scholarly journals QUANTITATIVE ANALYSIS OF DOCTORAL DISSERTATIONS IN SERBIA AT THE END OF 2019 ON THE TOPIC ARTIFICIAL INTELLIGENCE

2020 ◽  
Author(s):  
Olga Mirković Maksimović ◽  
Keyword(s):  
Artificial Intelligence ◽  
Quantitative Analysis ◽  
Initial State ◽  
Doctoral Dissertations

The Strategy for the Development of Artificial Intelligence (AI) for the period from 2020 to 2025 has been adopted in Serbia and the strategy proposal states that one of the goals is the development of science and innovation in the field of AI. Some of the indicators being measured are the number of patents as well as the number of published papers in this field. The initial state is currently unknown. The aim of this paper is to establish how many dissertations at which University and faculty have been defended on this topic, who are the most common mentors and members of the commissions. The paper deals with publicly available data of doctoral dissertations in Serbia and makes a quantitative analysis of doctoral dissertations on the topic of AI.

Quantitative Analysis of COVID-19 Patients

2021 ◽  
pp. 218-242
Author(s):  
Soobia Saeed ◽  
N. Z. Jhanjhi ◽  
Memood Naqvi ◽  
Mamoona Humayun ◽  
Vasaki Ponnusamy
Keyword(s):  
Artificial Intelligence ◽  
Weight Loss ◽  
Quantitative Analysis ◽  
High Resolution ◽  
Computerized Tomography ◽  
Respiratory Infections ◽  
Disease Outbreaks ◽  
Weight Loss Program ◽  
Pulmonary Lesions ◽  
The World

A new coronavirus-CoV-2 virus has caused disease outbreaks in many countries, and the number of cases is increasing rapidly through transmission from person to person. Clinical acoustics for SARS-CoV-2 patients are crucial to distinguish them from other respiratory infections. Symptomatic sufferers can also have pulmonary lesions on the photographs. A computerized tomography study in patients with suspected COVID-19 pneumonia consists of using a high-resolution approach (HRCT). Artificial intelligence applications need to be useful in categorizing the illness to an awesome severity and integrating the structured file, organized consistent with subjective issues, with objective and quantitative checks of the amount of the lesions. Data indicate the statistical document of the world in trendy. This method, with the aid of a coloring map, identifies floor glass in submission processing and separates it from consolidation and units it as a percentage in respect to the balanced weight loss program.

Artificial intelligence-assisted CT characterizations and quantitative analysis for differentiating pre-invasive lesions from invasive adenocarcinomas in pulmonary subsolid nodules ≤ 2cm.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21614-e21614
Author(s):  
Bingyu Zhang ◽  
Fenglei Yu ◽  
Muyun Peng
Keyword(s):  
Artificial Intelligence ◽  
Quantitative Analysis ◽  
Regression Model ◽  
Characteristic Curve ◽  
Maximum Diameter ◽  
Adenocarcinoma In Situ ◽  
Invasive Adenocarcinoma ◽  
Ct Value ◽  
Significant Difference ◽  
Preinvasive Lesions

e21614 Background: The use of artificial intelligence (AI) in medical imaging has dramatically improved the quality of segmentation including accuracy, efficiency and reproducibility. This study sought to determine whether AI-assisted computed tomography (CT) features and quantitative analysis of pulmonary subsolid nodules (SSNs) under 2cm could be used to differentiate preinvasive lesions from invasive adenocarcinomas. Methods: Clinical data and CT images of 297 preinvasive lesions and early invasive lung adenocarcinomas confirmed by surgery pathology with CT manifestations of SSNs under 2cm were retrospectively analysed. The nodules were divided into two groups: the preinvasive lesions (PILs, N = 115) including 7 cases of atypical adenomatous hyperplasia (AAH), 30 cases of adenocarcinoma in situ (AIS) and 78 cases of minimally invasive adenocarcinoma (MIA), and the invasive adenocarcinomas (IACs, N = 182). All CTs were processed by AI and the volume, mean CT value, consolidation-to-tumor ratio (CTR), mass and maximum diameter of each SSN were obtained. Results: The volume, mean CT value, CTR, maximum diameter and mass of nodules showed significant difference between the two groups (Table). Multivariate analysis was determined by logistic regression. The regression model between the two groups was logit(p) = -1.439-2.927Volume +0.0005(mean CT value)-0.463(CTR > 0.5) +0.238(maximum diameter)+6.298(mass).The receiver operating characteristic curve (ROC) showed that the mass can do the best prediction among all the independent factors with the areas under the curve(AUC) 0.748 at a cut-off value of 0.154, with the sensitivity of 70.9% and specificity of 70.4% .The AUC of the ROC using the regression probabilities of regression model was 0.769. Conclusions: AI-assisted CT characterizations may be promising tools to predict if SSNs under 2 cm have invaded. [Table: see text]

scholarly journals Substation Decision-making Platform Based on Artificial Intelligence

2021 ◽  
Author(s):  
Jianhua Qin ◽  
Xueqiong Zhu ◽  
Zhen Wang ◽  
Jingtan Ma ◽  
Shan Gao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Logistic Regression ◽  
Quantitative Analysis ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Final Decision ◽  
Equipment Failure ◽  
Operational Data ◽  
Electrical Data

In view of the actual needs faced by the substation maintenance, this paper proposes a kind of substation decision-making platform based on artificial intelligence. The platform formalizes and integrates the basic data, electrical data and the operational data of the equipment, qualitatively triggers the maintenance task abide by the result of the logistic regression model, provides further results of data processing through quantitative analysis, and provides knowledge navigation to the operation guidance of the corresponding equipment. The platform matches the electrical data with the inference engine stored in the knowledge base. If the data match the condition of the inference successfully, the inference is triggered and the action is executed. The result is provided to the relevant staff as a suggestion to assist the final decision. After the task is completed, the cause, effect and solution of the equipment failure are backfilled and expanded into the equipment base as a new instance.  

Role of Technology in Using Artificial Intelligence to Improve Educational Learning Challenges With Reference to India

2021 ◽  
pp. 681-703
Author(s):  
Seema Sahai ◽  
Sharad Khattar ◽  
Richa Goel
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Systematic Review ◽  
Quantitative Analysis ◽  
Technology Use ◽  
Education Technology ◽  
Learning Challenges ◽  
Research In Education ◽  
Quality In Higher Education

Artificial education intelligence (AIEd) is one of the emerging educational technological fields. A most logical question which comes up is, Is it possible to ensure quality in higher education? Can use of AI and sister technologies help us deliver in the mission? Will it be able to tackle all or most of shortcomings in the field of education? This study aims in a systematic review to provide an overview of AI applications research in education. Technology use in education and learning has undergone a remarkable transformation in the education sector. In order to accomplish this purpose, a quantitative analysis approach was used by open end questionnaire for a survey of scholars. This chapter examined the possible impacts of artificial intelligence on universities. The empirical findings indicate that the knowledge of AI is declining and there is a need to disperse knowledge of technology in higher education.

Evaluation of artificial intelligence-based quantitative analysis to identify clinically significant severe retinopathy of prematurity

Retina ◽  
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Jie Li ◽  
Kaide Huang ◽  
Rong Ju ◽  
Yuanyuan Chen ◽  
Mengyu Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Quantitative Analysis ◽  
Retinopathy Of Prematurity ◽  
Clinically Significant

scholarly journals Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference

2019 ◽  
Vol 1 (3) ◽  
pp. 945-961 ◽  
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer
Keyword(s):  
Artificial Intelligence ◽  
Quantitative Analysis ◽  
Hypothesis Testing ◽  
Statistical Inference ◽  
Data Science ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Hypothesis Tests ◽  
Analysis Methods ◽  
Procedural Components

Statistical hypothesis testing is among the most misunderstood quantitative analysis methods from data science. Despite its seeming simplicity, it has complex interdependencies between its procedural components. In this paper, we discuss the underlying logic behind statistical hypothesis testing, the formal meaning of its components and their connections. Our presentation is applicable to all statistical hypothesis tests as generic backbone and, hence, useful across all application domains in data science and artificial intelligence.

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