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آخرین شماره

No 16
Vol. 4 No. 4
Autumn 2016
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Predicting the price of electricity is very important because electricity can not be stored. To this end, parallel methods and adaptive regression have been used in the past. But because dependence on the ambient temperature, there was no good result. In this study, linear prediction methods and neural networks and fuzzy logic have been studied and emulated. An optimized fuzzy-wavelet prediction method is proposed to predict the price of electricity. In this method, in order to have a better prediction, the membership functions of the fuzzy regression along with the type of the wavelet transform filter have been optimized using the E.Coli Bacterial Foraging Optimization Algorithm. Then, to better compare this optimal method with other prediction methods including conventional linear prediction and neural network methods, they were analyzed with the same electricity price data. In fact, our fuzzy-wavelet method has a more desirable solution than previous methods. More precisely by choosing a suitable filter and a multiresolution processing method, the maximum error has improved by 13.6%, and the mean squared error has improved about 17.9%. In comparison with the fuzzy prediction method, our proposed method has a higher computational volume due to the use of wavelet transform as well as double use of fuzzy prediction. Due to the large number of layers and neurons used in it, the neural network method has a much higher computational volume than our fuzzy-wavelet method.
Keivan Borna - Sepideh Palizdar
DOI : 10.7508/jist.2016.04.001
کلمات کلیدی : prediction ، wavelet transform ، fuzzy logic ، bacteria foraging algorithm ، electricity market
In this article the hybrid optimization algorithm of differential evolution and particle swarm is introduced for designing the fuzzy rule base of a fuzzy controller. For a specific number of rules, a hybrid algorithm for optimizing all open parameters was used to reach maximum accuracy in training. The considered hybrid computational approach includes: opposition-based differential evolution algorithm and particle swarm optimization algorithm. To train a fuzzy system hich is employed for identification of a nonlinear system, the results show that the proposed hybrid algorithm approach demonstrates a better identification accuracy compared to other educational approaches in identification of the nonlinear system model. The example used in this article is the Mackey-Glass Chaotic System on which the proposed method is finally applied.
Hojjatollah Hamidi - Atefeh Daraei
DOI : 10.7508/jist.2016.04.002
کلمات کلیدی : Data mining ، Classification ، Heart disease ، Diagnosis ، Prognosis ، Treatment
Emotion is expressed via facial muscle movements, speech, body and hand gestures, and various biological signals like heart beating. However, the most natural way that humans display emotion is facial expression. Facial expression recognition is a great challenge in the area of computer vision for the last two decades. This paper focuses on facial expression to identify seven universal human emotions i.e. anger, disgust, fear, happiness, sadness, surprise, and neu7tral. Unlike the majority of other approaches which use the whole face or interested regions of face, we restrict our facial emotion recognition (FER) method to analyze human emotional states based on eye region changes. The reason of using this region is that eye region is one of the most informative regions to represent facial expression. Furthermore, it leads to lower feature dimension as well as lower computational complexity. The facial expressions are described by appearance features obtained from texture encoded with Gabor filter and geometric features. The Support Vector Machine with RBF and poly-kernel functions is used for proper classification of different types of emotions. The Facial Expressions and Emotion Database (FG-Net), which contains spontaneous emotions and Cohn-Kanade(CK) Database with posed emotions have been used in experiments. The proposed method was trained on two databases separately and achieved the accuracy rate of 96.63% for spontaneous emotions recognition and 96.6% for posed expression recognition, respectively
Mina Navraan - Nasrollah moghadam charkari - Muharram Mansoorizadeh
DOI : 10.7508/jist.2016.04.003
کلمات کلیدی : Facial emotion recognition ، Gabor filter ، Support Vector Machine (SVM) ، Eye region
Entity profiling (EP) as an important task of Web mining and information extraction (IE) is the process of extracting entities in question and their related information from given text resources. From computational viewpoint, the Farsi language is one of the less-studied and less-resourced languages, and suffers from the lack of high quality language processing tools. This problem emphasizes the necessity of developing Farsi text processing systems. As an element of EP research, we present a semantic approach to extract profile of person entities from Farsi Web documents. Our approach includes three major components: (i) pre-processing, (ii) semantic analysis and (iii) attribute extraction. First, our system takes as input the raw text, and annotates the text using existing pre-processing tools. In semantic analysis stage, we analyze the pre-processed text syntactically and semantically and enrich the local processed information with semantic information obtained from a distant knowledge base. We then use a semantic rule-based approach to extract the related information of the persons in question. We show the effectiveness of our approach by testing it on a small Farsi corpus. The experimental results are encouraging and show that the proposed method outperforms baseline methods.
Hojjat Emami - Hossein Shirazi - Ahmad Abdollahzadeh Barforoush
DOI : 10.7508/jist.2016.04.004
کلمات کلیدی : Web mining ، information extraction ، entity profiling ، Farsi language
Android has been targeted by malware developers since it has emerged as widest used operating system for smartphones and mobile devices. Android security mainly relies on user decisions regarding to installing applications (apps) by approving their requested permissions. Therefore, a systematic user assistance mechanism for making appropriate decisions can significantly improve the security of Android based devices by preventing malicious apps installation. However, the criticality of permissions and the security risk values of apps are not well determined for users in order to make correct decisions. In this study, a new metric is introduced for effective risk computation of untrusted apps based on their required permissions. The metric leverages both frequency of permission usage in malwares and rarity of them in normal apps. Based on the proposed metric, an algorithm is developed and implemented for identifying critical permissions and effective risk computation. The proposed solution can be directly used by the mobile owners to make better decisions or by Android markets to filter out suspicious apps for further examination. Empirical evaluations on real malicious and normal app samples show that the proposed metric has high malware detection rate and is superior to recently proposed risk score measurements. Moreover, it has good performance on unseen apps in term of security risk computation.
Mahmood Deypir - Ehsan Sharifi
DOI : 10.7508/jist.2016.04.005
کلمات کلیدی : Mobile Device Security ، Risk Computation ، Android Malwares ، Critical Permissions ، Security Metric
This research is an ongoing work for achieving consistency between topology control and QoS guarantee in MANET. Desirable topology and Quality of Service (QoS) control are two important challenges in wireless communication networks such as MANETs.In a Mobile Ad hoc Network, MANET, nodes move in the network area; therefore, the network topology is randomly and unpredictably changed. If the network topology is not controlled properly, the energy consumption is increased and also network topology probably becomes disconnected. To prevent from this situation, it is necessary to use desirable dynamic topology control algorithms such as k-edge connectivity methods. This papertries to improvethe three following parameters according to the k-edge connectivity concepts: (1) network performance, (2) reduce energy consumption, and (3) maintain the network connectivity. To achieve these goals, as a new method, we enhance k-edge connectivity methods using an improved definition of node density. The new method is called as: Node Density Based k-edge connected Topology Control (NDBkTC) algorithm. For the first time the node density definition is dynamically used. The new method, computes the node density based on a new equation which consists of the following factors: the relative velocity of nodes, distance between nodes, the number of nodes and the transmission range of nodes. The results show that our new method improves the network performance compared with the existing methods. Also we will show that the new method can holds QoS in a desirable tolerance range.
Mohsen Heydarian
DOI : 10.7508/jist.2016.04.006
کلمات کلیدی : Localtopology Control ، k-edge Connectivity ، Node Density; MANET ، Optimizedenergy Consumption ، QoS
The idea behind the research is to deal with real-time 3D imaging that may extensively be referred to the fields of medical science and engineering in general. It is to note that most effective non-contact measurement techniques can include the structured light patterns, provided in the surface of object for the purpose of acquiring its 3D depth. The traditional structured light pattern can now be known as the fringe pattern. In this study, the conventional approaches, realized in the fringe pattern analysis with applications to 3D imaging such as wavelet and Fourier transform are efficiently investigated. In addition to the frequency estimation algorithm in most of these approaches, additional unwrapping algorithm is needed to extract the phase, coherently. Considering problems regarding phase unwrapping of fringe algorithm surveyed in the literatures, a state-of-the-art approach is here organized to be proposed. In the aforementioned proposed approach, the key characteristics of the same conventional algorithms such as the frequency estimation and the Itoh algorithm are synchronously realized. At the end, the results carried out through the simulation programs have revealed that the proposed approach is able to extract image phase of simulated fringe patterns and correspondingly realistic patterns with high quality. Another advantage of this investigated approach is considered as its real-time application, while a significant part of operations might be executed in parallel.
Amir Hooshang Mazinan - Ali Esmaeili
DOI : 10.7508/jist.2016.04.007
کلمات کلیدی : High-Resolution Fringe Patterns; ، 3D Imaging ، Itoh Algorithm ، Wavelet Transformation
Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cloud computing, appropriate task scheduling techniques are needed. Due to the limitations and heterogeneity of resources, the issue of scheduling is highly complicated. Hence, it is believed that an appropriate scheduling method can have a significant impact on reducing makespans and enhancing resource efficiency. Inasmuch as task scheduling in cloud computing is regarded as an NP complete problem; traditional heuristic algorithms used in task scheduling do not have the required efficiency in this context. With regard to the shortcomings of the traditional heuristic algorithms used in job scheduling, recently, the majority of researchers have focused on hybrid meta-heuristic methods for task scheduling. With regard to this cutting edge research domain, we used HEFT (Heterogeneous Earliest Finish Time) algorithm to propose a hybrid meta-heuristic method in this paper where genetic algorithm (GA) and particle swarm optimization (PSO) algorithms were combined with each other. The results of simulation and statistical analysis of proposed scheme indicate that the proposed algorithm, when compared with three other heuristic and a memetic algorithms, has optimized the makespan required for executing tasks.
Amin Kamalinia - Ali Ghaffari
DOI : 10.7508/jist.2016.04.008
کلمات کلیدی : Cloud Computing ، Task Scheduling ، Genetic Algorithm ، Particle Swarm Optimization Algorithm

معرفي نشريه

صاحب امتیاز :Ict Research Institute at ACECR
مدیر مسئول :Habibollah Asghari
سردبیر :Masood Shafiei
هیئت تحریریه :
Abdolali Abdipoor
Mahmoud Naghibzadeh
Zabih Ghasemlooy
Mahmoud Moghavemi
Aliakbar Jalali
Ramazan Ali Sadeghzadeh
Hamidreza Sadegh Mohammadi
Saeed Ghazimaghrebi
Ahmad Khademzadeh
Shaban Elahi
Abbasali Lotfi
Alireza Montazemi
Ali Mohammad Djafari
Rahim Saeidi
شاپا :2322-1437
شاپا الکترونیکی :2345-2773

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