Baljeet Kaur
@sicsr.ac.in
Assistant Professor
Symbiosis Institute of Computer Studies and Research (SICSR), Pune.
PhD in Computer Application, with a rich academic & research experience, having 20 years of experience across renowned Universities. A keen researcher in the Interdisciplinary domain. Area of interests are Machine Learning, Graphical Databases, Information Security, and Wireless Networks. Contributed as resource person for guest lectures in India and abroad on various Technical Topics like Big Data Analytics using R, Cyber Security, Mobile Computing, Graph Database NEo4J. Published 18 quality research papers in various national/international conferences and Journals. Some of the papers are also Listed in Scopus. Possess extensive experience in teaching, training and research along with acquired skills of multitasking, crisis management, organizational skills, strong and effective communication skills, team work capacity with leading potentials.
RESEARCH INTERESTS
Machine Learning, Network Communication
FUTURE PROJECTS
Mobility Modeling of Mobile Ad-hoc Networks
Applications Invited
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sahil Y. Bansal, Baljeet Kaur, and Jatinderkumar R. Saini
IEEE
As there are numerous opportunities for competent people throughout the world, workers frequently switch employers to take advantage of these opportunities, which causes a high attrition rate inside the organization. All firms increasingly view employee attrition as a major problem because of its negative impact on workplace productivity and the timely achievement of corporate goals and vision. Businesses are using machine learning technology to estimate worker turnover rates in an effort to solve this problem. In order to most precisely anticipate employee attrition, various machine learning algorithms are investigated and their results are compared in this study. The current study also optimizes the results of the most efficient machine learning algorithm for the given data using the ROC method. Notably, optimization of machine learning algorithms has not been studied in earlier research works related to employee attrition. In the current study, an attempt is made to optimize the performance of the selected algorithm and a model is proposed.
Baljeet Kaur and Jatinderkumar R. Saini
IEEE
Customer segmentation is a very strong way to identify unsatisfied customers as well as loyal customers. It has become very crucial and mandatory for businesses to understand the customers and segment them according to their needs and desires. Many businesses struggle to manage cancellations and delays. The high number of cancellations is always a challenge for business houses. Every instance of cancellation can be a learning experience and an opportunity to understand the customers better. These insights can help businesses to improve their products and services. Now, as the usage of online gadgets has increased among customers and smart technologies are used in designing web applications, more data is available to understand customer behavior and predict their buying patterns. In the current era, customers are exposed to many online applications which pose tough competition among service providers. Businesses spend a lot to attract new customers. On the other side, retaining loyal customers is as crucial as identifying new customers. Identifying loyal customers helps to create a personalized approach that makes them feel valued. Understanding current customer priorities are more important than identifying the new customer. Machine learning is an effective technique to help segment loyal customers into actionable customers. This paper outlines the use of the K-means algorithm to identify loyal and prospective customers along with strategies to lower the cancellation rate. The current study uses the elbow curve method to identify the optimum number of clusters into which the customers could be segmented. This study will help businesses to seize new opportunities and gain customers for life.
Gunjan Behl, Deepali Shahane, Baljeet Kaur, and Shambhu Rai
Siree Journals
Pandemic challenges demanded immediate solutions and continues improvement in solutions on field which motivated the entire world’s research community to find an opportunity to provide speedy solutions to problems. Agile developments provide immediate improvements which functioned on the grounds of assorted health care units, medical facilities, pharmaceuticals and variants of COVID 19 cases. Agile developments proved its effectiveness for immediate solutions which take full advantage of aids to health and pharmaceutical organizations and also exploits worth rapport with health stakeholders. This springs a thirst for carrying out the study on agile developments and its effectiveness for health and pharmaceuticals so, the study focuses to design generic adaptive emergency agile model for Health care and pharmaceuticals to deal with Crisis pressure which will support COVID 19 medical research field.
Baljeet Kaur
IEEE
Data science is a blanket term for Machine learning, Artificial Intelligence and Big Data. Data science is being used by commercial and noncommercial organizations to get the insides of their customers, their behavior about selecting a product, insides of the staff. Many of the organizations use data science to provide better services to their customers. Financial Institutions use data science to analyze and predict stock markets, determine the risk of lending money, and learn how to attract new clients for their service. Government organizations get inside of public services by using data science and also use data structures to provide better services to citizens. Universities use data science to improve research and enhance study experience of students. Data grows exponentially over the period of time. The problem of working on large data sets can be solved by using the adept algorithm for your problem, use different data structures, and by relying on tools and libraries. The three main data structures used in data science are sparse matrix, hash functions and tree structures. Python has many tools that can deal with large data sets. In reality most of the data which is stored in a relational database is structured data. Now days the data retrieve from most of the websites is not structured and cannot be used by the relational databases. This paper describes how different tools in Python can improve user experience and provide better services to customers by using appropriate data structure and algorithm. This user experience and the better services to the customer boost the business and hence it is important for business strategy and ensures quality of experience (QoE) for the delivery services to their users.
B. Kaur
IEEE
Mobile ad hoc networks offer quick and easy deployment of network in situations where it is not possible otherwise. MANET offer unique benefits and versatility if the environment and application is appropriate and m-governance is one such application. MANET can be the best option for m-Governance services where there is no predefined infrastructure. Due to this reason MANETS can be easily adapted to m-Governance. Because of their ad hoc nature MANETS are vulnerable to security attacks. Most of the research in MANETS has focused on routing issues and security has been given a low priority. The layered architecture draws huge support with its success in case of Internet. The Cross Layer Design Architecture is becoming more popular with its performance improvements. In this paper, security architecture is proposed for Cross Layer Design Architecture of MANET. This paper analyzes the security mechanism in m-Governance applications in scope of proposed security architecture.