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@article{148260,
        author = {Akmal Ziyad Gyasoddin and Vishal Pushkarnath Marhatta and Manoj Vasudev Baviskar},
        title = {HELPING CYBER FORENSICS FOR WRITER IDENTIFICATION BY IDENTIFYING IMPORTANT FEATURES},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {6},
        number = {1},
        pages = {95-99},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=148260},
        abstract = {The cover of mystery gave by mobile phones paid early SIM cards, open Wi-Fi hotspots, and flowed frameworks like Tor has profoundly tangled the task of recognizing customers of online life in the midst of logical examinations. From time to time, the substance of a singular posted message will be the principle snippet of data to a maker's character. By what strategy would we have the option to exactly envision who that maker might be the time when the message may never outperform 140 characters on an organization like Twitter? For up to 50 years, language masters, PC specialists, and analysts of the humanities have been as one making automated methodologies to perceive essayists in light of the style of their composed work. All journalists have characteristics of inclination that effect the casing and substance of their created works. These traits can routinely be assessed and evaluated by using AI computations.We will try to analyze the result after classification and find out which word-n-gram and char-n-gram plays most important role.For this evaluation we will use an application developed by us named TweetiFi.},
        keywords = {},
        month = {},
        }