Multimodal approach for fall detection based on support vector machine

Kareem, Aythem Khairi; Alheeti, Khattab M. Ali

doi:10.1063/5.0115534

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2023

2024

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Cited by 2 publications

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“…Recall utilized to evaluates the model's capability to detect all positive cases out of the total actual positives, highlighting a low false negative rate (Kareem and Alheeti, 2022).…”

Section: True Positives (Tp) Precisionmentioning

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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“…Recall utilized to evaluates the model's capability to detect all positive cases out of the total actual positives, highlighting a low false negative rate (Kareem and Alheeti, 2022).…”

Section: True Positives (Tp) Precisionmentioning

confidence: 99%