The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

What is JavaScript and what does it do? Before you start learning something new, it’s important to understand exactly what it is and what it does. This is especially useful when it comes to mastering a new programming language. In simple terms, JavaScript is a programming language used to make websites interactive. If you think about the basic makeup of a website, you have HTML, which describes and defines the basic content and structure of the website, then you have CSS, which tells the browser how this HTML content should be displayed—determining things like color and font. With just HTML and CSS, you have a website that looks good but doesn’t actually do much. JavaScript brings the website to life by adding functionality. JavaScript is responsible for elements that the user can interact with, such as drop-down menus, modal windows, and contact forms. It is also used to create things like animations, video players, and interactive maps. Nowadays, JavaScript is an all-purpose programming language—meaning it runs across the entire software stack. The most popular application of JavaScript is on the client side (aka frontend), but since Node.js came on the scene, many people run JavaScript on the server side (aka backend) as well. When used on the client side, JavaScript code is read, interpreted, and executed in the user’s web browser. When used on the server side, it is run on a remote computer. You can learn more about the difference between frontend and backend programming here. JavaScript isn’t only used to create websites. It can also be used to build browser-based games and, with the help of certain frameworks, mobile apps for different operating systems. The creation of new libraries and frameworks is also making it possible to build backend programs with JavaScript, such as web apps and server apps. Is it still worth learning JavaScript in 2021? The world of web development is constantly moving. With so many new tools popping up all the time, it can be extremely difficult to know where you should focus your efforts. As an aspiring developer, you’ll want to make sure that what you’re learning is still relevant in today’s industry. If you’re having doubts about JavaScript, it’s important to know that, since its creation in 1995, JavaScript is pretty much everywhere on the web—and that’s not likely to change any time soon. According to the 2020 StackOverflow developer survey, JavaScript is the most commonly used programming language for the eighth year in a row. It is currently used by 94.5% of all websites and, despite originally being designed as a client-side language, JavaScript has now made its way to the server-side of websites (thanks to Node.js), mobile devices (thanks to React Native and Ionic) and desktop (courtesy of Electron). As long as people are interacting with the web, you can assume that JavaScript is highly relevant—there’s no doubt that this is a language worth knowing! With that in mind, let’s look at some of the key benefits of becoming a JavaScript expert. Why learn JavaScript? The most obvious reason for learning JavaScript is if you have hopes of becoming a web developer. Even if you haven’t got your heart set on a tech career, being proficient in JavaScript will enable you to build websites from scratch—a pretty useful skill to have in today’s job market! If you do want to become a web developer, here are some of the main reasons why you should learn JavaScript: JavaScript experts are versatile JavaScript is an extremely versatile language. Once you’ve mastered it, the possibilities are endless: you can code on the client-side (frontend) using Angular and on the server-side (backend) using Node.js. You can also develop web, mobile, and desktop apps using React, React Native, and Electron, and you can even get involved in machine learning. If you want to become a frontend developer, JavaScript is a prerequisite. However, that’s not the only career path open to you as a JavaScript expert. Mastering this key programming language could see you go on to work in full-stack development, games development, information security software engineering, machine learning, and artificial intelligence—to name just a few! Ultimately, if you want any kind of development or engineering career, proficiency in JavaScript is a must. JavaScript experts are in-demand (and well-paid) JavaScript is the most popular programming language in the world, so it’s no wonder that JavaScript is one of the most sought-after skills in the web development industry today. According to the Devskiller IT Skills and Hiring Report 2020, 72% of companies are looking to hire JavaScript experts. Enter the search term “JavaScript” on job site Indeed and you’ll find over 40,000 jobs requiring this skill (in the US). Run the same search on LinkedIn and the results are in excess of 125,000. At the same time, the global demand for JavaScript seems to outweigh the expertise available on the market. According to this 2018 HackerRank report, 48% of employers worldwide need developers with JavaScript skills, while only 42% of student developers claim to be proficient in JavaScript. And, in their most recent report for 2020, HackerRank once again reports that JavaScript is the most popular language that hiring mangers look for in a web developer candidate. Not only are JavaScript experts in demand—they are also well-paid. In the United States, JavaScript developers earn an average yearly salary of $111,953 per year. We’ve covered this topic in more detail in our JavaScript salary guide, but as you can see, learning JavaScript can really boost your earning potential as a developer. JavaScript is beginner-friendly Compared to many other programming languages, JavaScript offers one of the more beginner-friendly entry points into the world of coding. The great thing about JavaScript is that it comes installed on every modern web browser—there’s no need to set up any kind of development environment, which means you can start coding with JavaScript right away! Another advantage of learning JavaScript as your first programming language is that you get instant feedback; with a minimal amount of JavaScript code, you’ll immediately see visible results. There’s also a huge JavaScript community on sites like Stack Overflow, so you’ll find plenty of support as you learn. Not only is JavaScript beginner-friendly; it will also set you up with some extremely valuable transferable skills. JavaScript supports object-oriented, functional, and imperative styles of programming—skills which can be transferred to any new language you might learn later on, such as Python, Java, or C++. JavaScript provides a crucial introduction to key principles and practices that you’ll take with you throughout your career as a developer. Should you learn plain JavaScript first or can you skip to frameworks and libraries? When deciding whether or not to learn JavaScript, what you’re really asking is whether or not you should learn “vanilla” JavaScript. Vanilla JavaScript just means plain JavaScript without any libraries or frameworks. Let’s explore what this means in more detail now. What is meant by vanilla JavaScript, libraries, and frameworks? If you research the term “vanilla JavaScript”, you might run into some confusion; however, all you need to know is that vanilla JavaScript is used to refer to native, standards-based, non-extended JavaScript. There is no difference between vanilla JavaScript and JavaScript—it’s just there to emphasize the usage of plain JavaScript without the use of libraries and frameworks. So what are libraries and frameworks? JavaScript libraries and frameworks both contain sets of prewritten, ready-to-use JavaScript code—but they’re not the same thing. You can think of a framework as your blueprint for building a website: it gives you a structure to work from, and contains ready-made components and tools that help you to build certain elements much quicker than if you were to code them from scratch. Some popular JavaScript frameworks include Angular, React, Vue, and Node.js. Frameworks also contain libraries. Libraries are smaller than frameworks, and tend to be used for more specific cases. A JavaScript library contains sets of JavaScript code which can be called upon to implement certain functions and features. Let’s imagine you want to code a particular element into your website. You could write, say, ten lines of JavaScript from scratch—or you could take the condensed, ready-made version from your chosen JavaScript library. Some examples of JavaScript libraries include jQuery, Lodash, and Underscore. The easiest way to understand how frameworks and libraries work together is to imagine you are building a house. The framework provides the foundation and the structure, while the library enables you to add in ready-made components (like furniture) rather than building your own from scratch. You can learn more about the relationship between languages and libraries in this post explaining the main differences between JavaScript and jQuery. For now, let’s go back to our original question: How important is it to learn vanilla JavaScript? Should you learn vanilla JavaScript first? When it comes to learning JavaScript, it can be tempting to skip ahead to those time-saving frameworks and libraries we just talked about—and many developers do. However, there are many compelling arguments for learning plain JavaScript first. While JavaScript frameworks may help you get the job done quicker, there’s only so far you can go if you don’t understand the core concepts behind these frameworks. Frontend developer Abhishek Nagekar describes how not learning vanilla JavaScript came back to bite him when he started learning the JavaScript frameworks Node and Express: “As I went to write more and more code in Node and Express, I began to get stuck at even the tiniest problems. Suddenly, I was surrounded with words like callbacks, closures, event loop and prototype. It felt like I got a reintroduction to JavaScript, but this time, it was not a toddler playing in its cradle, it was something of a mysterious monster, challenging me on every other step for not having taken it seriously.” The above Tweet references a long-running joke within the developer community, and although it dates way back to 2015, it’s still highly relevant today. If you want to become a developer who can innovate, not just execute, you need to understand the underlying principles of the web—not just the shortcuts. This means learning vanilla JavaScript before you move on to frameworks. In fact, understanding plain JavaScript will help you later on when it comes to deciding whether to use a framework for a certain project, and if so, which framework to use. Why Study JavaScript? JavaScript is one of the 3 languages all web developers must learn: 1. HTML to define the content of web pages 2. CSS to specify the layout of web pages 3. JavaScript to program the behavior of web pages Learning Speed In this tutorial, the learning speed is your choice. Everything is up to you. If you are struggling, take a break, or re-read the material. Always make sure you understand all the "Try-it-Yourself" examples. The only way to become a clever programmer is to: Practice. Practice. Practice. Code. Code. Code ! Commonly Asked Questions How do I get JavaScript? Where can I download JavaScript? Is JavaScript Free? You don't have to get or download JavaScript. JavaScript is already running in your browser on your computer, on your tablet, and on your smart-phone. JavaScript is free to use for everyone.

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An e-commerce website selling it products, built with React, Tailwind CSS. here One can buy it products. If have any confusion about what product is suitable then we provide consultation service. One can book schedule and discuss about her needs.

Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.

Confusion Restaurant using React, redux, reactstrap and json-server

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React confusion Restaurant is coursera course project about a restaurant management system which is made up by React, Redux, JavaScript, Bootstrap and Css

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Website of Ristorante con fusion using React

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Json Server from the Confusion-React project

Front end react app that works together with the confusion-rest-api.

Coursera Native App..Confusion

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coursera full-stack module2

React beginner project. Demonstrates the more advanced usage of react and redux for various purposes and managing state in the web application.

Source Code for Reactjs course on Coursera. Includes all code for assignments and lesson follow along.

Reactjs Website

:octocat: Confusion React App for the Hong Kong University Coursera course of Front End Development with React.

Single Page Application, **MERN**

This is the final version of Restaurant Confusion, integrated and configured with firebase

Javascript Development App and React Library.

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