Nowadays, AI is being used by everyone. And the one important group of humans that is exploiting and taking advantage of this newfound technology is the students. Besides, the one important thing students desperately want but are too afraid to ask for that AI can easily provide them with is professional assignment help.
Artificial Intelligence, or AI, is the most impactful, innovative, and cutting-edge technology of the 21st century. The development of this technology has ushered in a new technological revolution in the world. While many celebrate its creation and its constant improvements. Others panic and fear that AI will take over the world and our lives, similar to Hollywood movie portrayals.
Whether AI will actually take over or not remains to be seen. For now, AI has lots of real-world applications. This article will tell students about the AI topics they can conduct their research on.
Resume Parser AI
Recruiters have to spend a lot of time reading through hundreds of resumes, skim-reading them, and identifying potential candidates with potential skills to fill certain positions at the office. Doing so can make them extremely tired and worn out since many resumes may also have similar formats. And since one position can have 100 applicants, it only increases the difficulty level. That is why this job has also been automated.
Now all a recruiter has to do is input certain keywords, and if the resumes contain those keywords, then those applicants will be shortlisted for the job. But there is also a drawback to this. Many people have this inside knowledge. So applicants try to stuff keywords in their resumes as much as possible to get shortlisted for the job.
Students can visit the companies that use this AI, asking them for assignment help and learning how their software works. What keywords do they insert to shortlist potential candidates? Using this as inspiration and knowing the drawbacks, students can create a Resume Parser AI using machine learning language that can scan and filter resumes for the skills and experience companies require making the keyword system obsolete.
The data for this AI software is present, but it’s in text form and is in its early stages. Students should develop and work on it, it can change the recruiting process forever. It can be created using NLTK Python.
AI That Detects Fake News
Fake news is information that is false or misleading. It is spread by pages and people in clickbait form to gain likes and follows. It is usually circulated online. Sometimes fake news is also used as propaganda. Often times fake news and real news are nearly impossible to distinguish.
Also, fake news can be harmful and cause mass panic among civilians. For example, during the Covid-19 pandemic, a lot of fake news was propagated and usually spread through online platforms, causing panic among people. Often times fake news can cause economic instability as well. For this, fake news must be detected before it spreads among civilians and misleads them, and causes mass panic.
For this project, the students, using the fake and real news datasets on the Kaggle website, can build an AI that detects fake news before it reaches a large audience. Students can employ the BERT machine learning model, which is pre-trained. BERT is an open-source NLP model. For those who don’t know, NLP stands for Natural Language Processing. You can start working on this project by loading BERT into Python and adding extra output layers for this task.
Deep Learning
Deep Learning is a subtype of machine learning that processes data and makes judgments based on that data by modeling the inner workings of the human brain. In essence, machine learning is implemented via artificial neural networks through deep learning. Similar to the networks in the human brain, these neural networks are interconnected in a web-like pattern.
Artificial neural networks’ web-like structure enables them to analyze data in a nonlinear manner, which gives them a considerable edge over conventional algorithms that can only do so. For this project, students may also need professional assignment help.
Machine Learning
Artificial intelligence is used in machine learning, which enables machines to learn a task through experience without being particularly programmed for it. The learning process begins when a human feeds high-quality information to an AI but not all at once. Little by little, information is fed to it to process it. Then using the data and various techniques, train the machines by creating various machine learning models. The type of data we have and the sort of task we’re seeking to automate will influence the algorithms we use.
Translator AI
If students are eager to learn about NLP or Natural Language Processing, then they should build a translator AI app with a transformer’s help. A transformer model analyses sentences to extract features and assess the significance of each word. Both the encoding and decoding halves of a transformer are end-to-end trained. You can use a transformer to develop your own AI translator application. To accomplish this, Python can load a pre-trained transformer model. Following that, turn the text to be translated into tokens and feed those tokens to the trained model.
Using online assignment helper, students can use the library of GluonNLP for this task. The train and test datasets for this AI project can also be loaded from this library.
An AI that Detects Fake Reviews of a Product
Many businesses create fake positive reviews of their product to increase their sales. People are misled to believe that a product has good reviews and is high quality. They purchase it only to find out that they have been ripped off and, most times, cannot even return the item for a refund. In this regard, students can build an AI to combat this problem. They can build using the dataset available at Kaggle, a Google subsidiary, by the name of “Deceptive Opinion Spam Corpus” Reviews of this are labeled, making half the work for the students easier. All they have to do is a little pre-processing and another minor adjustment before teaching their model how to process this information.
An AI that Detects Objects
With this project, you can showcase your expertise in the area of computer vision. By using computer vision algorithms in the background, an object identification system may recognize different types of items that are visible in an image. For example, an image of a person sitting using an iPad should label and identify the person as human and the iPad as a computer, technology, etc. For this project, students can use the dataset of “Open Images Object Detection” on Kaggle. An open-source object identification model that has already been trained, is known as SSD. This model can recognize items like tables, chairs, and books because it was trained on a dataset of common objects called COCO.
Violence Detection in Videos
Videos that display sensitive and violent content are damaging to a person’s mental health. Such videos should be flagged with a discretion warning and censored for those persons who dislike violence.
In this project, with help from UK assignment help, students can create a deep learning model that can detect videos that contain violence. It can also help generate an automatic warning asking viewers if they want to watch the video on their own or not. You can utilize datasets with both violent and nonviolent material to train this model. You can take image frames from these videos and use them to train a CNN. You can utilize a variety of pre-trained models to complete this assignment, including VGG16, VGG19, and Resnet50.
With the help of transfer learning, people were able to acquire high accuracy scores (over 90%) for this task. Because transfer learning employs models that have already been trained on millions of general images, these models typically outperform models that are taught from scratch.
Python Sign Language Recognition App
Communicating with persons who have hearing impairments might be difficult at times. Learning sign language can be difficult, and most lack the necessary skills.
In this project, you will create a Python sign-language recognition software. To accomplish this, you must complete the following steps:
- Use the World-Level American Sign Language video dataset, which contains roughly 2000 sign language classes. To train your model, you will need to extract frames from the data.
- You can load the previously trained Inception 3D model on the ImageNet dataset.
- This can be used to produce text labels for image frames of sign language gestures.
When you’re finished constructing the model, you can choose whether or not to deploy it. It is incredibly useful to create an application that helps persons with hearing impairments communicate with others who do not know ASL. It allows two persons who would not normally speak to communicate with one another.
Price Comparison Software
You may create an app in this AI project that allows customers to upload a photo of the item they want to purchase. The app will then search many online stores for the best price on the item. In this manner, the user receives the greatest available offer.
To construct an app like this, you must first develop an algorithm that can recognize items in an image. For example, if a user uploads a photo of a floral dress, the algorithm must accurately identify the color as well as the style of the clothing. You may also need Cheap assignment help while doing this project.
For this AI project, you can utilize transfer learning to train on top of models like VGG-16 with a pre-existing collection of item descriptions. Once the model is complete, you may provide the user with the option of specifying extra information about the items, brand, and outlet, among other things.
After gathering all this data, you must create an algorithm that detects online businesses based on the brand information provided. Create an automated tool that visits these sites and scrapes pricing data from at least three to four online businesses. Then, return to the user the site name and pricing information, as well as a link to where they can purchase the item. The item description based on the image submitted by the user is the only aspect of this project that uses AI. Everything else necessitates model deployment expertise, the ability to show information fast to the user, and a solid understanding of data science programming languages.
Model for Detecting Age
When we look at someone’s face, we can typically tell what age group they are. We can identify whether someone is youthful, middle-aged, or elderly. You can automate this procedure in this AI project by developing a deep learning age detection model. Companies frequently use demographic data to better promote their products and identify their target audience. This information, however, is not always straightforward to obtain.
For starters, users of social networking networks such as Facebook frequently lie about their age. This information is frequently buried and not made public. By creating an age recognition algorithm, you can easily anticipate a person’s age based on their profile picture and avoid wasting time scraping data that hasn’t been made public.
This is simple to achieve with the OpenCV library. OpenCV is a free and open-source image processing and computer vision library. It may be used to swiftly process picture data in order to recognize persons, objects, and even handwriting. You may easily install the OpenCV library and use it with Python. DNN (Deep Neural Networks) is an OpenCV module that may be used to import models from well-known deep learning frameworks. For this assignment, you can use the Caffe framework, which has previously-trained models for age and gender.
Detecting Pneumonia Using Python
Many diseases like pneumonia, tumors, and cancer have been diagnosed and detected through the help of AI-aided software. On Kaggle, there are accessible image datasets for disease identification. On Kaggle, you can experiment with disease prediction using one of these datasets: Chest X-Ray Images (Pneumonia Detection).
This collection contains three types of tagged lung X-Ray images: normal, bacterial, and viral. Based on an X-Ray photograph of the patient’s lungs, you can create a model that categorizes their health condition into one of these three groups.
To create this model, you can utilize the FastAI Python module. FastAI is an open-source library that enables users to quickly develop and train deep learning models for a variety of tasks, including computer vision and natural language processing (NLP).
This library provides a higher level of abstraction than Keras and is relatively simple to use for beginners. FastAI can tackle a problem that takes Keras over 30 lines of code in only five lines of code.
To build the classifier, download the ResNet50 previously-trained model from FastAI and run it on top of it. ResNet50 enables us to train neural networks with over 150 layers, and training on top of it yields good results. However, this task may not be easy. Students and amateur developers may require Online assignment help.
An App that Converts Images to a Pencil Sketch
You can construct a web application that turns a picture submitted by a user into a pencil sketch in this advanced-level artificial intelligence project.
You can accomplish this by taking the following steps:
- Create a front-end application that lets users upload any image they choose. This is possible with HTML and JavaScript.
- Use Python in the backend and import OpenCV.
- OpenCV has a package that lets you convert images to grayscale, invert the color of an image, and smoothen the image to make it look like a sketch.
- Once you obtain the final image, show it to the user on the screen.
Since tools that handle picture conversion are accessible, this is a reasonably simple AI project. The more difficult element is creating a working app with which users can interact because it necessitates knowledge of languages other than Python.
Model for Recognizing Hand Gestures
Python can be used to construct a gesture recognition web application. You may do this by using Kaggle’s hand gesture recognition database. This dataset contains 20,000 gestures that have been labeled.
This dataset can be trained on VGG-16. OpenCV can also be used to capture a live stream of video data and then use the model to identify and predict hand motions in real time. You may even create your own hand gesture recognition program. Deploy your model to a server and see it make predictions as users perform various hand motions.
AI That Predicts Animal Species
You may do this with Kaggle’s Animals-10 dataset. This dataset contains 10 different animal categories: cat, dog, butterfly, sheep, elephant, and squirrel, among others. This is a multi-class classification task in which you must predict the animal’s species based on its image in the dataset. For this purpose, you can utilize the VGG-16 pre-trained model. The Keras library can be used to import this model into Python.
VGG-16 is a Convolutional Neural Net (CNN) architecture trained on ImageNet, a database of over 14 million images. It includes images of ordinary objects, fruits, automobiles, and many animal species. After importing the VGG-16 model into Python, you can train on top of it using the tagged photos from the Kaggle dataset to categorize the ten different animal types.
Conclusion
All of the topics in the list above are meant to be fun, increasing students’ and amateur researchers’ knowledge. If you start working on them, some projects may succeed, and some may fail. What’s important is to not give up and keep trying. And if you fail, there is no shame in accepting professional assignment help from experts and experienced people.
