Optimizing Document Classification: Unleashing the Power of Genetic Algorithms

Mustafa, Ghulam; Rauf, Abid; Al-Shamayleh, Ahmad Sami; Sulaiman, Muhammad; Alrawagfeh, Wagdi; Afzal, Muhammad Tanvır; Akhunzada, Adnan

doi:10.1109/access.2023.3292248

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…In machine learning, the feature-ranking task is crucial because it helps to identify the most impactful feature for different tasks, such as prediction [ 27 ], model interpretability [ 28 ], and dimensionality reduction [ 29 ]. To address the first research question, we propose a technique called the Modified Recursive Elimination Technique (MERT).…”

Section: Methodsmentioning

confidence: 99%

MRET: Modified Recursive Elimination Technique for ranking author assessment parameters

Mustafa,

Rauf,

Tanvir Afzal

2024

PLoS ONE

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In scientific research, assessing the impact and influence of authors is crucial for evaluating their scholarly contributions. Whereas in literature, multitudinous parameters have been developed to quantify the productivity and significance of researchers, including the publication count, citation count, well-known h index and its extensions and variations. However, with a plethora of available assessment metrics, it is vital to identify and prioritize the most effective metrics. To address the complexity of this task, we employ a powerful deep learning technique known as the Multi-Layer Perceptron (MLP) classifier for the classification and the ranking purposes. By leveraging the MLP’s capacity to discern patterns within datasets, we assign importance scores to each parameter using the proposed modified recursive elimination technique. Based on the importance scores, we ranked these parameters. Furthermore, in this study, we put forth a comprehensive statistical analysis of the top-ranked author assessment parameters, encompassing a vast array of 64 distinct metrics. This analysis gives us treasured insights in between these parameters, shedding light on the potential correlations and dependencies that may affect assessment outcomes. In the statistical analysis, we combined these parameters by using seven well-known statistical methods, such as arithmetic means, harmonic means, geometric means etc. After combining the parameters, we sorted the list of each pair of parameters and analyzed the top 10, 50, and 100 records. During this analysis, we counted the occurrence of the award winners. For experimental proposes, data collection was done from the field of Mathematics. This dataset consists of 525 individuals who are yet to receive their awards along with 525 individuals who have been recognized as potential award winners by certain well known and prestigious scientific societies belonging to the fields’ of mathematics in the last three decades. The results of this study revealed that, in ranking of the author assessment parameters, the normalized h index achieved the highest importance score as compared to the remaining sixty-three parameters. Furthermore, the statistical analysis results revealed that the Trigonometric Mean (TM) outperformed the other six statistical models. Moreover, based on the analysis of the parameters, specifically the M Quotient and FG index, it is evident that combining these parameters with any other parameter using various statistical models consistently produces excellent results in terms of the percentage score for returning awardees.

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Section: Methodsmentioning

confidence: 99%

MRET: Modified Recursive Elimination Technique for ranking author assessment parameters

Mustafa,

Rauf,

Tanvir Afzal

2024

PLoS ONE

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“…generates a set of node features J n ={j 1 n , j 2 n , … … , j p n } given its output. The self-attention mechanism is used to evaluate the attention score between the l − th node and k − th node as shown in (6). However, Y s ∈ T f * f , Y m ∈ T f * f , additionally, the relative position for encoding in between the k − th node and l − th node is evaluated as r kl = [h sinw (z kx − z lx )]; h sinw (a kx − a lx )].…”

Section: Adaptive Fusion Layermentioning

confidence: 99%

“…The system enables streamlined organization and retrieval of documents, enhancing navigational efficiency and reducing the time needed to locate specific information. The process of categorizing documents is essential for ensuring the precision and safety of user interactions [6], [7]. Moreover, it serves to facilitate content filtering and enhance spam detection.…”

Section: Introductionmentioning

confidence: 99%

Graph attention-driven document image classification through DualTune learning

Shilpa,

Soma

2024

IJEECS

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Document image classification is a challenging task due to the complexity of information contained within documents, including text, images, and their spatial arrangement. Deep learning has become a pivotal tool for extracting and learning complex patterns. However, conventional methods often grapple with integrating different data modalities and minimizing redundancy, leading to a need for more advanced and efficient deep learning strategies. This study presents a new approach to document image classification, named graph attention-driven with dual tune learning (GAD-DTL). GAD-DTL employs dual-tune learning and graph attention networks. The methodology creates semantic region embedding within document images, which incorporate both textual and spatial data. A key feature of this approach is the adaptive fusion layer, which integrates different modalities and uses a graph attention layer to capture context within each region. To minimize redundancy in learned features, we implement two distinct learning techniques, relational and non-relational learning. This approach enhances document image classification by ensuring invariant representation and minimal redundancy in features.

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Enhancing author assessment: an advanced modified recursive elimination technique (MRET) for ranking key parameters and conducting statistical analysis of top-ranked parameter

Mustafa,

Rauf,

Afzal

2024

Int J Data Sci Anal

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Optimizing Document Classification: Unleashing the Power of Genetic Algorithms

Abstract: We extend our heartfelt gratitude and appreciation to Qatar National Library for their generous support in providing Open Access funding for this research.

Cited by 7 publications

References 48 publications

MRET: Modified Recursive Elimination Technique for ranking author assessment parameters

MRET: Modified Recursive Elimination Technique for ranking author assessment parameters

Graph attention-driven document image classification through DualTune learning

Enhancing author assessment: an advanced modified recursive elimination technique (MRET) for ranking key parameters and conducting statistical analysis of top-ranked parameter

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