scholarly journals The Effect of Law Students in Entrepreneurial Psychology Under the Artificial Intelligence Technology

2021 ◽  
Vol 12 ◽  
Author(s):  
Chengjin Xu ◽  
Zhe Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
College Students ◽  
Doctoral Students ◽  
Self Efficacy ◽  
Psychological Capital ◽  
Law Students ◽  
Four Dimensions ◽  
Significant Difference ◽  
The Difference

With the increasingly serious employment situation in China, the government and schools encourage college students to start businesses to alleviate employment pressure. College student's successful entrepreneurship depends on national preferential policies, social support, and, most importantly, their healthy and solid psychological quality and entrepreneurial psychological quality. The purpose is to understand the entrepreneurial psychology of college students and study the entrepreneurial psychological effect. Firstly, the four aspects of entrepreneurial psychology are summarized, including entrepreneurial awareness, entrepreneurial volition, entrepreneurial ability, and entrepreneurial personality. Secondly, the research status of college students' entrepreneurial psychology is reviewed, and the existing problems are pointed out. Thirdly, the combined model of wavelet transform and Neural Network (NN) is proposed, and the feasibility of the proposed model is evaluated through the analysis of college students' entrepreneurial psychology. The wavelet NN is used in experimental design to predict college students' entrepreneurial psychology, and the predicted results are compared with the actual value. From the perspective of the prediction results of entrepreneurial psychology, the combination of wavelet algorithm and neural network is more accurate for entrepreneurial psychology prediction and evaluation results of law students. Overall, the difference between the predicted value and the actual value is within 0.3 points, which is relatively stable. According to the analysis of single-factor results, the scores of students of different majors in the four dimensions of entrepreneurial psychology are all higher than 3.5, but there is no significant difference among the four dimensions (P > 0.05), indicating that the major has no significant impact on entrepreneurial psychology; law students with different educational backgrounds have significant differences in entrepreneurial psychology (P < 0.05), among which students with a master's degree have the strongest entrepreneurial will, while doctoral students have the lowest entrepreneurial will; in terms of entrepreneurial psychological capital, men's self-efficacy is higher than women's, and the difference is significant (P < 0.05). The difference between males and females in the scores of entrepreneurial psychological factors' four aspects is not very obvious. In terms of entrepreneurial psychological capital, males' self-efficacy is significantly higher than females' (P < 0.05). Artificial Intelligence (AI) technology has great application prospects in the prediction and evaluation of college students' entrepreneurial psychology, and college students' entrepreneurial psychology is highly correlated with gender and education.

scholarly journals Research on General Self-Efficacy and Test Anxiety of Normal University Students

2018 ◽  
Vol 5 (2) ◽  
pp. 191 ◽  
Author(s):  
Jian Tang ◽  
Qingmin Sun
Keyword(s):  
College Students ◽  
University Students ◽  
Test Anxiety ◽  
Self Efficacy ◽  
Significant Negative Correlation ◽  
Significant Difference ◽  
Normal University ◽  
The Difference ◽  
The Relationship ◽  
General Self Efficacy

<p><em>The general self-efficacy scale and test anxiety scale are utilized for the questionnaire survey among 188 normal university students. The relationship between their general self-efficacy and test anxiety, the difference of general self-efficacy in gender and major, and difference of test anxiety in gender and major are discussed. The results indicate that there is a significant negative correlation between general self-efficacy and test anxiety of normal college students; there is a significant difference in gender and major for general self-efficacy; there is no significant difference in gender but in major for test anxiety.</em></p>

scholarly journals Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Psychological Distress ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
The Neural Network ◽  
Significant Difference ◽  
Total Data

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.

scholarly journals Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA

2013 ◽  
Vol 7 (2) ◽  
pp. 189
Author(s):  
Silviani E Rumagit ◽  
Azhari SN
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Statistical Method ◽  
Moving Average ◽  
Electric Energy ◽  
Backpropagation Neural Network ◽  
The Difference ◽  
The Government ◽  
Increasing Demand

AbstrakLatar Belakang penelitian ini dibuat dimana semakin meningkatnya kebutuhan listrik di setiap kelompok tarif. Yang dimaksud dengan kelompok tarif dalam penelitian ini adalah kelompok tarif sosial, kelompok tarif rumah tangga, kelompok tarif bisnis, kelompok tarif industri dan kelompok tarif pemerintah. Prediksi merupakan kebutuhan penting bagi penyedia tenaga listrik dalam mengambil keputusan berkaitan dengan ketersediaan energi listik. Dalam melakukan prediksi dapat dilakukan dengan metode statistik maupun kecerdasan buatan.            ARIMA merupakan salah satu metode statistik yang banyak digunakan untuk prediksi dimana ARIMA mengikuti model autoregressive (AR) moving average (MA). Syarat dari ARIMA adalah data harus stasioner, data yang tidak stasioner harus distasionerkan dengan differencing. Selain metode statistik, prediksi juga dapat dilakukan dengan teknik kecerdasan buatan, dimana dalam penelitian ini jaringan syaraf tiruan backpropagation dipilih untuk melakukan prediksi. Dari hasil pengujian yang dilakukan selisih MSE ARIMA, JST dan penggabungan ARIMA, jaringan syaraf tiruan tidak berbeda secara signifikan. Kata Kunci— ARIMA, jaringan syaraf tiruan, kelompok tarif.  AbstractBackground this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.            ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary  use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly different. Keyword—ARIMA, neural network, tarif groups

scholarly journals Efektivitas PBL setting TTW dan TPS ditinjau dari prestasi belajar, berpikir kritis dan self-efficacy siswa

2018 ◽  
Vol 5 (1) ◽  
pp. 116 ◽  
Author(s):  
Wahyu Hardiyanto ◽  
Rusgianto Heri Santoso
Keyword(s):  
Critical Thinking ◽  
Self Efficacy ◽  
Problem Based Learning ◽  
T Test ◽  
Learning Achievement ◽  
Critical Thinking Ability ◽  
Significant Difference ◽  
Thinking Ability ◽  
Quasi Experimental ◽  
The Difference

Penelitian ini bertujuan untuk mendeskripsikan keefektifan pendekatan problem-based learning (PBL) setting think talk write (TTW) dan problem-based learning (PBL) setting think pair share (TPS) serta mendeskripsikan perbedaan keefektifan antara PBL setting TTW dan PBL setting TPS ditinjau dari prestasi belajar, kemampuan berpikir kritis dan self-efficacy siswa. Penelitian ini merupakan penelitian eksperimen semu. Instrumen yang digunakan untuk mengumpulkan data adalah tes prestasi belajar, tes kemampuan berpikir kritis dan angket self-efficacy siswa. Data yang dikumpulkan dianalisis dengan menggunakan one sample t-test, dan analisis multivariat (MANOVA). One sample t-test dilakukan untuk menguji keefektifan pendekatan PBL setting TTW dan keefektifan pendekatan PBL setting TPS, sedangkan analisis multivariat (MANOVA) dilakukan untuk menguji perbedaan keefektifan antara kedua treatment tersebut ditinjau dari prestasi belajar, kemampuan berpikir kritis dan self-efficacy siswa. Hasil analisis menunjukkan bahwa pendekatan PBL setting TTW dan pendekatan PBL setting TPS efektif ditinjau dari prestasi belajar, kemampuan berpikir kritis dan self-efficacy siswa. Selain itu hasil analisis multivariat menunjukkan bahwa tidak terdapat perbedaan keefektifan yang signifikan antara pendekatan PBL setting TTW dengan pendekatan PBL setting TPS ditinjau dari prestasi belajar, kemampuan berpikir kritis dan self-efficacy siswa. The Effectiveness of PBL Setting TTW and TPS Seen from Students Learning Achievement, Critical Thinking and Self-Efficacy  AbstractThis study aims to describe the effectiveness of problem-based learning (PBL) setting think talk write (TTW) and PBL setting think pair share (TPS) and describe the difference of the effectiveness between PBL setting TTW and PBL setting TPS in terms of learning achievements, critical thinking ability and self-efficacy of grade students. This research is quasi-experimental research. The research instruments to collect the data are a learning achievement test, a test to examine the ability to think critically and a self-efficacy questionnaire. One sample t-test was conducted to examine the effectiveness PBL setting TTW and PBL setting TPS. Meanwhile, multivariate test (MANOVA) was carried out to determine the difference between PBL setting TTW and PBL setting TPS. The results show that both PBL setting TTW and PBL setting TPS are effective in terms of students learning achievements, critical thinking ability, and self-efficacy and there is no significant difference between the effectiveness of PBL setting TTW and the effectiveness of PBL setting TPS in terms of learning achievements, critical thinking ability and student self-efficacy.

scholarly journals Impact of Artificial Intelligence, With and Without Information, on Pathologists’ Decisions: An Experiment

2021 ◽  
Author(s):  
Julien Meyer ◽  
April Khademi ◽  
Bernard Têtu ◽  
Wencui Han ◽  
Pria Nippak ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Aid ◽  
Online Survey ◽  
Decision Aids ◽  
Survey Study ◽  
Gleason Grade ◽  
Accuracy Rate ◽  
Significant Difference ◽  
The Difference ◽  
The Individual

Abstract Background: Artificial intelligence (AI) is rapidly gaining attention in medicine and in pathology in particular. While much progress has been made in refining the accuracy of algorithms, thereby increasing their potential use, we need to better understand how these algorithms will be used by pathologists, who will remain for the foreseeable future the decision-makers. The objective of this paper is to determine the propensity of pathologists to rely on AI decision aids and to investigate whether providing information on the algorithm impacts this reliance.Methods: To test our hypotheses, we conducted an experiment with within-subjects design using an online survey study. 116 respondent pathologists and pathology students participated in the experiment. Each participant was tasked with assessing the Gleason grade for a series of 12 prostate cancer samples under three conditions: without advice, with advice from an AI decision aid, and with advice from an AI decision aid with information provided on the algorithm, namely the algorithm accuracy rate and the algorithm model. Scores were computed by comparing the respondents’ scores with the “true” score at the individual-question level. A mixed effects logistic regression was used to analyze the difference in scores between the different conditions, controlling for the random effects of participants and images and to assess the interactions with Experience, Gender and beliefs towards AI.Results: Participant responses to the questions with AI decision aids were significantly more accurate than the control condition without aid. However, no significant difference was found when subjects were provided with additional accuracy rate and model information on the AI advice. Moreover, the propensity to rely on AI was found to relate to general beliefs on AI but not with particular assessments of the AI tool offered. Males also performed better in the No-aid condition but not in the AI-aid condition.Conclusions: AI can significantly influence pathologists and the general beliefs in AI could be major predictors of future reliance on AI by pathologists.

scholarly journals College Students’ Academic Achievement: Exploring the Role of Hope and Academic Self-Efficacy

2021 ◽  
Vol 8 (1) ◽  
pp. 4-23
Author(s):  
Esther C. Penzar ◽  
Munyi Shea ◽  
Cher N. Edwards
Keyword(s):  
College Students ◽  
Academic Achievement ◽  
Self Efficacy ◽  
Specific Domain ◽  
College Going ◽  
Significant Difference ◽  
First Generation College ◽  
Trait Hope ◽  
And Gender ◽  
Academic Self Efficacy

In the present study, the relationships among trait hope, academic self-efficacy, and academic achievement (self-reported GPA) were examined among college students. Demographic differences were analyzed based on college-going status, ethnicity, and gender. First-generation college-going students (FGCS) reported significantly lower levels of hope, academic self-efficacy, and academic achievement when compared to non-FGCS. Male students reported significantly lower academic self-efficacy compared to female students. There was no statistically significant difference between non-White and White students. Overall, academic self-efficacy was a stronger predictor of achievement than hope. Between the two subscales of trait hope, agency was more strongly correlated with academic achievement than pathways. Furthermore, a mediation analysis indicated that academic self-efficacy fully accounted for the relationship between agency and academic achievement, which suggests that perceived capacity and agency to perform tasks in a specific domain may be more strongly associated with academic achievement than a general sense of hope and motivation.

scholarly journals Psychometric Properties of the Psychological Capital Scale in the Student Context (PsyCap-S)

2021 ◽  
Vol 31 ◽  
Author(s):  
Fabiola Rodrigues Matos ◽  
Alexsandro Luiz De Andrade
Keyword(s):  
Academic Performance ◽  
Psychometric Properties ◽  
Self Efficacy ◽  
Psychological Capital ◽  
Validity And Reliability ◽  
Four Dimensions ◽  
Undergraduate Studies ◽  
Successful Academic

Abstract The resources provided by psychological capital can contribute to the successful academic performance of students, as well as to overcome obstacles and achieve established goals. There is an absence of a Brazilian instrument to measure psychological capital in students. Thus, this study aimed to develop and to seek evidence for the validity and accuracy of the psychological capital scale in the student context (PsyCap-S). The research was conducted based on two studies, with 697 students in each. In both samples the majority was composed of females who intended to enter undergraduate studies. The results indicated the validity and reliability of a structure of four dimensions (resilience, hope, self-efficacy, and optimism). Theoretical and practical dimensions of using the instrument are discussed, as well as implications for intervention in the context of the studies.

An artificial intelligence data mining technology based evaluation model of education on political and ideological strategy of students

2020 ◽  
pp. 1-12
Author(s):  
Zheng Rong ◽  
Zheng Gang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
College Students ◽  
Data Mining ◽  
University Students ◽  
Evaluation System ◽  
Evaluation Model ◽  
Ideological And Political Education ◽  
Political Education ◽  
Ideological Education

The student’s political and ideological practices is a vital portion of education, and it is related to optimization of task based on fundamental scenario in establishing morality. In order to establish a scientific, reasonable and operable evaluation model for students’ ideological education, and evaluate the status of college students’ ideological education. In this paper, firstly, in view of the shortcomings of evaluation objectives, single evaluation methods, lack of pertinence of evaluation indicators and subjectivity of evaluation standards in the current evaluation system of university students’ ideological and political education, the basic principles for constructing evaluation models of university students’ ideological and political education are put forward. Secondly, in case to meet changing needs of the times, an artificial neural network algorithm based on artificial intelligence data mining and a traditional multi-layer fuzzy evaluation model are designed to evaluate the ideological and political education of college students. This newly proposed model integrates learning, association, recognition, self-adaptive and fuzzy information processing, and at the same time, it overcomes their respective shortcomings. Finally, an example analysis is carried out with a nearby university as an example. The evaluation results display that the evaluation model of students’ ideological education established in this paper is in good agreement with the previous evaluation results. It fully shows that the comprehensive evaluation model of fuzzy neural network for college students’ ideological and political education established in this paper is scientific and effective.

scholarly journals Cooking Beliefs and Culinary Program Interest of College Students with Overweight and Obesity from Different Environments (P21-012-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Miriam Mahmood ◽  
Jennifer Kleiman ◽  
Rachel Ryan ◽  
Kayla Wong ◽  
Ronald Lu ◽  
...  
Keyword(s):  
College Students ◽  
Weight Loss ◽  
Self Efficacy ◽  
Weight Status ◽  
Undergraduate Research ◽  
Overweight And Obesity ◽  
Weight Loss Program ◽  
New York University ◽  
Tertiary Institution ◽  
Significant Difference

Abstract Objectives College students with overweight/obesity previously reported a lack of confidence in meal planning/production, which may contribute to current weight status and subsequent weight gain. The objectives of this study were to: 1) determine cooking beliefs of students with overweight/obesity from different environments and 2) assess interest in a culinary-focused, weight loss program. Methods Students with overweight or obesity (BMI > 25), ages 18–24, enrolled in New York University (NYU) or LaGuardia Community College (LCC) were recruited. Participants completed a Qualtrics survey that included: 1) Cooking Attitudes Subscale, 2) Cooking Behaviors Subscale, 3) Cooking Self-Efficacy Scale (SEC), 4) Self-Efficacy for Using Basic Cooking Techniques Scale (SECT) and 5) a culinary program preference questionnaire. Height and weight were objectively measured. Descriptive, Chi square, Kruskal-Wallis, and post hoc Dunn test statistics were conducted. Results Students (N = 91; 19.6 ± 1.6 years; BMI 31.7 ± 5.6) were 64% female and 24% non-Hispanic. Institution type was associated with ethnicity (P = 0.03), with a higher percentage of non-Hispanic students from NYU. NYU students had a significantly lower BMI (P = 0.01) and were younger (P = 0.005). There was a significant difference in the Cooking Behaviors Subscale between institutions, with NYU students having overall lower scores (P = 0.0001). For LCC, there was a significant difference in BMI between the lowest and third quartiles of SECT scores (P = 0.04); students with a higher BMI had lower scores. At NYU, there was a significant difference in BMI between the lowest and second (P = 0.004) and third (P = 0.01) quartiles of the Cooking Behaviors Subscale; the lowest quartile had a higher mean BMI. Regardless of institution, the majority of students were interested in participating in a culinary-focused weight loss program for 6–8 weeks. However, NYU students reported a greater interest in weekly group meetings (P = 0.0001). Conclusions There is heterogeneity in cooking beliefs by college environment and BMI. However, interest in a culinary-focused, weight loss program is high for both 2- and 4-year tertiary institution students with overweight/obesity. Focus groups will be used for the development of population specific interventions. Funding Sources NYU College of Arts and Science Dean's Undergraduate Research Fund Grant (Spring 2018).

An Artificial Intelligence Hypothetical Approach for Masseter Muscle Segmentation on Ultrasonography in Patients With Bruxism

2021 ◽  
pp. 232020682110056
Author(s):  
Kaan Orhan ◽  
Gokhan Yazici ◽  
Mehmet Eray Kolsuz ◽  
Nihan Kafa ◽  
Ibrahim Sevki Bayrakdar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Retrospective Study ◽  
Masseter Muscle ◽  
Area Under The Curve ◽  
Muscle Thickness ◽  
Human Observer ◽  
Manual Segmentation ◽  
Significant Difference ◽  
Muscle Segmentation

Aim: The present study is aimed to assess the segmentation success of an artificial intelligence (AI) system based on the deep convolutional neural network (D-CNN) method for the segmentation of masseter muscles on ultrasonography (USG) images. Materials and Methods: This retrospective study was carried out by using the radiology archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry in Ankara University. A total of 195 anonymized USG images were used in this retrospective study. The deep learning process was performed using U-net, Pyramid Scene Parsing Network (PSPNet), and Fuzzy Petri Net (FPN) architectures. Muscle thickness was assessed using USG by manual segmentation and measurements using USG’s software. The neural network model (CranioCatch, Eskisehir-Turkey) was then used to determine the muscles, following automatic measurements of the muscles. Accuracy, ROC area under the curve (AUC), and Precision-Recall Curves (PRC) AUC were calculated in the test dataset and compare a human observer and the AI model. Manual segmentation and measurements were compared statistically with AI ( P < .05). The Mann–Whitney U test was used to analyze whether there is a statistically significant difference between the predicted values and the actual values. Results: The AI models detected and segmented all test muscle data for FPN and U-net, while only two cases of muscles were not detected by PSPNet (false negatives). Accuracies of FPN, PSPNet, and U-net were estimated as 0.985, 0.947, and 0.969, respectively. Receiver operating characteristic scores of FPN, PSPNet, and U-net were estimated as 0.977, 0.934, and 0.969, respectively. The D-CNN measurements of the muscles were similar to manual measurements. There was no significant difference between the two measurement methods in three groups ( P > .05). Conclusion: The proposed AI system approach for the analysis of USG images seems to be promising for automatic masseter muscle segmentation and measurement of thickness. This method can help surgeons, radiologists, and other professionals such as physical therapists in evaluating the time correctly and saving time for diagnosis.

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