ICACT 2018
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/18944
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Item Forecasting Monthly Ad Revenue from Blogs using Machine Learning(3rd International Conference on Advances in Computing and Technology (ICACT ‒ 2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Dias, D.S.; Dias, N.G.J.Blogs emerged in the late 1990s as a technology that allows Internet users to share information. Since then, blogging has evolved to become a source of living to some and a hobby to others. A blog with rich content and regular traffic could easily be monetized through a number of methods. Affiliate marketing, Google AdSense, offering courses or services, selling eBooks and paid banner advertisements are some of the methods in which a blog could be monetized. There exists, a direct relationship on the revenue that can be generated through any of the above methods and the traffic that the blog gets. Google AdSense is the leader in providing ads from publishers to website owners. All bloggers or blogging website owners who have monetized their blogs, attempt to maximize their revenue by publishing articles in hope that it will generate the targeted revenue. On the other hand, bloggers or blogging website owners that hope to monetize their blog will be greatly benefitted if there was a way to forecast the monthly ad revenue that could be generated through the blog. But there exists no tool in the market that can help the bloggers forecast their ad revenue from the blog. In this research, we are looking at the possibility of finding an appropriate machine learning technique by comparing a linear regression, neural network regression and decision forest regression approaches in order to forecast the monthly ad revenue that a blog can generate to a greater accuracy, using statistics from Google Analytics and Google AdSense. As conclusion, the Decision Forest Regression model came out as the best fit with an accuracy of over 70%Item Virtual Airplay Drum Kit based on Hand Gesture Recognition(3rd International Conference on Advances in Computing and Technology (ICACT ‒ 2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Dias, D.S.; Perera, M.D.R.In the music industry, a drum kit plays a vital role in the production of masterpiece musical melodies. It is also one of the instruments that is in greatest demand by youngsters who are passionate in learning and practicing music. But acquiring a typical drum kit is become a difficult task because of its high cost as well as it requires a large storage space to hold. This research is targeted in examining the possibility of engineering a cost-effective solution to build a portable drum kit. In this approach, ultrasonic sensors are used in order to identify hand gestures. Ultrasonic sensor is used to measure the distance to an obstacle using the theories of sound reflectance. The obstacle in this scenario is the human palm. When the palm of the human is moved up and down above the ultrasonic sensor, mimicking the typical actions of playing a drum kit, the changes in distances to the palm are mapped to corresponding drum sounds using a sound generation algorithm. This algorithm is further optimized in such a way that it yields an optimal consistency in readings, regardless of the typical issues of the low cost ultrasonic sensor such as noise, low accuracy of distance readings and random loss of signal. The solution was tested with the feedback of the general audience and it yielded satisfactory results, in achieving our goal. In conclusion, this approach could be well used in reaching our goal based on over 75% of positive feedback (rated very good and good) received. But in order to improve its accuracy and efficiency, more expensive and more accurate distance sensors such as high precision ultrasonic sensors or infrared sensors could be used. The portability, the low cost of engineering, and yet the deliverance of acceptable level of quality of music, could be identified as the unique key point of this research.