Simulation Research on Intelligent Investment Agent Monitoring and Management System Optimization Based on Artificial Intelligence

2021 ◽  
Vol 7 (5) ◽  
pp. 2035-2044
Author(s):  
Man Lu
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Financial Risk ◽  
Optimization Problems ◽  
Principal Component ◽  
System Optimization ◽  
Investment Model ◽  
Simulation Research ◽  
Investment Models ◽  
Step Factor

Objectives: How to construct an effective investment early warning model, scientifically and accurately predict China’s extreme financial risks, and thus formulate effective measures to deal with and prevent risks, has become an important issue urgently needed to be solved by financial risk management departments and investors. Based on multi-step factor analysis and artificial intelligence classification, two main intelligent investment models based on artificial intelligence are designed in this paper. Firstly, the principal component analysis method and multi-step factor extraction method are used to select the variables of 28 Financial Indicators of listed companies, and a multi-factor analysis investment model is constructed. Secondly, a smart single classifier based on factor analysis is designed. The experimental results show that combined with multi-step factor analysis has a better warning effect. The final research results show that the algorithm can guarantee the convergence performance and dispersion performance for different optimization problems, and has the advantages of stability and robustness, which embodies the application value of the algorithm.

scholarly journals Educational Information System Optimization for Artificial Intelligence Teaching Strategies

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Taotang Liu ◽  
Zhongxin Gao ◽  
Honghai Guan
Keyword(s):  
Artificial Intelligence ◽  
Information System ◽  
Network Architecture ◽  
Optimization Problems ◽  
Optimization Methods ◽  
System Optimization ◽  
Engineering Problem ◽  
Engineering Practice ◽  
Local Optimum ◽  
Clustering Method

Under the background of the information age, scientific research and engineering practice have developed vigorously, resulting in many complex optimization problems that are difficult to solve. How to design more effective optimization methods has become the focus of urgent solutions in many academic fields. Under the guidance of such demand, intelligent optimization algorithms have emerged. This article analyzes and optimizes the modern artificial intelligence teaching information system in detail. On the basis of determining the network architecture, a detailed demand analysis was carried out, and the overall structure optimization of the network was given; the business process and data flow of the main modules of the website (resource center module and collaborative learning module) were optimized. In order to further enhance the local search ability of the algorithm, a multiclass interactive optimization algorithm is proposed in combination with the Euclidean distance-based clustering method, which changes the teaching mode from “one-person teaching” to “multiperson teaching.” This clustering method has lower complexity and is beneficial to enhance the utilization of neighborhood information. At the same time, in order to enhance the diversity of the population and strengthen the connection between the subgroups, after the teaching phase, the worst students in each subgroup are allowed to learn from the best teachers of the population, and after the learning phase, individuals in a random subgroup are allowed to learn from other subgroups. The algorithm was tested in the experimental environment of unconstrained, constrained, and an engineering problem. From the test results, it can be seen that the algorithm is not easy to fall into the local optimum. Compared with other algorithms, the solution accuracy is higher and the stability is better. And it performed well in engineering optimization problems, thus verifying the effectiveness of the strategy.

scholarly journals Metacognitive Rating Scale: A Study Applying a Korean Translation to Individuals with Schizophrenia

2021 ◽  
Vol 18 (13) ◽  
pp. 6853
Author(s):  
Mihwa Han ◽  
Kyunghee Lee ◽  
Mijung Kim ◽  
Youngjin Heo ◽  
Hyunseok Choi
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Mental Status ◽  
Rating Scale ◽  
Anger Expression ◽  
Principal Component ◽  
Negative Beliefs ◽  
Five Factors ◽  
Metacognitive Beliefs

Metacognition is a higher-level cognition of identifying one’s own mental status, beliefs, and intentions. This research comprised a survey of 184 people with schizophrenia to verify the reliability of the metacognitive rating scale (MCRS) with the revised and supplemented metacognitions questionnaire (MCQ) to measure the dysfunctional metacognitive beliefs of people with schizophrenia by adding the concepts of anger and anxiety. This study analyzed the data using principal component analysis and the varimax method for exploratory factor analysis. To examine the reliability of the extracted factors, Cronbach’s α was used. According to the results, reliability was ensured for five factors: positive beliefs about worry, negative beliefs about uncontrollability and danger of worry, cognitive confidence, need for control, and cognitive self-consciousness. The negative beliefs about uncontrollability and danger of worry and the need for control on anger expression, which were both added in this research, exhibited the highest correlation (r = 0.727). The results suggest that the MCRS is a reliable tool to measure the metacognition of people with schizophrenia.

scholarly journals Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ozan Karaca ◽  
S. Ayhan Çalışkan ◽  
Kadir Demir
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Medical Students ◽  
Data Science ◽  
Factor Model ◽  
Health Science ◽  
Measurement Tool ◽  
Validity And Reliability ◽  
Mathematical Concepts ◽  
Evaluation And Monitoring

Abstract Background It is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. In this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine. Methods To define medical students’ required competencies on AI, a diverse set of experts’ opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied. Results A total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach’s alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (χ2/df = 3.81, RMSEA = 0.094, SRMR = 0.057, CFI = 0.938, and NNFI (TLI) = 0.928). These values showed that the four-factor model has construct validity. Conclusions The newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow ‘a physician training perspective that is compatible with AI in medicine’ to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants’ end-course perceived readiness opportunities.

Use of the Perceived Physical Competence Scale with Adolescents with Disabilities

1995 ◽  
Vol 80 (2) ◽  
pp. 571-577 ◽  
Author(s):  
Taru Lintunen ◽  
Pilvikki Heikinaro-Johansson ◽  
Claudine Sherrill
Keyword(s):  
Gender Differences ◽  
Factor Analysis ◽  
Principal Component ◽  
Principal Component Factor Analysis ◽  
Validity And Reliability ◽  
Varimax Rotation ◽  
Physical Competence ◽  
Related Literature ◽  
Competence Scale ◽  
Two Factors

The construct validity and reliability of the 1987 Perceived Physical Competence Scale of Lintunen were examined to assess the applicability of the instrument for use with adolescents with disabilities. Subjects were 51 girls and 34 boys ( M age = 15.1 yr.) from several schools in central Finland. Principal component factor analysis with varimax rotation yielded the same two factors for adolescents with disabilities as reported for nondisabled adolescents in the related literature. Cronbach alphas for the two factors were .89 and .56. It was concluded that the scale is an appropriate measure for adolescents with disabilities. Statistical analysis indicated no gender differences for adolescents with disabilities. When compared with nondisabled groups in the related literature, these adolescents had perceived fitness similar to nondisabled peers but significantly lower than that of athletes without disabilities.

Optimum Selection of Hydraulic Devices for Water Hammer Control in the Pipeline Systems Using Genetic Algorithm

2003 ◽  
Author(s):  
Bong Seong Jung ◽  
Bryan W. Karney
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Optimization Problems ◽  
System Optimization ◽  
Water Hammer ◽  
Pipeline System ◽  
Optimal Size ◽  
Protection Strategy ◽  
Hydraulic Devices ◽  
Selection Of

Genetic algorithms have been used to solve many water distribution system optimization problems, but have generally been limited to steady state or quasi-steady state optimization. However, transient events within pipe system are inevitable and the effect of water hammer should not be overlooked. The purpose of this paper is to optimize the selection, sizing and placement of hydraulic devices in a pipeline system considering its transient response. A global optimal solution using genetic algorithm suggests optimal size, location and number of hydraulic devices to cope with water hammer. This study shows that the integration of a genetic algorithm code with a transient simulator can improve both the design and the response of a pipe network. This study also shows that the selection of optimum protection strategy is an integrated problem, involving consideration of loading condition, device and system characteristics, and protection strategy. Simpler transient control systems are often found to outperform more complex ones.

Developing the Organisational Justice Scale and Examining Teachers' and School Administrators' Views about Organisational Justice in Primary Schools

2021 ◽  
pp. 33-50
Author(s):  
Hasan Basri Memduhoðlu ◽  
Ali Ýhsan Yildiz
Keyword(s):  
Factor Analysis ◽  
Primary Schools ◽  
School Administrators ◽  
Data Gathering ◽  
Principal Component ◽  
Measurement Tool ◽  
Principal Component Factor Analysis ◽  
Reliable Measurement ◽  
Organisational Justice ◽  
Confirmatory Factor

The purpose of this study is to develop a reliable and valid measurement tool to explore views about organisational justice in schools and to examine teachers' and school administrators' views about organisational justice in primary schools. The sample of the study consisted of a total of 455 participants, 176 school administrators and 279 teachers from the primary schools in the Centre of Van. The Organisational Justice Scale, developed by the authors, was employed as data gathering tool. Principal Component Factor Analysis was used to determine the content and construct validities of the scale and Confirmatory Factor Analysis was employed to evaluate the obtained results. As a result of the study, the developed Organisational Justice Scale (OJS) was found to be a valid and reliable measurement tool for school applications.

scholarly journals Some Applications of Lévy Processes to Stochastic Investment Models for Actuarial Use

1998 ◽  
Vol 28 (1) ◽  
pp. 77-93 ◽  
Author(s):  
Terence Chan
Keyword(s):  
Time Series ◽  
Brownian Motion ◽  
Differential Equations ◽  
White Noise ◽  
Stochastic Differential Equations ◽  
Continuous Time ◽  
Investment Model ◽  
Stable Processes ◽  
Gamma Processes ◽  
Investment Models

AbstractThis paper presents a continuous time version of a stochastic investment model originally due to Wilkie. The model is constructed via stochastic differential equations. Explicit distributions are obtained in the case where the SDEs are driven by Brownian motion, which is the continuous time analogue of the time series with white noise residuals considered by Wilkie. In addition, the cases where the driving “noise” are stable processes and Gamma processes are considered.

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