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In this study feature Selection technique (FST) namely Particle Swarm Optimization (PSO) is used to optimize the features of diabetes datasets. There are different types of classifiers that give low performances. So we need an FST to combined classifier may be required for best results. We used FST to improve the overall performance of the classification model. Classification of diabetes dataset classifier C4.5 and Support Vector Machine (SVM) is applied. The selected feature of diabetes is applied to classifiers and a comparative study was conducted. The experimental outcome reveals that the C4.5 is performed better with selected features compared to other models.

Classification, C4.5, feature Selection technique, Particle Swarm Optimization, Support Vector Machine

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Sanat Kumar Sahu, "Early-Stage Diabetes Risk Detection Using Data Mining Techniques With Particle Swarm Optimization," International Journal of Computer Sciences and Engineering, Vol.9, Issue.9, pp.63-65, 2021.

Diagnosis of health conditions is an incredibly difficult and significant issue in the field of medical science. Classification, dimension reduction technique (DRT), feature selection techniques (FST) play a very important role in the quick and accurate identification of disease. The chronic kidneys disease (CKD) dataset is connected into three classification methods like RF, J48 and C5.0. The proposed ensemble model (RF, J48 and C5.0) gives better accuracy i.e. 99.75% contrast with all classifiers with selected feature subset. All classification models give a better outcome with proposed PC-DRT and GA-FST when contrasted with without FST. The outcomes showed that utilizing GA-FST has computationally enhanced the classification accuracy.

Classification, chronic kidneys disease, dimension reduction technique, ensemble model, feature selection techniques, genetic algorithm, principal component analysis

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Sanat Kumar Sahu, "A Novel Ensemble Model for Classification of Chronic Kidney Disease With Selected Features and Components," International Journal of Computer Sciences and Engineering, Vol.9, Issue.9, pp.66-69, 2021.

“Large sections of society today do not trust their government or election transparency. The issue with the current EVM system is that it can be easily manipulated by power-hungry organizations. Blockchain is a disruptive technology of current era and promises to improve the overall resilience of e-voting systems. This project presents an effort to leverage benefits of blockchain such as cryptographic foundations and transparency to achieve an effective scheme for e-voting. The proposed scheme conforms to the fundamental requirements for e-voting schemes and achieves end-to-end verifiability. We can achieve this e-voting scheme along with its implementation using Multichain platform. This project presents an effort to leverage benefits of blockchain such as cryptographic foundations and transparency to achieve an effective scheme for e-voting. This requires minimum requirements needed by a voter is a smartphone or a computer with a webcam and an internet connection. This private key public key wallet for users can completely eliminate chances of double voting. This will also lead to minimum involvement of human engagement and we can rely on a trusted software architecture. More number of voters would be able to participate in elections.

Block chain, EVM, Power Hungry, Resilence, Cryptography, Multichain, Decentrolised

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C. Gouthami, G. Vidyulatha, K. Bhavani, G. Akhila, "Blockchain Based Secure Online Voting System," International Journal of Computer Sciences and Engineering, Vol.9, Issue.9, pp.70-74, 2021.

In the present study Diabetics is one of the critical diseases which can fall at any group of age and gender. The major causes lead to diabetics is mostly inheritance, in a proper healthy lifestyle, Irregular food habits, stress, and no physical exercise. Prediction of Diabetics is a very important study since it is one of the leading causes of sudden kidney failures, heart attacks, and brain stroke etc. The diabetic patient treatment can be done through patient health history. The Doctor can find hidden information about the patient through healthcare applications and it will be used for effective decision-making for the patient’s health condition. The healthcare industry is also collecting a large amounts of patient health information from different data warehouses. Using these healthcare databases researchers used to extract information for predicting the diabetics of the patient. Researchers are focused on developing software with the help of machine learning methods that can help clinicians to make better decisions about a patient`s health based on their prediction and diagnosis. The main purpose of this program is to diagnose a patient`s diabetes using machine learning methods. A relative study of the various competences of machine learning approaches will be done through a graphical representation of the results. The goal and objective of this project is to predict the chances of diabetics then provide early treatment to patients, which will reduce the life-risk and cost of treatment. For this purpose a probability modeling and machine learning approach like Support Vector Machine algorithm Decision tree algorithm, Naive Bayes algorithm, Logistic regression algorithm are used to predict diabetics.

SVM (Support Vector Machine), Decision Tree, Naïve Bayes, Linear Regression, accuracy comparison, machine learning techniques, predicting data values, analysis and results.

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Ommi Ramu, Brahmaji Godi, Om Prakash Samantray, "Diabetic Disease Prediction System using Supervised Machine Learning Approaches," International Journal of Computer Sciences and Engineering, Vol.9, Issue.9, pp.75-82, 2021.

Rainfall is important for human beings, animals and plants for their survival. Rainfall depends on many variables such as wind speed, temperature, humidity etc. Mathematical modelling of rainfall data is a stochastic process. Several mathematical models based on the probability concept are available. These models help in knowing the probable weekly, monthly or annually rainfall. Over the past decade or so, a number of models have been developed to generate rainfall and runoff. Monthly rainfall and temperature were analyzed using time series analysis. In this paper we are fitted linear regression model and quartile regression model at various values of tau 0.25, 0.5 and 0.75 for North west India (NWI), West Central India (WCI), North East India(NEI), Central North East India (CNEI) and Peninsular India (PI). Best model among fitted four models is choosing by using root mean square error (RMSE) criteria.

Rainfall, Quantile Regression, Linear regression, RMSE

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S. Damodharan, S. Venkatramana Reddy, B. Sarojamma, "Quantile Regression Models for Rainfall Data," International Journal of Computer Sciences and Engineering, Vol.9, Issue.9, pp.83-85, 2021.