring leukemia in adults, accounting for about 30-25% of total leukemia. One of the important etiological causes of this leukemia is the disruption of the Nuclear Factor Kappa B (NF-kB) signaling pathway. The two proteins of Apoptosis-Inducing Ligand (APRIL) and B-Cell Activating Factor (BAFF) play a role in the pathogenesis of this leukemia by affecting the NF-kB signaling pathway. In this study, due to the effect of miRNAs in regulating many cellular processes, the prediction of the prominent miRNAs targeting APRIL and BAFF transcripts in B-cell CLL patients was evaluated using specific and different bioinformatics programs. Methods & Materials Afterwards retrieving the sequences of APRIL and BAFF proteins from the NCBI website, by using several programs including miRanda, TargetScan, miRWalk, DIANA and miRDB with different algorithms, the prediction of miRNAs targeting these genes was investigated. Ethical Considerations This study was approved by the Research Ethics Committee of Arak University of Medical Sciences. Results Based on the scoring system of bioinformatics programs, “hsa-miR-145-5p” and “hsa-miR-185-5p” were identified as miRNAs targeting APRIL gene, while “hsa-miR-424” and “hsa-miR-497”were miRNAs targeting BAFF gene. They were suggested for the practical studies in future. Conclusion Based on the important role of APRIL and BAFF genes in the normal process of cell death and B-cell evolution, it seems that the mi-RNAs predicted by bioinformatics programs using different algorithms can be used as a diagnostic molecular biomarker to identify B-cell CLL patients. [ABSTRACT FROM AUTHOR]