Sensitivity Analysis of Artificial Neural Networks Identifying JWH Synthetic Cannabinoids Built with Alternative Training Strategies and Methods

Burlacu, Catalina Mercedes; Burlacu, Adrian; Praisler, Mirela

doi:10.3390/inventions7030082

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Sensitivity (True Positive Rate): Sensitivity assesses the model's ability to identify true positive cases. Sensitivity will reflect how effectively the model can recognize images of the conjunctiva of the eye that show symptoms of anemia in the setting of this investigation [19], [77]. Sensitivity is required to ensure that the model can identify patients who may have anemia.…”

Section: E Evaluation Modelmentioning

confidence: 99%

Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI

Muljono,

Wulandari,

Azies

et al. 2024

IEEE Access

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This article evolved because several instances of anemia are still discovered too late, especially in communities with limited medical resources and access to laboratory tests. Invasive diagnostic technologies and expensive expenses are additional impediments to early diagnosis. To detect anemia, an effective, accurate, and non-invasive method is required. In this study, the conjunctival image of the eye is analyzed as a non-invasive method of detecting anemia. Various model approaches were tested in an endeavor to categorize anemic and healthy patients as accurately as possible. The Support Vector Machine (SVM) algorithm-integrated MobileNetV2 method was determined to be the most effective plan. With this combination, the accuracy of 93%, sensitivity of 91%, and specificity of 94%. These findings show that the model can successfully identify healthy patients while accurately identifying anemic patients. This method offers a non-invasive means of detecting anemia early on, making it promising for use in clinical settings. The SVM+MobileNetV2 technique relies on images of the conjunctiva of the eye and has the potential to improve healthcare by identifying people who may have had anemia earlier. This technique stands out as a solid option for the efficient and precise diagnosis of anemia when accuracy, sensitivity, and specificity are balanced.

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Section: E Evaluation Modelmentioning

confidence: 99%

Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI

Muljono,

Wulandari,

Azies

et al. 2024

IEEE Access

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“…In article [2], the authors presented alternative training methods that were evaluated for an Artificial Neural Network (ANN) intended to identify JWH synthetic cannabinoids. The authors employed the Python programming language along with the Neural Designer data science and machine learning platform to improve the model's performance in terms of output sensitivity.…”

mentioning

confidence: 99%

Editorial Note for the Special Issue: Perspectives and Challenges in Doctoral Research—Selected Papers from the 10th Edition of the Scientific Conference of the Doctoral Schools from the “Dunărea de Jos”

Rusu

Râpeanu

2023

Inventions

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Machine Learning Systems Detecting Illicit Drugs Based on Their ATR-FTIR Spectra

2023

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We present a comparative study aiming to determine the most efficient multivariate model screening for the main drugs of abuse based on their ATR-FTIR spectra. A preliminary statistical analysis of selected spectra data extracted from the public SWGDRUG IR Library was first performed. The results corroborated those of an exploratory analysis that was based on several dimensionality reduction methods, i.e., Principal Component Analysis (PCA), Independent Component Analysis (ICA), and autoencoders. Then, several machine learning methods, i.e., Support Vector Machines (SVM), eXtreme Gradient Boosting (XGB), Random Forest, Gradient Boosting, and K-Nearest Neighbors (KNN), were used to assign the drug class membership. In order to account for the stochastic nature of these machine learning methods, both models were evaluated 10 times on a randomly distributed subset of the whole SWGDRUG IR Library, and the results were compared in detail. Finally, their performance in assigning the class identity of three classes of drugs of abuse, i.e., hallucinogenic (2C-x, DOx, and NBOMe) amphetamines, cannabinoids, and opioids, were compared based on confusion matrices and various classification parameters, such as balanced accuracy, sensitivity, and specificity. The advantages of each of the illicit drug-detecting systems and their potential as forensic screening tools used in field scenarios are also discussed.

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Sensitivity Analysis of Artificial Neural Networks Identifying JWH Synthetic Cannabinoids Built with Alternative Training Strategies and Methods

Cited by 4 publications

References 14 publications

Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI

Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI

Editorial Note for the Special Issue: Perspectives and Challenges in Doctoral Research—Selected Papers from the 10th Edition of the Scientific Conference of the Doctoral Schools from the “Dunărea de Jos”

Machine Learning Systems Detecting Illicit Drugs Based on Their ATR-FTIR Spectra

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