Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
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This Author published in this journals
All Journal Universa Medicina Indonesian Journal of Electrical Engineering and Computer Science
Dash, Biswajit
Unknown Affiliation
Health Professions Immunology & microbiology Medicine & Pharmacology Public Health

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Dysmenorrhea and recent treatment options in adolescents and young adults Innocent Rani , Vanitha; Dash, Biswajit; Nancy Lal, Monica; P, Muthu Prasanna; Bagchi, Sovan; Aruna, V.; Prabha, K. Suria
Universa Medicina Vol. 43 No. 3 (2024)
Publisher : Faculty of Medicine, Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18051/UnivMed.2024.v43.349-362

Abstract

Dysmenorrhea, or primary dysmenorrhea (PD), marked by menstrual cramps in the lower abdomen, is a common but often overlooked condition affecting a considerable number of women. The impact of dysmenorrhea extends beyond physical pain, often causing considerable disruption to daily activities, work, and social interactions. It significantly affects the quality of life, often causing absenteeism from school or work. Increased intrauterine prostaglandin secretion is linked to pelvic pain in PD. Diagnosis relies mainly on clinical assessment, considering symptoms and physical examination, with treatment aimed to enhance the quality of life. These menstrual cramps are frequently accompanied by other symptoms, such as headaches and nausea, which are believed to be due to prostaglandins released as the endometrium breaks down. A literature search using the keywords dysmenorrhea, menstrual pain, and hormonal contraceptives was done using the following databases: Google Scholar, ProQuest, ScienceDirect, Web of Science, Pubmed, and Scopus for articles published  from   2015 to 2024. The literature study was done to find the connection of dysmenorrhea and menstrual pain with hormonal contraceptives. Common treatments include nonsteroidal anti-inflammatory drugs, hormonal contraceptives, and non-pharmacological interventions. This review provides an in-depth analysis of recent treatment advancements for dysmenorrhea, focusing on its pathophysiology, clinical diagnosis, and impact on women's quality of life. It evaluates current and emerging treatments, including pharmacological interventions, non-pharmacological therapies, surgical approaches, hormonal treatments, and investigational drugs, aiming to identify improvements in efficacy and side effects.
Empirical analysis of Bitcoin investment strategy: a comparison of machine learning and deep learning approach Tripathy, Nrusingha; Manchala, Yugandhar; Ghosh, Rajesh Kumar; Dash, Biswajit; Rout, Archana; Swain, Nirmal Keshari; Nayak, Subrat Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1745-1754

Abstract

A digital currency known as a cryptocurrency uses blockchain technology to record transactions electronically, guaranteeing security and transparency. Cryptocurrencies, in contrast to conventional hard currency, are virtual or soft currencies; that do not exist in the actual world like coins or banknotes. Since all transactions occur digitally, cryptocurrencies are decentralized and frequently stand-alone from conventional financial institutions. Peer-to-peer transfers, increased anonymity, and often quicker transaction processing without middlemen are made possible by this. In this study, two machine learning models; autoregressive integrated moving average (ARIMA), extreme gradient boosting (XGBoost), and two deep learning models; long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM) were compared. By employing past Bitcoin data from 2012 to 2020, we evaluated the models' mean absolute error (MAE) and root mean squared error (RMSE). Compared to other models, the Bi-LSTM model yields minimal RMSE scores of 67.18 and MAE scores of 24.73. This aids in capturing all temporal correlations, which are important for forecasting the price of Bitcoin.
Co-Authors Aruna, V. Bagchi, Sovan Ghosh, Rajesh Kumar Innocent Rani , Vanitha Manchala, Yugandhar Nancy Lal, Monica Nayak, Subrat Kumar P, Muthu Prasanna Prabha, K. Suria Rout, Archana Swain, Nirmal Keshari Tripathy, Nrusingha