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The rapid advancement of IoT technology presents transformative opportunities across various sectors, with education being a prominent beneficiary. Smart classrooms, a product of IoT integration, are being widely adopted to create technology-enhanced, student-centric learning environments that cater to students' information literacy needs, particularly during events like pandemics. This widespread adoption generates substantial amounts of educational data, commonly known as big data, necessitating innovative solutions for analysis and utilization. To solve these challenges, this paper proposes utilizing the Apriori algorithm—a data mining technique renowned for uncovering valuable patterns and associations within extensive datasets. This paper evaluates the impact of various information resources with differing quality, considering individuals' information literacy skills. Utilizing data mining techniques, it delves into university students' information literacy data, integrating it with the university library resources to establish a data-driven information literacy education model. It then focuses on criteria, components, and effective methods for instructing college students in information literacy. Finally, a diverse group of students, from first-year undergraduates to doctoral candidates at a specific university, is studied for their engagement in information literacy instruction. Based on the experimental findings, sophomore students exhibited the highest level of participation at 75.9% accuracy, while postgraduate students received more information literacy training than undergraduates and Ph.D. students. When comparing this method to others, such as SVM, KNN, LR, RF, and DT, it achieved superior performance. Additionally, the quality of information literacy training in university libraries was assessed through three dimensions: student learning, behavior, and achievements. Only junior, senior, and first-year graduate students scored above 4, with scores of 4.18, 4.15, and 4.26, respectively. [ABSTRACT FROM AUTHOR]

These days, the integration of big data and information systems enables industries such as sports, health, and medical sciences in resource optimization by enhancing their operational efficiency. Personalized health management, driven by advancements in big data analysis, feature extraction, and internet of things (IoT) has emerged as a prominent trend in the sports industry. Based on these advancements, this paper focuses on leveraging machine vision, big data, and IoT to conduct a comprehensive study for extracting football players' offense features by aiming to improve accuracy and ensure the players' safety in sports industry. First, this paper discusses a framework for sports health, which is based on big data analysis. Next, we discuss the use of big data and feature extraction for IoT and sports health informatics. The action image of an offense for football players is collected by an intelligent camera, and the gathered imaging code is read and analyzed by radio frequency technology. Using the recognition method of offense action using deep learning and motion information, the offense action's image features and the three-color features of video RGB are fused into deep learning features to complete the offense action recognition. Based on the offense action recognition, the Harris 3D operator is applied to construct the sequence potential function of the offense action, and the sequence possible function of the offense action is used as the operational technique for this purpose. In addition, the AdaBoost algorithm is used to comprehensively screen the characterized data of athletes' offense actions, and the filtered data are used as training samples to complete the feature extraction of athletes' offense actions. The experimental results show that the proposed method can effectively reduce the recognition error of offense action and improve the efficiency of action's feature extraction and the accuracy of feature extraction. [ABSTRACT FROM AUTHOR]

The mental health of college students not only affects their personal development but also impacts the environment of the campus. Moreover, it affects the harmony of the family and stability of the society. This phenomenon has attracted great attention from the society. New technological paradigms such as the Internet of Things (IoT) and data mining offer new opportunities to detect, assess, and care for the mental health of students early. This paper explains the psychological problems of college students and analyzes the application of IoT and data mining in the mental health of students. The contents of mental health in colleges are investigated, and big data techniques are employed to build an innovative model for students. First, an online psychological consulting platform is designed followed by online evaluation standards and online/offline teaching fusion courses to understand mental health. Next, a neural network algorithm is developed and trained on the datasets to accurately classify the contents of students' mental health, and the corresponding results are obtained. The model is evaluated in terms of accuracy. Results show that the proposed innovative model achieves better results with 88% accuracy. Through comparative analysis, it is demonstrated that the use of IoT and big data technology can provide technical support for the innovation of college student's mental health and education and can solve more problems efficiently. [ABSTRACT FROM AUTHOR]

Presently, the Internet of Things (IoT) and big data analytics technology offer enormous opportunities to disaster risk management services. Recent disaster risk management patterns involve disaster risk reduction to avoid future disaster risks, minimize current disastrous risks, resilience building, and disaster loss reduction. The main challenges in disaster management are communication within a disaster zone which is being disrupted. In this paper, the Internet of Things assisted disaster risk management framework (IOTDRMF) has been proposed for technical resources to communicate the emergency time and better visibility into reliable and prompt decision-making through observing, evaluating, and forecasting natural disasters. This IOTDRMF utilizes big data analytics to analyze disaster risk management build a kind of spatial data communication network infrastructure, making it a priority to establish rules, protocols, and knowledge sharing. The experimental results show the IOTDRMF, and big data analytics compensate for a weak communication network infrastructure and better decision-making to handle disaster risk management. [ABSTRACT FROM AUTHOR]