The COVID-19 virus has made a huge impact on people’s lives ever since the outbreak happened in December 2019. Unfortunately, not completely vanished from world yet, and thus, global agitation is still increasing with mutations variants of same. Early diagnosis best way to decline mortality risk associated it. This urges necessity developing new computational approaches that can analyze large d...

Abstract Oversampling is a promising preprocessing technique for imbalanced datasets which generates new minority instances to balance the dataset. However, improper generated instances, i.e., noise may interfere learning of classifier and impact it negatively. Given this, in this paper, we propose simple effective oversampling approach known as ASN-SMOTE based on k -nearest neighbors synthetic...

Because the numbers of cars reflect each person's travel behaviors for specific location, car ownership demand model plays a dominant role in analysis order to understand area's individual and household behaviors. However, study project master plan Khon Kaen expressway represented imbalanced data; namely, majority class minority were not equal. Before developing machine learning model, this sug...

Application of data mining methods as a decision support system has a great benefit to predict survival of new patients. It also has a great potential for health researchers to investigate the relationship between risk factors and cancer survival. But due to the imbalanced nature of datasets associated with breast cancer survival, the accuracy of survival prognosis models is a challenging issue...

Imbalanced training data always puzzles the supervised learning based emotion and sentiment classification. Several existing research showed that data sparseness and small disjuncts are the two major factors affecting the classification. Target to these two problems, this paper presents a word embedding based oversampling method. Firstly, a large-scale text corpus is used to train a continuous ...

To reduce the computational cost of the simulation of electromagnetic responses in geophysical settings that involve highly heterogeneous media, we develop a multiscale finite volume method with oversampling for the quasi-static Maxwells equations in the frequency domain. We assume a coarse mesh nested into a fine mesh, which accurately discretizes the setting. For each coarse cell, we independ...

The continuous rise in skin cancer cases, especially malignant melanoma, has resulted a high mortality rate of the affected patients due to late detection. Some challenges affecting success detection include small datasets or data scarcity problem, noisy data, imbalanced inconsistency image sizes and resolutions, unavailability reliability labeled (ground truth), imbalance datasets. This study ...

Two techniques for performing pixel level analog to digital conversion (ADC) are reviewed. The first is an oversampling technique which uses a one bit first order Ez modulator for each 2 x 2 block of pixels to directly convert photocharge to bits. Each modulator is implemented using 17 transistors. The second technique is a Nyquist rate multi—channel--bit—serial (MCBS) ADC. The technique uses s...

Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the automatic production of Use/Land Cover has been topic interest for remote sensing community several years, but it is still fraught with technical challenges. One such challenge imbalanced nature most remotely sensed data. The asymmetric class dis...