In this Repository, I upload my Research and Development Projects which I have Completed in Bachelor’s Degree (2015-19).
- Multilayer Perceptron and Support Vector Machine to filtering the TubeSpam Comments
- Conversational AI Chatbot Based on Encoder-Decoder Architectures with Attention Mechanism
In the recently advanced society, online social media sites like YouTube, Twitter, Facebook, LinkedIn, etc are very popular. People turn to social media for interacting with other people, gaining knowledge, sharing ideas, for entertainment and staying informed about the events happening in the rest of the world. Among these sites, YouTube has emerged as the most popular website for sharing and viewing video content. However, such success has also attracted malicious users, which aim to self-promote their videos or disseminate viruses and malware. These spam videos may be unrelated to their title or may contain pornographic content. Therefore, it is very important to find a way to detect these videos and report them. In this work, we have evaluated several top-performance classification techniques for such purpose. The statistical analysis of results indicates that the Multilayer Perceptron and Support Vector Machine show good accuracy results.
Project 2: Conversational AI Chatbot Based on Encoder-Decoder Architectures with Attention Mechanism
Conversational AI Chatbot development using Artificial Intelligence and Machine Learning technique is an interesting problem of Natural Language Processing. In many research and development projects scientists are using AI, Machine Learning algorithms and Natural Language Processing techniques for developing Conversational AI Chatbot. The research and development of automated help desk and customer services through these conversation agents are still under progress and experimentation. Conversational AI Chatbot is mostly deployed by financially organizations like the bank, credit card companies, businesses like online retail stores and startups. Virtual agents are adopted by businesses ranging from very small start-ups to large corporations. There are many AI Chabot development frameworks available in the market both program-based and interface based. But they lack the accuracy and flexibility in developing real dialogues. Among these popular intelligent personal assistants are Amazon’s Alexa, Microsoft’s Cortana and Google’s Google Assistant. The functioning of these agents is limited, and retrieval based agent which are not aimed at holding conversations that emulate real human interaction. Among current chatbots, many are developed using rule-based techniques, simple machine learning algorithms or retrieval based techniques which do not generate good results. In this paper, we have developed a Conversational AI Chatbot using modern-day techniques. For developing Conversational AI Chatbot, We have implemented encoder-decoder attention mechanism architecture. This encoder-decoder is using Recurrent Neural Network with LSTM (LongShort-Term-Memory) cells.