S3574 Duodenal Diverticula: An Unusual Cause of Dyspepsia

Din, Asim Tameez Ud; Siddiqui, Khaleeq H.; Chaudhary, Farooq Mohyud Din; Nawaz, Faisal A.; Ahmad, Sultan; Awais, Muhammad Faisal; Ullah, Muhammad Ahmed Saif; Tameez-ud-din, Ahsan; Din, Asma Tameez Ud; Chaudhary, Sana Mohyud Din

doi:10.14309/01.ajg.0000716344.13446.06

Cited by 2 publications

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“…Huang et al [28] introduces Dynamic Updated Ensemble (DUE), an algorithm for handling imbalanced non-stationary data streams with concept drift efficiently. Shao et al [29] addresses the streaming data with concept drift and limited resources, the algorithm employs dynamic micro-clusters for online semi-supervised learning, exhibiting superior classification performance. Raab et al [30] opines that real-time communication in social media and sensor data demands adaptable algorithms.…”

Section: Related Workmentioning

confidence: 99%

LCDDF: An Adaptive and Learning based Framework with Feature Selection for Efficient Detection of Concept Drift in Data Streams

Trupthi,

CH,

SNEHA

et al. 2024

Preprint

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In the contemporary era, there has been increased collaboration among machines and things due to innovative technologies like Internet of Things (IoT). With use cases of IoT pertaining to industries, there is unprecedented increase in data generation and dissemination resulting in large data streams. In this context, data stream analytics is given paramount importance but it suffers from concept drift issues leading to performance deterioration in many automation applications. There are many existing methods for automatic detection of concept drifts in data streams. However, there is need for an adaptive approach that learns dynamically through machine learning (ML) techniques. Another drawback of existing method is lack of efficient feature selection method that leverages drift detection performance. In this paper, we address these shortcomings by proposing a framework known as Learning based Concept Drift Detection Framework (LCDDF). We also proposed two algorithms, namely Concept Drift-aware Feature Engineering (CDFE) and Learning based Model Selection (LbMS), to realize the framework and improve the state of the art in detection accuracy. The former is used to perform feature engineering which concept drift-aware leading to improving quality of learning process. The latter detects best models for making an ensemble towards improving prediction performance. Our empirical study has revealed that the proposed framework with underlying algorithms outperform many state of the art methods.

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Section: Related Workmentioning

confidence: 99%

LCDDF: An Adaptive and Learning based Framework with Feature Selection for Efficient Detection of Concept Drift in Data Streams

Trupthi,

CH,

SNEHA

et al. 2024

Preprint

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Endoscopic findings in patients with persistent dyspepsia in a tertiary care hospital of south Punjab

Chaudhary

Gul

Hameed

et al. 2021

Preprint

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Background: One of the most common clinical problem encountered by physicians in clinical practice is dyspepsia. This symptom has great impact on quality of life of patients. There are numerous causes of dyspepsia, organic as well as functional. Endoscopy is the diagnostic test of choice in these patients. Aim: The aim of our study was to see the endoscopic findings in patients with persistent dyspepsia. Methods: Retrospective analysis of data of patients who underwent Esophagogastroduodenoscopy (EGD) for persistent dyspepsia was collected and evaluated. Results: There were 495 patients in our study, 244 females and 251 males, with a mean age of 41 years. Almost half of the patients belonged to 21-40 years age group. The most common endoscopic finding in patients with persistent dyspepsia was gastritis (n=219, 44.2%), followed by normal endoscopy (n= 94, 19%), incompetent lower esophageal sphincter (n=67, 13.5%), gastric malignancy (n=48, 9.7%). Ulcer disease was found in just 15 patients (3%). Conclusion: Most common endoscopic finding in patients with persistent dyspepsia was gastritis followed by normal endoscopy. Key Words: dyspepsia, endoscopy

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S3574 Duodenal Diverticula: An Unusual Cause of Dyspepsia

Cited by 2 publications

References 0 publications

LCDDF: An Adaptive and Learning based Framework with Feature Selection for Efficient Detection of Concept Drift in Data Streams

LCDDF: An Adaptive and Learning based Framework with Feature Selection for Efficient Detection of Concept Drift in Data Streams

Endoscopic findings in patients with persistent dyspepsia in a tertiary care hospital of south Punjab

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