The Development and Management Work Innovation of University Students by Artificial Intelligence and Deep Learning in the Wireless Network Era

Li, Qiao; Qi, Tiaolan; Liu, Lin

doi:10.1155/2022/7774185

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…MCS has been applied in mapping environmental noise, monitoring environmental pollution and urban traffic conditions, social networking, and medical care. Foreseeably, it will be applied to more business scenarios [13]. e structure division of the MSC system is shown in Figure 1.…”

Section: Establishment Of Mta Model Under Mobile Si Perceptionmentioning

confidence: 99%

Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes

Wang

2022

Security and Communication Networks

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The objective is to optimize the multitask assignment (MTA) in mobile crowdsensing (MCS) scenarios. From the perspective of reinforcement learning (RL), an Internet of Things (IoT) devices-oriented MTA model is established using MCS, IoT technology, and other related theories. Then, the data collected by the University of Cambridge and the University of St. Andrews are chosen to verify the three MTA algorithms on IoT devices. They are multistage online task assignment (MOTA), average makespan-sensitive online task assignment (AOTA), and water filling (WF). Experiments are designed by considering different algorithms’ MTA time consumption and accuracy in simple and complex task scenarios. The research results manifest that with a constant load or task quantity, the MOTA algorithm takes the shortest time to assign tasks. In simple task scenarios, MOTA is compared with the WF. The MOTA algorithm’s total moving distance is relatively short, and the task completion degree is the highest. AOTA algorithm lends best to complex tasks, with the highest MTA accuracy and the shortest time consumption. Therefore, the research on IoT devices’ MTA optimization based on RL in the MCS scenario provides a certain theoretical basis for subsequent MTA studies.

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Section: Establishment Of Mta Model Under Mobile Si Perceptionmentioning

confidence: 99%

Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes

Wang

2022

Security and Communication Networks

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“…An improved resource recommendation model, tailored to individual needs through the use of deep neural networks, has been developed to enhance independent learning and institutional management efficiency among college students (Li et al, 2022). The implementation of AI tools such as chatbots and code can have a profound impact on teaching and learning initiatives.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Generative Ai on Educational Sector

Singh

2024

IJSREM

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This study looks into how generative artificial intelligence (AI) is affecting the field of education. The study examines the possible advantages of using generative AI into instruction, such as more individualized learning opportunities, enhanced accessibility, and creative teaching strategies. The study also recognizes and examines the moral issues raised by the application of AI in education, highlighting the significance of upholding academic honesty and openness. It draws attention to the necessity of cautious application in order to guarantee that AI fosters fruitful learning results. The suggested research technique describes a mixed methods strategy that combines quantitative data analysis with qualitative information from surveys and interviews. The goal of the project is to provide best practices and standards for the appropriate integration of generative AI in education, while also identifying the potential and problems that this technology presents. The anticipated results include a thorough comprehension of how generative AI will affect education and how it might improve learning opportunities, ethical issues for responsible usage, and the identification of possible problems and solutions. In the end, the research hopes to promote a more inclusive and productive learning environment by aiding in the creation of useful frameworks for incorporating AI into educational settings. The education industry has drastically evolved in recent years, thanks to the remarkable advancements made by artificial intelligence. With the integration of generative AI technology, educators and students are now presented with a wealth of possibilities and opportunities. As AI continues to advance further, it holds great potential for revolutionizing education as we know it through improved efficiency measures that personalize learning experiences while enhancing overall student outcomes. Harnessing this power-packed tool in teaching environments creates an opportunity for educational institutions across various sectors allowing them efficient intelligent assessment tools coupled with personalized tutoring provisions towards addressing classroom weaknesses resulting in custom-made lesson plans aimed at betterment of individual academic progress developed using predictive models creating ample time such that teachers can focus solely on providing tailored instruction & support based exclusively around their Student's requirements& specifications. This groundbreaking technological development provided meaningfully leveraging Generative Intelligence efforts promises nothing short but will be instrumental when shaping how Education is structured from henceforth enabling automation capabilities within the Administrative tasks management system,& content-building-The advantages come twofold-Enhancement not only resides generally concerning Upgrade Learning Experience situations; there exist tailor solutions projecting Speaker understanding regarding distinctive needs pupils have always had throughout all levels ultimately leading to Developmental Progression heights hitherto attainable anticipated sooner than later.

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“…The utilization of artificial intelligence and deep learning has the potential to predict students' future learning performance based on their historical learning data and to recommend suitable content, thus significantly enhancing their learning efficiency. Li and colleagues concentrate their study on enhancing college students' growth and development through artificial intelligence and deep learning [12]. Firstly, a personalized learning resource recommendation model was constructed on an online education platform using deep neural networks (DNN), which was experimentally validated.…”

Section: Intelligent College Student Comprehensive Quality Evaluation...mentioning

confidence: 99%

Investigation of Artificial Intelligence algorithms in education

Lin,

Luo,

Qian

2023

ACE

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The application of Artificial Intelligence (AI) in education has garnered increasing attention in recent years. This study investigates the primary methods of AI in education, including The Teaching Machine System, Intelligent Tutoring Systems, Intelligent Educational Systems, and the Intelligent College Student Comprehensive Quality Evaluation Model. The Teaching Machine System employs pre-defined rules and algorithms to deliver personalized instruction to students. Intelligent Tutoring Systems use AI algorithms to provide real-time feedback and guidance to students, tailoring the learning experience to address their learning gaps. Intelligent Educational Systems use AI to assist with instructional design, assessment, and delivery, while the Intelligent College Student Comprehensive Quality Evaluation Model is an AI-based system designed to evaluate students' comprehensive quality. Furthermore, this paper provides relevant descriptions and insights into the applications of AI in language and mathematics learning, highlighting the limitations of current AI models in these areas. Despite the advances in AI, challenges still exist, including limitations in processing unstructured data and the need for more effective human-AI interactions. Nonetheless, the potential of AI to enhance the quality and accessibility of education remains promising, and further research is needed to explore its full potential in the field of education.

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The Development and Management Work Innovation of University Students by Artificial Intelligence and Deep Learning in the Wireless Network Era

Cited by 5 publications

References 24 publications

Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes

Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes

Impact of Generative Ai on Educational Sector

Investigation of Artificial Intelligence algorithms in education

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