• The Korean Journal of Ecology
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• 제12권4호
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• pp.257-284
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• 1989

This study was carried out to characterize pinus densiflora forests in middle province (Mt. Seolag, Mt. Taebaik) south province (Mt. Sokli, Mt. Jiri) and south-coast province (Mt. Daedun) of Korea. The appearance species in the P. densiflora alliance included 325 taxa and varied according to the direction of slopes. The steeper the slope was, the fewer number of taxa were observed. The floristic composition of south-coast province was gradually changing to the south hemispheric factors. Dominant species groups of P. densiflora alliance were classified into P. densiflora, Quercus serrata ( layer), Rhus trichocarpa ( layer), Lespedeza maximowiczii var. tomentella (S layer), Artemisia keiskeana, Carex humilis var. nana, Spodiopogon sibiricus (K layer). Quercus variabilis, Fraxinus sieboldiana and Styrax japonica association were formed under the P. densiflora alliance. Quercus, Rhus, Lespedeza and Rhododendron groups maintained high ecological relationships one another. The soil factors (pH, organic matters, and water field capacity)and relative light intensity tended to show negative correlation, which were significantly different among provinces. The P. densiflora forests of Korea were classified into one alliance and four associations, that is, pinion densiflorae Suz.-Tok. 1966, Quercetum variabilae ass. nov., Quercetum mongolicae ass. nov. Fraxinetum sieboldianae ass. nov. and Styraxetum japonicae ass. nov.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• 제40권1호
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• pp.23-28
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• 2008

Functional hanji was manufactured using the bast fiber of Broussonetia kazinoki and various inorganic compounds such as kaolin, talc, elvan, and ocher, and the physical and optical properties were investigated. The residual percentages of kaolin, talc, elvan and ocher in the functional hanji were above 50%. The density of the hanji increased with the increase of the content of inorganic compounds. The hanji manufactured using ocher showed the highest density. The breaking length and burst factor decreased with the increase of inorganic materials, indicating that physical properties of hanji were not improved by adding inorganic materials. The emission rates of far-infrared radiation increased in the hanji manufactured using inorganic materials. The higher emission rates were observed in the hanji with elvan or ocher. Addition of inorganic compounds to hanji showed the flame retardative effect. The colorfastness to light of the hanji with elvan or ocher was the degree of 4, which explained by the characteristic color of the inorganics.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.330-334
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• 2022

In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the right time. First problem is that How Farmers can predict the right crop for cultivation because famers have no knowledge about prediction of crop. Second problem is that which algorithm is best that provide the maximum accuracy for crop prediction. Therefore, in this research Author proposed a method that would help to select the most suitable crop(s) for a specific land based on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine learning. In this work, the author implemented Random Forest Classifier, Support Vector Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these algorithms with the training dataset and later these algorithms were tested with the test dataset. The author compared the performances of all the tested methods to arrive at the best outcome. In this way best algorithm from the mention above is selected for crop prediction.

• Journal of the Society of Disaster Information
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• 제19권4호
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• pp.924-935
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• 2023

Purpose: The central aim of this study is to leverage machine learning techniques for the classification of Intrusion Detection System (IDS) data, with a specific focus on identifying the variables responsible for enhancing overall performance. Method: First, we classified 'R2L(Remote to Local)' and 'U2R (User to Root)' attacks in the NSL-KDD dataset, which are difficult to detect due to class imbalance, using seven machine learning models, including Logistic Regression (LR) and K-Nearest Neighbor (KNN). Next, we use the SHapley Additive exPlanation (SHAP) for two classification models that showed high performance, Random Forest (RF) and Light Gradient-Boosting Machine (LGBM), to check the importance of variables that affect classification for each model. Result: In the case of RF, the 'service' variable and in the case of LGBM, the 'dst_host_srv_count' variable were confirmed to be the most important variables. These pivotal variables serve as key factors capable of enhancing performance in the context of classification for each respective model. Conclusion: In conclusion, this paper successfully identifies the optimal models, RF and LGBM, for classifying 'R2L' and 'U2R' attacks, while elucidating the crucial variables associated with each selected model.

• Mycobiology
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• 제51권5호
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• pp.343-353
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• 2023

Lichens play crucial roles in the ecosystems, contributing to soil formation and nutrient cycling, and being used in biomonitoring efforts to assess the sustainability of ecosystems including air quality. Previous studies on heavy metal accumulation in lichens have mostly relied on manipulated environments, such as transplanted lichens, leaving us with a dearth of research on how lichens physiologically respond to heavy metal exposure in their natural habitats. To fill this knowledge gap, we investigated lichens from two of South Korea's geographically distant regions, Gangwon Province and Jeju Island, and examined whether difference in ambient heavy metal concentrations could be detected through physiological variables, including chlorophyll damage, lipid oxidation, and protein content. The physiological variables of lichens in response to heavy metals differed according to the collection area: Arsenic exerted a significant impact on chlorophyll degradation and protein content. The degree of fatty acid oxidation in lichens was associated with increased Cu concentrations. Our research highlights the value of lichens as a bioindicator, as we found that even small variations in ambient heavy metal concentrations can be detected in natural lichens. Furthermore, our study sheds light on which physiology variables that can be used as indicators of specific heavy metals, underscoring the potential of lichens for future ecology studies.

• Asia pacific journal of information systems
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• 제33권3호
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• pp.737-767
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• 2023

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

• Journal of Korea Artificial Intelligence Association
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• 제2권1호
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• pp.7-14
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• 2024

In this paper, we explore the application of RNA-sequencing data and ensemble machine learning to predict lung cancer and treatment strategies for lung cancer, a leading cause of cancer mortality worldwide. The research utilizes Random Forest, XGBoost, and LightGBM models to analyze gene expression profiles from extensive datasets, aiming to enhance predictive accuracy for lung cancer prognosis. The methodology focuses on preprocessing RNA-seq data to standardize expression levels across samples and applying ensemble algorithms to maximize prediction stability and reduce model overfitting. Key findings indicate that ensemble models, especially XGBoost, substantially outperform traditional predictive models. Significant genetic markers such as ADGRF5 is identified as crucial for predicting lung cancer outcomes. In conclusion, ensemble learning using RNA-seq data proves highly effective in predicting lung cancer, suggesting a potential shift towards more precise and personalized treatment approaches. The results advocate for further integration of molecular and clinical data to refine diagnostic models and improve clinical outcomes, underscoring the critical role of advanced molecular diagnostics in enhancing patient survival rates and quality of life. This study lays the groundwork for future research in the application of RNA-sequencing data and ensemble machine learning techniques in clinical settings.

• Geomechanics and Engineering
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• 제37권6호
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• pp.539-554
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• 2024

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.

• The Korean Journal of Ecology
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• 제24권2호
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• pp.93-100
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• 2001

One-year-old cottonwood (Populus deltoides Bartr.) clones, which were classified as sensitive or tolerant, were exposed to 150 n1/1 ozone (O$_3$) over 8 days for 8 hours each day under glass chamber conditions with natural sunlight. The leaves of the sensitive clone had black stipple and bifacial necrosis after $O_3$ treatment. Photosynthesis and stomatal conductance were measured before, during, and after the $O_3$ treatment. The photosynthetic rates due to $O_3$ treatment were decreased 51 percent and 34 percent on the sensitive and tolerant clone, respectively. The stomatal conductance of the sensitive clone was more than 40 percent higher than that of the tolerant clone regardless of the $O_3$ treatment. As light intensity increased, the $O_3$ effect on photosynthesis was clear. Compared to the previous growth chamber studies, our natural light exposure system was able to maintain a stable photosynthetic responses of the control treatment throughout the fumigation period. In addition, changes in assimilation versus intercellular $CO_2$ concentration (A/C curves) showed that $O_3$ decreased the slope and asymptote of the curves for the sensitive clone. This indicates that $O_3$ decreases the biochemical capacity of photosynthesis on the sensitive clone. Chlorophyll contents and fluorescence of the two clones were analyzed to examine the $O_3$ effects on photosystem 11, but $O_3$ did not impact these variables on either clone. Although the tolerant clone did not show any foliar injury, we could not find any ecophysiological defensive responses to $O_3$ treated. Stomatal conductance of the tolerant clone was originally much lower than that of the sensitive one. Thus, the mechanisms of the tolerant clone in this system are to narrowly open stomata and efficiently maintain photosynthesis with a more durable biochemical apparatus of photosynthesis under $O_3$ stress. The sensitive clone has higher photosynthetic capacity and more efficient light reaction activity than the tolerant one under charcoal filtered condition, but is not as resilient under stress.