EDUCATION
An Impressive Way to Write an Excellent Research Proposal for your College
Published
12 months agoon
By
Saad MushtaqResearch Proposal
A research proposal is an assignment usually assigned at an advanced level of study. Not only that, it is assigned only at the academic level, sometimes you need to write a research proposal when you are already into your job. A research proposal describes the work that you are going to investigate; also you need to describe why the work is that much significant and how you will conduct the research work.
If you have been assigned with the one and want to create the best research proposal, then first of all, precisely find out the purpose of writing it and then accordingly, proceed with the steps described here. While writing, make sure to keep the purpose of writing in mind and organize your ideas and thoughts.
StepbyStep Instructions on How to Write the Best Research Proposal
Most of the time research proposals are written for the sole purpose of getting funding for their projects. That is to say, it should be written addressing to any educational institution or a funding body and thus should be designed in a convincing tone.
So, go through the basic research proposal format and you will get an idea on how to create one with proper guidelines:
1. Title Page
Similar to that of a thesis or dissertation, your research proposal should have a title page that will include the title of your project, your name, the name of your supervisor, and the name of your institution or department.
If your research proposal is too long, then you also need to add a table of contents to it for proper navigation.
2. Introduction
In the introduction, you must succinctly introduce the topic of research, provide proper background and context to it, and also you should write an outline on the research questions and problem statement. You can also include a separate section on the importance of research and its objectives.
3. Literature Review
In this section, not only you are required to show that you are familiar with the research topic but also convince the reader that your review has a strong and solid foundation in your project. Here you can also compare and contrast the main theories, debates, controversies, etc. Also, you need to discuss the strengths and weaknesses of different approaches.
4. Research Design and Methodologies
After writing the literature review, it is important to restate your main objectives. Also, you need to answer your research questions in the research design and methodology section. You need to also make an argument that is more valid and appropriate for answering your questions.
5. Inference and Contribution
To complete your research proposal on a strong note, not only you need to explore the research implications for theory or practice. Also, there will be scientific assumptions and strengthening a model or a theory. Moreover, you can also create a basis for further research and improve processes in a specialized field or location.
Are you facing problems in any part of your research proposal? Then the best way is to approach a reliable paper writer service.
6. Bibliography or Reference List
Your research proposal might also include a reference list as well as a bibliography section with proper citations. For every source that you will include in your research proposal, it should be properly cited so that the readers could find it credible enough.
In addition to those stated above, you must include a detailed timeline of the project along with its requirements and last but not least, proofread and edit your research proposal before submission. It is necessary important to check its structure and academic style for a better chance of approval.
Saad Mushtaq was born and raised in the busy city of Abbottabad. As a journalist, Saad Mushtaq has contributed to many online publications including the PAK Today and the Huffing Post. In regards to academics, Saad Mushtaq earned a degree in business from the Abbottabad UST, Havelian. Saad Mushtaq follows the money and covers all aspects of emerging tech here at The Hear Up.Thanks
EDUCATION
How to Prepare for Class 8 Maths Exams from NCERT Maths Book?
Published
2 weeks agoon
September 3, 2021Mathematics is not an easy subject if you are not familiar with the concepts completely. Therefore, for a student, it is immensely important to do maths every day. Regular studies are the first step to scoring well in the exam. However, you can always take additional help from the NCERT maths books.
NCERT Books can be a great help as they offer exercises to solve, easy explanations, ncert maths book class 8 solutions pdf free download, and more. Referring to these books after your child has completed the said chapter in the textbook can help gain a deeper insight. Now, before you start preparing for the exam, it is extremely important to understand the number of chapters present in the syllabus and do them one by one.
Syllabus of Class 8 Maths Exams
Topic: Rational Numbers
Introduction
Properties of Rational Numbers
Representing Rational Numbers on the Number Line
Rational Number between Two Rational Numbers
Topic: Linear Equations in One Variable
Introduction to linear equations
Solving Equations
Some Applications
Solving Equations where there are Variable on either side
Some More Applications
Reducing Equations to Simpler Form
Equations Reducible to the Linear Form
Topic: Understanding Quadrilaterals
Introduction
Polygons
A few Measures of the Exterior Angles of a Polygon
Kinds of Quadrilaterals
Some Special Parallelograms
Topic: Practical Geometry
Introduction
Constructing a Quadrilateral
Some Special Cases
Topic: Data Handling
Looking for Information
Organising Data
Grouping Data
Circle Graph or Pie Chart
Chance and Probability
Topic: Squares and Square Roots
Introduction
Properties of Square Numbers
Some More Interesting Patterns
Finding the Square of a Number
Square Roots
Square Roots of Decimals
Estimating Square Root
Topic: Cubes and Cube Roots
Introduction
Cubes
Cubes Roots
Topic: Comparing Quantities
Recalling Ratios and Percentages
Looking for the Increase and Decrease Percent
Finding Discounts
Prices That Are Related to Buying and Selling i.e.Profit and Loss
Sales Tax/Value Added Tax/Goods and Services Tax
Compound Interest
Deducing a Formula for Compound Interest
Rate Compounded Annually or Half Yearly (SemiAnnually)
Applications of Compound Interest Formula
Topic: Algebraic Expressions and Identities
What are Expressions?
Terms, Factors and Coefficients
Monomials, Binomials and Polynomials
Like and Unlike Terms
Subtraction and Addition of Algebraic Expressions
Introduction to Multiplication of Algebraic Expressions
Multiplying a Monomial by a Monomial
Multiplying a Monomial by a Polynomial
Multiplying a Polynomial by a Polynomial
What is an Identity?
Standard Identities
Applying Identities
Topic: Visualising Solid Shapes
Introduction
View of 3DShapes
Mapping Space Around Us
Faces, Edges and Vertices
Topic: Mensuration
Introduction
Let us Recall
Area of Trapezium
Area of General Quadrilateral
Area of Polygons
Solid Shapes
Surface Area of Cube, Cuboid and Cylinder
The volume of Cube, Cuboid and Cylinder
Volume and Capacity
Topic: Exponents and Powers
Introduction
Powers with Negative Exponents
Laws of Exponents
Using Exponents to Express Small Numbers and convert them into Standard Form
Topic: Direct and Inverse Proportions
Introduction
Direct Proportion
Inverse Proportion
Topic: Playing with Numbers
Introduction
Numbers in General Form
Game with Numbers
Letters for Digits
Test of Divisibility
Topic: Factorisation
Introduction
What is Factorisation?
Division of Algebraic Expressions
Division of Algebraic Expressions Continued (Polynomial / Polynomial)
Can you Find the Error?
Topic: Introduction to Graphs
Introduction
Linear Graphs
Some Applications
When it comes to mathematics, skipping any chapter is never advised. However, you can make sure your child studies regularly to avoid any issues. Some tips will make studies for exams easier, read ahead to know more.
How To Study From Maths NCERT Books?
Using NCERT books is very helpful because;
 The language used in the book is very simple, meaning, your child can read it himself.
 Reputed experts and teachers with vast experience have written the books.
 NCERT books are a genuine source of information.
 NCERT books are great for understanding the concepts easily.
Start ChapterWise
One of the best ways to make use of NCERT books is to study them chapterwise. It means, your child studies the chapter in the textbook once and then do the exercises and then move on to the NCERT books. This will ensure your child is completely prepared for the exams. It is important to give equal time to both books to ensure your child is completely prepared. Solving all the problems present in the book means more practice and more knowledge.
Make A Study Schedule
A study schedule is very important when it comes to studying for the exam. Now, you can make separate study schedules for your child one for the textbook and the other for the NCERT book. Or you can have one study schedule including both. Don’t overburden your child, but make sure you cover all the chapters from both books. When it comes to Mathematics, practice makes perfect.
Focus on Challenging Chapters
Your child needs to concentrate more on difficult topics, but without taking too much time. Maths is a subject where your child can score full marks. Therefore, make sure all chapters are covered.
Sample Question Papers
Solving sample papers is another great way to make sure your child is thorough. It can also help you understand their strengths and weaknesses, and since you will have ample time before the exams, you can work on any weaknesses.
Always Clear The Doubts
If your child has any doubts regarding a concept, make sure it gets cleared immediately. You can either talk to the teacher or hire an online tutor. It is also possible to understand the concept clearly with the help of the NCERT books.
Finally, remember, make sure your child remains calm and panicfree before and during the exam as it will help him remember everything more clearly. Also, try to make learning more fun, with quizzes, interactive games, and more as this will help your child learn better.
Khalil ur Rehman is a proud born and raised in Abbottabad. Khalil has worked as a journalist for nearly a decade having contributed to several large publications including the Yahoo News and The Verge. As a journalist for The Hear Up, Khalil covers climate and science news. [email protected]
EDUCATION
All You Need To Know About Molecular Spectroscopy
Published
1 month agoon
August 11, 2021Spectroscopy involves the investigation and measurement of spectra produced due to matter interacting to give off electromagnetic radiation. In other words, it refers to the dispersion of light into component colours. It involves the interactions between molecules and electromagnetic radiation. Therefore that tells you that molecules oscillate from a lower energy level to a higher energy one and back repeatedly. In the process, the molecules absorb and radiate electromagnetic radiation. Here includes everything you need to learn about Molecular Spectroscopy.
Understand the meaning of a Molecule
A molecule is a group of positively charged atoms surrounded by a considerable number of negatively charged electrons. That explains molecular stability since there is a balance between the attractive and repulsive forces of the negatively charged electrons and positively charged nuclei. Moreover, a molecule is also characterized by the resulting energy that emerges from these interacting forces.
Types of Molecular Spectroscopy
The electromagnetic spectrum of a series of wavelengths is produced whenever a matter is exposed to any electromagnetic radiation. As a result, the Molecules will absorb a certain amount of wavelengths to the vibrational, higher electronic, and rotational energy levels. Therefore, the series of wavelengths a given molecule absorbs gives off a distinct molecular spectrum that lies in the specific region of the electromagnetic spectrum. Here are three types of molecular spectra you should know:
 Pure Rotational Spectra
Pure rotational spectra occur when a molecule absorbs the least amount of energy that compels it to transit from one rotational level to the next but within the same vibrational level. It is possible to observe rotational spectra using the spectral region of Far Infrared and Microwaves. Moreover, the energies in these types of spectral regions are very small. That’s why they are referred to as all rotational spectra, the microwave spectra.
 Vibrational Rotational Spectra
Vibrational, rotational spectra occurs when a molecule absorbs sufficient energy that causes the molecule to move from one vibrational level to the next within the same electronic level. However, in this case, both vibrational and rotational transition takes place. That is how you end up with vibrational, rotational spectra. With vibrational spectra, it is easy to observe the spectra in the NearInfrared Spectral region. That’s called vibrational, rotational spectra, the Infrared spectra.
 Electronic Band Spectra
Lastly, electronic band spectra happen when the radiation’s exciting energy is large enough to aid the successful transition from one electronic level to the next. Both rotational and vibrational level changes accompany this transition. Additionally, a set of closely spaced lines appear for each vibrational transition. These closely spaced lines are known as bands Because the corresponding rotational level changes.
Molecular Spectroscopy primarily involves the excitation of atoms and molecules using photons. They are excited by either resonant vibrations or electronic transitions based on the induced quantum mechanical changes. While vibrational transitions correspond to changes made within the molecular vibrational states, they typically appear in the infrared region. On the other hand, Electronic transitions specifically correspond to changes in the electronic state of the molecules. Moreover, it often appears in the UVvisible region.
Khalil ur Rehman is a proud born and raised in Abbottabad. Khalil has worked as a journalist for nearly a decade having contributed to several large publications including the Yahoo News and The Verge. As a journalist for The Hear Up, Khalil covers climate and science news. [email protected]
EDUCATION
Difference between an Algorithm and a Model in Machine Learning
Published
1 month agoon
August 6, 2021If you are at a crossroads, wondering which data science certification to take for a highly relevant career upgrade, opt for a Machine Learning course at a reputed institute. It teaches you all the essentials of big data analytics and steers your career path to a more focused data scientist job role as a machine learning engineer.
Before registering for the course, you may like to clear some concepts about machine learning algorithms and models. So here we are, discussing what is an algorithm and a model? What is the difference between the two?
What is Machine Learning
IBM lays down the definition of machine learning: “Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.” It provides systems with the ability to automatically and iteratively learn and improve from experience. Machine learning is also an important element of data science, where algorithms train to make classifications or predictions, to uncover insights from massive quantities of data.
Embed Youtube video URL here: https://www.youtube.com/embed/ukzFI9rgwfU
What is an Algorithm in Machine Learning
Some definitions of machine learning encapsulate the “algorithm” component of machine learning.
A McKinsey & Co. Insight states that “Machine learning is based on algorithms that can learn from data without relying on rulesbased programming.” A University of Washington paper mentions how “Machine learning algorithms figure out how to perform important tasks by generalizing from examples.”
So what is an “algorithm” in machine learning? A machine learning algorithm is a program that provides systems the ability to learn on their own and improve from experience without being explicitly programmed. Machines adjust themselves to perform better as they are exposed to more data.
What is a Machine Learning Model
A machine learning model is a file that has been trained to recognize certain types of patterns. A set of data is used and provided with an algorithm to reason and learn from the data, and this is called training the model. Once the model is trained with the given set of training data, it can be used to reason with unseen data and make predictions about that data. For example, if you want to build an application that recognizes a user’s emotions based on facial expressions. The model can be trained by providing it with images of faces tagged with a certain emotion and then used in an application that can then recognize any user’s emotion.
A machine learning model is thus a condensed representation of what a machine learning system has learned. It is similar to a mathematical function that takes a request as input data, makes a prediction on that input data, and then serves a response.
The final set of trainable parameters of a model depends on the type of model. All machine learning models are categorized as either supervised or unsupervised. Where the model is a supervised model, it is further subcategorized as either a regression or classification model.
Difference between a Machine Learning Algorithm and a Machine Learning Model
An “algorithm” creates a machine learning “model.” A “model” is the output of a machine learning algorithm. The model represents what is learned by the algorithm. The Machine Learning model is “the “thing” that is saved after running an algorithm on training data and represents the rules, numbers, and any other algorithmspecific data structures required to make predictions.”
Examples
Examples of machine learning algorithms are linear regression, decision trees, convolutional neural networks, and reinforced learning. Some examples of machine learning models are Regression Models, Clustering Models, and Dimensionality Reduction Models.
Data set
Machine learning algorithms are executed in the code and run on data. Machine learning models are the output of the algorithms and consist of the model data and a prediction algorithm.
When you train an algorithm with known data, it becomes a model.
Thus, Model = Training (of an Algorithm + Data)
Mathematical equation
Every algorithm has a mathematical underpinning, which, when executed in a machine, forms a machine learning algorithm. A model is an equation structured by discovering the parameters in the equation of the algorithm. The model specifies which family of functions the learning algorithm can choose from when varying the parameters to reduce the training objective. (Deep learning book, Goodfellow et al., 2016).
Algorithms are methods undertaken to get a task done or solve a problem, while Models are welldefined computations that are a product of an algorithm.
An algorithm takes some value as input and produces some value as output and is thus a sequence of steps with flow and loops, etc., transforming input to output.
Approach
An algorithm is an approach you take to solve a problem. The model is what you get when you run the algorithm on the training data and what you further use to make predictions on new data. A new model may be generated using the same algorithm but with different data, and a new model can be generated from the same data using a different algorithm.
Training data vs. new data
An algorithm is implemented in a programming language. An algorithm takes an input set of data and outputs an equation which is a model. Model is the output of the machine learning algorithm.
An algorithm can be used on different sets of training data. A model is then used as the deployment vehicle, which can take any unseen data in the future and produce an output prediction. That model has both data and a procedure for how to use the training data to make a prediction on new data, almost like a prediction algorithm.
Storing the entire dataset
Models are always triggered by the algorithm but not always dependent on the data. Based on the purpose that your model serves, some models like the knearest neighbors store the entire dataset, which acts as the prediction algorithm.
Interrelationship
The algorithm behind the model matters most, as it is important to know which algorithm to apply to your model to yield the best predictions and right results. If the algorithm used is right, you will likely get a good model that works well on new data sets. However, a robust algorithm does not always yield a good model, as a machine learning model strategy depends on various factors other than the algorithm.
Ultimately, an algorithm is a few lines of code that you implement after much deliberation, while a perfectly working model is dependent on many other factors other than the algorithm or the training data.
Conclusion
Ultimately, a machine learning engineer works with big data to execute algorithms and build models for predictions. And a good way to kickstart a career in machine learning is to take a certification.
Khalil ur Rehman is a proud born and raised in Abbottabad. Khalil has worked as a journalist for nearly a decade having contributed to several large publications including the Yahoo News and The Verge. As a journalist for The Hear Up, Khalil covers climate and science news. [email protected]
Search Bar
BEST OFFER
Scannable texas real id
Fashion That Makes You Smile –Anti Social Social Club TShirts
What Benefits are Offered by WordPress Theme
Trending

TECH2 years ago
Free download mp3 mp3juices fast Mp3 and Mp4

NEWS2 years ago
WhatsApp Groups

OTHER2 years ago
Best websites to download free mp3 music

NEWS2 years ago
Celebrity that you never heard of..???

ENTERTAINMENT2 years ago
How to Download Music, MP3 Addresses and MP3 Direct Songs.

NEWS2 years ago
Traits in The Bathroom That May Cause Mold Growth

OTHER2 years ago
The best Black Friday deals 2019

GAMES2 years ago
Final Fantasy VIII Remake: Yoshinori Kitase would like to see the game created by young people