A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data

General surgery educational resources for Jordanian medical students

Background

To outline the resources deemed most beneficial to medical students during their general surgery clerkship, as well as to examine their link to students' general surgery scores and the usage of artificial intelligence in general surgery study.

Methods

A retrospective survey of Jordanian medical students from six universities was done between March and June 2023 using a 7-item questionnaire covering questions concerning general surgery study methods and scores. Descriptive statistics were used to evaluate demographic data. Chi-square is used to evaluate categorical data, with a P value <0.05 deemed significant.

Results

The average age of respondents was 23.3 years, and 54.2 % of the respondents were females, 47.8 % were from Mutah University. Most students (48.2 %) relied on tutor lectures. Students who studied through instructor lectures had the highest grades (9 % excellent, 17 % very good), followed by students who studied using surgery textbooks (6.8 % and 14.6 %, respectively). The relationship between the study method and academic achievement was statistically significant (P < 0.05).

Conclusions

Traditional face-to-face learning with instructor lectures and surgery textbooks is still the most efficient approach to attain the greatest scores. Medical students are still underutilizing artificial intelligence.

Artificial Intelligence in Surgical Learning

(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.

Retracted: A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data

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The Influence of Public Mental Health Based on Artificial Intelligence Technology on the Teaching Effect of Business Administration Major

As an emerging technology, public mental health based on artificial intelligence engineering has broad application and development prospects in improving teaching effects, realizing high efficiency and intelligence, and improving refined education. The application of public mental health based on artificial intelligence engineering in the practice of the teaching effect of business administration has a very positive significance for accurately discovering the mental health and teaching effect of business administration students. For the sake of improving the teaching effect of business administration major, we come up with a method for the influence of public mental health on the teaching effect of business administration profession based on artificial intelligence engineering. Firstly, we collect the video and audio of the teaching of business administration, conducts data screening and cleaning, and sorts out the video and audio synchronization clips as training data. Secondly, we propose a multimodal feature extraction network and a multimodal fusion network for the extraction and fusion of video and audio clips, respectively. Then, the fully connected network structure is used to evaluate and classify the effect of business administration professional teaching, and use mental health factors for evaluation to avoid human intervention and improve the prediction effect. Finally, through intensive experimental results, we prove that the method raised by us can use artificial intelligence engineering to evaluate the teaching effect of business administration majors from the perspective of public mental health and achieve good experimental results.

Exploring the Path of Enhancing Ideological and Political Education in Universities in the Era of Big Data

The emergence of the big data era has drastically altered people's lives and perceptions. One needs a thorough understanding of the topic to effectively apply big data's benefits to the ideological and political education work that colleges and universities carry out. By doing so, the advantages of big data can be better exploited and integrated into the educational process, enhancing the work's overall quality. To enhance the path of ideological and political education in colleges and universities, it is necessary to change according to the matter, advance according to the time, and make new changes according to the situation, and therefore, it is important to actively explore the path of ideological and political education in colleges and universities under the times. In this study, we research and analyze the opportunities and challenges facing the ideological and political education of universities in the era of big data, reexamine the subjective and objective environment in which the ideological and political education of universities is located, and explore the innovative development path of the ideological and political education of universities in the new environment. We will also encourage the innovative growth of ideological and political work in four areas, such as cultivating big data thinking innovation, working method innovation, working carrier innovation, and ideological work team construction, and conduct a ranking analysis on the significance of the exploration variables to improve the path of ideological work. The importance score measures the value of features in the construction of the ascending decision in the model, so the XGBoost algorithm is used to sort and analyze the significance of exploring variables to enhance the political and ideological work trajectory. The analysis of the experimental results shows that the innovation of working methods has greatly enhanced the conditions for carrying out ideological and political education in the new environment and has far-reaching implications and important significance for the innovation of ideological and political education in universities.

Construction of Hospital Human Resource Information Management System under the Background of Artificial Intelligence

Under the background of artificial intelligence (AI), a human resource information management system was designed to facilitate hospital human resource management and improve hospital management efficiency. Based on AI, SOA was constructed and Java2 platform enterprise edition (J2EE) was combined with Java to design and research hospital human resource information management system. In addition, the function and performance required by the system were tested. The results showed that the designed system showed high safety in requirement analysis and performance. The function focused mainly on the systematic analysis of personnel management, recruitment management, organization and personnel management, and patient medical information. The constructed system could work normally and achieve the efficiency of hospital human resource management. The evaluation response time of system home page access was less than 1 second when 300 users were concurrent, and the utilization rate of service CPU was lower than 50% without abnormal memory fluctuation. The concurrent response time of all 20 managers online was less than 5 seconds, and the utilization rate of the service was lower than 70%. When the information of 100 employees in the system was queried concurrently, the average CPU utilization of the database server exceeded 90%. After performance optimization, the test result showed that the transaction response time was reduced to 0.23 seconds, which met the target requirement. In conclusion, the proposed intelligent human resource management system could reduce hospital management cost and the high sharing of human resource information provided a reference for the decision-making system of hospital leaders.

Research on Music Style Classification Based on Deep Learning

Music style is one of the important labels for music classification, and the current music style classification methods extract features such as rhythm and timbre of music and use classifiers to achieve classification. The classification accuracy is not only affected by the classifier but also limited by the effect of music feature extraction, which leads to poor classification accuracy and stability. In response to the abovementioned defects, a deep-learning-based music style classification method will be studied. The music signal is framed using filters and Hamming windows, and the MFCC coefficient features of music are extracted by discrete Fourier transform. A convolutional recurrent neural network structure combining CNN and RNN is designed and trained to determine the parameters to achieve music style classification. Analysis of the simulation experimental data shows that the classification accuracy of the studied classification method is at least 93.3%, and the classification time overhead is significantly reduced, the classification results are stable, and the results are reliable.

Differences in Outcome and Comparison of Stress and Immune Status in Patients with Recurrent Common Bile Duct Stones after Biliary Tract Surgery Choosing Three Procedures (ERCP, OCBDE, and LCBDE) for Treatment

OBJECTIVE

To compare the effect of choosing ERCP, OCBDE, and LCBDE for the treatment of patients with recurrent common bile duct stones after biliary tract surgery.

METHOD

115 patients with recurrent common bile duct stones after biliary surgery in our hospital were retrospectively analyzed and divided into three groups according to the procedure, 36 patients in the ERCP group, 38 patients in the OCBDE group, and 41 patients in the LCBDE group, and compared the efficacy, stress status, and immune status of the three groups.

RESULT

The stone removal rates were 91.67%, 97.37%, and 97.56% in the ERCP, OCBDE, and LCBDE groups, respectively (P > 0.05). There were statistical differences between the ERCP, OCBDE, and LCBDE groups in terms of operative time, postoperative recovery time of exhaustion, recovery time of defecation, recovery time of feeding, and hospitalization time (P < 0.05). The postoperative complication rates were 8.33%, 10.53%, and 7.32% in the ERCP, OCBDE, and LCBDE groups, respectively (P > 0.05). The recurrence rates within 1 year after surgery were 2.78%, 7.89%, and 2.44% in the ERCP, OCBDE, and LCBDE groups, respectively (P > 0.05).

CONCLUSION

ERCP has short operative time, short hospital stay, and rapid postoperative recovery. LCBDE has mild trauma, and OCBDE has a wide range of application. Each of the three procedures has its own advantages and shortcomings, and the most appropriate procedure should be selected on the basis of comprehensive evaluation.