Reference no: EM132890932
In this report we will show how LinkedIn can improve data classification and privacy place for their users, members, recruiters that regulate how various of data are handled safely with an emerging technology.
Artificial Intelligence has been improved a lot in recent years such as algorithms, big data, and computing power. Here in this study, we will be introducing a technology called OS-ELM (Online sequential extreme learning machine) learning method in social networks for node classification) for data classification improvement in LinkedIn platform. Use of OS-ELM is more accurate and extremely faster algorithm in comparison to other AI technologies OS-ELM "can learn data one by one or chunk by chunk, with fixed or variable chunk sizes" and it will sort in to relevant categories.
According to cyber news Data scraping is a huge risk for LinkedIn and investing OS-ELM technology, LinkedIn can protect users sensitive data such as full names, email addresses, phone numbers, workplace information, and many more.
By implementing OS-ELM method will benefit LinkedIn huge cost reduction by 40% and the projected cost to implement this technology would be $4,000, 000. If LinkedIn cannot implement this technology they can be heavily fined for mishandling customers sensitive data and it's not good for the LinkedIn brand and reputation.
Use the above introduction and complete the following.
Analyse the alternatives (~800 words): (The two alternatives are Single OSELM and Multiple OSELM)
For each alternative carry out detailed critical analysis for the following areas.
OFeasibility: (can be a little bit technical) - talk about personnel (R&D), how long the project can be, how technology/IT assets can be obtained
OFinancial evaluation (talk about assumptions, provide a table similar as below)
OBenefits
OCosts (broad-brush figures, but understanding of different types of costs are important)
ORisks (risk register and matrix)
Attachment:- LinkedIn.rar