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AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics
AgroLens: یک معماری مزرعه هوشمند کمهزینه و سبز پسند برای پشتیبانی از تشخیص بیماریهای برگ در زمان واقعی-2022 Agriculture is one of the most significant global economic activities responsible for feeding the
world population of 7.75 billion. However, weather conditions and diseases impact production
efficiency, reducing economic activity and the food sovereignty of economies worldwide. Thus,
computational methods can support disease classification based on an image. This classification
requires training Artificial Intelligence (AI) models on high-performance computing resources,
usually far from the user domain. State of the art has proposed the concept of Edge Computing
(EC), which aims to bring computational resources closer to the domain problem to decrease
application latency and improve computational power closer to the client. In addition, EC has
become an enabling technology for Smart Farms, and the literature has appropriated EC to
support these applications. However, predominantly state-of-the-art architectures are dependent
on Internet connectivity and do not allow diverse real-time classification of diseases based on
crop leaf on mobile devices. This paper sheds light on a new architecture, AgroLens, built with
low-cost and green-friendly devices to support a mobile Smart Farm application, operational
even in areas lacking Internet connectivity. Among our main contributions, we highlight the
functional evaluation of AgroLens for AI-based real-time classification of diseases based on leaf
images, achieving high classification performance using a smartphone. Our results indicate that
AgroLens supports the connectivity of thousands of sensors from a smart farm without imposing
computational overhead on edge-compute. The AgroLens architecture opens up opportunities
and research avenues for deployment and evaluation for large-scale Smart Farm applications
with low-cost devices.
keywords: بیماری گیاهی | مزرعه هوشمند | اینترنت اشیا | یادگیری عمیق | سبز پسند| Plant disease | Smart Farm | Internet of Things | Deep learning | Green-friendly |
مقاله انگلیسی |
2 |
Boundary optimal control for antiplane contact problems with power-law friction
کنترل بهینه مرزی برای مشکلات تماس antiplane با اصطکاک قانون قدرت -2020 We consider a contact model with power-law friction in the antiplane context. Our study focuses on the boundary optimal control, paying special attention to optimality conditions and computational methods. Depending on the exponent of the power-law friction, we are able to deduce an optimality condition for the original problem or for a regularized version of it. Furthermore, we introduce and analyze a computational technique based on linearization, saddle point theory and a fixed point method. For a slightly modified optimal control problem, some numerical experiments are presented. Keywords: Nonlinear boundary value problem | Antiplane contact model | Power-law friction | Optimal control | Optimality conditions | Fixed point | Approximation |
مقاله انگلیسی |
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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 |
4 |
Cognitive solutions for security and cryptography
راه حل های شناختی برای امنیت و رمزنگاری-2019 This paper describes the new security solutions based on cognitive approaches and new computing paradigm called cognitive cryptography.
This new security area establish a new generation of computational methods and security systems, focused on creation intelligent
cryptographic algorithms and security protocols using cognitive information processing approaches. Such systems are designed for
semantic evaluation of encrypted data, and allow to select the most appropriate techniques of its encryption. This paper presents a possible
application of such techniques for different security tasks like authentication, secret sharing, secure data management etc. Additionally,
some cryptographic solutions inspired by biological models will be presented. Keywords: Cognitive cryptographic protocols | Bio-inspired cryptography | Personalized security systems |
مقاله انگلیسی |
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Mapping the political landscape of Persian Twitter: The case of 2013 presidential election
نقشه چشم انداز سیاسی توییتر فارسی: مورد انتخابات ریاست جمهوری سال 2013-2019 The fallacy of premature designations such as ‘‘Iran’s Twitter Revolution’’ can be attributed to the empirical gap in our
knowledge about such sociotechnical phenomena in non-Western societies. To fill this gap, we need in-depth analyses of
social media use in those contexts and to create detailed maps of online public environments in such societies. This paper
aims to present such cartography of the political landscape of Persian Twitter by studying the case of Iran’s 2013
presidential election. The objective of this study is twofold: first, to fill the empirical gap in our knowledge about
Twitter use in Iran, and second, to develop computational methods for studying Persian Twitter (e.g., effective methods
for analyzing Persian text) and identify the best methods for addressing different issues (e.g., topic detection and
sentiment analysis). During Iran’s 2013 presidential election, three million tweets were collected and analyzed using
social network analysis and machine learning. The findings provide a more nuanced view of the political landscape of
Persian Twitter and identify patterns in accordance with or in contrast to those identified in the English-speaking
Twittersphere around the 2013 presidential election. Persian Twitter was dominated by micro-celebrities, whereas
institutional elites dominated English discourse about Iran on Twitter. The results also illustrate that Persian Twitter
in 2013 was predominantly in favor of reformists. Finally, this study demonstrates that sentiment analysis toward political
name entities can be used efficiently for mapping the political landscape of conversation on Twitter.
Keywords: Twitter | Iran | social network analysis | political landscape | computational methods | Big Data |
مقاله انگلیسی |
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Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology
مالتیفیزیک عمیق: اتصال چند روانشناسی گسسته با یادگیری ماشین برای دستیابی به خودآموزی در مدلهای سیلیکو که همانند فیزیولوژی انسانی-2019 Objectives: The objective of this study is to devise a modelling strategy for attaining in-silico models replicating
human physiology and, in particular, the activity of the autonomic nervous system.
Method: Discrete Multiphysics (a multiphysics modelling technique) and Reinforcement Learning (a Machine
Learning algorithm) are combined to achieve an in-silico model with the ability of self-learning and replicating
feedback loops occurring in human physiology. Computational particles, used in Discrete Multiphysics to model
biological systems, are associated to (computational) neurons: Reinforcement Learning trains these neurons to
behave like they would in real biological systems.
Results: As benchmark/validation, we use the case of peristalsis in the oesophagus. Results show that the insilico
model effectively learns by itself how to propel the bolus in the oesophagus.
Conclusions: The combination of first principles modelling (e.g. multiphysics) and machine learning (e.g.
Reinforcement Learning) represents a new powerful tool for in-silico modelling of human physiology. Biological
feedback loops occurring, for instance, in peristaltic or metachronal motion, which until now could not be
accounted for in in-silico models, can be tackled by the proposed technique Keywords: Discrete multiphysics | Reinforcement Learning | Coupling first-principles models with machine | learning | Particle-based computational methods |
مقاله انگلیسی |
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Identifying the business and social networks in the domain of production by merging the data from heterogeneous internet sources
شناسایی کسب و کار و شبکه های اجتماعی در حوزه تولید ازطریق ادغام داده های حاصل از منابع داخلی ناهمگن-2018 People, companies, and institutions form networks as part of their technical, economic, and social activities. As a consequence, these networks have an influence on how companies conduct business. Recently, the Internet, professional, and scientific social networks have contributed to the ease and simplicity of the network forming process and the public availability of the corresponding data. We investigate whether useful information about the relationships between individuals, companies, and institutions for the domain of production can be extracted from publicly available, structured and unstructured data that is merged from various Internet sources. We demonstrate that relevant information about the structures of these networks can be obtained by merging publicly available data using a combination of advanced computational methods including web crawling, machine learning, and creating mash-ups of publicly available services. The feasibility and the applicability of the approach are shown for a case in the automotive domain. A potential use case of the resulting data is demonstrated, showing how the approach can be used to find specific skills and expertise for a scientific community consisting of people from industry and academia. The proposed approach can be used for the modelling and analysis of various forms of collaboration between and within businesses. As a tool, it could be used for the purposes of strategic networking, to facilitate the creation of project consortia, to identify competitors and other stakeholders in a certain domain, to pinpoint communication channels, to search for specific expertise, or to identify organisational and social structures within organisations.
keywords: Production |Network |Network analysis |Data analytics |Skill profiling |
مقاله انگلیسی |
8 |
Big Data and forensics: An innovative approach for a predictable jurisprudence
داده های بزرگ و پزشکی قانونی: رویکرد نوآورانه برای فقه قابل پیش بینی-2018 Nowadays, it is easy to trace a large amount of information on the web, to access docu
ments and produce a digital storage.
The current work is submitted as an introduction to an innovative system for the inves
tigation about notoriety of web data which is based on the evaluation of judicial sentences
and it is implemented to reduce the duration of all processes.
This research also aims to open some new conjoint debates about the study and ap
plication of statistical and computational methods to web data on new forensics topics:
text mining techniques enable us to obtain information which may be helpful to establish
a statistical index in order to describe the quality and the efficiency in terms of law. It is
also possible to develop an intelligent system about facts and judgments.
Keywords: Quality in law ، Efficiency in law ، Big data ، Literal text similarity ، Semantic text similarity |
مقاله انگلیسی |
9 |
The role of statistics in the era of big data: A computational scientist perspective
نقش آمار در دوران داده های بزرگ: دیدگاه علمی محاسباتی-2018 In their modern implementation, computational models based on first principles from
Physics can dramatically benefit from the recent explosion of Data Science. In fact, these
two branches of applied mathematics can virtuously interplay, and at a large extent they
already do.
Keywords: Numerical models ، Computational methods ، Data assimilation ، Uncertainty quantification ، Data analysis |
مقاله انگلیسی |
10 |
Computational prediction of chemical reactions: current status and outlook
پیش بینی محاسباتی واکنش های شیمیایی: وضعیت فعلی و چشم انداز-2018 Over the past few decades, various computational methods have become increasingly important for discovering and developing novel drugs. Computational prediction of chemical reactions is a key part of an efficient drug discovery process. In this review, we discuss important parts of this field, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics (QM/MM) to explore chemical reactions. We also outline potential future developments with an emphasis on pre-competitive collaboration opportunities.
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مقاله انگلیسی |