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نتیجه جستجو - آموزش ماشین

تعداد مقالات یافته شده: 9
ردیف عنوان نوع
1 Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining
چارچوب های یادگیری ماشین و داده کاوی برای پیش بینی پاسخ به دارو در سرطان: یک مرور کلی و رمان در فرآیند غربالگری سیلیکون بر اساس قاعده قاچاق انجمن-2019
A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big “omic” data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges tomaximizemodel performance. In this contextwe also describe a novel in silico screening process, based on Association RuleMining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large samplespaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.
Key words: Drug Response Prediction | Precision Medicine | Data mining | Machine Learning | Association Rule Mining
مقاله انگلیسی
2 Detection of peripheral arterial disease using Doppler spectrogram based expert system for Point-of-Care applications
تشخیص بیماری شریانی محیطی با استفاده از سیستم خبره مبتنی بر طیف سنجی داپلر برای کاربردهای نقطه مراقبت-2019
tPeripheral arterial disease (PAD) is a common manifestation of cardiovascular diseases and more preva-lent in underdeveloped countries. Ultrasound (US) is one of the preferred non-invasive diagnostictechniques for the evaluation of PAD. This work aims at achieving a low-cost PAD detection technique formass screening. A computer aided diagnosis (CAD) method has been proposed based on the Doppler bloodflow spectrograms of lower limb arteries. The proposed scheme initially removes noise from the spectro-gram (350 × 175 pixels) and extracts the hemodynamic features which are generally independent of theDoppler angle. From these, best feature subsets are selected using the wrapper algorithm and supervisedclassifiers are developed in a machine learning framework to perform using 10-fold cross-validationtechnique. Overall, 334 arterial segments of 60 subjects are investigated where reference measurementis taken from the triplex mode US scanning. The quantitative assessment using random forest based clas-sifier provides an accuracy of 84.37% and 87.93% for detecting the blood flow irregularities in above-kneeand below-knee arterial segments, respectively. To classify the arterial diseases into normal, stenosis andocclusion categories, support vector machine (SVM) classifier is found to provide 97.91% accuracy on theunknown testing dataset. Moreover, variations of diagnostic parameters around the proximal and distalarterial segments define the zone of significant stenosis. The degree of stenosis is determined to quantifythe severity of obstruction and the accuracy for stenosis greater than 50% is found to be 96.83%. Finally,smartphone application is implemented to Keywords:Ultrasonography | Peripheral artery disease | Features extraction |Machine learning |Androida
مقاله انگلیسی
3 Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring
هوش مصنوعی و آرتروپلاستی در یک مؤسسه واحد: برنامه های کاربردی دنیای واقعی آموزش ماشین به داده های بزرگ ، مراقبت های مبتنی بر ارزش ، سلامت موبایل و نظارت از راه دور بیمار-2019
Background: Driven by the recent ubiquity of big data and computing power, we established the Machine Learning Arthroplasty Laboratory (MLAL) to examine and apply artificial intelligence (AI) to musculoskeletal medicine. Methods: In this review, we discuss the 2 core objectives of the MLAL as they relate to the practice and progress of orthopedic surgery: (1) patient-specific, value-based care and (2) human movement. Results: We developed and validated several machine learning-based models for primary lower extremity arthroplasty that preoperatively predict patient-specific, risk-adjusted value metrics, including cost, length of stay, and discharge disposition, to provide improved expectation management, preoperative planning, and potential financial arbitration. Additionally, we leveraged passive, ubiquitous mobile technologies to build a small data registry of human movement surrounding TKA that permits remote patient monitoring to evaluate therapy compliance, outcomes, opioid intake, mobility, and joint range of motion. Conclusion: The rapid rate with which we in arthroplasty are acquiring and storing continuous data, whether passively or actively, demands an advanced processing approach: AI. By carefully studying AI techniques with the MLAL, we have applied this evolving technique as a first step that may directly improve patient outcomes and practice of orthopedics.
Keywords: machine learning | arthroplasty | value | big data | remote patient monitoring
مقاله انگلیسی
4 Parallel Design of a Product and Internet of Things (IoT) Architecture to Minimize the Cost of Utilizing Big Data (BD) for Sustainable Value Creation
معماری موازی طراحی محصول و اینترنت اشیاء (IoT) برای به حداقل رساندن هزینه استفاده از داده های بزرگ (BD) برای ایجاد ارزش پایدار-2017
Information has become today’s addictive currency; hence, companies are investing billions in the creation of Internet of Things (IoT) frameworks that gamble on finding trends that reveal sustainability and/or efficiency improvements. This approach to “Big Data” can lead to blind, astronomical costs. Therefore, this paper presents a counter approach aimed at minimizing the cost of utilizing “Big Data” for sustainable value creation. The proposed approach leverages domain/expert knowledge of the system in combination with a machine learning algorithm in order to limit the needed infrastructure and cost. A case study of the approach implemented in a consumer electronics company is also included.
Keywords: Internet of Things | Big Data | Product Design | Machine Learning | Sustainability
مقاله انگلیسی
5 Computer Vision in Automated Parking Systems: Design, Implementation and Challenges
دیدگاه کامپیوتر در سیستم های پارکینگ خودکار: طراحی، پیاده سازی و چالش ها-2017
Automated driving is an active area of research in both industry and academia. Automated Parking, which is automated driving in a restricted scenario of parking with low speed manoeuvring, is a key enabling product for fully autonomous driving systems. It is also an important milestone from the perspective of a higher end system built from the previous generation driver assistance systems comprising of collision warning, pedestrian detection, etc. In this paper, we discuss the design and implementation of an automated parking system from the perspective of computer vision algorithms. Designing a low-cost system with functional safety is challenging and leads to a large gap between the prototype and the end product, in order to handle all the corner cases. We demonstrate how camera systems are crucial for addressing a range of automated parking use cases and also, to add robustness to systems based on active distance measuring sensors, such as ultrasonics and radar. The key vision modules which realize the parking use cases are 3D reconstruction, parking slot marking recognition, freespace and vehicle/pedestrian detection. We detail the important parking use cases and demonstrate how to combine the vision modules to form a robust parking system. To the best of the authors knowledge, this is the first detailed discussion of a systemic view of a commercial automated parking system.
Keywords: Automated Parking | Automotive Vision | Autonomous Driving | ADAS | Machine Learning | Computer Vision | Embedded Vision | Safety critical systems
مقاله انگلیسی
6 Training My Car to See using Virtual Worlds
آموزش اتومبیل من برای دیدن با استفاده از جهان مجازی-2017
Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting in training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance.
Keywords: ADAS | Autonomous Driving | Computer Vision | Object Detection | Semantic Segmentation | Machine Learning | Data Annotation | Virtual Worlds | Domain Adaptation
مقاله انگلیسی
7 Easy gesture recognition for Kinect
تشخیص حرکت آسان برای کینکت-2014
Recent progress in entertainment and gaming systems has brought more natural and intuitive human– computer interfaces to our lives. Innovative technologies, such as Xbox Kinect, enable the recognition of body gestures, which are a direct and expressive way of human communication. Although current development toolkits provide support to identify the position of several joints of the human body and to process the movements of the body parts, they actually lack a flexible and robust mechanism to perform high-level gesture recognition. In consequence, developers are still left with the time-consuming and tedious task of recognizing gestures by explicitly defining a set of conditions on the joint positions and movements of the body parts. This paper presents EasyGR (Easy Gesture Recognition), a tool based on machine learning algorithms that help to reduce the effort involved in gesture recognition. We evaluated EasyGR in the development of 7 gestures, involving 10 developers. We compared time consumed, code size, and the achieved quality of the developed gesture recognizers, with and without the support of EasyGR. The results have shown that our approach is practical and reduces the effort involved in implementing gesture recognizers with Kinect. Keywords: Natural user interfaces Gesture recognition Machine learning Kinect Human-computer interaction Gesture-recognition framework
مقاله انگلیسی
8 Efficient crowdsourcing of unknown experts using bounded multi-armed bandits
جمعیت کارآمد کارشناسان ناشناخته با استفاده از راهزنان محدود شده مسلح-2014
Increasingly, organisations flexibly outsource work on a temporary basis to a global audience of workers. This so-called crowdsourcing has been applied successfully to a range of tasks, from translating text and annotating images, to collecting information during crisis situations and hiring skilled workers to build complex software. While traditionally these tasks have been small and could be completed by non-professionals, organisations are now starting to crowdsource larger, more complex tasks to experts in their respective fields. These tasks include, for example, software development and testing, web design and product marketing. While this emerging expert crowdsourcing offers flexibility and potentially lower costs, it also raises new challenges, as workers can be highly heterogeneous, both in their costs and in the quality of the work they produce. Specifically, the utility of each outsourced task is uncertain and can vary significantly between distinct workers and even between subsequent tasks assigned to the same worker. Furthermore, in realistic settings, workers have limits on the amount of work they can perform and the employer will have a fixed budget for paying workers. Given this uncertainty and the relevant constraints, the objective of the employer is to assign tasks to workers in order to maximise the overall utility achieved. To formalise this expert crowdsourcing problem, we introduce a novel multi-armed bandit (MAB) model, the bounded MAB. Furthermore, we develop an algorithm to solve it efficiently, called bounded ε-first, which proceeds in two stages: exploration and exploitation. During exploration, it first uses ε B of its total budget B to learn estimates of the workers’ quality characteristics. Then, during exploitation, it uses the remaining (1 − ε) B to maximise the total utility based on those estimates. Using this technique allows us to derive an O(B 23 ) upper bound on its performance regret (i.e., the expected difference in utility between our algorithm and the optimum), which means that as the budget B increases, the regret tends to 0. In addition to this theoretical advance, we apply our algorithm to real-world data from oDesk, a prominent expert crowdsourcing site. Using data from real projects, including historic project budgets, expert costs and quality ratings, we show that our algorithm outperforms existing crowdsourcing methods by up to 300%, while achieving up to 95% of a hypothetical optimum with full information. Keywords: Crowdsourcing Machine learning Multi-armed bandits Budget limitation
مقاله انگلیسی
9 روشی برای طبقه بندی و تعیین محل خطا در مدارات سه ترمیناله با استفاده از آموزش ماشین
سال انتشار: 2013 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 35
این مقاله یک روش مبتنی بر موج انتقال را برای طبقه¬بندی و تعیین محل خطا در سیستمهای انتقال توان سه ترمیناله ارائه می¬دهد. در روش پیشنهادی، از تبدیل موجک گسسته برای استخراج اطلاعات گذرا از از ولتاژهای ضبط شده استفاده شده است. سپس از طبقه¬بندی کننده¬های ماشین برداری پشتیبان برای طبقه-بندی نوع خطا و نیز خط معیوب در شبکه¬های انتقال استفاده شده است. نمودارهای بیولی برای الگوهای موج انتقال مورد مطالعه قرار گرفته¬اند و ضرایب موجک مود هوایی ولتاژ برای تعیین محل خطا به کار رفته است. از نرم¬افزار Alternate Transients Program(برنامه گذارهای متناوب) برای شبیه¬سازی حالات گذرا استفاده شده است. عملکرد این روش به ازای زوایای مختلف آغاز خطا، مقاومت¬های مختلف خطا، خطاهای امپدانس بالای غیرخطی و خطاهای غیرعادی مورد آزمایش قرار گرفته است که نتایج آن رضایتبخش می¬باشد.
کلمات کلیدی : طبقه بندی خطا | تعیین محل خطا | ماشین برداری پشتیبان(SVM) | شبکه سه ترمیناله | موج های انتقال | تبدیل موجک.
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