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Статті в журналах з теми "RDF dataset metrics"

Автор: Grafiati

Опубліковано: 10 березня 2023

Оновлено: 31 липня 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Sawalha, Samer, and Ghazi Al-Naymat. "An Adaptive Holt–Winters Model for Seasonal Forecasting of Internet of Things (IoT) Data Streams." IoT 6, no. 3 (2025): 39. https://doi.org/10.3390/iot6030039.

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In various applications, IoT temporal data play a crucial role in accurately predicting future trends. Traditional models, including Rolling Window, SVR-RBF, and ARIMA, suffer from a potential accuracy decrease because they generally use all available data or the most recent data window during training, which can result in the inclusion of noisy data. To address this critical issue, this paper proposes a new forecasting technique called Adaptive Holt–Winters (AHW). The AHW approach utilizes two models grounded in an exponential smoothing methodology. The first model is trained on the most curr

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12

Chen, Yanji, Mieczyslaw M. Kokar, Jakub Moskal, and Kaushik R. Chowdhury. "Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities." Applied Sciences 12, no. 23 (2022): 11946. http://dx.doi.org/10.3390/app122311946.

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Collaborative spectrum access requires wireless devices to perform spectrum-related tasks (such as sensing) on request from other nodes. Thus, while joining the network, they need to inform neighboring devices and/or the central coordinator of their capabilities. During the operational phase, nodes may request other permissions from the the controller, like the opportunity to transmit according to the current policies and spectrum availability. To achieve such coordinated behavior, all associated devices within the network need a language for describing radio capabilities, requests, scenarios,

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13

Makubhai, Shahin, Ganesh R Pathak, and Pankaj R Chandre. "Comparative analysis of explainable AI models for predicting lung cancer using diverse datasets." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1980. http://dx.doi.org/10.11591/ijai.v13.i2.pp1980-1991.

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Lung cancer prediction is crucial for early detec-tion and treatment, and explainable AI models have gained attention for their interpretability. This study aims to compare various explainable AI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, prepro-cessed, and used to train models such as Logistic Regression, SVC-Linear, SVC-rbf, Decision Tree, Random Forest, AdaBoost Classifier, and XGBoost Classifier. Preliminary results indicate that Random Forest achieved the highest accuracy of 98.9% across multiple data

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14

Yousaf, Saima, and Zainab Yousaf. "COMPOUND FACIAL EXPRESSION RECOGNITION BASED ON MOBILENET WITH DATA AUGMENTATION." Insights-Journal of Life and Social Sciences 3, no. 3 (Social) (2025): 269–77. https://doi.org/10.71000/8r543y25.

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Background: Facial expression recognition (FER) has emerged as a crucial tool in human-computer interaction, medical diagnostics, psychological analysis, and robotics. While prior research has focused extensively on basic facial expressions, compound emotions—combinations of two or more basic expressions—remain underexplored. A key limitation in existing datasets is class imbalance and poor organization, which affects training quality and leads to bias in deep learning models. Efficient recognition of both basic and compound expressions demands a balanced, well-structured dataset and advanced

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15

Shahin, Makubhai, R. Pathak Ganesh, and R. Chandre Pankaj. "Comparative analysis of explainable artificial intelligence models for predicting lung cancer using diverse datasets." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1980–91. https://doi.org/10.11591/ijai.v13.i2.pp1980-1991.

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Lung cancer prediction is crucial for early detection and treatment, and explainable artificial intelligence (XAI) models have gained attention for their interpretability. This study aims to compare various XAI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, preprocessed, and used to train models such as logistic regression (LR), support vector classifier (SVC)-linear, SVC-radial basis function (RBF), decision tree (DT), random forest (RF), adaboost classifier, and XGBoost classifier. Preliminary results indica

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16

Salim, Bryan Othman. "Performance Comparison of the Support Vector Machine Algorithm with RBF Kernel and Random Forest in Classifying Tourism Images of Nusa Penida." Internet of Things and Artificial Intelligence Journal 4, no. 4 (2024): 867–77. https://doi.org/10.31763/iota.v4i4.846.

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Indonesia holds vast tourism potential, including Nusa Penida, Bali, renowned for its natural beauty. However, the adoption of modern technology to support tourism promotion and management remains limited. This study aims to compare the performance of Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel and Random Forest algorithms in classifying images of three main tourist attractions in Nusa Penida: Angel’s Billabong, Broken Beach, and Kelingking Beach. The dataset consists of 450 images processed using the Histogram of Oriented Gradients (HOG) method for feature extraction.

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17

Ha, Hoang, Hieu Vu Trong, Trang Le Huyen, Dam Duc Nguyen, Indra Prakash, and Binh Thai Pham. "Investigation of the Gaussian Process with Various Kernel Functions for the Prediction of the Compressive Strength of Concrete." Engineering, Technology & Applied Science Research 15, no. 1 (2025): 19992–97. https://doi.org/10.48084/etasr.9125.

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The Compressive Strength of Concrete (CSC) is a critical parameter for evaluating the quality of concrete used in various construction projects, including buildings, bridges, and roads. The primary objective of this study is to examine the efficacy of a Gaussian Process (GP) Machine Learning (ML) model employing two kernel functions: Radial Basis Function (RBF) and Polynomial (POL), for predicting the CSC, considering readily quantifiable parameters. Based on these kernel functions, two models were created for this prediction, GP-RBF and GP-POL. The modeling process employed a total of 369 con

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18

Jerop, Brenda, and Davies Rene Segera. "An Efficient PCA-GA-HKSVM-Based Disease Diagnostic Assistant." BioMed Research International 2021 (October 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/4784057.

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Disease diagnosis faces challenges such as misdiagnosis, lack of diagnosis, and slow diagnosis. There are several machine learning techniques that have been applied to address these challenges, where a set of symptoms is applied to a classification model that predicts the presence or absence of a disease. To improve on the performance of these techniques, this paper presents a technique which involves feature selection using principal component analysis (PCA), a hybrid kernel-based support vector machine (HKSVM) classification model and hyperparameter optimization using genetic algorithm (GA).

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19

Varshini, A. Sri. "Face Recognition using SVM with the Help of Kernel Characteristics Function." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 793–97. http://dx.doi.org/10.22214/ijraset.2023.57436.

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Abstract: This project focuses on building a face recognition system using the Labeled Faces in the Wild (LFW) dataset.The dataset includes diverse facial images of individuals across various age groups, such as kids, women, men, and the elderly. The model employs a Support Vector Machine (SVM) with a radial basis function (RBF) kernel for effective classification. Preprocessing techniques like normalization, grayscale conversion, and face alignment enhance the dataset's quality. The system's performance is assessed using accuracy, precision, recall metrics, and a Receiver Operating Characteri

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20

Bukhowah, Rawan, Ahmed Aljughaiman, and M. M. Hafizur Rahman. "Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions." Electronics 13, no. 6 (2024): 1031. http://dx.doi.org/10.3390/electronics13061031.

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The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy

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21

Zhang, Zimo. "Cropland plot segmentation based on deep convolutional neural networks." Applied and Computational Engineering 46, no. 1 (2024): 214–24. http://dx.doi.org/10.54254/2755-2721/46/20241372.

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Cropland in mountainous areas has always been an area of focus for researchers, as it serves as the foundation for agricultural production and has profound impacts on regional economy and ecological stability. This study aims to address the challenging problem of cropland parcel segmentation in remote sensing images, driven by the increasing demand for accurate and efficient land-use monitoring. To achieve precise delineation of agricultural boundaries and overall regions, we utilized deep learning methods, including the RCF (Richer Convolutional Features) model and the UNet++ architecture. Th

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22

Mohammed, Yousra Abdulaziz, and Eman Gadban Saleh. "Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 1113–20. https://doi.org/10.11591/ijeecs.v21.i2.pp1113-1120.

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Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in developing countries such as Iraq. Our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done usi

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23

Pourramezan, Mohammad-Reza, Abbas Rohani, and Mohammad Hossein Abbaspour-Fard. "Unlocking the Potential of Soft Computing for Predicting Lubricant Elemental Spectroscopy." Lubricants 11, no. 9 (2023): 382. http://dx.doi.org/10.3390/lubricants11090382.

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Predictive maintenance of mechanical systems relies on accurate condition monitoring of lubricants. This study assesses the performance of soft computing models in predicting the elemental spectroscopy (Fe, Pb, Cu, Cr, Al, Si, and Zn) of engine lubricants, based on the electrical properties (ε′, ε″, and tan δ) of oil samples. The study employed a dataset of 49 lubricant samples, comprising elemental spectroscopy and dielectric properties, to train and test several soft computing models (RBF, ANFIS, SVM, MLP, and GPR). Performance of the models was evaluated using error metrics such as MAPE, RM

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24

Mohammed, Yosra Abdulaziz, and Eman Gadban Saleh. "Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 1113. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp1113-1120.

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<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of val

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25

Iriananda, Syahroni Wahyu, Renaldi Widi Budiawan, Aviv Yuniar Rahman, and Istiadi Istiadi. "Optimasi Klasifikasi Sentimen Komentar Pengguna Game Bergerak Menggunakan Svm, Grid Search Dan Kombinasi N-Gram." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 4 (2024): 743–52. http://dx.doi.org/10.25126/jtiik.1148244.

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Game online telah menjadi fenomena budaya signifikan dalam industri yang berkembang pesat. Pengguna dan pengembang game menggunakan analisis sentimen untuk memahami opini dan ulasan pemain, yang membantu dalam pengembangan dan peningkatan game. Penelitian ini melakukan klasifikasi sentimen menggunakan algoritma Support Vector Machine (SVM) dengan penerapan teknik N-Gram untuk seleksi fitur. Grid Search (GS) digunakan untuk optimasi hyperparameter guna mencapai akurasi optimal. Eksperimen dilakukan dengan berbagai skenario, termasuk variasi jumlah data, pengaturan hyperparameter, rasio dataset

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Blikon, Yohanes Balawuri. "PERBANDINGA KINERJA PENGKLASIFIKASI CITRA BUAH KAKAO SAKIT DAN SEHAT MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) DAN K-NEAREST NEIGHBORS (KNN)." Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 14, no. 1 (2023): 1–8. http://dx.doi.org/10.24176/simet.v14i1.9012.

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Kakao merupakan salah satu hasil bumi dibidang perkebunan. Perkebunan kakao dengan hasilnya yaitu biji kakao dapat diolah menjadi bahan dasar tepung atau coklat. Keberadaan perkebunan ini tentu perlu mendapat dukungan teknologi atau kecerdasan buatan untuk membantu proses pensortiran secara modern jika dilakukan penerapan conveyer belt atau model pemetikan otomatis masa depan menggunakan drone pemetik buah. Proses pensortiran yang dimaksud yaitu menggunakan model pengklasifikasian untuk mendeteksi dataset buah kakao sakit dan sehat. Penelitian ini membandingkan model klasifikasi Support Vector

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Das, Shuvendu, Karanvir Singh, and Kiranjeet Kaur. "Air Quality Prediction in Beijing: Machine and Deep Learning Analysis." ITM Web of Conferences 68 (2024): 01012. https://doi.org/10.1051/itmconf/20246801012.

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In densely populated urban hubs like Beijing, the presence of PM2.5, a critical air quality metric, poses significant hazards to human health and the environment. This study delves into predictive modeling approaches for forecasting PM2.5 concentrations in response to escalating concerns.We analyze a wide range of approaches, including RDF, CNN, STM and fundamental statistical techniques, by closely analyzing Beijing’s PM2.5 concentrations and historical meteorological information. According to our research, CNN outperforms LSTM and shows excellent accuracy in forecasting PM2.5 levels. While r

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Du, Xiaoshuang, Nan Qu, Xuexi Zhang, et al. "Accelerated First-Principles Calculations Based on Machine Learning for Interfacial Modification Element Screening of SiCp/Al Composites." Materials 17, no. 6 (2024): 1322. http://dx.doi.org/10.3390/ma17061322.

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SiCp/Al composites offer the advantages of lightweight construction, high strength, and corrosion resistance, rendering them extensively applicable across various domains such as aerospace and precision instrumentation. Nonetheless, the interfacial reaction between SiC and Al under high temperatures leads to degradation in material properties. In this study, the interface segregation energy and interface binding energy subsequent to the inclusion of alloying elements were computed through a first-principle methodology, serving as a dataset for machine learning. Feature descriptors for machine

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Lu, Tao, Yubian Wang, Chaohui Ma, Mingxing She, Min Liu, and Fenghui Dong. "Estimation of CS of the HPC with hybrid RBF neural networks." International Journal of Knowledge-Based and Intelligent Engineering Systems 29, no. 2 (2024): 188–201. https://doi.org/10.1177/13272314241295966.

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This study used a radial function neural network (RBFNN) to create a novel system for calculating high-performance concrete's (HPC) compressive strength (CS) modified with fly ash and blast furnace slag. These admixtures could affect the mechanical and physical properties of HPC, and determining it definitely requires experimental efforts and costs. Herein, alternative methods such as machine learning algorithms named RBFNN could be useful to address these questions. The SSA (Salp swarm algorithm) and the artificial hummingbird algorithm (AHA) were utilized in this work to find optimal values

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Tarun, Jaiswal, S. Jaiswal Dr., and Ragini Shukla Dr. "Soft Computing Techniques Based Image Classification using Support Vector Machine Performance." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 1645–50. https://doi.org/10.31142/ijtsrd23437.

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n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by

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Sofiane Kherrour. "Optimizing Drying Processes with Machine Learning: A Data-Driven Classification Approach." Journal of Information Systems Engineering and Management 10, no. 4 (2025): 1133–41. https://doi.org/10.52783/jisem.v10i4.10286.

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Efficient drying of medicinal and agricultural plants is critical for enhancing food security and maintaining product quality during storage. This study investigates the application of advanced machine learning models—XGBoost, Polynomial SVM (Poly-SVM), and Radial Basis Function SVM (RBF-SVM)—to classify the drying status of five medicinal plants: Moringa, Neem, Lemongrass, Mint, and Hibiscus. The models were trained and tested independently for each plant type using a dataset of 35,000 experimental trials, with environmental parameters such as Solar Radiation, Wind Speed, Altitude, Humidity,

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Gupta, Sahil, Sourabh, Rounak Kumar, Sourav Raj, Dr Vishal Shrivastava, and Dr Devesh Kumar Bandil. "ML-Based: Placement Prediction Application." International Journal of Emerging Science and Engineering 13, no. 6 (2025): 20–25. https://doi.org/10.35940/ijese.f2603.13060525.

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This research paper examines machine learning models in predicting student placement outcomes in technical education. Given the increasing focus on employability in higher education, institutions need strong predictive models to improve placement readiness. We perform a stringent comparison of four sophisticated machine learning methods—Random Forest, XGBoost, Logistic Regression with Regularisation, and Support Vector Machines with RBF Kernel—on a complete dataset involving academic, technical, and behavioral metrics. Our approach requires feature engineering methods and advanced hyperparamet

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33

Chandrashekariah, Yathish Aradhya Bandur, and Dinesha H. A. "Structured query language query join optimization by using rademacher averages and mapreduce algorithms." Bulletin of Electrical Engineering and Informatics 13, no. 3 (2024): 1730–40. http://dx.doi.org/10.11591/eei.v13i3.6837.

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Query optimization involves identifying and implementing the most effective and efficient methods and strategies to enhance the performance of queries. This is achieved by intelligently utilizing system resources and considering various performance metrics. Table joining optimization involves optimizing the process of combining two or more tables within a database. Structured query language (SQL) optimization is the progress of utilizing SQL queries in the possible way to achieve fast and accurate database results. SQL optimization is critical to decreasing the no of queries in research descri

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34

Panda, Mrutyunjaya. "Software Defect Prediction Using Hybrid Distribution Base Balance Instance Selection and Radial Basis Function Classifier." International Journal of System Dynamics Applications 8, no. 3 (2019): 53–75. http://dx.doi.org/10.4018/ijsda.2019070103.

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Software is an important part of human life and with the rapid development of software engineering the demands for software to be reliable with low defects is increasingly pressing. The building of a software defect prediction model is proposed in this article by using various software metrics with publicly available historical software defect datasets collected from several projects. Such a prediction model can enable the software engineers to take proactive actions in enhancing software quality from the early stages of the software development cycle. This article introduces a hybrid classifi

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35

Nahamizun Maamor, Hanita Daud, and Muhammad Naeim Mohd Aris. "A Comparative Study on the Performance of Covariance Functions in Gaussian Process Regression model: Application to Global Wheat Price." Journal of Advanced Research in Applied Sciences and Engineering Technology 42, no. 1 (2024): 215–25. http://dx.doi.org/10.37934/araset.42.1.215225.

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Gaussian Process Regression (GPR) is a nonparametric machine learning model that provides uncertainty quantification in making predictions. GPR utilizes several covariance functions (CFs) in the process of developing models to ensure high accuracy. There are five common CFs in GPR, which are the Radial Basis Function (RBF), Rational Quadratic (RQ), Periodic (Per), Matérn 3/2 (Mat 3/2), and Matérn 5/2 (Mat 5/2), where each covariance function (CF) has different characteristics and behaviors. This paper is to investigate the comparative performances of each CF when applied to the Global Wheat Pr

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Villa, Amalia, Abhijith Mundanad Narayanan, Sabine Van Huffel, Alexander Bertrand, and Carolina Varon. "Utility metric for unsupervised feature selection." PeerJ Computer Science 7 (April 21, 2021): e477. http://dx.doi.org/10.7717/peerj-cs.477.

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Feature selection techniques are very useful approaches for dimensionality reduction in data analysis. They provide interpretable results by reducing the dimensions of the data to a subset of the original set of features. When the data lack annotations, unsupervised feature selectors are required for their analysis. Several algorithms for this aim exist in the literature, but despite their large applicability, they can be very inaccessible or cumbersome to use, mainly due to the need for tuning non-intuitive parameters and the high computational demands. In this work, a publicly available read

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MUSUBAO SWAMBI, Patient, Albert Ntumba Nkongolo, Pierre Kafunda Katalay, Rostin Mabela Matendo Makengo, and Eugène Mbuyi Mukendi. "ARTIFICIAL LEARNING BASED ON KERNEL SVM FOR THE PREDICTION OF CARDIOVASCULAR DISEASE HYPERTENSION." Jurnal Techno Nusa Mandiri 22, no. 1 (2025): 28–34. https://doi.org/10.33480/techno.v22i1.6011.

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Hypertension, a critical risk factor for cardiovascular diseases, requires accurate early detection for effective management. This study examines the application of kernel-based Support Vector Machines (SVM) for predicting hypertension, utilizing advanced machine learning techniques to address the complex, non-linear relationships inherent in healthcare data. By employing various kernel functions, such as the radial basis function (RBF) and polynomial kernels, the study aims to enhance the model's ability to capture and interpret the nuanced patterns associated with hypertension risk. The rese

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38

Sahil, Gupta. "ML-Based: Placement Prediction Application." International Journal of Emerging Science and Engineering (IJESE) 13, no. 6 (2025): 20–25. https://doi.org/10.35940/ijese.F2603.13060525.

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<strong>Abstract:</strong> This research paper examines machine learning models in predicting student placement outcomes in technical education. Given the increasing focus on employability in higher education, institutions need strong predictive models to improve placement readiness. We perform a stringent comparison of four sophisticated machine learning methods—Random Forest, XGBoost, Logistic Regression with Regularisation, and Support Vector Machines with RBF Kernel—on a complete dataset involving academic, technical, and behavioral metrics. Our approach requires feature engine

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39

Sahil, Gupta. "ML-Based: Placement Prediction Application." International Journal of Emerging Science and Engineering (IJESE) 13, no. 6 (2025): 20–25. https://doi.org/10.35940/ijese.F2603.13060525/.

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Анотація:

<strong>Abstract:</strong> This research paper examines machine learning models in predicting student placement outcomes in technical education. Given the increasing focus on employability in higher education, institutions need strong predictive models to improve placement readiness. We perform a stringent comparison of four sophisticated machine learning methods—Random Forest, XGBoost, Logistic Regression with Regularisation, and Support Vector Machines with RBF Kernel—on a complete dataset involving academic, technical, and behavioral metrics. Our approach requires feature engine

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40

Gayatri Dwi Santika and Valiant Shabri Rabbani. "Stroke Disease Prediction Using Support Vector Machine Method." Proceeding of The International Conference of Inovation, Science, Technology, Education, Children, and Health 5, no. 1 (2025): 123–28. https://doi.org/10.62951/icistech.v5i1.274.

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Stroke is one of the leading causes of death globally and is particularly prevalent in Indonesia. Early prediction of stroke is critical to reducing the risk of long-term disability and mortality. This study aims to build a stroke prediction model using the Support Vector Machine (SVM) classification method. The dataset used is sourced from Kaggle, containing 5,110 records with class imbalance. To address the imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. The study evaluates model performance across multiple data splits (70:30, 80:20, 90:10)

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Lahon, Pranobjyoti, Aditya Bihar Kandali, Utpal Barman, Ruhit Jyoti Konwar, Debdeep Saha, and Manob Jyoti Saikia. "Deep Neural Network-Based Smart Grid Stability Analysis: Enhancing Grid Resilience and Performance." Energies 17, no. 11 (2024): 2642. http://dx.doi.org/10.3390/en17112642.

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With the surge in population growth, the demand for electricity has escalated, necessitating efficient solutions to enhance the reliability and security of electrical systems. Smart grids, functioning as self-sufficient systems, offer a promising avenue by facilitating bi-directional communication between producers and consumers. Ensuring the stability and predictability of smart grid operations is paramount to evaluating their efficacy and usability. Machine learning emerges as a crucial tool for decision-making amidst fluctuating consumer demand and power supplies, thereby bolstering the sta

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Guo, Zixin, and Ruizhi Yang. "A channel attention and feature manipulation network for facial expression recognition." Applied and Computational Engineering 6, no. 1 (2023): 1344–54. http://dx.doi.org/10.54254/2755-2721/6/20230751.

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Facial expression conveys a variety of emotional and intentional message from human beings, and automated facial expression recognition (FER) has become an ongoing and promising research topic in the field of computer vision. However, the primary challenge of FER is learning to discriminate similar features among different emotion categories. In this paper, a hybrid architecture using Efficient Channel Attention (ECA) residual network ResNet-18, and feature manipulation network is proposed to tackle the above challenge. First, the ECA residual network effectively extract input features with lo

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Anggraini, Dinda, Indra Gamayanto, and Sasono Wibowo. "Comparing Decision Tree and Support Vector Machines in Hospital Satisfaction." Journal of Applied Informatics and Computing 9, no. 2 (2025): 364–72. https://doi.org/10.30871/jaic.v9i2.9203.

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Patient satisfaction is a key indicator of hospital service quality. This study compares the performance of Decision Tree and Support Vector Machine (SVM) in classifying patient satisfaction at Harapan Hospital Magelang for service optimization. The dataset, derived from a 2024 survey, consists of 577 samples and 13 predictor variables, covering patient demographics and medical service aspects. Preprocessing includes data cleaning, normalization, encoding, and class balancing using SMOTE. The Decision Tree is applied with gini impurity and max_depth=11, while SVM uses the RBF kernel (C=100, ga

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Yaseen, Mohanad G., Ahmed Hussein Ali, and Mohammad Alajanbi. "An Enhanced Hybrid Genetic-JAYA Algorithm for Feature Selection and SVM Parameter Optimization in Intrusion Detection Systems: Evaluation on the CICIDS Dataset." SHIFRA 2025 (February 25, 2025): 98–109. https://doi.org/10.70470/shifra/2025/006.

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The changing scenario in the cybersecurity field requires intelligent and adaptive means of detecting sophisticated intrusions. This paper presents an improved hybrid Genetic-JAYA algorithm for simultaneous feature selection and hyperparameter tuning of an SVM-based IDS. Leveraging the CICIDS2017 dataset, which is well-known for its comprehensive representation of modern attack types, the model interprets the classification as a binary problem, distinguishing normal traffic from malicious activity. The presented hybridization employs the global exploration ability of the Genetic Algorithm (GA)

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Arciniegas-Ayala, Cristian, Pablo Marcillo, Ángel Leonardo Valdivieso Caraguay, and Myriam Hernández-Álvarez. "Prediction of Accident Risk Levels in Traffic Accidents Using Deep Learning and Radial Basis Function Neural Networks Applied to a Dataset with Information on Driving Events." Applied Sciences 14, no. 14 (2024): 6248. http://dx.doi.org/10.3390/app14146248.

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A complex AI system must be worked offline because the training and execution phases are processed separately. This process often requires different computer resources due to the high model requirements. A limitation of this approach is the convoluted training process that needs to be repeated to obtain models with new data continuously incorporated into the knowledge base. Although the environment may be not static, it is crucial to dynamically train models by integrating new information during execution. In this article, artificial neural networks (ANNs) are developed to predict risk levels

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Azi, Amanda, Robby Febrianur Saleh, Wildan Muhammmad Ardana, and Kusrini Kusrini. "Flood Prediction Using Support Vector Regression (Case Study of Floodgates in Jakarta)." Journal of Computer Networks, Architecture and High Performance Computing 6, no. 3 (2024): 1333–44. http://dx.doi.org/10.47709/cnahpc.v6i3.4360.

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Flood can be interpreted as an event that occurs suddenly and quickly enough where the water discharge in the drainage channel cannot be accommodated, so that the blocked area causes the water discharge in the drainage channel in several surrounding areas to overflow and is one of the natural disasters that occurs at an unexpected time and cannot be prevented, because of this, a prediction must be made to detect floods for the next day. Flood prediction is a crucial aspect of disaster management and mitigation, particularly in flood-prone areas such as Jakarta, Indonesia. This study aims to le

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Bashir, Kamal, Tianrui Li, and Mahama Yahaya. "A Novel Feature Selection Method Based on Maximum Likelihood Logistic Regression for Imbalanced Learning in Software Defect Prediction." International Arab Journal of Information Technology 17, no. 5 (2020): 721–30. http://dx.doi.org/10.34028/iajit/17/5/5.

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The most frequently used machine learning feature ranking approaches failed to present optimal feature subset for accurate prediction of defective software modules in out-of-sample data. Machine learning Feature Selection (FS) algorithms such as Chi-Square (CS), Information Gain (IG), Gain Ratio (GR), RelieF (RF) and Symmetric Uncertainty (SU) perform relatively poor at prediction, even after balancing class distribution in the training data. In this study, we propose a novel FS method based on the Maximum Likelihood Logistic Regression (MLLR). We apply this method on six software defect datas

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Parsaeian, Mahdieh, Mohammad Rahimi, Abbas Rohani, and Shaneka S. Lawson. "Towards the Modeling and Prediction of the Yield of Oilseed Crops: A Multi-Machine Learning Approach." Agriculture 12, no. 10 (2022): 1739. http://dx.doi.org/10.3390/agriculture12101739.

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Crop seed yield modeling and prediction can act as a key approach in the precision agriculture industry, enabling the reliable assessment of the effectiveness of agro-traits. Here, multiple machine learning (ML) techniques are employed to predict sesame (Sesamum indicum L.) seed yields (SSY) using agro-morphological features. Various ML models were applied, coupled with the PCA (principal component analysis) method to compare them with the original ML models, in order to evaluate the prediction efficiency. The Gaussian process regression (GPR) and radial basis function neural network (RBF-NN)

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Mugo, Robinson, and Sei-Ichi Saitoh. "Ensemble Modelling of Skipjack Tuna (Katsuwonus pelamis) Habitats in the Western North Pacific Using Satellite Remotely Sensed Data; a Comparative Analysis Using Machine-Learning Models." Remote Sensing 12, no. 16 (2020): 2591. http://dx.doi.org/10.3390/rs12162591.

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To examine skipjack tuna’s habitat utilization in the western North Pacific (WNP) we used an ensemble modelling approach, which applied a fisher- derived presence-only dataset and three satellite remote-sensing predictor variables. The skipjack tuna data were compiled from daily point fishing data into monthly composites and re-gridded into a quarter degree resolution to match the environmental predictor variables, the sea surface temperature (SST), sea surface chlorophyll-a (SSC) and sea surface height anomalies (SSHA), which were also processed at quarter degree spatial resolution. Using the

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Alshehri, Mohammed. "Breast Cancer Detection and Classification Using Hybrid Feature Selection and DenseXtNet Approach." Mathematics 11, no. 23 (2023): 4725. http://dx.doi.org/10.3390/math11234725.

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Breast Cancer (BC) detection and classification are critical tasks in medical diagnostics. The lives of patients can be greatly enhanced by the precise and early detection of BC. This study suggests a novel approach for detecting BC that combines deep learning models and sophisticated image processing techniques to address those shortcomings. The BC dataset was pre-processed using histogram equalization and adaptive filtering. Data augmentation was performed using cycle-consistent GANs (CycleGANs). Handcrafted features like Haralick features, Gabor filters, contour-based features, and morpholo

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