Browsing by Author "Gupta, Vedika"

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Flower Identification by Deep Learning Approach and Computer Vision
(2024-04-18) Mohaimenur Rahman, Md.; Mojumdar, Mayen Uddin; Jamil, Md Mashud; Chakraborty, Narayan Ranjan; Hasan, Rifat; Gupta, Vedika
This study employed deep learning methods like VGG19, Xception, CNN, DenseNet201, and InceptionV3 to identify flowers. After applying these models, a confusion matrix was applied to evaluate the performances of the techniques. At an astounding 94% accuracy, the CNN model outperformed. VGG19 and DenseNet201, which came in at 82%. Inception V3 performed worst with 23% of accuracy. The culmination of the study is the accurate measurement of unknown blooms in real time and analysis of the result based on accuracy for different types of algorithms. The program demonstrated the usefulness of cutting-edge deep learning algorithms and functions as an efficient tool for smooth and dependable flower detection.
Impact on GDP and the Stock Market During Pandemics or Epidemics of 21st Century
(Journal of Interdisciplinary Mathematics, 2023-09-29) Hossen, Md. Arman; Rahman, Md. Merazur; Nahid, Md. Nabil Ahmed; Islam, Rafiul; Banshal, Sumit Kumar; Gupta, Vedika; Dass, Pranav
"In this article, we present the effect on GDP growth rate during key major pandemics in the twenty-first century. Ebola, cholera, and the most dangerous pandemic, Covid-19, are all fatal pandemics. It was said that Coronavirus started spreading from Wuhan, China. The virus then afflicted the entire human race worldwide. The major goal of this paper is to compare the most catastrophic pandemic to another pandemic in terms of its effect on GDP. This study also examined the impact of the pandemic on the stock market. We offer a descriptive examination of economic impact over the course of 19 years for different countries."
Machine Learning Models for Maternal Health Risk Prediction based on Clinical Data
(2024-04-18) Arman Shifa, Hasin; Uddin Mojumdar, Mayen; Md. Mohaimenur Rahman; Ranjan Chakraborty, Narayan; Gupta, Vedika
In the healthcare industry, maternal health is of utmost importance because it directly affects the welfare of both mothers and infants. This study explores the crucial area of predicting maternal health risks with the goal of equipping healthcare professionals with precise tools for early risk assessment and intervention. The dataset being examined consists of 1102 painstakingly gathered examples that include 12 crucial attributes and were obtained from the closest hospital. Nine algorithms were utilized, leveraging machine learning skills, with XGBoost outperforming the others with 97.3% accuracy. The initial target of this research is to make it easier to precisely and comprehensively categorize maternal health risk factors, allowing for timely, focused interventions. This study has far-reaching implications for better healthcare resource allocation and, most importantly, the prospect of reducing detrimental maternal health events.
Source Recommendation System Using Context-based Classification: Empirical Study on Multi-level Ensemble Methods
(Scopus, 2024-08-19) Kafi, Abdullah Al; Banshal, Kafi, Sumit Kumar; Sultana, Nishat; Gupta, Vedika
Aim/Background: This research aims to develop an automated contextual classifier for scholarly papers by utilizing established algorithms and understanding the information retention of different parts of a scholarly article, such as the Abstract, Article Title, and Keywords. It also seeks to recommend a contextual classifier-based recommender system to help academics identify credible sources. Scholarly articles from various study fields often use similar terms in their titles and keywords. However, finding a publication venue can be challenging for researchers at the beginning of a scientific inquiry. Thus, it is crucial to classify information based on its context, especially when abstracts, keywords, and titles receive equal attention. Materials and Methods: An ensembled model was developed and trained using 114K instances from 38 classes of the Web of Science (WoS) dataset and 40 classes of the Dimensions dataset. The ensemble approach incorporated both machine learning and deep learning algorithms to build a diverse classifier. The model was evaluated by testing it with an 80:20 train-test split to assess performance. The classifier was further integrated into a recommender system designed to suggest probable publication sources based on given article information. Results: The ensemble classification approach demonstrated superior performance with faster inference and efficient training time. The balanced training model, tested on 114K instances, effectively categorized scholarly articles into one of 40 categories. The recommender system was capable of recommending up to 10 probable publication sources based on the article’s Title, Keywords, and Abstract. Models utilizing abstractions yielded the best results and provided a better understanding of the context in every iteration of the experiment. Conclusion: This study successfully developed an ensemble-based contextual classifier for academic papers, which can also function as a recommender system. The system aids researchers in choosing the most appropriate sources to publish by categorizing articles into 40 categories and suggesting credible publication venues. This approach simplifies the decision-making process for academics, enabling them to identify relevant publications and suitable sources for their work more efficiently.
Source Recommendation System Using Context-based Classification: Empirical Study on Multi-level Ensemble Methods
(Manuscript Techno Media Publisher, 2024-08-19) Kafi, Abdullah Al; Banshal, Sumit Kumar; Sultana, Nishat; Gupta, Vedika
Aim/Background This research aims to develop an automated contextual classifier for scholarly papers by utilizing established algorithms and understanding the information retention of different parts of a scholarly article, such as the Abstract, Article Title, and Keywords. It also seeks to recommend a contextual classifier-based recommender system to help academics identify credible sources. Scholarly articles from various study fields often use similar terms in their titles and keywords. However, finding a publication venue can be challenging for researchers at the beginning of a scientific inquiry. Thus, it is crucial to classify information based on its context, especially when abstracts, keywords, and titles receive equal attention. Materials and Methods An ensembled model was developed and trained using 114K instances from 38 classes of the Web of Science (WoS) dataset and 40 classes of the Dimensions dataset. The ensemble approach incorporated both machine learning and deep learning algorithms to build a diverse classifier. The model was evaluated by testing it with an 80:20 train-test split to assess performance. The classifier was further integrated into a recommender system designed to suggest probable publication sources based on given article information. Results The ensemble classification approach demonstrated superior performance with faster inference and efficient training time. The balanced training model, tested on 114K instances, effectively categorized scholarly articles into one of 40 categories. The recommender system was capable of recommending up to 10 probable publication sources based on the article’s Title, Keywords, and Abstract. Models utilizing abstractions yielded the best results and provided a better understanding of the context in every iteration of the experiment. Conclusion This study successfully developed an ensemble-based contextual classifier for academic papers, which can also function as a recommender system. The system aids researchers in choosing the most appropriate sources to publish by categorizing articles into 40 categories and suggesting credible publication venues. This approach simplifies the decision-making process for academics, enabling them to identify relevant publications and suitable sources for their work more efficiently.