Twitter being one of the most sophisticated social networking platforms whose users base is growing exponentially, terabytes of data is being generated every day. Technology Giants invest billions of dollars in drawing insights from these tweets. The huge amount of data is still going underutilized. The main of this paper is to solve two tasks. Firstly, to build a sentiment analysis model using BERT (Bidirectional Encoder Representations from Transformers) which analyses the tweets and predicts the sentiments of the users. Secondly to build a personality prediction model using various machine learning classifiers under the umbrella of Myers-Briggs Personality Type Indicator. MBTI is one of the most widely used psychological instruments in the world. Using this we intend to predict the traits and qualities of people based on their posts and interactions in Twitter. The model succeeds to predict the personality traits and qualities on twitter users. We intend to use the analyzed results in various applications like market research, recruitment, psychological tests, consulting, etc, in future.

Security is a major concern for wireless multimedia networks because of their role in providing various services. Traditional security techniques have inadequacies in identifying emerging security threats and also lacks in computing efficiency. Furthermore, conventional upper-layer authentication doesn’t provide any protection for physical layer, thus leading to leakage of privacy data. Keep these issues in mind, the paper has envisioned an artificial intelligence-based security authentication system that is lightweight, adaptive and doesn’t require any explicit programming. The neural network is built on convolutional filters which explore the data and learns the features or characteristic of the data. With this learned feature, the model will be able to recognize whether a wireless multimedia device present in a network is legitimate or not. Experimental analysis and validation have been performed on the trained model and ensure that the authentication of wireless multimedia devices can be achieved and also ensuring lightweight authentication system, which ensures less computation needs. The different neural model is also trained using gaussian noise of different standard deviation so that it can be used in a practical scenario like smart industry etc.  

Our life nowadays relies much on technologies and online services net banking, e-voting and so on. So, there is a necessity to secure the data that is transmitted through the internet. However, while performing decryption, it sometimes led to privacy violation so there is need to operate on users encrypted data without knowing the original plaintext.
This paper represents the implementation of two-layer cryptosystem using paillier and elgamal algorithm both following asymmetric encryption. It is mainly focusing the challenges of privacy protection and secure utilization of information, where homomorphy encryption is gaining attention. Additive homomorphism is used in paillier cryptosystem which is used in fields like secure biometrics and electronic voting. Elgamal ensures that paillier encrypted data is secured that ensures two-layer encryption.