Intelligent Mobile Coronavirus Recognition Centre Based on IEEE 802.15.4

Alzahrani, Abdulkareem; Alheeti, Khattab M. Ali; Thabit, Samer Salah; Al_Dosary, Duaa; Al-Ani, Muzhir Shaban

doi:10.3991/ijim.v15i16.24193

Cited by 4 publications

(2 citation statements)

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“…This algorithm provides a simple and understandable framework for capturing complex patterns within the data, incorporating both categorical and numerical attributes. It is sensitive to overfitting, which causes the use of regularization methods such as reducing to improve this potential point (Alheeti et al, 2023;Charbuty and Abdulazeez, 2021).…”

Section: Dtmentioning

confidence: 99%

An Ensemble Model for Detection of Adverse Drug Reactions

Nafea,

Ibrahim,

Mukhlif

et al. 2024

ARO

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The detection of adverse drug reactions (ADRs) plays a necessary role in comprehending the safety and benefit profiles of medicines. Although spontaneous reporting stays the standard approach for ADR documents, it suffers from significant under reporting rates and limitations in terms of treatment inspection. This study proposes an ensemble model that combines decision trees, support vector machines, random forests, and adaptive boosting (ADA-boost) to improve ADR detection. The experimental evaluation applied the benchmark data set and many preprocessing techniques such as tokenization, stop-word removal, stemming, and utilization of Point-wise Mutual Information. In addition, two term representations, namely, term frequency-inverse document frequency and term frequency, are utilized. The proposed ensemble model achieves an F-measure of 89% on the dataset. The proposed ensemble model shows its ability in detecting ADR to be a favored option in achieving both accuracy and clarity.

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Section: Dtmentioning

confidence: 99%

An Ensemble Model for Detection of Adverse Drug Reactions

Nafea,

Ibrahim,

Mukhlif

et al. 2024

ARO

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“…Pre-processing consists of drop-duplicated features, feature encoding and normalization [30]. This step is an essential phase in the performance development.…”

Section: Preprocessingmentioning

confidence: 99%

A Comparative Study for SDN Security Based on Machine Learning

Alheeti

Alzahrani

Alamri

et al. 2023

Int. J. Interact. Mob. Technol.

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In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN). The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM). The proposed approach is tested experimentally and evaluated using an available NSL–KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies.

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