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.

   Cardiovascular diseases, particularly arrhythmias, have been among the leading causes of mortality in recent years. Consequently, the medical community has been actively seeking efficient and rapid methods for diagnosing these conditions. To enhance diagnostic speed and minimize potential human errors, the use of automated methods for detecting arrhythmias has gained significant attention. This study aims to achieve accurate and timely detection of various arrhythmias with minimal computational complexity and a reduced number of features. in this thesis, three types of arrhythmias—atrial, sinus, and ventricular—are analyzed, with each category comprising 100 ECG signal samples sourced from the SHEDB database. Two models, the Multilayer Perceptron (MLP) neural network and the Radial Basis Function (RBF) neural network, were employed for arrhythmia classification. Results indicate that the MLP model, achieving a test accuracy of 97.8%, significantly outperformed the RBF model, which achieved a test accuracy of 76.7%. These models were selected to reduce computational overhead compared to more complex models like Convolutional Neural Networks (C  ). furthermore, various temporal, statistical, and frequency domain features were examined during the feature extraction process. The best performance was achieved using eight selected features: Root Mean Square (RMS), Waveform Length (WL), Absolute Sum of Squares( ASS), Mean (MEAN), Skewness (SKW), Kurtosis (KUR), Dominant Frequency (DF), and Amplitude of Dominant Frequency (AFDF).

  Diabetes mellitus is a chronic metabolic disease characterized by the body's inability to effectively use blood sugar or produce sufficient insulin to regulate it. If not properly diagnosed and treated, this disease can lead to serious complications such as heart disease, kidney damage, nerve disorders, and blindness. Given the increasing global prevalence of diabetes, early identification and prediction of this disease is of paramount importance. This research focuses on predicting the onset of diabetes using machine learning algorithms. For this purpose, the Pima Indian Diabetes dataset is employed, which includes features such as age, weight, blood pressure, fasting blood glucose levels, Body Mass Index (BMI), number of pregnancies, family history of diabetes, and other biological parameters. These data, extracted from a population of Native American women, are used to train and test various machine-learning models. In this study, different algorithms including Logistic Regression, XGBoost, AdaBoost, LightGBM, Decision Tree, CatBoost, and Gradient Boosting, were employed to predict the onset of diabetes. The results of this research, which compares different algorithms, particularly boosting algorithms, indicate that some of these algorithms demonstrate higher accuracy in predicting diabetes and can be used as effective tools for early detection and optimal management of the disease. The models achieved the following accuracy: Logistic Regression (0.92), XGBoost (0.96), AdaBoost (0.94), LightGBM (0.96), Gradient Boosting (0.91), and Decision Tree (0.91), with the best performance achieved by CatBoost with an accuracy of 0.98. Finally, suggestions for future research are offered.

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   

توسعه زندگي بشري منجر به ايجاد رخدادهايي متنوع در سطح جامعه شده است، دولت ها جهت كنترل اين رخدادها موجودتي به نام قانون را ايجادكرده اند تا به وسيله آن، رخدادهاي بشري را كنترل كنند. از اين حيث شناخت دقيق قوانين جهت دفاع از حقوق فردي، جمعي و يا قضاوت، رخداد ها بر اساس اين قوانين امري بسيار پيچيده است. چرا كه استنباط هر شخص از رخداد و قوانين بر اساس دانش، تجربه، شخصيت و احساسات است. با افزايش اين رخدادها خصوصا رخدادهاي يكسان و به طبع آن افزايش پرونده هاي دادرسي، شواهد و نظرات متنوع نسبت به رخدادها، منجر به ذهني شدن رسيدگي به رخدادهاي يكسان شده است، از اين رو بنا بر اينكه عدالت در صدور آراي قضايي مهمترين اولويت يك دستگاه قضايي است ذهني شدن قضاوت در پرونده هاي مشابه، عدالت در صدور آراي قاضيي در پرونده هاي مشابه را زير تحت تأثير قرار ميدهد. وجود ابزار و الگوريتم هاي شباهت سنجي با استفاده از هوش مصنوعي ميتواند جهت استفاده كارشناسان حقوقي و نيز دادخواهان بسيار مفيد واقع شود.   اين شباهت سنجي به طرفين دعوي، وكلا و قضات كمك ميكند كه آراي صادره نسبت به يك رخداد يكسان را مشاهده كرده و نسبت به آن وحدت رويه داشته باشند. وحدت رويه موضوعي است كه باعث ميشود قضات در تصميم گيري نسبت به پرونده هاي مشابه بتوانند اعمال نظري دقيق تري انجام دهند و در تصميم گيري نسبت به يك موضوع اجماع نظر داشته و در برخورد با موارد مشابه سليقه اي برخورد نشود. در شباهت سنجي قوانين و آراي صاده مشكلات و چالش هاي فراواني   وجود دارد كه يكي از مهمترين آنان عبارت است از زبان قوانين و عدم دسته بندي هاي لازم در اين متنون است. براي ارتباط و شباهت سنجي متون قضايي با وجود محدوديت ها و چالش هاي موجود از يادگيري عميق در زمينه پردازش زبان طبيعي(NLP) استفاده خواهيم كرد. براي پردازش زبان آراي صادره نيازمند به يك الگوريتم پردازش زبان، براي زبان مورد نظر هستيم. استفاده از يك سيستم شباهت سنجي مبتني بر هوش مصنوعي ميتواند به عنوان يك ابزار قابل اتكا براي كارشناسان قضاييي مورد استفاده قرارگيرد.   

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.   

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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.   

سرطان ريه شايع ترين سرطان در دنيا است. در اين پژوهش با استفاده از مدل يادگيري عميق توانستيم سرطان ريه را به دو دسته تومور و سالم طبقه بندي كنيم.

  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.

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

  Abstract Today, the security debate is considered an important and challenging issue. Older tools such as usernames and passwords alone are not responsive and reliable. That is why, day by day, in many areas, we need tools to identify individuals based on vital signs. With the advent of biometric knowledge, common methods of authentication in biometric systems have changed. Recently, the use of electrical brain signals (EEG) in biometric systems has been considered by researchers as an attractive and practical branch of research because it has two main advantages: First, this signal must be recorded from a living person in a normal mental state. Second, the EEG signal, unlike many other biometrics, is the result of a set of internal and cortical events in the brain that make it impossible to mimic. In this study, a data set with two different stimuli (relaxation and concentration) has been used that in the first period of time people are in a state of relaxation and in the second period of time people are in a state of concentration. An electrode is used to process and record EEG signals, then the analog signals are converted into digital signals. In this research, EEG data set with 109 topics has been used. In order to improve the performance of the authentication system in this study, instead of extracting features and selecting optimal features, deep features have been used. The results of our experiments on Albasri database with 99% accuracy indicate that using deep features and neural network algorithm Convolution using the genetic algorithm (GPCNN) is significantly improved over other electrical signal-based authentication systems of the brain, and shows a clear vision of the practical and commercial use of brain electrical signals in future authentication systems.

AbstractThe increasing number of private cars on the tra  ortation routes causes a heavy traffic load. In many countries, high occupancy vehicles (HOVs) have been developed to reduce the traffic load on special lines. Also, only buses, police vehicles, fire trucks, emergency vehicles, and personal vehicles with capacities to carry more than one passenger are allowed to use these lines. Another issue in monitoring the tra  ortation and traffic of vehicles is the observance of driving rules within the vehicle compartment. These rules include the drivers' use of seat belts while driving, and the accurate and automatic detection of these rules is of particular importance. In this paper, we propose a method based on deep learning models for simultaneous detection of the occupants and the status of driver's seat belt. In this method, first, the windshield is detected using the YOLOv5s network. Then, we determine the presence of a person in the passenger compartment using the front seat passenger detector model. Finally, using the deep learning-based image >Keywords: Car occupant detection, Seat belt status detection, Automated tra  ort images analysis, deep learning, transfer learning, YOLOv5, ResNet34, TPP,   , PMT   

به طور معمول يك قاضي بر اساس دانش، تجربه، شخصيت و احساسات خود قضاوت مي‌كند. با افزايش تعداد پرونده‌ها، بررسي اسناد و شواهد به صورت دقيق دشوار است و ممكن است قضاوت‌ها ذهني‌تر شوند. همچنين با افزايش حجم كاري، يك قاضي ممكن است بيش از حد تحت فشار قرار گرفته و نتواند يك قضاوت با كيفيت انجام دهد. پيش بيني حكم دادگاه توسط الگوريتم‌هاي هوش مصنوعي، علاوه بر قضات، مي‌تواند جهت استفاده كارشناسان حقوقي و نيز دادخواهان بسيار مفيد واقع شود. همچنين اين نوع پيش‌بيني مي‌تواند به عنوان يك خدمت مشاوره‌اي آنلاين به آحاد جامعه ارائه شود تا قبل از طرح دعوي در محاكم قضايي و تنظيم دادخواست يا شكواييه، نسبت به نتيجه احتمالي درخواست خود آگاهي يافته و چه بسا همين امر سبب كاهش چشمگير پرونده‌ها و نيز كاهش هزينه‌هاي سرسام‌آور گرفتن وكيل در برخي موارد براي قشر كمتر برخوردار گردد. اين نوع پيش‌بيني همچنين به وكلا و طرفين دعوي كمك مي‌كند كه قبل از رفتن به دادگاه اقدامات لازم را انجام دهند. از ديگر كاربردهاي اين پژوهش مي‌توان كمك به صدور دستور تشكيل دادگاه‌هاي تجديد نظر در صورت مغايرت راي دادگاه بدوي با حكم پيش‌بيني شده توسط مدل هوش مصنوعي اشاره كرد. با وجود آن‌كه متن‌كاوي و كاربردهاي آن به طور گسترده در حوزه‌هاي مختلف مورد استفاده قرار گرفته، اما تنها مطالعات معدودي متن‌كاوي را در زمينه‌هاي قضايي به كار گرفته‌اند. اين پايان‌نامه، اولين پژوهش مدون در حوزه متن‌كاوي اسناد قضايي فارسي مي‌باشد. در اين پايان‌نامه به پيش‌بيني حكم دادگاه در پرونده‌هاي مرتبط با خريد، نگهداري، مخفي كردن يا حمل مواد مخدر با استفاده از تكنيك‌هاي يادگيري ماشين و يادگيري عميق، با بررسي تاثير جنبه احساسات و هيجانات قاضي در شدت حكم صادره، در مجازات‌هاي شلاق، جريمه نقدي و حبس، پرداخته شده‌است. براي اين منظور ابتدا متون و اسناد 6000 پرونده قضايي را پيش‌پردازش نموده، سپس با استفاده از پيكره احساسات و هيجانات NRC، گرايش مثبت يا منفي و نوع هيجان موجود در پرونده‌ها را بررسي و نمره‌گذاري كرديم. در ادامه با روش‌هاي گوناگون يادگيري ماشين و يادگيري عميق، مدلسازي احساسات را انجام داديم كه از ميان روش‌هاي پياده‌سازي شده، روش TFIDF + SVM بيشترين دقت را كسب نمود. سپس به تجزيه و تحليل 8 نوع هيجان موجود در پرونده‌ها پرداخته و به صورت طبقه‌بندي چند برچسبه آن‌ها را مدل‌سازي نموديم كه به صورت ميانگين، الگوريتم TFIDF + SVM بيشترين دقت را داشت. در گام بعد، ميزان مجازات‌هاي در نظر گرفته شده در پرونده‌ها را در دو دسته مخففه و مشدده طبقه‌بندي نموده و به روش‌هاي يادگيري ماشين، يادگيري ماشين جمعي و يادگيري عميق، به مدلسازي آن‌ها اقدام نموديم كه در نهايت از ميان روش‌هاي بررسي شده، در مجازات شلاق روش TFIDF + Adaboost، در مجازات جريمه نقدي روش BERT و در مجازات زندان روش Skipgram + LSTM + CNN، بيشترين دقت را كسب نمودند. در نهايت به منظور تخصيص هر يك از برچسب‌هاي مجازات شلاق، جريمه نقدي و زندان، هر الگوريتمي كه بيشترين دقت را داشت انتخاب نموده و دقت آن را در شرايطي كه داده ما متون قضايي به علاوه نمره احساسات پرونده، متون قضايي به علاوه نمره هيجانات پرونده، متون قضايي به علاوه نمره احساسات و نمره هيجانات پرونده باشد را محاسبه نموديم. نتايج اين پژوهش نشان مي‌دهد كه استفاده از نمره احساسات و هيجانات، باعث افزايش دقت پيش‌بيني حكم دادگاه براي هر سه مجازات مورد بررسي(شلاق، جريمه نقدي، زندان) مي‌گردد. همچنين مجازات شلاق بيشترين تاثير و مجازات زندان كمترين تاثير را از احساسات و هيجانات مي‌گيرد. در ضمن در مجموع احساسات تأثير بيشتري نسبت به هيجانات در پيش‌بيني راي دادگاه دارند. كليدواژه‌ها: پيش‌بيني حكم دادگاه، متن‌كاوي، يادگيري ماشين، يادگيري عميق، تحليل احساسات، تحليل هيجانات   

  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.   

Every day a large number of comments are published by users on the web, especially on social networks, online review sites in forums and social networks. Due to the huge volume of this data and textual information, their analysis by humans is very difficult, time consuming and practically impossible; so we need a system that can automatically analyze comments. Sentiment analysis is the best solution to this problem. Sentiment analysis is a subset of natural language processing. And it is a process that examines people's concerns, views, and feelings by identifying the positive, negative, and neutral aspects of writing. The corona virus has become a storm on social media. As awareness of this disease increases, messages and posts confirm its existence. The social network Twitter has shown a similar effect to the number of messages related to Covid19. Which has had unprecedented growth in recent times. In this study, the analysis of Twitter Persian messages about the coronavirus was performed using machine learning. The success of machine learning has been discussed in many applications due to its ability to automatically extract features and learn complex patterns. The purpose of this study is to provide a model for analyzing and classifying the Sentiment of Twitter users using machine learning algorithms. In this research, using machine learning algorithms such as decision tree, SVM, logistic regression to approach the emotions of Persian tweets, an acceptable result has been obtained. Similarly, the accuracy of the decision tree algorithm was 83%, the support vector machine 81% and the logistic regression 77%. The decision tree algorithm has the best accuracy. Keywords: Sentiment analysis, Coronavirus pandemic, Twitter social networks, Machine learning.   

   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

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  

The diagnosis of cancer is mainly performed by visual analysis of the pathologists, through examining the morphology of the tissue slices and the spatial arrangement of the cells under a microscope. If the microscopic image of a specimen is not stained, it will look colorless and textured. Therefore, chemical staining is required to create contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, cutting thicknesses, and laboratory protocols, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to interpretive disparity among pathologists more, is one of the main challenges in designing robust and flexible systems for automated analysis. Various strategies for normalizing stain have been proposed as a pre-processing step in automated pipeline systems. In this thesis, the stain normalization for Hematoxylin and Eosin (H&E) stained histopathology images has been performed based on the Pix2Pix framework derived from conditional generative adversarial networks (cGAN). The proposed approach is called "Stain-to-Stain Translation" (STST). This method learns not only the specific color distribution but also the preserves corresponding histopathological pattern. Also, unlike previous methods that depended on a reference image, this method uses the distribution of all images in the training set for learning. The STST method has achieved significant results, both quantitative and qualitative evaluation, against some of the best methods. Based on the obtained results, it can be shown that STST, besides the very high perceptual similarity between the ground truth and the restained image, outperformed other stain normalization methods examined on the processing time metric. It also in a clinical use-case, namely breast cancer tumor name="_ftnref1" title="">[1]. [1] https://github.com/pegahsalehi/Stain-to-Stain-Translation   

Abstract You've definitely seen a variety of movies and animations in the cinema that make for spectacular special effects. These special effects are quite similar to the real world, and the movements of the characters are similar to those in the real world. With the advent of 3D motion capture technology and its move to computers, movies, computer games and especially animations have entered a new world. When movies started using 3D models, the goal was for them to create real motion and speed up the workflow so that the motion was not manually animated. The solution is to capture the movements of an actor in 3D and apply them to 3D computer models. The purpose of this technology is to allow us to create more effective and realistic characters and effects that we were not able to do before. The purpose of this dissertation is to design and implement an actor's face recording system using digital image processing techniques and machine learning algorithms, which is performed without the use of a specific hardware system. In this design, the camera is first captured by a computer webcam and then detected the face in the image and then the key points of the face are detected, then the two-dimensional points are identified. The camera parameters and two-dimensional mapping algorithms and their combination with facial feature points are mapped to the 3D coordinate space and a three-dimensional model of the face is created. This three-dimensional model is independent of head rotation and a particular face. Finally, the data obtained from the previous steps are transferred to a 3D virtual character in Maya 3D software by connecting to a TCP / IP socket.  

an independent unified section.  

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.   

  Deep Packet I  ection has always been a challenge of performance and a matter of throughput in computer networks. Therefor a lot of different methods have been invented to enhance the operation of DPI in networks. Using probabilistic filters for DPI is an approach which has been taken is recent years. Probabilistic filters are some kind of data structure which are used for membership test among a set of items. These filters can result a false positive answer. One of constraints of using probabilistic filters is incapability of efficient scaling specially when they are used in a software running by CPU. To solve this problem implementing DPI utility on a scalable parallel architecture can be a good solution. On the other hand, emergence of new networks paradigms like software defined networks added new difficulties in monitoring networks. In the base situation, to perform deep packet i  ection in a software defined network, the whole task is delegated to the controller and this makes the controller overloaded thus creating a network bottleneck. This situation created an intensive need for an architecture and new design of deep packet i  ection which is fast, scalable and flexible to fit in SDN networks. The new design should also decrease the workload of controller which is related to deep packet i  ection. In this thesis we try to design, implement, and evaluate a new method that hits needed criteria.

Human Identification Based on Ear Biometric Employing a Hybrid Approach  

  Fundamental frequency estimation is one of the most important issues in the field of speech processing. An accurate estimate of the fundamental frequency plays a key role in the field of speech and music analysis. So far, various methods have been proposed in the time- and frequency-domain. However, the main challenge is the strong noises in speech signals. In this paper, to improve the accuracy of fundamental frequency estimation, we propose a method for optimal combination of fundamental frequency estimation methods, in noisy signals. In this study, to discriminate voiced frames from unvoiced frames in a better way, the Voiced/Unvoiced (V/U) scores of four pitch detection methods are combined both linearly and nonlinearly. These methods are: Autocorrelation, Yin, YAAPT and SWIPE. After identifying the Voiced/Unvoiced label of each frame, the fundamental frequency (F0) of the frame is estimated using the SWIPE method. The optimal coefficients for linear combination are determined using the regularized least squares method with Tikhonov regularization. To evaluate the proposed method, 10 speech files (5 female and 5 male voices) are selected from the PTDB-TUG standard database and the results are presented in terms of SDFPE, MFPE, FPE, GPE, VDE, PTE and FFE standard error criteria. The results of the experiments indicate that the linear combination method (on various SNRs) made GPE error 22.98%, VDE error 26.16%, PTE error 9.26%, and FFE error rate of 32.72% (relative) And the nonlinear combining method reduces the GPE error by 30.64%, the VDE error by 33.58%, the PTE error by 9.58%, and the FFE error by 39.86%, as compared to the popular speech frequency extraction methods. 

  SRM motors have attracted considerable attention due to its low cost and robust structure, high efficiency, and the ability to track at variable and high speed and high ambient temperature. SRM engines are one of the oldest types of electric motors that were left out of the lack because of proper control systems, but today with modern semiconductor technology, SRM engines can be made cheap and even easier than induction motors, and will Compete with all other electric motors soon. One of the problems with this machine is the speed control complexity that is influenced by the effect of the voltage thresholds of each phase and the detection pulses which applied on phases but nowadays engineers are working to overcome the obstacles that cause these problems over time.

Classification of Motor Imagery Tasks for Brain Computer Interface Applicatio  

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

  موضوع بهبود كيفيت صدا امروزه به يكي از موضوعات مهم و اساسي روز تبديل‌شده است .ازاين‌رو بهبود گفتارهاي آغشته به نويز يكي از موضوعات مهم در حوزه پردازش سيگنال است و در موارد بسياري مثل تشخيص صدا، شناسايي احساسات صوتي و...كاربرد دارد. تضعيف نويز به‌نحوي‌كه اختلالي در سيگنال اصلي به وجود نياورد يك چالش مهم براي بهبود صدا محسوب مي‌شود. روش‌هاي مختلفي براي كاهش نويز ارائه‌شده‌اند كه ازجمله روش‌هاي پايه مي‌توان به روش تفريق طيفي ، تبديل موجك، و...ساير موارد اشاره كرد. موضوع تحقيق اين پايان­نامه نيز بررسي نويز موجود در سيگنالِ گفتار، حذف و يا كاهش آن نويز ازسيگنال گفتارِنويزي و ايجاد بهبود در سيگنال‌هاي گفتارِ آغشته به نويز مي­باشد.در اين پايان­نامه دو   روش جديد براي كاهش نويز موجود در سيگنال گفتار نويزي ارائه داده ايم . در روش اول ، يك روش تخمين نويز براي نويزهاي غير ايستان همراه با اعمال تبديل موجك بر روي سيگنال و استفاده از الگوريتم بهينه‌سازي گروه ذرات با رفتار كوانتومي،را به صورت تركيبي با روش Bayesian ارائه داده‌ايم تا نويزهاي موجود در سيگنال نويزي را حذف كند و سيگنال بازيابي شده به سيگنال اصلي نزديك‌تر باشد.در روش دوم نيز با اعمال تبديل موجك بر روي سيگنال و تركيب آن با روش SMPR   روشي جديد براي كاهش نويز ارائه داده ايم. روش­هاي پيشنهادي نسبت به روش‌هاي موردتحقيق در اين پايان­نامه بهتر عمل مي‌كنند و منجر به كاهش نويز از سيگنال با كمترين اعوجاج مي‌شوند.

Overpopulation in metro-polices has led to a space reduction in the cities and a tendency to use underground spaces. Underground structures, especially in cities with large populations, are built for various needs. Different solutions have been proposed for traffic problems; some of the most important of these solutions include the building subways, intersections, urban trains, and etc. Contrary to popular belief that the earthquake impacts on underground structures and tunnels are trivial, there are many cases in science research in recent years that show the significance of earthquake destruction effects on these structures. With the disclosure of the importance of the presence of underground spaces on the seismic response at the Earths surface in this regard, the researchers paid attention to studying analytical, numerical and physical modeling. Considering with seismicity of the Kermanshah city, I have tried to by using finite element ABAQUS software study the effect of earthquake on the site condition of Kermanshah metro tunnel by defining the suitable nonlinear behavior for materials used in modeling. Each one of BH-7, BH-8 and BH-9 bore holes have been analyzed in 3 different steps: first, Frequency analysis, then free field analysis (without tunnel), and finally the main model. The results derived from the time historical analysis of the three BH-7, BH-8 and BH-9 bore holes show that the maximum amplification occurs in the BH-9 borehole which is the most critical borehole in terms of amplification received waveforms on the earth surface. Although the maximum amplification occurs in the BH-9 borehole, the highest maximum stress occurs in the tunnel cover at the site of the BH-8 borehole due to the location of this borehole which is near the bedrock. On the all, the amount of damage on the dependent tunnels depends on the geotechnical characteristics of the layers, the content and intensity of the earthquake record, the amplification that occur in the soil profile, the amount of tunnel overhead, the strength of the forming materials covering the tunnel with concrete and the type and the distance from the bedrock.

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.  

  يكي از مباحث مهم در پردازش صوت، پردازش فايل‌هايي است كه در آن مخلوطي از گفتار انسان، سكوت و موزيك وجود دارد. به عنوان نمونه مي‌توان به فايل‌هاي ضبط شده از رسانه‌هاي راديويي، تلويزيوني و ماهواره‌اي اشاره كرد كه حاوي سيگنال‌هاي صوتي متنوعي هستند.در برخي از كاربردها مانند كاهش حجم، افزايش كيفيت، شناسايي و كاربردهاي ديگر نياز به جداسازي گفتار انسان و يا به عبارتي حذف سكوت، موزيك و يا نويزهاي محيطي از سيگنال‌هاي صوتي به‌وجود مي‌آيد. سيستم‌هاي جداسازي گفتار را مي‌توان نوعي از سيستم‌هاي شناسايي گفتار انسان و يا سيستم‌هاي دسته‌بندي كننده‌ي سيگنال‌هاي صوتي دانست كه از آنها براي جداسازي، شناسايي و يا نشانه گذاري قسمت‌هايي از سيگنال صوتي كه شامل گفتار انسان است، استفاده مي‌شود.براي انجام عمليات جداسازي گفتار انسان از سيگنال‌هاي صوتي از روش‌ها و رويكرد‌هاي گوناگوني بهره گرفته‌مي‌شود. هدف ما در اينجا ارائه روشي مناسب وكارا   در قسمت استخراج ويژگي (feature extraction) و هم‌چنين در قسمت دستبه‌بندي (classification) با استفاده از الگوريتم‌هاي قدرتمند و پيشنهادي و نوين براي رسيدن به دقت بالا و كارايي بيشتر مي‌باشد.

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 >

هدف از شناسايي گوينده ، تمايز قائل شدن بين افراد از طريق تفاوت در ويژگيهاي گفتار آنهاست. به اين معني افراد نه تنها در ويژگيهايي مانند اثر انگشت و برخي ويژگيهاي شناخته شده از هم قابل تفكيك هستند، بلكه مي­توان از تفاوتهاي ديگري مانند، شكل دستگاه صوتي و ويژگيهايي مثل لحن، لهجه، طرز بيان و ... نيز بهره برد. روشهاي زيادي براي مدل كردن سيگنال صوتي، بصورتي قابل تحليل بوجود آمده­اند. از جمله­ي اين روشها مي­توان به روش مدل مخلوط گوسي و مدل پس­زمينه جهاني استفاده كرد. از اين مدل براي تشكيل ابربردارهاي گوسي استفاده شده است. ابربردارهاي گوسي بردارهايي با بعد ثابت هستند كه از سال 2006، توسط كمپبل تعريف شده­اند. و در سيستمهاي شناسايي گوينده مورد استفاده قرار گرفته­اند. مشكل اين ابربردارها، بعد بالاي آنهاست كه موجب افزايش پيچيدگي محاسباتي شده است. براي مقابله با اين مشكل، از روشهاي كاهش بعد مانند بدست آوردن بردار i-vector مربوط به هرگوينده استفاده شده است. در اين تحقيق مؤلفه­هاي گوسي كه براي مدل كردن i-vectorها استفاده شده اند با توجه به مقدار آماره باوم ولچ مرتبه صفر آنها به دو دسته مؤلفه­هاي كم اهميت و مؤلفه­هاي مؤثر دسته­بندي شده­اند. از هركدام از اين مجموعه­ها عناصري بصورت تصادفي حذف مي­گردد كه تعداد اين عناصر حذفي در دو مجموعه متفاوت است. براي ارزيابي عملكرد سيستم از پايگاه داده TIMIT استفاده شده است. ميانگين خطاي EER روش پيشنهادي نسبت به كمترين مقدار خطاي EER در ساير روشها 56درصد كاهش داشته است.كلمات كليدي: ابربردار، i-vector، نمايش تنك، ماتريس نگاشت، شناسايي گوينده، مدل مخلوط گوسي، مدل پس زمينه جهاني  

Studies to date of the existence of a difference in peoples emotions. This vision for all researchers, particularly developers of computer games is valuable, Because by increasing the touchscreen and an increase in this type of game on our phones, the question arises, "Is touching behavior reflects the mood of the players?" If we can recognize the user’s emotions, according to the emotions of users, game design can control the amount and intensity of the game and to minimize the damaging effects of such games. . In this study, we characterized human touch in time on a touch screen use, So that we can distinguish between emotions and personality of users. In this study, using figures factor in diagnosing mental states were able to carefully 91/90 percent and 97/79 in the best position to do character recognition accuracy. In addition to this we got a result and it is other aspects of the recovery process does not recognize the characters in the parameters may in the algorithms of the parameters of feature in the evaluation of personality dimensions will be deleted when emotions are evaluated. But if consider arousal dimension moods and personality aspects to evaluating mood and personality dimensions also approached carefully 98/52 and will have a positive influence in results.

Abstract- The need to private and sensitive information security on multi-touch devices like smartphones and tablets is one of the main problems in information security. Methods that are commonly used passwords and tokens that have a lot of obstacles and challenges. Biometric authentication methods, these methods are a good alternative to overcome the problems. The introduction of biometric based smartphone touchscreen for user authentication is based on finger touch and movement. The purpose of this Study is to examine method of authentication using biometric behavior based on specific gesture for unlocking the device based in existing designs is safe. In this study, by extracting a large number of features and using Distance learning with Genetic Programming, With high accuracy in authentication based on finger multi-touch touch screen to unlock the device achieved.

امروزه اقتصاد هاي مختلف، تجربه هاي زيادي در زمينه نوسانات اقتصادي بدست آورده اندكه شامل دوران هاي رونق و ركود اقتصادي مي باشد.با توجه به اين كه يكي از موضوع هاي بسيار با اهميت در حوزه اقتصاد كلان ،تثبيت اقتصادي ورسيدن به اهداف اصلي كلان اقتصادي از جمله   رشد اقتصادي،افزايش اشتغال و كاهش تورم مي باشد،بنابراين به منظور تحقق اين اهداف و كاهش زيان هاي ناشي از سيكل هاي تجاري، سياستگذاران و برنامه ريزان اقتصادي   همواره تلاش مي كنندتا با كنترل اين نوسانات اقتصادي تا حد ممكن اين اهداف را تحقق بخشند. بنابراين به منظور تحقق هرچه بيشتر اين اهداف، پيش بيني ادوار تجاري در اقتصاد كلان همواره داراي اهميت مي باشد و بخش مهمي از فرآيند تصميم گيري وسياست گذاري اقتصادي را در هر كشور تشكيل مي دهد.   در اين پژوهش از داده هاي فصلي طي دوره ي زماني بين سال هاي 1353تا1393 استفاده گرديده است و به منظور پيش بيني وقوع ركوداقتصادي 115 فصل را بعنوان مجموعه آموزش و به صورت نمونه گيري بدون جايگذاري و 49 فصل را به عنوان مجموعه آزمايش در نظر گرفته شده است.در اين پژوهش در مرحله اول ، ابتدا با توجه به مطالعات صورت گرفته در زمينه ادوار اقتصادي كشور ايران مجموعه اي از متغير هاي موثر بر بروز و پيش بيني اين ادوار معرفي مي گردد سپس با استفاده از تكنيك داده كاوي و روش طبقه بندي موثرترين متغير ها بر بروز اين ادوار شناسايي مي گردد. سپس در مرحله مدل سازي مدل درختان تقويت كننده، در ابتدا با توجه به مجموعه كل داده ها، پارامتر هاي تنظيم كننده   بر اساس معيار هاي دقت مدل Accuracyو kapa بر اساس بيشترين دقت و كمترين RMSE بهينه يابي شده ومناسب ترين مدل در مرحله ساختاري تنظيم مي گردد .سپس اين بهينه يابي در شرايط انتخاب موثرتن شاخص ها نيز صورت مي گيرد و مدل نهايي در زمينه پيش بيني ادوار اقتصادي تعيين مي گرد . در مرحله سوم   براساس بهينه يابي صورت گرفته از مدل و پارامتر هاي تنظيمي مدل، مدل نهايي پيش بيني تنظيم گرديده وفرآيند پيش بيني صورت مي گيرد ودر مرحله آخردقت پيش بيني هاي انجام شده توسط مدل نهايي   RTبه وسيله منحني ارزيابي عمليات گيرنده(ROC) ارزيابي مي گردد.كه نتايج نشان مي دهد كه مساحت سطح زير اين نمودار بالاي 70 درصد است و اين امر ملاكي از دقت بالاي پيش بيني مدل مي باشد . همچنين مدل BRT در مقايسه با دومدل پروبيت و پروبيت بيزين كه در اين پژوهش مورد بررسي قرار گرفتند دقت بيشتري دارد.  

An Evaluation of Enterprise Architecture Frameworks for E-government

Augmented Reality (AR) applications rely on automatically matching a captured visual scene to an image in a database. The task of the thesis is to develop a technique which recognizes paintings displayed in an exhibition. Such a scheme would be useful as part of an electronic museum guide; the user would point his camera-phone at a painting of interest and would see/hear commentary based on the recognition result. Applications of this kind are usually referred to as "augmented reality" applications. Implemented on hand-held mobile devices, called "mobile augmented reality." We are interested in the image processing part of the problem.In this thesis, recognize image at the museum and a gallery is done. Photographed a database of Iraqi National museum and Free drawing exhibition in Ministry of culture and media in Baghdad. Recognize image evaluation parameters are time and accuracy. Features that are extracted from the images for the first time are Histogram in the different bin: histogram 256 bin, histogram 18 bin, and histogram 12 bin, Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Local configuration pattern (LCP). Also, these methods are compared with the three methods Scale Invariant Feature Transform (SIFT), Speed Up Robust Features (SURF), The combination of SIFT –SURF which has been used in past articles.The results showed that the best algorithms for image recognition are HOG-Histogram algorithm using SVM ltr">

 ارزيابي ويژگي هاي بافت تصاوير به منظور تعيين شكستگي استخوان