Prashasti Baranwal

Bio: Prashasti Baranwal is an academic researcher from VIT University. The author has contributed to research in topics: Encryption & Health care. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Analytical Comparison of Machine Learning Techniques for Liver Dataset

Abstract: Researchers all across the world have been working extensively for developing system in health care domain. Many people are struggling to clear their doubts about health issues by doctors or other medical personnel to confirm or clarify their diagnosis. The health management system is an end user support and online consultation system. The system is fed with counts of various pigments and chemicals present in an individual’s body which are necessary for determining liver health. Four algorithms have been implemented for this. This paper summarizes the predicted accuracy, precision and F-score of various machine learning algorithms and compares them to find the best suited algorithm to predict the impact of the liver diseases.

Implementation and Analysis of Various Visual Cryptographic Techniques for Sensitive Data Protection

Abstract: With the advancement of technology, more and more personal data are digitized, leading to increasing vulnerabilities in data. Protection of this data is a paramount importance for today’s era. Visual cryptography is the cryptographic technique in which various forms of visual data like pictures, texts, etc. are encrypted such that they cannot be directly read and need to be visually decrypted for use. Sensitive data like defense data involves diverse content types and different formats of data related to criminology, military, aeronautics, communications and space flights. Communicating the crucial data safely is a topic of vital concern for government. This paper intends to analyze the various cryptographic techniques and explore appropriate visual cryptographic solution for securing the sensitive data.

Machine Learning in liver disease diagnosis: Current progress and future opportunities

Abstract: There has been a rapid growth in the use of automatic decision-making systems and tools in the medical domain. By using the concepts of big data, deep learning, and machine learning, these systems extract useful information from large medical datasets and help physicians in making accurate and timely decisions regarding predictions and diagnosis of diseases. In this regard, this study provides an extensive review of the progress of applying Artificial Intelligence in forecasting and detecting liver diseases and then summarizes related limitations of the studies followed by future research.

Analysis of Machine Learning Algorithms on Cancer Dataset

Abstract: The most promising of all cancers that are prevailing among and the primary source of women’s deaths worldwide is the cancerous breast cells. Accurate discovery of this type of cancer cells is essential in its early stages, which can be attained via. various data mining and machine learning techniques. Therefore, a comparative analysis among different machine learning techniques such as Random Forest, Support Vector Machine, Naive Bayes, Decision Tree, Neural Networks and Logistic Regression is conducted. It is determined using the WEKA tool. Also, the selected machine learning algorithms are evaluated based on accuracy in prediction results and performance comparison of each classifier with a ROC curve on multiple classifiers is performed.

Prediction of COVID-19 using Machine Learning Techniques

Abstract: Background: Over the past 4-5 months, the Coronavirus has rapidly spread to all parts of the world. Research is continuing to find a cure for this disease while there is no exact reason for this ou ...