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All Journal Bulletin of Electrical Engineering and Informatics
Zellou, Ahmed
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Electrical & Electronics Engineering

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A systematic literature review on data quality assessment Reda, Oumaima; Benabdellah, Naoual Chaouni; Zellou, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5667

Abstract

Defining and evaluating data quality can be a complex task as it varies depending on the specific purpose for which the data is intended. To effectively assess data quality, it is essential to take into account the intended use of the data and the specific requirements of the data users. It is important to recognize that a standardized approach to data quality assessment (DQA) may not be suitable in all cases, as different uses of data may have distinct quality criteria and considerations. In order to advance research in the field of data quality, it is useful to determine the current state of the art by identifying, evaluating, and analyzing relevant research conducted in recent years. In light of this objective, the study proposes a systematic literature review (SLR) as a suitable approach to examine the landscape of data quality and investigate available research specifically pertaining to DQA. The findings of our SLR clearly reveal and demonstrate the criticality of data quality and point to new directions for future study and have consequences for researchers and practitioners interested in defining and assessing data quality.
Automatic keyphrases extraction: an overview of deep learning approaches Ajallouda, Lahbib; Fagroud, Fatima Zahra; Zellou, Ahmed; Benlahmar, El habib
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4130

Abstract

Automatic keyphrases extraction (AKE) is a principal task in natural language processing (NLP). Several techniques have been exploited to improve the process of extracting keyphrases from documents. Deep learning (DL) algorithms are the latest techniques used in prediction and extraction of keyphrases. DL is one of the most complex types of machine learning, relying on the use of artificial neural networks to make the machine follow the same decision-making path as the human brain. In this paper, we present a review of deep learning-based methods for AKE from documents, to highlight their contribution to improving keyphrase extraction performance. This review will also provide researchers with a collection of data and information on the mechanisms of deep learning algorithms in the AKE domain. This will allow them to solve problems encountered by AKE approaches and propose new methods for improving key-extraction performance.
A scoring approach for detecting fake reviews using MRCS similarity metric enhanced by personalized k-means Ennaouri, Mohammed; Zellou, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8288

Abstract

Online commerce has grown in the digital age, and as a result, consumers now depend more than ever on other consumer feedback to make informed purchasing decisions. However, as the importance of reviews has increased, so has the prevalence of fake ones, which now infiltrate platforms and manipulate users' perceptions. This presents a significant challenge to preserving confidence and integrity in online marketplaces. This study addresses the difficulty of identifying fake reviews by introducing a distinctive methodology that incorporates advanced natural language processing (NLP) tools. By including a new metric, mean review cosine similarity (MRCS), which enhances textual similarity assessment for more accurate detection, we improve the identification process. Additionally, an exaggeration detection technique is included, enhancing the model's capacity to identify deceptive variations in review content. Furthermore, an adaptive clustering method differs from traditional k-means classification through modifying clusters to adjust to the constant evolution of misleading linguistic patterns. Empirical validation on the Yelp labeled dataset demonstrates the approach's accuracy (90%), with high precision (89%), recall (95%), and F1 score (92%), indicating its effectiveness and highlighting areas for further refinement.
Fuzzy logic: a novel approach to compound noun extraction Rassam, Latifa; Zellou, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8293

Abstract

Compound noun extraction from textual documents presents a unique challenge due to the inherent complexity and variability in linguistic structures. Traditional approaches often struggle to accurately capture the nuanced semantics of compound nouns, primarily due to their rigid reliance on exact matches. In response, this research underscores the pivotal role of fuzzy logic in addressing the challenges associated with ambiguity and imprecision within compound noun extraction. Leveraging the inherent flexibility of fuzzy logic, we propose a novel approach that surpasses the limitations of traditional methods. Our method embraces the adaptability of fuzzy logic, providing a powerful and context-aware solution for compound noun extraction. Empirical evaluation demonstrates superior performance, with a macro precision of 0.572, recall of 0.607, and F-measure of 0.589, compared to traditional approaches. By incorporating fuzzy logic, our approach excels in handling variations and uncertainties present in natural language, ultimately offering a more accurate and nuanced representation of compound nouns within textual documents. This research not only advances the field of compound noun extraction but also underscores the efficacy of fuzzy logic in overcoming challenges associated with linguistic intricacies in information extraction tasks.
Co-Authors Ajallouda, Lahbib Benabdellah, Naoual Chaouni Benlahmar, El habib Ennaouri, Mohammed Fagroud, Fatima Zahra Rassam, Latifa Reda, Oumaima