Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms

Papineni, Swarajya Lakshmi V.; Yarlagadda, Snigdha; Akkineni, Haritha; Reddy, A. Mallikarjuna

doi:10.14445/22315381/ijett-v69i1p204

Cited by 12 publications

(4 citation statements)

References 18 publications

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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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“…Second, there has been a scarcity of investigations that delve into the extraction of facial features while simultaneously employing feature selection techniques. Third, the predominant exploration of deep learning methods has often been confined to datasets of small sizes which raised concerns about the applicability of these techniques in larger and more diverse datasets [29]. Lastly, the diversity inherent in demographic characteristics, such as age, culture, and backgrounds has posed a challenge to achieve consistent and uniform outcomes.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Depression Prediction Accuracy Using Filter and Wrapper-Based Visual Feature Extraction

Mamidisetti,

Reddy

2023

JAIT

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The pressing need for Artificial Intelligence (AI) applications in healthcare is evident, particularly in the context of depression prediction. Literature underscores the significance of visual cues as crucial indicators of depression. Primary objective of this work is to design a complete machine learning pipeline for more accurate depression prediction, which includes several stages like: data collection stage, feature extraction stage, feature selection stage, classification stage, and performance evaluation stage. Data collection involved video recording of participants (n = 219) while conducting emotion elicitation (triggering emotions by showing photos/videos) to depressed and nondepressed subjects. Then, numerous visual features like geometrical features and facial action unit features were extracted. Filter and Wrapper Feature Selection (FS)methods were used to extract the optimal feature set from high-dimensional visual features. In the Filter method, experiments are conducted using three strategies: quasiconstant strategy, mutual information gain, and linear discriminant analysis. In the wrapper method, experiments are conducted using three strategies: forward selection, backward elimination, and recursive feature elimination. Accuracy for the classification of non-depressed or depressed subjects was used as the performance metric. Obtained results with an accuracy of 85.6% show that the backward elimination approach (even though only ten features were selected) outperformed other experiments conducted in current work and also with the state-of-the-art methods. In addition to this, our method is also applied to publicly available benchmarking dataset to show its effectiveness on diverse dataset. These findings demonstrate the applicability of visual features using filter and wrapper feature selection method is reliable in depression prediction.Hence implications extend to a potential application in mental health assessment.

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“…In the first fold, ML algorithms need numerical data to work and using SMOTE analysis, it is recognized that most of the symptoms are empty and the dataset is unbalanced. So, the model has applied central tendency nature "mode", to replace the missing fields of categorical data [25]. In the second fold, the model performed label encoding to transform the data from categorical to numerical as shown in figure 7.…”

Section: Data Pre-processingmentioning

confidence: 99%

A Hypertuned Pipeline Vector Using Meta Classifier Technique for Feature Selection in Multi Disease Prediction

Indupalli,

Pradeepini

2023

IJRITCC

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Automation of health sector plays a very important role especially during this pandemic due to the side effects of either vaccination or attack of the COVID. Most of the researchers designed a system to predict whether a person suffers from a particular disease or not. Few researchers worked on prediction variants of a single disease based on symptoms but due to this COVID-19, different people are getting attacked with different diseases as a side effect. This proposed system aims to identify the multiple diseases that a person may suffer from based on the symptoms. In this paper, the dataset obtained from the open access repository “Kaggle” contains 17 symptoms combinations to identify the one of the 41 types of diseases as class label. All the symptoms may not be important for identification, so in this model, the important features are identified using the pipeline vector of different Machine Learning approaches are passed as base line classifier and decision tree classifier as meta line to the elimination function. The model has got “99.48%” accuracy for selecting the essential features using bagging and boosting algorithms.

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Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms

Cited by 12 publications

References 18 publications

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

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

Enhancing Depression Prediction Accuracy Using Filter and Wrapper-Based Visual Feature Extraction

A Hypertuned Pipeline Vector Using Meta Classifier Technique for Feature Selection in Multi Disease Prediction

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