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Abualhaj, Mosleh M
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Mitigating Healthcare Information Overload: a Trust-aware Multi-Criteria Collaborative Filtering Model Shambour, Qusai Y; Abualhaj, Mosleh M; Abu-Shareha, Ahmad; Hussein, Abdelrahman H; Kharma, Qasem M
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.297

Abstract

The rapid growth of online health information resources has made it difficult for users, as well as providers of healthcare, to cope with large volumes of information that are becoming increasingly complex. Hence, there is an urgent demand for developing new advanced recommendation techniques in the healthcare domain to enhance decision-making processes. However, most current health recommendation systems, which recommend personalized healthcare services and items such as diagnoses, medications, and doctors based on users' health conditions and needs, are hindered by the data sparsity issue that compromises the reliability of their recommendations. In this paper, we intend to address this issue by proposing a Trust-aware Multi-Criteria Collaborative Filtering model for recommendation services in the healthcare domain. This model leverages multi-criteria ratings and integrates user-item trust relationships to improve the precision and coverage of recommendations, thus facilitating more informed healthcare choices that align closely with their individual needs. Our empirical analysis on two healthcare multicriteria rating datasets, including those with sparse data, shows the proposed model's superior performance over existing baseline methods. On the RateMDs dataset, our model improved the average MAE by 24% and RMSE by 19% compared to baseline methods. For the WebMD dataset, it enhanced the average MAE by 6% and RMSE by 2%. In sparse data scenarios, the model boosted the average MAE by 18% and Coverage by 6% compared to baseline approaches.
ARP Spoofing Attack Detection Model in IoT Network using Machine Learning: Complexity vs. Accuracy Alsaaidah, Adeeb; Almomani, Omar; Abu-Shareha, Ahmad Adel; Abualhaj, Mosleh M; Achuthan, Anusha
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.374

Abstract

Spoofing attacks targeting the address resolution protocol, or the so-called ARP, are common cyber-attacks in IoT environments. In such an attack, the attacker sends a fake message over a local area network to spoof the users and interfere with the communication transferred from and into these users. As such, to detect such attacks, there is a need to check the network gateways and routers continuously to capture and analyze the transmitted traffic. However, there are three major problems with such traffic data: 1) there are substantial irrelevant data to the ARP attacks, 2) there are massive patterns in the way by which the spoof can be implemented, and 3) there is a need for fast processing of such data to reduce any delay resulting from the processing stage. Accordingly, this paper proposes a detection approach using supervised machine learning algorithms. The focus of this paper is to show the tradeoff between speed and accuracy to offer various solutions based on the demanded quality. Various algorithms were tested to find a solution that balanced time requirements and accuracy. As such, the results using all features and with various feature selection techniques were reported. Besides, the results using simple classifiers and ensemble learning algorithms were also reported. The proposed approach is evaluated on an IoT network intrusion dataset (IoTID20) collected from different IoT devices. The results showed that the highest accuracy is obtained using the RF classifier with a subset of features produced by the wrapper technique. In such a case, the accuracy obtained was 99.74%, with running time equal to 305 milliseconds. However, If time is more critical for a given application, then DT can be used with the whole feature set. In such a case, the accuracy was 99.41%, with running time equal to 11  milliseconds.
Spam Feature Selection Using Firefly Metaheuristic Algorithm Abualhaj, Mosleh M; Hiari, Mohammad O; Alsaaidah, Adeeb; Al-Zyoud, Mahran; Al-Khatib, Sumaya
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.336

Abstract

This paper presents a novel method for improving spam detection by utilizing the Firefly Algorithm (FA) for feature selection. The FA, a bio-inspired metaheuristic optimization algorithm, is applied to identify the most relevant features from the ISCX-URL2016 dataset, which contains 72 features. By balancing exploration (searching for new solutions) and exploitation (focusing on the best solutions), FA is able to effectively reduce the feature space from 72 to 31 features. This reduction improves model efficiency without sacrificing performance, as only the most impactful features are retained for the classification task. The selected features were then used to train three machine learning classifiers: Decision Tree (DT), Gradient Boost Tree (GBT), and Naive Bayes (NB). Each classifier's performance was evaluated based on accuracy, with DT achieving the highest accuracy of 99.81%, GBT achieving 99.70%, and NB scoring 90.33%. The superior performance of the DT algorithm is attributed to its ability to handle non-linear relationships and high-dimensional data, making it particularly well-suited for the FA-selected features. This combination of FA for feature selection and DT for classification demonstrates significant improvements in spam detection performance, highlighting the importance of selecting the most relevant features. The results show that by reducing the dimensionality of the dataset, the FA algorithm not only accelerates the classification process but also enhances detection accuracy.
Co-Authors Abu-Shareha, Ahmad Abu-Shareha, Ahmad Adel Achuthan, Anusha Al-Khatib, Sumaya Al-Zyoud, Mahran Almomani, Omar Alsaaidah, Adeeb Hiari, Mohammad O Hussein, Abdelrahman H Kharma, Qasem M Shambour, Qusai Y