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Top 10 Rules to identify keywords for your Research Paper | Research Voyage

Top 10 Rules to Identify Keywords for your Research Paper

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With  humongous volume of  data  available on internet,  gleaning the appropriate documents  from the searched documents has become a great challenge.  To  explore  the articles we want, we use search engines and type in keywords that cut down the total results. Keywords, therefore, are vital for filtering the abundant amount of resources available. Keywords are one of the parameters  used for searching an article in a database or a search engine, that in turn retrieves a bunch of results ranked according to relevancy. The appearance of the document higher in the order is directly proportional to the number of  relevant keywords used in that document. Then how to make our research paper rank higher in the search results and not lost in the ocean of the documents stored on the net? The answer is of course choosing the right and relevant key words. The purpose of keywords in a research article  is to help other researchers find your paper when they are conducting a search on the topic. In a research article, abstracts are usually followed by a list of keywords selected by the author. Picking the apt keywords is crucial, because these are used for indexing purposes. Well-picked keywords help your article  to be more easily searched and cited.  Keywords define the domain, sub domain, topic, research objective, etc. that are covered by the article. Most of the search engines, citation indexing databases, or journal websites refer keywords to decide where and when to display your article to the researchers who are searching articles related to your article. Keywords make your article easily  searchable and ensure that your article gets more citations.  Hence it is essential to include and select relevant keywords and filter-out the large body of unwanted material.

Let us take an example to see why keywords are useful. A paper titled ” New  approaches Leaf Image processing”  describes, how some  Machine Learning Algorithms will help in  identifying the leaf spot diseases  like Bacterial Blight at the earlier stage using Image Processing techniques.  However, neither the title mentions this, nor does it mention the names of those Machine Learning Algorithms. Suitable keywords for such a paper will include i) Machine Learning Algorithms like Support Vector Machine, Reinforcement Learning  ii) Image Processing steps like Image Segmentation, Image Clustering etc and iii) Leaf Spot disease names Bacterial Blight, Anthracnose etc. Search for any of those names will lead a  researcher to this paper.

Here are a few tips that will help you create relevant and effective keywords for your paper:

  1. Start thinking about the terms you use to search for papers related to your topic. Possibly these are the terms used by other researchers for searching the topic of your interest. These terms can be ideal keywords for your Research Article.

  2. Most of the times it is assumed that keyword means a single word, however as per the research  Search Engine users are becoming more specific and they have understood  that a single keyword is probably going to be too broad of a search to return the articles  they’re looking for. A good example is what happens when you do a search for  security. You might be in need of  articles written on  network security for cloud computing  but doing a quick search on Google with the keyword security gives you results as varied as  articles on security article in Wikipedia, Security of nation , the Social Security number,  security jobs in your local area or a recently released movie with title “security”. Using the keyword phrase  network security for cloud computing on the other hand, turns up a couple of research articles about  network security for cloud computing. keywords should ideally be phrases of 2-3 words; No doubt you can give single word keyword but it may lead to many false matches. Note that actually keywords are not simply set of words instead they are phrases.

    Examples : Soft Computing for Leaf Image Analysis, Support Vector Machine for Fruit quality Identification, Neural Network for Leaf Image Extraction

  3. Identify the generally used alternate terms for the words written in your title. That is, include significant abbreviations, acronyms, and other short-form or substitute names for your paper. But care should be taken while using acronyms that may have other meanings. WWW would be a abbreviation since most hits would relate to the Internet. Similarly other abbrevations like   i) SVM for Support Vector Machine, ii) NN for Neural Network and iii) Soft Computing for Neural Network , Fuzzy Logic and Genetic Algorithm. can be used

  4. Do not use words or phrases from the title as keywords. Keywords should contain a list of words that supplement your title’s content. This is  because  most of the search engines and journal databases use Research Title for indexing purpose.

    TITLE: “Leaf Image Analysis for Pathological Issues Using  Soft Computing Techniques”

    KEYWORDS: Unsupervised Neural Networks, Leaf Spot disease Identification,  Disease spot  extraction using Image Processing  .

  5. Keywords should contain words and phrases that suggest what the topic is about. Also include words and  phrases that are closely related to your topic. (For example, if the paper is about  Image Processing for Leaf Spot Identification and Disease Classification use words like  Plant Pathology, Bacterial Blight in pomegranate, Leaf Disease spot Identification etc.

  6. If your research revolves around a key method or technique, make sure the term for it is located in your keyword.Example: If the method uses Multi Spectral Camera for image capturing and processing then Keywords must contain terms like Multi Spectral Image Analysis.

  7. If the paper focuses on a particular region use that as a keyword. If the paper is on Voice Analysis of Southern Indian Community for Age Identification then  Keyword can be “Southern Indian Community”  If the paper is Pomegranate Leaf Spot Identification using Support Vector Machine and if the pomegranate considered are from Indian sub continent  then key word can be ” Pomegranate plant of Indian subcontinent“.

  8. If your article is  about developing application Check whether potential applications, issues or phenomena can serve as keywords (Smart City Construction, Plant Pathology,  Medical Image Processing for Skin Cancer, Sensor for Tsunami).

  9. The most important experimental techniques used in your article are worth considering as keywords, for example, Stereo Vision for Robot,  X-ray  analysis for pomegranate fruit etc.

  10. Some sites such as the one offered by the University of Texas(http://www.lib.utexas.edu/keywords/index.php) provide keyword generators or keyword planners to help you think of other terms you could include.

Let me show you an example of a Research Paper with Title, Abstract and set of possible Keywords:

TitleLeaf Disease Feature Identification and Extraction Using Deep Neural Network

ABSTRACT

: 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 spot disease identification has matured significantly throughout the years, and many advances image processing techniques continue to be made, allowing  new  techniques  to be applied to new and more demanding pathological problems. In this paper, a method for detection and classification of leaf spot diseases affecting Pomegranate crop  is  developed using  Deep learning Neural networks. Throughout, we  have presented tables and charts  to compare the performance of  the proposed method  with state of the art techniques. Experimental results show that  the Deep Neural Networks  handle uncertainty effectively and  they can be trained with limited data sets. The paper has also  made suggestions for  future research  directions.

Keywords :

Image Processing for plant pathology, diseased leaf spot extraction,  leaf spot disease classification, segmentation of leaf spot disease, Machine learning for Leaf disease identification, Pomegranate leaf disease identification, Pomegranate leaf disease classification.

 
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