• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• Advances in Computational Design
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• v.7 no.3
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• pp.253-279
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• 2022

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.485-496
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• 2023

In this study, we aimed to apply multispectral imaging (713 - 920 nm, 10 bands) for maturity classification and recognition of oriental melons grown in hydroponic greenhouses. A total of 20 oriental melons were selected, and time series multispectral imaging of oriental melons was 7 - 9 times for each sample from April 21, 2023, to May 12, 2023. We used several approaches, such as Savitzky-Golay (SG), standard normal variate (SNV), and Combination of SG and SNV (SG + SNV), for pre-processing the multispectral data. As a result, 713 - 759 nm bands were preprocessed with SG for the maturity classification of oriental melons. Additionally, a Light Gradient Boosting Machine (LightGBM) was used to train the recognition model for oriental melon. R² of recognition model were 0.92, 0.91 for the training and validation sets, respectively, and the F-scores were 96.6 and 79.4% for the training and testing sets, respectively. Therefore, multispectral imaging in the range of 713 - 920 nm can be used to classify oriental melons maturity and recognize their fruits.

• The Journal of Korean Medicine
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• v.45 no.1
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• pp.80-99
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• 2024

Objectives: The objective of this study was to review clinical studies conducted over the last ten years that investigated weight or fat loss interventions that can preserve muscle or fat-free mass in Sarcopenic obesity Methods: PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Research Information Sharing Service (RISS) and Korea Studies Information Service (KISS) were searched for Randomized clinical trials that had investigated all-type of interventions on the management of sarcopenic obesity from October 2013 to September 2023. Results: A total of 14 studies met all the inclusion criteria. Interventions that increase muscle mass while reducing body fat at the same time included resistance training (including using elastic bands) and whole-body electromyostimulation(WB-EMS) in exercise intervention and Hypocaloric high-protein diet in nutritional intervention, exercise and nutritional combined intervention, and combination intervention of electrical acupuncture and amino acid supplementation. Among them, the most positive method of changing the body composition in sarcopenic obesity was the electric acupuncture and amino acid supplements. Conclusion: Varying diagnostic criteria and management interventions for sarcopenic obesity in the included studies made it hard to maintain homogeneity across the studies. Well-defined criteria for diagnostic sarcopenic obesity should be considered. In addition, since all of the interventions examined did not show sufficient clinical effectiveness, follow-up studies are needed to confirm effective interventions for sarcopenic obesity patients in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.76-88
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• 2015

This paper presents a classification method based on the combination of active learning with temporal contextual information extracted from past land-cover maps for the classification of crop cultivation areas. Iterative classification based on active learning is designed to extract reliable training data and cultivation rules from past land-cover maps are quantified as temporal contextual information to be used for not only assignment of training data but also relaxation of spectral ambiguity. To evaluate the applicability of the classification method proposed in this paper, a case study with MODIS time-series vegetation index data sets and past cropland data layers(CDLs) is carried out for the classification of corn and soybean in Illinois state, USA. Iterative classification based on active learning could reduce misclassification both between corn and soybean and between other crops and non crops. The combination of temporal contextual information also reduced the over-estimation results in major crops and led to the best classification accuracy. Thus, these case study results confirm that the proposed classification method can be effectively applied for crop cultivation areas where it is not easy to collect the sufficient number of reliable training data.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.536-543
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• 2023

Speaker diarization, which labels for "who spoken when?" in speech with multiple speakers, has been studied on a deep neural network-based end-to-end method for labeling on speech overlap and optimization of speaker diarization models. Most deep neural network-based end-to-end speaker diarization systems perform multi-label classification problem that predicts the labels of all speakers spoken in each frame of speech. However, the performance of the multi-label-based model varies greatly depending on what the threshold is set to. In this paper, it is studied a speaker diarization system using single-label classification so that speaker diarization can be performed without thresholds. The proposed model estimate labels from the output of the model by converting speaker labels into a single label. To consider speaker label permutations in the training, the proposed model is used a combination of Permutation Invariant Training (PIT) loss and cross-entropy loss. In addition, how to add the residual connection structures to model is studied for effective learning of speaker diarization models with deep structures. The experiment used the Librispech database to generate and use simulated noise data for two speakers. When compared with the proposed method and baseline model using the Diarization Error Rate (DER) performance the proposed method can be labeling without threshold, and it has improved performance by about 20.7 %.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.261-269
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• 2018

This study was conducted to identify the effects of repeated cardiopulmonary resuscitation (CPR) training using smart learning on nursing students' knowledge, self-efficacy, and clinical competency. A quasi-experimental nonequivalent control group, pretest-posttest design was used. The subjects of the study were 102 nursing college students who had received CPR training for 6 months. The CPR training was divided into smart learning, lecture education, and practical education. Data were collected from November to December, 2016 and analyzed by descriptive statistics, ${\chi}^2$-test, t-test and one way ANOVA using the SPSS/WIN 21.0 program. The scores of cardiopulmonary resuscitation knowledge were higher in the lecture education group than the practical education group and the smart learning group. Scheffe's post hoc test revealed a statistically significant difference among groups (F=8.23, p=<.001). The self-efficacy of the practical education group was higher than that of the lecture education group and smart learning group, but this difference was not significant (F=2.46, p=.091). The clinical competency of the practical education group and smart learning group were higher than that of the lecture education group. Scheffe's post hoc test revealed that the value of clinical competency differed significantly among groups (F=59.90, p=<.001). Overall, the results showed that effective education differs based on nursing students' knowledge, self-efficacy, ad clinical competency. Combination training would be required for more effective repeated cardiopulmonary resuscitation training.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.253-256
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• 2014

Background: The standard treatment in the metastatic colorectal cancer consists of 5-FU based infusional regimens. However, with oral fluoropyrimidines, equal tumor responses may be obtained. Capecitabine causes macrocytosis of the cells by inhibition of DNA synthesis. In this context, a relationship was found between mean corpuscular volume (MCV) and response to therapy in breast cancer patients treated with Capecitabine, but whether this relationship also pertains in colorectal cancer has not been established. Materials and Methods: A total of 102 metastatic colorectal cancer patients treated with a oxaliplatin (XELOX)${\pm}$Bevacizumab combination were retrospectively evaluated. Patients were randomized into three groups. Hematological parameters (MCV, MPV, PCT, PLT, NLR) were recorded retrospectively, before treatment and after 3 cycles of chemotherapy. Results: After three cycles of therapy, 20 (19.6%) patients had progressive disease (PD), 41 (40.1%) had stable disease (SD), and 41 (40.1%) demonstrated a partial response (PR). In 62 (60.7%) treatment was with capesitabin plus XELOX therapy, and in 40 (39.2%) it was XELOX-Bevacizumab combination therapy. There was no difference among three groups before the treatment in terms of MCV, MPV, PCT, PLT, and NLR. MCV showed significant increase in chemotherapy response groups (PR and SD). In addition, a significant decrease was observed for platelet count in chemotherapy response groups. While NLR decrease was seen in only a PR group, PCT decrease was observed in all three groups. PCT and PLT values were higher in patients receiving Bevacizumab. Conclusions: PLT, PCT, MPV, and NLR values were decreased due to Capecitabine-based chemotherapy, however MCV was increased. PCT and PLT values were higher in patients who received Bevacizumab than those who did not. MCV, PLT, and NLR can be considered as important factors in predicting response to colorectal carcinoma treatment.

• PNF and Movement
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• v.5 no.2
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• pp.37-46
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• 2007

Purpose : The purpose of this study was to examine the effects of combination of isotonic and rhythmic stabilization in proprioceptive neuromuscular facilitation on trunk stability and balance in elderly people with chronic low back pain. Subjects : Thirty-two elderly people ($73.78{\pm}8.49$ years of age) who had complaints of CLBP were randomly assigned to 2 groups: experimental group and control group. Methods : Subject trained with combination of isotonic and rhythmic stabilization in proprioceptive neuromuscular facilitation for 4weeks with aim of improving trunk stability and balance. Weight distribution, trunk muscle balance, static balance ability and dynamic balance ability were measured before, at the end of training. Results : Data were analyzed using two-way ANOVA. After the exercise programs, there were significant differences in the weight distribution, trunk muscle balance and dynamic balance ability between the experimental and control group. However there was no significant difference in the one leg stance test(p<.05). Conclusion : This study suggest that PNF programs may be appropriate for improving trunk stability and balance in elderly people with CLBP.