Research Proposal in Machine Learning

Developing a machine learning-based research proposal includes detecting a particular problem or interesting field, exploring previous research, generating a project-related queries or theory and building a technique to overcome the problem. We open the gateway for your PhD or MS by writing a compelling research proposal in machine learning. Within the given tome we will provide you with a clear research proposal in outstanding quality.  The introduction will be keenly designed as per the context of your work.

Below, we discuss about the procedural flow and instance of research proposal based on machine learning:

Procedural Guide:

1. Topic: Offer a proper and detailed topic for our research project.
2. Introduction: Precisely describe the problem or interesting field we intend to explore.

• Background Detail
• Research Importance

3. Literature Review:

• To offer information, we explain significant research.
• Our work detects challenges or queries that remain unanswered.

4. Objectives/Research Queries/Hypotheses:

• Specifically demonstrate what we aim to accomplish.
• For evaluation, prepare particular queries or hypotheses.

5. Techniques:

• Define the dataset that we will utilize.
• Describe the machine learning approaches and methods that our project aims to employ.

6. Expected Results:

• Forecast possible outcomes or discoveries for our research.

7. Timeline:

• State an evaluated timeline for our project tasks.

8. Resources:

• Demonstrate the resources that our research requires like particular datasets or computational frameworks.

9. References:

• Mention all the sources that we mentioned in our project.

Sample  Research Proposal:

Topic: Predictive Modeling for Early Detection of Chronic Kidney Disease Using Machine Learning.

Introduction: We introduce that Chronic Kidney Disease (CKD) is considered as a world-wide health issue. Early identification guide to appropriate intervention and potentially reduce the disease development. For early identification, machine learning provides the efficiency to use huge datasets.  

Literature Review: From the analysis, we state that various research projects employed statistical techniques for CKD forecasting. Through the utilization of these approaches, there is a chance to enhance accuracy due to the emergence of machine learning. Also, for CKD forecasting, there is insufficient research in manipulating deep learning frameworks.

Objectives:

• In identifying early-stage CKD, we estimate the predictive accuracy of machine learning frameworks.
• Our goal is to differentiate the efficiency of conventional ML techniques like Random Forest and SVM with deep learning frameworks.

Techniques:

• Dataset: For this research, we use datasets having lab findings, patient clinical data and demographic data.
• ML Methods: Our project employs Random Forest, SVM and an easiest Neural Network.
• Validation Plan: To validate our framework, the stratified K-fold cross-validation technique is useful for us.

Expected Results: Initially, we predict that deep learning framework will offer excellent efficiency in early-stage CKD identification because of its capability to detect data’s complex factors.  

Timeline:

• Months 1-2: Data Gathering and Preprocessing.
• Months 3-4: Carry out framework execution and training for conventional ML techniques.
• Months 5-6: Execution and training of deep learning framework.
• Month 7: Evaluation and comparison of outcomes.
• Month 8: Summarizing and representing results.

Resources:

• We use available clinical dataset with CKD labels
• To train a deep learning framework, our research uses a needed computational framework.

The above mentioned instance is the easiest approach. However, based on the educational or business factors, we need more information. Every time make sure that our project is modified to the needs of our academy, advisory groups or funding community.