Original article was published by Kajal Yadav on Artificial Intelligence on Medium
1. Sentimental analysis for depression based on social media post
This topic is so sensitive to be considered nowadays and in urgent need to do something about it. There are more than 264 million individuals worldwide who are suffering from depression. Depression is the main cause of disability worldwide and is a significant supporter of the overall global burden of disease and nearly 800,000 individuals consistently bite the dust because of suicide every year. Suicide is the second driving reason for death in 15–29-year-olds. Treatment for depression is often delayed, imprecise, and/or missed entirely.
Internet-based life gives the main edge chance to change early melancholy mediation services, especially in youthful grown-ups. Consistently, roughly 6,000 Tweets are tweeted on Twitter, which relates to more than 350,000 tweets sent for each moment, 500 million tweets for every day, and around 200 billion tweets for each year.
As indicated by the Pew Research Center, 72% of the public uses some sort of internet-based life. Datasets released from social networks are important to numerous fields, for example, human science and brain research. But the supports from a specialized point of view are a long way from enough, and explicit methodologies are desperately out of luck.
By analyzing linguistic markers in social media posts, it’s possible to create a deep learning model that can give an individual insight into his or her mental health far earlier than traditional approaches.
2. Sports match video to text summarization using neural network
So this project idea is basically based on getting precise summary out of Sports match videos. There are sports websites that tell about highlights of the match. Various models have been proposed for the task of extractive text summarization but neural networks do the best job. As a rule, Summarization alludes to introducing information in a brief structure, concentrating on parts that convey facts and information, while safeguarding the importance.
Automatically creating an outline of a game video gives rise to the challenge of distinguishing fascinating minutes, or highlights, of a game.
So, one can achieve that using some deep learning techniques like 3D-CNN (three-dimensional convolutional networks), RNN(Recurrent neural network), LSTM (Long short term memory networks) and also through Machine learning algorithms by dividing the video into different sections and then applying SVM(Support vector machines), NN(Neural Networks), k-means algorithm.
For better understanding, do refer to the attached articles in detail.
3. Handwritten equation solver using CNN
Among all the issues, handwritten mathematical expression recognition is one of the confounding issues in the region of computer vision research. You can train Handwritten equation solver by handwritten digits and mathematical symbols using Convolutional Neural Network (CNN) with some image processing techniques. Developing such a system requires training our machines with data, making it proficient to learn and make the required prediction.
Do refer to the below-attached articles for better understanding.
4. Business meeting summary generation using NLP
Ever got stuck in a situation, where everyone wants to see a summary not full reports. Well, I face it during my school and college days where we spend a lot of time preparing a whole report but the teacher only has time to read the summary.
Summarization has risen as an inexorably helpful way to tackle the issue of data over-burden. Extracting information from conversations can be of very good commercial and educational value. This can be done by feature capture of the statistical, linguistic, and sentimental aspects with the dialogue structure of the conversation.
Manually changing the report to a summed up form is too time taking, isn’t that so? But one can rely on Natural Language Processing (NLP) techniques to achieve that.
Text summarization using deep learning can understand the context of the entire text. Isn’t it a dream come true for all of us who need to come up with a quick summary of a document !!
Do refer to the below-attached articles for better understanding.
5. Facial recognition to detect mood and suggest songs accordingly
The human face is an important part of an individual’s body and it particularly plays a significant role in knowing a person’s state of mind. This eliminates the dreary and tedious task of manually isolating or grouping songs into various records and helps in generating an appropriate playlist based on an individual’s emotional features.
People tend to listen to music based on their mood and interests. One can create an application to suggest songs for users based on their mood by capturing facial expressions.
Computer vision is an interdisciplinary field that helps convey a high-level understanding of digital images or videos to computers. computer vision components can be used to determine the user’s emotion through facial expressions.
There are these APIs too that I found interesting and useful, although I didn’t work on these but attaching here with a hope that these will gonna help you.
6. Finding out habitable exo-planet from images captured by space vehicles like Kepler
In the most recent decade, over a million stars were monitored to identify transiting planets. Manual interpretation of potential exoplanet candidates is labor-intensive and subject to human mistake, the consequences of which are hard to evaluate. Convolutional neural networks are fit for identifying Earth-like exoplanets in noisy time-series data with more prominent precision than a least-squares strategy.
7. Image regeneration for old damaged reel picture
I know, how time- consuming and painful it is to get back your old damaged photo in the original form as it was earlier. So, this can be done using deep learning by finding all the image defects (fractures, scuffs, holes), and using Inpainting algorithms, one can easily discover the defects based on the pixel values around them to restore and colorize the old photos.
8. Music generation using deep learning
Music is an assortment of tones of various frequencies. So, the Automatic Music Generation is a process of composing a short piece of music with the least human mediation . Recently, Deep Learning engineering has become the cutting edge for programmed Music Generation.
I hope you guys will find this article informative & useful for you. Do share your thoughts about these project ideas in the comment box & do let me know about other cool ideas if you have any✌️