1. Sentiment 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.
You Are What You Tweet
Detecting Depression in Social Media via Twitter Usage
Early Detection of Depression: Social Network Analysis and Random Forest Techniques
Depression detection from social network data using machine learning techniques
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.
Scene Classification for Sports Video Summarization Using Transfer Learning
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.
#machine-learning #artificial-intelligence #data-science #python #deep-learning #deep learning
