Utilizing Mining Techniques for Attributes’ Intra-Relationship Detection, a Collaborative Approach

Idrees, Amira M.; Almazroi, Abdulwahab Ali; Khedr, Ayman E.

doi:10.1080/10447318.2022.2112029

Cited by 4 publications

(7 citation statements)

References 42 publications

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“…Other approaches introduced the idea of determining the mean value which could be directly affected by outliers in [35]. The present study follows the collaborative approach which effectiveness is confirmed in different research such as [36] [37]. The main idea is identifying the initial point based on the contribution of different measures such as mode, median, and chi-square based on the type of the attributes.…”

Section: The Proposed Predictive Wkm_id3 Algorithmmentioning

confidence: 70%

“…In this study, identifying key attributes is performed by applying a set of weighting methods and retrieving the features with the highest significance. The research follows the study [37] whose scope was to select the most contributing features in customers' responses. The research aimed at identifying the significant attributes.…”

Section: ) Identify Key Attributes Phasementioning

confidence: 99%

“…As discussed, the aim of the approach in [37] is feature selection, therefore the prior research accepted the optimal as well as the accepted attributes as the final deliverable.…”

Section: ) Identify Key Attributes Phasementioning

confidence: 99%

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A Proposed Multi-Level Predictive WKM_ID3 Algorithm, Toward Enhancing Supply Chain Management in Healthcare Field

Khedr,

Alsahafi,

Idrees

2023

IEEE Access

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This research proposes a multi-level predictive algorithm based on the k-means algorithm with multiple adaptations. The research highlights the main limitations of k-means and proposes a set of adaptations that enhance the clustering task results in accuracy with minimal centroid distance error. The study proposes three enhancements in selecting the number of clusters, identifying the initial point, and exploring the contributing features set. Moreover, prediction is performed through a multi-level paradigm targeting performance enhancement by following the partitioning approach. The research experimental study focused on applying the proposed algorithm to the healthcare supply chain as it is one of the most influential factors for health services delivery. The proposed algorithm is applied to a dataset that is offered by USAID which includes 31622 records with 104 attributes. The proposed algorithm aims to predict the valued information in the supply chain progress such as predicted delivery time, predicted delay time, and other vital attributes. The data source is the USAID dataset; however, the research utilized the dataset for multiple objectives in vital aspects prediction of the healthcare supply chain with an average accuracy of 97.45%.

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Section: The Proposed Predictive Wkm_id3 Algorithmmentioning

confidence: 70%

Section: ) Identify Key Attributes Phasementioning

confidence: 99%

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A Proposed Multi-Level Predictive WKM_ID3 Algorithm, Toward Enhancing Supply Chain Management in Healthcare Field

Khedr,

Alsahafi,

Idrees

2023

IEEE Access

Self Cite

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“…This step determines the attributes' weights following the proposed strategy in [31]. The features' significance is determined based on the collaboration of a set of weighting methods.…”

Section: First Step: Determine the Features' Significance Levelmentioning

confidence: 99%

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics—A Supply Chain Backlog Elimination Framework

Hachaichi,

Khedr,

Idrees

2024

CMC

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The diversity of data sources resulted in seeking effective manipulation and dissemination. The challenge that arises from the increasing dimensionality has a negative effect on the computation performance, efficiency, and stability of computing. One of the most successful optimization algorithms is Particle Swarm Optimization (PSO) which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task. This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance. On the other hand, this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance. The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features. To confirm the proposed algorithm results, the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3% for random forest and a maximum of 99.0 for Naïve Bayes. Moreover, the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets. The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

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“…On the other hand, feature selection techniques are proposed in different research which tackles various issues such as in the education field [7], [8], and construction field [9]. Moreover, features selection techniques also proved their effectiveness in other research directions such as in Recommendation systems sentimental analysis [10], classification [11] [12] [13], query answering [14] [8], decision support systems [15] [16] , and Internet of Things (IoT) [17].…”

Section: Introductionmentioning

confidence: 99%

Iterative Feature Selection-Based DDoS attack Prevention Approach in Cloud

Naiem

Khedr

Idrees

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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Distributed Denial of Service (DDOS) attacks aim to exploit the capacity and performance of a network's infrastructure, making the cloud environment one of the biggest targets for attackers. Many efforts are being made in the field of technology to prevent them from disrupting the services provided. Machine Learning techniques are a means to protect against DDOS attacks. Data preprocessing, feature selection, and classifiers are the main components of any prevention framework. The focus of this study is to find and enhance the feature selection approach for increasing the accuracy of the classifiers in detecting DDOS attacks from regular traffic. We used four different techniques, including Pearson Correlation Coefficient (PCC), Random Forest Feature Importance (RFFI), Mutual information (MI), and Chi-squared(X2) measure which we tested on different classifiers. The first selection approach was based on the feature’s independency level then the second iteration was based on the feature’s importance. We also examined the claim of dropping attacks from the dataset for better accuracy. The best performing set of features was from using PCC and RFFI together for feature selection with average accuracy and precision of 99.27% and 97.60%, which is higher than the use of PCC for both measures by almost 2%. The accuracy is also higher by nearly 12% from the same approach dropping 50% of the attacks.

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Utilizing Mining Techniques for Attributes’ Intra-Relationship Detection, a Collaborative Approach

Cited by 4 publications

References 42 publications

A Proposed Multi-Level Predictive WKM_ID3 Algorithm, Toward Enhancing Supply Chain Management in Healthcare Field

A Proposed Multi-Level Predictive WKM_ID3 Algorithm, Toward Enhancing Supply Chain Management in Healthcare Field

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics—A Supply Chain Backlog Elimination Framework

Iterative Feature Selection-Based DDoS attack Prevention Approach in Cloud

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