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.

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.   

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 >

Advances in Blockchain and distributed ledger technologies are driving the rise of incentivized social media platforms over Blockchains. Blockchain-based online social media is decentralized social media that uses blockchain technology to reward users' social activities and store information. In order to protect the privacy of users and expose fake news.    In this study, presents an empirical analysis of Steemit, a key representative of these emerging platforms, to understand and evaluate the actual level of decentralization in these modern social media platforms. Similar to Bitcoin, Steemit is operated by a decentralized community, where 21 members are periodically elected to cooperatively operate the platform through the Delegated Proof-of-Stake (DPoS) consensus protocol.    Our study performed on 539 million operations performed by 1.12 million Steemit users during the period 2016/03 to 2018/08 reveals that the actual level of decentralization in Steemit is far lower than the ideal level, indicating that the DPoS consensus protocol may not be a desirable approach for establishing a highly decentralized social media platform. For this reason, in this dissertation, we tried to provide a solution to the problem of decentralization of the consensus algorithm used in Steemit social media. Our solution to this problem is to replace its consensus algorithm with a more advanced consensus algorithm called the Algorand, which can form a committee without elections involving user interaction. Algorand is a new cryptography that proposes a new Byzantine agreement algorithm that allows choices to be made by randomly validated cryptographic functions rather than by users.    Using the simulator design as well as the API published by Algorand's team, we explored its three main aspects of decentralization, high scalability and security, and show that Algrand can be a good alternative to the DPOS algorithm. Be in Steemit.      

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   

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.   

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.   

  ?_ 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

   Due to the complexity of medical decisions, the application ofinformation systems to support these decisions has increased. The presence oflarge and unknown variables means more complexity of decision-making. Given thevariables’ frequency and interference in the medical field, physicians cantime on the assessment of the decision more. In order to design medical expertdecide faster and more efficiently through using expert systems and spend theiror clinical guidelines and entered into the knowledge base. I.e. the knowledgesystems, specialized knowledge is extracted from the experts in the given areaand experience of the professionals in different fields can be entered into theto recommendations at any time and place will increase with these systems. Thisdecisions of different people, and eventually, the speed of analysis and accessis very important for medical decisions. According to the above, it isconcepts, the use of fuzzy logic in this area is effective; since fuzzy systemsrecognized that we are faced with serious problems in the process of medicaldiagnosis and performance of physicians that necessitates a collective wisdomFurthermore, due to the inherent ambiguity in the definitions of medicalto improve the quality of treatment with the help of expert systems.can play a valuable role in the diagnosis of diseases [24, 31, 32, and 33].diagnose the individual’s disease, if any, through using the fuzzy system andHence, in this study, through using fuzzy system, we are aimed at creating aset of fuzzy rules in order to achieve a higher-level series of information byanalyzing the initial data in blood test results such as red blood cells,tests.hematocrit, white blood cells, hemoglobin, platelets, etc. Our goal is tomedical rules as well as the high-level information obtained from the blood

  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.

Recognizing the emotional states using matching point  

  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.  

هدف اين تحقيق، ايجاد يك سيستم كامپيوتري مؤثر سيستماتيك آموزشي براي طراحي اشكال ساده براي افراد نابينا است (پروتوتايپ)، بنابراين ما يك سيستم آموزشي براي كودك عرب ايجاد كرديم كه در همان زمان، قرآن كريم را نجات داد. طراحي ELMS بايد به حداكثر رساندن نتايج آموزشي براي كانديد نامزد ما در اينجا، روش برنامه نويسي پويا با توجه به مجموعه اي از حروف شناخته شده است. صداي نزديك به كلمه واژگاني با مقايسه كلمه كليدي با تمام الگوها در كتابخانه گوگل و انتخاب آن كه داراي حداقل فاصله (مشابه) با بستر مطلق است و سرعت پاسخ به سرعت بستگي دارد از اينترنت يك حالت كد در صدا و كد ديگري در متن است. تبديل كد در نرم افزار python انجام شده است كه با كد API گوگل كار مي كند. تعويض بين (Speech to command) (متن به گفتار) چندين مرحله دارد. اما در دسته كلي، دو مدل اصلي كه عبارتند از: 1-متن به گفتار 2- گفتار به فرمان. اولين گام ورود به متن عربي به كامپيوتر و شناسايي متن و تبديل متن به فايل صوتي است. بيشتر خطا در مرحله دوم به دليل اين ماژول بسته به دستگاه ورودي (ميكروفون)، سرعت اينترنت و سر و صدا در اطراف فرد و كيفيت صدا رخ مي دهد، تمام دستورات در سيستم عمل مي كنند (بازي سوره قرآن) و غيره تا زماني كه تمام سوره هايي كه در LMS ما ذخيره مي شوند، اين 10 قرآن سورات كوتاه (سوره القطار، سوره فلاع، سوره النس، سوره الطوف، سوره الفيل، سوره النشره، سوره آل -Asr، سوره القرية، القادر و سوره الاخلا) پس از آن درصد خطا در ماژول STC در بيست افراد عرب 10? براي سوره القرية، 5? سوره الاراي، 40? براي سوره النشره، 5? براي سورات الفيل، 0? براي سوره آل نوجوان، 0? براي سوره النس، 0? براي القاد، 15? براي سوره القوث، 25? براي سوره الاخلا و 5? براي سوره الفلق، ما الگوريتم اين ELMS را ساختيم، اما مشكل بزرگي كه من با آن مواجه شدم، معرفي زبان عربي به برنامه بود. دليل اين امر اين است كه زبان عربي زود هنگام در جهان vo تشخيص يخ در مقايسه با زبان انگليسي.  

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. using the sensor to detect the position of the rotor increases the price and complexity of the engine structure, and in addition, in the event of burning or dusting and the mass of each sensor and also noise on sensor cables which given the fact that it is used in industrial environments or electrical appliances, It is likely that this engine will not be welcomed, but in a sensorless method that uses feedback from different parts of the engine and using a microprocessor and writing complex codes to detect the position of the rotor and start the engine and take a long time It takes itself and needs much more science, but all of these happen just one time, and the engine's surroundings make it easier to control other engine parameters.

  Proper user interface design is one of the most important issues in software production. The user understands only the interface to which it relates, and recognizes it as software. Therefore, user interfaces play an important role in the acceptance of the software. Developing a software without designing a suitable user interface will result in its disapproval. The acceptance of a UI is not limited to technical factors because of the user interface is used by the human, and human decisions are depended on psychological factors. As a result, one of the issues that involved in accepting or rejecting the user interface is the human-psychological factor. On the other hand, designing an appropriate user interface often requires the right feedback from the evaluators of that user interface and then applying the correct changes to the final software product.by investigating the majority of papers and computer-implemented studies, and especially the design of the user interface, In this field, we find that in most of these papers, the evaluators are only selected based on their expertise and academical degrees to evaluate the user interface of a system, and the importance of psychological factors and personality traits on the quality of examining different aspects of a system by evaluators is not significant. Therefore, it can be presented that in evaluating the user interface for a software product, the condition of expertise is not sufficient. Depending on the psychological nature of the study, Evaluators should have the reasonable level of emotional intelligence.In this research, we investigate emotional intelligence as one of the most influential factors of human-computer interaction. According to the effect of emotional intelligence on evaluating the user interface of a software by the human, we will provide a method for selecting proper persons to evaluate the user interface.Therefore, we used the Bar-On's emotional intelligence questionnaire to measure the emotional intelligence of the subjects. The Nielsen's criteria questionnaire is used for evaluating the effectiveness of the user interface, of the Shagerdaneh learning management system. Investigating psychological factors (emotional intelligence) and proving their impacts on the quality of analyzing the user interface and finally filtering the evaluators who have a normal rating in these emotional intelligence features are part of the objectives of this study.The statistical population consists of 35 software professionals. The system used for this research is the user interface of the Shagerdaneh learning management system, which was previously designed by the author of this research. The required information will be collected from the questionnaire. For determining the feasibility of the project, normalization and correlation of data will be analyzed by    software. In order to investigate the effect of evaluators' emotional intelligence on how to evaluate the user interface and providing a prediction model for input data, we use multiple regression methods in genetic programming using the GPTIPS version 2 toolkit in MATLAB software version 2017. We also use clustering methods to evaluate the work's accuracy. All methods and tools are described in Chapter three.The results of this research prove the effect of emotional intelligence on the way that evaluators investigate the user interface. After filtering, seven persons were selected from 35 samples as the appropriate evaluators. In order to verify the accuracy of the proposed method in this study, the scores of the seven selected users were compared with the expert views in this field, and more than 71% of them was close to expert Opinions.Increasing the quality of the web content by examining the emotional intelligence of content providers and teaching emotional intelligence to them, is a suggestion for future studies.

Classification of Motor Imagery Tasks for Brain Computer Interface Applicatio  

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

Ontology Model For Data Integration In Gas And Oil Industry

هدف اصلي اين مطالعه كاهش پرت هگزان در صنعت روغنكشي از دانه روغني سويا مي باشد.بدين منظور سيستم مينرال اويل كه به هدف بازگرداني بخارات متصاعد شده هگزان در محيط   و چگالش آن جهت استفاده مجدد در سيستم مي باشد مورد بازبيني و شبيه سازي قرار گرفت.تست هاي آزمايشگاهي جهت تشخيص ميزان حلال در روغن معدني ،از نوع كروماتوگرافي گازي بوده و نتايج در نرم افزار طراحي آزمايشات چهار فاكتوري -3سطحي بر پايه RSM   پياده و مورد تحليل قرار گرفتند.در اين كار چهار پارامتر موثر بر كاهش اتلاف هگزان شامل تناژ دانه ورودي ، دماي آب ورودي سرد،دماي ستون گرم و دبي سيالات تحت آزمايش مد نظر قرار داده شد.در نهايت بعد از شبيه سازي نتايج   بدست آمده مشخص گرديد كه افزايش تناژدانه و بالا نگه داشتن آن ، پايين نگه داشتن دماي آب ورودي به كندانسور سيستم ،بالا نگهداشتن دماي ستون دفع از طريق بخار زنده در يك رنج مشخص و افزايش دبي سيال جاذب در محدوده مناسب با شرايط عملياتي برج همگي عوامل مثبت در بازيابي بهتر حلال   از روغن معدني هستند.

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 >

One of the most important subjects that matters to either government organization and non-government specially for Judicial, security and law enforcement systems is preventing forgery, Undoubtedly, in order to prevent, it is necessary to take this issue into the account. As the field of knowledge and technology progressing, the methods of committing crime are also evolving in various dimensions and levels. Therefore, obtaining effective results in the prevention of documents forgery requires the application of indicators and security components in documents as well as utilizing modern technologies in the prevention and protection of documents forgery. Establish bank checks in a digital and secure way, and preventing counterfeiting of information, faces two major challenges including error and security. In this thesis, by implementation of digital signature based on elliptical curves all check data is generated from this type of signature, tended to keep information safe and secure by appointing this signature as resistant watermarking to the blue component of the image and DTC middle frequencies domain. Even though, AES cryptography and logistic mapping are used to increase the security of cache information.Besides, the inclusion of fragile cryptographic information and reconstruction of this information to investigate the non-manipulation of the red component of the image by encryption and decryption elliptical curve in the spatial domain being concluded.‎ While data transferring, due to the noise and interference, the data can be subjected to be incorrect. In this thesis applying codes‎ BCH(31‎, ‎16)it is possible to correct up to three possible errors. ‎The production of digital signature is done applying the software of ‎SAGE‎ and the placement of the information cryptography and reconstruction of this information is implemented by MATLAB software.

  AbstractThe most significant tool in combating crime is the fight against money laundering. Detecting “suspicious transactions of money laundering” in banks is the biggest challenge of combating money laundering. Lack of attention to the context of the bank account owner’s is resulting in low efficiency of anti-money laundering approaches. The aim of this study is to provide a method of detecting suspicious transactions based on data-mining techniques such as a statistical method for analyzing “contextual outlier transactions” by targeting money launderer’s transactions in integration stage. The method of this study was context analyzing, and its population includes 1.8 million simulated transactions belongs to 1008 people from 48 different contexts over a period of 6 years. Simulators probably distributions came from the Kolmogorov-Smirnov test on 50 person cross-sectional actual bank transactions sample. The transactions collected over field research. Due to the unavailability of sufficient numbers of actual bank transactions, simulated transactions used. By simulating, the ability to create scenarios that may not provide in the real world is possible. Testing the idea of the research resulted in 100% True Positive Rate and 1.14% False Positive Rate, compared to most methods, tangible progress achieved. The study findings showed attending to bank account owner’s context, promotes the quality of the methods used in detecting money laundry.  Keywords: Money-laundry, Context, Contextual Variable, Behavioral Variable, Working Set Window

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.

In recent years iris recognition has attracted the attention of many researchers and it also has been used in many real-world applications. In iris recognition, segmentation phase always has been the one of the challenging problems and it always consumes significant processing time. On the other hand features in a classification task play a major role and as the selected features are good the performance of the classifier can be improved. Particle swarm optimization algorithm is an evolutionary algorithm and it successfully has been used in many optimization problems. We have used this algorithm to select the most appropriate features in an iris recognition task and in this way we have learned a near optimum distance metric. In addition, in this study we have provided an effective and simple method to detect the iris area that could greatly improve iris area detection process speed. To evaluate the proposed method two data sets CASIA Interval and IITD have been tested and the results have been very promising.

An Evaluation of Enterprise Architecture Frameworks for E-government

  امنيت يكي از اركان موجودات زنده و احساس امنيت يكي از اساسيترين نيازهاي نوع بشر است. امروزه با گستري وسايل ارتباطي و حجم اطلاعات مبادله شده در شبكههاي رايانهاي و همچنين توسعه و پيشرفتهاي صنعت مخابرات ند رسانه اي، مفهوم مخابرات تصويري متحول شده است. امنيت رسانه هاي ديجيتال يكي از مسائل مهم و مطرا جامعه رمزنگاري در دنياي امروز است. با توجه به كاربرد روزافزون رايانه و گستري زيرساخت هاي ارتباطي مثل شبكههاي سيار و اينترنت، حفظ محرمانگي و تاييد صحت تصاوير روز به روز اهميت بيشتري مييابد. با توجه به كاربرد روزافزون اينترنت و افزايش حجم اطلاعات مبادله شده، حفظ امنيت و تاييد صحت مخابره شده كه ميتواند كاربردهايي در امور تجاري، نظامي و حتي پزشكي داشته باشند نيز روز به روز اهميت بيشتري ميابد. در جهان ديجيتال امروزي امنيت تصاوير ديجيتال بيش از پيش اهميت پيدا كرده است. در سالهاي اخير سرعت زيادي در رشد انتقال تصاوير ديجيتال از طريق كامپيوتر بويژه اينترنت صورت گرفته است.به عنوان مثال ما در اين پاياننامه از يك روي رمزنگاري تصوير صحبت ميكنيم كه در امور پزشكي كه مرتبط با عكسبرداري از بيماران ميباشد و همچنين صحت و امنيت اطلاعات بيمار از اهميّت بالايي برخوردار است، استفاده ميشود. اين روي از رمزنگاري مبتني بر ميدان گالواست و ما آن را به طور كامل پيادهسازي كرده و علاوه بر بيان نقاط ضعف آن، راهي براي بهبود كارايي اين روي را بيان ميكنيم

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">

E-Learning is a new tool for education purposes which helps to improve the education quality and learning based on new communication technologies. Considering the effect of matching the learning method with user’s capabilities and emotions, today E-Learning systems are designed to be intelligent. These systems identify the mentioned capabilities, and try to make the simulation of the virtual environment in E-Learning as close as possible to the real world and reach the education’s goal which is making the user learn new materials. In the education environment, lack of learning in the user side and as a result giving wrong answers to the assignments is a consequence of having issues in the education system or also can be caused by user’s emotional issues (e.g. stress). In the current research, an Intelligent Tutoring System (ITS) will be design and implemented which in order to figure out the reason of user’s mistake uses the “sample tracing” method. In this method the solution for each problem is divided into separate and serial phases in which the user’s results are being analyzed in each phase. Because using touch screen displays is more similar to learning tools like pen and paper comparing to mouse and keyboard, this system uses this kind of interface to interact, and discover mentioned properties and take proper reactions.

With increasing use of computers, electronic devices and human interaction with computer in the broad spectrum of human life, the role of emotion regulation and increasing positive emotional status shows more. On the other hand by increasing computer programs, graphical interface software play a major role in computer programs thanks to direct communication with users. Furthermore the studies have shown that the colors are one of the most influential basic functions in vision, identify, interpret, and understanding of their senses. It can be said, colors have impact on emotional status of individuals and can change emotional status. In this thesis, by learning the reactions of users with different personality types against each color, communication between the users emotional status and user personality were modeled with color for excitation control as variable. For   learning was used   a memory-based system and user’s interface color will change according to   the positive and negative experiences of users with different characters. For the evaluation and testing of a programming language   C ++   learning system, it has been done   in three different modes: (1) basic mode   without no color change, (2) by changing the color and AUBUE   method (3) by changing the color and memory-based learning method. I   each   method   were tested by 16 men and   8 women. In addition for the testing phase of third method, these tools was used by 30 individuals also for data analysis was used one-way ANOVA. The end result of comparison between testing methods demonstrated the superiority of memory-based learning in all three parameters means emotional control, enhancing positive emotional status and reducing negative emotional status. Moreover the accuracy of memory- based learning method was 70 % percent. Hence by learning of users experiences against each color and by considering their characters we can control users emotional status precisely enough.