In this research, a hybrid algorithm called WOA-Q Learning is proposed for real-time task scheduling in Fog-Cloud environments. The algorithm integrates the global exploration capability of the Whale Optimization Algorithm (WOA) with the adaptive decision-making mechanism of Q-Learning, aiming to optimize resource allocation and minimize delay. Simulations conducted in MATLAB across scenarios with 25 to 100 tasks demonstrated that the proposed method outperforms benchmark algorithms such as EDF, PSO, WOA, and QL in four key performance metrics: total delay, energy consumption, deadline miss ratio, and scheduling efficiency. The WOA-Q algorithm achieved up to 25% reduction in total delay, 20% lower energy consumption, and a deadline miss ratio of around 0.05. Although its execution time is slightly higher due to computational complexity, the overall performance improvement justifies the trade-off. The results confirm that combining metaheuristic and reinforcement learning techniques provides an effective and intelligent approach for real-time scheduling, with significant potential for applications in IoT, edge computing, and industrial control systems.          Keywords: Optimization, Real-time scheduling, Cloud-fog environment, Internet of Things      

   Abstract Digitalization in the field of government and then land administration as a government service, has come to the integration of processes and data through a single Point of Access called the single-window system. However, assessing the maturity level of this system and tracking its progress remains a major challenge. This thesis aims to propose a descriptive maturity model for evaluating the land administration single-window system, also known as Iranland . The proposed model is developed through a literature review and the integration of reference methodologies, frameworks and models such as the World Trade Organization’s Single Window Assessment Methodology (SWAM), the World Customs Organization's (WCO) Single Window Maturity Model, the Capability Maturity Model Integration (CMMI), the Open Group Architecture Framework (TOGAF), the United Nations e-Government Development Index (EGDI), the Organization for Economic Cooperation and Development (OECD) maturity frameworks, Iran’s five-level e-Government maturity model, and several other models and frameworks. In this proposed maturity model, the maturity of the system is assessed based on several key process areas, including integration level, data management, user interface, stakeholder participation, tra  arency, system performance, smart monitoring and control, and more. The proposed maturity model is presented in five maturity levels (Initial, Standard, Integrated, Advanced, and Optimization & Innovation), which align with phases A, B, and C of the TOGAF architecture development method, CMMI integration, and Iran’s five-level e-Government maturity model. The model was evaluated through comparative analysis with its reference models. The results indicate that the key benefits of the proposed maturity model are the integration of criteria from several recognized models, covering diverse indicators, and its compatibility with the specific characteristics of the land management single-window system. However, the model also has limitations, including the lack of a precise method for assessing weight to indicators, limited coverage of advanced security considerations, and dependency on data quality and availability, which could be explored in future research. Keywords: Maturity model, land administration single window system, single window, e-Government, maturity assessment.

In recent years, people's use of digital products such as websites and mobile applications has increased significantly. On the other hand, the way users interact with the product is one of the important factors in its success. As a result, experts in this field are trying to improve methods and standards related to user experience design by analyzing and measuring criteria. One of the effective methods in user experience research is the use of a questionnaire so that the target users and their needs can be properly met so that the final product has the desired function for the users. There are different questionnaires, each of which has examined user feedback from a specific aspect such as aesthetics, usability, or emotions. However, the modular evaluation questionnaire of the key components of the user experience, or meCUE for short, tests different dimensions effective in the user experience in a simultaneous and standard way. This questionnaire, which has obtained good results in the German language in various tests, was then translated into English and Indonesian languages according to a reliable process in two other articles, and the quality of the questionnaire in the target language was evaluated with criteria such as reliability, Cronbach's alpha test, and measurement. Internal consistency has been evaluated. In this research, three translators first translated the meCUE questionnaire into Farsi based on the international principles of cross-cultural adaptation and evaluated it with various criteria including reliability, Cronbach's alpha test, and internal consistency. To evaluate the questionnaire, it has been used on a comprehensive Iranian application called Rubika with more than 30 million active installations. In this way, 30 application users answered the questionnaire first, and using the obtained results, the criteria mentioned above were calculated to ensure the accuracy of the translation. This questionnaire can be used as the first step in user experience research. In the next step, changes were made in the user interface of the application according to the answers of the users to the questionnaire and also by using the interview tool and Nielson's exploratory evaluation principles. Then the changes applied by 14 people were evaluated with the help of usability testing. To test usability, both people who have not used Rubik's and people who were users were used. In this way, the bias and familiarity of the participants towards the current state of the application are adjusted, the current and potential users of the application are equally considered and a more accurate evaluation of the applied changes is obtained. The results of the usability test showed that the changes applied to the Rubika user interface increased the usability score by 45% on average. In addition, the users who participated in the usability test answered a series of questions regarding the comparison of the current user interface and some proposed changes, as a result of which all the proposed changes were evaluated positively by the majority of users. Keywords: user experience evaluation, human-computer interaction, meCUE questionnaire, user experience improvement of Persian smartphone application, usability testing, Iranian mobile phone application   

رايانش مه يك زيرساخت توزيع شده با امكان ارتباط، ذخيره‌سازي و محاسبه در لبه يك شبكه محلي و بسيار پوياست. فاصله زياد سرويس­گيرنده­هاي يك محيط محلي با ابر و همچنين تعداد بسيار بالاي درخواست‌ها از ساير محيط­ها كه حساس به تأخير هستند مشكلاتي را در ارائه خدمات ابري به وجود آورده است. درنتيجه استفاده از قابليت محاسباتي منابع بيكار محلي و نزديك به دستگاه‌هاي انتهايي همانند خودروهاي با/بدون سرنشين و ايجاد يك شبكه ad-hoc تحت عنوان رايانش مه وسايل نقليه سبب كاهش ارسال درخواست‌ها به ابر و همچنين كاهش زمان پاسخ مي‌شوند. با اين ‌وجود محدوديت منابع در رايانش مه وسايل نقليه در مقايسه با ابر باعث ايجاد مشكلاتي از قبيل يافتن منابع آزاد از نظر توان محاسباتي و همچنين دسترس‌‌پذيري منابع در ارائه سرويس مطلوب به مشتري­ها مي­شود. درنتيجه تلاش براي پيش­بيني درست ميزان منابع درخواستي هر وظيفه مي­تواند از هدر رفتن منابع محدود گره­هاي مه جلوگيري كند كه اين امر نيازمند روش‌هايي از قبيل يادگيري ماشين است تا بر اساس درخواست/پاسخ‌هاي دستگاه‌هاي انتهايي بتواند رفتار محيط را تا حدودي ياد گرفته و جهت رسيدن به كيفيت مطلوب سرويس­دهي، مقادير مناسبي از منابع را در اختيار آن­ها قرار دهد. در اين پژوهش با استفاده از يادگيري تقويتي عميق QL روشي براي برنامه‌ريزي و پيش‌بيني منابع مورد‌نياز يك مشتري خودرو هوشمند با معماري سه‌لايه رايانش خودرويي در بهينه‌سازي تخصيص منابع و بهبود عملكرد كلي سيستم ارائه شده است.اين روش با استفاده از قابليت‌هاي هوش مصنوعي و يادگيري تقويتي، رويكردي پويا و تطبيقي براي مديريت منابع در يك محيط محاسباتي مه ارائه مي‌دهد. دو الگوريتم اصلي براي مسئله پيش‌بيني و تخصيص منابع در اين تحقيق پيشنهاد شده است. در انتها بر مبناي روش پيشنهادي از ابزارها و مجموعه داده­هاي مناسب جهت ارزيابي استفاده مي‌شود. داده‌هاي مورداستفاده هم مي‌توانند يك بازه‌اي مشاهده شده از دنياي واقعي باشند و هم مي‌توانند از طريق ابزارهاي شبيه‌سازي مانند Matlab توليد شوند. ديتاست مورداستفاده شامل وضعيت‌هاي خودروهاي كلاينت، درخواست‌هاي آنها، گره‌هاي مه، تحركات آنها و وظايف درخواست شده از سمت كلاينت‌ها در يك بازه زماني خاص است. يافته‌هاي كليدي اين مطالعه نشان مي‌دهد كه يادگيري تقويتي QL مي‌تواند به طور مؤثري ميزان متناسب تخصيص منابع را با يادگيري از تجربيات گذشته و تصميم‌گيري آگاهانه پيش‌بيني كند. با آموزش و به روزرساني مستمر عامل يادگيري Q، سيستم مي‌تواند با شرايط متغير سازگار شود و تصميمات تخصيص منابع را بر اساس اطلاعات بلادرنگ اتخاذ كند. همچنين نتايج آزمايش‌ها اثربخشي روش پيشنهادي را در بهينه‌سازي تخصيص منابع نشان مي‌دهد. عامل يادگيري تقويتي QL اقدامات بهينه‌اي را ياد مي‌گيرد كه مصرف منابع را به حداقل مي‌رساند درحالي‌كه الزامات عملكرد سيستم مه را برآورده مي‌كند. اين منجر به بهبود كارايي، كاهش تأخير و افزايش قابليت اطمينان سيستم مي‌شود.   

Abstract Objective: Due to the increasing growth of image transmission in computer networks, it is very important to provide a suitable level of security to protect these images, which can be ensured by using different encryption methods. Image encryption methods based on chaos theory are known as a more effective and safer solution in image encryption due to the unique characteristics of chaos functions, such as sensitivity to initial values and parameters and high scrambling power. This study was conducted with the aim of determining the optimal chaotic mapping for encryption of four different groups of images including face images, fingerprint images, satellite images and medical images and increasing the encryption speed. Research method: First, texts and articles related to image encryption were studied using chaotic mappings. Using these studies, 11 one-dimensional and two-dimensional chaotic maps were investigated. In the implementation phase, 40 images were encrypted with these 11 maps using the Python programming language. Then, in the evaluation stage of encrypted images, the encryption quality was checked with the help of criteria such as image histogram and correlation between image pixels. After the evaluation stage, it was determined that for the encryption of each of these image grou   which mapping is more appropriate? In the end, the encryption speed was increased by using parallelization techniques. Findings: The result of this study was to determine the appropriate chaotic mapping for encryption of each of the four image groups and the parallelization of chaotic key generation. Also, the chaotic function of sinusoidal mapping was improved by making changes in the equation of this mapping. After analyzing the encrypted images, it was determined that Logistic 2, Logistic 3, Duffing, and Sinusoidal mappings are the optimal mappings for face, medical, fingerprint, and satellite image encryption, respectively. It was also found that chaotic quadratic mapping has the highest speed of generating chaotic keys. Conclusion: One of the available methods to ensure the security of images during transmission in computer networks is image encryption. In image encryption, one of the important steps is to generate encryption keys. Pseudo-random keys can be generated by using chaotic functions. There are different types of chaotic functions that it is better to choose a suitable function for image encryption according to the type of image. The use of chaos functions increases the security factor of image encryption; but it usually requires a lot of calculations. This volume of calculations can reduce the encryption speed. To solve this problem, different parallelization methods can be used. Keywords: image encryption, chaos functions, optimal mapping, parallelization   

   Parkinson's disease is a brain disorder caused by damage to dopamine producing cells in the brain. People with Parkinson's disease have symptoms such as tremors and slowness of movement, which makes it difficult for these people to control their movements. Parkinson's is usually diagnosed based on tests done by a neurologist. Actions such as; Analysis of the patient's medical history, examination of symptoms, neurological and physical examination. Therefore, the identification of Parkinson's disease is a long-term process that always requires the availability of all the patient's information (history) and their careful study in each session. Therefore, according to the conditions and problems that exist in this field, misdiagnosis is among the possibilities according to its risks. One of the solutions used today to prevent such mistakes is the use of automatic machine learning detection systems. Considering the mentioned issues and problems, this study tests a two-way LSTM model with two activation functions, Softsign and Tanh, for the automatic diagnosis of Parkinson's disease based on the gait analysis of PD people. The raw data of VGRF signals obtained from the Physionet database were tested in the proposed model to classify PD and healthy subjects. Experiments show the high efficiency of the proposed method in diagnosing Parkinson's disease based on the analysis of movement signals related to people's walking. The proposed algorithm achieved 97.1% accuracy. Among the methods investigated in this study, the presented method has obtained the best performance in the diagnosis of Parkinson's disease using movement signals related to walking. These results show that this model can learn efficient features from existing data that can be useful in clinical diagnosis.

In recent years, with the growth ofscience and technology and the creation of competitive markets in variousindustries, the need for quality control, measuring the quantitative andqualitative parameters of the final product has become very important. Having aquality product is the most important part of a production line, so that todaythere are few advanced factories where part of the production is not controlledby intelligent machine vision and image processing applications. Qualitymanagement in real time and on line provides the possibility of increasingproduction efficiency effectively.In this thesis, anattempt has been made to research and examine advanced techniques in the fieldsof image processing, machines and learning in order to improve the quality ofwood defect detection as a basic material in the wood products industry. The maingoal of this project is to improve the accuracy and ability to detect wooddefects through the use of advanced tools and techniques in the fields of imageand machine processing. In this study, the "Wood_patches" dataset isused, which includes many images of healthy and unhealthy wood of differenttypes. Also, in order to further evaluate and deepen the effectiveness of themodels, the "Leather Defect" dataset is also used, where there arehealthy and unhealthy leather >In the first proposed approach for predicting wood defects,three main steps are performed for independent feature extraction and>The second proposed approach is prediction by extractingcombined features and >

پيشرفت روزافزون فنّاوري در عرصه‌هاي مختلف علوم و تأثير آن بر زندگي انسان امروزي، تجارب احساسي، عاطفي و ادراكي را به‌شدت در كانون توجه طراحان قرار داده است. در اين خصوص، طراحي بر اساس رضايتمندي، خوشايندي، احساسات و عواطف دروني انسان عاملي بسيار مهم و تأثيرگذار در فرايند طراحي محصول شناخته مي‌شود. به دنبال شيوع و فراگيري ويروس كرونا در جهان، ساختار آموزش عالي نيز، مانند بسياري از بخش‌هاي ديگر زندگي انسان، دست‌خوش تغييرات عمده شد.   شركت دانشجويان در كلاس‌ها ي آنلاين، آزمون‌ها و انجام امور اداري به‌صورت غيرحضوري موجب استفاده بيشتر دانشجويان از وبسايت دانشگاه‌ها شده است. استاندارد نبودن طراحي وبسايت باعث مي‌شود زمان زيادي از دانشجويان گرفته شود تا به اهداف موردنظرشان برسند. بنابراين گنجاندن عناصر احساسي كه مي‌توانند شادي، لذت و علاقه را تشويق كنند، بسيار مهم است. اين تحقيق از مهندسي كانسي استفاده كرده است تا احساسات كاربر را به مولفه‌هاي طراحي رابط تبديل كند و نشان دهد كاربر از رابط كاربري چه مي‌خواهد. 50 كلمه‌ي كانسي از طريق پرسشنامه بين 50 دانشجو توزيع گرديد و از بين آن‌ها 12 كلمه جهت ارزيابي پارامترهاي طراحي بر اساس احساسات كاربران انتخاب شد. بر اساس كلمات كانسي انتخاب شده پارامترها و قوانيني براي طراحي رابط كاربري استخراج شد. اين قوانين در يك پايگاه دانش جمع‌آوري گرديد كه طراحان مي‌توانند با مراجعه به آن بر اساس احساس موردنظرشان براي طراحي، پارامترهاي طراحي متناسب با آن احساس را دريافت كرده و طرح كاربرپسند خود را ترسيم كنند.   

   Abstract: Today, with the increasing expansion of social networks, users have access to the opinions and views of other people. These opinions often contain valuable information that can be analyzed to understand people's tastes and tendencies and to identify their positive, negative or even neutral opinions on various issues. But since the volume of these data and the speed of their production is surprisingly high, analyzing it by humans is a difficult, time-consuming and practically impossible task; Therefore, there is a need for a system that can automatically analyze comments. Sentiment analysis is the solution to this problem. Sentiment analysis is a process that is able to discover people's views, attitudes and feelings from their writings. Sentiment analysis or opinion analysis is a subset of text mining and natural language processing, the purpose of which is to automatically extract users' views on various issues. Microblog is a type of social network where users try to share their short texts with others. Twitter is one of the most popular microblogs in which the maximum size of each tweet is 280 characters, and this feature has made Twitter a suitable platform for knowing the opinions of users. In this thesis, sentiment analysis has been done on 7306 Farsi tweets extracted from the Twitter social network on the topic of Corona vaccine. For this purpose, tweets were considered in three >Keywords: text mining, sentiment analysis, corona vaccine, natural language processing, machine learning, deep learning, Twitter social network

   In recent decades, due to the growth and development of mobile telecommunication equipment and portable systems, RF researchers and designers have focused more on designing circuits with low voltage and power consumption. Most systems are now wireless, and reducing power consumption is important, which can lead to increased battery life. One of the important parts in receiver systems is the low-noise amplifier, which should be designed with low power. In this thesis, a narrowband low noise amplifier with low energy consumption and high voltage gain is presented using 0.18 ?m RF CMOS technology, so that in the proposed amplifier, the threshold voltage of the transistor can be reduced from the bias technique. body and to reduce the supply voltage and current, current reuse technique has been used. Due to the use of noise removal technique in the proposed circuit, it has resulted in acceptable noise reduction, and with the appropriate selection of circuit elements, a compromise between circuit parameters has been created. The results of the investigations show that the gain of the proposed low-noise amplifier is 13.8 dB, S11 is less than -14.37 dB, and the noise figure is 2 dB at the central frequency of 2.4 GHz. Also, the linearity is -2.5 dBm and the power consumption at the power supply voltage of 1 V is 3.79 milliwatts. The use of such a circuit can greatly contribute to the design of low-power, high-performance wireless communication systems. With further modifications, it can also be used in IoT applications where low power consumption is critical. Overall, this work shows a promising trend for the development of compact, low-power and efficient amplifiers using advanced RF CMOS technologies.

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The stock market in general is very unpredictable in nature. Many factors may play a role in determining the price of a particular stock, such as market trends, supply and demand ratios, the global economy, public sentiment, sensitive financial information, earnings announcements, historical prices, and many more. The challenge of accurate forecasting But, with the help of new technologies such as data mining and machine learning, we can analyze big data and create an accurate forecasting model that avoids some human errors. In this work, the closing prices of specific stocks are predicted from sample data using a supervised machine learning algorithm. Specifically, a Recurrent Neural Network (RNN) algorithm is used on stock time series data. The predicted closing prices are checked against the actual closing price. In this research, we investigate the problem of stock market forecasting using Recurrent Neural Network (RNN) with short-term memory (LSTM). The purpose of this research is to investigate the feasibility and performance of LSTM in stock market forecasting. We optimize the LSTM model by testing different configurations, for example, the number of neurons in the hidden layers and the number of samples, respectively. We have used historical stock price data collected from the Yahoo Finance database to train our model. Nevertheless, based on the forecasting results of the LSTM model, we used it to predict the stock value in the coming days for the final stock price. The results show that the LSTM model with 4 layers has a higher accuracy and the lowest error (about 0.1771, 2101 and 0.1617) compared to the rest of the layers for prediction.   

   Abstract Today, the expansion of the use of the Internet has made it possible for millions of users around the world to access online social networks. On the other hand, these networks have been in the focus of users' attention in the dissemination of information, especially in areas such as viral marketing advertising, improving recommender systems, transferring time-sensitive information, guiding public opinion, promoting national security, sociology, etc. One of the important issues surrounding the dissemination of information in online social networks is the issue of effective dissemination of the message at a suitable speed. For this purpose, it is necessary to identify the influential users in a suitable way. In this thesis, it is proposed to identify influential people based on the reactive information of users and according to their sphere of influence. For this purpose, first, the value of network communication is determined based on the reactive information of users (reply and retweet), and then, the structure of the network is divided into its constituent communities. In the next step, centrality criteria are used to evaluate the importance of each member of the community, and at the end, influential nodes are identified in their sphere of influence. The effectiveness of the proposed method using a real database that includes the retweet and reply information of Twitter users; has been tested. Due to the lack of a database that has the tagging of influential people and reactive information at the same time; from one of the methods of information dissemination in social networks that has an acceptable correlation with the proposed method; Used. At the end, the obtained results are compared with previous similar methods. The results of the evaluations showed that the method proposed in this thesis can identify effective users with more accuracy, efficiency and speed. Also, the evaluation results showed that the proposed method can achieve 87.33% detection accuracy in identifying effective users, which shows an improvement of at least 13% compared to the compared methods.    Keywords: social network analysis, identification of influential users, identification of communities, graph analysis.   

The world around us is full of images. Pictures are documents that, by recording a moment, become the narrator of a world of words. City cameras create, record, and store thousands of traffic images every second. Proper processing of these images can help train models based on deep learning. Such models are used in object recognition and image captioning and will be used in cases such as voice assistants and self-driving cars. In this thesis, a method is introduced to convert traffic images into their descriptions. The presented description is based on prominent objects from images and deep learning and includes three basic steps. In the first stage, data processing and methods such as data augmentation are performed on training images. In the second step, appropriate features are extracted from the images. For this purpose, four deep neural networks named VGGNet, EfficientNetB0, InceptionV3, and ResNet50 have been investigated to extract image features. According to the number of layers in the architecture of each of these deep neural networks, the fine-tuning technique has been applied to improve the accuracy of detecting traffic objects. In the third step, two neural networks, LSTM and Transformer, have been used to convert image features into text. Finally, the optimal solution will be introduced, which will significantly increase the quality of the output sentences. In total, two methods were introduced. Based on the Transformer network, the second method showed better accuracy than the first. The MS-COCO dataset was used to evaluate the proposed methods. For this purpose, a subset including 8,000 images and ten classes of traffic objects in the MS-COCO dataset has been separated and pre-processed. The accuracy of the model introduced in the BLEU evaluation criteria is 65.3595%.

  With the rapid growth of the economy and the expansion of the stock market, analyzing and forecasting the stock price and comparing various price forecasting methods, and analyzing the trend of the stock market are more necessary and at the same time popular.The stock market is difficult to predict due to its volatile nature.There are no rules for predicting what will happen to stocks in the future.Accurate forecasting is a big challenge because market trends are always changing depending on many factors. In this research, the goal is to analyze the price and trend of the total index and stocks using machine learning techniques.This work includes two approaches. In the first approach, using the historical data of oil, gold, dollar, some other foreign indices, shares of some large stock exchange companies inside Iran, stock indices, and technical indices extracted from them between 11/13/2012 and 05/21/ 2022, was shown that with the help of artificial intelligence and machine learning algorithm (MLP), it is possible to find the factors and indicators that affect the total index of the Tehran stock market and try to better predict prices with the help of them and machine learning algorithms.The results indicate that the LSTM network with two recurrent layers and the optimal time step is a suitable and time-consuming but high-accuracy network for price and time series forecasting, which has the best results with minimal error compared to other machine learning methods such as nearest neighbor. followedIn the second approach, using some machine learning algorithms and technical indicators and past price information of a specific stock (steel from 03/11/2007 to 08/30/2022), the goal was to analyze the stock trend and check the buying and selling signals that With the help of the Bollinger Band indicator and a buy and sell risk factor, you can find the right time signals for the predicted data.

تشخيص وسيله­­نقليه، يك بخش مهم در حمل و نقل و هوش مصنوعي است. وسايل­نقليه مي­توانند در قسمت­هاي مختلف تصاوير قرار بگيرند. پيشرفت هاي اخير در روش هاي تشخيص، منجر به طيف وسيعي از تكنيك هاي مختلف شده كه مي­تواند براي شناسايي و تشخيص وسايل­نقليه مورد استفاده قرار گيرد. يادگيري عميق، در سال‌هاي اخير است كه كاربردهاي قابل توجهي در روش­هاي تشخيص وسايل نقليه دارد. باتوجه به اهميت تشخيص وسيله­نقليه در سيستم­هاي حمل و نقل هوشمند، در اين پايان­نامه به بررسي و تشريح روش­هاي تشخيص وسايل­نقليه مختلف از تصاوير دوربين­هاي ترافيكي پرداخته­ و نيز از معماري قدرتمندي به نام يولو[1] براي تشخيص وسايل­نقليه روي ديتاست BVMMR استفاده مي­كنيم.­ به دليل تغييرپذيري در محيط‌هاي رانندگي، تشخيص خودرو ممكن است با مشكلات و چالش‌هاي متفاوتي مواجه شود، مثلا ظاهر وسايل­نقليه در اندازه، شكل و رنگ متفاوت، روشنايي خاص، شرايط آب و هوا و.. است. معماري، YOLOv5 شامل چهار بخش اصلي ورودي، backbone ، neck   و خروجي است. ترمينال ورودي عمدتاً شامل پيش پردازش داده ها است، از جمله افزايش داده موزاييك و پر كردن تطبيقي تصوير. شبكه backbone   عمدتاً از يك شبكه جزئي چند مرحله­اي (CSP) براي كاهش مقدار محاسبات و افزايش سرعت استنتاج و   ادغام هرمي فضايي (  ) براي استخراج feature map   با اندازه­هاي مختلف از ورودي تصوير با هدف بهبود دقت تشخيص با كانولوشن چندگانه و pooling استفاده مي­كند. در شبكه neck، از ساختارهاي هرمي ويژگي FPN و PAN استفاده مي­شود. با استفاده از معماري يولو نسخه پنجم[2] آموزش داده شده، موقعيت خودروها و نوع و دسته­ي آن­ها را نيز مشخص كرده­ايم و به   98.88% و دقت مجموع 99.73% و نيز سرعت 0.03 ثانيه براي تشخيص اشيا موجود در يك تصوير دست مي­يابيم كه خود گواهي بر مناسب بودن اين روش براي كاربردهاي بلادرنگ[3] مي­باشد. كلمات كليدي: تشخيص وسايل­نقليه، تشخيص اشيا، يولو، يادگيري عميق، سرعت و دقت بالا در تشخيص اشيا، شناسايي نوع و مدل وسايل نقليه [4]. [1]. you only look once (YOLO) [2] YOLOv5 [3] real-time

Network traffic classification has been studied for two decades and has been applied to a wide variety of applications, including network traffic management, security in firewalls, and intrusion detection systems. Traditional network traffic classification methods, including port-based methods, deep packet i  ection, and traditional machine learning methods, have been widely used in the past. But due to the dramatic changes that happened in the field of traffic on the Internet, especially the increase in encrypted traffic, as well as the need for these methods to extract features from data streams manually by experts in this field, which was time-consuming, expensive and error-prone. , the accuracy of these methods decreased dramatically. This caused the emergence of newer methods in the field of network traffic classification. Deep learning methods, which are a subset of machine learning science, were able to quickly open their place in this field by automatically extracting features from traffic flows and removing the need for feature extraction by experts, as well as the high accuracy they showed in traffic classification. Also, techniques such as data fusion techniques, as helpful techniques that can be used to further increase accuracy and improve network traffic classification, have come to the aid of these methods. In this research, an attempt has been made to use recurrent deep networks and cumulative cryptography to extract features from the high-level ISCX VPN-non VPN traffic data set. Then, by applying the data integration technique at the feature level, the features extracted from the mentioned networks will be brought to an optimal set of features and finally increase the accuracy in traffic classification. It should be noted that traffic classification is done by the multi-layer perceptron deep network. According to the evaluations, the accuracy of the proposed model in network traffic classification has reached 99.1%.  

ا رشد پلتفرم ها و برنامه هاي كاربردي شبكه هاي اجتماعي آنالين، روزانه مقادير زيادي محتواي متني توسط كاربر به روشهاي مختلف مانند نظرات، تحليلها، اخبار و پيام هاي متني كوتاه ايجاد مي شود. در نتيجه، كاربران اغلب براي استخراج اطالعات مفيد در مورد موضوع مورد بحث اين گونه محتوا را پالش برانگيز ميدانند. امروزه براي 1 استخراج راحتتر اطالعات مفيد از روشي به نام استخراج موضوع استفاده ميكنند. استخراج موضوع با اسستقاده از يك سري محاسبات آماري خالصه يا موضوع اصلي سند مورد نظر را از متن بيرون ميكشد، كه با اين كار ميتوان با مشكالت كمتري به تجزيه و تحليل اسناد پرداخت. در اين پژوهش قصد داريم با استفاده از روشهاي يادگيري عميق همچون)DNN,LSTM )يك شبكه يادگيري عميق جهت استخراج موضوع با دقت بيشتر از كارهاي انجام شده در اين زمينه طراحي كنيم. ديتابيسي كه در اين پژوهش بر روي آن كار خواهيم كرد ديتابيسي متني شامل اخبار است. كه در ابتدا با استفاده از تكنيكهاي پيشپردازش متن )تبديل كردن تمامي حروف موجود در دادههاي متني به »حروف كوچك« )letters Lowercase ،)پاك كردن عالئم نقطهگذاري )Punctuations ،)پاك كردن »كلمات بي اثر« )Stopwords ،)مصدر سازي كلمات)Stemming ) )عمليات نرمل 2 سازي را انجان داديم. از LDA بعنوان روش يادگيري شبكه استفاده ميكنيم به بياني واضح تر شبكه يادگيري عميق بر اساس تكنيك استخراج موضوع LDA كار خواهد كرد. نتيجه اين پژوهش دقت بشتر شبكه يادگيري نسبت به شبكههاي ساخته شده در كارهاي پيشين است كه توانستهايم دقت شبكه بر روي ديتابيس مورد نظر را نسبت به آنها بيشتر كنيم.  

Abstract

  The amount of text that is produced every day increases dramatically. Therefore, efficient and effective techniques and algorithms are needed to discover useful patterns. With the pervasiveness of social networks in recent years, despite their positive applications, spreading rumors has become easier and more common. Rumors are a security challenge on social media, as a malicious node can easily discredit or isolate its goals by spreading a rumor. Therefore, detecting rumors is an important challenge in soft security mechanisms such as trust and reputation. In this study, we used the machine learning approach of the LSTM deep learning model and deep neural network to simplify feature extraction and create a strong ability to learn, and automatically detect features compared to traditional machine learning methods. LSTM neural network Due to its special architecture, it is very suitable for working with sequential data, especially textual data. But the performance of this network is highly dependent on the regulation of its hyperparameters, so a new approach to improve the result using a genetic algorithm to regulate the hyperparameters of the deep neural network is proposed. The standard genetic algorithm has its own problems, including the speed of convergence in this algorithm. In this study, we have solved this problem by adjusting and formulating the rate of algorithm processes based on two criteria of suitability and diversity, and we have reached a detection accuracy of 0.93%. ام.

Pilates includes set of motions, Focusing on the simultaneous useof mind and body, to increase the body's resistance,. uses gravity, body weight andspecial devices. If for any reason a person intends to perform these movementsat home without a trainer, there are various commercial software that play therole of trainer; But these softwares do not have a guide or software monitor togive the user proper feedback on the correctness of the movements. Thisdissertation addresses the issue of the correctness of the user's Pilatesmovements by providing an approach based on image processing techniques.In this study, computer analysis of 6 main Pilates movements in theabsence of a Pilates instructor has been performed. To do this research, asuitable data set is needed first. To collect data, 20 main body joints wereextracted from deep video in each 3D frame using a Kinect sensor. The resultingdata set contains 300 records that are collected in a fixed location fromdifferent users. The proposed method consists of four steps. First, thethree-dimensional coordinates of the 20 main joints are extracted from theinput. In the second step, the required preprocessors include calculating the 4main body angles, namely the angles of the knees and elbows in each frame,applying the Savitzky Golay filter, and the PiecewiseAggregate Approximation. In the third step, various functions were proposed to calculatethe data distance, which are Dynamic TimeWarping, Hausdorff,fast Dynamic Time Warping, and providean improved distance function based on fast Dynamic Time Warping. In the fourth stage, learning and >After >  

nowadayswe have entered a new era of information exchange due to the widespread use of mobile devices andthe Android operating system is the most popularmobile operating system in the world. Simultaneously with applications, manymalicious applications with different purposes and forms for the Androidoperating system are being developed and released. Despite the increasing developmentof Iranian programs in software stores, it has never been investigated how muchof the malware is possible among them, that it may endanger the safety of usersor with other targets such as high volume of advertising, to offend users. For this reason, we decided tocreate models based on the static permission feature using a secure data setusing nine machine learning classifiers as well as a deep learning approach toclassify more than four hundred Iranian applications which wererandomly downloaded from the Cafe Bazaar store, in two categories: maliciousand Benign application. Then we will analyze the results and also by making allthe mentioned models on the samples downloaded from the Cafe Bazaar store, wewill complete our evaluations on the effect of the permission feature indetecting Iranian malware. Theresults obtained from the models made as well as the scanned samples downloadedfrom Cafe Bazaar store in the reputable site of Virus Total show that more thanfifty percent of the samples downloaded from Cafe Bazaar store are malware. Therefore, in order to increase theconfidence of Iranian users that the downloaded Android applications are notmalicious, the current approach to screening applications should bereconsidered before placing them in software stores.

   In today's information age, it is a prerequisite that we have reliable information before making any decision. In this regard, location-based social networks have become an important program in location-based social networks as an effective way to help users find attractive places and recommend points of interest. Recently, they have gained a lot of popularity. Adding a location dimension to these networks makes their information closer to reality by creating a bridge between virtual social networks and the real world. The purpose of creating these networks is to provide location-related services; By allowing users to share experiences and visited locations with other users in different geographical locations. Location-based social networks are rich resources for data mining and information discovery by obtaining and updating the information of their users around the world. Recommended systems are also a special type of intelligent systems that take advantage of users' past rankings. Collaborative filtering is one of the most common approaches used for recommendation systems, although this method can sometimes present challenges such as cold start. Cold start occurs due to data scatter and is based on the fact that most users only connect to a small number of possible locations and the recommendation system for ranking some items or new users lacks data; Not available or only a small amount of data available. Solving this problem can greatly improve the user experience and trust in recommender systems. In this dissertation, we try to use machine learning and deep learning algorithms to provide a spatial recommendation system with a participatory filtering approach. Therefore, by implementing the torsional neural network algorithm on Yelp data set and presenting experimental results, we show that the proposed method can perform better than other related methods. Keywords: recommendation system, collaborative filtering, location recommendation, location-based social networks, deep learning, convolutional neural network

بدون شك امروزه يكي از چالش ­هاي مهم مراكز داده ابري مصرف انرژي بسيار زياد است. از طرفي احتمال خرابي يك سرور در يك مركز داده با تعداد زياد سرور و از دست رفتن ماشين ­هاي مجازي روي آن امري اجتناب ناپذير است. يك روش سنتي براي افزايش قابليت دسترسي سرورها و در نتيجه افزايش قابليت اطمينان ماشين مجازي استفاده از افزونگي مي­ باشد بطوريكه تعداد ماشين مجازي يكسان روي سرورهاي مختلف اجرا شوند كه در صورت بروز خرابي در يك سرور نسخه ­هاي پشتيبان روي سرورهاي ديگر كار مورد نظر را انجام دهند. استفاده از افزونگي منجر به افزايش مصرف انرژي در مراكز داده ابري مي­ شود، لذا بهينه ­سازي اين دو پارامتر نياز به مصالحه دارد. اين پايان نامه روشي جهت تخصيص ماشين ­هاي مجازي با در نظر گرفتن مصرف انرژي و قابليت اطمينان را تحت عنوان روش P21(Placement method with 2 active replication and 1 inactive replication) ارائه مي ­دهد. ايده اصلي روش پيشنهادي در نظر گرفتن دو نسخه­ ي فعال و يك نسخه­ ي غيرفعال افزونگي مي ­باشد. جايگذاري ماشين­ هاي مجازي روي سرورهايي با بالاترين مقدار تابع هدف صورت مي ­گيرد. در همين راستا روش ارائه شده، 2 نسخه­ ي فعال از افزونگي را به عنوان نسخه ­ي اصلي و نسخه­ ي اول پشتيبان در نظر مي­ گيرد و يك نسخه­ ي غير فعال افزونگي را به صورت رزرو دارد. در ابتداي كار، زماني كه ماشين مجازي بر روي سرور نسخه ­ي اصلي جايگذاري مي­ شود همزمان يك نسخه­ ي پشتيبان به صورت سينك روي نسخه­ ي پشتيبان فعال پردازش مي­ شود. زماني كه سرور دچار مشكل شود به دليل حفظ دسترس ­پذيري، عمليات فعال سازي سرور پشتيبان غير فعال آغاز مي­ گردد. بلافاصله، از اطلاعات و پردازش ­هاي صورت گرفته بر روي سرور نسخه­ ي فعال يك نسخه (image) تهيه مي­ شود و پس از فعال سازي سرور غير فعال به آن منتقل مي­ گردد. به همين روش مي­ توان قابليت اطمينان سيستم را بالا برد و با استفاده از قابليت رزرو بودن نسخه­ ي پشتيبان مي­توان از تعداد سرور كمتري استفاده كرد. به دليل عدم پشتيباني قابليت اطمينان در شبيه ­ساز كلودسيم، جهت ارزيابي روش پيشنهادي يك شبيه ­ساز به زبان جاوا پياده ­سازي شده ­است. در اين ارزيابي 8 آزمايش با باركاري مختلف از جمله زمان ورود و پايان تسك­ ها، تعداد هسته­ هاي مورد نياز هر تسك و تاخير بين تسك­ ها مورد بررسي قرار گرفته­ است. اين 8 آزمايش به ازاي تفاوت در تعداد سرورها، توان، دسترس پذيري و تعداد هسته مي­ باشند. نتايج شبيه ­سازي نشان مي­ دهد كه مصرف انرژي براي حالت­ هاي مختلف بين 38-1 درصد كمتر و قابليت اطمينان بين 58/2-01/0 درصد افزايش را نسبت به روش­ هاي مشابه دارد.كلمات كليدي: رايانش ابري، جايگذاري ماشين ­هاي مجازي، قابليت اطمينان، مصرف انرژي، افزونگي

   With advances in microelectromechanical systems (MEMS), researchers have now become interested in the systems operating based on inertial signals. In fact, inertial signals have proven useful in different areas due to advances in their manufacturing technology, availability, and inexpensiveness as well as the development of powerful processing methods such as deep learning techniques. Handwritten character recognition (HCR) is among such areas. This paper aimed to design, implement, and evaluate a novel system for the recognition of handwritten Farsi characters extracted from an inertial pen. For this purpose, a wireless inertial pen was designed. Its motion trajectory was then determined by combining the signals of its angular velocity and acceleration and using the concepts of navigation systems such as quaternion in order to estimate the position signals of characters. A convolutional neural network (CNN) was also employed to facilitate the extraction of high-level features and classification of characters. The position signal was also extracted as an image used for model learning to enhance the classifier efficiency. The experimental results indicated the CNN-6 architecture outperformed the other CNN-n architectures in terms of character classification accuracy. According to the evaluation of the proposed method through test data, character recognition accuracies of Farsi letters and numbers were reported 91.06% and 94.52%, respectively. In comparison with the previous systems, the proposed method managed to improve the recognition of handwritten Farsi characters.

  Abstract:Today, using artificial intelligent and machine learning algorithms, we see improvements in intelligent tra  ortation industry, especially automatic vehicles that can analyze the information about them by using advanced sensors and machine vision techniques. The main challenge in design of this type of vehicles is identification of other vehicles around the vehicle’s path. The main objective of this thesis is to provide a method for identifying type of emergency vehicles based on deep learning. Considering the importance of passing emergency vehicles on roads and streets, non - drive vehicles should be able to identify this type of vehicles with high accuracy and have an appropriate response.In this thesis, to identify the type of emergency vehicles, methods based on deep learning are proposed which feature extraction and ltr">

The Internet of Things is a network of physical objects connected by the Internet. The Internet of Things covers a variety of areas, including home automation, industrial processes, human health monitoring, and environmental monitoring. The future of objects is the future of the Internet and will be useful for anything available in the world. Internet of Things, despite many benefits, also create security and privacy challenges. IoT systems are very vulnerable, so an intrusion detection system requires IoT environments. Intrusion detection systems are an important tool for protecting networks and information systems. The purpose of an intrusion detection system is not preventing attack and only discover and identify attacks and identify security bugs in the system or computer network and its announcement to the system administrator. Despite the fact that decades are developed from the development of previous influence systems, these systems still face challenges to improve diagnosis accuracy. Many intrusion detection systems are still suffering from high false alert rates, so many researchers have focused on developing intrusion detection systems with high detection rates and reducing the wrong alert rate. Since the network environment changes quickly, a variety of new attacks appear. Therefore, we need to develop intrusion detection systems that can identify unknown attacks. To solve these problems, researchers have begun to focus on building intrusion detection systems using machine learning methods. Deep learning is a branch of machine learning, based on a set of algorithms that are trying to model high-level abstract concepts in the data. Therefore, in this dissertation, the process of detecting intrusion on the Internet of things with a deep learning approach has been addressed. In this thesis, an attack identification method and anomalies based on the combination of deep learning CNN-LSTM algorithms are used in the Bot-IoT data set. In order to evaluate performance, the indicators of accuracy, accuracy and reminders have been used. According to the results obtained in the proposed method, the detection accuracy of 99.98% is obtained.   

   Skin cancer is one of the most common cancers in human societies and its prevalence is increasing dramatically. Melanoma is one of the most dangerous types of skin cancer, and the more the skin lesion grows, the lower the chance of cure. Early detection of cancer plays an important role in its treatment. Definitive treatment of melanoma cancer is possible with early detection. In this dissertation, a new method for diagnosing skin cancer was presented. In this method, two types of discrete and stationary wavelet transform were first applied to the images. A number of statistical features were then extracted from these converted images. Also, various global, local, etc. features were applied to the gray and color surface images. In the next step, to improve the results, the extraction features were combined to obtain the best combination of features that >Keywords: Melanoma Cancer, Discrete Wavelet Transform, Stationary Wavelet Transform, Least Squares Support Vector

AbstractPredicting the stock market is an important and challenging task. Traditional stock market forecasting methods uses only historical stock trading data and related numerical indicators, but with the grows of information about the stock market on the Web, researchers began to use this valuable information to increase the accuracy of stock value forecasting. In many previous studies, only one additional data source has been used to combine with the historical stock data source, which can not show the impact of other information on the stock market price trend properly. And in many studies, they have relied on one learning algorithm, which means that we can not achieve the most accurate forecast for stock value.In this study, by collecting three different stock data sources (historical stock data source, social network data source and daily news data source), we tried to use different aspects affecting stock value in predicting stock value to be more accurate than The traditional way. To do this, we first analyzed the opinions extracted about the stock, from the Twitter social network and the daily news data source extracted from the Reddit news website, using a hybrid opinion mining model, and from this, emotional indicators such as The polarity and subjectivity of each sentence were extracted. Then, by combining these indicators with the historical stock data source, we proceeded to create the final composite data source. Then, by using different >The results of this study showed that in Apple, Cisco and Boeing stocks, the use of information combination has improved the accuracy of stock value forecasting to 65%, and with the analysis of the principal component, this amount reached over 80%, which compared to The traditional method, which is less than 60%, is a good improvement. The experiments also showed that the use of XGBoost >Keywords: stock market prediction, information combination, sentiment Analysis, social network  

چكيده تشخيص سن استخوان روشي است كه به طور مكرر براي ارزيابي ناهنجاري رشد و تشخيص و درمان اختلالات غدد درون‌ريز و سندرم­هاي كودكان بيمار انجام مي‌شود. چندين دهه است كه تعيين سن استخواني با ارزيابي بصري از رشد اسكلت دست چپ انجام مي­شود و معمولاً از روش مرجع G&am   استفاده مي­شود. با پيدايش تصويربرداري ديجيتال، تلاش­هاي زيادي براي ايجاد روش­هاي پردازش تصوير انجام شده است كه به طور خودكار ويژگي­هاي اصلي مراحل تشكيل استخوان را براي ارزيابي مؤثر و دقيق­تر سن استخواني استخراج مي­كند. بااين‌حال ماهيت ذهني روش­هاي دستي، تعداد زياد مراكز استخوان در دست و تغييرات گسترده در مراحل استخوان‌سازي سبب پيچيدگي ارزيابي سن استخواني شده است و يك چالش براي طراحي الگوريتم­هاي كامپيوتري تشخيص خودكار در اين حوزه است. هدف: اين مطالعه با هدف ارائه يك روش جديد براي كاهش خطاي روش­هاي ذهني و بهبود روش­هاي اتوماتيك موجود در تخمين سن انجام شده است. روش: اين مدل روي 1400 تصوير از كودكان سالمِ صفر تا هجده سال از چهار قاره پياده‌سازي شده است. با استفاده از تكنيك­هاي پردازش تصوير در محيط برنامه‌نويسي متلب شش ناحيه در دست استخراج شدند؛ تجزيه‌وتحليل مراكز استخوان و محاسبه سن در هركدام از اين ناحيه­ها توسط تكنيك­هاي يادگيري عميق در محيط برنامه‌نويسي پايتون انجام شده است. دسته‌بندي نهايي نيز بر مبناي ميانگين رأي‌گيري صورت‌گرفته است. نتيجه: در مدل ارائه شده تمام سنين رشد و چهار نژاد آسيايي، آفريقايي، اروپايي و آمريكايي در نظر گرفته شده است. در قسمت پيش­پردازش تمام انگشت­هاي دست و مچ دست به‌درستي استخراج شده­اند. براي تشخيص نهايي سن از چند شبكه عصبي پيچشي و يك Ensemble بين آنها استفاده شده است. روش پيشنهادي به طور ميانگين 81 درصد دقت در تشخيص داشته است. اين دلايل نشان­دهنده برتري مدل پيشنهادي در مقايسه با ديگر مدل­هاي ارائه شده است. كلمات كليدي: اختلالات رشد، سن استخواني، روش Greulich and Pyle، روش Tanner-Whitehouse، مناطق اوليه رشد (ديافيز)، مناطق ثانويه رشد (اپيفيزها)، استخوان­هاي مچ (Carpal)، تصاوير ديجيتال (x-ray Image)، يادگيري عميق، شبكه­هاي عصبي پيچشي (CNN)، Ensemble، ميانگين رأي‌گيري (Average Voting).  

Aspect-based sentiment classification is one of the most challenging fields in natural language processing. Researchers have used a variety of traditional and machine learning methods. Traditional methods do not make good use of the interaction between data, and we must manually them but deep learning methods, on the other hand, can consider both data specify the feature for widely used in text processing, image processing, and many other fields, and obtain interaction and latent features. Therefore, these methods have recently been networks, attention-based approach, etc. have been introduced for Aspect-based state-of-the-art result. Many deep learning methods such as convolutional sentiment classification, but each has advantages and disadvantages. For to parallelize and extract local features within the text and the attention example, convolutional networks, better than other networks, have the ability approach also has the ability to focus more on the more important parts of the introduced according to the idea of ??extracting the local features of networks sentence. The Burt network was also introduced in 2018 to summarize text in search engines. In this thesis, simple and chain local attention models are using local attention. Then, by applying the attention approach to the lower convolution and more focus on the most important parts with the approach of attention and mapping of words to the vector by Burt network. It can be hoped models, low level and aspect-related features are provided for the upper layer that these networks will cover each other's shortcomings. In the proposed layer, the high-level features are extracted and used for classification. comparable to the superior models in the classification of aspect-based sentiment. Experimental results showed that the proposed models have achieved result  

Abstract Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the Graph Neural Network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices by active vertices. The method is tested on three scientific bibliography datasets: DBLP, Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of the next article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBLP and Pubmed datasets, respectively.   

  The main purpose of human data to collection is to understand the thinking of other human beings. This unconscious tendency has led researchers to analyze information in order to understand and analyze the minds of other human beings. Today, with the advancement of information platforms such as the Internet, social networks, etc., it is easy to gather the information you need. Today, social networks are one of the most important aspects of people's lives, and on the other hand, these networks have made huge profits by exploring the general information of users. The aim of this study is to investigate the text to find out the mood of people in typing texts. In this study, 14,000 tweets related to airlines were used to analyze emotions in three categories: positive, negative and neutral. The final proposal has three steps. In the first step, we perform the pre-processing operation on the database. In the second step, using the Imperialist Competitive Algorithm, we extract the main words from all the existing words. Keywords are the words that have the most impact on categorization. We then use the convolution neural network to extract more features. In the last step, we perform the classification operation using the multilayer perceptron neural network (MLP). At the end, using the final proposed design, we achieved precision, accuracy and recall of 0.990, 0.983 and 0.875, respectively. The results indicate that the final proposed design is desirable.

تهديدهاي امنيتي وب به­طور روزافزون در حال افزايش است. ماهيت شبكه اينترنت به صفحات وب بدخواه اين اجازه را مي­دهد تا خود را به‌عنوان "صفحات امن" نشان دهند و متعاقباً برخي از كاربراني كه آگاهي كافي ندارند در دام اين وب­سايت­ها گرفتار شوند. يكي از حملات رايج اين حوزه، حمله Cross-Site Scripting(XSS) است. اين حمله با تزريق اسكريپت­هاي مخرب به ورودي­هاي صفحات وب رخ مي­دهد، زماني كه كاربر صفحه آلوده مورد نظر را بازديد كند به وقوع مي­پيوندد. روش مرسوم براي شناسايي صفحات مخرب وب، استفاده از فهرست‌هاي سياه است. اين فهرست‌هاي سياه، توسط سازمان­هاي مورد اعتماد و داوطلب تهيه مي­شود و سپس توسط مرورگرهاي مدرن مانند كروم و فايرفاكس استفاده مي­شود. با توجه به اينكه، ماهيت صفحات وب به‌طور مداوم در حال تغيير است، اين روش در شناسايي تهديدهاي جديد ناكارآمد است رويكرد ديگر، استفاده از روش­هاي يادگيري ماشين است كه تصميم­گيري­هاي پيچيده‌تري نسبت به روش انساني مي­توانند اتخاذ كنند. روش­هاي يادگيري ماشين با تحليل ايستاي متن(بدون اجراي كد) اين كار را انجام مي­دهند اما هنوز هم عدم شناسايي صحيح در بسياري از برنامه­هاي جاري، منجر به فعال شدن كدهاي مخرب شده و آسيب وارد مي­كنند. در اين پژوهش هدف ما شناسايي وب­سايت­هاي مخرب با استفاده از تركيب تحليل ايستا و پوياي(با اجراي كد) است، كه به كمك اين دو رويكرد ابتدا، چالش­هاي رمزگشايي و مبهم­سازي را حل كرده و سپس ويژگي­هاي استخراج شده را تحليل مي­كنيم. نتايج اين تجزيه و تحليل نشان مي­دهد كه رويكرد پيشنهاد شده با الگوريتم طبقه­بندي درخت تصادفي، پيوندهاي صفحات وب را با دقت 97.11 درصد شناسايي مي­كند.   

  Abstract Social networks has attracted many users. These social networks have enabled user to connect to each other and share text, image and videos. A social network that allows users to share their location is named a location-based social network. Users can leave their tips on places they have visited and share it with others. User feedback is reflection of how they feel about the places they have visited. In social networks, people are connected to each other’s. One of the issues of these networks is the prediction of communication that may be created between two users in the future. Link prediction is the name chosen for this issue. There are many approaches used to predict links. Network structure information, user information such as their interests and characteristics, and location information that users have visited are used to predict links. User’s sentiment is one of the information that can be used to improve link prediction. Their tips can be analyzed to gain a sentiment for users in location-based social networks. This can provide a new algorithm for link prediction by combining the information of network structure, the information of the places they visited and their sentiments. The algorithm was tested on a foursquare network dataset, and it was found to perform better than one that does not use user sentiment. Therefore, it can be concluded that the role of sentiment is effective in creating new links among users. Keywords: social network, location-based social networks, link prediction, sentiment, location sharing

  ?_ Abtract The present study was conducted under the title "Data exchange Protocol between Appointment services Based on Health Data Exchange Center (IX Health)" in 1398. the purpose of the research is to exchange information between health systems. the ability to communicate between appointment systems is one of the key factors in patient satisfaction in receiving medical services, reducing patient and physician waiting time, and so on. in this research, we first examine the systems integration architecture as well as the architecture of the National Center for Information Exchange and the National Center for Health Services and then examine the protocol for data interchange between the systems based on the protocol presented in the electronic health record the proposed protocol focuses on the possibility of data interchange between the delivery systems by providing a communication protocol implemented with the use of php programming language, larval framework and phpstorm environment, the results and outputs of the program show that it is possible to exchange data between queuing systems by providing communication protocol. obviously, this reduces the waiting time for the patient and the physician to increase speed and improve efficiency in medical centers. we also showed that using this communication protocol, it was possible to refer from one system to another. Keywords: Data Exchange, Scheduling. Health

  Electronic service providers discovered the importance of evaluating their services with market competition. Because the competition is in such a way that any weaknesses in the mindset of customers and the attraction of new customers over a short period can bring the organization into an abyss. In the research literature, we introduced the important concepts of research. In the sequel, we will look at the history of the research and the ongoing efforts. By examining different models, we concluded that the role of the user in different models was not considers sufficiently. In some studies, the user's satisfaction has been overlook unaware that different users have different characters and different tastes. Regardless of these differences, we will not be able to assess accurately the quality of a web service from the user's perspective. Through the Myers-Briggs test, we divided the users into 16 personality categories. Fuzzy was chose as a suitable method because of the close proximity to user interactions. After reviewing some fuzzy methods, Topsis method was select as a suitable method because of high accuracy and unlimited in the number of interviewees and criteria. In Topsis method, we needed to weigh it to the criteria, which used from improve fuzzy AHP method. Finally, we distributed a questionnaire that analyzed the first 60 first questions of personality testing and 42 subsequent questionnaires on quality of web service with Mellat Bank as a well-known Web service. Hundred completed questionnaires filled in for us had remarkable results. As expected, users with different personalities arranged different levels of satisfaction and, in some cases, even contradicted the criteria. In the results of the research, we arrange the criteria for each character as well as the order of the characters according to their satisfaction with the criteria.

  The users and the common goals and objectives and put them in a batch, the proposed algorithm uses an distributed and interactive particle pooling algorithm. The distributed and interactive particle pool algorithm is a version of the PSO that can process each section of the database or each dimension of the target separately. Therefore, the proposed algorithm is well suited to distributed processing platforms such as Spark. The simulation results, while confirming the accuracy of the proposed method with the collaborative refinement, show that the proposed system for recommendation in the Kalandays is about 64 times faster than conventional processing platforms.

  AbstractAdvances in Internet technologies have led to the popularity of technology-based self-services, with the design of such services becoming increasingly important. This thesis identified the key service attributes driving adoption and use of transactional e-government services, and citizens’ preference structures across these attributes by using technology-based services in the public sector. An unsolved quest still however is how to categorize such e-services. Stage-models are today dominating for pinpointing high-range  characteristics of e-services. The classification of the services helps in understanding their importance. As a conceptual category, one can distinguish between economic and information services. At the same time there is a flaw that there are no good models for categorizing services. Efforts have been made to use such models as the Classification Diamond for electronic services. Hence, the main purpose of this thesis is to introduce a new and easy-to-use and well-form model for the classification of e-government services. In this thesis, a review was initially carried out on the most popular models of e-government services categorization. In the research that took place, the ESI model has a more coherent structure for classifying e-government services. In contrast, the rhombus model is a graphical model that has a well-formed character. Then, a classification model was first introduced for the Iraqi government services using the ESI model. This model is then upgraded in the form of a Diamond model. So, in the rhombus model, classification information is filled in from the table ESI. Presence, Non-Presence, Government performative, Citizen informative, and Financial and Non- Financial. Within each of these categories sub categories such as separate vs. compound, and individual vs. general is used for the purpose of make an even more fine-grained classification.  

  The database used in this study is a 10000 sample medical database from a cohort study conducted in ravansar city, IRAN. This database consist variables like biochemical tests, CBC tests, anthropometric data and lifestyle variables. The main reason of creating this database was to have a baseline source for storing ravansar city residents’ information to follow up in a 15 year prospective cohort study. Therefore in this study the effort was to extract interesting relationships between these variables and chronic diseases using genetic programming. In contrast to majority of data mining researches that take performance and creating a novel model as the main purpose, this study has chosen knowledge extraction. At first by using the feature selection ability of genetic programming we have tried to reveal the most related variable for each disease. Rule mining is done by association rule mining algorithm. An evaluation process of the GP results is conducted by the extracted rules to check the correctness of GP’s results. Features selection and rule generation are done separately for biochemical test and CBC tests based on their different definition. From the diseases we have chosen diabetes and hypertension as the goal diseases because of the quality of the data they have. For diabetes ALP, TR, GGT, BU are considered as the most related variables while for hypertension FBS, ALP, LDL, TR are the most important ones. Results considering CBC variables were not mentionable.

معرفي روشي براي تحليل احساس داده هاي حجيم (مطالعه موردي تويتر)

Communication problem with a simple idea can be transferred to a facility management network infrastructure / system / centralized system, solved, so that hardware can remain as part of the network data (like hardware available) and tooling to the control unit to Annie on the device. Hadoop has given birth several years, the question arises whether its functioning can be improved. The answer can be quite overwhelming with the composition and performance of Hadoop-based software and networking replied. with the networking issues, the work will pay its processing and network-based software this task is delegated.

  DN is a new paradigm in computer networks based on global view provided form separation of data plane and control plane. This separation is possible by means of an API between the switches and the controllers such as OpenFlow. Logical centralized in SDN by global view of the network can help to improve network management, load balancing, routing and security. Logically centralized controller allows SDN load balancer to allocate the new incoming flow to the best possible server, efficiently. SDN load balancers mostly operate on L4 in OSI model and decide based on the L2/4 headers and this conditions cause limitations in implementation of networks when Back-end servers are not replica. In this case a data base is needed to store mapping between content and controller and with each incoming flow to the Frontend load balancer the controller allocates that flow to the server containing the request content. To implement the L7 load balancer (application layer) there are traditional methods such as Delayed Binding and TCP Socket Migration and this project discuss the implementation of Delayed Binding based on SDN concepts and also the best server should be chose regarding to the network global view, traffic load and response time of the Back-end server that contains request content. The implementation of this method is done by using a virtual switch named Open vSwitch in a virtual machine monitor or hypervisor and Floodlight controller and the results of the implementation has been shown in this project. The average improvement of response time in comparison with three other algorithms, the L  RT, Round Robin and Random selection methods are 19.58%, 33.94% and 57.41% respectively. Furthermore, the average improvement of throughput in comparison with three other algorithms are 16.52%, 29.72%, and 58.27%, respectively.

  .With the advancement of computer networks and the presence of the Internet,our lifestyles have changed. This also has affected people’s jobs, companies’ andorganizations’ activities. Increasing the communication has led to the need for datasecurity and secure transactions. Information security is critical for communications,especially in financial and economic transactions in the digital world. Cryptographyis used to provide secure transactions. Preventing unauthorized people fromaccessing data is one of the most challenging areas in transmitting information viathe Internet. One common approach for protecting data against adversaries is encryptingit. There are many different encryption techniques. In this work, Ellipticcurve cryptography is used. Elliptic curve cryptography is a public key cryptographymethod, which similar to other cryptography methods such as RSA, provides a givensecurity with a short length key. This thesis presents a method to automate the processof transferring medical information and records while ensuring the security ofthe transaction. Furthermore, a secure method for encrypting data and informationin processes such as authentication, transferring data to the insurance

Ontology Model For Data Integration In Gas And Oil Industry

يكي از مباحث مهم در جامعه امروزي كه دغدغه بسياري از كارشناسان و همچنين كاربران مي‌باشد بحث امنيت و تشخيص و تاييد هويت است. مردم خواستار اقدامات امنيتي بي­عيب، ساده و كاربرپسند هستند. بيومتريك، احراز هويت افراد براساس ويژگي­هاي منحصربفرد و متمايز كننده ، مقاوم و قابل­سنجش است كه بتواند جهت تعيين يا تأييد هويت افراد بكار رود. شناسايي از طريق بيومتريك، شناسايي يك فرد براساس صفات فيزيولوژي، رفتاري و شيميايي يك شخص است. تشخيص هويت از طريق بيومتريك مزاياي بسياري دارد و تاكنون روش­هاي مختلفي ارائه شده است. روش­هاي بكار رفته در هر دوره قوت و ضعف فناوري آن را به همراه دارد. در بين ويژگي­هاي بيومتريك مختلف استفاده از الگوي رگ دست افراد يكي از مناسب­ترين و قابل اطمينان­ترين خصيصه­هاي بيومتريكي مي­باشد كه ما در اين پايان­نامه به آن مي­پردازيم. سيستم­هاي تصديق هويت مبتني بر الگوي رگ دست شامل چندين مرحله مختلف از قبيل پيش­پردازش، استخراج ويژگي الگوي رگ­ها و تطابق الگو است. در سال­هاي اخير روش­هاي مختلفي براي هر كدام از اين مراحل ارائه شده است. در اين پايان نامه، تمركز ما بر روي استخراج ويژگي و بكارگيري توصيفگرهاي بافت تصوير و تركيب چند توصيفگر مي­باشد. به منظور استخراج ويژگي توصيفگرهاي الگوي باينري يكنواخت، الگوي باينري يكنواخت مستقل از چرخش و كوانتيزه ساز فاز محلي مستقل از چرخش به كار گرفته شده است. همچنين در روش پيشنهادي تركيب چند توصيفگر را نيز بررسي نموده ايم. در ادامه براي طبقه بندي تصاوير، سه طبقه بند متفاوت ماشين بردار پشتيبان، درخت تصميم و كا نزديك­ترين همسايه بكار گرفته شده است. براي ارزيابي دقيق روش پيشنهادي، از مجموعه داده PUT Hand Vein   كه خود شامل دو مجموعه داده از تصاوير رگ كف دست و رگ پشت دست است، استفاده شده است. پايگاه داده شامل 1200 تصوير رگ كف دست و همچنين 1200 تصوير رگ پشت دست است. همچنين پارامتر دقت طبقه بندي تصاوير و زمان محاسبات اندازه گيري شده است. نتايج بدست آمده از اجراي اين الگوريتم­ها و تركيبات مختلف آنها نشان مي­دهد كه بهترين الگوريتم تركيب الگوي باينري يكنواخت و كوانتيزه ساز فاز محلي است كه دقت اين روش در تصاوير رگ كف دست براي دست راست 99 درصد و براي دست چپ 33/ 99 درصد با طبقه­بند ماشين بردار پشتيبان بدست آمده است. در تصاوير رگ پشت دست براي دست راست مقدار دقت طبقه بندي 83/97 درصد و براي دست چپ 66/97 درصد با بكارگيري طبقه­بند كا نزديكترين همسايه بدست امده است. علاوه بر اين در مقايسه با روش هاي پيشين، نتايج بدست آمده از روش پيشنهادي بهبود دقت را نشان مي­دهد.  

بيماري پاركينسون يكي از بيماري­هاي شايع عصبي است. اين بيماري با مشكلات حركتي براي بيماران همراه مي­باشد كه موجب عدم توانايي كاركردن و ديگر پيامدها مي­باشد. در اين پايان­نامه، سعي شده تصاوير مربوط به دست نوشته افرادي كه تست پاركينسون داده­اند به صورت اتوماتيك توسط روش­هاي پردازش تصاوير بررسي شوند و بيمارها و غير بيمار ها با متد­هاي پردازش ماشين و يادگيري ماشين تفكيك شوند. ويژگي­هاي الگوي باينري محلي و چندي­كردن فاز محلي براي اولين بار در مسئله­ي طبقه­بندي افراد سالم و بيمار پاركينسون بكار برده مي­شوند و پارامترهاي دقت شناسايي،   دقت،فراخواني وF-score   ارزيابي مي­شوند. روش پيشنهادي شامل سه قسمت است: پيش پردازش، استخراج ويژگي­ و كلاس بندي. در بخش پيش پردازش، نرمال سازي، قطعه­بندي مبتني بر عمليات ريخت­شناسي و فيلتر مات بر روي تصوير انجام مي­گردد. سپس، در بخش استخراج ويژگي براي تصوير، دست خط و خط چاپي از هم جدا شده و سپس با هم مقايسه مي­شوند تا ويژگي­هاي مربوط به آن به دست آيد. براي مشخص كردن نقاط متناظر روي دست خط و خط چاپي از اختلاف دو تصوير و همچنين ميانگين­گيري استفاده شده است. در ادامه، ويژگي­هاي بدست آمده كه مبتني بر اطلاعات آماري تصوير مي­باشد، بدست مي­آيد. در مرحله­ي بعد سه طبقه­بند مختلف ماشين بردار پشتيبان، نايو بيز و كا نزديك­ترين همسايه به منظور دسته بندي افراد سالم و بيمار پاركينسون بكار گرفته شده است. براي ارزيابي روش پيشنهادي و مقايسه با روش­هاي پيشين، از مجموعه داده Hand PD استفاده شده و از 90 درصد داده­ها براي آموزش و از 10 درصد براي تست استفاده كرده­ايم. نتايج به­دست آمده نشان مي­دهد كه بهترين الگوريتم در بين طبقه­بندها نايو بيز بوده است كه دقت اين روش براي طبقه­بندي افراد سالم و بيمار با بدست آوردن اطلاعات آماري   تصاوير، برابر با 32/85 است . همچنين در ادامه تاثير بكارگيري دو توصيفگر الگوي باينري محلي و الگوي چندي­ساز فاز محلي، بررسي شده است كه طبقه­بند نايوبيز بيشترين دقت را براي الگوي باينري محلي برابر با مقدار 77/87 و براي الگوي چندي­ساز فاز محلي برابر با 59/85   نتيجه داده است. در مجموع نتيجه­  hy  hy;ي بدست آمده از روش پيشنهاد شده نشان مي­دهد كه اين روش نسبت به روش­هاي اخير 9 درصد افزايش در دقت تشخيص داشته­است.   

Heart disease is one of the major causes of disability in adults and one of the main causes of death in the developed countries. Although significant progress has been made in the diagnosis and prediction of heart disease, further investigation is still needed.Data mining techniques have been applied magnificently in many fields, including science, the web, business, bioinformatics, and on different types of data such as sensor data, visual, textual. Medical data is still information rich, but knowledge poor. Data mining is a tool that we can use it to predict or detect the heart diseases based on previous data in the standard dataset. >The objective of this thesis is to design, implement and introduce a new hybrid >One hybrid >One part of our proposed method (Approach two) has used CFS algorithm for selecting the best features at the feature level. In all approaches, KNN, DT, NB and SVM >We designed and implemented a method, which uses single >The best result of the base method for Cleveland dataset was equal to 83.82% of >Finally, maximum >

  Energy consumption in Computer networks in recent years, due to the notable grow of the users and demanding of multimedia services have been increased. To preserve the environment, decreasing of energy consumption has been attended, specifically. Energy consumption is investigated from different aspects. In a network, different protocols have been defined which affect on energy consumption. Energy consumption in a protocol is defined based on the generated load on link and necessary time to transfer  the generated load. TCP is a protocol that assures a flow will arrive the destination surely. Therefore, generates a notable volume of the  load because of the  acknowledge  acket  which increase  the load on a related link.In this thesis, energy consumption is investigated  from the software point of view and is tried to decrease the number of acknowledge packets to improve the energy consumption beside of reliability control. The achieved  energy efficiency  improvement in this work is   12.09%. The proposed approach in this work may cause the decreasing of throughput in online networks like VOIP wich can be ignored  generally.

An Evaluation of Enterprise Architecture Frameworks for E-government