Journal of Applied Artificial Intelligence <p>Journal of Applied Artificial Intelligence (JAAI) is an international and interdisciplinary scholarly peer reviewed journal on artificial intelligence published by Saba Publishing.<br />JAAI devoted entirely to Artificial Intelligence and welcomes papers in the overall field including, but not limited to, machine learning and cognition, deep learning, supervised learning, unsupervised learning, classification, regression, clustering, big and streaming data, optimization algorithms, feature selection and extraction, pattern recognition, bio-informatics, uncertain information processes, recommender systems, E-service personalization, distributed and parallel processing, computer vision, neural networks, natural language processing, heuristic search, multi-objective optimization, multi-agent systems, advances in social network systems, reasoning under uncertainty, forecasting and predication models as well as other hot topics.</p> <p><strong>Editor in Chief: <a href="" target="_blank" rel="noopener">Dr Nibras Abdullah</a></strong><br /><strong>ISSN (online)</strong>: <a href="" target="_blank" rel="noopener">2709-5908</a><br /><strong>Frequency:</strong> Quarterly</p> en-US Wed, 30 Dec 2020 20:14:52 +0000 OJS 60 Analysis of Cyber Bullying on Facebook Using Text Mining <p>Cyberbullying is a type of cybercrime that involves the use of the internet and other information technology resources to deliberately insult, embarrass, harass, bully, and threaten people online. The ubiquity of internet connectivity has enabled an increase in the volume and pace of cyberbullying activities because the criminals no longer need to be physically present when committing the crime. This work aims to analyze and predict cyberbullying on Facebook using Naïve Bayes algorithm. The score accuracy, classification report, and confusion matrix are also employed to assess the performance of the classifier. The accuracy of the classifier is 0.95(95%) which means the model can predict 95 of every 100 instances correctly. Also, the result of the experimental analysis shows that Naïve Bayes is effective in classifying a word into a bully or non-bully word and can identify the category of the bully word that is being sent online.</p> NASIRU ISHOLA ALIYU, Abdulrahaman Musbau Dogo , Fatimah Olajumoke Ajibade, Tosho Abdurauf Copyright (c) 2020 Journal of Applied Artificial Intelligence Wed, 30 Dec 2020 00:00:00 +0000 Machine Learning Methods for Analysing the Impact of Social Media on Students Academic Performance <p>Today Social networking is a common apporch, mostly along with youngster. The social media impact on the academic performance of students become fundamental part to determine. Social Networking Sites (SNS) for example, twitter facebook are presently an vital medium that are used to connect peoples and different&nbsp; associations approximately the world&nbsp; Technology is thriving quick with the passage of time, and the youngsters are trapped in this swift revolutionize. In this paper we find out in which ways social sites affects student’s academic performance and recognize the impact of social sites on our educational system. The reason at the back huge use of social networking sites. Social sites networking (SNS) have grow from being merely libertine stages for personl &nbsp;use to awesome progressive structure that utilized both blend and inside and remotely for relationship and joined exertion with shafts separated stakeholders. Generally among youthful understudies, Social sites networks is an unmistakable example today In this paper by using <strong>student-por.csv</strong> and <strong>student-mat.csv</strong> dataset, we work on the classifier on&nbsp; the social media impact on students academic performances. For this purposes we choose K nearest-neighbor (KNN), support vector machine (SVM) and Linear regression algorithm using python that predict slightly better and give right prediction about the student’s academic performance. &nbsp;In social networking model our work has enhanced our imminent.&nbsp; The results reveal that there is strong relationship between social media and student’s academic performance. This paper shows that mostly students have cell phones and also have internet availability so they use 40 mints form 4 hours daily and not give attentions to their studies, all these activities on internet effect the student’s academic performance. This study will commence with introduction and background of existing studies. After that on the basis of background there will be a research question.</p> Kiran Shahzadi, Muhammad Shahzad Sarfraz, Mazhra Saqib, Hamza Mansoor Shah Copyright (c) 2020 Journal of Applied Artificial Intelligence Wed, 30 Dec 2020 00:00:00 +0000