The Potential of Big Data Application in Mathematics Education in Malaysia

Jin, Sim Jia; Abdullah, Abdul Halim; Mokhtar, Mahani; Kohar, Umar Haiyat Abdul

doi:10.3390/su142113725

Cited by 7 publications

(3 citation statements)

References 94 publications

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“…Urban transportation big data is based on intelligent transportation systems and analyzes the traffic data around the city. It has characteristics such as volume, velocity, diversity, veracity, connectivity, and value, namely 6V features [26]. Liu provides a specific method for monitoring urban uneven development using remote sensing images.…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches

Wang

Zhao

et al. 2023

Sustainability

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Nowadays, urban areas are experiencing heavy traffic, and governments are implementing various policies to manage it. For example, in China, trucks are prohibited from entering urban areas during the daytime to reduce traffic congestion. However, we have found that this policy is not cost-efficient for logistics, which includes gas fees, air pollution fees, and wear and tear expenses, as it cannot adjust to real-time traffic conditions. To minimize logistics costs in real-time, we propose DeepPlan, a deep-learning-based model that optimizes urban planning. Our model calculates the optimal route for each truck based on real-time traffic data in urban areas. We learned the optimal route from the trace data of taxi drivers who are experienced in minimizing logistics costs. Our experimental results show that DeepPlan outperforms existing urban plans by 25% and works well in various circumstances, including different weather and unexpected events.

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Section: Literature Reviewmentioning

confidence: 99%

Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches

Wang

Zhao

et al. 2023

Sustainability

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“…The discipline went through an "AI winter" in the 1970s and 1980s, marked by low interest and slow growth. However, the advent of big data necessitated the development of big data analytics (algorithms and computer applications) for assisting with expedited decisionmaking (Jin et al, 2022;Liu et al, 2022), improved computing power, and advances in machine learning, AI has witnessed a rebirth in the twenty-first century (Sevnarayan, 2024;Williamson, 2017). AI applications have permeated various industries and disciplines, including education, healthcare, banking, transportation, gaming, and customer service.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in mathematics education: The good, the bad, and the ugly

Opesemowo,

Ndlovu

2024

JPR

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“…In other words, the contribution of MapReduce on ideological evaluation of students would likely be indirect, as it could be used to process and analyze data related to student beliefs and attitudes, but it would not contribute to the actual evaluation of those beliefs and attitudes. The evaluation itself would still require human judgment, critical thinking, and analysis [10]. The contribution of security in MapReduce can be important in ensuring the privacy and protection of student data related to ideology.…”

Section: Introductionmentioning

confidence: 99%

Secure MapReduce Framework with Data Compression Model to Evaluate Ideological Evaluation Contribution on Students

2023

IJRITCC

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Ideological education is the process of transmitting and instilling a particular set of beliefs, values, and worldviews in individuals, with the goal of shaping their attitudes, behaviors, and perceptions. In colleges, ideological education plays an important role in shaping students' values, beliefs, and attitudes toward society, politics, and culture. The primary objective of ideological education in colleges is to promote critical thinking skills, social responsibility, and civic engagement among students. However, the use of big data in ideological education also raises concerns about privacy and ethical considerations. It is important to ensure that students' personal information is kept secure and that data is only used for educational purposes. It is also important to ensure that the use of big data does not lead to bias or discrimination of certain students or groups based on their demographics or backgrounds. This paper constructed a framework of Stream Cipher MapReduce Fractal Index (ScMFI). The ScMFI model performs the processing of information about students in ideological education in colleges. Through the Fractal Index Tree data is compressed with the MapReduce Framework. Finally, the ScMFI model uses the Stream Cipher process for data processing and storing in the cloud environment. The developed ScMFI model contribution of ideological education toward students’ awareness is classified as a machine learning model. The simulation analysis stated that ScMFI model minimizes the data storage, encryption and decryption time.

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The Potential of Big Data Application in Mathematics Education in Malaysia

Cited by 7 publications

References 94 publications

Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches

Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches

Artificial intelligence in mathematics education: The good, the bad, and the ugly

Secure MapReduce Framework with Data Compression Model to Evaluate Ideological Evaluation Contribution on Students

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