An Intelligent Cognitive-Inspired Computing with Big Data Analytics Framework for Sentiment Analysis and Classification

Integrated design of financial self-service terminal based on artificial intelligence voice interaction with the rapid development of science and technology, artificial intelligence technology is deepening in the field of intelligence and automation. The financial industry is the lifeblood of a country’s economy, with great growth potential and high growth rate. The integrated design of intelligent financial self-service terminal has become an important topic in the field of rapid development of social economy and science and technology. Therefore, this paper designs the integration of financial self-service terminal based on artificial intelligence voice interaction. First, this paper introduces the meaning and composition of financial self-service terminal integration, then studies the voice interaction principle based on artificial intelligence technology, and designs the integrated structure of financial self-service terminal with voice interaction. After that, this paper makes a series of tests on voice interaction technology, user experience, and the performance of financial self-service terminal. Finally, the test results of voice interaction are as follows: the delay estimation results of voice interaction of the terminal are relatively accurate, and the error points are basically within five sampling points, which indicate that the delay estimation algorithm is practical. The endpoint detection method based on CO complexity can effectively overcome the impact of noise environment on speech endpoint detection system and is suitable for the requirements of robust speech recognition system. Considering that the actual application scenario of voice positioning can judge the speaker’s position and turn to the speaker’s direction during human–computer interaction, the azimuth error is acceptable within a few degrees to meet the application requirements. The direction angle error is acceptable within a few degrees to meet the application requirements. The accuracy of the improved algorithm is improved in intercepting effective speech signals. The terminal has short running time and delay time, small memory, and central processing unit (CPU) occupation and can meet the needs of users. The speech recognition accuracy of the financial self-service terminal basically reaches more than 80%, which can basically meet the daily needs.

Section: Discussion On the Integration Of Financial Self-service Term...mentioning

confidence: 99%

Integrated Design of Financial Self-Service Terminal Based on Artificial Intelligence Voice Interaction

Chen

Chen²,

Zhao

2022

“…Finally, it trains and recognizes the annotated feature information through the support vector machine. It constructs different classification models through two different classification ideas and finally analyzes the results ( Jain et al, 2022 ).…”

Section: English Flipped Classroom Teaching Mode Methods Based On Emo...mentioning

confidence: 99%

English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

Lin

2022

With the development of modern information technology, the flipped classroom teaching mode came into being. It has gradually become one of the hotspots of contemporary educational circles and has been applied to various disciplines at the same time. The domestic research on the flipped classroom teaching mode is still in the exploratory stage. The application of flipped classroom teaching mode is still in the exploratory stage. It also has many problems, such as low class efficiency, poor teacher-student interaction, outdated teaching modes, not student-centered, etc., which lead to poor students’ enthusiasm for learning. Therefore, the current English flipped classroom teaching mode still needs to be tested and revised in practice. Combined with emotion recognition technology, this paper analyzes speech emotion recognition, image emotion recognition, and audition emotion recognition technology and conducts a revision test for the current English flipped classroom teaching mode. It uses the SVM algorithm for one-to-one method and dimension discretization for emotion recognition, and finds that the recognition results after different dimension classification recognition are improved for each emotion. Among them, the recognition rate of different dimension classification recognition methods is 2.6% higher than that of one-to-one method. This shows that under the same conditions, the emotion recognition technology of different dimension classification recognition methods is higher.

“…Corporal punishment is a physical and psychological abuse of students, and at the same time, it does not exclude the insult to their mind and personality. There is an essential difference between corporal punishment and punishment ( Syafriadi et al, 2021 ; Jain et al, 2022 ). First of all, discipline emphasizes the word “precept,” which is aimed at guiding and educating, and does not have the purpose of harming the physical and mental health of students.…”

Section: The Influence Of Teacher Discipline On Teaching Effect and S...mentioning

confidence: 99%

The Influence of Teacher Discipline on Teaching Effect and Students’ Psychology in Universities and the Normative Suggestions for Discipline Behavior

2022

In today’s educational environment, with the popularization of laws, more and more students pay attention to the maintenance of their own rights. However, due to the misinterpretation of punishment, it is very easy to mistake teacher punishment for “corporal punishment.” Therefore, it is particularly important to investigate the impact of teacher discipline on students. This paper first collects some knowledge related to the research based on the research results of scholars, and then makes a detailed analysis of this research from two aspects. It, respectively, introduces the influence of teacher discipline on teaching effect and students’ psychology in universities, and the normative suggestions for discipline behavior in this paper. It then uses formulas to explain how the teaching and learning optimization algorithm works. Finally, it analyzes the changes among teachers’ discipline, students’ psychology, and coping style through experiments. The results showed that urban students had the highest probability of being disciplined for being late, at 53%, and the lowest probability of being disciplined for not completing homework, at 34%.