• Journal of Appropriate Technology
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• v.7 no.1
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• pp.33-40
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• 2021

The government of Uganda, which belongs to East Africa, approved the National Vison Statement, "A transformed Ugandan society from a Peasant to a Modern and Prosperous Country within 30 years". However, the Uganda is facing the problem of unbalanced development between urban and rural area in spite of the government's efforts. In particular, the urban-rural education gap is emerging as a problem that could negatively affect national development plans. In this paper, we explain the reasons why Uganda's urban-rural educational imbalance is accelerating. In addition, we would like to introduce a way to reduce the educational imbalance by using appropriate technology of ICT such as the electronic library system.

• Biomedical Science Letters
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• v.29 no.3
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• pp.168-177
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• 2023

Recently, as a health problem of the elderly in an aging society, the risk of nutritional imbalance and weakening of immunity due to deterioration of masticatory function has been mentioned. In order to solve this problem, this study was conducted to investigate the effect of cyclophosphamide (CPA)-induced immunosuppression in mice induced by fermented samultang (FST) porridge on the markers related to immune activity function. ICR Mouse was divided into 6 groups of 7 animals each. Experimental groups were set as normal control group, CPA-administration group, positive control group, and FST-administration experimental group (0.25%, 0.5%, 1.0%). In groups except for the normal control group, 100 µL of CPA dissolved in 0.9% NaCl at a concentration of 150 mg/kg was injected twice at the start of the experiment and after 3 days to induce immunosuppression. As a result of analyzing the cell proliferation capacity of splenocytes, all B and T cells decreased in the CPA-administered group and increased in a concentration-dependent manner in the FST-administered group. In addition, IgA measured to evaluate the effect of improving immunity showed high values in medium and high concentration FST (P<0.05). These results can be expected as an effective solution to improve the nutritional imbalance of the elderly.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.179-190
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• 2022

Data imbalance refers to a phenomenon in which the number of data in one category is too large or too small compared to another category. Due to this, it has been raised as a major factor that deteriorates performance in machine learning that utilizes classification algorithms. In order to solve the data imbalance problem, various ovrsampling methods for amplifying prime number distribution data have been proposed. Among them, SMOTE is the most representative method. In order to maximize the amplification effect of minority distribution data, various methods have emerged that remove noise included in data (SMOTE-IPF) or enhance only border lines (Borderline SMOTE). This paper proposes a method to ultimately improve classification performance by improving the processing method for anomaly data in the traditional SMOTE method that amplifies minority classification data. The proposed method consistently presented relatively high classification performance compared to the existing methods through experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.209-215
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• 2018

This paper present a 7-level PWM inverter adopting voltage balancing control to series-connected input capacitors. The prior proposed 7-level PWM inverter consists of dc input source, three series-connected capacitors, two bidirectional switch modules, and an H-bridge. This circuit topology is useful to increase the number of output voltage levels, however it fails to generate 7-level in output voltage without consideration for voltage balancing among series-connected capacitors. Capacitor voltage imbalance is caused on the different period between charging and discharging of capacitor. To solve this problem, we uses the amplitude modulation of carrier wave, which is used to produce the center output voltage level. To verify the validity of the proposed control method, we carried out computer-aided simulation and experiments using a prototype.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.135-142
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• 2015

This paper applied a VAR model to analyze the effects of RMB exchange rate brought to processing trade, non-processing trade and FDI. Then we can get the results that the appreciation of RMB could not solve the problem of US trade deficit. It is more likely that the appreciation just can transfer the trade imbalance to other country with US, which could not radically solve the economic problems of US. Also this paper find that the data of service trade is surplus while the main goods deficit was occur in advanced technology product, especially in the information & communications trade And US has real advantage in these industries, so the situation will be changed if US decreased the barrier in these industries. In that way, the imbalance situation should be greatly reduced.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.197-206
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• 2019

Telemarketing has been used in finance due to the reduction of offline channels. In order to select telemarketing target customers, various machine learning techniques have emerged to maximize the effect of minimum cost. However, there are problems that the class imbalance, which the number of marketing success customers is smaller than the number of failed customers, and the recall rate is lower than accuracy. In this paper, we propose a method that solve the imbalanced class problem and increase the recall rate to improve the efficiency. The hybrid sampling method is applied to balance the data in the class, and the stacked deep network is applied to improve the recall and precision as well as the accuracy. The proposed method is applied to actual bank telemarketing data. As a result of the comparison experiment, the accuracy, the recall, and the precision is improved higher than that of the conventional methods.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.808-813
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• 2022

This study presents the problems of data imbalance, varying difficulties across target objects, and small objects in construction object segmentation for far-field monitoring and utilize compound loss functions to address it. Construction site scenes of assembling scaffolds were analyzed to test the effectiveness of compound loss functions for five construction object classes---workers, hardhats, harnesses, straps, hooks. The challenging problem was mitigated by employing a focal and Jaccard loss terms in the original loss function of LinkNet segmentation model. The findings indicates the importance of the loss function design for model performance on construction site scenes for far-field monitoring.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.585-587
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• 2023

Long-tailed datasets have an imbalanced distribution because they consist of a different number of data samples for each class. However, there are problems of the performance degradation in tail-classes and class-accuracy imbalance for all classes. To address these problems, this paper suggests a learning method for training of long-tailed dataset. The proposed method uses and combines two methods; one is a resampling method to generate a uniform mini-batch to prevent the performance degradation in tail-classes, and the other is a reweighting method to address the accuracy imbalance problem. The purpose of our proposed method is to train the learning models to have uniform accuracy for each class in a long-tailed dataset.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.287-294
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• 2007

Many classification algorithms for real world data suffer from a data class imbalance problem. To solve this problem, various methods have been proposed such as altering the training balance and designing better sampling strategies. The previous methods are not satisfy in the distribution of the input data and the constraint. In this paper, we propose a focused sampling method which is more superior than previous methods. To solve the problem, we must select some useful data set from all training sets. To get useful data set, the proposed method devide the region according to scores which are computed based on the distribution of SOM over the input data. The scores are sorted in ascending order. They represent the distribution or the input data, which may in turn represent the characteristics or the whole data. A new training dataset is obtained by eliminating unuseful data which are located in the region between an upper bound and a lower bound. The proposed method gives a better or at least similar performance compare to classification accuracy of previous approaches. Besides, it also gives several benefits : ratio reduction of class imbalance; size reduction of training sets; prevention of over-fitting. The proposed method has been tested with kNN classifier. An experimental result in ecoli data set shows that this method achieves the precision up to 2.27 times than the other methods.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.349-374
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• 2019

A class imbalance problem arises when one class outnumbers the other class by a large proportion in binary data. Studies such as transforming the learning data have been conducted to solve this imbalance problem. In this study, we compared resampling methods among methods to deal with an imbalance in the classification problem. We sought to find a way to more effectively detect the minority class in the data. Through simulation, a total of 20 methods of over-sampling, under-sampling, and combined method of over- and under-sampling were compared. The logistic regression, support vector machine, and random forest models, which are commonly used in classification problems, were used as classifiers. The simulation results showed that the random under sampling (RUS) method had the highest sensitivity with an accuracy over 0.5. The next most sensitive method was an over-sampling adaptive synthetic sampling approach. This revealed that the RUS method was suitable for finding minority class values. The results of applying to some real data sets were similar to those of the simulation.