Prediction of Indian Petrol Price Using Machine Learning Algorithm

A Forecasting of oil prices has always been a matter of great importance due to its influence in driving a
country’s economy. As a matter of fact, the petroleum industry is considered to be the biggest contributor
in the industrial sector in terms of providing raw materials to the other industries and generating
revenues. Due to the non-linear and unpredictable nature of the oil prices, a lot of forecasting techniques
have been developed and used to check whether they are capable of forecasting the oil prices
satisfactorily. In this paper the Artificial Neural Network and Multiple regression technique is used to
forecast the prices of Petrol in India for a 11 years period (2008-2018). The results obtained are compared
with the actual prices for the above time period. It is observed that the overall accuracy considering
petrol price shows promising results thus justifying them capable of forecasting the prices of the petrol in India.

Data Leakage Detection and Prevention: Review and Research Directions

Disclosure of confidential data to an unauthorized person, internal or
external to the organization is termed as data leakage. It may happen inadvertently
or deliberately by a person. Data leakage inflicts huge financial and nonfinancial
losses to the organization. Whereas data is a critical asset for an organization, recurrent
data leakage incidents create growing concern. This paper defines data leakage
detection and prevention system and characterizes it based on different states of data,
deployment points and leakage detection approaches. Further, this paper follows a
systematic literature review considering a decade of the existing research efforts and
makes a critical analysis thereof to highlight the issues and research gaps therein.
The paper then proposes important research directions in the field of data leakage
detection and prevention. This review helps fellow researchers and interested readers
understand the research problem, appreciate the state-of-the-art techniques addressing
the research problem, draw attention toward the research challenges and derive
motivation for further research in this promising field.

Data Breach in Cloud: Challenges, Countermeasures and Future Directions

Cloud computing is a relatively new area in the field of computing that provides scalable, on-demand access to shared resources from a remote location over the Internet. It offers pay-as-you-go pricing for consumption of resources and significantly reduces both capital as well as operational expenditures incurred in acquiring computing infrastructure. As a result, cloud computing is rapidly gaining popularity and organizations of all sizes are embracing it. However, cloud computing is not devoid of issues. It poses several security concerns to organizations, out of which data breach is the most serious one. The paper provides a comprehensive understanding of data breach problem in cloud computing. It analyzes the challenges and proposes research directions in the field to help fellow researchers understand the research problem and derive motivation for further research in this promising field.

A Comprehensive review on Cloud Computing and Security Issues

Cloud computing means on demand delivery of IT resources via the Internet. It provides a solution of IT infrastructure
in low cost. Cloud computing is a flexible infrastructure, Internet centric approach and ease of access. There are many
organizations accepted cloud computing services in terms of storage, computation and IT services. In order to provide privacy
preventing services to the individuals as well as organizations, cloud computing process should concerned about security issues.
This paper presents brief idea about cloud computing and latest survey on security issues .

Content and Popularity-Based Music Recommendation System

Nowadays the future of many modern technologies includes Machine Learning and Deep learning
methodologies. One of the prominent applications of these tehnologies is Recommender system. In this paper we
are going to design, build and implement a song recommender system using popularity and content relied
recommendation. We will also discuss the various techniques and their pros and cons and try to build a good
music recommender system. We will mostly focus on content based, popularity based and collaborative based
filtering algorithm and also try to combine them using hybrid approach. We utilized MAE for comparing the
several procedures implemented here for the recommendation. Out of all procedure used, SVD performed well
with MAE of 1.60 while KNN didn’t perform that well as we had very less features of song with mean absolute
error of 2.212. User relied and Item relied prototype performed the best with MAE of 0.931 and 0.629.

A Study on Data Leakage Challenges in Cloud Environment

Recommender System on Books: A Hybrid Approach

The Role of Machine Learning in Addressing Security Challenges for IoT Applications

Secure Framework for Data Leakage Detection and Prevention in IoT Application

An Intelligent Disease Prediction and Drug Recommendation Prototype by Using Multiple Approaches of Machine Learning Algorithms

A predictive prototype for the identification of diseases relied on the symptoms described by patients

Application of ensemble learning in predicting heart failure