Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles

Amirruddin, Amiratul Diyana; Muharam, Farrah Melissa; Tan, Ngai Paing; Ismail, Mohd Khairi

doi:10.1016/j.compag.2021.106646

Cited by 41 publications

(20 citation statements)

References 68 publications

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“…To obtain more robust and accurate calibrations, a re-balancing approach was adopted to obtain class-balanced distribution. Re-balancing has been previously adopted in NIR calibration using different mathematical approaches, but ultimately by under-sampling the majority class and/or generating a new representative of the minority class [ 46 , 47 , 48 ]. Moreover, a recent study evidenced that the combination of oversampling and downsampling techniques performed better than using exclusively one or the other [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

Ghilardelli

Barbato

Gallo

2022

Toxins

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Mycotoxins should be monitored in order to properly evaluate corn silage safety quality. In the present study, corn silage samples (n = 115) were collected in a survey, characterized for concentrations of mycotoxins, and scanned by a NIR spectrometer. Random Forest classification models for NIR calibration were developed by applying different cut-offs to classify samples for concentration (i.e., μg/kg dry matter) or count (i.e., n) of (i) total detectable mycotoxins; (ii) regulated and emerging Fusarium toxins; (iii) emerging Fusarium toxins; (iv) Fumonisins and their metabolites; and (v) Penicillium toxins. An over- and under-sampling re-balancing technique was applied and performed 100 times. The best predictive model for total sum and count (i.e., accuracy mean ± standard deviation) was obtained by applying cut-offs of 10,000 µg/kg DM (i.e., 96.0 ± 2.7%) or 34 (i.e., 97.1 ± 1.8%), respectively. Regulated and emerging Fusarium mycotoxins achieved accuracies slightly less than 90%. For the Penicillium mycotoxin contamination category, an accuracy of 95.1 ± 2.8% was obtained by using a cut-off limit of 350 µg/kg DM as a total sum or 98.6 ± 1.3% for a cut-off limit of five as mycotoxin count. In conclusion, this work was a preliminary study to discriminate corn silage for high or low mycotoxin contamination by using NIR spectroscopy.

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Section: Discussionmentioning

confidence: 99%

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

Ghilardelli

Barbato

Gallo

2022

Toxins

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“…Moreover, the variables and collected data should be justified by having demonstrated pertinence to healthcare; unnecessary variables should not be collected [18]. This includes avoiding datasets known to be imbalanced or biased [50][51][52][53] and data types that will not be used, such as photographs of people when training text-based Fig. 1 Ethical AI framework algorithms [32].…”

Section: Data Collectionmentioning

confidence: 99%

“…Use diverse datasets [46][47][48][49] 3. Voluntarily increase diversity of imbalanced datasets [50][51][52][53] 4. (Synthetically) create diverse datasets for training and accessing models [54,55] 5.…”

Section: Data Collectionmentioning

confidence: 99%

Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers

2022

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Artificial intelligence (AI) offers much promise for improving healthcare. However, it runs the looming risk of causing individual and societal harms; for instance, exacerbating inequalities amongst minority groups, or enabling compromises in the confidentiality of patients’ sensitive data. As such, there is an expanding, unmet need for ensuring AI for healthcare is developed in concordance with human values and ethics. Augmenting “principle-based” guidance that highlight adherence to ethical ideals (without necessarily offering translation into actionable practices), we offer a solution-based framework for operationalising ethics in AI for healthcare. Our framework is built from a scoping review of existing solutions of ethical AI guidelines, frameworks and technical solutions to address human values such as self-direction in healthcare. Our view spans the entire length of the AI lifecycle: data management, model development, deployment and monitoring. Our focus in this paper is to collate actionable solutions (whether technical or non-technical in nature), which can be steps that enable and empower developers in their daily practice to ensuring ethical practices in the broader picture. Our framework is intended to be adopted by AI developers, with recommendations that are accessible and driven by the existing literature. We endorse the recognised need for ‘ethical AI checklists’ co-designed with health AI practitioners, which could further operationalise the technical solutions we have collated. Since the risks to health and wellbeing are so large, we believe a proactive approach is necessary for ensuring human values and ethics are appropriately respected in AI for healthcare.

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“…e LMT uses cross-validation to find LogitBoost iterations that do not overfit the training data [40].…”

Section: Logistic Model Tree (Lmt) Classifier Lmt Is a Classification...mentioning

confidence: 99%

CFSBFDroid: Android Malware Detection Using CFS + Best First Search-Based Feature Selection

Sharma

Agrawal

Kumar

et al. 2022

Mobile Information Systems

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With the fast development of smartphone technology and mobile applications, the mobile phone has become the most powerful tool to access the Internet and get various services with one click. Meanwhile, susceptibilities of the application are the primary hazard to the security of Android devices. Due to these weaknesses, an attacker can easily hack the confidential data of the mobile phone. The malware application automatically performs fraudulent activities on mobile phones without the user's knowledge. Thus, these attacks are the major threats to the security of mobile phones. To detect malicious applications installed on Android smartphones, we have conducted a study that focuses on permissions and intent-based mechanisms. The study was done in three phases: in the first phase, the dataset was created by extracting intents and permissions from APK files; in the second phase, correlation-based feature selection (CFS) and best first search (BFS) were combined to select the most representative features from the feature space of the extracted dataset; and in the third phase, machine learning (ML) techniques were trained and tested against the preprocessed dataset obtained in the second phase. The accuracy, precision, recall, F1 score, and error metrics of seven machine learning techniques (REPTree, Rule PART, RF, SMO, SGD, MCC, and LMT) were demonstrated over the Android dataset.

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Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles

Cited by 41 publications

References 68 publications

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers

CFSBFDroid: Android Malware Detection Using CFS + Best First Search-Based Feature Selection

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