Browsing by Author "Islam, Linta"

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A Predictive Analysis of Chronic Kidney Disease by Exploring Important Features
(Daffodil International University, 2022-01-09) Rahman, Mafizur; Islam, Linta; Rana, Masud; Tazim, Malika Zannat; Sorna, Jannatul Ferdous; Alvi, Syada Tasmia
Chronic Kidney Disease is an incurable disease which causes damages to the functions of a kidney gradually. Only proper treatment can prevent the disease from getting worse. Because of proper knowledge about kidney disorders, people had to suffer from this deadly disease. Thus, in this paper, we analyzed certain key features and noticed several interesting relationships with the disease by considering the actual perception of people. We also predict kidney disease by employing various machine learning algorithms including Logistic Regression, Naive Bayes, SVM and KNN. By applying PCA, we observe that there is an improvement in the accuracy for predicting the disease. SVM outperforms other algorithms with 98% accuracy in predicting chronic kidney disease. In future, we will try to find some significant hypothesis that helps us to prevent the disease better.
Automated Invasive Cervical Cancer Disease Detection at Early Stage through Suitable Machine Learning Model
(SN Applied Sciences, Springer, 2021-09-16) Jahan, Sohely; Islam, M. D. Saimun; Islam, Linta; Rashme, Tamanna Yesmin; Prova, Ayesha Aziz; Paul, Bikash Kumar; Islam, M. D. Manowarul; Mosharof, Mohammed Khaled
Cervical cancer is a common cancer that affects women all over the world. This is the fourth leading cause of death among women and has no symptoms in its early stages. At the cervix, cervical cancer cells develop slowly. If it can be detected early, this cancer can be successfully treated. Health professionals are now facing a major challenge in detecting such cancer until it spreads rapidly. This study applied various machine learning classification methods to predict cervical cancer using risk factors. The main aim of this research work is to be described of the performance variation of eight most classifications algorithm to detect cervical cancer disease based on the selection of various top features sets from the dataset. Multilayer Perceptron (MLP), Random Forest and k-Nearest Neighbor, Decision Tree, Logistic Regression, SVC, Gradient Boosting, AdaBoost are examples of machine learning classification algorithms that have been used to predict cervical cancer and help in early diagnosis. A variety of approaches are used to avoid missing values in the dataset. To choose the various best features, a combination of feature selection techniques such as Chi-square, Select Best and Random Forest was used. The performance of those classifications is evaluated using the accuracy, recall, precision and f1-score parameters. On a variety of top feature sets, MLP outperformed other classification models. The majority of classification models, on the other hand, claim to have the highest accuracy on the top 25 features in dataset splitting ratio (70:30). For each model, the percentage of correctly classified instances has been presented and all of the results are then discussed. Medical professionals will be able to use the suggested approach to perform research on cervical cancer.
BSEVOTING
(Scopus, 2021) Alvi, Syada Tasmia; Islam, Linta; Rashme, Tamanna Yesmin; Uddin, Mohammed Nasir
In a democratic society, a citizen's ability to vote is regarded as one of the most significant legal rights he or she may exercise. For e-voting methods, blockchain presents new possibilities to properly meet transparency, integrity, anonymity and many other security properties. As the need for blockchains keeps rising, demand for bigger, more scalable, more adaptable, and more cost-effective multipurpose chain is also high. Conventional blockchains are incapable of meeting all of these demands. To solve the problems (mostly performance) associated with main blockchains, sidechain technology has recently evolved as a separate chain connected to the main chain that runs in parallel with transactions. Our proposed method is designed to operate on a public blockchain, but we separate the storage of voting information of each candidate using a sidechain to offer a cost-effective blockchain-based voting mechanism by ensuring the security properties such as anonymity, integrity, privacy, security, fairness, receipt freeness and so many. In future, we will broadly discuss and implement this voting system.
DVTChain
(Daffodil International University, 22-07-01) Alvi, Syada Tasmia; Uddin, Mohammed Nasir; Islam, Linta; Ahamed, Sajib
Voting is a fundamental democratic activity. Many experts believe that paper balloting is the only appropriate method to ensure everyone’s right to vote. But this method is prone to errors and abuse. Many nations utilize digital voting methods to solve the difficulties of paper balloting. A single flaw in digital voting may lead to massive vote-rigging. Election voting methods must be legal, accurate, safe, and convenient. However, issues with digital voting methods may restrict acceptance. Due to its end-to-end verification capabilities, blockchain technology was developed to address these problems. To guarantee We have used blockchain technology anonymity, privacy, verifiability, mobility, integrity, security, and fairness in voting. By using blockchain our proposed system ensures security, privacy, and integrity. This system provides voter anonymity by keeping the voter information as a hash in the blockchain. It also provides fairness by keeping the casted vote encrypted till the ending time of the election. After ending time, the voter can verify their casted vote, ensuring verifiability. To test our protocol, we put it on Ethereum 2.0, a blockchain platform that uses Solidity as a programming language to create smart contracts. The adoption of smart contracts provides a safe means for performing voter verification, ensuring the correctness of voting results, making the counting system public, and protecting against fraudulent activities. We analyzed the system’s performance based on security and gas costs. It improves in terms of security characteristics and the related cost for the necessary infrastructure.