Monitoring Commercial Diffusion of Technology Using Qualitative Text Content Classification: The Case of Ai Diffusion Among S&P 500 Companies

2021 ◽  
Author(s):  
Tomasz Mucha ◽  
Timo Seppälä
Keyword(s):  
Diffusion Of Technology ◽  
Content Classification ◽  
Text Content

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

Survey of Diffusion of Technology in Small Businesses

2010 ◽  
Vol 4 (3) ◽  
pp. 40-52
Author(s):  
Alev Efendioglu ◽  
◽  
Tugba Karabulut ◽  
Eugene Muscat ◽  
◽  
...  
Keyword(s):  
Small Businesses ◽  
Diffusion Of Technology

The Huguenots and the Diffusion of Technology. I

1952 ◽  
Vol 60 (4) ◽  
pp. 294-311 ◽  
Author(s):  
Warren C. Scoville
Keyword(s):  
Diffusion Of Technology

scholarly journals A Spectral-Based Approach for BCG Signal Content Classification

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1020
Author(s):  
Mohamed Chiheb Ben Nasr ◽  
Sofia Ben Jebara ◽  
Samuel Otis ◽  
Bessam Abdulrazak ◽  
Neila Mezghani
Keyword(s):  
Human Body ◽  
Supervised Classification ◽  
Unsupervised Classification ◽  
Gaussian Mixture ◽  
K Nearest Neighbors ◽  
Content Classification ◽  
Extraction Step ◽  
Vital Sign Measurement ◽  
Definition Of ◽  
Spectral Flatness Measure

This paper has two objectives: the first is to generate two binary flags to indicate useful frames permitting the measurement of cardiac and respiratory rates from Ballistocardiogram (BCG) signals—in fact, human body activities during measurements can disturb the BCG signal content, leading to difficulties in vital sign measurement; the second objective is to achieve refined BCG signal segmentation according to these activities. The proposed framework makes use of two approaches: an unsupervised classification based on the Gaussian Mixture Model (GMM) and a supervised classification based on K-Nearest Neighbors (KNN). Both of these approaches consider two spectral features, namely the Spectral Flatness Measure (SFM) and Spectral Centroid (SC), determined during the feature extraction step. Unsupervised classification is used to explore the content of the BCG signals, justifying the existence of different classes and permitting the definition of useful hyper-parameters for effective segmentation. In contrast, the considered supervised classification approach aims to determine if the BCG signal content allows the measurement of the heart rate (HR) and the respiratory rate (RR) or not. Furthermore, two levels of supervised classification are used to classify human-body activities into many realistic classes from the BCG signal (e.g., coughing, holding breath, air expiration, movement, et al.). The first one considers frame-by-frame classification, while the second one, aiming to boost the segmentation performance, transforms the frame-by-frame SFM and SC features into temporal series which track the temporal variation of the measures of the BCG signal. The proposed approach constitutes a novelty in this field and represents a powerful method to segment BCG signals according to human body activities, resulting in an accuracy of 94.6%.

scholarly journals Systemic Lupus Erythematosus before and after COVID-19 Lockdown: How the Perception of Disease Changes through the Lenses of Narrative Medicine

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 726
Author(s):  
Fulvia Ceccarelli ◽  
Venusia Covelli ◽  
Giulio Olivieri ◽  
Francesco Natalucci ◽  
Fabrizio Conti
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Narrative Medicine ◽  
Good Control ◽  
Final Analysis ◽  
Point Of View ◽  
Systemic Lupus ◽  
Before And After ◽  
Text Content ◽  
Insight Into

Background: The COVID-19 pandemic contributes to the burden of living with different diseases, including Systemic Lupus Erythematosus (SLE). We described, from a narrative point of view, the experiences and perspectives of Italian SLE adults during the COVID-19 emergency, by distinguishing the illness experience before and after the lockdown. Methods: Fifteen patients were invited to participate. Illness narratives were collected between 22 and 29 March 2020 using a written modality to capture patients’ perspectives before and after the COVID-19 lockdown. We performed a two-fold analysis of collected data by distinguishing three narrative types and a qualitative analysis of content to identify the relevant themes and sub-themes reported. Results: Eight narratives included in the final analysis (mean length 436.9 words) have been written by eight females (mean age 43.3 ± 9.9 years, mean disease duration 13.1 ± 7.4 years). Six patients provided a quest narrative, one a chaos and the remaining one a restitution narrative. By text content analysis, we identified specific themes, temporally distinct before and after the lockdown. Before COVID-19, all the patients referred to a good control of disease, however the unexpected arrival of the COVID-19 emergency broke a balance, and patients perceived the loss of health status control, with anxiety and stress. Conclusions: We provided unique insight into the experiences of people with SLE at the time of COVID-19, underlining the perspective of patients in relation to the pandemic.

Text Content Based Layout Analysis

2020 ◽  
Author(s):  
Jose Ramon Prieto ◽  
Vicente Bosch ◽  
Enrique Vidal ◽  
Dominique Stutzmann ◽  
Sebastien Hamel
Keyword(s):  
Layout Analysis ◽  
Text Content
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