Published in 2026
T. Jayanthi, V.Geetha
Stem cells have the remarkable potential to develop into many different cell types in the body during early life and growth. In addition, in many tissues they serve as a sort of internal repair system, dividing essentially without limit to replenish other cells as long as the person or animal is still alive. When a stem cell divides, each new cell has the potential either to remain a stem cell or become another type of cell with a more specialized function, such as a muscle cell, a red blood cell, or a brain cell. Transplantation is the important process in the medical field where healthy stem cells can be transplanted to the donors who in need of those. The difficulty in stem cell transplantation is finding the donors. HLA typing is the process for finding the unrelated donors for stem cell transplantation. Research on stem cells continues to advance knowledge about how an organism develops from a single cell and how healthy cells replace damaged cells in adult organisms. Stem cell research is one of the most fascinating areas of contemporary biology, but, as with many expanding fields of scientific inquiry, research on stem cells raises scientific questions as rapidly as it generates new discoveries. Data mining is one such field which helps can be incorporated in the various activities of the medical field, thus making it beneficial. The database is used to search for similar genes which in turn help in the process of HLA typing.
Stem cells, HLA Typing, Genes, Transplantation.
V. Geetha, T. Jayanthi
Data mining holds great potential for the healthcare industry to enable health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs. Some experts believe the opportunities to improve care and reduce costs concurrently could apply to as much as 30% of overall healthcare spending. This could be a win/win overall. But due to the complexity of healthcare and a slower rate of technology adoption, our industry lags behind these others in implementing effective data mining and analytic strategies. Most of the systems rarely use the huge clinical data where vital information is hidden. As these systems create huge amount of data in varied forms but this data is seldom visited and remain untapped. So, in this direction lots of efforts are required to make intelligent decisions. The diagnosis of this disease using different features or symptoms is a complex activity. This research intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research particularly in Heart Disease Prediction.
Data mining, Decision Tree, Neural Network, Naive Bayes, cardiovascular disease.
K.Ganagavalli, V.Krishnamoorthy, A.Banu
In the upcoming computer world cloud computing has been becoming an emerging technology for sharing resources and outsourcing of data. While storing the data in cloud, the data owners need not to run and keep their data in their personal devices. Instead, the data will be maintained by the cloud servers which are maintained by a third party. From users’ point of view, putting sensitive data on the cloud and losing control of these data may increase the risk of being abused. Access control is a mechanism that is used for preventing unauthorized access of the resource. This service provides controls over the data such that who can access to a resource, under what conditions their access can occur, and also restricts the actions performed by a person on the resources. To achieve this, an identity-based proxy re-encryption scheme has been adopted which allows a user to encrypt his data under his identity and to delegate his data management capability to the cloud. The cloud, which could grant the access to an authorized user by transforming the cipher text encrypted with the data owner’s identity to the one with the sharer’s identity. This algorithm achieves more security than the existing algorithms in data management and also restricting the resource access control.
Cloud, Access control, Proxy Re-Encryption.
M.Devaraj, N. Santhana Krishna
In data mining applications, there are various kinds of missing values in experimental datasets. Nonsubstitution or inappropriate treatment of missing values has a high probability to cause a lot of warnings or errors. Besides, many classification algorithms are very sensitive to the missing values. Because of these, handling the missing values is an important phase in many classification or data mining task. This paper introduces traditional EM algorithm and disadvantage of the EM algorithm. This paper proposes a new method to implement the missing values based on EM algorithm, which uses Naive Bayesian to improve the accuracy. We conclude by classifying seeds dataset and vertebral columns dataset and comparing the results to those obtained by applying two other missing value handling methods: the traditional EM algorithm and the non-substitution method. The experimental results prove a stable algorithm for improving the data classification accuracy on large datasets, which contain a lot of missing values.
Data mining, Data cleaning, Expectation Maximization Algorithm