Fardin Abdali-Mohammadi

   Research Aim: Phishing is a major security threat in online payments, where users are tricked into disclosing sensitive financial information through fake payment gateway pages. This study aims to design and implement an accurate and reliable model for detecting fraudulent payment gateway pages using Convolutional Neural Networks (CNN). Research method: In this research, a dataset of 80,000 samples was collected, including 50,000 legitimate pages and 30,000 phishing pages. The data were preprocessed by rendering HTML pages into images, resizing, normalization, and data augmentation before being input into the network. The CNN model, with a multi-layer architecture consisting of convolutional, pooling, and fully connected layers, extracted spatial features of the pages and >Findings: Quantitative results showed that the proposed model achieved an accuracy of 97%, a recall of 97.8%, and an area under the ROC curve (AUC) of 0.991 in distinguishing phishing pages from legitimate ones. Qualitative analysis indicated that the model effectively detected highly similar pages, with false negatives occurring only when static real screenshots were used. Comparisons with text-based features and >Conclusion: Considering its high accuracy, fast prediction speed, and deployability in practical environments, the proposed model can be applied in banking systems, browsers, and online payment applications. Future research suggestions include incorporating multimodal features, lightweight models for mobile applications, and automatic model retraining to further enhance online payment security. Keyword: Online Security, Fraud Detection, Deep Learning, Image Analysis, Financial Transaction Safety

In the seismic design of steel structures, the use of efficient lateral load-bearing systems capable of dissipating energy and controlling displacements is of great importance. Steel shear walls are widely used, but the main challenge with thin plates is the phenomenon of out-of-plane buckling, which can occur before full yielding and reduce the system's load-bearing capacity. This research aims to evaluate the potential of using aluminum shear walls as a ductile alternative by numerically and parametrically investigating the seismic performance of steel frames strengthened with this system. The present study was conducted using the Finite Element Method in ABAQUS software. To ensure the accuracy of the modeling, a numerical model was first developed and validated against the results of a reputable experimental study on a steel shear wall, achieving a suitable agreement between the force-displacement curves. In the main phase of the research, a comprehensive parametric study was performed on a single-story, single-bay steel frame strengthened with an aluminum shear wall. The primary variables included the aluminum panel thickness (at three levels: 1, 2, and 3 mm) and the ratio of the wall width to the bay span (at ten levels from 0.1 to 1.0), forming a total of 30 models. All models were analyzed under quasi-static cyclic loading, and indicators such as strength, stiffness, and energy absorption were extracted. Finally, the effect of adding a cross-shaped stiffener to one of the models was also evaluated. The results showed that adding an aluminum shear wall significantly improves the seismic performance of the frame. In the optimal case (F-1-3), the ultimate strength increased by 2.41 times, the initial stiffness by 12.42 times, and the energy absorption capacity by 5.81 times compared to the bare frame. It was found that increasing both the thickness and width of the wall directly leads to the enhancement of all performance indicators and, by controlling buckling, shifts the structural behavior towards the formation of a uniform and stable diagonal tension field. Furthermore, the addition of a stiffener, with a 42% increase in ultimate strength, proved its effectiveness as a viable strategy for optimization and further increasing the system's load-bearing capacity.   

  Abstract Multiple sclerosis (MS), as a chronic and disabling disease of the central nervous system, has created many challenges in the field of diagnosis and treatment for doctors and health systems. Rapid and accurate identification of lesions caused by this disease in MRI images due to structural similarities with other brain tissues requires the use of accurate and advanced image processing and machine learning methods. In this study, an optimized model called DE-CNN-Gray is presented for automatic diagnosis of MS from gray-scale MRI images. In this model, a convolutional neural network is first designed and then the network structure including the number of layers and effective parameters is optimized using the Differential Evolution algorithm. The main goal of this optimization was to increase the classification accuracy and reduce the computational complexity of the model. Model evaluation using 5-Fold validation showed that the proposed method performed very well in identifying MS patients with an accuracy of 99.40%, sensitivity of 98.89%, positive accuracy of 99.90%, and F1 score of 99.33%. The results show that the DE-CNN-Gray method, using gray images and meta-heuristic algorithms, can be used as an accurate, fast, and low-cost tool for developing MS diagnosis systems and play an effective role in improving the treatment process and reducing treatment costs . Keywords: MS, Deep Learning, Convolutional Neural Network, Differential Evolution Algorithm, MRI, DE-CNN-Gray,Medical Diagnosis, Image Processing.

Sleep is one of the fundamental human needs that significantly impacts physical and mental health. Stress during sleep can lead to sleep disorders and related health issues, making accurate prediction of sleep stress particularly important. This thesis explores the detection of sleep stress using deep learning, specifically focusing on LSTM and GRU recurrent neural network models, as well as a hybrid model combining the two. The aim of this research is to provide an efficient and accurate model for predicting and detecting sleep stress based on the SaYoPillow dataset. We used 10-fold Cross Validation. Various models were evaluated, and the results showed that the hybrid Bidirectional LSTM-GRU model achieved the best performance with an accuracy of 1.00, precision of 1.00, recall of 1.00, and an F1 score of 1.00, outperforming individual LSTM and GRU, and MLP models in detecting all 5 levels of sleep stress. The use of a confusion matrix and evaluation metrics such as accuracy, precision, recall, and F1 score demonstrated that the hybrid model not only has high accuracy in detecting positive cases but also reduces errors related to identifying negative cases. This research highlights that deep learning models, particularly the hybrid Bidirectional LSTM-GRU model, can be effective tools for detecting sleep stress, thereby contributing to improved sleep quality and overall health. The development of these models can assist healthcare professionals in providing appropriate preventive and therapeutic strategies for managing sleep stress.

   Epilepsy is a common neurological disorder characterized by recurrent seizures. Research indicates that approximately 30% of epilepsy patients are resistant to pharmaceutical treatments or surgical interventions. Abnormal brain activity, known as the pre-ictal state, typically begins a few minutes before a seizure occurs. Electroencephalography (EEG) is a practical technique for recording brain electrical activity and aiding in the diagnosis of epilepsy. Seizure prediction and assistance for epilepsy patients remain significant challenges in preventing seizure-related complications and improving the quality of life for individuals affected by this condition. Accurate prediction of the onset of the pre-ictal state can help reduce the adverse effects of seizures for patients and their caregivers by providing timely care. The objective of this thesis is to develop a system that enhances evaluation metrics for seizure prediction using deep learning methods. In this study, the CHB-MIT dataset, comprising scalp EEG signals, has been utilized, and the proposed method was evaluated on 24 patients from this dataset. To predict seizures, deep learning-based models and knowledge distillation techniques were employed for model compression, aiming to reduce time and hardware costs and enable real-time application of the network. The teacher model, designed as a patient-independent framework with 22 channels and preprocessed mel-spectrogram inputs, employs a 3D convolutional neural network. This model achieved an accuracy of 87.52%, sensitivity of 88.82%, specificity of 85.97%, and an F1 score of 86.56%. Subsequently, the knowledge distillation technique was applied. By utilizing this approach and employing a single electrode, we identified two electrodes (Electrode 20 and Electrode 22) with superior performance compared to others. The proposed method, for Electrode 20, achieved accuracy84.56%, sensitivity86.76%, specificity82.77%, and F1-score values of 83.63%, and for Electrode 22, achieved accuracy84.30%, sensitivity86.45%, specificity82.93%, and F1-score values of 83.35%, enabling seizure prediction 30 minutes before onset. The results obtained from our proposed method were compared with advanced seizure prediction techniques. The proposed method demonstrated superior performance in terms of accuracy, sensitivity, specificity, and F1 score, highlighting its effectiveness in seizure prediction.

  dar jameh kononi ranandegi baraye kar, zandegi ejtemaei, tafarih, tahsil, faalitenpehei egtesadi ve sayer jonbacpanteoja manpam est. ema tasadef vasail naghliyeh motori yeki az moteghirehaye mokharb, maloolit ve morg mohsub mi shod. bar asas motaleat enjam shodeh, chehar amel esli mosar dar vaghu havades ranandegi vojud dard keh shamel moteghirehaye ensani, jadeh, vasileh naghliyeh ve mohit mi bashod. tahlil tasadef jadegyi dar iran neshan midehad keh moteomtarin amel dar vaghu tasadefat ranandegi dar iran, amel ensani est. benabrain, yeki az moteghirehaye ravaneshenakhti mw?sar dar khatrat ranandegi, masoulitpaziri ranandegan est. npamchenin nezameh bavarehya ra mitavan yeki az moteghirehaye moteomi danst keh ertabat mosteghimi ba no raftar ranandegi dard. benabrain, tamrakz in pajoosh bar farayand masoulit paziri bar asas nezam etegadi dar bin ranandegan est. zira zehor khatrat ranandegi bah delil afzayesh raftarehaye ghiraadi ro bah afzayesh est. motaleh hazar bah rosh tosifi npambastegi bud. azmodninpehei pajoosh 3?0? nafar az ranandegan shehar ravansar bodand keh ba rosh nemonehgiri dar dasteres entekhab shodand. shiveh tajziyeh vatahlil dadegeya dar do bakhsh tosif dadegeya ve estanbat dadegeya bud. netaij neshan dad masoulitpaziri ve nezam bavarehya bar kanpash tasadefat ranandegi mosar est. dar tabiyn netaij bah dast amodeh bayad goft keh sistam masoulit paziri ve bavar naqsh mahvari dar kanpash tasadefat jadegyi dard. ba dark tasir in avamel mitavan esteratzhinpehei mw?sari baraye ertaghaye imni jadegeya ijad kard.

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

     Medical imaging plays an important role in clinical diagnosis and treatment. Medical imaging is a way to show the anatomical structures of the body with the help of X-rays, which is obtained from computed tomography and magnetic resonance imaging. But often this type of photography is more suitable for physiological function than anatomy. With the development of computer and imaging technology, medical imaging has greatly affected the medical field. Since the quality of medical imaging has had a great impact on disease diagnosis, medical image processing has become one of the most important clinical applications that store and retrieve images for the future, which are prerequisites for accurate storage of these images. Bones are solid organs in the human body that protect many important organs such as the brain, heart, lungs and other internal organs. The human body has 206 bones of different shapes, sizes and sizes. The largest bone is the femur, and the smallest is the ossicle. A common problem in humans is "bone fracture". A bone fracture can be caused by an accident or any other case where a lot of pressure is applied to the bone. There are different types of bone fractures: oblique, compound, comminuted, spiral, girrin's stick1 and transverse. Compared to other methods, X-ray imaging provides precise details of bones and less details of tissue and muscle, which makes it easier to detect fractures

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

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

Abstract Due to the progress of science and technology in various cultural, social and economic fields, the need to receive data from various databases based on extracting information patterns to achieve life-giving research results is increasing day by day. On the other hand, maintaining the security of private data of individuals and organizations is a key issue in this field, ignoring it will lead to incorrect information and customer dissatisfaction, and will ultimately lead to incorrect research results. One of the most important information data in this regard is medical data, which even according to medical regulations, respecting people's privacy and keeping information confidential is essential. With the world entering the channel of the global epidemic of the corona virus, controlling the epidemic in the first place and finding its cure in the second place has become the challenge of the scientists and doctors of the world. In this regard, the computer science community has offered its role to control the corona virus epidemic to the world. Using deep learning to detect covid-19 from X-ray images becomes a fast method to diagnose patients and manage care services for patients. To achieve better results, we need a lot of data from different information sources, and data privacy as a barrier in this way will prevent engineers from achieving this important goal. Therefore, by introducing federated learning, which is a nascent leap towards creativity and better results, we will introduce its advantages and challenges and simulate it in the diagnosis of Covid-19 and try to take a small step to achieve more accurate results with more data. And of course, more organized to train neural network models, in the meantime, by presenting a strong aggregate approach, we were able to surpass our competitors with an accuracy of 97.04.      Keywords: Federated learning, covid19, x-ray, deep learning

  The

  امروزه با افزايش ارتباط بين فناوريهاي رايانهاي و حوزه پزشكي، واسطهاي مغز و كامپيوترتأثير مهمي در زمينههاي مختلف از جمله تشخيص فعاليت تصور حركت، بازشناسي احساسات، تشخيص بيماري صرع، امتياز بندي سطح خواب و باركاري ذهني دارند. تشخيص تصور حركت يكي از تكنيكهاي مبتني بر واسط مغز و كامپيوتر است. اين تكنولوژي با پردازش سيگنالهاي مغز و استخراج الگو از يكي از مهمترين سيگنالها در تشخيص اين نوع EEG تأثير به سزايي بر مطالعه ذهن وكاركردهاي آن دارد. سيگنال ،MI سيگنالهاي فعاليت است. اين پژوهش به طراحي، پياده سازي و ارزيابي يك روش جديد براي تشخيص تصور حزكت دست انسان مي پردازد. مغز انسان اين قابليت را دارد كه از طريق ارتباط بين نواحي و اثرگذاري بر يكديگر منجر به فعاليت هاي شناختي شود. به عبارت ديگر مغز از نواحي مختلفي تشكيل شده است كه هر كدام از آن ها يا به طور جداگانه يا تعاملي منجر به اجراي وظايف مختلف توسط انسان مي شود. اين روش جديد، از قابليت ذكر شده جهت ارزيابي عملكرد مغز و تشخيص تصور حركت دست انسان استفاده كرده است. در اين تحقيق تلاش شده است كه اطلاعات دقيق تر و كامل تري جهت ارزيابي مدل پيشنهادي، استفاده شود. به اين منظور براي تحليل سيگنال هاي حاصل از تصور حركت، از رويكرد مسئله معكوس به كار برده شد كه به اطلاعات آناتوميكي مغز حين تصور حركت دست EEG دسترسي دارد. در اين راستا از بازسازي منبع سه بعدي كه شامل مراحل مدل سازي فضاي منبع، مدل پيشرو و مسئله معكوس است، به كار برده شد. باتوجه به اطلاعات به دست آمده، از اتصال مؤثر (يكي از سه نوع اتصال بين نواحي مغز) مبتني بر مدل سازي علّي پويا استفاده گرديد كه گراف مربوط به نواحي مرتبط با تصور حركت طراحي و پياده سازي شود. نواحي مؤثر به كمك بازسازي منبع به دست آمده است. اين نوع مدل سازي بهتر مي تواند اتصالات جهت دار و علّي بين نواحي مغز و نقش مؤثر فعاليت نورون هاي قشر مغز را در ايجاد و اجراي تصور حركت تفسير كند. به دليل اينكه اطلاعات حاصل از گراف مدل سازي علّي پويا يك ماتريس مجاورت از مقادير اتصال مؤثر بين نواحي، ناشي از تعامل و اثر گذاري قشرهاي مغز است براي سهولت در استخراج ويژگي هاي سطح بالاي تصور حركت، از تكنيك شبكه عصبي كانولوشن گراف گونه جهت طبقه بندي نوع تصور حركت به كار برده شد. اين شبكه عصبي از طريق ماتريس مجاورت، جهت بين نواحي را تشخيص مي دهد و اطلاعات يال و رئوس گراف را براي استخراج ويژگي به كار مي برد. نتايج اين روش دقت بالاتري را در مقايسه با 5 و 10 لايه جهت تشخيص (GCN_ نشان داده است كه اجراي شبكه عصبي با 15 لايه ي كانولوشني ( 15 0% است. در مقايسه با / 0% و 99 / نوع تصور حركت را داشته است. دقت حاصل براي تصور حركت دست راست و چپ به ترتيب 95 پژوهش هاي پيشين نيز، روش پيشنهادي توانسته است دقت تشخيص را افزايش دهد.

  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   

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

In the last century, remote sensing imagery has been a major source of information in many applications, such as land cover detection, resource management, and monitoring. These images include a variety of aerial and satellite imagery, the use of which has expanded dramatically by placing various cameras and sensors on airplanes and other aircrafts. Detection and extraction of surface water is one of the main applications of remote sensing images that play a key role in controlling resources and preventing floods and crises such as drought. To date, various methods such as image thresholding, index detection, edge-based detection, and machine learning methods such as support vector machine have been used to improve the quality of water detection in images; However, the main application of these methods has been in problems where the water areas are not very dispersed and the water body has smoother boundaries and in images that include challenges such as the existence of water dispersed areas or narrow rivers, almost none - did not provide acceptable accuracy. Despite these issues, deep neural networks have obtained the state of the art results in the field of remote sensing image segmentation. In this research, a hybrid architecture called "stacked ensemble model" is presented to pixelwise >Based on the obtained results, the stacked ensemble method proposed in this thesis has succeeded in receiving the best result and also achieving the first rank among the participants of AIcrowd LNDST water body segmentation challenge which was held in August 2020.[1]    Key Words: Remote Sensing, Satellite Imagery, Water Body, Surface Water, Semantic Segmentation, Deep Learning [1] https://www.aicrowd.com/challenges/ai-for-good-ai-blitz-3/problems/lndst/leaderboards   

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

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

   With advances in Internet technology and easy access to Internet networks, digital images that are available to the public can be modified, manipulated, and copied illegally without any qualitative loss. Without respecting the ownership of this data, the desired content can be reproduced and distributed on a large scale. Digital watermarking appears as a branch of data concealment science and protects the ownership and copyright of these images. In this dissertation, the idea of designing and implementing an invisible digital image watermarking method in the field of Lifting Wavelet Transform and Singular Value Decomposition in order to improve image Robustness and increase the reliability of image watermarking system using The Three Module Redundancy Technique is proposed. First, the Lifting Wavelet Transform is applied to the host image, and then its three high frequency su  ands divided into 8×8 non overlapping blocks. In each frequency subbands, the Lifting Wavelet Transform is converted to each block, and four frequency subbands ll, lh, hl and hh are obtained for each block. Then the lh frequency subband is selected from each block and the singular value Decomposition is applied to the selected frequency subband. In order to increase the security of the hidden image against alteration and manipulation, the hidden image is first encrypted using the improved Arnold Transform. Following the technique of the Three Module Redundancy, the Singular Values of the watermark image are inserted into the host image by adding the Singular Values of each frequency subbands. The three optimal scale factors used by the ant bee colony algorithm to determine the balance between Robustness and imperceptability are used to insert the watermark image. Evaluations show that the average hidden Robustness of different images against signal processing and geometric attacks is more than 96% and the average imperceptability of the hidden images is more than 50 dB. According to the assessments, the proposed method is more Robustness and imperceptability than the existing works, and the proposed system is a fault tolerance system that can work properly under different circumstances. Also, by using the improved Arnold conversion to encrypt the watermark image, attempt has been made to increase the image security.

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.   

در اين مطالعه عددي، اثر شكل نانوذرات بر روي عملكرد حرارتي-هيدروليكي نانوسيال آب-آلومينا در هيت سينك داراي ريز مجراها با هندسه­هاي مختلف براي خنك كاري پردازشگرهاي الكترونيكي مورد مطالعه قرار گرفته است. اين مطالعه، براي پنج شكل نانوذره پلاكتي، تيغه­اي، مكعب مربع، بيضوي و استوانه­اي در چهار عدد رينولدز 400، 800، 1200 و 1600 در چاه گرمايي داراي ريزمجرا­ها با چهار مقطع دايروي، مثلثي، بيضوي و شش ضلعي انجام شده است. غلظت نانوسيال ثابت و مقدار آن برابر با 1% در نظر گرفته شده است. در اين مطالعه مشاهده شد كه هيت سينك با مقطع مثلثي بيشترين ضريب انتقال حرارت جابجايي را ايجاد مي­كند، و پس از آن، هيت سينك با مقاطع بيضوي، شش ضلعي و دايروي به ترتيب موجب ضرايب انتقال حرارت بزرگ­تري مي­شوند. همچنين، نانوذرات با شكل پلاكتي بيشترين افت فشار را ايجاد مي­كنند، و پس از آن، نانوذرات با شكل­ها­ي استوانه­اي، تيغه­اي، مكعب مربع و بيضوي به ترتيب موجب افت شار بزرگ­تري مي­شوند. در نهايت با ارائه يك پاراكتر مناسب (FoM) كه معرف نسبت افزايش نرخ انتقال حرارت جابجايي به افزايش افت فشار است، ميزان بهره­وري انرژي مورد ارزيابي قرار گرفته است.   

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.  

Abstract    Industrial processes that involve the heating and cooling of a variety of fluids flowing through the ducts are very extensive and today represent some of the most common and important processes in engineering. Actually, in heat engineering, forced convection is probably one of the most effective and widely used heat transfer tools. Metals in their solid form have much higher thermal conductivity than fluids, which is why it is expected that fluids containing metallic suspended particles or metal oxide will have higher thermal conductivity than pure fluids. In the present work, the effect of magnetic field on the fluid flow and heat transfer of a nanofluid in the tube is experimentally studied, and the nanofluid is a type of Fe3O4   magnetic nanofluid with distilled water base fluid. After designing the experimental apparatus, experiments conducted to investigate the effect of the main operating variables such as voltage applied to an electric field applied with an alternating current (V), nanofluid concentration (C) and the intensity of the inlet fluid flow to the sub-field (Q) on the difference between the inlet and outlet temperature of the nanofluid. The channel crossing was selected based on the design of the experiments using response surface methodology based on the Box-Behnken model. The values of the variables in the study of the effect of applied voltage are 40, 80 and 120 volts, concentration of nanoparticles in solution of 0, 0.02 and 0.04   gL   and fluid flow intensity of 180, 360 and 540 L

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

   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

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

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

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.  

  Sandwich structures are now widely used in aircraft, industry, rocket, marine, and medical industries due to its advantages such as high strength, lightness and crack-resistant properties. For this purpose, in this dissertation, analytical modeling and numerical simulation of the low velocity shock load on the sandwich beams with thick core and metallic procedures are carried out. The ultimate goal of this thesis is to obtain the process of high plastic deformation based on the strengths of the sandwich core, as well as the calculation of the transverse displacement of the lower sandwich of the beam under the influence of elastic vibration. In the analytic modeling model, using the Galerkin method, the equations governing the height, velocity and acceleration of the sandwich beam are extracted. Further, for analytical modeling of the low velocity shock load on the beams sandwich, matlab software is used as well as numerical simulation of this process using Abkhus software. In analytical modeling and numerical simulation, three locations are considered for applying sandwich blow times. These three locations are 30 cm from the free end of the beam, in the middle of the beam, and 30 cm from the sandwich support axis, respectively. In the results section, a comparison of the final upper extremity profiles for the three places of impact loading was compared for analytical modeling and numerical simulation. In the next section, the comparison of the high degree of deviation in mass and the various impact velocities for the three locations of impact loads has been compared for analytical modeling and numerical simulation. Finally, the comparison of the effect of the core strength, the effect of the impact mass and the effect of the pickup width on the shape of the final profile on the sandwich of the beam for the three places of impact load was compared.

  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.

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

Classification of Motor Imagery Tasks for Brain Computer Interface Applicatio  

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

ارائه يك مدل بهينه سازي فرا ابتكاري و استفاده آن در زمانبندي وظايف سيستم هاي ناهمگن

Ontology Model For Data Integration In Gas And Oil Industry

  AbstractIn this thesis, a model has been introduced to simulate the capture efficiency of 3D high gradient magnetic filters for the separation of iron sulfide particles from the amine solution in the sweetening gas process. The geometry of the filter consists of a matrix of rods located in a channel with a square cross section. In order to study this problem, initially the Navier-Stokes equations coupled with Ampere equation are solved numerically in a 2D geometry. Having the flow field, the problem of particle capturing has been studied due to Lagrangian viewpoint and the effects of governing parameters including the particle diameter, fluid velocity and the geometrical parameters of the matrix have been investigated. Finally, the results of 2D analysis have been extended to simulate the particle capturing in a 3D filter. The 2D numerical simulations have been performed with COMSOL MULTIPHISICS 5.0 based on the finite element method and the extension of results to the 3D case has been carry out with proper code in MATLAB. This code interpolates or extrapolates the 2D results for any cases rather than those simulated in COMSOL. It has been demonstrated that the capture efficiency of filters with triangular configuration is higher than the rectangular one in the same conditions because the triangular configuration provides a better fluid mixing. However, the difference between capture efficiency of these two filters decreases with the increase in fluid velocity or distance between the matrices or decrease in particle diameter. Also, it has been demonstrated that the capture efficiency of these filters is directly related to the particles diameter and reversely dependent to the fluid velocity and the distance between the matrices. In addition, the performance of these filters are investigated in the case of non-uniform distribution of particles in the inlet. In order to demonstrate a useful graph, the performance contours of the 3D filter in the operational conditions are presented in terms of Reynolds number and the distance between the matrices. These counters show that the capturing efficiency of 60% will maintained when the dimensionless distance between the matrices is less than 1.2 independent of the Reynolds number.   Keywords: Magnetic filter, Capture efficiency, High gradient magnetic separation, Particle tracing

Analytical modeling of functionally graded carbon nanotube reinforced composite plates under low velocity impact

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.

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.