• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.179-184
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• 2021

In our study we performed a sentiments analysis from the images. For this purpose, we used 153 images that contain: people, animals, buildings, landscapes, cakes and objects that we divided into two categories: images that suggesting a positive or a negative emotion. In order to classify the images using the two categories, we created two models. The SAI-G model was created with Google's AutoML Vision service. The SAI-C model was created on the Clarifai platform. The data were labeled in a preprocessing stage, and for the SAI-C model we created the concepts POSITIVE (POZITIV) AND NEGATIVE (NEGATIV). In order to evaluate the performances of the two models, we used a series of evaluation metrics such as: Precision, Recall, ROC (Receiver Operating Characteristic) curve, Precision-Recall curve, Confusion Matrix, Accuracy Score and Average precision. Precision and Recall for the SAI-G model is 0.875, at a confidence threshold of 0.5, while for the SAI-C model we obtained much lower scores, respectively Precision = 0.727 and Recall = 0.571 for the same confidence threshold. The results indicate a lower classification performance of the SAI-C model compared to the SAI-G model. The exception is the value of Precision for the POSITIVE concept, which is 1,000.

• Journal of the Korean Society for Library and Information Science
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• v.8
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• pp.73-101
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• 1981

Retrieval effectiveness is the principal criterion for measuring the performance of an information retrieval system. The effectiveness of a retrieval system depends primarily on the extent to which it can retrieve wanted documents without retrieving unwanted ones. So, ultimately, effectiveness is a function of the relevant and nonrelevant documents retrieved. Consequently, 'relevance' of information to the user's request has become one of the most fundamental concept encountered in the theory of information retrieval. Although there is at present no consensus as to how this notion should be defined, relevance has been widely used as a meaningful quantity and an adequate criterion for measures of the evaluation of retrieval effectiveness. The recall and precision among various parameters based on the 'two-by-two' table (or, contingency table) were major considerations in this paper, because it is assumed that recall and precision are sufficient for the measurement of effectiveness. Accordingly, different concepts of 'relevance' and 'pertinence' of documents to user requests and their proper usages were investigated even though the two terms have unfortunately been used rather loosely in the literature. In addition, a number of variables affecting the recall and precision values were discussed. Some conclusions derived from this study are as follows: Any notion of retrieval effectiveness is based on 'relevance' which itself is extremely difficult to define. Recall and precision are valuable concepts in the study of any information retrieval system. They are, however, not the only criteria by which a system may be judged. The recall-precision curve represents the average performance of any given system, and this may vary quite considerably in particular situations. Therefore, it is possible to some extent to vary the indexing policy, the indexing policy, the indexing language, or the search methodology to improve the performance of the system in terms of recall and precision. The 'inverse relationship' between average recall and precision could be accepted as the 'fundamental law of retrieval', and it should certainly be used as an aid to evaluation. Finally, there is a limit to the performance(in terms of effectiveness) achievable by an information retrieval system. That is : "Perfect retrieval is impossible."

• Korean Journal of Fisheries and Aquatic Sciences
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• v.57 no.5
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• pp.581-588
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• 2024

In this, we study developed a machine-learning system that can effectively detect giant jellyfish Nemopilema nomurai by collecting videos of their appearances. Surveys were conducted in the East China Sea, South Sea, and Jeju coastal waters, which are presumed to be jellyfish migration routes. Video data were collected using GoPro cameras, and images were extracted at 1 fps to train the YOLOv8 Nano and Medium models. The YOLOv8 Nano model achieved an F1 score of 0.83 with high confidence and maintained high precision in the precision-recall curve, demonstrating its effectiveness in predicting jellyfish occurrences. The YOLOv8 nano model demonstrated excellent reliability and precision, indicating its potential for effective jellyfish detection. However, to improve the performance of the model even further, data from various environments must be collected and additional validations must be performed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5159-5178
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• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

• Journal of the Korean Society for Library and Information Science
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• v.16
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• pp.177-205
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• 1989

Retrieval effectiveness is the principal criteria for measuring the performance of an information retrieval system. This paper deals with the characteristics of 'relevance' of information and various measuring techniques of retrieval effectivess. The outlines of this study are as follows: 1) Relevance decision for evaluation should be devided into the user-oriented and the system-oriented decisions. 2) The recall-precision measure seems to be user-oriented, and the recall-fallout measure to be system-oriented. 3) Many of composite measures can not be justified III any rational manner unfortunately. 4) The Swets model has demonstrated that it yields, in general, a straight line instead of a curve of varying curvature and emphasized the fundamentally probabilistic nature of information retrieval. 5) The Cooper model seems to be a good substitute for precision and a useful measure for systems which ranked documents. 6) The Rocchio model were proposed for the evaluation of retreval systems which ranked documents, and were designed to be independent of cut-off. 7) The Cawkell model suggested that the Shannon's equation for entropy can be applied to measuring of retrieval effectiveness.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.290-300
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• 2024

This study presents a comprehensive evaluation of various machine learning models for predicting heart failure outcomes. Leveraging a data set of clinical records, the performance of Logistic Regression, Support Vector Machine (SVM), Random Forest, Soft Voting ensemble, and XGBoost models are rigorously assessed using multiple evaluation metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The analysis reveals that the XGBoost model outperforms the other techniques across all metrics, exhibiting the highest AUC score, indicating superior discriminative ability in distinguishing between patients with and without heart failure. Furthermore, the study highlights the importance of feature importance analysis provided by XGBoost, offering valuable insights into the most influential predictors of heart failure, which can inform clinical decision-making and patient management strategies. The research also underscores the significance of balancing precision and recall, as reflected by the F1-score, in medical applications to minimize the consequences of false negatives.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.49-55
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• 2013

In this paper, we proposed a good classifier to match different spatial data sets by applying evaluation of classifiers performance in data mining and biometrics. For this, we calculated distances between a pair of candidate features for matching criteria, and normalized the distances by Min-Max method and Tanh (TH) method. We defined classifiers that shape similarity is derived from fusion of these similarities by CRiteria Importance Through Intercriteria correlation (CRITIC) method, Matcher Weighting method and Simple Sum (SS) method. As results of evaluation of classifiers performance by Precision-Recall (PR) curve and area under the PR curve (AUC-PR), we confirmed that value of AUC-PR in a classifier of TH normalization and SS method is 0.893 and the value is the highest. Therefore, to match different spatial data sets, we thought that it is appropriate to a classifier that distances of matching criteria are normalized by TH method and shape similarity is calculated by SS method.

• ETRI Journal
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• v.44 no.4
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• pp.573-587
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• 2022

The agricultural sector is completely different from other sectors since it completely relies on various natural and climatic factors. Climate changes have many effects, including lack of annual rainfall and pests, heat waves, changes in sea level, and global ozone/atmospheric CO₂ fluctuation, on land and agriculture in similar ways. Climate change also affects the environment. Based on these factors, farmers chose their crops to increase productivity in their fields. Many existing agricultural ontologies are either domain-specific or have been created with minimal vocabulary and no proper evaluation framework has been implemented. A new agricultural ontology focused on subdomains is designed to assist farmers using Jaccard relative extractor (JRE) and Naïve Bayes algorithm. The JRE is used to find the similarity between two sentences and words in the agricultural documents and the relationship between two terms is identified via the Naïve Bayes algorithm. In the proposed method, the preprocessing of data is carried out through natural language processing techniques and the tags whose dimensions are reduced are subjected to rule-based formal concept analysis and mapping. The subdomain ontologies of weather, pest, and soil are built separately, and the overall agricultural ontology are built around them. The gold standard for the lexical layer is used to evaluate the proposed technique, and its performance is analyzed by comparing it with different state-of-the-art systems. Precision, recall, F-measure, Matthews correlation coefficient, receiver operating characteristic curve area, and precision-recall curve area are the performance metrics used to analyze the performance. The proposed methodology gives a precision score of 94.40% when compared with the decision tree(83.94%) and K-nearest neighbor algorithm(86.89%) for agricultural ontology construction.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.354-361
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• 2023

Most of spinal diseases are diagnosed based on the subjective judgment of a specialist, so numerous studies have been conducted to find objectivity by automating the diagnosis process using deep learning. In this paper, we propose a method that combines segmentation and feature extraction, which are frequently used techniques for diagnosing spinal diseases. Four models, U-Net, U-Net++, DeepLabv3+, and M-Net were trained and compared using 1000 X-ray images, and key-points were derived using Douglas-Peucker algorithms. For evaluation, Dice Similarity Coefficient(DSC), Intersection over Union(IoU), precision, recall, and area under precision-recall curve evaluation metrics were used and U-Net++ showed the best performance in all metrics with an average DSC of 0.9724. For the average Euclidean distance between estimated key-points and ground truth, U-Net was the best, followed by U-Net++. However the difference in average distance was about 0.1 pixels, which is not significant. The results suggest that it is possible to extract key-points based on segmentation and that it can be used to accurately diagnose various spinal diseases, including spondylolisthesis, with consistent criteria.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.179-186
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• 2008

In this paper we analyse the effects of Bayesian algorithm in teaming class imbalance problems and compare the performance evaluation methods. The teaming performance of the Bayesian algorithm is evaluated over the class imbalance problems generated by priori data distribution, imbalance data rate and discrimination complexity. The experimental results are calculated by the AUC(Area Under the Curve) values of both ROC(Receiver Operator Characteristic) and PR(Precision-Recall) evaluation measures and compared according to imbalance data rate and discrimination complexity. In comparison and analysis, the Bayesian algorithm suffers from the imbalance rate, as the same result in the reported researches, and the data overlapping caused by discrimination complexity is the another factor that hampers the learning performance. As the discrimination complexity and class imbalance rate of the problems increase, the learning performance of the AUC of a PR measure is much more variant than that of the AUC of a ROC measure. But the performances of both measures are similar with the low discrimination complexity and class imbalance rate of the problems. The experimental results show 4hat the AUC of a PR measure is more proper in evaluating the learning of class imbalance problem and furthermore gets the benefit in designing the optimal learning model considering a misclassification cost.