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ردیف | عنوان | نوع |
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1 |
Image2Triplets: A computer vision-based explicit relationship extraction framework for updating construction activity knowledge graphs
Image2Triplets: چارچوب استخراج رابطه صریح مبتنی بر بینایی ماشین برای به روز رسانی نمودارهای دانش فعالیت های ساخت-2022 Knowledge graph (KG) is an effective tool for knowledge management, particularly in the architecture,
engineering and construction (AEC) industry, where knowledge is fragmented and complicated. However,
research on KG updates in the industry is scarce, with most current research focusing on text-based KG
updates. Considering the superiority of visual data over textual data in terms of accuracy and timeliness, the
potential of computer vision technology for explicit relationship extraction in KG updates is yet to be ex-
plored. This paper combines zero-shot human-object interaction detection techniques with general KGs to
propose a novel framework called Image2Triplets that can extract explicit visual relationships from images
to update the construction activity KG. Comprehensive experiments on the images of architectural dec-
oration processes have been performed to validate the proposed framework. The results and insights will
contribute new knowledge and evidence to human-object interaction detection, KG update and construc-
tion informatics from the theoretical perspective.
© 2022 Elsevier B.V. All rights reserved. keywords: یادگیری شات صفر | تشخیص تعامل انسان و شی | بینایی ماشین| استخراج رابطه صریح | نمودار دانش | Zero-shot learning | Human-object interaction detection | Computer vision | Explicit relationship extraction | Knowledge graph |
مقاله انگلیسی |
2 |
Deep unsupervised methods towards behavior analysis in ubiquitous sensor data
روش های عمیق بدون نظارت برای تجزیه و تحلیل رفتار در داده های حسگر همه جا حاضر-2022 Behavioral analysis (BA) on ubiquitous sensor data is the task of finding the latent distribution of
features for modeling user-specific characteristics. These characteristics, in turn, can be used for a
number of tasks including resource management, power efficiency, and smart home applications.
In recent years, the employment of topic models for BA has been found to successfully extract the
dynamics of the sensed data. Topic modeling is popularly performed on text data for mining
inherent topics. The task of finding the latent topics in textual data is done in an unsupervised
manner. In this work we propose a novel clustering technique for BA which can find hidden
routines in ubiquitous data and also captures the pattern in the routines. Our approach efficiently
works on high dimensional data for BA without performing any computationally expensive
reduction operations. We evaluate three different techniques namely Latent Dirichlet Allocation
(LDA), the Non-negative Matrix Factorization (NMF), and the Probabilistic Latent Semantic
Analysis (PLSA) for comparative study. We have analyzed the efficiency of the methods by using
performance indices like perplexity and silhouette on three real-world ubiquitous sensor datasets
namely, the Intel Lab, Kyoto, and MERL. Through rigorous experiments, we achieve silhouette
scores of 0.7049 over the Intel Lab dataset, 0.6547 over the Kyoto dataset, and 0.8312 over the
MERL dataset for clustering. In these cases, however, it is di cult to validate the results obtained as
the datasets do not contain any ground truth information. Towards that, we investigate a self-
supervised method that will be capable of capturing the inherent ground truths that are avail-
able in the dataset. We design a self-supervised technique which we apply on datasets containing
ground truth and also without. We see that our performance on data without ground truth differs
from that with ground truth by approximately 8% (F-score) hence showing the efficacy of self-
supervised techniques towards capturing ground truth information. keywords: تحلیل داده های فراگیر | تحلیل رفتار | یادگیری خود نظارتی | Ubiquitous data analysis | Behavior analysis | Self supervised learning |
مقاله انگلیسی |
3 |
Multimodal biometric monitoring technologies drive the development of clinical assessments in the home environment
فن آوری های نظارت بیومتریک چند حالته ، توسعه ارزیابی بالینی را در محیط خانه هدایت می کند-2021 Biometric monitoring technologies (BioMeTs) have attracted the attention of the health care community because of their user-friendly form factor and multi-sensor data-collection capabilities. The potential benefits of remote monitoring for collecting comprehensive, longitudinal, and contextual datasets span therapeutic areas, and both chronic and acute disease settings. Importantly, multimodal BioMeTs unlock the ability to generate rich contextual data to augment digital measures. Currently, the availability of devices is no longer the main factor limiting adoption but rather the ability to integrate fit-for-purpose BioMeTs reliably and safely into clinical care. We provide a critical review of the state of art for multimodal BioMeTs in clinical care and identify three unmet clinical needs: 1) expand the abilities of existing ambulatory unimodal BioMeTs; 2) adapt standardized clinical test protocols ("spot checks’’) for use under free living conditions; and 3) develop novel applications to manage rehabilitation and chronic diseases. As the field is still in an early and quickly evolving state, we make practical recommendations: 1) to select appropriate BioMeTs; 2) to develop composite digital measures; and 3) to design interoperable software to ingest, process, delegate, and visualize the data when deploying novel clinical applications. Multimodal BioMeTs will drive the evolution from in-clinic assessments to at-home data collection with a focus on prevention, personalization, and long-term outcomes by empowering health care providers with knowledge, delivering convenience, and an improved standard of care to patients.“The whole is greater than the sum of its parts” – Aristotle Keywords: Digital medicine | Wearables | Multimodal assessment | Digital measures |
مقاله انگلیسی |
4 |
Age estimation from the biometric information of hand bones_ Development of new formulas
برآورد سن از اطلاعات بیومتریک استخوان های دست: توسعه فرمول های جدید-2021 Biometric monitoring technologies (BioMeTs) have attracted the attention of the health care community because
of their user-friendly form factor and multi-sensor data-collection capabilities. The potential benefits of remote
monitoring for collecting comprehensive, longitudinal, and contextual datasets span therapeutic areas, and both
chronic and acute disease settings. Importantly, multimodal BioMeTs unlock the ability to generate rich
contextual data to augment digital measures. Currently, the availability of devices is no longer the main factor
limiting adoption but rather the ability to integrate fit-for-purpose BioMeTs reliably and safely into clinical care.
We provide a critical review of the state of art for multimodal BioMeTs in clinical care and identify three unmet clinical needs: 1) expand the abilities of existing ambulatory unimodal BioMeTs; 2) adapt standardized clinical test protocols ("spot checks’’) for use under free living conditions; and 3) develop novel applications to manage rehabilitation and chronic diseases. As the field is still in an early and quickly evolving state, we make practical recommendations: 1) to select appropriate BioMeTs; 2) to develop composite digital measures; and 3) to design interoperable software to ingest, process, delegate, and visualize the data when deploying novel clinical applications. Multimodal BioMeTs will drive the evolution from in-clinic assessments to at-home data collection with a focus on prevention, personalization, and long-term outcomes by empowering health care providers with knowledge, delivering convenience, and an improved standard of care to patients. Keywords: Digital medicine | Wearables | Multimodal assessment | Digital measures |
مقاله انگلیسی |
5 |
Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
به سمت یک چارچوب پردازش در زمان واقعی بر اساس بهبود انواع شبکه عصبی مکرر توزیع شده با fastText برای تجزیه و تحلیل داده های بزرگ اجتماعی-2020 Big data generated by social media stands for a valuable source of information, which offers an
excellent opportunity to mine valuable insights. Particularly, User-generated contents such as
reviews, recommendations, and users’ behavior data are useful for supporting several marketing
activities of many companies. Knowing what users are saying about the products they bought or
the services they used through reviews in social media represents a key factor for making decisions.
Sentiment analysis is one of the fundamental tasks in Natural Language Processing.
Although deep learning for sentiment analysis has achieved great success and allowed several
firms to analyze and extract relevant information from their textual data, but as the volume of
data grows, a model that runs in a traditional environment cannot be effective, which implies the
importance of efficient distributed deep learning models for social Big Data analytics. Besides, it
is known that social media analysis is a complex process, which involves a set of complex tasks.
Therefore, it is important to address the challenges and issues of social big data analytics and
enhance the performance of deep learning techniques in terms of classification accuracy to obtain
better decisions.
In this paper, we propose an approach for sentiment analysis, which is devoted to adopting
fastText with Recurrent neural network variants to represent textual data efficiently. Then, it
employs the new representations to perform the classification task. Its main objective is to enhance
the performance of well-known Recurrent Neural Network (RNN) variants in terms of
classification accuracy and handle large scale data. In addition, we propose a distributed intelligent
system for real-time social big data analytics. It is designed to ingest, store, process,
index, and visualize the huge amount of information in real-time. The proposed system adopts
distributed machine learning with our proposed method for enhancing decision-making processes.
Extensive experiments conducted on two benchmark data sets demonstrate that our
proposal for sentiment analysis outperforms well-known distributed recurrent neural network
variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory
(BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach
using the three different deep learning models. The results show that our proposed approach
is able to enhance the performance of the three models. The current work can provide
several benefits for researchers and practitioners who want to collect, handle, analyze and visualize
several sources of information in real-time. Also, it can contribute to a better understanding
of public opinion and user behaviors using our proposed system with the improved
variants of the most powerful distributed deep learning and machine learning algorithms.
Furthermore, it is able to increase the classification accuracy of several existing works based on
RNN models for sentiment analysis. Keywords: Big data | FastText | Recurrent neural networks | LSTM | BiLSTM | GRU | Natural language processing | Sentiment analysis | Social big data analytics |
مقاله انگلیسی |
6 |
Emotional Text Mining: Customer profiling in brand management
متن کاوی عاطفی: نمایه سازی مشتری در مدیریت برند-2020 The widespread use of the Internet and the constant increase in users of social media platforms has made a large amount of textual data available. This represents a valuable source of information about the changes in people’s opinions and feelings. This paper presents the application of Emotional Text Mining (ETM) in the field of brand management. ETM is an unsupervised procedure aiming to profile social media users. It is based on a bottom-up approach to classify unstructured data for the identification of social media users’ representations and sentiments about a topic. It is a fast and simple procedure to extract meaningful information from a large collection of texts. As customer profiling is relevant for brand management, we illustrate a business application of ETM on Twitter messages concerning a well-known sportswear brand in order to show the potential of this procedure, high- lighting the characteristics of Twitter user communities in terms of product preferences, representations, and sentiments. Keywords: Emotional Text Mining | Brand management | Twitter | Network analysis | Customer profiling |
مقاله انگلیسی |
7 |
Semantic approach to compliance checking of underground utilities
رویکرد معنایی برای بررسی انطباق ابزارهای زیرزمینی-2020 Utility regulations stipulate the spatial configurations between underground utilities and their surroundings to
avoid interferences and disruptions of utility services. Utility compliance checking aims to detect spatial noncompliances
in underground utilities by examining geospatial data of utilities and their surroundings against
textual data of utility regulations. However, the integration of heterogeneous utility geospatial and textual data
for compliance checking remains a big challenge. This paper presents a semantic approach to integrate heterogeneous
data and enable automated compliance checking of underground utilities through logic and spatial
reasoning. The approach consists of the following key components: (1) four interlinked ontologies that provide
the semantic schema for heterogeneous data relevant to utility compliance checking, (2) two data convertors for
the conversion of heterogeneous data from proprietary formats into a common and interoperable format following
the semantic schema, and (3) a query mechanism with spatial extensions for the detection of noncompliant
utility instances. The approach was tested on a sample utility database, and the results demonstrate
the success of the proposed approach in the integration of heterogeneous data from multiple sources and automated
detection of spatial non-compliances in underground utilities. In addition to utility compliance
checking, the approach can be extended to other application cases where both data integration from multiple
sources and spatial reasoning are required. Keywords: Ontology | Data integration | Semantic reasoning | Utility compliance checking |
مقاله انگلیسی |
8 |
Uncovering cyberincivility among nurses and nursing students on Twitter: A data mining study
کشف فضای مجازی در بین پرستاران و دانشجویان پرستاری در توییتر: مطالعه داده کاوی-2019 Background: Although misuse of social networking sites, particularly Twitter, has occurred, little is known about
the prevalence, content, and characteristics of uncivil tweets posted by nurses and nursing students.
Objective: The aim of this study was to describe the characteristics of tweets posted by nurses and nursing
students on Twitter with a focus on cyberincivility.
Method: A cross-sectional, data-mining study was held from February through April 2017. Using a data-mining
tool, we extracted quantitative and qualitative data from a sample of 163 self-identified nurses and nursing
students on Twitter. The analysis of 8934 tweets was performed by a combination of SAS 9.4 for descriptive and
inferential statistics including logistic regression and NVivo 11 to derive descriptive patterns of unstructured
textual data.
Findings: We categorized 413 tweets (4.62%, n=8934) as uncivil. Of these, 240 (58%) were related to nursing
and the other 173 (42%) to personal life. Of the 163 unique users, 60 (36.8%) generated those 413 uncivil posts,
tweeting inappropriately at least once over a period of six weeks. Most uncivil tweets contained profanity
(n=135, 32.7%), sexually explicit or suggestive material (n=37, 9.0%), name-calling (n=14, 3.4%), and
discriminatory remarks against minorities (n=9, 2.2%). Other uncivil content included product promotion,
demeaning comments toward patients, aggression toward health professionals, and HIPAA violations.
Conclusion: Nurses and nursing students share uncivil tweets that could tarnish the image of the profession and
violate codes of ethics. Individual, interpersonal, and institutional efforts should be made to foster a culture of
cybercivility. Keywords: Civility | Cyberincivility | Education | Incivility | Nurses | Nursing | Nursing students | Social media | Social networking sites | Twitter |
مقاله انگلیسی |
9 |
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children’s surgery
استخراج داده های خاص و اختصاصی بیمار با فناوری های یادگیری ماشین برای پیش بینی لغو جراحی کودکان-2019 Background: Last-minute surgery cancellation represents a major wastage of resources and can cause significant
inconvenience to patients. Our objectives in this study were: 1) To develop predictive models of last-minute
surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from
two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact
children’s risk of day-of-surgery cancellation.
Methods and findings: We extracted five-year datasets (2012–2017) from the Electronic Health Record at
Cincinnati Children’s Hospital Medical Center. By leveraging patient-specific information and contextual data,
machine learning classifiers were developed to predict all patient-related cancellations and the most frequent
four cancellation causes individually (patient illness, “no show,” NPO violation and refusal to undergo surgery
by either patient or family). Model performance was evaluated by the area under the receiver operating characteristic
curve (AUC) using ten-fold cross-validation. The best performance for predicting all-cause surgery
cancellation was generated by gradient-boosted logistic regression models, with AUC 0.781 (95% CI:
[0.764,0.797]) and 0.740 (95% CI: [0.726,0.771]) for the two campuses. Of the four most frequent individual
causes of cancellation, “no show” and NPO violation were predicted better than patient illness or patient/family
refusal. Models showed good cross-campus generalizability (AUC: 0.725/0.735, when training on one site and
testing on the other). To synthesize a human-oriented conceptualization of pediatric surgery cancellation, an
iterative step-forward approach was applied to identify key predictors which may inform the design of future
preventive interventions.
Conclusions: Our study demonstrated the capacity of machine learning models for predicting pediatric patients
at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The
approach offers the promise of targeted interventions to significantly decrease both healthcare costs and also
families’ negative experiences. Keywords: Pediatric surgery cancellation | Quality improvement | Predictive modeling | Machine learning |
مقاله انگلیسی |
10 |
Using big data database to construct new GFuzzy text mining and decision algorithm for targeting and classifying customers
استفاده از بانک اطلاعاتی داده های بزرگ برای ساخت الگوریتم تصمیم گیری متن کاوی GFuzzy برای هدف قرار دادن و طبقه بندی مشتریان-2019 After an enterprise builds a data warehouse, it can record information related to customer interactions using
structured and unstructured data. The intention is to convert these data into useful information for decisionmaking
to ensure business continuity. Hence, this study proposes a new Chinese text classification model for the
project management office (PMO) using fuzzy semantics and text mining techniques. First, content analysis is
performed on the unstructured data to convert important textual information and compile it into a keyword
index. Next, a classification and decision algorithm for grey situations and fuzzy (GFuzzy) is used to categorize
textual data by three characteristics: maximum impact, moderate impact, and minimum impact. The purpose is
to analyze consumer behaviors for the accurate classification of customers. Lastly, a more effective marketing
strategy is formulated to target the various customer combinations, growth models, and the best mode of service.
A company database of interactions with customers is used to construct a text mining model and to analyze the
decision process of its PMO. The purpose is to test the feasibility and validity of the proposed model so that
enterprises are provided with better marketing strategies and PMO processes aimed at their customers Keywords: Big data warehouse | Content analysis | Data mining | Fuzzy grey situation decision-making algorithm | Project management office | Customer relations management |
مقاله انگلیسی |