Topological to deep learning era for identifying influencers in online social networks :a systematic review

Rashid, Yasir; Bhat, Javaid Iqbal

doi:10.1007/s11042-023-16002-8

Cited by 4 publications

(3 citation statements)

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“…Furthermore, social networks facilitate the formation of interest-based communities, enabling users to discover visually similar images and explore related content that aligns with their preferences. This integration of social networks and CBIR opens up exciting possibilities for visual search, recommendation systems, and personalized content curation, fostering a more engaging and interactive online experience (39) .…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Delving into the Depths of Image Retrieval Systems in the Light of Deep Learning: A Review

Khan,

Bhat

2023

IJST

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Objective:The objective of this study is to conduct a comprehensive review of existing research and literature in the field of Content-Based Image Retrieval (CBIR). This review highlights the key challenges associated with the extraction and representation of visual semantics of images. This paper discusses the measure used computing similarity and ranking of retrieved images by CBIR system. The review discusses limitation of traditional approaches and also highlights the challenges with the current deep learning methods in semantic feature representation, defining the similarity metrics and indexing. This paper also highlights scalability and generalization challenges in implementing real environment. Methods: A thorough literature review was conducted on wellestablished databases, including Scopus, Web of Science, IEEE Xplore, ACM, and Science Direct, employing appropriate keywords. Mention the period of coverage. Pertinent search terms encompassed local feature representation, global feature representation, low-level features, high level features, semantic gap, image embeddings, handcrafted features, deep learning, image descriptors, similarity, and image indexing, with the aim of exploring content-based image retrieval systems. Comparative analysis was performed on the chosen articles, taking into account factors such as algorithms, methodologies, datasets, and evaluation metrics. The results discussed using comparative analysis, ensuring a comprehensive overview of recent literature on content-based image retrieval, offering valuable insights and highlighting emerging trends in the field. Findings : The research uncovers the novelty in the realm of contentbased image retrieval (CBIR) by highlighting the challenge of high-level visual semantics when comparing images, as perceived by humans. It emphasizes that feature extraction methods and choices significantly influence CBIR system performance, stressing the importance of selecting suitable features and similarity measures based on image dataset characteristics and application requirements. The study underscores the persistent obstacle of the semantic gap between low-level visual features and high-level semantic concepts, encouraging exploration of diverse approaches like deep learning, relevance feedback, and ontology-based methods to bridge this gap. Particularly, deep learnhttps://www.indjst.org/

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Section: Deep Learning Methodsmentioning

confidence: 99%

Delving into the Depths of Image Retrieval Systems in the Light of Deep Learning: A Review

Khan,

Bhat

2023

IJST

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“…Numerous studies have been conducted using machine learning, deep learning, fuzzy logic, and data mining tools and methodologies to predict cardiac disease. Researchers have employed a variety of datasets, algorithms, and procedures [7]; the findings they have seen thus far and future work will be used to determine the most effective techniques for diagnosing cardiovascular disease. The literature review has been divided into three categories on the basis of techniques including deep learning, machine learning, and ensemble learning.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a result, it has become critical for the precise and accurate prognosis of heartrelated disorders. Many academics from all around the world began researching the prediction of cardiac problems by analyzing enormous databases for this purpose [7]. Various deep learning approaches have the capability of working on enormous datasets and drawing relevant results [8].…”

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confidence: 99%

A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques

Nissa,

Jamwal,

Neshat

2024

Computation

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This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is paramount for implementing preventive measures and timely interventions. The World Health Organization (WHO) reports that cardiovascular diseases, responsible for an alarming 17.9 million annual fatalities, constitute a significant 31% of the global mortality rate. The intricate clinical landscape, characterized by inherent variability and a complex interplay of factors, poses challenges for accurately diagnosing the severity of cardiac conditions and predicting their progression. Consequently, early identification emerges as a pivotal factor in the successful treatment of heart-related ailments. This research presents a comprehensive framework for the prediction of cardiovascular diseases, leveraging advanced boosting techniques and machine learning methodologies, including Cat boost, Random Forest, Gradient boosting, Light GBM, and Ada boost. Focusing on “Early Heart Disease Prediction using Boosting Techniques”, this paper aims to contribute to the development of robust models capable of reliably forecasting cardiovascular health risks. Model performance is rigorously assessed using a substantial dataset on heart illnesses from the UCI machine learning library. With 26 feature-based numerical and categorical variables, this dataset encompasses 8763 samples collected globally. The empirical findings highlight AdaBoost as the preeminent performer, achieving a notable accuracy of 95% and excelling in metrics such as negative predicted value (0.83), false positive rate (0.04), false negative rate (0.04), and false development rate (0.01). These results underscore AdaBoost’s superiority in predictive accuracy and overall performance compared to alternative algorithms, contributing valuable insights to the field of cardiovascular health prediction.

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An insight into topological, machine and Deep Learning-based approaches for influential node identification in social media networks: a systematic review

Rashid,

Bhat

2024

Multimedia Systems

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Topological to deep learning era for identifying influencers in online social networks :a systematic review

Cited by 4 publications

References 100 publications

Delving into the Depths of Image Retrieval Systems in the Light of Deep Learning: A Review

Delving into the Depths of Image Retrieval Systems in the Light of Deep Learning: A Review

A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques

An insight into topological, machine and Deep Learning-based approaches for influential node identification in social media networks: a systematic review

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