In week 3, we were supposed to prepare the project proposal and the plan

1.   Research Questions

To generate the project report with detailed information about the algorithms used in big data analytics, I’m supposed to carry out a comprehensive research on the following areas.

• What are the Methodologies and algorithmic analysis used in ML to organise the data?

 

• What are the Machine Learning algorithms used to analyse and generate information?

 

• The research would be mainly focused on the commonly used algorithms in the field. In these algorithms, I would be detailed analyse on the requirements to use the selected algorithm and what specific outcome can be generated using the selected algorithm. Some of the algorithms that would be researched are listed below.

K-Means Clustering Algorithm                       Linear Regression Algorithms

Association Rule Mining Algorithm                 Regression Algorithms

Support vector machine (SVM)

2.   Conceptual or theoretical framework

In the research, I will be using ‘qualitative theoretical framework’ as the research framework. This approach would aid me have a wide understanding about the area and be able to gain knowledge from previously carried out research projects. The research projects that are being used in this project, are from expertise that have carried out comprehensive research in the particular area which would help to me understand, learn and research in depth about my selected topic.

3.   Methodology

5.1 Research data collection and analysis

In this research project, a detailed analysis would be carried out on 12-15 previously published research papers and journal articles from different authors. These research projects should have adequate information on the area and should be relevant to my research since my research project is mainly a theoretical framework project depending on these selected projects. The selected resources would be analysed and a critique would be documented in the project with an informative research paper on algorithms as the final outcome of the project.

5.2 Sample research papers

The following are two of the research papers that I intend to use in my project.

Bhargavi, P. (2018). Machine Learning Algorithms in Big data Analytics. Research Gate.

Bruce, S., Li, Z., Yang, H.-C., & Mukhopadhyay, S. (2019). Nonparametric Distributed Learning Architecture for Big Data: Algorithm and Applications. IEEE Xplore .

 
5.3 Research Method

The planned research project is ‘a qualitative framework project’ with consist of analysis of other research papers. In my research I would not be introducing new algorithms or frameworks but to analyse and construct a comprehensive report with existing algorithms. Therefore, the research method using would be ‘report analysis research method’

For the research project, a project plan was created with the tasks that have to be completed in order to successfully complete the project.

The tasks and the estimated dates are shown below.

In my research, during the week 2 I have been writing on the Abstract on my selected topic.  Apart from that, I have been producing an report assignment on the basic outline of my research project.

While writing my Abstract and conducting some research on existing published journals on my selected topic I have found new two other suitable journals which can be used for my project.

During the class I was able to finalise the topic after discussing more on the project with my supervisor.

After all, This week was productive where I was able to get more understanding on the depth of the subject and as well as successfully completing my first assignment on the project.

 

1. Alamlahi, Y., & Muthana, A. (2018). An Email Modelling Approach for Neural Network Spam Filtering to Improve Score-based Anti-spam Systems. Modern Education and Computer Science Press.

2. Ahsan, M. I., Nahian, T., Kafi, A. A., Hossain, M. I., & Shah, F. M. (2016). An Ensemble approach to detect Review Spam using hybrid Machine Learning Technique. IEEE Xplore, 388-394.

3.

Alurkar, A. A., Ranade, S. B., Joshi, S. V., Ranade, S. S., Sonewar, P. A., Mahalle, P. N., & Deshpande, A. V. (2017). A Proposed Data Science Approach for Email Spam Classification using Machine Learning Techniques. IEEE Xplore.

 

 

Emerging technologies and Innovation

 

About me: 

­­I’m Nathali Jayasinghe who is currently studying for my Master Degree in Information Technology in Charles Sturt University. For the subject Emerging Technologies and Innovations, I have chosen one of the innovative and fundamental topics in Big Data Analytics Field, which is ‘Statistical Algorithms’. In my study, I would be deeply focusing on Statistical Algorithms which are used in Big Data Analytics. I have selected this topic because all the big data analytics are carried out using different kind of Statistical Algorithms. I always wanted to explore on big data analytics field, Therefore it is essential to have a proper understanding on its fundamentals. This comprehensive study would aid me to achieve my requirement.

 

Project Title: Machine Leaning Algorithms in Spam Detection

 

I will be blogging the weekly updates on my study using the below blog site.

https://thinkspace.csu.edu.au/nathali

 

Project Problem Domain: In the Information Management Field, one of the most critical and important area is ‘how to deal with the massive amount of rapidly increasing of meta data in the current world since everything is becoming digitalized’. In here, it is important to looking to different and each phase which meta data undergo: Techniques using to gather data, the data storing phase, securing the collected data phase and possibly the most critical phase of how the data is interpreted. Applying the most effective data analyzing techniques and algorithms are the keys to extract massive amount of meaningful information from the collected big data. The organizations who are able to do so gain a huge competitive advantage over the other in the industry.

 

Background/Context/Description:

In the current environment, the organizations are focusing on the methods which can derive the collected data into a meaningful and valuable information. This derived information should be valid, accurate, usable and relevant to the organization. Statistical Algorithms comes into picture in this context. Although organizations have been collecting and analyzing their data from a long time ago, having specific algorithms to analyze big data is relatively new. In here, different statistical models are created with effective algorithms to sort the data, classify into different categories, and process to result in valuable information.

 

Project Aim/Objectives:

In my comprehensive study the main objective would be to dive into the different algorithms and techniques which are being used in the big data analytics and to understand the characteristics, similarities and differences, the effectiveness and suitable environments to use the specific algorithm.

 

Scope:

There are different types and methods of Statistical Algorithms which cater to specific environments and requirements. I would be focusing on mainly used and effective algorithms in the field.

 

Deliverables/Outcomes:

In this project the findings and the deliverables would be presented as a paper after the successful completion.

 

Resources:

Bhargavi, P. (2018). Machine Learning Algorithms in Big data Analytics. Research Gate.

 

Bruce, S., Li, Z., Yang, H.-C., & Mukhopadhyay, S. (2019). Nonparametric Distributed Learning Architecture for Big Data: Algorithm and Applications. IEEE Xplore .