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Open Access Article
1 - A New Algorithm for Fastintra-Frame Modes Selection in H.264/Avc Video Coding
Mahnaz Nejadali mahdi jafari majid mohammadiBy the increasing of video communication in portable and functional devices, encoders design with low complexity and high performance are required. H.264 / AVC standard offers higher compression efficiency than previous standards. But this standard by employing several MoreBy the increasing of video communication in portable and functional devices, encoders design with low complexity and high performance are required. H.264 / AVC standard offers higher compression efficiency than previous standards. But this standard by employing several powerful coding techniques, considerably increased complexity at the encoder. This paper presents a new algorithm to reduce the complexity of the H.264/AVC encoder. The proposed method uses simple directional masks, neighboring blocks modes and detection of 4×4 and/or 16×16 intra estimation modes with determination of quantization parameters for fast mode selection in Intra-Frame Modes prediction. Experimental results show that the proposed method reduces maximum 29% of the encoding time, while has little effect on visual quality and PSNR. Manuscript profile -
Open Access Article
2 - Comparing A Hybridization of Fuzzy Inference System and Particle Swarm Optimization Algorithm with Deep Learning to Predict Stock Prices
Majid Abdolrazzagh-Nezhad mahdi kheradPredicting stock prices by data analysts have created a great business opportunity for a wide range of investors in the stock markets. But the fact is difficulte, because there are many affective economic factors in the stock markets that they are too dynamic and compl MorePredicting stock prices by data analysts have created a great business opportunity for a wide range of investors in the stock markets. But the fact is difficulte, because there are many affective economic factors in the stock markets that they are too dynamic and complex. In this paper, two models are designed and implemented to identify the complex relationship between 10 economic factors on the stock prices of companies operating in the Tehran stock market. First, a Mamdani Fuzzy Inference System (MFIS) is designed that the fuzzy rules set of its inference engine is found by the Particle Swarm Optimization Algorithm (PSO). Then a Deep Learning model consisting of 26 neurons is designed wiht 5 hidden layers. The designed models are implemented to predict the stock prices of nine companies operating on the Tehran Stock Exchange. The experimental results show that the designed deep learning model can obtain better results than the hybridization of MFIS-PSO, the neural network and SVM, although the interperative ability of the obtained patterns, more consistent behavior with much less variance, as well as higher convergence speed than other models can be mentioned as significant competitive advantages of the MFIS-PSO model Manuscript profile -
Open Access Article
3 - Apocalyptic in Sohrab Sepehri’s Hasht Ketab
mostafa gorjiApocalyptic literature is one of the genre in the area of Sacred Books and religious-faith texts. Apocalyptic literature foresee past and future events by tools of various types based on the culture dominating the society and its opinions and express it in form of art, MoreApocalyptic literature is one of the genre in the area of Sacred Books and religious-faith texts. Apocalyptic literature foresee past and future events by tools of various types based on the culture dominating the society and its opinions and express it in form of art, literature and by codified and symbolic language. Top and complete type of this, exists in Hannah Sacred Book, Nostrdamous universal apocalyptic and Farsi Shah Nemat-U Allah Vali literature. (Divan Ashaar, 1373:228). This issue have been circulated in any period of art and literature specially in Farsi contemporary literature. Among these we can mention Sohrab Sepehri’s, Forokh Farrokhzad and the likes ones poetries. This article tries to criticize and analyze its type of literature in the past and its origins and then criticize Sepehri’s Hasht Ketan from this view point and analyze the apocalyptic singled out in the ten areas such as dream and the likes. The major points is that pointing the first apocalyptic (poetic revelation and apocalyptic), Sohrab points to its second meaning namely apocalyptic while pointing the manifestation related tools proior to deal with the prediction itself and the event concerning the Last Time. Manuscript profile -
Open Access Article
4 - Prediction of Deadlocks in Concurrent Programs Using Neural Network
Elmira Hasanzad babamir babamirThe dependability of concurrent programs is usually limited by concurrency errors like deadlocks and data races in allocation of resources. Deadlocks are difficult to find during the program testing because they happen under very specific thread or process scheduling an MoreThe dependability of concurrent programs is usually limited by concurrency errors like deadlocks and data races in allocation of resources. Deadlocks are difficult to find during the program testing because they happen under very specific thread or process scheduling and environmental conditions. In this study, we extended our previous approach for online potential deadlock detection in resources allocated by multithread programs. Our approach is based on reasoning about deadlock possibility using the prediction of future behavior of threads. Due to the nondeterministic nature, future behavior of multithread programs, in most of cases, cannot be easily specified. Before the prediction, the behavior of threads should be translated into a predictable format. Time series is our choice to this conversion because many Statistical and Artificial Intelligence techniques can be developed to predict the future members of the time series. Among all the prediction techniques, artificial neural networks showed applicable performance and flexibility in predicting complex behavioral patterns which are the most usual cases in real world applications. Our model focuses on the multithread programs which use locks to allocate resources. The proposed model was used to deadlock prediction in resources allocated by multithread Java programs and the results were evaluated. Manuscript profile -
Open Access Article
5 - Representing a Content-based link Prediction Algorithm in Scientific Social Networks
Hosna Solaimannezhad omid fatemiPredicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. A MorePredicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. As a part of recent improvements of research, far much attention is devoted to the computational analysis of these social networks. The dynamics of these networks makes them challenging to study. Link prediction is one of the main problems in social networks analysis. If we represent a social network with a graph, link prediction means predicting edges that will be created between nodes in the future. The output of link prediction algorithms is using in the various areas such as recommender systems. Also, collaboration prediction between two authors using their research interests is one of the issues that improve group researches. There are few studies on link prediction that use content published by nodes for predicting collaboration between them. In this study, a new link prediction algorithm is developed based on the people interests. By extracting fields that authors have worked on them via analyzing papers published by them, this algorithm predicts their communication in future. The results of tests on SID dataset as coauthor dataset show that developed algorithm outperforms all the structure-based link prediction algorithms. Finally, the reasons of algorithm’s efficiency are analyzed and presented Manuscript profile -
Open Access Article
6 - Using Discrete Hidden Markov Model for Modelling and Forecasting the Tourism Demand in Isfahan
Khatereh Ghasvarian Jahromi Vida Ghasvarian JahromiTourism has been increasingly gaining acceptance as a driving force to enhance the economic growth because it brings the per capita income, employment and foreign currency earnings. Since tourism affects other industries, in many countries, tourism is considered in the MoreTourism has been increasingly gaining acceptance as a driving force to enhance the economic growth because it brings the per capita income, employment and foreign currency earnings. Since tourism affects other industries, in many countries, tourism is considered in the economic outlook. The perishable nature of most sections dependent on the tourism has turned the prediction of tourism demand an important issue for future success. The present study, for the first time, uses the Discrete Hidden Markov Model (DHMM) to predict the tourism demand. DHMM is the discrete form of the well-known HMM approach with the capability of parametric modeling the random processes. MATLAB Software is applied to simulate and implement the proposed method. The statistic reports of Iranian and foreign tourists visiting Isfahan gained by Iran Cultural Heritage, Handicrafts, and Tourism Organization (ICHHTO)-Isfahan Tourism used for simulation of the model. To evaluate the proposed method, the prediction results are compared to the results from Artificial Neural Network, Grey model and Persistence method on the same data. Three errors indexes, MAPE (%), RMSE, and MAE, are also applied to have a better comparison between them. The results reveal that compared to three other methods, DHMM performs better in predicting tourism demand for the next year, both for Iranian and foreign tourists. Manuscript profile -
Open Access Article
7 - Overcoming the Link Prediction Limitation in Sparse Networks using Community Detection
Mohammad Pouya Salvati Jamshid Bagherzadeh Mohasefi Sadegh SulaimanyLink prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A maj MoreLink prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A major issue confronted by all these methods, is that many of the available networks are sparse. This results in high volume of computation, longer processing times, more memory requirements, and more poor results. This research has presented a new, distinct method for link prediction based on community detection in large-scale sparse networks. Here, the communities over the network are first identified, and the link prediction operations are then performed within each obtained community using neighbourhood-based methods. Next, a new method for link prediction has been carried out between the clusters with a specified manner for maximal utilization of the network capacity. Utilized community detection algorithms are Best partition, Link community, Info map and Girvan-Newman, and the datasets used in experiments are Email, HEP, REL, Wikivote, Word and PPI. For evaluation of the proposed method, three measures have been used: precision, computation time and AUC. The results obtained over different datasets demonstrate that extra calculations have been prevented, and precision has been increased. In this method, runtime has also been reduced considerably. Moreover, in many cases Best partition community detection method has good results compared to other community detection algorithms. Manuscript profile -
Open Access Article
8 - Nonlinear Regression Model Based on Fractional Bee Colony Algorithm for Loan Time Series
Farid Ahmadi Mohammad Pourmahmood Aghababa Hashem KalbkhaniHigh levels of nonperforming loans provide negative impacts on the growth rate of gross domestic product. Therefore, predicting the occurrence of nonperforming loans is a vital issue for the financial sector and governments. In this paper, an intelligent nonlinear model MoreHigh levels of nonperforming loans provide negative impacts on the growth rate of gross domestic product. Therefore, predicting the occurrence of nonperforming loans is a vital issue for the financial sector and governments. In this paper, an intelligent nonlinear model is proposed for describing the behavior of nonperforming loans. In order to find the optimal parameters of the model, a new fractional bee colony algorithm (BCA) based on fractional calculus techniques is proposed. The inputs of the nonlinear model are the loan type, approved amount, refund amount, and economic realm. The output of the regression model is that whether the current information is for a nonperforming loan or not. Consequently, the model is modified to detect the status of a loan. So, the modified model predicts the occurrence of a nonperforming loan and determines the loan status, i.e., current, overdue, and nonperforming. The proposed procedure is applied to data gathered from an economic institution in Iran. The findings of this study are helpful for the managers of banks, and financial sectors to forecast the future of the loans and, therefore, manage the budget for the upcoming loan requests. Manuscript profile -
Open Access Article
9 - Deep Learning Approach for Cardiac MRI Images
Afshin Sandooghdar Farzin YaghmaeeDeep Learning (DL) is the most widely used image-analysis process, especially in medical image processing. Though DL has entered image processing to solve Machine Learning (ML) problems, identifying the most suitable model based on evaluation of the epochs is still an o MoreDeep Learning (DL) is the most widely used image-analysis process, especially in medical image processing. Though DL has entered image processing to solve Machine Learning (ML) problems, identifying the most suitable model based on evaluation of the epochs is still an open question for scholars in the field. There are so many types of function approximators like Decision Tree, Gaussian Processes and Deep Learning, used in multi-layered Neural Networks (NNs), which should be evaluated to determine their effectiveness. Therefore, this study aimed to assess an approach based on DL techniques for modern medical imaging methods according to Magnetic Resonance Imaging (MRI) segmentation. To do so, an experiment with a random sampling approach was conducted. One hundred patient cases were used in this study for training, validation, and testing. The method used in this study was based on full automatic processing of segmentation and disease classification based on MRI images. U-Net structure was used for the segmentation process, with the use of cardiac Right Ventricular Cavity (RVC), Left Ventricular Cavity (LVC), Left Ventricular Myocardium (LVM), and information extracted from the segmentation step. With train and using random forest classifier, and Multilayer Perceptron (MLP), the task of predicting the pathologic target class was conducted. Segmentation extracted information was in the form of comprehensive features handcrafted to reflect demonstrative clinical strategies. Our study suggests 92% test accuracy for cardiac MRI image segmentation and classification. As for the MLP ensemble, and for the random forest, test accuracy was equal to 91% and 90%, respectively. This study has implications for scholars in the field of medical image processing. Manuscript profile -
Open Access Article
10 - Examining contract termination in Iranian law by comparative study with the principles of international trade agreements
Masoumeh Ghadirian Mozafar Bashokouh Alireza Lotfi DudranThe upward trend in the internationalization of contracts, especially international business contracts, requires the adoption of laws in harmony with other well-known legal systems. To avoid problems due to differences in the methods and methods used to regulate interna MoreThe upward trend in the internationalization of contracts, especially international business contracts, requires the adoption of laws in harmony with other well-known legal systems. To avoid problems due to differences in the methods and methods used to regulate international trade, the coordinator internal regulations are imperative with the passage of laws and other regulations. Regarding this issue, the present article deals with the comparative analysis of the guarantees of violations of contractual obligations in Iranian law and the principles of international trade agreements with the aim of explaining the rules governing the guarantee of implementation and its implementation with domestic law. Problems and issues related to the performance of contractual obligations are a series of substantive issues, and the most important part of the contracts is the guarantee, which is described in the Unidroit principles in chapter seven, which is discussed in the treatise. The performance bonus that is presented in most legal systems In case of violation of contractual obligations, they can be resorted to out of three categories. At first sight, it may require the execution of the same contractual obligations from the obligated party. The second one can request alternative compensation from a committed one, which is usually done by paying money and finally, it can cancel the contract, which seeks to examine the distinction and sharing of the guarantees of violations of contractual obligations in Iranian law and the principles of international commercial contracts. According to the research, despite the many similarities between the two, differences it is also seen that they are not so important and can be adjusted by using other institutions in Iranian law. Manuscript profile -
Open Access Article
11 - Speech Compression Based on Linear Prediction Model and Voiced and Unvoiced Cycles
K. YaghmaieVariable rate signal compression has found many applications where there is no serious limitation on delay and the signal parameters are not very susceptible to errors. Methods used to apply variable rate coding usually rely on the redundancies included in the signal. MoreVariable rate signal compression has found many applications where there is no serious limitation on delay and the signal parameters are not very susceptible to errors. Methods used to apply variable rate coding usually rely on the redundancies included in the signal. Such methods are different in final bit rate, quality of the synthetic signal and computational requirements. This paper presents a novel method for compression of speech signal in a variable scheme. Based on the known linear prediction method, a simple and efficient model is developed in which segments of the speech signal are classified as voiced or unvoiced using the innovative voiced and unvoiced cycle concept. Manuscript profile -
Open Access Article
12 - Model Reference Adaptive Control Design for a Teleoperation System with Output Prediction
K. Hosseini-Sunny H. R. Momeni F. Janabi-SharifiIn this paper a new control scheme is proposed to ensure stability and performance of the teleoperation systems while a wide range of time delay of transmission line is allowed. For this mean, time delay is estimated and used to predict the plant output. A model referen MoreIn this paper a new control scheme is proposed to ensure stability and performance of the teleoperation systems while a wide range of time delay of transmission line is allowed. For this mean, time delay is estimated and used to predict the plant output. A model reference adaptive controller (MRAC) is designed for the master site using the predicted output of the plant. The proposed control system indicates good stability and force tracking performance. For the slave site, an independent MRAC is designed and it is shown that a good tracking for the position and velocity signals is achieved. Manuscript profile -
Open Access Article
13 - Speech Coding Using Non-linear Prediction Based on Volterra Series Expansion
M. H. Savoji Gh. AlipoorIn recent years there has been a growing interest to employ non-linear predictive techniques and models in speech coding to further reduce bit-rate and therefore channel bandwidth. Usually neural nets are used for this purpose that result in an additional up to 3dB redu MoreIn recent years there has been a growing interest to employ non-linear predictive techniques and models in speech coding to further reduce bit-rate and therefore channel bandwidth. Usually neural nets are used for this purpose that result in an additional up to 3dB reduction in the excitation signal energy. Non-linear prediction can also be performed based on Volterra series expansion wherein the expansion is usually limited to first and second terms, for simplicity (quadratic prediction). Early studies have shown that employing Volterra filters results in a much higher reduction in excitation signal energy (6 to 10 dB), as compared with neural nets. But, because of instability, this reduction can not be materialized in terms of bit-rate reduction or signal to noise improvement. This instability in the decoder is triggered by computational errors (i.e. due to quantization of the excitation signal) and high sensitivity of algorithms to these errors. In the original work, presented here, the instability in the codec is studied in both forward and backward prediction schemes using LS and LMS algorithms respectively. It is shown that stability can be obtained at the cost of losing most of saving in excitation signal energy where final reduction level is as much as for neural nets. With forward prediction, after stabilizing, in spite of a small increasing in the operational complexity for 20 to 45% of frames including the quadratic term will be beneficial. So a scheme is developed to perform non-linear prediction only on these frames. This algorithm results in an improvement of up to 4 dB in final signal to noise ratio. Sequential backward quadrant prediction, although much more interesting from implementation point of view, does not lead to an appreciable better performance over linear prediction. Manuscript profile -
Open Access Article
14 - Spectral Shaping of Reconstruction Noise in Backward ADPCM Coding
قاسم علیپور محمدحسن ساوجیThe main idea in ADPCM coding is to remove the redundancies of the speech signal before quantization. One of the important characteristics of this coding scheme is the spectral flatness of the reconstruction noise in spite of its low level. It has been tried, in the pre MoreThe main idea in ADPCM coding is to remove the redundancies of the speech signal before quantization. One of the important characteristics of this coding scheme is the spectral flatness of the reconstruction noise in spite of its low level. It has been tried, in the present research, to improve the perceptual quality of the reconstructed signal by shaping the spectrum of the reconstruction noise using an all-zero filter in the backward ADPCM coding. By doing so, a useful compromise is achieved between the level and the spectral shape of the reconstruction noise. The obtained results show an improvement in the perceptual quality of the reconstructed signal (higher PESQ score) and an increase in the noise level (lower SNR). Manuscript profile -
Open Access Article
15 - A Multi-Criteria Decision Making Mechanism for Data Offloading from Cellular Networks to Complementary Networks
M. Fallah Khoshbakht saleh Yousefi B. Ghalebsaz JeddiDue to proliferation of smart phones, data traffic in cellular networks has been significantly increasing, which has resulted in congestions in cellular networks. Data offloading to a complementary network such as Wi-Fi has been identified as a rational and cost-effecti MoreDue to proliferation of smart phones, data traffic in cellular networks has been significantly increasing, which has resulted in congestions in cellular networks. Data offloading to a complementary network such as Wi-Fi has been identified as a rational and cost-effective solution to these congestions. In this paper, a multi-criteria offloading (MCO) mechanism is proposed to select the best transfer mode among: cellular delivery, delay-tolerant offloading (DTO) to a complementary network, and peer-assisted offloading (PAO). The proposed MCO mechanism utilizes TOPSIS multi-criteria decision analysis method and a prediction model for the Wi-Fi connection pattern. The decision criteria include: the fraction of total users’ request satisfied by offloading, data transfer costs of cellular operator to users, data transfer bandwidth of users in both cellular and complementary networks, and total users’ power consumption. To evaluate the proposed mechanism various scenarios have been simulated, and the results show that the MCO mechanism can successfully take into account the preferences of the cellular operator and its users. Through simulations, the MCO mechanism demonstrated superior performance in comparison with other proposed solutions in the literature in terms of balancing the load on the network, reducing the cost of the cellular operator, and reducing energy consumption of the users. Manuscript profile -
Open Access Article
16 - A Novel Link Prediction Approach on Social Networks
S. Rezavandi Shoaii H. ZareNowadays the network science has been attracted many researchers from a wide variety of different fields and many problems in engineering domains are modelled through social networks measures. One of the most important problems in social networks is the prediction of ev MoreNowadays the network science has been attracted many researchers from a wide variety of different fields and many problems in engineering domains are modelled through social networks measures. One of the most important problems in social networks is the prediction of evolution and structural behavior of the networks that is known as link prediction problem in the related literature. Nowadays people use multiple and different social networks simultaneously and it causes to demonstrate a new domain of research known as heterogenous social networks. There exist a few works on link prediction problem on heterogenous networks. In this paper, first a novel similarity measure for users in heterogenous networks is defined. Then a novel link prediction algorithm is described through a supervised learning approach which is consisted by the generated features from the introduced similarity measures. We employ the standard evaluation criteria for verification of the proposed approach. The comparison of the proposed algorithm to the other well-known earlier works showed that our proposed method has better performance than the other methods based on testing on several network datasets. Manuscript profile -
Open Access Article
17 - Human Activity Recognition using Switching Structure Model
Mohammad Mahdi Arzani M. Fathy Ahmad AkbariTo communicate with people interactive systems often need to understand human activities in advance. However, recognizing activities in advance is a very challenging task, because people perform their activities in different ways, also, some activities are simple while MoreTo communicate with people interactive systems often need to understand human activities in advance. However, recognizing activities in advance is a very challenging task, because people perform their activities in different ways, also, some activities are simple while others are complex and comprised of several smaller atomic sub-activities. In this paper, we use skeletons captured from low-cost depth RGB-D sensors as high-level descriptions of the human body. We propose a method capable of recognizing simple and complex human activities by formulating it as a structured prediction task using probabilistic graphical models (PGM). We test our method on three popular datasets: CAD-60, UT-Kinect, and Florence 3D. These datasets cover both simple and complex activities. Also, our method is sensitive to clustering methods that are used to determine the middle states, we evaluate test different clustering, methods. Manuscript profile -
Open Access Article
18 - A Prediction-Based Load Distribution Approach for Software-Defined Networks
Hossein Mohammadi سیداکبر مصطفویSoftware-defined networking is a new network architecture which separates the control layer from the data layer. In this approach, the responsibility of the control layer is delegated to the controller software to dynamically determine the behavior of the entire network MoreSoftware-defined networking is a new network architecture which separates the control layer from the data layer. In this approach, the responsibility of the control layer is delegated to the controller software to dynamically determine the behavior of the entire network. It results in a flexible network with centralized management in which network parameters can be well controlled. Due to the increasing number of users, the emergence of new technologies, the explosive growth of network traffic, meeting the requirements of quality of service and preventing underload or overload of resources, load balancing in software-based networks is of substantial importance. Load imbalance increases costs, reduces scalability, flexibility, efficiency, and delay in network service. So far, a number of solutions have been proposed to improve the performance and load balancing in the network, which take into account different criteria such as power consumption and server response time, but most of them do not prevent the system from entering the load imbalance mode and the risks of load imbalance. In this paper, a predictive load balancing method is proposed to prevent the system from entering the load imbalance mode using the Extreme Learning Machine (ELM) algorithm. The evaluation results of the proposed method show that in terms of controller processing delay, load balance and response time, it performs better than CDAA and PSOAP methods. Manuscript profile -
Open Access Article
19 - Inferring Diffusion Network from Information Cascades using Transitive Influence
Mehdi Emadi Maseud Rahgozar Farhad OroumchianNowadays, online social networks have a great impact on people’s life and how they interact. News, sentiment, rumors, and fashion, like contagious diseases, are propagated through online social networks. When information is transmitted from one person to another in a so MoreNowadays, online social networks have a great impact on people’s life and how they interact. News, sentiment, rumors, and fashion, like contagious diseases, are propagated through online social networks. When information is transmitted from one person to another in a social network, a diffusion process occurs. Each node of a network that participates in the diffusion process leaves some effects on this process, such as its transmission time. In most cases, despite the visibility of such effects of diffusion process, the structure of the network is unknown. Knowing the structure of a social network is essential for many research studies such as: such as community detection, expert finding, influence maximization, information diffusion, sentiment propagation, immunization against rumors, etc. Hence, inferring diffusion network and studying the behavior of the inferred network are considered to be important issues in social network researches. In recent years, various methods have been proposed for inferring a diffusion network. A wide range of proposed models, named parametric models, assume that the pattern of the propagation process follows a particular distribution. What's happening in the real world is very complicated and cannot easily be modeled with parametric models. Also, the models provided for large volumes of data do not have the required performance due to their high execution time. However, in this article, a nonparametric model is proposed that infers the underlying diffusion network. In the proposed model, all potential edges between the network nodes are identified using a similarity-based link prediction method. Then, a fast algorithm for graph pruning is used to reduce the number of edges. The proposed algorithm uses the transitive influence principle in social networks. The time complexity order of the proposed method is O(n3). This method was evaluated for both synthesized and real datasets. Comparison of the proposed method with state-of-the-art on different network types and various models of information cascades show that the model performs better precision and decreases the execution time too. Manuscript profile -
Open Access Article
20 - Modeling Mud Loss in Asmari Formation Using Geostatistics in RMS Software Environment in an Oil Field in Southwestern Iran
Kioumars Taheri Farhad Mohammad TorabStudying lost circulation in Asmari formation is very important because about 25% to 40% of drilling costs is allocated to drilling mud expenses. Considering that Studied oil field encounters severe mud loss in Asmari formation, therefore the purpose of this study is re MoreStudying lost circulation in Asmari formation is very important because about 25% to 40% of drilling costs is allocated to drilling mud expenses. Considering that Studied oil field encounters severe mud loss in Asmari formation, therefore the purpose of this study is recognition of the lost circulation zones and illustrating the mud loss distribution in Asmari formation. The mud loss maps in Asmari field were plotted in RMS software using moving average algorithm method. For this purpose, the data of 363 wells in this oil field was processed after data preparation, for mapping and 3D modeling of 11 different zones in Asmari formation. The data processing includes different stages such as elimination of outliers, normal transformation, drawing the histogram, variography and estimation and modeling. In this research, the geostatistical kriging method was also used for estimation and 3D modeling of mud loss in Asmari formation so that the output of geostatistical modeling method shows the localized and better results. Consequently, by applying and analysis of results, the 2D and 3D models of mud loss in Asmari formation were demonstrated. By simulation and modeling of mud loss and its comparison with reservoir fault modeling and production indexes plots, it was identified that the dominant mud losses are related to fault zone fractures and in minor cases the increasing of mud weight is the reason of mud loss. Applying appropriate operations such as under balance drilling (UBD) and suitable well placement, use of drilling mud with proper mud weight in severe mud loss points, use of NIF and MMH especial drilling muds with lowest formation damage, or a combination of these methods are suggested for mud loss control in critical points of the oil field. Manuscript profile -
Open Access Article
21 - Implementation of Machine Learning Algorithms for Customer Churn Prediction
Manal Loukili Fayçal Messaoudi Raouya El YoubiChurn prediction is one of the most critical issues in the telecommunications industry. The possibilities of predicting churn have increased considerably due to the remarkable progress made in the field of machine learning and artificial intelligence. In this context, w MoreChurn prediction is one of the most critical issues in the telecommunications industry. The possibilities of predicting churn have increased considerably due to the remarkable progress made in the field of machine learning and artificial intelligence. In this context, we propose the following process which consists of six stages. The first phase consists of data pre-processing, followed by feature analysis. In the third phase, the selection of features. Then the data was divided into two parts: the training set and the test set. In the prediction process, the most popular predictive models were adopted, namely random forest, k-nearest neighbor, and support vector machine. In addition, we used cross-validation on the training set for hyperparameter tuning and to avoid model overfitting. Then, the results obtained on the test set were evaluated using the confusion matrix and the AUC curve. Finally, we found that the models used gave high accuracy values (over 79%). The highest AUC score, 84%, is achieved by the SVM and bagging classifiers as an ensemble method which surpasses them. Manuscript profile -
Open Access Article
22 - Modeling for Predicting Domestic Demands for Recreational Tourism in Tehran
ebtehal zandiRecreational tourism is an important form of domestic tourism in Tehran, based on the statistics of the National Center of Statistics and the views of the experts. This paper tried to propose models for predicting effective variables on predicting domestic demands for r MoreRecreational tourism is an important form of domestic tourism in Tehran, based on the statistics of the National Center of Statistics and the views of the experts. This paper tried to propose models for predicting effective variables on predicting domestic demands for recreational tourism in Tehran. The study used the monthly information between 2001 and 2015. The independent variable was the number of domestic recreational tourists in Tehran, and the dependent variables were selected based on Delphi and Fuzzy DEMATEL techniques. The model framework was a combination of regression, the fuzzy neural network, and SVR algorithm. The combinations of these methods helped measure prediction errors and compare methods. Results showed that the proposed hybrid approach of regression and Adaptive Neuro-Fuzzy Inference System (ANFIS) could have a better prediction compared to other methods for predicting domestic recreational tourism. Manuscript profile -
Open Access Article
23 - Stock Price Movement Prediction Using Directed Graph Attention Network
Alireza Jafari Saman HaratizadehPrediction of the future behavior of the stock market has always attracted researchers' attention as an important challenge in the field of machine learning. In recent years deep learning methods have been successfully applied in this domain to improve prediction perfor MorePrediction of the future behavior of the stock market has always attracted researchers' attention as an important challenge in the field of machine learning. In recent years deep learning methods have been successfully applied in this domain to improve prediction performance. Previous studies have demonstrated that aggregating information from related stocks can improve the performance of prediction. However, the capacity of modeling the stocks relations as directed graphs and the power of sophisticated graph embedding techniques such as Graph Attention Networks have not been exploited so far for prediction in this domain. In this work, we introduce a framework called DeepNet that creates a directed graph representing how useful the data from each stock can be for improving the prediction accuracy of any other stocks. DeepNet then applies Graph Attention Network to extract a useful representation for each node by aggregating information from its neighbors, while the optimal amount of each neighbor's contribution is learned during the training phase. We have developed a novel Graph Attention Network model called DGAT that is able to define unequal contribution values for each pair of adjacent nodes in a directed graph. Our evaluation experiments on the Tehran Stock Exchange data show that the introduced prediction model outperforms the state-of-the-art baseline algorithms in terms of accuracy and MCC measures. Manuscript profile -
Open Access Article
24 - Long-Term Software Fault Prediction Model with Linear Regression and Data Transformation
Momotaz Begum Jahid Hasan Rony Md. Rashedul Islam Jia UddinThe validation performance is obligatory to ensure the software reliability by determining the characteristics of an implemented software system. To ensure the reliability of software, not only detecting and solving occurred faults but also predicting the future fault i MoreThe validation performance is obligatory to ensure the software reliability by determining the characteristics of an implemented software system. To ensure the reliability of software, not only detecting and solving occurred faults but also predicting the future fault is required. It is performed before any actual testing phase initiates. As a result, various works on software fault prediction have been done. In this paper presents, we present a software fault prediction model where different data transformation methods are applied with Poisson fault count data. For data pre-processing from Poisson data to Gaussian data, Box-Cox power transformation (Box-Cox_T), Yeo-Johnson power transformation (Yeo-Johnson_T), and Anscombe transformation (Anscombe_T) are used here. And then, to predict long-term software fault prediction, linear regression is applied. Linear regression shows the linear relationship between the dependent and independent variable correspondingly relative error and testing days. For synthesis analysis, three real software fault count datasets are used, where we compare the proposed approach with Naïve gauss, exponential smoothing time series forecasting model, and conventional method software reliability growth models (SRGMs) in terms of data transformation (With_T) and non-data transformation (Non_T). Our datasets contain days and cumulative software faults represented in (62, 133), (181, 225), and (114, 189) formats, respectively. Box-Cox power transformation with linear regression (L_Box-Cox_T) method, has outperformed all other methods with regard to average relative error from the short to long term. Manuscript profile -
Open Access Article
25 - Application of Machine Learning in the Telecommunications Industry: Partial Churn Prediction by using a Hybrid Feature Selection Approach
Fatemeh Mozaffari Iman Raeesi Vanani Payam Mahmoudian Babak SohrabiThe telecommunications industry is one of the most competitive industries in the world. Because of the high cost of customer acquisition and the adverse effects of customer churn on the company's performance, customer retention becomes an inseparable part of strategic d MoreThe telecommunications industry is one of the most competitive industries in the world. Because of the high cost of customer acquisition and the adverse effects of customer churn on the company's performance, customer retention becomes an inseparable part of strategic decision-making and one of the main objectives of customer relationship management. Although customer churn prediction models are widely studied in various domains, several challenges remain in designing and implementing an effective model. This paper addresses the customer churn prediction problem with a practical approach. The experimental analysis was conducted on the customers' data gathered from available sources at a telecom company in Iran. First, partial churn was defined in a new way that exploits the status of customers based on criteria that can be measured easily in the telecommunications industry. This definition is also based on data mining techniques that can find the degree of similarity between assorted customers with active ones or churners. Moreover, a hybrid feature selection approach was proposed in which various feature selection methods, along with the crowd's wisdom, were applied. It was found that the wisdom of the crowd can be used as a useful feature selection method. Finally, a predictive model was developed using advanced machine learning algorithms such as bagging, boosting, stacking, and deep learning. The partial customer churn was predicted with more than 88% accuracy by the Gradient Boosting Machine algorithm by using 5-fold cross-validation. Comparative results indicate that the proposed model performs efficiently compared to the ones applied in the previous studies. Manuscript profile -
Open Access Article
26 - Wide Area out of Step Prediction of Interconnected Power System Using Decision Tree C5.0 Based on WAMS Data
Soheil RanjbarThis paper presents a new method for Out-of-Step detection in synchronous generators based on Decision Tree theory. For distinguishing between power swing and out-of-step conditions a series of input features are introduced and used for decision tree training. For gener MoreThis paper presents a new method for Out-of-Step detection in synchronous generators based on Decision Tree theory. For distinguishing between power swing and out-of-step conditions a series of input features are introduced and used for decision tree training. For generating input training samples, a series of measurements are taken under various faults including operational and topological disturbances. The proposed method is simulated over 10 machines 39-bus IEEE test system and the simulation results are prepared as input-output pairs for decision tree induction and deduction. The merit of proposed out-of-step protection scheme lies in adaptivity and robustness of input features under different input scenarios Manuscript profile -
Open Access Article
27 - Predicting the workload of virtual machines in order to reduce energy consumption in cloud data centers using the combination of deep learning models
Zeinab Khodaverdian Hossein Sadr Mojdeh Nazari Soleimandarabi Seyed Ahmad EdalatpanahCloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted usi MoreCloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted using live migration of Virtual Machines (VMs) and their consolidation into the small number of Physical Machines (PMs). However, the selection of the appropriate VM for migration is an important challenge. To solve this issue, VMs can be classified according to the pattern of user requests into Delay-sensitive (Interactive) or Delay-Insensitive classes, and thereafter suitable VMs can be selected for migration. This is possible by virtual machine workload prediction .In fact, workload predicting and predicting analysis is a pre-migration process of a virtual machine. In this paper, In order to classification of VMs in the Microsoft Azure cloud service, a hybrid model based on Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. Microsoft Azure Dataset is a labeled dataset and the workload of virtual machines in this dataset are in two labeled Delay-sensitive (Interactive) or Delay-Insensitive. But the distribution of samples in this dataset is unbalanced. In fact, many samples are in the Delay-Insensitive class. Therefore, Random Over-Sampling (ROS) method is used in this paper to overcome this challenge. Based on the empirical results, the proposed model obtained an accuracy of 94.42 which clearly demonstrates the superiority of our proposed model compared to other existing models. Manuscript profile -
Open Access Article
28 - Liquidity Risk Prediction Using News Sentiment Analysis
hamed mirashk albadvi albadvi mehrdad kargari Mohammad Ali Rastegar Mohammad TalebiOne of the main problems of Iranian banks is the lack of risk management process with a forward-looking approach, and one of the most important risks in banks is liquidity risk. Therefore, predicting liquidity risk has become an important issue for banks. Conventional m MoreOne of the main problems of Iranian banks is the lack of risk management process with a forward-looking approach, and one of the most important risks in banks is liquidity risk. Therefore, predicting liquidity risk has become an important issue for banks. Conventional methods of measuring liquidity risk are complex, time-consuming and expensive, which makes its prediction far from possible. Predicting liquidity risk at the right time can prevent serious problems or crises in the bank. In this study, it has been tried to provide an innovative solution for predicting bank liquidity risk and leading scenarios by using the approach of news sentiment analysis. The news sentiment analysis approach about one of the Iranian banks has been used in order to identify dynamic and effective qualitative factors in liquidity risk to provide a simpler and more efficient method for predicting the liquidity risk trend. The proposed method provides practical scenarios for real-world banking risk decision makers. The obtained liquidity risk scenarios are evaluated in comparison with the scenarios occurring in the bank according to the guidelines of the Basel Committee and the opinion of banking experts to ensure the correctness of the predictions and its alignment. The result of periodically evaluating the studied scenarios indicates a relatively high accuracy. The accuracy of prediction in possible scenarios derived from the Basel Committee is 95.5% and in scenarios derived from experts' opinions, 75%. Manuscript profile -
Open Access Article
29 - Design and implementation of a survival model for patients with melanoma based on data mining algorithms
farinaz sanaei Seyed Abdollah Amin Mousavi Abbas Toloie Eshlaghy ali rajabzadeh ghotriBackground/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of MoreBackground/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of the disease may cause death due to the spread of the disease to internal organs. The National Cancer Institute reported that approximately 99,780 people were diagnosed with melanoma in 2022, and approximately 7,650 died. Therefore, this study aims to develop an optimization algorithm for predicting melanoma patients' survival. Methodology: This applied research was a descriptive-analytical and retrospective study. The study population included patients with melanoma cancer identified from the National Cancer Research Center at Shahid Beheshti University between 2008 and 2013, with a follow-up period of five years. An optimization model was selected for melanoma survival prognosis based on the evaluation metrics of data mining algorithms. Findings: A neural network algorithm, a Naïve Bayes network, a Bayesian network, a combination of decision tree and Naïve Bayes network, logistic regression, J48, and ID3 were selected as the models used in the national database. Statistically, the studied neural network outperformed other selected algorithms in all evaluation metrics. Conclusion: The results of the present study showed that the neural network with a value of 0.97 has optimal performance in terms of reliability. Therefore, the predictive model of melanoma survival showed a better performance both in terms of discrimination power and reliability. Therefore, this algorithm was proposed as a melanoma survival prediction model. Manuscript profile -
Open Access Article
30 - Relationship Between Perceived Stress And Craving Usage with Predicting Relapse in Stimulants Users Mediated By Self-Control
roya jalili Javad KHalatbari Hassan Ahadai shohreh ghorban shiroudiThe purpose of the present study was to investigate the relationship between perceived stress and craving for consumption with predicting people's return to stimulants with the mediation of self-control. This research was method descriptive-correlative. The statistical MoreThe purpose of the present study was to investigate the relationship between perceived stress and craving for consumption with predicting people's return to stimulants with the mediation of self-control. This research was method descriptive-correlative. The statistical population of this research included all people who used stimulants, hospitalized in care centers in Tehran in 1400 (2019). For sample selection, 250 persons were randomly selected based on the structural modeling formula. In order to collect information, Cohen's Perceived Stress Scale, Franken's Substance Craving, Wright's Substance Return Prediction and Tanji's Self-Control Scales were used. The analysis of the research data, using the path analysis method, showed that there is a positive and significant relationship (P<0.01) between perceived stress and predicting return to stimulants with the mediation of self-control and between drug craving and predicting return to stimulants with The mediation of self-control. Also, there were a negative and significant relationship (P<0.01) between self-control and predicting return to stimulants. Thus, it can be concluded that with increase in perceived stress and the urge to use, the return to use of stimulants increases and the amount of self-control decreases, and with decrease of self-control, the return to use of stimulants increases. Manuscript profile -
Open Access Article
31 - An Overview of Artificial Intelligence Applications in Prediction and Diagnosis of Diseases Occurrence in Veterinary Medicine: Challenges and Techniques
Mahdi Bashizadeh Parham Soufizadeh Mahdi Zamiri Ayda Lamei Matin Sotoudehnejad Mahsa Daneshmand Melika Ghodrati Erika Isavi Hesameddin AkbareinEarly diagnosis of diseases is one of the main goals of health and wellness centers. Timely diagnosis can reduce the potential damage of diseases. The importance of this issue in veterinary medicine multiplies due to its combination with economic goals. Therefore, a pre MoreEarly diagnosis of diseases is one of the main goals of health and wellness centers. Timely diagnosis can reduce the potential damage of diseases. The importance of this issue in veterinary medicine multiplies due to its combination with economic goals. Therefore, a predictive approach is necessary for early diagnosis of diseases. This approach should be evidence-based and highly accurate. It should also be economically efficient. Artificial intelligence is the simulation of human intelligence and judgment by a computer or a robot that is programmed or trained to perform tasks that normally need human abilities. The emergence of artificial intelligence and machine learning techniques in today's world has improved the existing functions in health care systems. So that with the application of this technology, a significant progress has been made in the procedures of event prediction and disease diagnosis, management and health at the macro level, etc. Furthermore, the scope of diagnosable diseases is extensive, encompassing any ailment for which relevant data can be processed by artificial intelligence algorithms. The trained model has the capability to diagnose a wide range of diseases, with accuracy contingent upon factors such as disease indicators, collected data, and other pertinent variables. In this review article, the most important applications of artificial intelligence in veterinary medicine will be mentioned, and in general, these applications will be examined in various fields such as diagnosis of common diseases, differential diagnosis, prediction of disease occurrence, veterinary diagnostic imaging techniques, veterinary clinical pathology, etc. In addition, the challenges in this field will also be mentioned. This article is a review of recent studies in this fiel. Manuscript profile
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