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Fulltext An approach for removing redundant data from RFID data streams

Mahdin H, Abawajy J

Sensors (Basel), 2011;11(10):9863-77.
PMID: 22163730 DOI: 10.3390/s111009863

Radio frequency identification (RFID) systems are emerging as the primary object identification mechanism, especially in supply chain management. However, RFID naturally generates a large amount of duplicate readings. Removing these duplicates from the RFID data stream is paramount as it does not contribute new information to the system and wastes system resources. Existing approaches to deal with this problem cannot fulfill the real time demands to process the massive RFID data stream. We propose a data filtering approach that efficiently detects and removes duplicate readings from RFID data streams. Experimental results show that the proposed approach offers a significant improvement as compared to the existing approaches.
Fulltext Clone tag detection in distributed RFID systems

Kamaludin H, Mahdin H, Abawajy JH

PLoS One, 2018;13(3):e0193951.
PMID: 29565982 DOI: 10.1371/journal.pone.0193951

Although Radio Frequency Identification (RFID) is poised to displace barcodes, security vulnerabilities pose serious challenges for global adoption of the RFID technology. Specifically, RFID tags are prone to basic cloning and counterfeiting security attacks. A successful cloning of the RFID tags in many commercial applications can lead to many serious problems such as financial losses, brand damage, safety and health of the public. With many industries such as pharmaceutical and businesses deploying RFID technology with a variety of products, it is important to tackle RFID tag cloning problem and improve the resistance of the RFID systems. To this end, we propose an approach for detecting cloned RFID tags in RFID systems with high detection accuracy and minimal overhead thus overcoming practical challenges in existing approaches. The proposed approach is based on consistency of dual hash collisions and modified count-min sketch vector. We evaluated the proposed approach through extensive experiments and compared it with existing baseline approaches in terms of execution time and detection accuracy under varying RFID tag cloning ratio. The results of the experiments show that the proposed approach outperforms the baseline approaches in cloned RFID tag detection accuracy.
Fulltext Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes

Jawad MS, Dhawale C, Ramli AAB, Mahdin H

MethodsX, 2023;10:102124.
PMID: 36974325 DOI: 10.1016/j.mex.2023.102124

Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance. This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects:•Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures.•Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises.
Corrigendum to "Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes" [MethodsX Volume 10(2023) 102124]

Jawad MS, Dhawale C, Ramli AAB, Mahdin H

MethodsX, 2023 Dec;11:102324.
PMID: 37637288 DOI: 10.1016/j.mex.2023.102324

[This corrects the article DOI: 10.1016/j.mex.2023.102124.].
Fulltext A refactoring categorization model for software quality improvement

Almogahed A, Mahdin H, Omar M, Zakaria NH, Gu YH, Al-Masni MA, et al.

PLoS One, 2023;18(11):e0293742.
PMID: 37917752 DOI: 10.1371/journal.pone.0293742

Refactoring, a widely adopted technique, has proven effective in facilitating and reducing maintenance activities and costs. Nonetheless, the effects of applying refactoring techniques on software quality exhibit inconsistencies and contradictions, leading to conflicting evidence on their overall benefit. Consequently, software developers face challenges in leveraging these techniques to improve software quality. Moreover, the absence of a categorization model hampers developers' ability to decide the most suitable refactoring techniques for improving software quality, considering specific design goals. Thus, this study aims to propose a novel refactoring categorization model that categorizes techniques based on their measurable impacts on internal quality attributes. Initially, the most common refactoring techniques used by software practitioners were identified. Subsequently, an experimental study was conducted using five case studies to measure the impacts of refactoring techniques on internal quality attributes. A subsequent multi-case analysis was conducted to analyze these effects across the case studies. The proposed model was developed based on the experimental study results and the subsequent multi-case analysis. The model categorizes refactoring techniques into green, yellow, and red categories. The proposed model, by acting as a guideline, assists developers in understanding the effects of each refactoring technique on quality attributes, allowing them to select appropriate techniques to improve specific quality attributes. Compared to existing studies, the proposed model emerges superior by offering a more granular categorization (green, yellow, and red categories), and its range is wide (including ten refactoring techniques and eleven internal quality attributes). Such granularity not only equips developers with an in-depth understanding of each technique's impact but also fosters informed decision-making. In addition, the proposed model outperforms current studies and offers a more nuanced understanding, explicitly highlighting areas of strength and concern for each refactoring technique. This enhancement aids developers in better grasping the implications of each refactoring technique on quality attributes. As a result, the model simplifies the decision-making process for developers, saving time and effort that would otherwise be spent weighing the benefits and drawbacks of various refactoring techniques. Furthermore, it has the potential to help reduce maintenance activities and associated costs.
Robustness evaluations of pathway activity inference methods on gene expression data

Hui TX, Kasim S, Aziz IA, Fudzee MFM, Haron NS, Sutikno T, et al.

BMC Bioinformatics, 2024 Jan 12;25(1):23.
PMID: 38216898 DOI: 10.1186/s12859-024-05632-w

BACKGROUND: With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches.
RESULTS: Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets.
CONCLUSION: However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.