Category: Research Paper

“One’s research is only as good as one’s proposal.”

Paul T.P Wong

Ph.D., C.Psych.

(Trinity Western University, Canada)

You know you have to write an astounding research proposal to get the funds rolling or get accepted into the institute you are intrigued to get into. The purpose of your research paper is to “convince” the people that your research is worth pursuing.

But, what you don’t know is where to start and how to even start your journey of writing a mind-blowing research proposal?

Don’t you worry, we are here at your service. We will guide you through the process of how to write a research proposal in simple and crisp steps.

1. Research and preparation

As you have to start from the beginning, the best way to start is by researching the topic. This is the time for your theoretical as well as practical work. You are making your job easier, by doing a thorough research and by doing your experiments to collect your data for your research.

• Finalize your research topic and the purpose of your research.
• Read all the available materials that you can find about your topic. This will help you to establish the gap that is available between the existing research and what you are proposing.
• Take notes, save data, mark quotes and important lines which you think are going to be helpful in your work.
• Create your questionnaire and conduct your interviews. Keep all the data in chronological order.
• Seek out an advisor. An advisor will guide you through the process of writing a research paper. Their insights and advice will help you in perfecting your research proposal.

2. Title

A title is the window of your paper for the readers. The title is what is gonna keep the readers interested in going further into your research paper.

The title should be:-

• Short and understandable. It should also have the keyword of the research topic.
• Interesting and Quirky.
• It predicts the content.
• It reflects the tone of the writing.

The title page consists:-

• Title of the paper.
• Name of the Researcher and the designation.
• Name of the institute (including the logo is optional).
• Name of the supervisor (Optional)

To have a precise title page, do check out the institute’s instructions on the research paper.

3. Introduction

The introduction is the part where you lay down your framework. It involves your

• Introduction about the topic. Add some interesting quotes, facts, or trivia to make it engaging for the reader.
• They provide the background of the topic, followed by the context of the topic.
• Introduce your research problem including the questions and objectives.
• Then give a context of what is already known and what needs to be done.
• Provide your hypothesis and the purpose of why it is important to pursue the problem.
• At the end, define key concepts if required.

4. Literature Review

It is the section where you lay out what you have understood about the research problem from the available literature. This is your foundation, on which you are building your home. A strong literature review showcases that you have strong knowledge and understanding of the existing piece of work.

A good literature review includes:-

• A critical review of the existing literature. Provide new insights to lay the foundation of your concept.
• Provide the credits and citations to the authors or media that you are using.
• Compare and Contrast the existing literature.
• Showcase the gap that exists between the existing literature and your proposed theory.

5. Methods

The method or the methodology is the process that you will use to tackle your research problem. You will show your process of how you are going to conduct your research. This is the section where you are going to describe how you conducted your research to get your data.

It involves:-

• Explain whether your research is qualitative or quantitative.
• Participants- The people who are going to take part in your research.
• Materials- The materials like questionnaires, experiments, or visual aids which you are using to conduct your research.
• Procedure- How are you going to conduct the research? How are you going to interview the participants and the activities involved in it? List all the steps here in details.

6. Results

This is the segment where you showcase the results that you garnered from the methodology. All your findings, observations, interpretations should be listed. This is not the section where you give your opinion, so keep the results as facts not opinions.

7. Discussions

Now, you have listed everything you found, this discussion section involves what you have figured out and what the reader should be looking forward to in the proposal. Give a summary of your findings, the implications of it, the limitations of your findings, the future directions that succeeding members should follow. End it with a conclusion.

8. Bibliography

The bibliography is the section where you provide the names of the research materials that you have used in your research. List all the sources here.

Points to consider-

• If the research proposal is for funding purposes, then after bibliography add the time frame required to finish the research and include the analysis of the budget that you are seeking.
• Be very clear with your methodology and avoid unclear methodology.
• Put your emphasis on the fact that your research is worth pursuing.
• Proofread your proposal and take advice from your supervisor.

Theories are vital for research. In a quantitative study, theories aim to provide an answer to the research question. On the other hand, theories in qualitative study play a varied role. That is, it may either lead to the final outcome of the study or may provide a ‘new lenses’ through which they can look at the complex issue in a new perspective, focus on various aspects of data or perform data analysis & interpret the results. While theories draw a connection, theoretical framework defines the purpose of the study and lays a foundation to the research document.

As stated by Eisenhart “ theoretical framework is the structure that guides research by depending on a formal theory that is constructed using the coherent explanation of specific phenomenon and relationships”. Theoretical framework caters as a guide on which the entire study is built. Presented in the initial section of a research document, the theoretical framework offers a rationale to investigate a specific research problem. It provides a structure that defines how epistemologically, philosophically, analytically and methodologically the research document is structured and organised. 

Goals of a theoretical framework

As said earlier, theoretical framework provides a rationale for investigation and demonstrates that the research was not unanticipated. The main goals of theoretical framework are:

• Defining significant concepts
• Explaining assumptions and expectations
• Analysee, choose and combine similar/relevant theories

Developing a theoretical framework

To develop a theoretical framework, a researcher is required to have a deep understanding of the following three aspects.

1. The problem must establish a connection between two factors resulting in a quandary that cause further investigation. The problem statement defines the central problem and areas that require further research/investigation. 
2. Purpose describes the aims and outcomes of the problem. 
3. Significance, as the name suggests, explains the importance of your study and how it contributes to the existing knowledge. 

Steps involved in developing a theoretical framework are:

1. Choose key concepts – Identify the crucial terms from the research question and problem statement. Since concepts include multiple definitions, you can use a theoretical framework to explain the meaning of each term. For example, ABC institute was facing increased student absenteeism. It aimed at improving student presentism and assumes that fun learning plays a key role in achieving the process. To investigate the issue, research questions, objective and problem statement was defined. ‘How the student presentism can be improved?’ was the research question, ‘including fun learning’ was the objective and ‘increase in student absenteeism’ was the problem statement. Here, fun learning & student presentism are the key focus of the study and the theoretical framework must describe these concepts. 
2. Define and assess theories – To identify key concepts, conduct a literature review and determine how previous researchers have defined the key concepts and drew connections between them. Further, when working on the framework, compare and analyse the approaches previously used. This is followed by explaining the model that fits best for your study and justifying the reasons behind using the same. In complex research, you can combine theories from various fields and create a unique framework. However, if there exists a well-established theory and you are not using it, justify the reason for the same.  
3. Describe the significant contributions – After establishing links between the existing theories, the next (final) step is to explain how your research fits in the field of interest. Describe how the theory will be tested and how the results contribute to the existing information. Explain if you have used a specific theory as a basis for understanding and interpreting the data. Demonstrate if your theory would challenge or criticise the existing theories and did you combine theoretical methods to obtain a new approach. 

Why is this framework important?

• Theoretical framework lets you clarify the implicit theory in a defined and clear manner. 
• It considers other frameworks and reduces the bias in the data interpretation.
• It explains to the reader your perspective and context.
• It includes a coherent and interrelated set of ideas and forms generalisation about observations.
• It explains the central phenomenon, events or relationships.
• If required, theoretical framework can be used to develop a hypothesis. 

Theoretical frameworks are of various types. Depending on the type of study, the perspective in which you are approaching the study and the key concept to be conveyed, choose an apt theory and lay a strong foundation for your research document.

In the recent decade, Artificial Intelligence (AI) has made noteworthy contributions ranging from speech recognition, self-driving cars to a humanoid robot. Today, Artificial Intelligence has become the talk of the town and has given scope to several debates. While some call AI as ‘cognitive computing’, others rebrand it as ‘machine intelligence’.  

There is no iota of doubt that each area (network with memory, generative models, etc.) of Artificial Intelligence has impacted the world around us. But when compared to other major AI areas, it is Reinforcement Learning (RL) that has taken the technology world by storm.

Reinforcement Learning, also known as adaptive dynamic programming (ADP), is considered to be a robust tool for solving complex decision-making theories. It is a paradigm for learning through trial & error approach and allows machines to determine the specific behavior to maximize its performance. 

Some of the popular contributions of reinforcement learning include:

• Scalable deep RL with importance weighted actor-learner architecture – The core idea behind this project is to develop a scalable and fast policy gradient agent, IMPALA (importance weighted actor-learner architecture) to collect experience that is passed to the central learner. IMPALA can be incorporated either using single or multiple-learners conducting synchronous updates. The key benefit of this study is high data throughput rates can be achieved efficiently. 
• Model-free deep RL for model-based control – Model-free reinforcement learning algorithms have the potential to achieve the asymptotic performance (but are not efficient). On the other hand, model-based reinforcement learning algorithms best for higher asymptotic bias and are efficient. Here, the major idea is to combine the benefits of model-based & model-free reinforcement learning and accomplish asymptotic performance that is close to model-free algorithms. 
• Hierarchical imitation & reinforcement learning – This project introduces a hierarchical guidance framework that combines reinforcement learning & imitation learning (IL) to find solutions to problems that can be segregated into subtasks. The algorithm framework leveraging the hierarchical structure of issues such as labeling high-level trajectory with suitable macro-corrections, ignoring the sub policy if the macro-action is incorrect. The final outcome of the study implied that hierarchical imitation learning needs fewer labels than standard ones. 
• Unsupervised predictive memory in a goal-oriented agent – To overcome the issue of problem partial observability, a new model known as memory, RL, and interference network (MERLIN). This model offers a new approach to incorporate memory into the model. The major idea here is to segregate MERLIN into 2 components known as a memory-based predictor (MBP) and policy network which receives state variables. The outcome of the study concludes that the combination of predictive modeling and memory improves the performance of RL agents. 

With its growing importance, reinforcement learning is finding its applications in various sectors. 

 1. Manufacturing industry:- In the manufacturing sector, reinforcement learning can be used to pick devices and put them in a container with precision and at higher speeds. This is accomplished by memorizing objects and gaining knowledge. This technique, along with robots can also be used by the eCommerce businesses to sort out the products and deliver them to the customers. 

2. Power systems:- Optimisation and reinforcement learning techniques can be utilized to evaluate the security of electric power systems and improve the performance of microgrid. Adaptive learning approaches can be employed to create control & protection devices. One of the advantages of using RL technique is that it lets you develop a controlled structure for distributed generation sources, governs the communication topology graph, and controls the voltage level of an automatic microgrid. 

3. Finance industry:- Today, RL is widely used in the banking sector for training systems to maximize and optimize the financial objectives. Also, trading strategies can be analyzed precisely using a reinforcement learning technique. The added benefit of using this technique is its ability to study an optimal trading strategy and to increase the value of the portfolio using one programming instruction.  

4. Inventory management:- Coordination of inventory policies adopted by manufacturers, suppliers, and distributors for smooth flowing of materials while reducing the cost is the major issue faced by the inventory management. RL algorithms can be developed to minimize transmitting time stocking, retrieving the products and optimizing warehouse operations. 

Due to the continuous advancements, technology giants are articulating the significant long-term RL strategies and their outcomes. However, due to its limitations like large time-consumptions to perform activities when the action space is large, using RL can be quite challenging and would require further study to resolve the hardships. 

Artificial Intelligence (AI) needs no introduction in today’s tech-savvy transforming world. With its increased popularity and wide coverage across industries, world is waiting for new innovation in open AI or deep mind. In recent times, researchers are trying to cover the complete spectrum of themes pertaining to AI, and derive knowledge from its applications, algorithms, theory, software and hardware infrastructure. 

Decades of research in Artificial Intelligence have developed terrifying technologies that are proving to be of immense benefit to the society and industry. AI systems have advanced to such an extent that they can now identify objects in images & videos, translate across multiple languages, streamline manufacturing processes, etc. 

With growing times, AI study is focusing on mimicking human intelligence. For instance, one method to acquire knowledge from data is to develop artificial neural networks. These neural networks are composed of neuronline units that adjust how they fire based on the data. However, in several cases, the approaches designed are engineered merely for performance and does not have any resemblance with human intelligence. For e.g., programs that are designed to chess game can consider several possible moves than humans. 

Artificial Intelligence has grown beyond academic focus on algorithms, theories and has moved  into a context of social and interactive experimentation, continuous data collection, and massive amounts of knowledge about a constantly changing world. But the roadmap identifies a number of stages involved in moving AI to the higher level. 

The rise of AI

Researchers all around are performing advanced studies in this field and are delivering news-breaking research papers around AI, of which include: 

1. Computational approach to edge detection – The success of this method is defined by a set of objectives for the computation of edge points. The defined goals should be precise to delimit the desired behaviour of the detector while forming minimal assumptions about the form of the solutions. In addition, this paper also demonstrates a general approach, known as the feature synthesis, for the fine-to-coarse integration of data from operators at various scales. This assists in establishing the fact that the performance of the edge detector performance can be enhanced considerably by extending the operator point spread function along the edge.
2. A threshold selection approach from gray-level histogram – This study paper discusses an unsupervised and nonparametric approach of automatic threshold choice for image segmentation. The paper explores how an optimal threshold is chosen by the discriminant criterion to expand the separability of the resultant groups in gray levels. The approach makes use of only the zeroth and first-order cumulative actions of the gray-level histogram. The paper validates the approach by demonstrating several experimental results. Also, this method can be applied across multiple threshold problems effortlessly. 
3. Distinctive image features from scale invariant keypoints – This research paper delves into the approach for extracting distinctive invariant features from pictures. This method can be used to conduct reliable matching between various views of a scene or an object. The features are invariant to image rotation & scale and are displayed to offer robust matching across a wide range of affine distortion, the addition of noise, change in illumination and change in 3D viewpoint. The paper additionally explores an approach which leverages the features for the image recognition process. This method helps in identifying objects among occlusion and clutter while accomplishing real-time performance.
4. Induction of decision trees – The technology for developing knowledge-based systems by inductive inference has been exhibited in several practical applications. This paper summarises the methods used to synthesise decision trees used in a variety of systems. It specifically explains one such system, ID3. It explains how the ID3 algorithm was incorporated, tested with selected example problems, in detail. In addition to the illustrations of current research directions, the paper also discusses detailed limitations of the basic algorithm, besides correlating the two approaches of overcoming it. 
5. Batch normalisation: Accelerating deep network training by reducing internal covariate shift – This study talks about how complex is the training process of deep neural networks considering the fact that the distribution of inputs of each layer modifies during training. This approach draws its strength by performing the normalisation for each training mini-batch. Batch normalisation enables us to utilise much higher learning rates. In some cases, it also nullifies the need for dropout. This is the outcome of modification in the parameters of the previous layers. 

None can deny the fact that Artificial Intelligence has a tremendous effect on our day-to-day life. Its applications have made their presence felt across the globe. In the coming time, AI technology will be incorporated in almost every device, field and application. 

But making significant progress towards the advanced level AI isn’t possible in isolation. Researchers claim that long-term objectives of understanding intelligence and developing intelligent machines are ambitious and bold. 

That is, taking Artificial Intelligence to the next level would require bringing together the algorithms, resources, and theories. Without the appropriate resources, AI research is limited and foundational questions would remain unanswered.