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).
ردیف | عنوان | نوع |
---|---|---|
1 |
A new pyramidal opponent color-shape model based video shot boundary detection
A new pyramidal opponent color-shape model based video shot boundary detection-2020 Video shot boundary detection (VSBD) is one of the most essential criteria for
many intelligent video analysis-related applications, such as video retrieval,
indexing, browsing, categorization and summarization. VSBD aims to segment
big video data into meaningful fragments known as shots. This paper put forwards
a new pyramidal opponent colour-shape (POCS) model which can detect abrupt
transition (AT) and gradual transition (GT) simultaneously, even in the presence
of illumination changes, huge object movement between frames, and fast camera
motion. First, the content of frames in the video subjected to VSBD is represented
by the proposed POCS model. Consequently, the temporal nature of the POCS
model is subjected to a suitable segment (SS) selection procedure in order to
minimize the complexity of VSBD method. The SS from the video frames is
examined for transitions within it using a bagged-trees classifier (BTC) learned
on a balanced training set via parallel processing. To prove the superiority of the
proposed VSBD algorithm, it is evaluated on the TRECVID 2001,
TRECVID2007 and VIDEOSEG2004 data sets for classifying the basic units of
video according to no transition (NT), AT and GT. The experimental evaluation
results in an F1-score of 95.13%, 98.13% and 97.11% on the TRECVID 2001,
TRECVID2007 and VIDEOSEG2004 data sets, respectively. Keywords: Shot Boundary Detection | Abrupt Transition | Gradual Transition | Opponent Color space | Ensemble Algorithm |
مقاله انگلیسی |
2 |
Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions
رویکردهای هوش محاسباتی برای طبقه بندی داده های پزشکی: مرور، چالش های آینده و مسیرهای تحقیقاتی-2018 The explosive growth of data in volume, velocity and diversity that are produced by medical applications
has contributed to abundance of big data. Current solutions for efficient data storage and management
cannot fulfill the needs of heterogeneous data. Therefore, by applying computational intelligence (CI) ap
proaches in medical data helps get better management, faster performance and higher level of accuracy
in detection. This paper aims to investigate the state-of-the-art of computational intelligence approaches
in medical data and to categorize the existing CI techniques, used in medical fields, as single and hybrid.
In addition, the techniques and methodologies, their limitations and performances are presented in this
study. The limitations are addressed as challenges to obtain a set of requirements for Computational In
telligence Medical Data (CIMD) in establishing an efficient CIMD architectural design. The results show
that on the one hand Support Vector Machine (SVM) and Artificial Immune Recognition System (AIRS) as
a single based computational intelligence approach were the best methods in medical applications. On
the other hand, the hybridization of SVM with other methods such as SVM-Genetic Algorithm (SVM-GA),
SVM-Artificial Immune System (SVM-AIS), SVM-AIRS and fuzzy support vector machine (FSVM) had great
performances achieving better results in terms of accuracy, sensitivity and specificity.
Keywords: Computational intelligence ، Medical application ، Big data ، Detection ، Ensemble algorithm |
مقاله انگلیسی |