Intelligent Assessment System Research Articles

Study on the balance and gait characteristics of subjects with generalized joint hypermobility residing in high-altitude using wearable devices: a cross-sectional study

PurposeTo investigate the characteristics of balance and gait functions in Generalized Joint Hypermobility (GJH) subjects residing in high-altitude areas.MethodsThis study included 61 university students (28 with GJH and 33 healthy controls) all from the high-altitude region of Linzhi, Tibet Autonomous Region. The Riablo™ wearable intelligent rehabilitation assessment and training system was used to assess static balance (with eyes open and closed) and gait function (during flat walking) in both groups.ResultsCompared to healthy subjects, GJH subjects exhibited significantly impaired balance, indicated by an increased distance of the center of pressure position from the ideal center of gravity(EO: P = 0.007, EC: P = 0.031) and greater amplitude of center of pressure displacements (EO: P = 0.043, EC: P = 0.032). Gait velocity(P = 0.007), stride length(P = 0.012), and swing stance phase of the gait cycle(P = 0.046) were significantly reduced in GJH subjects compared to healthy subjects. A significant increase in the flat-foot phase of the gait cycle(P = 0.022) was observed in GJH subjects compared to healthy subjects.ConclusionThe current study demonstrated that GJH subjects residing in high-altitude areas exhibit impairments in balance and gait, providing a basis for training and prevention strategies tailored for this population. And this study used the wearable intelligent rehabilitation evaluation and training system in high-altitude areas, providing methodological references for scientific research on balance and gait function under non laboratory conditions.Trial registrationControlled Trials No.102772023RT133, Registered 13 October 2023.

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ДИСТАНЦИОННОМ ОБУЧЕНИИ: НОВЫЕ ПОДХОДЫ И ИНСТРУМЕНТЫ

This article discusses new approaches and tools based on artificial intelligence that can be effectively used in teaching. In particular, the analysis will be carried out on adaptive learning, intelligent knowledge assessment systems, virtual assistants and chatbots, as well as the use of analytical data to predict student academic success. Particular attention is paid to how these technologies can improve the quality of the educational process, improve interaction between students and teachers, and provide a more personalized experience. The article also discusses the challenges and problems associated with the implementation of AI in the educational process, including data protection issues, ethical aspects of the use of AI, as well as issues of technology availability in different regions.

Intelligent transportation storage condition assessment system for fruits and vegetables supply chain using internet of things enabled sensor network

Intelligent transportation storage condition assessment system for fruits and vegetables supply chain using internet of things enabled sensor network

Intelligent assessment system of material deterioration in masonry tower based on improved image segmentation model

Accurate and timely data collection of material deterioration on the surfaces of architectural heritage is crucial for effective conservation and restoration. Traditional methods rely heavily on extensive field surveys and manual feature identification, which are significantly affected by objective conditions and subjective factors. While machine vision-based methods can help address these issues, the accuracy, intelligence, and systematic nature of material deterioration assessment for large-scale masonry towers with complex geometries still require significant improvement. This research focuses on the architectural heritage of masonry towers and proposes an intelligent assessment system that integrates an improved YOLOv8-seg machine vision image segmentation model with refined 3D reconstruction technology. By optimizing the YOLOv8-seg model, the system enhances the extraction capabilities of both detailed and global features of material deterioration in masonry towers. Furthermore, by complementing it with image processing methods for the global visualization of large-scale objects, this research constructs a comprehensive intelligent assessment process that includes "deterioration feature extraction—global visualization—quantitative and qualitative comprehensive assessment." Experimental results demonstrate that the intelligent assessment system significantly improves the performance of target feature extraction for material deterioration in masonry towers compared to existing methods. The improved model shows improvements of 3.39% and 4.55% in the key performance metrics of mAP50 and mAP50-95, respectively, over the baseline model. Additionally, the efficiency of global feature extraction and visualization of material deterioration increased by 66.36%, with an average recognition accuracy of 95.78%. Consequently, this system effectively overcomes the limitations and subjective influences of field surveys, enhancing the objectivity and efficiency of identifying and analyzing material deterioration in masonry towers, and providing invaluable data support for the subsequent preservation and restoration efforts.

Estimation of pathological subtypes in subsolid lung nodules using artificial intelligence

ObjectiveThis study aimed to investigate the value of artificial intelligence (AI) for distinguishing pathological subtypes of invasive pulmonary adenocarcinomas in patients with subsolid nodules (SSNs). Materials and methodsThis retrospective study included 110 consecutive patients with 120 SSNs. The qualitative and quantitative imaging characteristics of SSNs were extracted automatically using an artificially intelligent assessment system. Then, radiologists had to verify these characteristics again. We split all cases into two groups: non-IA including 11 Atypical adenomatous hyperplasia (AAH) and 25 adenocarcinoma in situ (AIS) or IA including 7 minimally invasive adenocarcinoma (MIA) and 77 invasive adenocarcinoma (IAC). Variables that exhibited statistically significant differences between the non-IA and IA in the univariate analysis were included in the multivariate logistic regression analysis. Receiver operating characteristic (ROC) analyses were conducted to determine the cut-off values and their diagnostic performances. ResultsMultivariate logistic regression analysis showed that the major diameter (odds ratio [OR] = 1.38; 95 % confidence interval [CI], 1.02–1.87; P = 0.036) and entropy of three-dimensional(3D) CT value (OR = 3.73, 95 % CI, 1.13–2.33, P = 0.031) were independent risk factors for adenocarcinomas. The cut-off values of the major diameter and the entropy of 3D CT value for the diagnosis of invasive adenocarcinoma were 15.5 mm and 5.17, respectively. To improve the classification performance, we fused the major diameter and the entropy of 3D CT value as a combined model, and the (AUC） of the model was 0.868 (sensitivity = 0.845, specificity = 0.806). ConclusionThe major diameter and entropy of 3D CT value can distinguish non-IA from IA. AI can improve performance in distinguishing pathological subtypes of invasive pulmonary adenocarcinomas in patients with SSNs.

A deep introspection into the role of ChatGPT for transforming hospitality, leisure, sport, and tourism education

This perspective article explores the emerging role of artificial intelligence (AI), specifically the OpenAI language model ChatGPT, in the field of Hospitality, Leisure, Sport, and Tourism (HLST) education. With the capacity to personalize education, streamline administrative tasks, and provide data-driven insights, AI holds the potential to revolutionize teaching, learning, and research in these sectors. However, the integration of AI in education also presents challenges, including data privacy, quality control, and the risk of widened digital divide. We delve into these challenges and discuss future directions for the harmonious integration of AI into the HLST educational landscape. Future directions include improved AI-educator collaboration, adaptive and personalized learning, intelligent assessment systems, and the promotion of lifelong learning. This exploration of the transformative potential of AI in education offers valuable insights for educators, policymakers, and researchers in the HLST sectors, highlighting both the challenges to navigate and the opportunities to seize in the journey towards an AI-enhanced educational future.

Developing a smart green supplier risk assessment system integrating natural language processing and life cycle assessment based on AHP framework: An empirical study

Global carbon reduction drives the transformations of corporations, emphasizing carbon and environmental impact within supply chains. A critical challenge in risk management is converting unstructured data into quantifiable information, and ruining the conversion often leads to ineffective risk assessments. While artificial intelligence is increasingly used in supply chain risk management, previous studies seldom integrate text analysis for risk extraction and lack accurate risk assessment methods. This study pioneers an intelligent green supplier risk assessment system integrating natural language processing (NLP) and life cycle assessment (LCA) based on an analytic hierarchy process (AHP) framework. The proposed approach utilizes NLP keyword extraction to identify and assess risk and uses LCA to uncover environmental risks, minimizing negative impacts. The text classification model attains 81.7 % accuracy, ensuring a trustworthy risk assessment system. The proposed method could significantly improve risk analysis efficiency, foster sustainable procurement, and elevate enterprise competitiveness in sustainable business practices.

A Cyber-physical-social systems approach to the semantic segmentation of pulmonary embolism

Cyber-Physical-Social Systems (CPSS) epitomize the modern era’s intelligent connectivity. They integrate physical devices, computer networks, and social networks, forming an innovative paradigm for intelligent systems. Utilizing CPSS to enhance intelligence, automation, and remote services in healthcare represents a primary research focus. Pulmonary embolism, a prevalent condition resulting from the blockage of the pulmonary artery and its branches by emboli, leads to a spectrum of clinical syndromes marked by impaired pulmonary circulation and right heart dysfunction, contributing to sudden and unpredictable fatalities. Nevertheless, the diagnosis of pulmonary embolism remains challenging due to non-specific clinical presentations, constrained diagnostic capabilities, delayed diagnoses, insufficient physician knowledge, and suboptimal diagnostic techniques. Consequently, we introduce the innovative LSCU-Net architecture within the CPSS framework, designed to develop an automated segmentation and intelligent assessment system for pulmonary embolism, facilitating its automated and intelligent detection. The experimental findings demonstrate that the model accurately segments pulmonary embolism, evidenced by a Jaccard index of 0.6958, a Dice coefficient of 0.8193, a Mean Pixel Accuracy (mPA) of 0.8519, and an accuracy of 0.9993. Empirical studies reveal that our proposed model substantially surpasses existing models in performance. Consequently, this model can aid physicians in the diagnosis of pulmonary embolism during clinical practice. The established pulmonary embolism automatic segmentation and assessment system also showcases the application successes of CPSS in intelligent remote healthcare. The system’s development and deployment not only streamline physicians’ diagnostic processes but also elevate public health standards and advance CPSS research within the medical domain.

DEVELOPMENT OF INTELLIGENT SYSTEMS FOR AUTOMATIC ASSESSMENT OF STUDENTS' ACADEMIC ACHIEVEMENTS

to analyse the development of artificial intelligence systems for automatic assessment of students' learning achievements. Methodology: to achieve this goal, the scientific methods of analysis and synthesis, content analysis, SWOT analysis, comparison, and typology were used. Results: it has been established that among the key advantages is a significant increase in the objectivity of the assessment of students' knowledge and skills. It is important to consider the acceleration of the process of checking the results, which saves time and effort for teachers. Another important advantage is the provision of real-time feedback during assessment. Scientific novelty: It has been established that one of the major problems is the possibility of bias and inequality in the educational system. Given that intelligent systems are based on certain algorithms, any bias or false information in the initial data can lead to biased results. Additional challenges include the excessive mechanisation of the assessment process, which does not always allow for the individual characteristics of each student, as well as ensuring appropriate protection of personal data. Conclusions: Intelligent student assessment systems are a powerful tool in countering corruption schemes in the educational system, especially in developing countries.

Design and Implementation of Intelligent Data Security Risk Assessment and Management System

Data is facing increasingly complex and diverse security risks. To have a better control on these, an Intelligent Data Security Risk Assessment and Management system emerges. This study takes a comprehensive literature research and case analysis method to have an in-depth research on the design and implementation of Intelligent Data Security Risk Assessment and Management System. Based on the collection and analysis of relevant literature, the key features and technical content of the system are introduced. The successful experience and challenges in the practical application of the system are explored through case analysis. The results indicate that the evaluation accuracy of the system is between 91% and 98%. The design and implementation of the Intelligent Data Security Risk Assessment and Management System greatly enhances the risk management capability of the organization. The system, leveraging on intelligent technology, is efficient, accurate in response and a comprehensive management of data security risk.

ИНТЕГРАЦИЯ НЕЙРОННЫХ СЕТЕЙ В УЧЕБНУЮ ПРАКТИКУ СРЕДНЕГО ПРОФЕССИОНАЛЬНОГО ОБРАЗОВАНИЯ

The article is devoted to the study of the integration of neural networks into the educational practice of secondary vocational education. The paper considers key areas of application of neural networks, including adaptive learning systems, intelligent knowledge assessment systems, virtual mentors and analysis of educational data. The study is aimed at assessing the impact of such technologies on the quality of education, as well as on the development of digital competencies in students. The main advantages and risks of using neural networks are identified, recommendations for the successful implementation of these technologies in the educational process are proposed.

Research on student engagement in distance learning in sustainability science to design an online intelligent assessment system

Research on student engagement in distance learning in sustainability science to design an online intelligent assessment system

Artificial Intelligence and Underfunded Education

There has been a lot of general research on education, but the unique topic of underfunded education has been largely untouched. Millions of students around the world get a low-quality education due to problems in underfunded education such as the lack of experienced teachers, a high student-teacher ratio and outdated curricula. This negatively impacts them and their respective countries in the future due to subpar performance both in school and in the workplace; further exacerbating the educational and economic disparities between countries. The lack of research in this area is due to the various unique challenges facing underfunded education and the difficulty in solving them with conventional methods. However, many tools that utilise Artificial Intelligence (AI) such as adaptive learning systems, chatbots, virtual assistants and Intelligent Assessment Systems can be used to solve or at least manage these problems, making the process of education more streamlined, efficient and effective. The purpose of this study is to explore the possibility of implementing AI into underfunded education and the numerous benefits that doing so would bring. We also outline the various challenges that would be faced in implementation and possible solutions to them.

The Swallowing Intelligent Assessment System Based on Tongue Strength and Surface EMG

Dysphagia is a very important issue in modern society, and it is common in stroke patients and the elderly. Many studies have shown that tongue strength can be used as an evaluation criterion for swallowing function. This work has implemented a methodology to assess and improvise the swallowing function of dysphagia patients. To execute the same, a tongue pressure instrument was used as a tool to assess tongue strength, and a surface electromyography instrument was used to collect electrical data on larynx muscles. In addition to this, an assessment task has been carried out that is combined with interesting games to increase users’ willingness. After completing the task, the system collects tongue pressure and muscle electrical data. We use the Scoring Function calculation to quantify the user’s swallowing performance quality. In addition to calculating the quality score of the motion, we also extract features from the collected data, build a variety of machine learning models to compare each model’s classification effectiveness and select the best model to correctly predict the level of the user’s swallowing function. Through this evaluation system, we hope to provide fast and accurate evaluation results, so that medical personnel can have more convenient and effective tools for dysphagia diagnosis and rehabilitation training.

Exploring the Use of Artificial Intelligence in Teaching Management and Evaluation Based on Citation Space Analysis

This article examines the use of artificial intelligence in teaching management and teaching evaluation based on citation space analysis. First, the sources and development of artificial intelligence at home and abroad are presented, then the trend of artificial intelligence research in education is analyzed. Then, the application of artificial intelligence in education from two aspects - management and assessment of teaching, including intelligent teaching management systems and intelligent assessment systems - is examined in detail. Finally, directions for development and prospects for future applications of AI in teaching management and assessment are suggested.

"Mathematics Learning Quality Intelligent Assessment and Strategy Implementation System: A Metacognitive Intervention Study for Ineffective Mathematics Learners"

"Mathematics Learning Quality Intelligent Assessment and Strategy Implementation System: A Metacognitive Intervention Study for Ineffective Mathematics Learners"

Sponge City Construction and Urban Economic Sustainable Development: An Ecological Philosophical Perspective.

The Party's 14th Five-Year-Plan and the 2035 Visionary Goals point out that green and sustainable development is the direction of China's road in the present age, and provide a theoretical basis for further improvement of ecological civilization. "Sponge city" is a new type of urban construction idea in China; moving from pilot to vigorous implementation at present, it is an important element of China's promotion of green development and development of ecological civilization. At present, a number of sponge city pilot projects have been built in China, and evaluation of their effects is already a matter of urgency. The overall planning of China's current policies in sponge city construction and the specific analyses conducted by experts from both subjective and objective aspects have not been able to completely fill the gap in this regard, thus making it particularly urgent to conduct in-depth studies. Based on this, this paper discusses the performance assessment of sponge cities in China on the basis of the prediction and analysis of the development trend of sponge cities in China. In the performance assessment system, the correctness and timeliness of the index system should be considered in terms of practical effects; in the city performance assessment, the ideas of new city development such as low-carbon cities and smart cities should be combined to build a comprehensive and multi-perspective intelligent assessment system, so as to provide a strong boost to promote the development of city construction and its evaluative research. Firstly, a system-dynamic model is applied to sort out and combine its internal operation mechanism, and a set of evaluation systems based on the ecological philosophical perspective of the sponge city and urban sustainable development performance is established. Second, to improve the accuracy of the research results, parallelism tests and robustness analysis were conducted on this performance index evaluation system. The study's results show that sponge city construction has achieved good results in sustainable urban development and has contributed to future development.

Intelligent Urban Sensing for Gas Leakage Risk Assessment

In order to mitigate gas leakage damage, it is important to assess systematic risk of gas leakage in utility networks. In practice, leakage detection systems heavily rely on tedious and expensive human efforts, such as manually checking and assessing the risk, and thus only a very limited portion of the gas pipeline network can usually be assessed during a period of time. Therefore, it is critical to develop tools for automatically and systematically evaluating the whole gas pipeline network and revealing high-risk sites for checking in a timely fashion. To this end, in this paper, we develop an intelligent gas leakage risk assessment system based on the analysis of large-scale multi-source data, such as gas pipeline data, Point of Interests (POIs) data, and human mobility data. However, it is a non-trivial endeavor to design such a risk assessment system due to the unclear leakage mechanism, complex environmental conditions, and large size of the pipeline network. To address these challenges, both internal features of gas pipelines and external environmental features should be exploited. Specifically, we design a novel urban sensing technique to extract environmental features by analyzing human mobility data. Then, a joint learning neural network is developed to assess the leakage risk by integrating both internal and external features. Moreover, an intelligent risk assessment system is implemented and deployed for experiments in real-world scenarios. The results demonstrate the effectiveness of our system for citywide gas leakage risk assessment. Indeed, the proposed system could help to locate the potential leakage sites in a timely way.

Wearable Intelligent Machine Learning Rehabilitation Assessment for Stroke Patients Compared with Clinician Assessment.

In order to solve the shortcomings of the current clinical scale assessment for stroke patients, such as excessive time consumption, strong subjectivity, and coarse grading, this study designed an intelligent rehabilitation assessment system based on wearable devices and a machine learning algorithm and explored the effectiveness of the system in assessing patients' rehabilitation outcomes. The accuracy and effectiveness of the intelligent rehabilitation assessment system were verified by comparing the consistency and time between the designed intelligent rehabilitation assessment system scores and the clinical Fugl-Meyer assessment (FMA) scores. A total of 120 stroke patients from two hospitals participated as volunteers in the trial study, and statistical analyses of the two assessment methods were performed. The results showed that the R2 of the total score regression analysis for both methods was 0.9667, 95% CI 0.92-0.98, p < 0.001, and the mean of the deviation was 0.30, 95% CI 0.57-1.17. The percentages of deviations/relative deviations falling within the mean ± 1.96 SD of deviations/relative deviations were 92.50% and 95.83%, respectively. The mean time for system assessment was 35.00% less than that for clinician assessment, p < 0.05. Therefore, wearable intelligent machine learning rehabilitation assessment has a strong and significant correlation with clinician assessment, and the time spent is significantly reduced, which provides an accurate, objective, and effective solution for clinical rehabilitation assessment and remote rehabilitation without the presence of physicians.

Development and Application of an Intelligent Assessment System for Mathematics Learning Strategy among High School Students—Take Jianzha County as an Example

To improve the quality of mathematics learning for high school students in economically disadvantaged areas, and promote education equity and sustainable development, this study developed the Mathematics Learning Strategies Intelligent Assessment and Strategy Implementation System by using artificial intelligence technology. The system fuses assessment scales, a set of norms, improvement strategies, and the intelligent assessment and strategy implementation program into an organic whole. The system can intelligently output all participants’ diagnosis results of the mathematics learning strategy in batches and automatically propose targeted improvement strategies for every participant. By applying the intelligent system to Jianzha County, Huangnan Tibetan Autonomous Prefecture, Qinghai Province, China, the results show that the mathematical learning strategies of high school students in Jianzha County were at a middle level; mathematical cognitive strategies and mathematical resource-management strategies need to be improved. The system’s effectiveness in practical applications was later tested via both quantitative methods, such as questionnaire surveys and testing, and qualitative methods, such as interviews, as well as evaluation by self and others. By intervening with participants according to the strategy implementation program provided by the system, it was found that their mathematics learning strategy level improved. The results of the study show that the system can accurately diagnose the level of mathematics learning strategies of high school students and that interventions based on the improvement measures can improve students’ mathematics learning strategy and mathematics achievements, indicating that the system is effective.