Similarity Matching of Pairs of Text using CACT Algorithm

Kumar, Ch. Narendra; Kumar, V Pavan; Reddy, D. Narasimha

doi:10.35940/ijeat.f8685.088619

Cited by 11 publications

(2 citation statements)

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“…PAN 2015 dataset contains tweets in English, Spanish, Italian and Dutch languages. This helps in identifying Age [21][22][23][24][25][26][27][28][29][30][31] Gender, language variety and personality type. PAN 2016 dataset contains tweets in English, Spanish and Dutch languages.…”

Section: Dataset Description and Metrics A Dataset Descriptionmentioning

confidence: 99%

Contextually Propagated Term Weight based Approach for Author Profiling: Gender, Age and Language Variety Prediction

Swapna M

2024

jes

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Author profiling creates characterization of an author based on attributes such as age, gender, language, dialect region variety, personality and so on. In recent years it has garnered significant attention for its varies applications across forensic linguistics, marketing, cybersecurity and social media analytics. Most of the research focused on stylistic, content based and term weight measures based feature representations. We observed that context semantics is not considered in the feature representation. In this propose contextually propagated term weight measures for feature representation. We implemented SVM, Random Forest and XG Boost machine learning algorithms on those feature representations. The results demonstrated that the proposed contextually propagated term weight with inverse category frequency outperformed the existing methods.

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Section: Dataset Description and Metrics A Dataset Descriptionmentioning

confidence: 99%

Contextually Propagated Term Weight based Approach for Author Profiling: Gender, Age and Language Variety Prediction

Swapna M

2024

jes

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“…The predicted values are represented on the X-axis, while the actual values are depicted on the Y-axis. The confusion matrix consists of four elements: TN, FP, TP, and FN [27,28,29].…”

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confidence: 99%

Enhanced Brain Tumor Classification: A Hybrid Classifier Approach

G.Srinivasa Rao

2024

jes

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Early detection of brain tumors is crucial for enhancing patient survival rates and treatment effectiveness. Analyzing magnetic resonance imaging (MRI) images by manually is a difficult task. Therefore, there is a requirement for digital techniques to improve the accuracy of tumor diagnosis. Study of this work was to enhance the performance of Machine Learning classifiers rather than move to deep learning. Machine Learning classifiers are advantageous compared to deep learning methods because of their ability to train on small datasets, low computing time complexity, affordability, and ease of adoption by individuals with less expertise The proposed methodology encompasses image processing, segmentation, feature extraction, feature reduction, and classification, leveraging techniques such as Otsu’s thresholding, GLCM, BWT, and PCA to extract pertinent information while minimizing dimensionality. By integrating a hybrid classifier that combines SVM, DT, and KNN, along with ensemble techniques, significant improvements in classification accuracy and robustness are achieved. Experimental results on the TCGA-GBM dataset showcase accuracy of 96.9%, with high specificity (95.14%) and sensitivity (97.14%) for classify normal tumors (benign tumors) and cancerous tumors (malignant tumors). Furthermore, the proposed methodology demonstrates efficiency with relatively low execution time, rendering it suitable for real-world applications.

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