• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Journal of Internet Computing and Services
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• v.25 no.5
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• pp.95-105
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• 2024

With the recent adoption of AI by various clothing shopping platforms and related industries to meet consumer needs and enhance purchasing power, the necessity for accurate classification of clothing categories and colors has surged. This paper aims to address this issue by developing a deep learning model that classifies various clothing items and their colors within a single image using buyer review images. After directly crawling buyer review image data and performing various preprocessing steps such as data augmentation, we utilized the YOLOv10 model to detect clothing objects and classify them into categories. Subsequently, to improve color extraction, we implemented a cropping method to isolate clothing regions in the images and calculated the similarity with a color chart to extract the most similar color names. Our experimental results show that our approach is effective, with performance increasing with model size and augmentation scale. The employed model showed stable performance in both clothing category and color extraction, proving its reliability. The proposed system not only enhances customer satisfaction and purchasing power by accurately classifying clothing categories and colors based on user review images but also lays the foundation for further research in automated fashion analysis. Moreover, it possesses the scalability to be utilized in various fields of the related industry, such as fashion trend analysis, inventory management, and marketing strategy development.