Browsing by Author "Keya, Sadia Ahmed"

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    Online Secure Banking Transaction in Concern of User Information
    (Daffodil International University, 2020-12-08) Keya, Sadia Ahmed; Tasnim, Mir Noshin
    In today’s modern world, security is the main essential part of an online transaction. We want to transact money as well as want to have full protection of that transaction. Particularly the individuals of this nation are generally working class or under neediness so they are worried about their money getting hacked on the online. In this project a Steganographic based encryption is shown for hiding data and proposed a system which can be used in an online banking system. The leading motive is to veil the undercover secret message inside the image utilizing the LSB(Least Significant Bit) technique and the Digital Steganography. Providing an extra level of security that ensures that no data can be accessed by any unwanted third party. This proposed method provides the Hybrid Steganography technique with the combination of LSB Steganography and Digital Steganography. Steganographic LSB technique is a least significant bit based encryption. All the media files like image, audio,video and also messages can be concealed inward the cover image. This two methods are used in this proposed project to conceal valuable information hidden under an image. Digital Steganography is the leading art of hiding the secret information inside of a cover image or media files without invading unintended users. Combination of both techniques conserves and provides security by enveloping the undercover hidden message. Our method will provide a commencing level of security to all the valuable transactions.
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    Risk Factor Prediction of Chronic Kidney Disease Based On Machine Learning Algorithms
    (Scopus, 2020) Islam, Md. Ashiqul; Akter, Shamima; Hossen, Md. Sagar; Keya, Sadia Ahmed; Tisha, Sadia Afrin; Hossain, Shahed
    Chronic kidney disease (CKD) is an increasing medical issue that declines the productivity of renal capacities and subsequently damages the kidneys. CKD is very common nowadays; cardiovascular infection and end-stage renal illness are two life threatening diseases that can be caused as after-effects of CKD. These are conceivably preventable through early recognizable conditions and treatment of people who are in danger. The expectation of medical problems is a very troublesome assignment. CKD is particularly one of the most lethal diseases in the clinical field. Before it becomes too late to recognize CKD forecast, to get rid of risks, the prediction of risk factor is a major necessary step in the immediate stage. In this research work six algorithms such as Naïve Bayes, Random forest, Simple logistic regression, Decision Stump, Linear regression model, simple linear regression model is used to predict the risk factors of CKD. Considering the orderly execution and investigations of these strategies, six algorithms give a superior and quicker characterization execution. Six individual algorithms are applied to the dataset and the best outcomes have been acquired through the classification of predicting risk factors.