Shabana Pathan
@stvincentngp.edu.in
Assistant Professor in Department of Information Technology
St.Vincent pallotti College of Engineering &Technology
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Shabana Pathan and Sanjeev Kumar Sharma
Auricle Technologies, Pvt., Ltd.
Performance of decision trees is assessed by prediction accuracy for unobserved occurrences. In order to generate optimised decision trees with high classification accuracy and smaller decision trees, this study will pre-process the data. In this study, some decision tree components are addressed and enhanced. The algorithms should produce precise and ideal decision trees in order to increase prediction performance. Additionally, it hopes to create a decision tree algorithm with a tiny global footprint and excellent forecast accuracy. The typical decision tree-based technique was created for classification purposes and is used with various kinds of uncertain information. Prior to preparing the dataset for classification, the uncertain dataset was first processed through missing data treatment and other uncertainty handling procedures to produce the balanced dataset. Three different real-time datasets, including the Titanic dataset, the PIMA Indian Diabetes dataset, and datasets relating to heart disease, have been used to test the proposed algorithm. The suggested algorithm's performance has been assessed in terms of the precision, recall, f-measure, and accuracy metrics. The outcomes of suggested decision tree and the standard decision tree have been contrasted. On all three datasets, it was found that the decision tree with Gini impurity optimization performed remarkably well.
Et al. Archana V. Potnurwar
Science Research Society
In order to improve healthcare outcomes, this study investigates the synergistic potential of combining Internet of Things (IoT), artificial intelligence (AI), and smart city innovations. The study examines how the confluence of various technologies creates a cohesive ecosystem, enhancing patient care, accessibility, and overall system efficiency against the backdrop of current healthcare difficulties. Using a conceptual framework, the study tackles privacy and data security issues in this networked healthcare environment. Methodologically, case studies and surveys are used in conjunction with quantitative and qualitative methods to examine the effects of this integration. Initial results show better patient outcomes, more accessibility to healthcare, and higher operational effectiveness. The consequences, difficulties, and moral issues surrounding the integration are all covered throughout the conversation. In addition to offering insightful information to the healthcare field, the research suggests directions for further investigation. In summary, this research proposes a holistic strategy for improving healthcare by carefully combining IoT, AI, and smart city innovations.
Rashmi Welekar, Farhadeeba Shaikh, Abhijit Chitre, Kirti Wanjale, Shabana Pathan, and Anil Kumar
Taru Publications
The exchange of medical information has been drastically altered by patient-centered developments such as personal health records (PHR). By giving patients a place to handle their own PHR on a unified transactional platform, personal health record (PHR) services increase the efficiency with which medical information may be kept, accessed, and transferred. With the ultimate objective of providing patients with total surveillance under data, our findings is focused on creating a state-of-the-art infrastructure for the safe transfer of personal health data via cloud computing. Patients have the option of encrypting their PHR files, which provides an additional layer of security and allows them to set access control limits such as who has access to their files and to what degree. When data is encrypted in the cloud, only approved users may access it. Using cloud-based platforms to share health records raises concerns over confidentiality and privacy, which are addressed by the proposed method. Patients may still benefit from data interchange for the goal of better healthcare thanks to the frameworkâs provision of an encrypted PHR file option. This framework may accommodate attribute-based encryption (ABE) and other kinds of granular security. These measures ensure that people may continue to have access to, and make changes to, their own medical data, even when they are stored on the cloud. This article presents research that attempts to meet the demands of patients while also providing a safe method of transferring individual health information through cloud computing.