A Hybrid Feature Selection Framework for Predicting Students Performance

Zaffar, Maryam; Hashmani, Manzoor Ahmed; Habib, Raja; Quraishi, KS; Irfan, Muhammad; Alqhtani, Samar M.; Hamdi, Mohammed

doi:10.32604/cmc.2022.018295

Cited by 15 publications

(8 citation statements)

References 57 publications

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“…One of the challenges educational organizations encounter is elevating the standard of instruction [8]. This is necessary not only to develop a more advanced level of knowledge but also to provide efficient learning environments that enable students to complete their coursework.…”

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Student's Academic Achievement in Genetic Algorithms Based on Mobile Learning Applications

Li,

Rahman,

Zhang

2024

Int. J. Interact. Mob. Technol.

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Several scholars have focused on this area due to the significance of mobile technology in the educational process, resulting in a substantial body of scholarly work. The primary objective of this study is to investigate the impact of mobile learning on students’ academic performance. The meta-analysis approach was utilized in this research. The available research was examined using several databases to find the relevant studies that were within the scope of the investigation. The study’s inclusion criteria and components were implemented following a literature review. This research proposes a mobile education system approach based on genetic algorithms to address the issues with the current system. The analysis findings indicate that most of them stay at around 10%. The salary only slightly increases when numerous individuals teach, but it also falls within a regular, appropriate range that can accommodate more complex tasks. According to the factor analysis findings, the influence of mobile educational devices on students’ learning performance varied depending on the course and subject. However, it remained constant regardless of the students’ education level and implementation duration. Apart from the previously mentioned research findings, this article also includes a descriptive analysis of the studies that were part of the meta-analysis.

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Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Student's Academic Achievement in Genetic Algorithms Based on Mobile Learning Applications

Li,

Rahman,

Zhang

2024

Int. J. Interact. Mob. Technol.

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“…The global and local switching optimization stages are the necessary conditions for the accurate operation of the swarm intelligence algorithm. The HHO algorithm first realizes the transition between the global and local exploitation stages through the energy equation (3). If |E| ≥ 1 , the algorithm will perform the global exploration stage, if |E| < 1 , the algorithm will perform the local mining stage.…”

Section: Transitory Phasementioning

confidence: 99%

“…These algorithms have the advantages of simple principle and easy implementation, and their search mechanism can better obtain the global optimal solution of a given objective function. Therefore, metaheuristic algorithms are widely used in various engineering fields, such as path planning [2], feature selection [3], scheduling optimization [4], image segmentation [5], and model prediction [6], etc. According to the source of inspiration for different algorithms, they are usually classified into four major categories.…”

Section: Introductionmentioning

confidence: 99%

A mixed Harris hawks optimization algorithm based on the pinhole imaging strategy for solving numerical optimization problems

Zeng

Zhang

et al. 2023

J Supercomput

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To address the shortcomings of the Harris Hawks Optimization (HHO) algorithm in solving complex high-dimensional optimization problems with a slow convergence speed, low accuracy, and the high likelihood to fall into local optimum, a Mixed Harris Hawks Optimization (MHHO) algorithm based on the pinhole imaging strategy is proposed. Firstly, the pinhole imaging strategy is used to enable the Harris' hawks to approach the optimal solution faster and accelerate convergence. Secondly, the spiral parameter is introduced into the exploration phase to help the searching paths of the Harris' hawks more diverse, and improve the global search ability of the algorithm. Finally, the greedy strategy of the aquila optimization algorithm and the position update strategy of the flower pollination optimization algorithm are embedded in the Article Title exploitation stage to make the algorithm jump out of local optimum effectively. To verify the effectiveness of the proposed MHHO algorithm, it is compared with the classical HHO algorithm and 16 other state-ofthe-art algorithms, and extensively tested on 23 well-known benchmark functions, the IEEE CEC2017 test set, and three complex constrained engineering optimization problems. The test results show that MHHO outperforms the classical HHO algorithm and other 16 algorithms, and has a faster convergence speed and higher convergence accuracy.

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“…Hybrid feature selection framework using feature-fusion was performed 22 to classify the significant features and insignificant features.…”

Section: Related Workmentioning

confidence: 99%

Targeted projection pursuit similarity based attribute selection for academic performance prediction

Ramanathan

Balasubramanian

2022

Concurrency and Computation

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The performance of student in the academic field reveals the consideration over researchers to enhance student's weakness. With the consumption of high potential factors from the dataset, accurate student performance prediction is carried out. Targeted projection pursuit similarity based attribute selection (TPPS-AS) technique is designed to improve the student academic performance prediction. TPPS is a machine learning technique that observes the given input. The relevant attributes from the multidimensional space is determined by TPPS. Several research works were recognized recently to conclude the high potential factors for observing student academic performances. A novel technique is designed in this research work to improve the student academic performance prediction in a taken dataset by choosing more relevant attributes. To detect the student academic performance with better accuracy and lesser time, proposed TPPS-AS technique is employed. The performance of student academic performance prediction is improved by TPPS-AS technique through the attribute selection with higher accuracy. With this proposed technique, prediction accuracy of the student academic performance is increased after the relevant attributes selection process.

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A Hybrid Feature Selection Framework for Predicting Students Performance

Abstract: the proposed approach gives more than 90% accuracy on benchmark dataset that is better than the results of existing approach.

Cited by 15 publications

References 57 publications

An Analysis of Student's Academic Achievement in Genetic Algorithms Based on Mobile Learning Applications

An Analysis of Student's Academic Achievement in Genetic Algorithms Based on Mobile Learning Applications

A mixed Harris hawks optimization algorithm based on the pinhole imaging strategy for solving numerical optimization problems

Targeted projection pursuit similarity based attribute selection for academic performance prediction

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