Informatics Publishing Limited

Sweta kumari ¹, Gagandeep Nagra ¹, Pradeep Manjrekar ², Renu Verma ³

Affiliations
1 Padmashree Dr. D.Y. Patil University Navi Mumbai’s Department of Business Management, CBD Belapur, Navi Mumbai - 400614, IN
2 Research and Consultancy and Extension Centre, Padmashree Dr. D.Y. Patil University Navi Mumbai’s Department of Business Management, CBD Belapur, Navi Mumbai - 400614, IN
3 IBS School, Gurgaon, IN

Abstract

Personal financial management is increasingly important, and not just for investors. It is becoming essential for the average family trying to decide how to balance its budget, buy a home, fund the children's education and ensure an income when they retire. In the current financial crisis, people are uniquely impacted by household financial complexities. Moments of financial trouble are teachable opportunities to learn about personal finance and to improve their own money management skills. However, comprehensive strategies for educating them about personal finance have not yet emerged.

This research explores the state of people's financial education in India and its impact on their investment decisions. The study analyses the relationship between financial knowledge and other factors like age, gender and education The research was carried out among 100 investors in Mumbai using a structured questionnaire Data was analysed with the help of SPSS software .Research findings gave an insight into major areas focused on existing and potential issues related to personal financial management .The paper has major implications in identifying the key opportunities for Indian investors as well as policy makers in designing suitable programs for financial planning of investors.

Keywords

Financial Education, Personal Finance, Financial Planning, Money Management, Financial Trouble.

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Mohanapriya Mohana Arumugam ¹, Sweta Kumari ²

Affiliations
1 Vellore Institute of Technology, IN
2 VIT University, IN

Abstract

Our work presents a data mining approach, using Krill herd optimization algorithm to generate more comprehensive classification of breast cancer dataset. Hybrid krill herd algorithm (HKH) is proposed which is suitable for generation of optimized classification rules. HKH algorithm has a very flexible encoding where each member of population (krill herd) corresponds to a classification rule. Each krill herd consist of many krills corresponding to antecedents of classification rule. For breast cancer classification, Wisconsin diagnostic breast cancer data set created from fine needle aspiration biopsy of breast mass was used. Using this optimization algorithm a very comprehensive and simple classification rule is obtained for breast cancer classification represented in form of If-Then rule. The obtained rule can be used to further classify breast tissue specimens into malignant and benign classes, thus supporting the cancer diagnosis.

Keywords

Swarm optimization algorithm; classification rule; rule mining; WDBC dataset

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Sweta Kumari ¹, Varsha Singh ¹

Affiliations
1 National Institute of Technology, Raipur, IN

Abstract

Wireless sensor networks aim to improve different aspects of our lives. This technology is experiencing true expansion for the past decades. Main concept in these systems is a sensor node, small microprocessor integrated with number of sensors. The sensor functions imply the WSNs' course of utilization. Possibilities are enormous. This paper focuses on sensor dataminning based on wireless sensor network. Sensor dataminning is very useful in various application like Traffic control, Disease diagnose, Animal tracking and etc.

Keywords

Wireless Sensor Networks (WSNs), Sensor Nodes, Utilization, Existing Medical Applications (EXMAs), Healthcare.

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Sweta Kumari ¹, Soniya Pardeshi ¹, Sumedha S. Deshmukh ¹

Affiliations
1 National Chemical Laboratory, Biochemical Sciences Division, Pune, Maharashtra, IN

Abstract

Streptomyces aureofaciens (NCIM 2614) culture produced extracellular RNase (291U/ml)

During screening among 9 cultures from NCIM division in MGYP medium.

 Effect of media components on RNase production showed that organism could grow in simple PG (0.8% peptone and 1% glucose) medium to replace costly MGYP medium. Medium optimization studies resulted in 2.2-fold increase in RNase activity (291-669 U/ml) and 1.6-fold increase in specific activity (5836-9211). Production profile of Streptomyces aureofaciens (2614) in PG medium (pH 7.0) at 28^οC showed that organism produces maximum RNase (669U/ml) and DNase activity (153.3U/ml) in 72 hrs and thus can be classified as non-specific nuclease. The ratio of RNase to DNase is 4: 1 suggesting that organism prefers RNA to DNA as substrate. RNase activity could be detected in the absence of metal ions while DNase activity required metal ions.

During purification of RNase enzyme, heat treatment is not a suitable method as enzyme is not thermo stable. Ammonium sulphate precipitation and dialysis resulted in 89 % recovery with 1.13-fold purification. The enzyme eluted unbound on anion exchanger (DEAE sephadex) with 46.5 % recovery which was loaded on cation exchanger (CM cellulose) and eluted with buffer containing 0.2 M salt (11% recovery). The enzyme was further loaded on gel filtration column

(FPLC Superose-12) with 11% recovery and 18.6-fold purification.

Keywords

DNase, RNase, Production, Purification, Specific Activity.

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