Intelligent Campus Research Articles

Research and Design of Intelligent Parking Management System based on the YOLO Algorithm

Given the difficulty in managing parking lots and inefficient utilization of parking spaces at university and college campuses, this paper designs an intelligent campus parking management system with functions such as license plate recognition (LPR), online parking space reservations, campus parking navigation, and mobile app payment through the parking space detection technology based on You Only Look Once (YOLO) algorithm, Internet of Things (IoT) technology, and cloud platform technology. This system obtains information relating to license plates, parking spaces, etc. from sensing nodes with the IoT technology and transmits the collected information through NB-IoT technology to a cloud platform for storage and management to facilitate information exchange between hardware and software. This paper describes the architecture of the intelligent parking management system, parking space detection technology based on the YOLOv4 algorithm, and hardware and software design of the system. The paper proposes an effective solution to parking problems facing colleges and universities. The paper concludes that the intelligent campus parking management system based on the YOLOv4 algorithm can prevent the haphazard parking of vehicles and traffic congestion at open campuses and therefore has important value for application and popularization.

Research on the Application of Big Data and Cloud Computing Technology in Smart Campus

The current education field is experiencing an innovation driven by big data and cloud technologies, and these advanced technologies play a central role in the construction of smart campuses. Big data technology has a wide range of applications in student learning behavior analysis, teaching resource management, campus safety monitoring, and decision support, which improves the quality of education and management efficiency. Cloud computing technology supports the integration, distribution, and optimal use of educational resources through cloud resource sharing, virtual classrooms, intelligent campus management systems, and Infrastructure-as-a-Service (IaaS) models, which reduce costs and increase flexibility. This paper comprehensively discusses the practical application of big data and cloud computing technologies in smart campuses, showing how these technologies can contribute to the development of smart campuses, and laying the foundation for the future innovation of education models.

Design and Implementation of an Intelligent Campus Second-hand Trading Platform based on Microservices and Cloud Computing

With the rapid development of smart campuses, traditional campus second-hand trading methods can no longer meet the growing needs of students. This paper proposes a design scheme for an intelligent campus second-hand trading platform based on microservices and cloud computing, which provides customized trading experience for users by introducing advanced recommendation algorithms. The microservice architecture gives the platform extremely high module decoupling and autonomous scalability, ensuring the high availability and agile response of the system. The recommendation system uses a hybrid algorithm, combining content filtering and collaborative filtering, and continuously optimizes the recommendation results through machine learning, so as to accurately capture user preferences. One of the features of the platform is its community-driven data feedback mechanism, which not only can adjust the recommendation strategy in real-time but also can promote the sustainable development of campus economy and culture through data mining. In addition, the research explores a novel credit evaluation system design to establish a trustworthy trading environment. The experimental results show the technical innovation and high efficiency of the platform in multiple dimensions, which is significantly superior to the existing solutions. The successful implementation of this research indicates that the campus trading platform is moving towards a more intelligent and personalized direction, and is expected to play a key role in the smart campus ecosystem.

Student Classroom Teaching Behavior Recognition Based on DSCNN Model in Intelligent Campus Education

Student Classroom Teaching Behavior Recognition Based on DSCNN Model in Intelligent Campus Education

Research on the Path of The Digital Transformation of Education in The Era of Artificial Intelligence

The digital transformation of education is a process of driving the ecological reform of education through digital technology, so as to play the effect of education. This paper discusses the core concepts and challenges of the digital transformation of education. In the process of the digital transformation, schools face campus walls elimination, school boundaries continuously reconstruction, curriculum mode subversion, teaching practice restructuring, teachers 'role transformation, professional ability needs to be reconstructed; students' ability weakening and learning ecology needs to be reshaped. Schools need to improve the digital infrastructure, build multiple digital platform, strengthen the systematic training of teachers, to carry out the integration of multimodal learning analysis of learning process tracking, provide man-machine collaborative personalized learning support and service four ways to build intelligent campus, comprehensive innovation teaching space, improve teachers' quality, create learning ecology at full speed.

Developing an Application for Document Analysis with Latent Dirichlet Allocation: A Case Study in Integrated Quality Assurance System

Background As part of the transition of every higher education institution into an intelligent campus here in the Philippines, the Commission of Higher Education has launched a program for the development of smart campuses for state universities and colleges to improve operational efficiency in the country. With regards to the commitment of Camarines Sur Polytechnic Colleges to improve the accreditation operation and to resolve the evident problems in the accreditation process, the researchers propose this study as part of an Integrated Quality Assurance System that aims to develop an intelligent model that will be used in categorizing and automating tagging of archived documents used during accreditation. Methods As a guide in modeling the study, the researchers use an agile method as it promotes flexibility, speed, and, most importantly, continuous improvement in developing, testing, documenting, and even after delivery of the software. This method helped the researchers design the prototype with the implementation of the said model to aid the file searching process and label tagging. Moreover, a computational analysis is also included to understand the result from the devised model further. Results As a result, from the processed sample corpus, the document labels are faculty, activities, library, research, and materials. The labels generated are based on the total relative frequencies, which are 0.009884, 0.008825, 0.007413, 0.007413, and 0.006354, respectively, that have been computed between the ratio of how many times the term was used in the document and the total word count of the whole document. Conclusions The devised model and prototype support the organization in file storing and categorization of accreditation documents. Through this, retrieving and classifying the data is easier, which is the main problem for the task group. Further, other clustering, modeling, and text classification patterns can be integrated into the prototype.

Innovation of University Management Model under the Background of Intelligent Campus Platform Services

The rapid development of information technology has made intelligent campus platforms an emerging tool in university management. As an important component of higher education, innovation in the management mode of universities has a profound impact on improving education quality, optimizing resource allocation, and promoting the comprehensive development of students. This article aims to explore the innovation of university management models based on intelligent campus platform services, and provide theoretical guidance and suggestions for managers through research and analysis of relevant theories and practices.

Modeling document labels using Latent Dirichlet allocation for archived documents in Integrated Quality Assurance System (IQAS)

Background As part of the transition of every higher education institution into an intelligent campus here in the Philippines, the Commission of Higher Education has launched a program for the development of smart campuses for state universities and colleges to improve operational efficiency in the country. With regards to the commitment of Camarines Sur Polytechnic Colleges to improve the accreditation operation and to resolve the evident problems in the accreditation process, the researchers propose this study as part of an Integrated Quality Assurance System that aims to develop an intelligent model that will be used in categorizing and automating tagging of archived documents used during accreditation. Methods As a guide in modeling the study, the researchers use an agile method as it promotes flexibility, speed, and, most importantly, continuous improvement in developing, testing, documenting, and even after delivery of the software. This method helped the researchers design the prototype with the implementation of the said model to aid the file searching process and label tagging. Moreover, a computational analysis is also included to understand the result from the devised model further. Results As a result, from the processed sample corpus, the document labels are faculty, activities, library, research, and materials. The labels generated are based on the total relative frequencies, which are 0.009884, 0.008825, 0.007413, 0.007413, and 0.006354, respectively, that have been computed between the ratio of how many times the term was used in the document and the total word count of the whole document. Conclusions The devised model and prototype support the organization in file storing and categorization of accreditation documents. Through this, retrieving and classifying the data is easier, which is the main problem for the task group. Further, other clustering, modeling, and text classification patterns can be integrated into the prototype.

The design of university security intelligent management platform based on VR technology

Abstract This paper combines the actual situation of security management in colleges and universities, and analyzes the demand for security innovative management platform in colleges and universities to facilitate teachers and students to live happily and research in a comfortable and modernized campus. According to the different functionality, the innovative campus system can be divided into the 3D campus roaming navigation module based on VR technology, the intelligent attendance module based on the combination of convolutional neural network and face recognition, and the induced parking module based on ant colony algorithm. The structure of each module and the operation process are all described in detail. Simulation experiment analysis is used empirically to analyze the intelligent campus management platform. The results show that the method of this paper has an average pass rate of about 89.55% for a given number of vehicles, and compared with the pre-optimization period, the waiting time for parking has been reduced by 19.5%, with an optimization rate range of 72.37%. Even after the optimization of the method in this paper can not wholly avoid the parking wait, but it can reduce the parking wait time, which for the parking lot, can ease the parking congestion in the peak time. The theoretical guidance provided by this study is used to construct a more brilliant campus, promoting the development of security and intelligent management in colleges and universities.

A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring

The recent advancements in artificial intelligence have brought about significant changes in education. In the context of intelligent campus development, target detection technology plays a pivotal role in applications such as campus environment monitoring and the facilitation of classroom behavior surveillance. However, traditional object detection methods face challenges in open and dynamic campus scenarios where unexpected objects and behaviors arise. Open-World Object Detection (OWOD) addresses this issue by enabling detectors to gradually learn and recognize unknown objects. Nevertheless, existing OWOD methods introduce two major uncertainties that limit the detection performance: the unknown discovery uncertainty from the manual generation of pseudo-labels for unknown objects and the known discrimination uncertainty from perturbations that unknown training introduces to the known class features. In this paper, we introduce a Parallel OWOD Framework with Uncertainty Mitigation to alleviate the unknown discovery uncertainty and the known discrimination uncertainty within the OWOD task. To address the unknown discovery uncertainty, we propose an objectness-driven discovery module to focus on capturing the generalized objectness shared among various known classes, driving the framework to discover more potential objects that are distinct from the background, including unknown objects. To mitigate the discrimination uncertainty, we decouple the learning processes for known and unknown classes through a parallel structure to reduce the mutual influence at the feature level and design a collaborative open-world classifier to achieve high-performance collaborative detection of both known and unknown classes. Our framework provides educators with a powerful tool for effective campus monitoring and classroom management. Experimental results on standard benchmarks demonstrate the framework’s superior performance compared to state-of-the-art methods, showcasing its transformative potential in intelligent educational environments.

Retracted: Construction and Management of Intelligent Campus Based on Student Privacy Protection under the Background of Artificial Intelligence and Internet of Things

Retracted: Construction and Management of Intelligent Campus Based on Student Privacy Protection under the Background of Artificial Intelligence and Internet of Things

USING FEATURE SELECTION AND ACO ALGORITHM FOR OPTIMIZING SMART CLASSROOM

The smart education had a huge impact on learning and teaching, so it must be effective and highly efficient. An efficient smart campus or smart classroom will make the learning more and more easily, the students could learn and give the best activities. In addition, the teachers will be able to make right decisions. To achieve this goal, the smart classroom's conditions must be ideal. Since ACO (ant colony optimization algorithm) is a meta heuristic algorithm, in this paper, it is found that ACO, in conjunction with a machine learning classifier, was an effective method used in feature selection for selecting best features from an intelligent campus data set to create an environment that is conducive to academic success and student learning, such as (humidity and temperature), lighting and sound pressure levels, wind direction, and raw rainfall amounts (among other variables). In this contribution to get the most accurate results, the ACO algorithm was combined with a logistic regression classifier that was used to select the best features. The accuracy of the proposed model was 0.927438624 and 0.898268071 for two sets of data back to the School of Design and Environment 4, building located at the National University of Singapore

Safe campus: An intelligent campus video surveillance system for crowd analysis

Nigerian campuses comprises of diverse culture, ethnicity and religion. Controlling these campuses is a very big deal. Cameras are installed to help security personnel in carrying out these enormous tasks. However, the installed closed circuit television [CCTV] cameras are only used for evidence sourcing rather than prevention of campus vices. With the use of appropriate technique, campus vices can be prevented thus securing the campus. In this research, an Artificial Intelligent (AI) video surveillance system was developed. The system captures, analyses video for any abnormal behaviour and alerts relevant personnel for appropriate required action. The scheme uses crowd surge a crowd analyzer for videos with vulnerability and threats. The result shows that when deployed at strategic flagged locations early campus vices are detected and reported to relevant personnel whom take appropriate actions and measures to curb escalation of the vices.

Behavior Recognition of College Students Based on Improved Deep Learning Algorithm

With the vigorous development of intelligent campus construction, great changes have taken place in the development of information technology in colleges and universities from the previous digital to intelligent development. In the teaching process, the analysis of students' classroom learning has also changed from the previous manual observation to intelligent analysis. Based on this, this paper studies the behavior recognition of college students based on the improved deep learning algorithm. Based on a brief analysis of the research background of behavior recognition, the research framework of college students' behavior recognition is constructed. Finally, the authors designed an experiment to evaluate the accuracy of classroom student behavior recognition analysis. The results show that the improved recognition of college students' behavior based on deep learning algorithm can improve the recognition accuracy.

Modeling document labels using Latent Dirichlet allocation for archived documents in Integrated Quality Assurance System (IQAS)

Background: As part of the transition of every higher education institution into an intelligent campus here in the Philippines, the Commission of Higher Education has launched a program for the development of smart campuses for state universities and colleges to improve operational efficiency in the country. With regards to the commitment of Camarines Sur Polytechnic Colleges in improving the accreditation operation and to resolve the evident problems in the accreditation process, the researchers propose this study as part of an Integrated Quality Assurance System that aims to develop an intelligent model that will be used in categorizing and automating tagging of archived documents used during accreditation. Methods: As a guide in modeling the study, the researchers use an agile method as it promotes flexibility, speed, and, most importantly, continuous improvement in developing, testing, documenting, and even after delivery of the software. This method helped the researchers in designing the prototype with the implementation of the said model to aid the process in file searching and label tagging. Moreover, a computational analysis is also included to further understand the result from the devised model. Results: As a result, from the processed sample corpus, the document labels are faculty, activities, library, research, and materials. The labels generated are based on the total relative frequencies which are 0.009884, 0.008825, 0.007413, 0.007413, 0.006354, respectively, that have been computed between the ratio on how many times the term was used in the document and the total word count of the whole document. Conclusions: The devised model and prototype support the organization in file storing and categorization of accreditation documents. Through this, it is easier to retrieve and classify the data, which is the main problem for the task group. Further, other patterns in clustering, modeling, and text classification can be integrated in the prototype.

Investigation on Smart Campus Management Platform Based on Digital Twin

With the improvement of digitalization and the development of information technology, intelligent campus management has become an important research direction in the current education field. Based on digital twin technology, this article proposed a design scheme for a smart campus management platform to address the current problems in campus management. This could manage and serve students, teachers, parents, and other aspects, and achieve the mining and analysis of school big data. Through the implementation and application of the platform, the efficiency and accuracy of campus management could be improved, and the modernization of education could be further promoted. The smart campus management platform of digital twin utilized digital twin technology to digitize the campus environment, equipment, students, teaching resources, and other information of the school, thus achieving three-dimensional visualization, intelligent analysis, and feedback. The experimental results showed that the security of the smart campus management platform designed in this article exceeded 90% and performed well in system testing.

Intelligent Campus Security Warning and Decision Making Based on Feature-Aware Recognition Technology

Intelligent Campus Security Warning and Decision Making Based on Feature-Aware Recognition Technology

Retracted: Intelligent Campus Resource Sharing System Based on Data Fusion Sensor

Retracted: Intelligent Campus Resource Sharing System Based on Data Fusion Sensor

Campus Abnormal Behavior Recognition With Temporal Segment Transformers

The intelligent campus surveillance system is beneficial to improve safety in school. Abnormal behavior recognition, a field of action recognition in computer vision, plays an essential role in intelligent surveillance systems. Computer vision has been actively applied to action recognition systems based on Convolutional Neural Networks (CNNs). However, capturing sufficient motion sequence features from videos remains a significant challenge in action recognition. This work explores the challenges of video-based abnormal behavior recognition on campus. In addition, a novel framework is established on long-range temporal video structure modeling and a global sparse uniform sampling strategy that divides a video into three segments of identical durations and uniformly samples each snippet. The proposed method incorporates a consensus of three temporal segment transformers (TST) that globally connects patches and computes self-attention with joint spatiotemporal factorization. The proposed model is developed on the newly created campus abnormal behavior recognition (CABR50) dataset, which contains 50 human abnormal action classes with an average of over 700 clips per class. Experiments show that it is feasible to implement abnormal behavior recognition on campus and that the proposed method is competitive with other peer video recognition in terms of Top-1 and Top-5 recognition accuracy. The results suggest that TST-L+ can improve campus abnormal behavior recognition, corresponding to Top-1 and Top-5 accuracy results of 83.57% and 97.16%, respectively.

Intelligent Campus Informatization Management Model Based on BD and AI

Intelligent Campus Informatization Management Model Based on BD and AI