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 |
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731 |
A computer vision system for early stage grape yield estimation based on shoot detection
سیستم بینایی ماشین برای ارزیابی عملکرد انگور در مرحله اولیه بر اساس تشخیص ساقه-2017 Counting grapevine shoots early in the growing season is critical for adjusting management practices but is challenging to automate due to a range of environmental factors.This paper proposes a completely automatic system for grapevine yield estimation, comprised of robust shoot detection and yield estimation based on shoot counts produced from videos. Experiments were conducted on four vine blocks across two cultivars and trellis systems over two seasons. A novel shoot detection framework is presented, including image processing, feature extraction, unsupervised feature selection and unsupervised learning as a final classification step. Then a procedure for converting shoot counts from videos to yield estimates is introduced.The shoot detection framework accuracy was calculated to be 86.83% with an F1-score of 0.90 across the four experimental blocks. This was shown to be robust in a range of lighting conditions in a commercial vineyard. The absolute predicted yield estimation error of the system when applied to four blocks over two consecutive years ranged from 1.18% to 36.02% when the videos were filmed around E-L stage 9.The developed system has an advantage over traditional PCD mapping techniques in that yield variation maps can be obtained earlier in the season, thereby allowing farmers to adjust their management practices for improved outputs. The unsupervised feature selection algorithm combined with unsupervised learning removed the requirement for any prior training or labeling, greatly enhancing the applicability of the overall framework and allows full automation of shoot mapping on a large scale in vineyards.© 2017 Elsevier B.V. All rights reserved.1. Keywords:Grape yield estimation | Shoot detection | Feature selection | Data classification | Vineyard mapping |
مقاله انگلیسی |
732 |
Sustainable supply chain management: framework and further research directions
مدیریت زنجیره تامین پایدار: چارچوب و مسیرهای تحقیقات بیشتر-2017 This paper argues for the use of Total Interpretive Structural Modeling (TISM) in sustainable supply chain
management (SSCM). The literature has identified antecedents and drivers for the adoption of SSCM.
However, there is relatively little research on methodological approaches and techniques that take into
account the dynamic nature of SSCM and bridge the existing quantitative/qualitative divide. To address
this gap, this paper firstly systematically reviews the literature on SSCM drivers; secondly, it argues for
the use of alternative methods research to address questions related to SSCM drivers; and thirdly, it
proposes and illustrates the use of TISM and Cross Impact Matrix-multiplication applied to classification
(MICMAC) analysis to test a framework that extrapolates SSCM drivers and their relationships. The
framework depicts how drivers are distributed in various levels and how a particular driver influences
the other through transitive links. The paper concludes with limitations and further research directions.
Keywords: Sustainable supply chain | Total Interpretive Structural Modeling | MICMAC | Drivers |
مقاله انگلیسی |
733 |
Modification of supervised OPF-based intrusion detection systems using unsupervised learning and social network concept
اصلاح سیستم های تشخیص نفوذ مبتنی بر OPF تحت نظارت با استفاده از مفهوم یادگیری بدون نظارت و شبکه های اجتماعی-2017 Optimum-path forest (OPF) is a graph-based machine learning method that can overcome some lim
itations of the traditional machine learning algorithms that have been used in intrusion detection sys
tems. This paper presents a novel approach for intrusion detection using a modified OPF (MOPF) algo
rithm for improving the performance of traditional OPF in terms of detection rate (DR), false alarm rate
(FAR), and time of execution. To address the problem of scalability in large datasets and also for achieving
high attack recognition rates, the proposed framework employs the k-means clustering algorithm, as a
partitioning module, for generating different homogeneous training subsets from original heterogeneous
training samples. In the proposed MOPF algorithm, the distance between unlabeled samples and the root
(prototype) of every sample in OPF is also considered in classifying unlabeled samples with the aim of
improving the accuracy rate of traditional OPF algorithm. Moreover, the centrality and the prestige
concepts in the social network analysis are employed in a pruning module for determining the most
informative samples in training subsets to speed up the traditional OPF algorithm. The experimental
results on NSL-KDD dataset show that the proposed method performs better than traditional OPF in
terms of accuracy rate, DR, FAR, and cost per example (CPE) evaluation metrics.
Keywords: Optimum-path forest | Classification | Clustering | Pruning | Centrality | Prestige | Social network analysis |
مقاله انگلیسی |
734 |
Gene selection for tumor classification using neighborhood rough sets and entropy measures
انتخاب ژن برای طبقه بندی تومور با استفاده از مجموعه های ناهموار همسایگی و اندازه گیری آنتروپی-2017 With the development of bioinformatics, tumor classification from gene expression data becomes an
important useful technology for cancer diagnosis. Since a gene expression data often contains thousands
of genes and a small number of samples, gene selection from gene expression data becomes a key step for
tumor classification. Attribute reduction of rough sets has been successfully applied to gene selection
field, as it has the characters of data driving and requiring no additional information. However, traditional
rough set method deals with discrete data only. As for the gene expression data containing real-value or
noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification
accuracy. In this paper, we propose a novel gene selection method based on the neighborhood rough set
model, which has the ability of dealing with real-value data whilst maintaining the original gene classi
fication information. Moreover, this paper addresses an entropy measure under the frame of neighbor
hood rough sets for tackling the uncertainty and noisy of gene expression data. The utilization of this
measure can bring about a discovery of compact gene subsets. Finally, a gene selection algorithm is
designed based on neighborhood granules and the entropy measure. Some experiments on two gene
expression data show that the proposed gene selection is an effective method for improving the accuracy
of tumor classification.
Keywords: Gene selection | Neighborhood rough sets | Tumor classification | Entropy measure | Gene expression data |
مقاله انگلیسی |
735 |
A novel semantic representation for eligibility criteria in clinical trials
بازنویسی معنایی جدید برای معیارهای واجد شرایط در آزمایشات بالینی-2017 Eligibility Criteria (EC) comprise an important part of a clinical study, being determinant of its cost, dura
tion and overall success. Their formal, computer-processable description can significantly improve clin
ical trial design and conduction by enabling their intelligent processing, replicability and linkability with
other data. For EC representation purposes, related standards were investigated, along with published lit
erature. Moreover, a considerable number of clinicaltrials.gov studies was analyzed in collaboration with
clinical experts for the determination and classification of parameters of clinical research importance. The
outcome of this process was the EC Representation; a CDISC-compliant schema for organizing criteria
along with a patient-centric model for their formal expression, properly linked with international classi
fications and codifications. Its evaluation against 200 randomly selected EC indicated that it can ade
quately serve its purpose, while it can be also combined with existing tools and components
developed for both EC specification and especially application to Electronic Health Records.
Keywords: Clinical trials | Eligibility criteria representation | Reference models | Temporal constraints | Bioinformatics | Semantic web |
مقاله انگلیسی |
736 |
Development pattern, classification and evaluation of the tourism academic community in China in the last ten years: From the perspective of big data of articles of tourism academic journals
الگوی توسعه، طبقه بندی و ارزیابی جامعه دانشگاهی گردشگری در چین در ده سال گذشته: از منظر داده های بزرگ از مقالات مجلات علمی گردشگری-2017 This paper reports findings from an analysis of 16,024 tourism academic papers published in the major
social science journals of China as ranked by CSSCI, and CSCD for the period from 2003 to 2012. The paper
ranks and evaluates journals and institutions related to tourism, and thus shows a comprehensive picture
of the academic development in Chinas tourism academic research over the decade. The paper used a
creative research method to discover the phenomenon and issues not previously identified by re
searchers and provides a sound foundation to further develop big data analytical methods in China.
Keywords:Tourism|Academic community|Academic journal|Big data|Ranking |
مقاله انگلیسی |
737 |
A statistical infinite feature cascade-based approach to anomaly detection for dynamic social networks
یک رویکرد مبتنی بر آبشاری ویژگی نامحدود اماری برای تشخیص ناهنجاری برای شبکه های اجتماعی پویا-2017 The development of methods for anomaly detection in dynamic ubiquitous online social networks is crit
ical to coincide with the growth in social network usage. This paper presents a novel statistical approach
to anomaly detection in dynamic social networks. The approach relies upon the fact that the network
dynamics can be driven by microscopic features of each node that dynamically cascade to neighboring
nodes over time. The proposed approach consists of two main components: (1) normal modeling compo
nent and (2) anomaly detection component. The former component is involved in three main processes,
governing the network dynamics. The first process is the features’ birth, death, and lifetime, which is as
sumed to follow a realistic statistical distribution in this paper for the very first time. The second process
is the evolution of nodes’ features that is modeled by an Infinite Factorial Hidden Markov Model (IFHMM),
considering feature cascade. The feature cascade is a phenomenon that explicitly describes how the past
features of each node affect the features of its neighboring nodes in future. The third process modeled
in this paper is the relationship between nodes’ features and link generation in dynamic social networks.
The latter component of the proposed approach provides a new method to quantize deviation of net
work dynamics from the normal behavior. Some Markov Chain Monte Carlo (MCMC) sampling strategies
have been used to simulate parameters of the proposed model, given social network data. The proposed
anomaly detection approach is validated by experiments on synthetic and real social network datasets.
Experimental results show that this approach outperforms other related approaches in terms of some
statistical performance measures, especially applied to binary normal-abnormal classification test.
Keywords: Dynamic social networks | Anomaly detection | Feature cascade | Statistical modeling |
مقاله انگلیسی |
738 |
Ant colony optimization based hierarchical multi-label classification algorithm
الگوریتم طبقه بندی چند برچسبی سلسله مراتبی مبتنی بر بهینه سازی کلونی مورچه ها-2017 There exist numerous state of the art classification algorithms that are designed to handle the data with
nominal or binary class labels, where a sample belongs to only a single class label. In these problems,
known as flat classification problems, class labels are independent of each other. Unfortunately, on the
other hand, less attention is given to the genre of classification problems where samples may belong to
several classes and at the same time the class labels are organized based on a structured hierarchy; such
as gene ontology, protein function prediction, test scores, web page categorization, text categorization
etc. This article presents a novel Ant Colony Optimization based hierarchical multi-label classification
algorithm that can handle such a complex instance of classification problems and can incorporates the
given class hierarchy during its learning phase. The algorithm produces IF-THEN ordered rule list to learn
a comprehensible model which can easily be verified by experts. It exploits positive correlation between
the domain values of two related attributes to improve the discrimination power of resultant classifica
tion model, up to a significant level. The paper contains rich details regarding hierarchical single label
(or single path) and multi-label classification problems and different categories of corresponding solu
tions. The proposed method is evaluated on sixteen most challenging bioinformatics datasets; some of
these containing hundreds of attributes and thousands of class labels. At the end, the proposed method is
compared with four recent state of the art hierarchical multi-label classification algorithms. The empir
ical evaluation confirms the promising ability of the proposed technique for hierarchical multi-label
classification task.
Keywords: Hierarchical multi-label classification | Ant colony optimization | Hierarchical single label classification | Bioinformatics data sets with gene | ontology and FunCat | Protein function prediction | Correlation based IF-THEN rule list | HmAntMiner-C |
مقاله انگلیسی |
739 |
Designing a national science and technology evaluation system based on a new typology of international practices
طراحی یک سیستم ارزیابی علم و فناوری ملی براساس نوع جدیدی از شیوه های بین المللی-2017 This paper aims to provide a new classification of national science and technology (S & T) evaluation systems.
This evaluation system will consider five analytical dimensions extracted from international practices consisting
of the following: evaluation system function, evaluation interactions framework, evaluation organization,
evaluation model of funding, and process of result evaluation. The classification proposed in the present paper is
intended for application in detecting the current position of and expanding suitable evaluation systems based on
the countries native context as a national analysis tool (especially for late-comer countries). Therefore, in the
case of Iran, we reviewed both the existing and optimized modes of national science and technology evaluation
systems. The results show that the existing evaluation system in Iran is not optimized, so evolutionary changes
are required for obtaining the desired system goals. Policy results of the mentioned classification as well as
national science and technology evaluation systems are considered. In general, it appears that such a descriptive
analytical typology can be applicable for all countries. However, the classification is specifically applied for
designing an optimized S & T evaluation system in Iran.
Keywords: Science and technology evaluation systems | Latecomer countries | International practices | Explanatory typology | Iran |
مقاله انگلیسی |
740 |
Mutually-exclusive-and-collectively-exhaustive feature selection scheme
طرح انتخاب ویژگی به طور متقابل منحصر به فرد و جمعی جامع-2017 In the fields of machine learning and data mining, feature selection methods are used to identify the
most cost-effective predictors and to give a deeper understanding of pattern recognition and extraction.
This study proposes a novel mutually-exclusive-and-collectively-exhaustive (MECE) feature selection
scheme. Based on the MECE principle in decision science, the scheme, which has three stages including
evaluation of independence, evaluation of importance and evaluation of completeness, aims to identify
the independent and important variables with complete information. A case study of fault classification
in semiconductor manufacturing and a study of breast cancer relapse identification in bioinformatics are
used to validate the proposed scheme. The results demonstrate that the proposed MECE scheme selects
fewer variables, avoids the multicollinearity problem, and improves fault classification accuracy in the
two case studies.
Keywords: Feature selection | Mutually-exclusive-and-collectively | exhaustive | Data mining | Semiconductor manufacturing | Bioinformatics |
مقاله انگلیسی |