Universal algorithms for multinomial logistic regression under Kullback–Leibler game

Dzhamtyrova, Raisa; Kalnishkan, Yuri

doi:10.1016/j.neucom.2019.06.105

Cited by 4 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multinomial logistic regression analysis predicts categorical variables or the probability of participating in a category as a dependent variable based on multiple independent variables [37]. This type of regression analysis is often used as it does not require regularity, linearity, or homoscedasticity [38,39]. In addition, multinomial logistic regression analysis uses a maximum likelihood estimation to assess the probability of categorical participation [40].…”

Section: Methodsmentioning

confidence: 99%

Youth’s Entrepreneurial Intention: A Multinomial Logistic Regression Analysis of the Factors Influencing Greek HEI Students in Time of Crisis

et al. 2022

View full text Add to dashboard Cite

Governments’ coronavirus disease (COVID-19) measures have forced the community to stay at home. During this period, youths have had time to think about their career paths. For some, a conventional eight-hour job in a private company is undesirable; meanwhile, entrepreneurship can mitigate the adverse effects of the crisis, such as unemployment. Accordingly, this study investigates the intention of Greek youths that study in a higher education institution (HEI) to engage in entrepreneurship in times of crisis, and highlights the factors that drive them to enter the business arena. This study designed and distributed a questionnaire to 369 Greek youths who were studying at the University of Thessaly. The data from the empirical research were used to develop a multinomial logistic regression model to investigate youths’ entrepreneurial intention and reveal the driving factors. The results showed that during times of crisis, youths appeared hesitant to enter the business arena. However, those who were more confident indicated that entrepreneurship could provide them with greater satisfaction. Driving factors for engaging in entrepreneurship were family and friends. Inhibiting factors for establishing a new business were having previous work experience in a family business and lack of educational knowledge. This study contributes to the understanding of youths’ entrepreneurial intention and the driving factors that play a key role in shaping this intention in Greece during a crisis period, since there are few studies on this topic.

show abstract

Section: Methodsmentioning

confidence: 99%

Youth’s Entrepreneurial Intention: A Multinomial Logistic Regression Analysis of the Factors Influencing Greek HEI Students in Time of Crisis

et al. 2022

View full text Add to dashboard Cite

show abstract

“…ISSN 2563-7568 The DNN has also been compared with other statistical models, including multinomial logistic regression [43], linear support vector machine (SVM) [44], SVM with Gaussian kernel [45] and Extreme Learning Machine (ELM) [46]. Table 3 shows the evaluation on the test data using metrics such as accuracy, sensitivity, specificity, and area under receiver operating curve (ROC).…”

Section: Machine Learningmentioning

confidence: 99%

Development of Conversational Artificial Intelligence for Pandemic Healthcare Query Support

Woo,

Gao,

Al-Nima

et al. 2020

Int J Auto AI Mach learn

View full text Add to dashboard Cite

The paper proposes and describes the development of conversational artificial intelligence (AI) agent to support hospital healthcare and COVID-19 queries. The conversational AI agent is called “Akira” and it is developed using deep neural network and natural language processing. It is capable of reading the inputs from the user, understanding the input and identifying the intention, and outputting messages towards the user, and these steps are iterated until the user prompts to exit or the programme is terminated. A deep learning model has been trained, and Akira could converse with the user ranging from the conversation over 7 topics related to COVID-19, common cold and flu, mental health, sexual health, abortions, allergens, drugs and medicine. The paper also describes the importance of designing an interactive human-user interface when dealing with conversational agent. In addition. the context of ethical issues and security concerns when designing the agent has been taken into consideration and discussed. The conversational agent is demonstrated to answer queries from a pool of 57 participants.

show abstract

“…Unlike other methods that require regular testing to assess water quality, logistic modeling can provide valuable information on coliform growth and help in decision making much faster and more effectively (Dzhamtyrova & Kalnishkan, 2020). The present study aimed to perform mathematical modelling using a logistic deterministic model under controlled laboratory conditions, i.e.…”

Section: Introductionmentioning

confidence: 99%

Logistic Model for Predicting Coliform Growth in Wastewater Effluent From the Chambo River (Ecuador)

Béjar-Suárez,

Muyulema-Allaica,

Béjar Cáceres

et al. 2024

J. Professional Business Review

View full text Add to dashboard Cite

Purpose: The objective of this work is to apply a predictive model of bacterial growth to determine the concentration of total coliforms, under controlled laboratory conditions, in wastewater from the city of Riobamba that flows into the Chambo River (Ecuador). Theoretical Framework: Wastewater pollution is a global problem that affects water quality and public health. Population growth has a drastic impact on the contamination of water bodies, particularly in Ecuador, since most of the effluents are not adequately treated, which becomes a public health and environmental problem. For this reason, over the years, total and fecal coliforms have been used as indicators of water quality. Design/Methodology/Approach: The study used a mathematical modeling approach of a logistic and deterministic type, and calculated through an executable command designed in the integrated development environment (IDE) with the Visual C# programming language (logic) and the XAML design language (interface). Findings: The results achieved are 1) the theoretical construction of a mathematical model (logistic model) starting from a differential equation and the taking of three experimental measurements, 2) the computer construction of the bacterial growth model and 3) it has been demonstrated that there is agreement between the values obtained from the model of total coliform growth and the experimental results with a margin of error of less than 1%. Research, Practical & Social Implications: This information can be used by stakeholders to determine actions that could improve current conditions. Originality/Value: The result of this study emphasizes that the model proposed can reproduce the growth of coliforms up to a maximum growth point, it is necessary to continue with the development of the same in the stationary phase of bacterial growth and then in the decay phase.

show abstract

Universal algorithms for multinomial logistic regression under Kullback–Leibler game

Cited by 4 publications

References 9 publications

Youth’s Entrepreneurial Intention: A Multinomial Logistic Regression Analysis of the Factors Influencing Greek HEI Students in Time of Crisis

Youth’s Entrepreneurial Intention: A Multinomial Logistic Regression Analysis of the Factors Influencing Greek HEI Students in Time of Crisis

Development of Conversational Artificial Intelligence for Pandemic Healthcare Query Support

Logistic Model for Predicting Coliform Growth in Wastewater Effluent From the Chambo River (Ecuador)

Contact Info

Product

Resources

About