Moldy apple core is an internal fruit disease that poses a threat to consumer health. In this study, synthetic minority over-sampling technology (SMOTE) based model of moldy was proposed solve the problem poor performance due imbalanced sample distribution in spectral nondestructive detection core. Two different methods, random under-sampling (RUS) and SMOTE, were used balance original samples....

Tracer Study is a mandatory aspect of accreditation assessment in Indonesia. The Indonesian Ministry Education requires all Indonesia Universities to anually report graduate tracer study reports the government. also needed by University evaluating success learning that has been applied curriculum. One things need be evaluated level absorption graduates into working industry, so machine model as...

Since overfitting due to imbalanced data can cause prediction errors during the learning process of machine and degrades performance model (e.g., sensitivity), it is necessary add an additional sampling technique in development step reduce overcome this issue, addition selecting a algorithm suitable for data. This study examined Alzheimer's patients living South Korea understand predictors anxi...

Abstract Mitigating the impact of class-imbalance data on classifiers is a challenging task in machine learning. SMOTE well-known method to tackle this by modifying class distribution and generating synthetic instances. However, most SMOTE-based methods focus phase selection, while few consider generation. This paper proposes hypersphere-constrained generation mechanism (HS-Gen) improve minorit...

Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuris...

BACKGROUND The IOM report, Preventing Medication Errors, emphasizes the overall lack of knowledge of the incidence of adverse drug events (ADE). Operating rooms, emergency departments and intensive care units are known to have a higher incidence of ADE. Labor and delivery (L&D) is an emergency care unit that could have an increased risk of ADE, where reported rates remain low and under-reportin...

MOTIVATION With the rapid increase of infection resistance to antibiotics, it is urgent to find novel infection therapeutics. In recent years, antimicrobial peptides (AMPs) have been utilized as potential alternatives for infection therapeutics. AMPs are key components of the innate immune system and can protect the host from various pathogenic bacteria. Identifying AMPs and their functional ty...

The Saarbruecken Voice Database (SVD) is a public database used by voice pathology detection systems. However, the distributions of pathological and normal samples show clear class imbalance. This study aims to develop system for classification voices that uses efficient deep learning models based on various oversampling methods, such as adaptive synthetic sampling (ADASYN), minority technique ...

In this paper, q-Gaussian Radial Basis Functions are presented as an alternative to Gaussian Radial Basis Function. This model is based on q-Gaussian distribution, which parametrizes the Gaussian distribution by adding a new parameter q. The q-Gaussian Radial Basis Function allows different Radial Basis Functions to be represented by updating the new parameter q. For example, when the q-Gaussia...

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Classimbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the...