content mining
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Author(s):  
Prachi Juneja

The objective of our work is to take apart unique data mining methods and procedures in the healthcare system that can use an assumption for coronary disease structure and their impact investigation. A coronary disease prediction model, which executes the data mining method, can help the therapeutic experts perceive the coronary sickness status subject to the patient's clinical data. Data mining description techniques for the great fundamental initiative in human system are specifically Decision trees, Naive Bayes, Neural Networks and Support Vector Machines. Hybridizing or merging any of these calculations makes decisions snappier and assigned dynamically. Information mining is a notable new improvement for extracting hypermetropic and critical information from enormous data sets to build significant and novel encounters. Using impelled data mining systems to extract essential information has been considered a fanatic method to improve human management organization's quality and precision while trimming down the social protection cost and execution time. Using this technique can expect the early detection of coronary disease. Using more information properties, for instance, could develop controllable and natural danger factors, progressively detailed results. Can also broaden this strategy. It can use an extensive part of data properties. Other data mining strategies use for forecasts, such as clustering, time series plan, and association rules. The unstructured data open in the human system industry information base can mine using content mining.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mouhamed Gaith Ayadi ◽  
Riadh Bouslimi ◽  
Jalel Akaichi

2021 ◽  
Vol 123 (13) ◽  
pp. 561-578
Author(s):  
Cecilia Pasquinelli ◽  
Mariapina Trunfio ◽  
Simona Rossi

PurposeThis study aims to frame the authenticity–standardisation relationship in international gastronomy retailing and explores how and to what extent the food place of origin and the urban context in which the gastronomy stores are located shape customers' in-store experience.Design/methodology/approachThis paper analyses the case of Eataly, which combines specialty grocery stores and restaurants disseminating the Italian eating style, quality food and regional traditions internationally. Facebook reviews (1,018) of four Eataly stores – New York City, Rome, Munich and Istanbul were analysed, adopting a web content mining approach.FindingsPlace of origin, quality and hosting city categories frame the gastronomic in-store experience. Standardisation elements (shared across the four analysed stores) and authenticity elements (specific to a single store) are identified towards defining three archetypical authenticity–standardisation relationships, namely originated authenticity, standardised authenticity and localised authenticity.Originality/valueThis study proposes original modelling that disentangles the authenticity–standardisation paradox in international gastronomy retailing. It provides evidence of the intertwining of the place of origin and the city brand in customers' in-store experience.


2021 ◽  
pp. 146144482110588
Author(s):  
Peng Zheng ◽  
Paul C Adams ◽  
Jiejie Wang

Better understanding of social media uses in crisis situations can help improve disaster management by policy-makers, organizations, businesses, and members of the public. It can also build theoretical understanding of how social life and citizenship incorporate social media usage. This study tracks the evolution of public sentiment in Wuhan, China, during the first 12 weeks after the identification of COVID-19 on the Chinese microblogging platform Sina Weibo. Data consist of 133,079 original Sina Weibo posts dealing with the novel coronavirus. The relative prevalence of eight different emotion groups is traced longitudinally using the ROST Content Mining System and the Emotion Vocabulary of Dalian University of Technology. The study finds a progression from confusion/fear, to disappointment/frustration, to depression/anxiety, then finally to happiness/gratitude. It argues that this progression indexes the changing affective energies of digital medical citizenship, which in turn indicates the context for intervention in future crises.


2021 ◽  
pp. 159-172
Author(s):  
Priyanka Shah ◽  
Hardik B. Pandit

2021 ◽  
Author(s):  
Nur Fitriyani Che Razali ◽  
Masurah Mohamad ◽  
Khairulliza Ahmad Salleh ◽  
Muhammad Hafizuddin Abd Rahman Sani ◽  
Lathifah Alfat

2021 ◽  
Vol 12 ◽  
Author(s):  
Pedro Farias ◽  
Romeu Francisco ◽  
Lorrie Maccario ◽  
Jakob Herschend ◽  
Ana Paula Piedade ◽  
...  

Tellurium (Te) is a metalloid with scarce and scattered abundance but with an increased interest in human activity for its uses in emerging technologies. As is seen for other metals and metalloids, the result of mining activity and improper disposal of high-tech devices will lead to niches with increased abundance of Te. This metalloid will be more available to bacteria and represent an increasing selective pressure. This environmental problem may constitute an opportunity to search for microorganisms with genetic and molecular mechanisms of microbial resistance to Te toxic anions. Organisms from Te-contaminated niches could provide tools for Te remediation and fabrication of Te-containing structures with added value. The objective of this study was to determine the ability of a high metal-resistant Paenibacillus pabuli strain ALJ109b, isolated from high metal content mining residues, to reduce tellurite ion, and to evaluate the formation of metallic tellurium by cellular reduction, isolate the protein responsible, and determine the metabolic response to tellurite during growth. P. pabuli ALJ109b demonstrated to be resistant to Te (IV) at concentrations higher than reported for its genus. It can efficiently remove soluble Te (IV) from solution, over 20% in 8 h of growth, and reduce it to elemental Te, forming monodisperse nanostructures, verified by scattering electron microscopy. Cultivation of P. pabuli ALJ109b in the presence of Te (IV) affected the general protein expression pattern, and hence the metabolism, as demonstrated by high-throughput proteomic analysis. The Te (IV)-induced metabolic shift is characterized by an activation of ROS response. Flagellin from P. pabuli ALJ109b demonstrates high Te (0) forming activity in neutral to basic conditions in a range of temperatures from 20°C to 37°C. In conclusion, the first metabolic characterization of a strain of P. pabuli response to Te (IV) reveals a highly resistant strain with a unique Te (IV) proteomic response. This strain, and its flagellin, display, all the features of potential tools for Te nanoparticle production.


Author(s):  
Monther Khalafat ◽  
Ja'far S. Alqatawna ◽  
Rizik M. H. Al-Sayyed ◽  
Mohammad Eshtay ◽  
Thaeer Kobbaey

<p class="0abstract">Today, the influence of the social media on different aspects of our lives is increasing, many scholars from various disciplines and majors looking at the social media networks as the ongoing revolution. In Social media networks, many bonds and connections can be established whether being direct or indirect ties. In fact, Social networks are used not only by people but also by companies. People usually create their own profiles and join communities to discuss different common issues that they have interest in. On the other hand, companies also can create their virtual presence on the social media networks to benefit from this media to understand the customers and gather richer information about them. With all of the benefits and advantages of social media networks, they should not always be seen as a safe place for communicating, sharing information and ideas, and establishing virtual communities. These information and ideas could carry with them hatred speeches that must be detected to avoid raising violence. Therefore, web content mining can be used to handle this issue. Web content mining is gaining more concern because of its importance for many businesses and institutions.  Sentiment Analysis (SA) is an important sub-area of web content mining.  The purpose of SA is to determine the overall sentiment attitude of writer towards a specific entity and classify these opinions automatically. There are two main approaches to build systems of sentiment analysis: the machine learning approach and the lexicon-based approach. This research presents the design and implementation for violence detection over social media using machine learning approach. Our system works on Jordanian Arabic dialect instead of Modern Standard Arabic (MSA). The data was collected from two popular social media websites (Facebook, Twitter) and has used native speakers to annotate the data. Moreover, different preprocessing techniques have been used to show their effect on our model accuracy. The Arabic lexicon was used for generating feature vectors and separate them to features set. Here, we have three well known machine learning algorithms: Support Vector Machine (SVM), Naive Bayes (NB) and k-Nearest Neighbors (KNN). Building on this view, Information Science Research Institute’s (ISRI) stemming and stop word file as a result of preprocessing were used to extract the features. Indeed, several features have been extracted; however, using the SVM classifier reveals that unigram and features extracted from lexicon are characterized by the highest accuracy to detect violence.</p>


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