با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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21 |
Unsupervised classification of multi-omics data during cardiac remodeling using deep learning
طبقه بندی بدون نظارت شده داده های چند omics در طی بازسازی قلب با استفاده از یادگیری عمیق-2019 Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries.
By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological
interactions and networks that were previously unidentifiable. However, to effectively perform integrative
analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in
the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during
cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)-
based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional
embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded
clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a
joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering,
partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the
Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological
pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched
pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised
DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics. Keywords: Cardiovascular | Clustering | Multi-omics Time-series | Unsupervised deep learning | Integrative analysis |
مقاله انگلیسی |
22 |
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 |
23 |
Bio-inspired cryptosystem with DNA cryptography and neural networks
رمزنگاری زیستی الهام گرفته از رمزنگاری DNA و شبکه های عصبی-2019 Bio-Inspired Cryptosystems are a modern form of Cryptography where bio-inspired and machine learning tech- niques are used for the purpose of securing data. A system has been proposed based on the Central Dogma of Molecular Biology (CDMB) for the Encryption and Decryption Algorithms by simulating the natural processes of Genetic Coding (conversion from binary to DNA bases), Transcription (conversion from DNA to mRNA) and Translation (conversion from mRNA to Protein) as well as the reverse processes to allow for encryption and de- cryption respectively. All inputs are considered to be in the form of blocks of 16-bits. The final outputs from the blocks can be concatenated to form the final cipher text in the form of protein bases. A Bidirectional Asso- ciative Memory Neural Network (BAMNN) has been trained using randomized data for key generation which is capable of saving memory space by remembering and regenerating the sets of keys in a recurrent fashion. The proposed bio-inspired cryptosystem shows competent encryption and decryption times even on large data sizes when compared with existing systems. Keywords: Cryptosystem | Bio-inspired | Central dogma | Key generation |
مقاله انگلیسی |
24 |
Translating public order: Colonial, transnational and international genealogies
ترجمه نظم عمومی: شجره نامه استعماری ، فراملی و بین المللی-2019 Post-colonial legal geographies intersect productively with an emergent global War on Terror discourse, by embedding local cases within a powerful and growing narrative of international concern for security, translating “peace and harmony” into public order. This article traces translations of public order in three distinct but interlocking arena within which the legal circulates: the impact of British colonial legal frameworks on post-colonial legal decision making; the transnational practice of cross-jurisdictional citation and transjudicial communication; the effects of the international war on terror on internal security arrangements. Seeking the making of increasingly global notions of public order in domestic courts in Malaysia and Pakistan, it places common law practices and institutions in the context of a series of networked internationalisms underwritten by the colonial legacies of the British Indian empire, practices and institutions which are now implicated in new international networks undergirded by the security concerns of the United States. From the vantage point of these Muslim majority allies in the War on Terror, this article builds upon recent efforts to reconsider empire and law as key factors in the study of international history and international relations, making visible a series of interlinked domestic, transnational and global spaces in which agents, institutions and ideas travel, conditioned by both imperial and international logics. It finds that what the colonial legacy of British law made possible – legal logics in which religious freedom is understood through the calculus of internal security – the war on terror has made far more likely. |
مقاله انگلیسی |
25 |
Dynamic time warping for reducing the effect of force variation on myoelectric control of hand prostheses
تار شدن زمان پویا برای کاهش تأثیر تغییر نیرو در کنترل میو الکتریک پروتزهای دستی-2019 Research in pattern recognition (PR) for myoelectric control of the upper limb prostheses has been extensive.
However, there has been limited attention to the factors that influence the clinical translation of this technology.
A relevant factor of influence in clinical performance of EMG PR-based control of prostheses is the variation in
muscle activation level, which modifies the EMG patterns even when the amputee attempts the same movement.
To decrease the effect of muscle activation level variations on EMG PR, this work proposes to use dynamic time
warping (DTW) and is validated on two databases. The first database, which has data from ten intact-limbed
subjects, was used to test the baseline performance of DTW, resulting in an average classification accuracy of
more than 90%. The second database comprised data from nine upper limb amputees recorded at three levels of
force for six hand grips. The results showed that DTW trained at a single force level achieved an average
classification accuracy of 60± 9%, 70± 8%, and 60± 7% at the low, medium and high force levels respectively
across all amputee subjects. The proposed scheme with DTW achieved a significant 10% improvement in classification
accuracy when trained at a low force level when compared to the traditional time-dependent power
spectrum descriptors (TD-PSD) method. Keywords: Electromyography (EMG) | Dynamic Time Warping (DTW) | Pattern Recognition (PR) | Force level variation | Classification |
مقاله انگلیسی |
26 |
Translational machine learning for psychiatric neuroimaging
یادگیری ماشین ترجمه ای برای تصویربرداری عصبی روانی-2019 Despite its initial promise, neuroimaging has not been widely translated into clinical psychiatry to assist in the
prediction of diagnoses, prognoses, and optimal therapeutic strategies. Machine learning approaches may enhance
the translational potential of neuroimaging because they specifically focus on overcoming biases by optimizing
the generalizability of pipelines that measure complex brain patterns to predict targets at a singlesubject
level. This article introduces some fundamentals of a translational machine learning approach before
selectively reviewing literature to-date. Promising initial results are then balanced by the description of limitations
that should be considered in order to interpret existing research and maximize the possibility of future
translation. Future directions are then presented in order to inspire further research and progress the field
towards clinical translation. Keywords: Machine learning | Neuroimaging | Translational psychiatry | MRI Deep learning |
مقاله انگلیسی |
27 |
Explicit Separable two dimensional Moment Invariants for object recognition
ویژگی های بدون تغییر زمانی دوبعدی قابل تفکیک صریح برای تشخیص اشیا-2019 Image moment invariants has been widely used in the fields of pattern recognition and computer vision, since they are able to represent
pattern features independently of geometric transformations. Currently, Separable Moments and their invariants are gaining
more interest, due to their capability for combining the basic properties of different orthogonal moments. However, most of the
existing separable moment invariants are derived indirectly from the geometric invariants, based on the relationship orthogonal
polynomials and the geometric basis. Therefore, in this paper, we propose a direct approach to construct a set of discrete separable
Tchebichef-Krawtchouk Moment Invariants which are simultaneously invariant to Rotation, Scaling and Translation transformation,
based on the explicit form of the Tchebichef and Krawtchouk polynomials. Consequently, the experimental and theoretical
results validate the effectiveness of the proposed method and show their superiority in image classification and pattern recognition
in comparison with the existing methods Keywords: Moment Invariants | Explicite Method | Separable Moments | Krawtchouk Polynomials | Tchebichef Polynomials | Pattern Recognition |
مقاله انگلیسی |
28 |
استفاده از شبکه های عصبی موجی فازی تابعی ترکیبی با یک الگوریتم بهینه سازی مبتنی بر تدریس – یادگیری برای تشخیص بیماری پزشکی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 15 - تعداد صفحات فایل doc فارسی: 58 تشخیص صحیح بیماری پزشکی، یک مسئله مهم در دسته بندی تلقی می شود. هدف اصلی فرآیند دسته بندی، تعیین دسته ای است که یک الگوی خاص به آن تعلق دارد. در این مقاله یک روش دسته بندی جدید برمبنای ترکیبی از الگوریتم بهینه سازی مبتنی بر تدریس – یادگیری (TLBO) و شبکه عصبی موجی فازی (FWNN) با شبکه عصبی ارتباطی تابعی (FLNN)، پیشنهاد می شود. به علاوه، از الگوریتم TLBO برای راه اندازی شبکه عصبی موجی فازی تابعی ترکیبی جدید (FFWNN) و بهینه سازی پارامترهای یادگیری که عبارتند از وزن، اتساع و ترجمه، استفاده می شود. برای ارزیابی عملکرد روش پیشنهادی، از 5 سری داده پزشکی استاندارد استفاده شد: سرطان سینه، بیماری قلبی، هپاتیت، دیابت پیمای هندی و آپاندیس. کارآیی روش پیشنهادی با استفاده از اعتبارسنجی تقاطعی 5 باره و اعتبارسنجی تقاطعی 10 باره ازنظر مربع خطای میانگین، دقت دسته بندی، زمان اجرا، حساسیت، اختصاصی بودن و کاپا بررسی می شود. نتایج آزمایش نشان می دهند که کارآیی روش پیشنهادی برای مسئله های دسته بندی پزشکی برای سری های داده ای سرطان سینه، بیماری قلبی، هپاتیت، بیماری های پیمای هندی و آپاندیس ازنظر دقت پس از 30 اجرا برای هر سری داده ای با پیچیدگی محاسباتی پایین، به ترتیب برابر با 309/98، 1/91، 39/91، 67/88 و 51/93 درصد می باشد. به علاوه، مشاهده شده است که روش پیشنهادی درمقایسه با عملکرد سایر روشهای یافت شده در مطالعات قبلی مرتبط، عملکرد کارآمدی دارد.
کلیدواژه ها: شبکه عصبی موجی فازی (FWNN) | شبکه عصبی ارتباطی تابعی (FLNN) | الگوریتم بهینه سازی مبتنی بر تدریس- یادگیری (TLBO) | شبکه عصبی موجی فازی تابعی (FFWNN) |
مقاله ترجمه شده |
29 |
Classification of multichannel surface-electromyography signals based on convolutional neural networks
طبقه بندی سیگنال های الکترومیوگرافی سطحی چند کاناله بر اساس شبکه های عصبی کانونشنال-2019 Electromyography is a science that studies or detects bioelectrical activity of muscles to analyze skills and
morphological changes of the neuromuscular system and contributes to studies on the neuromuscular system.
Surface electromyography (SEMG) signal is a bioelectrical signal emitted when nervous and muscular activities
are recorded from the surface of human skeletons by means of poles, which can reflect the functional state of
nerves and muscles under non-invasive conditions on a real-time basis. SEMG signals found a wide application in
different fields including prosthesis control, sports medicine, rehabilitation medicine, and clinical diagnosis.
However, how to efficiently exact features from SEMG signals to realize accurate recognition of action modes is a
key issue for the practice of electromyography-controlled prostheses and to achieve precision of rehabilitation
treatment. Deep learning reveals drastic changes in many fields of machine learning, including machine vision
and voice recognition, over the past few years. We use convolutional neural networks (CNNs) to extract deep
features from SEMG signals and classify actions. CNNs exhibit good translation invariance due to its characteristics
of local connection and weight sharing. If SEMG signals were applied in the modeling of electromyography
signal recognition, then the diversity of electromyography signal itself can be overcome using invariance
in convolutions. Therefore, in this study, the spectrogram obtained by analyzing electromyography
signals is proposed to be used as an image. Intensively used deep convolutional networks in the image were also
adopted to conduct the gesture motion recognition of SEMG signals. Keywords: Motor rehabilitation | Electromyography | CNN | Spectrogram | Pattern recognition |
مقاله انگلیسی |
30 |
Fractional-order orthogonal Chebyshev Moments and Moment Invariants for image representation and pattern recognition
لحظات متعامد مرتبه کسری چیبیشف برای نمایش تصویر و تشخیص الگو-2019 In this paper, we present a new set of fractional-order orthogonal moments, named Fractional-order
Chebyshev Moments (FCM). We initially introduce the necessary relations and properties to define the
FCM in the Cartesian coordinates. Then, we provide the theoretical framework to construct the Fractionalorder
Chebyshev Moment Invariants (FCMI), which are invariants with respect to rotation, scaling and
translation transforms. In addition, we devoted a substantial attention to enhance their computational
time and numerical accuracy. Consequently, the numerical experiments are carried out to demonstrate
the validity of the introduced fractional-order moments and moment invariants in comparison with the
classical methods, with regard to image representation capability and object recognition accuracy on several
publicly available databases. The presented theoretical and experimental results demonstrate the efficiency
and the superiority of the proposed method. Keywords: Fractional-order orthogonal moments | Fractional-order Chebyshev polynomials | Moment invariants | Image representation | Pattern recognition | Fast and accurate computation |
مقاله انگلیسی |