Relevant bibliographies by topics / Hybrid score-accuracy function

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Academic literature on the topic 'Hybrid score-accuracy function'

Author: Grafiati
Published: 2 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Hybrid score-accuracy function"

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Mukanya Ntumba, Benjamin, Jean Paul Ngbolua Koto-Te-Nyiwa, Blaise Bikandu Kapesa, and Nathanael Kasoro Mulenda. "Hybrid Approach for Protein Secondary Structure Prediction with KNN, SVM, and Neural Network Algorithms." Journal of Innovation Information Technology and Application (JINITA) 7, no. 1 (2025): 68–80. https://doi.org/10.35970/jinita.v7i1.2658.

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One of the main challenges in bioinformatics is predicting the structures of macromolecules, particularly nucleic acids and proteins. In this study, we propose a hybrid approach integrating K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Neural Network (NN) algorithms. We perform an in-depth analysis using various metrics, including accuracy, Q3 score, ROC, and precision-recall curves. Based on the RS126 dataset, we compared our hybrid model with individual approaches, revealing that our model achieves an accuracy of 80% and a Q3 score of 86%, outperforming each of the algorithms
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Petmezas, Georgios, Grigorios-Aris Cheimariotis, Leandros Stefanopoulos, et al. "Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function." Sensors 22, no. 3 (2022): 1232. http://dx.doi.org/10.3390/s22031232.

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Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consuming process that requires high medical expertise. The capabilities that deep learning offers could be exploited in order that robust lung sound classification models can be designed. In this paper, we propose a novel hybrid neural model that implements
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Altun, Murat, Hüseyin Gürüler, Osman Özkaraca, Faheem Khan, Jawad Khan, and Youngmoon Lee. "Monkeypox Detection Using CNN with Transfer Learning." Sensors 23, no. 4 (2023): 1783. http://dx.doi.org/10.3390/s23041783.

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Monkeypox disease is caused by a virus that causes lesions on the skin and has been observed on the African continent in the past years. The fatal consequences caused by virus infections after the COVID pandemic have caused fear and panic among the public. As a result of COVID reaching the pandemic dimension, the development and implementation of rapid detection methods have become important. In this context, our study aims to detect monkeypox disease in case of a possible pandemic through skin lesions with deep-learning methods in a fast and safe way. Deep-learning methods were supported with
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Polisi, Xhoena, Arban Uka, Daniel Avdiu, and Dimitrios A. Karras. "Efficient and accurate cell image segmentation through optimizer and loss function fine-tuning with quantization and pruning." Edelweiss Applied Science and Technology 9, no. 3 (2025): 1675–91. https://doi.org/10.55214/25768484.v9i3.5668.

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Convolutional deep learning is commonly and frequently used nowadays for high data throughput in image analysis and computer vision applications. The size of such models depends on the number of hyperparameters and the precision required for each hyperparameter. The training of these models requires enormous computational power for images of high dimensionality, resulting in trained models of large sizes. To solve this challenge, several approaches have been used, including quantization and pruning. These techniques have been proven to be effective in reducing the size of the models at the exp
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Azmi, Fadhillah, and Amir Saleh. "A Hybrid Algorithm for Multiple Disease Prediction: Radial Basis Function and Logistic Regression." International Journal of Science and Healthcare Research 9, no. 2 (2024): 363–68. http://dx.doi.org/10.52403/ijshr.20240246.

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Disease prediction is an important aspect of modern medicine, which aims to diagnose disease early and provide appropriate treatment to patients. This research uses a hybrid approach that combines the RBF (Radial Basis Function) kernel algorithm with logistic regression to predict various diseases in medical datasets. This method is intended to improve prediction performance by exploiting the advantages of each algorithm. This research uses a dataset containing medical information about several diseases collected from the Kaggle dataset. First, the RBF kernel is applied to transform the data f
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Zhang, Zhengjin, Qilin Wu, Yong Zhang, and Li Liu. "Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration." PeerJ Computer Science 9 (April 21, 2023): e1338. http://dx.doi.org/10.7717/peerj-cs.1338.

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In recent years, recommendation systems have already played a significant role in major streaming video platforms.The probabilistic matrix factorization (PMF) model has advantages in addressing high-dimension problems and rating data sparsity in the recommendation system. However, in practical application, PMF has poor generalization ability and low prediction accuracy. For this reason, this article proposes the Hybrid AdaBoost Ensemble Method. Firstly, we use the membership function and the cluster center selection in fuzzy clustering to calculate the scoring matrix of the user-items. Secondl
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Farias, Jorge G., Lisandra Herrera-Belén, Luis Jimenez, and Jorge F. Beltrán. "PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning." International Journal of Molecular Sciences 25, no. 19 (2024): 10267. http://dx.doi.org/10.3390/ijms251910267.

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Protamines play a critical role in DNA compaction and stabilization in sperm cells, significantly influencing male fertility and various biotechnological applications. Traditionally, identifying these proteins is a challenging and time-consuming process due to their species-specific variability and complexity. Leveraging advancements in computational biology, we present PROTA, a novel tool that combines machine learning (ML) and deep learning (DL) techniques to predict protamines with high accuracy. For the first time, we integrate Generative Adversarial Networks (GANs) with supervised learnin
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Nuanmeesri, S. "A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification." Engineering, Technology & Applied Science Research 11, no. 5 (2021): 7678–83. http://dx.doi.org/10.48084/etasr.4455.

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Analysis of the symptoms of rose leaves can identify up to 15 different diseases. This research aims to develop Convolutional Neural Network models for classifying the diseases on rose leaves using hybrid deep learning techniques with Support Vector Machine (SVM). The developed models were based on the VGG16 architecture and early or late fusion techniques were applied to concatenate the output from a fully connected layer. The results showed that the developed models based on early fusion performed better than the developed models on either late fusion or VGG16 alone. In addition, it was foun
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Based, Md Abdul, Elias Ur ,. Rahman, and Mohammad Shorif Uddin. "Development of an Electronic Voting System using Blockchain Technology and Deep Hybrid Learning." WSEAS TRANSACTIONS ON COMPUTERS 23 (August 14, 2024): 194–203. http://dx.doi.org/10.37394/23205.2024.23.18.

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Democratic people cannot function properly in today's sophisticated societies (where voting is a prominent issue) without electronic voting technologies. This study explores the use of hybrid learning algorithms for biometric authentication of voters, and blockchain technology for secure electronic voting. The thorough analysis includes a collection of more than 50,000 fingerprint samples using custom Convolutional Neural Network (CNN), VGG16, VGG19, Xception, and Inception. The algorithms are evaluated using F1-score, recall, accuracy, and precision. By combining Random Forest with a speciall
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Ahadi, Seyedeh Aridis, Kian Jazayeri, and Sahand Tebyani. "Detecting Suicidality from Reddit Posts Using a Hybrid CNN - LSTM Model." JUCS - Journal of Universal Computer Science 30, no. 13 (2024): 1872–904. https://doi.org/10.3897/jucs.119828.

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The identification of individuals who indicate suicidal behaviors on social media platforms has become more significant in recent years. The utilization of textual data may help in the development of systems aimed at predicting individuals' mental health. This article proposes an integrated framework for the identification of suicidal thoughts in social media through the implementation of a layered classifier model consisting of a convolutional neural network (CNN) and a long short-term memory (LSTM) model. Various combinations of embedding techniques, activation functions, and solver
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Books on the topic "Hybrid score-accuracy function"

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Kaufmann, Philipp A., and Oliver Gaemperli. Hybrid Cardiac Imaging. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0028.

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Assessment of both coronary anatomy and myocardial perfusion are equally important for the appropriate treatment of patients with stable coronary artery disease. Cardiac hybrid imaging allows integration of coronary anatomy and perfusion in one all-in-one image, thereby avoiding mental integration of findings. In selected subgroups of patients, cardiac hybrid imaging has demonstrated superior diagnostic accuracy compared to single modalities. The combination of coronary anatomy and function provides incremental prognostic information and improves risk stratification of patients with suspected
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Conference papers on the topic "Hybrid score-accuracy function"

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Junos, Mohamad Haniff, Anis Salwa Binti Mohd Khairuddin, Muhammad Izhar Kairi, and Yosri Mohd Siran. "Automatic Object Detection in Oil Palm Plantation using a Hybrid Feature Extractor of YOLO-based Model." In International Technical Postgraduate Conference 2022. AIJR Publisher, 2022. http://dx.doi.org/10.21467/proceedings.141.8.

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The current manual harvesting process is very laborious and time-consuming. Implementing a machine vision-based automated crop harvesting system may minimize operational costs and increase productivity. This paper aims to develop a one-stage object detection model with high accuracy, lightweight size, and low computing cost. A novel PalmYOLO model is proposed by modifying the architecture of the YOLOv3 tiny model to localize and detect oil palm tree, grabber and Fresh Fruit Bunch (FFB) in varied environmental conditions. The PalmYOLO model employed a lightweight-hybrid feature extractor compos
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Journal articles
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