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Добірка наукової літератури з теми "RDF dataset metrics"

Автор: Grafiati

Опубліковано: 10 березня 2023

Оновлено: 31 липня 2025

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Статті в журналах з теми "RDF dataset metrics"

Mountantonakis, Michalis, and Yannis Tzitzikas. "Content-based Union and Complement Metrics for Dataset Search over RDF Knowledge Graphs." Journal of Data and Information Quality 12, no. 2 (2020): 1–31. http://dx.doi.org/10.1145/3372750.

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Xia, Jianglin. "Credit Card Fraud Detection Based on Support Vector Machine." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 93–97. http://dx.doi.org/10.54097/hset.v23i.3202.

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Due to the increasing popularity cashless transactions, credit card fraud has become one of the most common frauds and caused huge harm to the financial institutions and individuals in real life. In this academic paper, the algorithm Support Vector Machine (SVM) is used to build models to deal with the credit card fraud detection problem with the performance metrics AUC and F1-score. The experiment dataset is named Credit Card Transactions Fraud Detection Dataset from the Kaggle website. After the step of preprocessing, the dataset is split into the training, testing and validation dataset wit

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Rahman, Rinsy, Dola Saha, Winniecia Dkhar, Sathyendranath Malli, and Neil Barnes Abraham. "Development of a machine learning predictive model for early detection of breast cancer." F1000Research 14 (February 5, 2025): 164. https://doi.org/10.12688/f1000research.161073.1.

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Background Breast cancer remains a significant global health concern, with over 7.8 million cases reported in the last five years. Early detection and accurate classification are crucial for reducing mortality rates and improving outcomes. Machine learning (ML) has emerged as a transformative tool in medical imaging, enabling more efficient and accurate diagnostic processes. Objective This study aims to develop a machine learning-based predictive model for early detection and classification of breast cancer using the Wisconsin Breast Cancer Diagnostic dataset. Methods The dataset, comprising 5

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Chan, Philip TH, and Terry HW Chan. "Machine learning model for dissolved gas analysis: methodological review with a case in Hong Kong." HKIE Transactions 31, no. 4 (2024): 1–11. https://doi.org/10.33430/v31n4thie-2024-0022.

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Dissolved gas analysis is a valuable diagnostic tool used to monitor transformer health by analysing the gases dissolved in insulation oil. However, its practical application is hindered by the absence of a universal standard, leading to varied interpretations and implementations across different contexts. Scholars have turned to machine learning to advance DGA anomaly detection, but the existing literature prioritises model development over methodological rigour; issues such as dataset imbalance, appropriate evaluation metrics, and testing and validation procedures are often overlooked. This

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Wang, Ke, Ligang Cheng, and Bin Yong. "Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification." Remote Sensing 12, no. 13 (2020): 2154. http://dx.doi.org/10.3390/rs12132154.

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Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we

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Kim, Eungi. "Region Partitioning Framework (RCF) for Scatterplot Analysis: A Structured Approach to Absolute and Normalized Data Interpretation." Metrics 2, no. 2 (2025): 6. https://doi.org/10.3390/metrics2020006.

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Scatterplots can reveal important data relationships, but their visual complexity can make pattern identification challenging. Systematic analytical approaches help structure interpretation by dividing scatterplots into meaningful regions. This paper introduces the region partitioning framework (RCF), a systematic method for dividing scatterplots into interpretable regions using k × k grids, in order to enhance visual data analysis and quantify structural changes through transformation metrics. RCF partitions the x and y dimensions into k × k grids (e.g., 4 × 4 or 16 regions), balancing granul

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Qian, Yuqing, Tingting Shang, Fei Guo, et al. "Identification of DNA-binding protein based multiple kernel model." Mathematical Biosciences and Engineering 20, no. 7 (2023): 13149–70. http://dx.doi.org/10.3934/mbe.2023586.

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<abstract> <p>DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linea

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Zhao, Qinghe, Zifang Zhang, Yuchen Huang, and Junlong Fang. "TPE-RBF-SVM Model for Soybean Categories Recognition in Selected Hyperspectral Bands Based on Extreme Gradient Boosting Feature Importance Values." Agriculture 12, no. 9 (2022): 1452. http://dx.doi.org/10.3390/agriculture12091452.

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Soybeans with insignificant differences in appearance have large differences in their internal physical and chemical components; therefore, follow-up storage, transportation and processing require targeted differential treatment. A fast and effective machine learning method based on hyperspectral data of soybeans for pattern recognition of categories is designed as a non-destructive testing method in this paper. A hyperspectral-image dataset with 2299 soybean seeds in four categories is collected. Ten features are selected using an extreme gradient boosting algorithm from 203 hyperspectral ban

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Manurung, Jonson, and Hondor Saragih. "Performance Comparison of Naive Bayes and Support Vector Machine Algorithms in Spambot Classification in Emails." International Journal of Basic and Applied Science 13, no. 3 (2024): 137–45. https://doi.org/10.35335/ijobas.v13i3.522.

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In the ever-growing digital era, email spam is a serious threat that affects user productivity and information security. This study aims to analyze the comparative effectiveness of Naive Bayes and SVM algorithms with radial basis function (RBF) kernels in classifying spambots in emails. The methodology used includes collecting email datasets, applying both algorithms for classification, and evaluating performance using accuracy, precision, recall, and f1-score metrics. The results showed that SVM RBF performed better than Gaussian Naive Bayes, with significant improvements in all evaluation me

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Sumiati, Ruzita, Moh Chamim, Desmarita Leni, Yazmendra Rosa, and Hanif Hanif. "Modeling Mechanical Component Classification Using Support Vector Machine with A Radial Basis Function Kernel." Jurnal Teknik Mesin 16, no. 2 (2023): 165–74. http://dx.doi.org/10.30630/jtm.16.2.1250.

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The process of identification and classification of products in the era of modern manufacturing industries has become a crucial pillar in enhancing efficiency, productivity, and product quality. In this research, the modeling of manufacturing product classification, such as mechanical components consisting of four classes: bolts, washer, nuts, and locating pin, was conducted. The proposed model in this study is the Support Vector Machine (SVM) with Radial Basis Function (RBF). The dataset used consists of digital images of mechanical components, with each component having 400 samples, resultin

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Дисертації з теми "RDF dataset metrics"

Soderi, Mirco. "Semantic models for the modeling and management of big data in a smart city environment." Doctoral thesis, 2021. http://hdl.handle.net/2158/1232245.

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The overall purpose of this research has been the building or the improve- ment of semantic models for the representation of data related to smart cities and smart industries, in such a way that it could also be possible to build context-rich, user-oriented, ecient and eective applications based on such data. In some more detail, one of the key purposes has been the modelling of structural and the functioning aspects of the urban mobility and the produc- tion of instances exploiting the Open Street Map, that once integrated with trac sensors data, it has lead to the building and display

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Частини книг з теми "RDF dataset metrics"

Wentzel, Bianca, Fabian Kirstein, Torben Jastrow, Raphael Sturm, Michael Peters, and Sonja Schimmler. "An Extensive Methodology and Framework for Quality Assessment of DCAT-AP Datasets." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-41138-0_17.

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AbstractThe DCAT Application Profile for Data Portals is a crucial cornerstone for publishing and reusing Open Data in Europe. It supports the harmonization and interoperability of Open Data by providing an expressive set of properties, guidelines, and reusable vocabularies. However, a qualitative and accurate implementation by Open Data providers remains challenging. To improve the informative value and the compliance with RDF-based specifications, we propose a methodology to measure and assess the quality of DCAT-AP datasets. Our approach is based on the FAIR and the 5-star principles for Li

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Rizvi, Syed Zeeshan, Muhammad Umar Farooq, and Rana Hammad Raza. "Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images: Case Study of Crowd Counting." In Digital Interaction and Machine Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_7.

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AbstractHumans are able to perceive objects only in the visible spectrum range which limits the perception abilities in poor weather or low illumination conditions. The limitations are usually handled through technological advancements in thermographic imaging. However, thermal cameras have poor spatial resolutions compared to RGB cameras. Super-resolution (SR) techniques are commonly used to improve the overall quality of low-resolution images. There has been a major shift of research among the Computer Vision researchers towards SR techniques particularly aimed for thermal images. This paper

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Harkat, Houda, Jose Nascimento, Alexandre Bernardino, and Hasmath Farhana Thariq Ahmed. "Fire images classification using high order statistical features." In Advances in Forest Fire Research 2022. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_31.

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Wildfires and forest fires have devastated millions of hectares of forest across the world over the years. Computer vision-based fire classification, which classifies fire pixels from non-fire pixels in image or video datasets, has gained popularity as a result of recent innovations. A conventional machine learning-based approach or a deep learning-based approach can be used to distinguish fire pixels from an image or video. Deep learning is currently the most prominent method for detecting forest fires. Although deep learning algorithms can handle large volumes of data, typically ignore the d

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Тези доповідей конференцій з теми "RDF dataset metrics"

Gao, Hanning, Lingfei Wu, Po Hu, and Fangli Xu. "RDF-to-Text Generation with Graph-augmented Structural Neural Encoders." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/419.

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The task of RDF-to-text generation is to generate a corresponding descriptive text given a set of RDF triples. Most of the previous approaches either cast this task as a sequence-to-sequence problem or employ graph-based encoder for modeling RDF triples and decode a text sequence. However, none of these methods can explicitly model both local and global structure information between and within the triples. To address these issues, we propose to jointly learn local and global structure information via combining two new graph-augmented structural neural encoders (i.e., a bidirectional graph enco

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Shi, Yu, and Rolf D. Reitz. "Assessment of Multi-Objective Genetic Algorithms With Different Niching Strategies and Regression Methods for Engine Optimization and Design." In ASME 2009 Internal Combustion Engine Division Spring Technical Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/ices2009-76015.

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In previous study [1] the Non-dominated Sorting Genetic Algorithm II (NSGA II) [2] performed better than other popular Multi-Objective Genetic Algorithms (MOGA) in engine optimization that sought optimal combinations of the piston bowl geometry, spray targeting, and swirl ratio. NSGA II is further studied in this paper using different niching strategies that are applied to the objective-space and design-space, which diversify the optimal objectives and design parameters accordingly. Convergence and diversity metrics are defined to assess the performance of NSGA II using different niching strat

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Amorim Neto, Hugo de A., Luis Loo, Marcelo G. P. de Lacerda, Ulisses Braga Neto, and Fernando Buarque de L. Neto. "Towards a Surrogate-assisted PALLAS algorithm for Gene Regulatory Network Inference." In Simpósio Brasileiro de Bioinformática. Sociedade Brasileira de Computação, 2024. https://doi.org/10.5753/bsb.2024.245586.

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This paper analyzes the application of surrogate models to improve the efficiency of Gene Regulatory Network (GRN) inference from time-series data. A Radial Basis Function (RBF) surrogate model was integrated with the Penalized mAximum LikeLihood and pArticle Swarms (PALLAS) using a Mixed Fish School Search (MFSS) algorithm to reduce the computational cost associated with evaluating the penalized log-likelihood (PLL) fitness function. Experimental results on the p53-MDM2 negative-feedback loop GRN dataset demonstrate that the surrogate-assisted approach significantly reduced fitness function c

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Bao, Kaige, and Ang Li. "An Efficient Program to Detect DDoS Attacks using Machine Learning Algorithms." In 3rd International Conference on Advances in Computing & Information Technologies. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131507.

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This paper investigates the efficacy of machine learning algorithms for the detection of Distributed Denial of Service (DDoS) attacks [4][5]. The study explores different approaches, including Support Vector Machines (SVM), logistic regression, and decision trees, and evaluates their performance using metrics such as accuracy, precision, recall, and F1-score [6]. The results demonstrate the effectiveness of SVM models with polynomial or radial basis function (RBF) kernels, logistic regression models with a polynomial degree of 4, and decision tree models with depths exceeding 10 [7][8]. These

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Kornev, Denis, Roozbeh Sadeghian, Stanley Nwoji, Qinghua He, Amir Gandjbbakhche, and Siamak Aram. "Machine Learning-Based Gaming Behavior Prediction Platform." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001826.

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Brain disorders caused by Gaming Addiction drastically increased due to the rise of Internet users and Internet Gaming auditory. Driven by such a tendency, in 2018, World Health Organization (WHO) and the American Medical Association (AMA) addressed this problem as a “gaming disorder” and added it to official manuals. Scientific society equipped by statistical analysis methods such as t-test, ANOVA, and neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG), has achieved significant success in brain ma

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Звіти організацій з теми "RDF dataset metrics"

Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41302.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR

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