Hate Classifier for Social Media Platform Using Tree LSTM

Social-media without a doubt is one of the most noteworthy developments ever. From associating with individuals across the globe for sharing of data and information in an infinitesimal of a second, online media stages have enormously altered the method of our lives. This is joined by a steadily expanding utilization of social media, less expensive cell phones, and the simplicity of web access which have additionally prepared for the huge development of social media. To place this into numbers, according to an ongoing report, billions of individuals all over the planet presently utilize web-based media every month, and a normal amount of almost 2 million people new clients are going along with them consistently. While web-based media stages have permitted us to interface with others and fortify connections in manners that were not conceivable previously. Unfortunately, they have additionally turned into the default gatherings for can’t stand discourse. Online hate is a wild issue, with the adverse result of disallowing client support in web-based conversations and causing mental mischief to people. Since hate is pervasive across friendly, media stages, our objective was to foster a classifier that is feasible to train classifiers that can identify hateful remarks with strong execution and with the portion of misleading up-sides and negatives staying inside sensible limits

Music Feature Extraction And Recommendation Using CNN Algorithm

In this age of technological advancements, it has become considerably easier for an individual to access a variety of music from a significant number of sources. Today, there are a multitude of songs of varying diversity available to users. Therefore, it becomes difficult for users to manually discover new music that may suit their liking. Thus arises the need for a system that will help the music streaming applications to recommend new music to their users that will befit their music taste, based on some predetermined criteria. With the ever-expanding user and song database, the system must also be dynamic and its recommendations must be up-to-date and accurate. Therefore, there is a strong demand for a well-qualified music recommendation system. The proposed system focuses of technical features of audio. The main purpose of this systems is to classify songs in different genre using Deep Learning Algorithm. There are two main approaches for implementing these system, viz, Feature Extraction and Content Based Filtering

Job Recommendation System Using Hybrid Filtering

As for today’s era, recruitment can be considered as one of most difficult process to undergo for job seeking candidate. Many fresher candidates face issue while job recruitment process to undergo which field of interest. The proposed system will help the user to overcome this difficulties by matching their work experience, skills and other details with appropriate companies suitable for respective user. The system will also help experienced users in getting their intended job on the basis of their last job profile. The job recommendation algorithm developed is tedious nor complicated and will be using user-friendly approach to implement job search.The proposed system consist of user dataset with various attributes and company dataset with company details. The profile matching of user with the respective companies can be done using various recommendation algorithms such as content-based,collaborative and hybrid filtering. Since, the content-based and collaborative approach have their own disadvantages, so here implement hybrid filtering which overcomes the disadvantages of the content-based and collaborative filtering. The user can expect a well-proof recommendation from our model. The Project will focus of developing the job recommendation system using hybrid filtering. As for today’s era, recruitment can be considered as one of most difficult process to undergo for job seeking candidate. Here, our job recommendation system comes in picture which neither is tedious nor complicated and makes use of user-friendly approach and helps user to accomplish the task easily.The project will also be focusing on developing the android application which will add a better user interface. The Android application will be user friendly and the user just have to fill in basic details such as his past years of experiences, project, internship, etc. That’s it,the rest part of recommending the job to the users will be done safely by the recommendation model of this project