دانلود و نمایش مقالات مرتبط با classification::صفحه 1
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نتیجه جستجو - classification

تعداد مقالات یافته شده: 356
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1 مشتقات ثابت دو بعدی تفکیک پذیر صریح برای تشخیص جسم
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 19
مشتقات ثابت تصویر به طور گسترده ای در زمینه های تشخیص الگو و دید رایانه مورد استفاده قرار گرفته اند، زیرا آنها قادر به ارائه الگوی ویژگی های مستقل تبدیل هندسی هستند. در حال حاضر، ثابت های تفکیک پذیر و مشتقات آنها به دلیل توانایی در ترکیب ویژگی های اساسی ثابت های متعامد مختلف، بیشتر مورد توجه قرار گرفته است. با این حال، بسیاری از مشتق های ثابت تفکیک پذیر موجود، به طور غیرمستقیم از مشتق های هندسی و بر اساس رابطه چندجمله ای متعامد و هندسی، به دست می آیند. بنابراین، در این مقاله، رویکرد مستقیمی برای ساخت مجموعه ای از مشتق های ثابت تفکیک پذیر گسسته Chebichef-Krawtchouk پیشنهاد شد که در آن به طور همزمان مشتق برای چرخش، مقیاس پذیری و تبدیل انتقال فراهم می شود و مبتنی بر فرم صریح چند جمله ای Tchebichef و Krawtchouk است. در نتیجه، نتایج تجربی و نظری اثربخشی روش پیشنهادی اثبات شد و ارجحیت آنها در طبقه بندی تصویر و شناخت الگو در مقایسه با روش های موجود نشان داده شد.
کليدواژه: مشتقات غیرمستقیم | روش صریح | ثابت تفکیک پذیر | چندجمله ای Krawtchouk | چندجمله ای Tchebichef | تشخیص الگو
مقاله ترجمه شده
2 Unsupervised by any other name: Hidden layers of knowledge production in artificial intelligence on social media
بدون نظارت با هر نام دیگری: لایه های پنهان تولید دانش در هوش مصنوعی در رسانه های اجتماعی-2019
Artificial Intelligence (AI) in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work well—with a high degree of autonomy and automation. We argue that we need to analyze the process of applying machine learning in depth and highlight at what point human knowledge production takes place in seemingly autonomous work. This article reintroduces classification theory as an important framework for understanding such seemingly invisible knowledge production in the machine learning development and design processes. We suggest a framework for studying such classification closely tied to different steps in the work process and exemplify the framework on two experiments with machine learning applied to Facebook data from one of our labs. By doing so we demonstrate ways in which classification and potential discrimination take place in even seemingly unsupervised and autonomous models. Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards governance of artificial intelligence.
Keywords: Artificial intelligence | machine learning | classification | social media| Facebook | discrimination | bias
مقاله انگلیسی
3 Friction, snake oil, and weird countries: Cybersecurity systems could deepen global inequality through regional blocking
اصطکاک، روغن مار، و کشورهای عجیب و غریب: سیستم های امنیت سایبری می تواند نابرابری جهانی را از طریق مسدود سازی منطقه ای تقویت کند-2019
In this moment of rising nationalism worldwide, governments, civil society groups, transnational companies, and web users all complain of increasing regional fragmentation online. While prior work in this area has primarily focused on issues of government censorship and regulatory compliance, we use an inductive and qualitative approach to examine targeted blocking by corporate entities of entire regions motivated by concerns about fraud, abuse, and theft. Through participant-observation at relevant events and intensive interviews with experts, we document the quest by professionals tasked with preserving online security to use new machine-learning based techniques to develop a ‘‘fairer’’ system to determine patterns of ‘‘good’’ and ‘‘bad’’ usage. However, we argue that without understanding the systematic social and political conditions that produce differential behaviors online, these systems may continue to embed unequal treatments, and troublingly may further disguise such discrimination behind more complex and less transparent automated assessment. In order to support this claim, we analyze how current forms of regional blocking incentivize users in blocked regions to behave in ways that are commonly flagged as problematic by dominant security and identification systems. To realize truly global, non-Eurocentric cybersecurity techniques would mean incorporating the ecosystems of service utilization developed by marginalized users rather than reasserting norms of an imagined (Western) user that casts aberrations as suspect.
Keywords: Regional blocking | machine learning | classification | inequality | discrimination | security
مقاله انگلیسی
4 Text Mining Based on Tax Comments as Big Data Analysis Using SVM and Feature Selection
متن کاوی براساس نکات مالیاتی به عنوان تجزیه و تحلیل داده های بزرگ با استفاده از SVM و انتخاب ویژگی-2018
The tax gives an important role for the contributions of the economy and development of a country. The improvements to the taxation service system continuously done in order to increase the State Budget. One of consideration to know the performance of taxation particularly in Indonesia is to know the public opinion as for the object service. Text mining can be used to know public opinion about the tax system. The rapid growth of data in social media initiates this research to use the data source as big data analysis. The dataset used is derived from Facebook and Twitter as a source of data in processing tax comments. The results of opinions in the form of public sentiment in part of service, website system, and news can be used as consideration to improve the quality of tax services. In this research, text mining is done through the phases of text processing, feature selection and classification with Support Vector Machine (SVM). To reduce the problem of the number of attributes on the dataset in classifying text, Feature Selection used the Information Gain to select the relevant terms to the tax topic. Testing is used to measure the performance level of SVM with Feature Selection from two data sources. Performance measured using the parameters of precision, recall, and Fmeasure.
Keywords: Text Mining; Tax Comments; Support Vector Machine; Feature Selection
مقاله انگلیسی
5 A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier
استخراج ویژگی منحصر به فرد با استفاده از MRDWT برای طبقه بندی خودکارضربان قلب غیر طبیعی از داده های بزرگ ECG با چند لایه طبقه بندی احتمالی شبکه عصبی-2018
This paper employs a novel adaptive feature extraction techniques of electrocardiogram (ECG) signal for detection of cardiac arrhythmias using multiresolution discrete wavelet transform from ECG big data. In this paper, five types ECG arrhythmias including normal beats have been classified. The MIT-BIH database of 48 patient records is utilized for detection and analysis of cardiac arrhythmias. Proposed feature extraction utilizes Daubechies as wavelet function and extracts 21 feature points which include the QRS complex of ECG signal. The Multilayered Probabilistic Neural Network (MPNN) classifier is pro posed as the best-suited classifier for the proposed feature. Total 1700 ECG betas were tested using MPNN classifier and compared with other three classifiers Back Propagation (BPNN), Multilayered Perceptron (MLP) and Support Vector Machine (SVM). The system efficiency and performance have been evaluated using seven types of evaluation criteria: precision (PR), F-Score, positive predictivity (PP), sensitivity (SE), classification error rate (CER) and specificity (SP). The overall system accuracy, using MPNN technique utilizing the proposed feature, obtained is 99.53% whereas using BPNN, MLP and SVM provide 97.94%, 98.53%, and 99%. The processing time using MPNN classifier is only 3 s which show that the proposed techniques not only very accurate and efficient but also very quick.
Keywords: Signal processing ، Artificial intelligence ، Pattern recognition ، Soft computing ، Wavelet transform
مقاله انگلیسی
6 مسیرهای به سیستم های پرداخت بیمارستان مبتنی بر DRG در ژاپن، کره و تایلند
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 22
کشورهای آسیایی در حال تلاش برای دستیابی به پوشش بهداشت جهانی هستند، تا از کیفیت مراقبت بهداشتی اطمینان حاصل کنند. یک مولفه، هزینه های بیمارستان را از طریق ایجاد اصلاحات در پرداخت کنترل می کند. در این مقاله تجربیات مربوط به استفاده از گروه های تشخیصی مرتبط (DRG) بر مبنای رویکرد های پرداخت بیمارستانی در سه کشور آسیایی را مورد بررسی قرار می دهیم و خواهیم پرسید که آیا یک کشور آسیایی راهی برای استفاده ازDRG دارد. ما برای اولین بار در مسائل فنی تمرکز می کنیم و با بحث درباره چالش های پیاده سازی و سوالات سیاسی همراه می شویم. ما ادبیات را مرور کردیم و به عنوان یک تیم تخصصی بر مبنای بررسی اسناد موجود از ژاپن، جمهوری کره و تایلند کار می کنیم. ما طراحی سیستم های پرداخت مبتنی بر مورد، تجربه آنها در زمینه اجرا، شواهدی در مورد تأثیر در ارائه خدمات و درس هایی که برای منطقه آسیا از آنها گرفته شده است، را مورد بررسی قرار دادیم. ما دریافتیم که کشورها ابتدا باید زیرساخت های مناسب، ظرفیت منابع انسانی و سیستم های مدیریت اطلاعات را ایجاد کنند. پوشش حجم و قیمت با درجه بالایی از استقلال بیمارستان گاهی اوقات ضروری است. به جای معرفی یک سیستم طبقه بندی کامل در یک زمینه، این کشورها به صورت مرحله ای DRG ها را اعمال می کنند، در برخی موارد بیمارستان ها برای شرکت در مرحله اول ( مثل کره) داوطلب می شوند و در دیگران موارد ، با استفاده از ترکیبی از واحد های مختلف برای پرداخت بیمارستان، از جمله طول اقامت و هزینه خدمات (ژاپن) اعمال می شود. سیستم های پرداخت موردی یک پاناسی نیستند. ارزش آنها بستگی به طراحی و اجرای آنها و ظرفیت سیستم بهداشتی دارد.
کلمات کلیدی: DRG | گروه های تشخیصی مرتبط | پرداخت مبتنی بر مورد | سیستم پرداخت بیمارستان
مقاله ترجمه شده
7 Big Data Challenges and Data Aggregation Strategies in Wireless Sensor Networks
چالش های داده بزرگ و استراتژی های جمع آوری داده ها در شبکه های حسگر بی سیم-2018
The emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.
INDEX TERMS: Big data, data aggregation, wireless sensor networks
مقاله انگلیسی
8 School accountability and standard-based education reform: The recall of social efficiency movement and scientific management
مسئولیت پذیری مدرسه و اصلاح آموزش مبتنی بر استاندارد: یادآوری جابجایی کارآمدی اجتماعی و مدیریت علمی-2018
This study examines issues of success/failure, performance, and effectiveness in the contemporary reform discourses of school accountability. It pays attention to a particular meaning of success/failure and its implications for current public school systems and education reform movements, not merely as a school accountability issue, but as an ontological one, which is inscribed in the politics of inclusion and exclusion. The mechanisms of standardization, classification, and normalization embedded in the practices of high-stakes testing are reconsidered through an analysis of the discussion of social efficiency in the early twentieth century. The study also examines its recall several decades later as part of the quality control and management of individuals, schools, and states. In exploring connectedness between the old and new levels of social efficiency, this study suggests that success/failure becomes reinscribed as a particular system of reasoning to normalize the subjectivity in discourses of the current American education reform and OECD’s PISA. The study concludes by criticizing the rationale of recent educational reforms based on the search for past and present social efficiency movements in schools.
keywords: Social efficiency movement |Scientific management |Standard-based education reform |OECD PISA
مقاله انگلیسی
9 A framework for big data analytics approach to failure prediction of construction firms
چارچوبی برای رویکرد تحلیل داده های بزرگ برای پیش بینی شکست شرکت های ساختمانی-2018
This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers.
Key Words: Big data analytics; failure prediction models; construction businesses; machine learning; MapReduce/Spark
مقاله انگلیسی
10 Big data analytics enabled by feature extraction based on partial independence
تحلیل داده های بزرگ فعال شده توسط قابلیت استخراج بر اساس استقلال جزئی -2018
Complex cells in primary visual cortex (V1) selectively respond to bars and edges at a particular location and orientation. Namely, they are relatively invariant to the phase as well as selective to the frequency and orientation emerging from natural images that are analogous to the characteristics of complex cells in V1 with the energy function of receptive fields (RFs) from tuning curve test with sinusoidal function in our related jobs. In this paper, we propose a feature learning algorithm based on the overcomplete AISA to apply on big data in parallel computing. In order to demonstrate the effectiveness of the overcomplete AISA features in the classification task, two feature representation architectures are evolved into the par tial independent signal bases and partial independent factorial representation, respectively. Experiments on four datasets (Coil20, Extended YaleB, USPS, PIE), acquired conjunction with two classification archi tectures based on the overcomplete AISA features, show that the classification accuracy is mostly higher than those obtained from the other ICA related features and two other sparse representation features with a small number of training samples via nearest neighbor (NN) classification method.
Keywords: Independent Component(IC) ، Overcomplete features ، Sparse representation ، Big data
مقاله انگلیسی
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