Computer recognition of sign language is an important research problem for enabling communication with hearing impaired people. This project introduces an efficient and fast algorithm for identification of the number of fingers opened in a gesture representing an alphabet of the Binary Sign Language. The system does not require the hand to be perfectly aligned to the camera. The project uses image processing system to identify, especially English alphabetic sign language used by the deaf people to communicate. The basic objective of this project is to develop a computer based intelligent system that will enable dumb people significantly to communicate with all other people using their natural hand gestures. The idea consisted of designing and building up an intelligent system using image processing, machine learning and artificial intelligence concepts to take visual inputs of sign language's hand gestures and generate easily recognizable form of outputs. Hence the objective of this project is to develop an intelligent system which can act as a translator between the sign language and the spoken language dynamically and can make the communication between people with hearing impairment and normal people both effective and efficient. The system is we are implementing for Binary sign language but it can detect any sign language with prior image processing

With the ever-growing number of online social media platforms, the world has shrunk even further with regards to communication and knowledge-sharing perspective. However, communication, at times, can be deterrent when misused using such widespread social media tools. The acts of terrorism become seemingly convenient as the barrier of communication is nullified. This propagation of hateful content becomes much more easier and even recruiting anti-socials gets easier. Oddly, these social media platforms are the ones that prove essential during such crisis situations. This paper reviews most of the works reported by various authors in the last 10 years on the use of social media during a time of terrorist attack to addressing how to use social media for public communication with the emergency organization and military or police during terrorist attack, how to perform post-attack social media analytic and how to detect acts of terrorism, unrest, and hatred using social media analytic. With this objective, the authors also hope to inspire other researchers to work in this direction and use this review as a guide for instigating future research to counter-attack terrorism as it is the need of the hour for our country in the wake of recent Uri and Pulwama attack.

No abstract

Trust is becoming a very important part of social network from the security point of view. In this project, we introduce a framework for handling trust in social networks, which is based on reputation mechanism. The reputation mechanism captures the implicit and explicit connections between the network members, analyses the semantics and dynamics of these connections, and provides personalized user recommendations to the network members. Based on the trust semantics, the system will provide the positive (trustworthy) recommendations and the negative (untrustworthy) recommendations. Along with this our system provides one more interesting mode i.e. public profile matching that preserves privacy on social networks. This profile matching contributes in reputation ratings required for suggestions of friend list. In order to compute the reputation of each member, we adopt several other properties of trust such as, transitivity, personalization, and context, and draw ideas from sociology axioms. Trust is not perfectly transitive in social networks, in that trust decays along the transition path, but it is generally agreed that it can be communicated between people. Along with trust generation percentile of profile matching is also considered for personal recommendation.

Trust is becoming a very important part of social network from the security point of view. In the proposed system, a framework is introduced for handling trust in social networks, which is based on reputation mechanism. The reputation mechanism captures the implicit and explicit connections between the network members, analyses the semantics and dynamics of these connections, and provides personalized user recommendations to another network members. Based on the trust semantics, the system will provide the positive recommendations i.e. list of trustworthy users and the negative recommendations i.e. list of untrustworthy users. Along with this, the proposed system provides one more interesting mode i.e. public profile matching that preserves privacy on social networks. This profile matching contributes in reputation ratings required for suggestions of friend list. The main focus is on providing negative recommendations. In order to compute the reputation of each member, we adopt several other properties of trust such as, transitivity, personalization, and context, and draw ideas from sociology axioms. Trust is not perfectly transitive in social networks, in that trust decays along the transition path, but it is generally agreed that it can be communicated between people. Along with trust generation percentile of profile matching is also considered for personal recommendation.