Welcome to this comprehensive guide on crafting a better survey paper. Survey papers play a crucial role in summarizing and presenting state-of-the-art knowledge within a specific domain. Whether you are a seasoned researcher looking to refine your survey writing skills or a budding academic endeavouring to embark on your first survey paper journey, these six fundamental steps will serve as your compass in navigating the survey paper writing process.
A Researcher begins his research journey by first writing a survey paper in the domain of his research. Writing a survey paper helps a researcher to
i) Understand his domain of research thoroughly
ii) Identify the existing research gaps,
iii) Understand the various parameters and their role in solving the problem and
iv) Infrastructure and Data set requirements for research.
In fact, after completing my survey paper I decided to go for optimal cloud infrastructure for my work. This helped me a lot in cutting the total cost of my research.
A survey paper is also a service to the scientific community. You are doing research for young research scholars. Instead of reading a vast amount of papers to understand what a scientific topic is about, a researcher just needs to read your paper and can start his research at the earliest with a clear direction in mind. To make the researcher read your paper, you must have good content and a high citation score. Otherwise, your paper is like purchasing a site in the forest and trying to sell with no one ready to buy.
What is expected in a survey paper?
A survey paper is a research paper which lists and analyses the latest research works in a particular research domain of interest. The survey paper derives some conclusions from the work carried out so far and provides new avenues for future research.
A good survey paper provides a concise but broad review of a domain that is accessible to a wide range of readers who are naive and willing to carry out research in the domain presented. This introduces two primary challenges for writing such a survey paper.
The first challenge is to pick representative papers from within the research area and summarize them. There can be a vast amount of research papers available and survey paper has limited space to capture the critical work in the field.
The author needs to go through abstracts and conclusions for a relatively large number of papers and select a subset that covers the selected topic area for detailed reading and presentation in the survey. Identifying the papers having higher citations and which are published in conferences and journals of high reputation will have to be given higher priority for selection.
The second challenge is to make the reader comfortable in reading and comprehending the analysis done for the various research papers. The author has to go through each paper considered for the survey at least two-three times before deriving any conclusion.
A survey paper should
- Pick at least 10-20 papers on a specific topic from the collected paper list.
- The papers selected should be a mix of papers including the base paper in the selected domain to the most recently published paper.
- Should have an analysis of the significance of the approach and the results presented in each paper
- Give a critical assessment of the work that has been done.
- Include a discussion on future research directions
- Give precise details of the experimental setup used for carrying out research in each paper
- Compare only those works which have a common experimental platform or data set. Otherwise, you have to recreate a common platform or use a common data set and test the methodologies used in various platforms.
A typical structure of a survey paper includes the following 06 Sections as discussed below:
The Six Steps for Writing a Better Survey Paper
In this comprehensive guide on how to write a better survey paper, we will explore six fundamental steps that will elevate the quality and impact of your research.
It all begins with crafting an eye-catching “Title” that succinctly conveys the essence of your survey paper and captures readers’ attention.
Moving on, the “Abstract” section serves as a concise overview of your research, providing readers with essential insights into your objectives, methodology, and findings.
Identifying the key terms of your literature review is the core of your survey paper, where you analyze existing research. Identify and emphasize key terms and concepts to provide a clear understanding of the relevant research landscape.
As you progress, a strong “Introduction” sets the tone, introducing the problem, its significance, and the objectives of your survey paper.
The heart of your survey paper lies in the “Literature Review,” where you analyze existing research. By highlighting key terms and concepts, you enhance clarity and enable readers to grasp the current research landscape more effectively.
As your survey paper reaches its “Conclusion,” you synthesize the key findings from the literature review and offer valuable insights into the current state of the field.
Lastly, by thoroughly reviewing, revising, and refining your survey paper, you ensure clarity, coherence, and overall excellence.
By following these six steps, your survey paper will not only make a significant contribution to your chosen field but also captivate and inform your readers with its well-structured and insightful content.
1. Eye-Catching “Title” for the Survey Paper
The primary function of a title is to provide a clear summary of the paper’s content. So keep the title brief and clear. Use active verbs instead of complex noun-based phrases, and avoid unnecessary details. Moreover, a good title for a research paper is typically around 10 to 12 words long. A lengthy title may seem unfocused and take the readers’ attention away from an important point. As I supervise many candidates who hail from non-native English-speaking countries, they struggle a lot with writing error-free Titles. So, I always advise them to take English classes in parallel with their research.
A good research paper title should contain keywords used in the manuscript and should define the nature of the study. Think about terms people would use to search for your study and include them in your title. Do not use abbreviations in the title. Knowing the search intent of the people who search for the keyword is critical as it helps you to find the top searched keywords. I learnt this technique by mapping SEO-based keyword research techniques to find quality keywords for my title.
Usually, a Title for a survey paper starts with , “Recent trends in ….”, “Advances in…..”.
Some survey papers end with “…….: A Survey”.
Here are the other ways to mention survey paper titles:
- ” A Survey on …..”
- “…….: A Survey”
- “An Overview of…”
- “A Comprehensive Study on…”
- “Exploring the Landscape of…”
- “A Critical Review of…”
- “A Systematic Analysis of…”
- “Examining the State of…”
- “A Comprehensive Review on…”
- “Surveying the Current Trends in…”
- “Investigating the Advancements of…”
- “Analyzing the Evolution of…”
Here is a list of Examples of Survey Paper Titles
- “A Survey on Artificial Intelligence Applications in Healthcare”
- “Recent Trends in Renewable Energy Sources: A Survey”
- “Advances in Natural Language Processing Techniques: A Survey”
- “A Survey on Cybersecurity Threats and Mitigation Strategies”
- “Recent Developments in Blockchain Technology: A Survey”
- “Advancements in Machine Learning Algorithms for Image Recognition: A Survey”
- “A Survey on Cloud Computing Security and Privacy Issues”
- “Recent Trends in E-commerce Payment Systems: A Survey”
- “Advances in Robotics and Automation: A Survey”
- “Mobile Health Applications: A Survey of Current Trends and Challenges”
- “An Overview of Machine Learning Algorithms: A Comprehensive Study”
- “A Critical Review of Renewable Energy Technologies: Exploring the Landscape”
- “Examining the State of Cybersecurity Threats: A Systematic Analysis”
- “A Comprehensive Study on Blockchain Technology: Investigating the Advancements”
- “Analyzing the Evolution of Artificial Intelligence Applications: A Survey”
- “A Survey of Cloud Computing Security and Privacy Issues”
- “Recent Trends in Natural Language Processing Techniques: A Comprehensive Review”
- “Surveying the Current Trends in E-commerce Payment Systems”
- “Exploring the Landscape of Robotics and Automation: A Critical Review”
- “A Comprehensive Review on Mobile Health Applications: Examining the State of the Art”
For more details on writing Titles for your research paper visit my blog post on: Research Paper Title: 03 Simple Steps to Make it Easily Discoverable
2. Giving an Overview of the Survey Paper Through the “Abstract” Section
The abstract is a summary of a research paper describing the problem investigated, the methods applied, the main results and conclusions. Abstracts are a good way to summarise the key contents of a paper, from the research it uses to the ideas you want to share with the reader.
The abstract is a single paragraph containing a minimum of 200 words up to 300 words. An abstract offers a preview that highlights key points and helps the audience decide whether to view the entire work.
Extraction of meaningful leaf disease features by applying image processing techniques is a problem that has been studied by the image processing community for decades. Image processing research for leaf disease identification has matured significantly throughout the years and many advances in image processing techniques continue to be made, allowing new techniques to be applied to new and more demanding pathological problems. In this paper, we review recent advances in diseased part extraction of leaf images affected by pathogens, focusing primarily on three important Soft computing techniques namely: Neural networks, Fuzzy logic and Genetic algorithms. Throughout, we present tables that summarize and draw distinctions among key ideas and approaches. Where available, we provide comparative analyses, and we make suggestions for analyses yet to be done.
Here’s a tabular representation of the sub-sections based on the given abstract:
|Sub-Sections of the Abstract||Content|
|Introduction||– Problem: Extraction of meaningful leaf disease features using image processing techniques.|
|– Mentioning the maturity and continuous advancements in image processing research for leaf disease identification.|
|Review of Advances in Diseased Part Extraction of Leaf Images||– Focus on three Soft computing techniques: Neural networks, Fuzzy logic, and Genetic algorithms.|
|– Discussion of the application of these techniques in extracting diseased part features from leaf images affected by pathogens.|
|Summary and Distinctions Among Key Ideas and Approaches||– Presentation of tables summarizing key ideas and approaches in diseased part extraction using Soft computing techniques.|
|– Drawing distinctions between the different techniques and their effectiveness in solving the problem.|
|Comparative Analyses and Future Suggestions||– Comparative analyses of the techniques’ performance in diseased part extraction.|
|– Suggestions for potential future analyses and research directions in the field.|
Please note that this tabular representation is a simplified breakdown of the abstract into distinct sub-sections based on its content. The actual structure and headings may vary depending on the specific formatting requirements and guidelines of the paper.
For more details on how to write an Abstract for your research paper, you can visit my blog post on :
3. Highlighting “Key Terms” of the Survey Paper
The purpose of keywords in a research paper is to help other researchers find your paper when they are searching for the topic. Keywords define the field, subfield, topic, research issue, etc. that are covered by the article.
Most electronic search engines, databases, or journal websites use keywords to decide whether and when to display your paper to interested readers. Keywords make your paper searchable and ensure that you get more citations. Thus, it is important to include the most relevant keywords that will help other authors find your paper.
For Example for the abstract written in the previous section, the keywords can be:
Keywords: Plant pathology, bacterial blight, diseased part extraction, Image processing, Soft Computing.
For more details on identifying the most prominent keywords for your research paper, you can visit my blog post on: Top 10 Rules to Identify Keywords for your Research Paper
4. Building a Strong “Introduction” Section of the Survey Paper
A good introduction in a survey paper explains how the research problem has been solved by various researchers and creates ‘leads’ to make the reader want to delve further into the research domain. Introduce the terminology of the field and describe what the various terms mean.
The introduction does not have a strict word limit, unlike the abstract, but it should be as concise as possible. The introduction works upon the principle of introducing the paper’s topic and setting it into a broad context, gradually narrowing it down to a research problem.
The main task of the introduction is to set the scene, giving your paper a context and seeing how it fits in with previous research in the field. The first few paragraphs of your introduction can be based on a historical narrative, from the very first research in the field to the current day.
The entire introduction should logically end with the research question. The reader, by the end of the introduction, should know exactly what research issue you are trying to survey with your paper.
Here’s a tabular format of the introduction for your survey paper:
|Background and Objective||– Objective: Explore advancements in extracting meaningful leaf disease features through image processing techniques.|
|– Importance: Significance of the problem and its relevance in the image processing community.|
|Terminologies and Definitions||– Introducing key terminologies used in the research domain and defining their significance.|
|Scope and Focus||– Defining the scope of the survey paper and narrowing down to the core research problem.|
|Historical Narrative||– Presenting a historical perspective, tracing the evolution of image processing techniques for leaf disease identification.|
|Setting the Context||– Establishing the context of the survey paper within the broader landscape of previous research in the field.|
|Creating ‘Leads’ for Further Exploration||– Emphasizing the aim to provide readers with insights that encourage them to delve deeper into the research domain.|
|Research Question||– Concluding the introduction with a clear statement of the central research question to be addressed in the survey paper.|
|– Guiding readers towards an exploration of recent advances in diseased part extraction using Soft computing techniques.|
Please note that the actual sub-section headings and their order may vary depending on the specific content and focus of your survey paper. The table above provides a general structure based on the information given in the previous response.
Example of an Introduction:
In this survey paper, we aim to explore the advancements made in extracting meaningful leaf disease features through image processing techniques. Over the years, this intricate problem has garnered significant attention from the image-processing community. Our objective is to provide a comprehensive overview of the diverse approaches employed by various researchers to address this challenge successfully.
To set the context, we will introduce the key terminologies used in this research domain and define their significance. As we delve into the topic, we will progressively narrow down the scope, focusing on the core research problem of extracting diseased parts from leaf images.
Throughout the introduction, we will present a historical narrative, tracing the evolution of image processing techniques for leaf disease identification from their inception to the current state-of-the-art methodologies.
By adopting this approach, we aim to give readers a clear and concise understanding of the research landscape in this field. As we progress, we will create ‘leads’ that encourage readers to delve deeper into the intricacies of diseased part extraction.
By the end of this introduction, readers will have a definitive grasp of the central research question we address in this survey paper. We will culminate with a concise statement of the research issue, guiding readers towards an exploration of recent advances in diseased part extraction using prominent Soft computing techniques, specifically Neural networks, Fuzzy logic, and Genetic algorithms.
Example text included under each subsection heading:
|Background and Objective||In this survey paper, we aim to explore the advancements made in extracting meaningful leaf disease features through image processing techniques. Over the years, this intricate problem has garnered significant attention from the image processing community. Our objective is to provide a comprehensive overview of the diverse approaches employed by various researchers to address this challenge successfully.|
|Terminologies and Definitions||To set the context, we will introduce the key terminologies used in this research domain and define their significance. As we delve into the topic, we will progressively narrow down the scope, focusing on the core research problem of extracting diseased parts from leaf images.|
|Historical Narrative||Throughout the introduction, we will present a historical narrative, tracing the evolution of image processing techniques for leaf disease identification from their inception to the current state-of-the-art methodologies. By adopting this approach, we aim to give readers a clear and concise understanding of the research landscape in this field.|
|Setting the Context||We will establish the context of the survey paper within the broader landscape of previous research in the field. This will highlight the significance of the problem and its relevance in the image processing community.|
|Creating ‘Leads’ for Further Exploration||Our intention is to create ‘leads’ for readers, encouraging them to delve deeper into the intricacies of diseased part extraction. By providing insights into the historical developments and the current state of the art, readers will be motivated to explore the diverse approaches employed by researchers in this area.|
|Research Question||By the end of this introduction, readers will have a definitive grasp of the central research question we address in this survey paper. We will culminate with a concise statement of the research issue, guiding readers towards an exploration of recent advances in diseased part extraction using prominent Soft computing techniques, specifically Neural networks, Fuzzy logic, and Genetic algorithms.|
For more details on writing the introduction section, you can visit my blog post on: How to Write an Effective Research Paper Introduction in 03 easy steps?
5. The “Literature Review” of the Survey Paper
The Literature Review has to be based on the specific theme of research which will help the reader in focusing his/her research on specific concepts. In this section, the provided details themes serve as a foundation for your Survey Paper. To create a comprehensive survey paper, it is essential to extend each theme with detailed analysis, research gaps, in-depth block diagrams, functioning descriptions, comparative analysis, and other relevant elements. By thoroughly exploring and analyzing the existing literature, you can enrich the survey paper with critical insights, identify research challenges, and provide valuable contributions to the field. Your thorough examination will contribute to a complete and well-rounded survey paper on the chosen topic.
Some possible themes can be:
i. Complexity of the problem:
There can be various types of solutions for a given problem domain and the author has to organize them in the increasing level of complexity or scale.
Literature Review: Complexity of Scene Analysis in Image Processing
In the field of Image Processing, scene analysis emerges as a core problem, where researchers seek to extract meaningful information from visual data. The solutions for scene analysis can range from simple grayscale images with few objects against a constant background to complex images with multiple objects of varying shapes and colours against diverse backgrounds.
At the basic level of complexity, researchers have explored methods for segmenting simple grayscale images containing only one or two objects of identical shapes against a uniform background. Early studies in the literature focused on techniques like thresholding and edge detection to identify and distinguish objects. (Reference: Smith et al., 2005; Johnson and Brown, 2008)
Moving up the complexity ladder, the literature presents solutions for scenes with multiple objects of different shapes but with a consistent background. Researchers have proposed methods such as region growing and contour tracing to extract relevant objects from such images. (Reference: Lee and Kim, 2010; Chen et al., 2012)
As the complexity further increases, scene analysis encompasses images with diverse objects of varying shapes and colours set against complex backgrounds. In these cases, advanced algorithms like Convolutional Neural Networks (CNNs) and deep learning techniques have been deployed to achieve accurate and robust object recognition. (Reference: Wang et al., 2016; Zhang and Li, 2018).
Additionally, research has expanded into real-world scenarios, where scene analysis encounters challenges such as occlusions, illumination variations, and cluttered backgrounds. Addressing these complexities, the literature explores techniques like scale-invariant feature transform (SIFT) and histogram of oriented gradients (HOG) to handle object detection and recognition in challenging environments. (Reference: Liu et al., 2019; Park and Lee, 2020)
The progression of complexity in scene analysis solutions reveals the evolution of Image Processing techniques to accommodate real-world challenges. By organizing the literature review based on the increasing level of complexity, this survey paper aims to assist readers in understanding the advancements made in addressing scene analysis across diverse image scenarios.
ii. Static vs. Dynamic:
Many fields can be organized by static techniques, dynamic techniques, and even hybrid. For example Static or Dynamic Routing in Computer Networking.
Literature Review: Static vs. Dynamic Resource Allocation in Cloud Computing
Resource allocation is a critical aspect of Cloud Computing, ensuring efficient utilization of computational resources to meet user demands and optimize system performance. The literature reveals two prominent approaches to resource allocation in Cloud Computing: Static and Dynamic allocation.
Static Resource Allocation involves allocating resources based on predetermined configurations and user-defined policies. Researchers have proposed various static resource allocation algorithms, such as Round-Robin and First-Come-First-Serve (FCFS), to allocate resources in a fixed manner without considering varying resource demands (Reference: Smith et al., 2015; Johnson and Brown, 2017).
In contrast, Dynamic Resource Allocation refers to adaptive resource allocation that adjusts resources in real-time based on changing workload conditions. Dynamic resource allocation algorithms, such as Elastic Load Balancing and Auto-scaling, continuously monitor resource usage and adjust allocations to optimize performance and maintain service-level agreements (Reference: Lee and Kim, 2018; Chen et al., 2020).
The literature also explores hybrid resource allocation techniques that combine elements of both static and dynamic approaches. Hybrid approaches aim to strike a balance between the predictability of static allocation and the responsiveness of dynamic allocation. For instance, researchers have proposed a hybrid approach that initially uses static allocation for steady-state workloads but switches to dynamic allocation during periods of sudden resource demand spikes (Reference: Wang et al., 2019; Zhang and Li, 2021).
By analyzing the literature on static, dynamic, and hybrid resource allocation techniques in Cloud Computing, this survey paper provides readers with insights into the trade-offs between these approaches. Additionally, it highlights the evolution of resource allocation strategies and their impact on cloud performance, cost efficiency, and scalability.
Please note that the references used in this example are imaginary.
iii. Segregating the Design Space:
Many systems are made up of components, so maybe for a computer network paper, the author could divide the problems into a physical layer, application layer, session layer, transport layer, data link layer and physical layer.
Literature Survey: Segregating the Design Space in AI-based Recommender Systems
In recent years, Artificial Intelligence (AI) has driven significant advancements in recommender systems, revolutionizing personalized recommendations across various domains. To comprehensively analyze the design space of AI-based recommender systems, researchers have categorized these systems into different architectural layers, each responsible for specific aspects of recommendation.
At the Data Collection and Preprocessing Layer, researchers focus on gathering and preprocessing vast amounts of user data to build comprehensive user profiles. Techniques like collaborative filtering and content-based filtering have been explored to analyze user preferences and item characteristics (Reference: Smith et al., 2022; Johnson and Brown, 2023).
Moving up the architectural layers, the Feature Engineering and Representation Learning Layer aims to extract meaningful features and embeddings from the data. Deep Learning models like Neural Collaborative Filtering (NCF) and Transformer-based architectures have gained attention for their ability to learn rich representations from user-item interactions (Reference: Lee and Kim, 2021; Chen et al., 2022).
The Recommendation Algorithm Layer is responsible for developing algorithms that generate personalized recommendations based on user preferences. Recent advancements include hybrid recommendation techniques that combine collaborative and content-based filtering, as well as reinforcement learning approaches for sequential recommendation tasks.
At the Interpretability and Fairness Layer, researchers focus on ensuring transparency and fairness in recommender system outputs. Explainable AI (XAI) techniques and fairness-aware recommendation algorithms have been studied to provide users with insights into the reasons behind recommendations and mitigate potential biases (Reference: Wang et al., 2022; Zhang and Li, 2023).
The Deployment and Evaluation Layer involves the real-world implementation of AI-based recommender systems. Researchers investigate methods to deploy models at scale while considering resource constraints and latency requirements. Moreover, the evaluation of recommender systems includes metrics such as accuracy, diversity, and serendipity to assess overall performance and user satisfaction.
By segregating the design space of AI-based recommender systems into these distinct architectural layers, this survey paper aims to provide readers with an organized understanding of the state-of-the-art approaches in personalized recommendations. These architectural layers serve as a structured framework for exploring the latest advancements and challenges in AI-driven recommendation technologies.
Please note that the references used in this example are imaginary
iv. Major Approaches in a Specific Domain:
Every domain usually has two to three major classes on which all the issues in that domain are addressed or the advancements in that domain are identified.
i) In Software Testing: Black box or White box testing
ii) In Networking: Wired or Wireless Networking and
iii) In Image processing Spatial or Temporal based Image Processing etc.
iv) In Telcom it is 2G, 3G,4G and 5G etc.
Literature Survey: Major Classes in Telecommunication Technology – 2G, 3G, 4G, and 5G
The domain of Telecommunication Technology has witnessed significant advancements over the years, leading to the emergence of major classes based on different generations of mobile communication systems. This survey paper aims to explore and analyze the key characteristics and advancements of each major class – 2G, 3G, 4G, and 5G.
At the inception of mobile communication, 2G (Second Generation) technology marked the transition from analog to digital communication. Researchers have extensively studied various 2G technologies, including GSM (Global System for Mobile Communications) and CDMA (Code Division Multiple Access) to provide basic voice and text messaging services (Reference: Smith et al., 2016; Johnson and Brown, 2018).
The evolution of mobile communication led to the introduction of 3G (Third Generation) technology, which brought about significant improvements in data transfer capabilities. With 3G technologies like UMTS (Universal Mobile Telecommunications System) and EV-DO (Evolution-Data Optimized), researchers explored higher data speeds, enabling services like mobile internet browsing and video streaming.
Subsequent advancements led to the deployment of 4G (Fourth Generation) technology, revolutionizing the mobile communication landscape. LTE (Long-Term Evolution) and WiMAX (Worldwide Interoperability for Microwave Access) are examples of 4G technologies that offered high-speed data transfer, low latency, and enhanced network capacity (Reference: Lee and Kim, 2020; Chen et al., 2022).
Currently, the telecommunications industry is witnessing the widespread adoption of 5G (Fifth Generation) technology, promising even more transformative capabilities. Researchers have delved into mmWave (millimeter-wave) frequencies, Massive MIMO (Multiple-Input Multiple-Output), and Network Slicing, enabling ultra-fast data speeds, low latency, and supporting the Internet of Things (IoT) (Reference: Wang et al., 2022; Zhang and Li, 2023).
This survey paper aims to provide readers with a comprehensive understanding of the major classes in Telecommunication Technology – 2G, 3G, 4G, and 5G. By analyzing the key characteristics and advancements of each generation, researchers can gain insights into the technological progression that has shaped modern mobile communication systems.
Please note that the references used in this example are imaginary
v. History of Development:
Some research domains like Cloud Computing, Big Data Management, Mobile Technology, Television technology etc. are linear. Such developments can be explained in chronological order.
One can find various options for a selected domain of research and it is this organization that is the challenging part of writing a survey paper.
The following points are to be elaborated for each paper which is surveyed as a part of writing the survey paper.
– What are you going to tell about the paper under consideration?
– Research direction of the paper
– Methods, mathematical modelling and approach or algorithms used to solve the problem: eg. Fuzzy logic, Gaussian process, neural network etc.
– Whether the paper considers theoretical issues of the concept or solves any application using the concept?
– Is the paper considered the continuation of another work? is it an improvement on another work?
– How validation of work is done i)through theoretical proofs? ii) simulation? iii)hardware test bed? or iv) real-life deployment?
– How is the work compared with other methods? and under what circumstances does the method under consideration perform better?
-On what parameters the paper under consideration stands apart from other papers like i)higher performance? ii) higher robustness? iii) lower computational complexity?
The author of each survey paper must be acknowledged by citing the paper referred to. In your survey do indicate the author names as well: Graham and Bell  have identified the importance of training, Patric et.al.  developed a simple methodology etc.
There are two reasons for this. One is the gratitude towards the authors, to whose work you are referring. Second, your reader will come to know the core people in the area in which he intends to carry out his future research. It is always good to mention in which particular country/University/Lab the work was carried out.
Survey Paper: Advancements in Cloud Computing: A Chronological Perspective
Cloud Computing has emerged as a transformative technology, revolutionizing the way computing resources are provisioned and utilized. This survey paper aims to explore the chronological advancements in Cloud Computing, focusing on key research directions, methodologies, and comparisons with other methods.
Paper 1: “Virtualization in Cloud Computing” – John et al. 
- In this paper, John et al. present the concept of virtualization and its application in Cloud Computing.
- Research Direction: The paper emphasizes the benefits of virtualization in enabling multi-tenancy and resource isolation in cloud environments.
- Methods: The paper discusses various virtualization techniques, including full virtualization and para-virtualization, to optimize performance and resource utilization.
- Application: The paper showcases how virtualization facilitates the seamless deployment of multiple applications on a shared physical infrastructure.
Paper 2: “MapReduce: Simplified Data Processing on Large Clusters” – Dean and Ghemawat 
- This influential paper introduces the MapReduce programming model for the efficient processing of large-scale data in Cloud Computing.
- Research Direction: The paper focuses on distributed data processing, fault tolerance, and scalability in Cloud environments.
- Methods: The MapReduce paradigm leverages parallelization and fault tolerance to process massive datasets.
- Application: The paper demonstrates how MapReduce can efficiently perform data-intensive tasks, such as web indexing and log processing.
Paper 3: “Machine Learning as a Service (MLaaS) in Cloud Computing” – Smith and Patel 
- In this work, Smith and Patel explore the concept of providing Machine Learning capabilities as a service in the Cloud.
- Research Direction: The paper delves into the integration of Machine Learning algorithms in Cloud platforms to enable MLaaS.
- Methods: The paper discusses various ML algorithms, including neural networks and decision trees, used for predictive analytics.
- Application: The paper showcases real-life deployments of MLaaS for applications like fraud detection and sentiment analysis.
Paper 4: “Serverless Computing: The Next Paradigm Shift in Cloud Architecture” – Lee et al. 
- Lee et al. present the concept of serverless computing and its potential impact on Cloud architecture.
- Research Direction: The paper explores the benefits of serverless computing in terms of cost-efficiency and scalability.
- Methods: The paper explains the use of Function as a Service (FaaS) to deploy event-driven applications without managing server infrastructure.
- Application: The paper highlights the practical applications of serverless computing for IoT data processing and real-time data analytics.
Through theoretical analysis and simulation studies, these papers validate their proposed methodologies and demonstrate the effectiveness of their approaches. Each work compares its method with existing techniques, highlighting higher performance and lower computational complexity in specific scenarios (Reference: Johnson and Brown , Chen et al. ).
The acknowledgement of the authors’ contributions is essential to show gratitude and establish recognition within the research community. John et al.  conducted their work at the University of XYZ, while Lee and Patel  carried out their research at ABC Labs.
This chronological survey paper aims to provide readers with a comprehensive understanding of the evolution of Cloud Computing, the core research directions, and the key contributors in this domain.
Please note that the references used in this example are imaginary
6. The “Conclusion” Section of the Survey Paper
The conclusion must answer the queries presented by your survey goals and objectives. The conclusion must be written in an interesting yet academic manner. No emotions should be attached to your conclusions but a commentary in the third person is required. Being the final portion of your survey paper, the conclusion serves as the researcher’s final say on the subject of the survey.
The conclusion must be a synthesis of the survey results with
i) an interpretation of each result
ii) the proposal of a course of action based on the result and
iii) a solution to the issues that emerged from the survey.
The tone of the conclusion should match that of the results and the rest of the data collection process. The conclusion should be able to wrap up the entire survey from the formulation of survey goals up to the satisfaction of such objectives.
After roughly two decades of research on leaf image analysis for pathological issues, many elements of pathological issues associated with leaves are well understood. In particular, accurate and efficient algorithms for leaf-diseased spot extraction are now well known.
As a result, during the past few years, we have seen the focus turn from the fundamentals of disease spot extraction to more difficult problems such as, the type of the leaf disease and the stage of the leaf disease. Few algorithms in this context are available. However, a comprehensive evaluation and comparison of these more advanced algorithms has yet to be done.
One of our goals in this review is to consolidate existing quantitative results and to carry out comparative analyses. We believe that much of the leaf image analysis for pathological work in the coming decade should and will be bolstered by more complete quantitative performance evaluations. The recent article by Wimar  is a promising first step.
Perhaps the most practically significant advance in the last decade has been the appearance of machine learning algorithms. However current implementation of Machine learning algorithms is still relatively simplistic. More demanding potential applications require algorithms to be very precise and reliable. This remains a challenging research topic that we predict will see progress in the coming decade.
For further details on writing the conclusion section, you can visit my blog post on : Art of Writing Conclusion Section to your Research Paper
Before We Close….
While writing a blog post, I realized I couldn’t cover everything about crafting survey papers. That’s where “WRITING LITERATURE SURVEY PAPER: A STEP BY STEP GUIDE” comes in. It’s an incredible book that simplifies the writing process using a single example, “The Role of Artificial Intelligence in Healthcare Diagnostics.” Imagine having a friendly mentor explaining each step. Although a blog post can’t teach everything, this book breaks it down so you can become a survey paper writing expert.
A survey paper plays a crucial role in providing readers with a systematic overview of existing research, methodologies, and advancements in a specific field. By adhering to the outlined steps, the survey paper can effectively convey valuable insights and contribute to the understanding of the chosen research domain.
Frequently Asked Questions:
How many references do I need for 2000 words survey paper?
At least 10 quality publications need to be referred for a 2000-word survey paper.
Whether survey papers are counted for the Ph.D. submission?
No. Survey papers are not counted for Ph.D. thesis submission.
Whether Scopus indexed journals accept survey papers for publication?
Yes. Many Scopus-indexed journals accept high-quality survey papers for publication.
What is the difference between a Survey Paper and a Review Paper?
In short, a Survey Paper provides a comprehensive summary of existing research on a specific topic, presenting the state-of-the-art and research trends. On the other hand, a Review Paper offers a critical evaluation and analysis of the literature, identifying gaps and suggesting future research directions.