Application of machine learning and data visualization techniques for decision support in the insurance sector Seema Rawat, Aakankshu Rawat, Deepak Kumar, A. Sai Sabitha International Journal of Information Management Data Insights, 2021 The insurance industry has a giant role in the sustainable economic growth of any country. With an increase in the number of insurance buyers, it has become an absolute necessity for an insurance company to have a detailed claim analysis system in place. Claim Analysis is performed by insurance companies to distinguish between fraudulent and genuine claims. Apart from that, Claim Analysis can also be used to understand the client strata in a much better way and implement the results further during the underwriting and acceptance/denial stage of policy enrollment. The main objective of this research work is to identify meaningful and decisive factors for claim filing and acceptance in a learning context through exploratory data analysis (EDA) and feature selection techniques. Also, machine learning algorithms are applied to the datasets and are evaluated using performance metrics.
Comparative Study of Deep Learning Models for COVID-19 Diagnosis Muskan Tomar, Tanishq Arora, A. Sai Sabitha, Nitasha Hasteer Icsccc 2021 International Conference on Secure Cyber Computing and Communications, 2021 Corona Virus Disease 2019(COVID-19) spread far and wide in numerous nations in early 2020, causing the world to face an existential health crisis. This pandemic continues to have a devastating effect on the global population and by now it has infected more than a few million individuals around the world. One significant obstacle in controlling the spreading of this virus is that the initial system for addressing this infectious disease was not clear. A basic advancement in the struggle opposite the COVID-19 pandemic is early screening and dependable diagnosis utilizing computerized detection of lung infections. Computed Tomography (CT) scans and X-rays imagery offers great potential help to clinical specialists tackling COVID-19. An efficient Deep Learning diagnosis application needs to be developed so that accurate and precise prediction can be done for the disease. This paper introduces dataset analysis and comparative evaluation of deep learning models for creating disease diagnosis application using image processing. Comparison is done using three main deep learning models -Convolutional Neural Network (CNN), Support Vector Machine (SVM) Logistic Regression. Dataset analysis and model selection is a crucial phase for developing a predictive deep learning algorithm. This analysis is done for better results and is done using Orange data mining software.
Application of Clustering Algorithm for Effective Customer Segmentation in E-Commerce Ritu Punhani, V. P. S Arora, Sai Sabitha, Vinod Kumar Shukla Proceedings of 2nd IEEE International Conference on Computational Intelligence and Knowledge Economy Iccike 2021, 2021 Due to the huge volume of customers in market and many platforms used by customers for purchasing, the focus turns to e-commerce organizations. It has become important for an organization to segment and cluster their customers and thereby take essential actions to survive against other competitive organizations. Since there are so many options, each organization must satisfy the demands of their customers or they might lose them to other alternatives that already exist in the market. Since the digital market is growing at a lightning speed the requirement of providing a complete experience to users becomes even more essential. In this paper, the dataset of an ecommerce site has been taken to identify all the parameters for analysis, few of them are - date, customer id, product category, payment method, value, time onsite, clicks InSite. The focus of this paper is to analyse the database on above defined parameters by using K-Mean algorithm. Every business in the market should have an effective strategy to address the people and retain their profitable users for its growth. Nowadays, users need personalisation therefore it has now become a need to prioritize experiences or you can’t stand against competitors. Summing up, the paper focuses on introducing customer segmentation, it’s basics, explaining why it is needed in the digital market, filtering the customer data effectively and analysis.
Segmenting e-commerce customer through data mining techniques Ritu Punhani, V.P.S Arora, A. Sai Sabitha, Vinod Kumar Shukla Journal of Physics Conference Series, 2021 Business-to-business e-commerce is in much attention nowadays, mainly due to its growing use. In today’s world, it has become imperative for companies to segment their customers and thereby take required measures to survive against other companies. Since there exist a lot, each company must fulfil the demands of their users or they might lose them to other alternatives that exist. This report aims to analyse the customer data from two years: 2018 and 2019 of the company: Autofurnish.com and thereby recommend methods to increase customer influx and give suggestions on what mistakes should not be repeated by the company in future for better performance and sales. To analyse the database, RapidMiner tool has been used. RapidMiner is an open source predictive analytic software that gives support regarding data mining. It lets the user to build models based on their needs and gives solutions quickly. For this analysis, K- means algorithm clustering will be used. Clustering is dividing groups based on similarities and K means is one of the very commonly used methods to do so. In the software, data has been imported having information about customers which is then analysed to prove different results draw a contrast between two years as told before keeping customer segmentation in mind based on various attributes given in the dataset. Relationships between the features are identified as assess to company’s performance.
Mapping Student Performance with Employment Using Fuzzy C-Means Ishwank Singh, Sai Sabitha, Tanupriya Choudhury, Archit Aggarwal, Bhupesh Kumar Dewangan International Journal of Information System Modeling and Design, 2020 Technical organisations are ranked based on performance indicators like resources, students' intake, global reputation, and research activities. Student performance and placement are important factors in deciding the ranking of a university. Student performance analysis is a recent and widely researched domain aimed at reforming the education system. The analysis assists institutions to understand and improve their performance and educational outcomes. Admissions, academics, and placement are the three most significant processes during which the large amount of data is gathered within a university and there is a requirement of analysis. The data mining techniques are used for data analysis processes and it encompasses data understanding, pre-processing, modelling, and implementation. In this research work, fuzzy c-means clustering technique is used to understand fuzziness of student performance, classify and map the student performance to employability. To understand this objective, the dataset has been collected from universities, pre-processed, and analysed.
Designing Ontology for Massive Open Online Courses using Protégé Shivangi Gupta, A. Sai Sabitha Icrito 2020 IEEE 8th International Conference on Reliability Infocom Technologies and Optimization Trends and Future Directions, 2020 Aimed at the problem of dropout in massive open online courses, this research work addresses the challenges and attributes of attrition. It investigates various factors to increase the effectiveness and efficiency in mooc platforms. The issue is not only interaction with the instructor but the necessity to create a social environment. The semantic web is an elongation of the present web that facilitates the meaning of information rather than syntax of the distinct vocabularies to the people and computers. The fundamental technique of semantic web mining is ontology. We have proposed semantic web mining by designing ontology for mooc platforms that can allow different mooc platforms to interact and create social environment for their users.
A review on data mining techniques towards water sustainability issues Ranjan Kumar Panda, A. Sai Sabitha, Vikas Deep Recent Advances in Computer Science and Communications, 2020 Sustainability is defined as the practice of protecting natural resources for future use without harming the nature. Sustainable development includes the environmental, social, political, and economic issues faced by human being for existence. Water is the most vital resource for living being on this earth. The natural resources are being exploited with the increase in world population and shortfall of these resources may threaten humanity in the future. Water sustainability is a part of environmental sustainability. The water crisis is increasing gradually in many places of the world due to agricultural and industrial usage and rapid urbanization. Data mining tools and techniques provide a powerful methodology to understand water sustainability issues using rich environmental data and also helps in building models for possible optimization and reengineering. In this research work, a review on usage of supervised or unsupervised learning algorithms in water sustainability issues like water quality assessment, waste water collection system and water consumption is presented. Advanced technologies have also helped to resolve major water sustainability issues. Some major data mining optimization algorithms have been compared which are used in piped water distribution networks.
Implementation of PingER on android mobile devices using firebase Ananthnarayan Rajappa, Aayush Upadhyay, A. Sai Sabitha, Abhay Bansal, Bebo White, Les Cottrell Proceedings of the Confluence 2020 10th International Conference on Cloud Computing Data Science and Engineering, 2020
Edge Analytics for Building Automation Systems: A Review Arushi Sharma, A. Sai Sabitha, Abhay Bansal Proceedings IEEE 2018 International Conference on Advances in Computing Communication Control and Networking Icacccn 2018, 2018
Clustering Analysis of Pinger Network Data for Vardha Cyclone Ankita Gupta, Reyhan Gupta, Harshit Sinha, A. Sai Sabitha, Abhay Bansal, Les Cotrell, Bebo White Proceedings of the 8th International Conference Confluence 2018 on Cloud Computing Data Science and Engineering Confluence 2018, 2018
Smart sentimental agent analysis through live streaming data Pratham Sharma, Tanupriya Choudhury, A Sai Sabitha, Gaurav Raj Proceedings of the 2018 International Conference on Communication Computing and Internet of Things Ic3iot 2018, 2018
Crime Analysis Using K-Means Clustering Anant Joshi, A. Sai Sabitha, Tanupriya Choudhury Proceedings 2017 International Conference on Computational Intelligence and Networks Cine 2017, 2017
Implementation of PingER on Android Rohan Sampson, Shivnarayan Rajappa, A. Sai Sabitha, Abhay Bansal, Bebo White, Les Cottrell Proceedings of the 7th International Conference Confluence 2017 on Cloud Computing Data Science and Engineering, 2017
A walk through of software testing techniques Jai Gaur, Akshita Goyal, Tanupriya Choudhury, Sai Sabitha Proceedings of the 5th International Conference on System Modeling and Advancement in Research Trends Smart 2016, 2017
Analysis and clustering of PingER network data Anwesha Mal, A. Sai Sabitha, Abhay Bansal, Bebo White, Les Cottrell Proceedings of the 2016 6th International Conference Cloud System and Big Data Engineering Confluence 2016, 2016
Landscape analysis of patent dataset Journal of Intellectual Property Rights, 2016
