Search Results - (( model evaluation based algorithm ) OR ( label classification model algorithm ))
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1
A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms
Published 2024“…The multi-tier model filters the news label correctly predicted by the first two-tier layer. …”
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2
Modified word representation vector based scalar weight for contextual text classification
Published 2024“…Based on the acquired results, the experiments reveal that the modified word vectors algorithm can effectively alter original LLM-generated word vectors to reflect intended contexts and can outperform baseline scores in contextual text classification tasks. …”
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3
Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad
Published 2023“…This project implements the Naive Bayes algorithm as the classification algorithm. The collected data from LinkedIn profiles then undergoes data preprocessing. …”
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4
A New Machine Learning-based Hybrid Intrusion Detection System and Intelligent Routing Algorithm for MPLS Network
Published 2024“…The ML-based routing algorithm is compared to the conventional routing algorithm, Routing Information Protocol version 2 (RIPv2). …”
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5
Improving multi-resident activity recognition in smart home using multi label classification with adaptive profiling
Published 2018“…When the data are induced with the lower quality model, the performance is also truncated. Furthermore, there is tendency that multi label classifications used instead of traditional single label classification technique. …”
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6
The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers
Published 2024“…The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. …”
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7
Context enrichment framework for sentiment analysis in handling word ambiguity resolution
Published 2024“…Machine learning algorithms are deployed to perform sentiment classification. …”
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8
A direct ensemble classifier for learning imbalanced multiclass data
Published 2013“…The learning framework consists of ensemble learning and decision combiner model with general supervised learning algorithms as base learner. …”
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9
Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification
Published 2023“…To address these issues, we propose a comprehensive CNN-based model for real-time detection and classification of five primary emotions: anger, happiness, neutrality, sadness, and surprise. …”
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10
Flow-based approach on bro intrusion detection
Published 2017“…Then, the model made use the machine learning classification algorithms for attribute evaluation and Bro policy scripts for detecting malicious flows. …”
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11
Adaptive Similarity Component Analysis in Nonparametric Dynamic Environment
Published 2011“…Data arrives from operational field in a stream model and similarity-based classification algorithms must identify them with acceptable performance. …”
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12
Cyber parental control framework for objectionable web content classification and filtering based on topic modelling using enhanced latent dirichlet allocation / Hamza H. M. Altart...
Published 2023“…Despite substantial advancements in automating web classification that combines web mining and content classification methods, the study identifies a gap in applying advanced machine learning algorithms for superior objectionable web content classification. …”
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13
Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques
Published 2025“…The models achieve classification accuracy of 85.40%, 80.76%, 82.46%, and 90.71%, respectively. …”
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14
A coherent knowledge-driven deep learning model for idiomatic - aware sentiment analysis of unstructured text using Bert transformer
Published 2023“…Machine learning and deep neural networks have shown promise in accurately representing and classifying sentiment, but they require large amounts of labeled data to train the models. In this context, the proposed novel strategy aims to eliminate the need for human labeling of the idiomatic lexicon and fine-tuning the classifier to handle the sentiment classification of tweets containing idiomatic expressions. …”
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15
A web-based image recognition system for detecting harumanis mangoes / Mohamad Shahmil Saari, Romiza Md Nor and Huzaifah A Hamid
Published 2020“…Furthermore, the accuracy of the image classification can be improved by increasing the number of datasets, the distance of images from the camera, and the labelling process. …”
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16
Bacterial image analysis using multi-task deep learning approaches for clinical microscopy
Published 2024“…All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models.…”
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17
Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie
Published 2019“…However, it is difficult to determine which single-model is the best classification technique in a specific application domain since a single learning algorithm may not uniformly outperform other algorithms over various datasets. …”
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18
Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout
Published 2025“…The study employed two MLC frameworks: Problem Transformation methods (Binary Relevance, Classifier Chains, Label Power Set, and Calibrated Label Ranking) and Algorithm Adaptation. …”
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19
The classification of FTIR plastic bag spectra via label spreading and stacking
Published 2021“…Four pipelines were investigated, consisting of two machine learning algorithms, a stacked model that stacks the KNN, SVM and RF algorithms together, and Label spreading, as well as two different dimensionality reduction methods namely; SVD and UMAP. …”
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20
Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz
Published 2020“…In order to evaluate the model, we calculate the accuracy. Accuracy is fraction of labels that the network predicts correctly. …”
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