Adverse Drug Reaction Detection Using Latent Semantic Analysis

Nafea, Ahmed Adil; Omar, Nazlia; Alani, Mohammed M.

doi:10.3844/jcssp.2021.960.970

Cited by 17 publications

(16 citation statements)

References 26 publications

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“…The first baseline proposed LSA with ML algorithms (Nafea, Omar and AL-Ani, 2021), while the second baseline utilized LSA with ANN (Nafea, Omar and Al-qfail, 2023). Both baselines and current study utilized TF or TF-IDF for feature extraction.…”

Section: Resultsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

“…In Nafea, Omar and AL-Ani (2021), this study proposed LSA to detecting ADR from social networks, where individuals articulate their perspectives on medications. The previous studies predominantly leaned on trigger terms for ADR detection, this method needed regular updates to hold novel side effects and pertinent medical entities.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Model for Detection of Adverse Drug Reactions

Nafea,

Ibrahim,

Mukhlif

et al. 2024

ARO

Self Cite

View full text Add to dashboard Cite

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“…This capability is crucial for accurately classifying different gestures based on visual input. Moreover, efficientNet B3 is part of the EfficientNet family, which is a group of Convolutional Neural Network (CNN) models designed for efficient performance [23], [24], [25]. The EfficientNet models use a systematic approach to scaling called compound scaling, which uniformly scales the network depth, width, and resolution with a set of fixed scaling coefficients.…”

Section: Convolutional Neural Network and Efficientnet B3 Modelmentioning

confidence: 99%

Deep Learning Enhanced Hand Gesture Recognition for Efficient Drone use in Agriculture

Srinil,

Thongnim

2024

ijacsa

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The use of deep learning in unmanned aerial vehicles (UAVs), or drones, has greatly improved various technologies by making complex tasks easier, faster, and requiring less human help. This study looks into how artificial intelligence (AI) can be used in farming, especially through creating a system where drones can be controlled by hand gestures to support agricultural activities. By using a special type of AI called a Convolutional Neural Network (CNN) with an EfficientNet B3 model, this research developed a gesture recognition system. It was trained on 1,393 pictures of different hand signals taken under various light conditions and from three different people. The system was evaluated based on its training and testing performance, showing very high scores in terms of loss, accuracy, F1 score, and the Area Under the Curve (AUC), which means it can recognize gestures accurately and work well in different situations. This has big implications for farming, as it gives farmers an easy way to control drones for tasks like checking on crops and spraying them precisely, which also helps keep them safe. This study is an important step towards smarter farming practices. Moreover, the system's ability to perform well in different settings shows it could also be useful in other areas like construction, where drones need to operate precisely and flexibly.

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“…Stage 3 This is the main building block on which the proposed approach is based. A neural network consists of 3 types of layers [13].…”

Section: Proposed Model Annmentioning

confidence: 99%

An Effective Deep Learning Model for Surface-Enhanced Raman Spectroscopy Detection Using Artificial Neural Network

Nafea,

Kareem,

Khalaf

et al. 2023

Babylonian Journal of Machine Learning

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Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for molecular sensing and has gained significant attention due to its high sensitivity and selectivity. SERS based on deep learning technology have been used in this study of materials, biological recognition, food safety, and intelligence. Deep learning techniques have shown tremendous potential in various scientific fields, including spectroscopy-based detection methodologies. In this study, we propose an effective deep learning approach for SERS detection using an artificial neural network (ANN). Then, the results are compared using two datasets batch1 and batch2. This study used Rhumamine 6G (R6G) as an aim molecule in this study. The experimental develops show the effectiveness of proposed ANN

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Adverse Drug Reaction Detection Using Latent Semantic Analysis

Cited by 17 publications

References 26 publications

An Ensemble Model for Detection of Adverse Drug Reactions

An Ensemble Model for Detection of Adverse Drug Reactions

Deep Learning Enhanced Hand Gesture Recognition for Efficient Drone use in Agriculture

An Effective Deep Learning Model for Surface-Enhanced Raman Spectroscopy Detection Using Artificial Neural Network

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